(upbeat music) >> Welcome back to Supercloud 2, an open collaborative where we explore the future of cloud and data. Now, you might recall last August at the inaugural Supercloud event we validated the technical feasibility and tried to further define the essential technical characteristics, and of course the deployment models of so-called supercloud. That is, sets of services that leverage the underlying primitives of hyperscale clouds, but are creating new value on top of those clouds for organizations at scale. So we're talking about capabilities that fundamentally weren't practical or even possible prior to the ascendancy of the public clouds. And so today at Supercloud 2, we're digging further into the topic with input from real-world practitioners. And we're exploring the intersection of data and cloud, And importantly, the realities and challenges of deploying technology for a new business capability. I'm pleased to have with me in our studios, west of Boston, Veronika Durgin, who's the head of data at Saks. Veronika, welcome. Great to see you. Thanks for coming on. >> Thank you so much. Thank you for having me. So excited to be here. >> And so we have to say upfront, you're here, these are your opinions. You're not representing Saks in any way. So we appreciate you sharing your depth of knowledge with us. >> Thank you, Dave. Yeah, I've been doing data for a while. I try not to say how long anymore. It's been a while. But yeah, thank you for having me. >> Yeah, you're welcome. I mean, one of the highlights of this past year for me was hanging out at the airport with you after the Snowflake Summit. And we were just chatting about sort of data mesh, and you were saying, "Yeah, but." There was a yeah, but. You were saying there's some practical realities of actually implementing these things. So I want to get into some of that. And I guess starting from a perspective of how data has changed, you've seen a lot of the waves. I mean, even if we go back to pre-Hadoop, you know, that would shove everything into an Oracle database, or, you know, Hadoop was going to save our data lives. And the cloud came along and, you know, that was kind of a disruptive force. And, you know, now we see things like, whether it's Snowflake or Databricks or these other platforms on top of the clouds. How have you observed the change in data and the evolution over time? >> Yeah, so I started as a DBA in the data center, kind of like, you know, growing up trying to manage whatever, you know, physical limitations a server could give us. So we had to be very careful of what we put in our database because we were limited. We, you know, purchased that piece of hardware, and we had to use it for the next, I don't know, three to five years. So it was only, you know, we focused on only the most important critical things. We couldn't keep too much data. We had to be super efficient. We couldn't add additional functionality. And then Hadoop came along, which is like, great, we can dump all the data there, but then we couldn't get data out of it. So it was like, okay, great. Doesn't help either. And then the cloud came along, which was incredible. I was probably the most excited person. I'm lying, but I was super excited because I no longer had to worry about what I can actually put in my database. Now I have that, you know, scalability and flexibility with the cloud. So okay, great, that data's there, and I can also easily get it out of it, which is really incredible. >> Well, but so, I'm inferring from what you're saying with Hadoop, it was like, okay, no schema on write. And then you got to try to make sense out of it. But so what changed with the cloud? What was different? >> So I'll tell a funny story. I actually successfully avoided Hadoop. The only time- >> Congratulations. >> (laughs) I know, I'm like super proud of it. I don't know how that happened, but the only time I worked for a company that had Hadoop, all I remember is that they were running jobs that were taking over 24 hours to get data out of it. And they were realizing that, you know, dumping data without any structure into this massive thing that required, you know, really skilled engineers wasn't really helpful. So what changed, and I'm kind of thinking of like, kind of like how Snowflake started, right? They were marketing themselves as a data warehouse. For me, moving from SQL Server to Snowflake was a non-event. It was comfortable, I knew what it was, I knew how to get data out of it. And I think that's the important part, right? Cloud, this like, kind of like, vague, high-level thing, magical, but the reality is cloud is the same as what we had on prem. So it's comfortable there. It's not scary. You don't need super new additional skills to use it. >> But you're saying what's different is the scale. So you can throw resources at it. You don't have to worry about depreciating your hardware over three to five years. Hey, I have an asset that I have to take advantage of. Is that the big difference? >> Absolutely. Actually, from kind of like operational perspective, which it's funny. Like, I don't have to worry about it. I use what I need when I need it. And not to take this completely in the opposite direction, people stop thinking about using things in a very smart way, right? You like, scale and you walk away. And then, you know, the cool thing about cloud is it's scalable, but you also should not use it when you don't need it. >> So what about this idea of multicloud. You know, supercloud sort of tries to go beyond multicloud. it's like multicloud by accident. And now, you know, whether it's M&A or, you know, some Skunkworks is do, hey, I like Google's tools, so I'm going to use Google. And then people like you are called on to, hey, how do we clean up this mess? And you know, you and I, at the airport, we were talking about data mesh. And I love the concept. Like, doesn't matter if it's a data lake or a data warehouse or a data hub or an S3 bucket. It's just a node on the mesh. But then, of course, you've got to govern it. You've got to give people self-serve. But this multicloud is a reality. So from your perspective, from a practitioner's perspective, what are the advantages of multicloud? We talk about the disadvantages all the time. Kind of get that, but what are the advantages? >> So I think the first thing when I think multicloud, I actually think high-availability disaster recovery. And maybe it's just how I grew up in the data center, right? We were always worried that if something happened in one area, we want to make sure that we can bring business up very quickly. So to me that's kind of like where multicloud comes to mind because, you know, you put your data, your applications, let's pick on AWS for a second and, you know, US East in AWS, which is the busiest kind of like area that they have. If it goes down, for my business to continue, I would probably want to move it to, say, Azure, hypothetically speaking, again, or Google, whatever that is. So to me, and probably again based on my background, disaster recovery high availability comes to mind as multicloud first, but now the other part of it is that there are, you know, companies and tools and applications that are being built in, you know, pick your cloud. How do we talk to each other? And more importantly, how do we data share? You know, I work with data. You know, this is what I do. So if, you know, I want to get data from a company that's using, say, Google, how do we share it in a smooth way where it doesn't have to be this crazy, I don't know, SFTP file moving. So that's where I think supercloud comes to me in my mind, is like practical applications. How do we create that mesh, that network that we can easily share data with each other? >> So you kind of answered my next question, is do you see use cases going beyond H? I mean, the HADR was, remember, that was the original cloud use case. That and bursting, you know, for, you know, Thanksgiving or, you know, for Black Friday. So you see an opportunity to go beyond that with practical use cases. >> Absolutely. I think, you know, we're getting to a world where every company is a data company. We all collect a lot of data. We want to use it for whatever that is. It doesn't necessarily mean sell it, but use it to our competitive advantage. So how do we do it in a very smooth, easy way, which opens additional opportunities for companies? >> You mentioned data sharing. And that's obviously, you know, I met you at Snowflake Summit. That's a big thing of Snowflake's. And of course, you've got Databricks trying to do similar things with open technology. What do you see as the trade-offs there? Because Snowflake, you got to come into their party, you're in their world, and you're kind of locked into that world. Now they're trying to open up. You know, and of course, Databricks, they don't know our world is wide open. Well, we know what that means, you know. The governance. And so now you're seeing, you saw Amazon come out with data clean rooms, which was, you know, that was a good idea that Snowflake had several years before. It's good. It's good validation. So how do you think about the trade-offs between kind of openness and freedom versus control? Is the latter just far more important? >> I'll tell you it depends, right? It's kind of like- >> Could be insulting to that. >> Yeah, I know. It depends because I don't know the answer. It depends, I think, because on the use case and application, ultimately every company wants to make money. That's the beauty of our like, capitalistic economy, right? We're driven 'cause we want to make money. But from the use, you know, how do I sell a product to somebody who's in Google if I am in AWS, right? It's like, we're limiting ourselves if we just do one cloud. But again, it's difficult because at the same time, every cloud provider wants for you to be locked in their cloud, which is why probably, you know, whoever has now data sharing because they want you to stay within their ecosystem. But then again, like, companies are limited. You know, there are applications that are starting to be built on top of clouds. How do we ensure that, you know, I can use that application regardless what cloud, you know, my company is using or I just happen to like. >> You know, and it's true they want you to stay in their ecosystem 'cause they'll make more money. But as well, you think about Apple, right? Does Apple do it 'cause they can make more money? Yes, but it's also they have more control, right? Am I correct that technically it's going to be easier to govern that data if it's all the sort of same standard, right? >> Absolutely. 100%. I didn't answer that question. You have to govern and you have to control. And honestly, it's like it's not like a nice-to-have anymore. There are compliances. There are legal compliances around data. Everybody at some point wants to ensure that, you know, and as a person, quite honestly, you know, not to be, you know, I don't like when my data's used when I don't know how. Like, it's a little creepy, right? So we have to come up with standards around that. But then I also go back in the day. EDI, right? Electronic data interchange. That was figured out. There was standards. Companies were sending data to each other. It was pretty standard. So I don't know. Like, we'll get there. >> Yeah, so I was going to ask you, do you see a day where open standards actually emerge to enable that? And then isn't that the great disruptor to sort of kind of the proprietary stack? >> I think so. I think for us to smoothly exchange data across, you know, various systems, various applications, we'll have to agree to have standards. >> From a developer perspective, you know, back to the sort of supercloud concept, one of the the components of the essential characteristics is you've got this PaaS layer that provides consistency across clouds, and it has unique attributes specific to the purpose of that supercloud. So in the instance of Snowflake, it's data sharing. In the case of, you know, VMware, it might be, you know, infrastructure or self-serve infrastructure that's consistent. From a developer perspective, what do you hear from developers in terms of what they want? Are we close to getting that across clouds? >> I think developers always want freedom and ability to engineer. And oftentimes it's not, (laughs) you know, just as an engineer, I always want to build something, and it's not always for the, to use a specific, you know, it's something I want to do versus what is actually applicable. I think we'll land there, but not because we are, you know, out of the kindness of our own hearts. I think as a necessity we will have to agree to standards, and that that'll like, move the needle. Yeah. >> What are the limitations that you see of cloud and this notion of, you know, even cross cloud, right? I mean, this one cloud can't do it all. You know, but what do you see as the limitations of clouds? >> I mean, it's funny, I always think, you know, again, kind of probably my background, I grew up in the data center. We were physically limited by space, right? That there's like, you can only put, you know, so many servers in the rack and, you know, so many racks in the data center, and then you run out space. Earth has a limited space, right? And we have so many data centers, and everybody's collecting a lot of data that we actually want to use. We're not just collecting for the sake of collecting it anymore. We truly can't take advantage of it because servers have enough power, right, to crank through it. We will run enough space. So how do we balance that? How do we balance that data across all the various data centers? And I know I'm like, kind of maybe talking crazy, but until we figure out how to build a data center on the Moon, right, like, we will have to figure out how to take advantage of all the compute capacity that we have across the world. >> And where does latency fit in? I mean, is it as much of a problem as people sort of think it is? Maybe it depends too. It depends on the use case. But do multiple clouds help solve that problem? Because, you know, even AWS, $80 billion company, they're huge, but they're not everywhere. You know, they're doing local zones, they're doing outposts, which is, you know, less functional than their full cloud. So maybe I would choose to go to another cloud. And if I could have that common experience, that's an advantage, isn't it? >> 100%, absolutely. And potentially there's some maybe pricing tiers, right? So we're talking about latency. And again, it depends on your situation. You know, if you have some sort of medical equipment that is very latency sensitive, you want to make sure that data lives there. But versus, you know, I browse on a website. If the website takes a second versus two seconds to load, do I care? Not exactly. Like, I don't notice that. So we can reshuffle that in a smart way. And I keep thinking of ways. If we have ways for data where it kind of like, oh, you are stuck in traffic, go this way. You know, reshuffle you through that data center. You know, maybe your data will live there. So I think it's totally possible. I know, it's a little crazy. >> No, I like it, though. But remember when you first found ways, you're like, "Oh, this is awesome." And then now it's like- >> And it's like crowdsourcing, right? Like, it's smart. Like, okay, maybe, you know, going to pick on US East for Amazon for a little bit, their oldest, but also busiest data center that, you know, periodically goes down. >> But then you lose your competitive advantage 'cause now it's like traffic socialism. >> Yeah, I know. >> Right? It happened the other day where everybody's going this way up. There's all the Wazers taking. >> And also again, compliance, right? Every country is going down the path of where, you know, data needs to reside within that country. So it's not as like, socialist or democratic as we wish for it to be. >> Well, that's a great point. I mean, when you just think about the clouds, the limitation, now you go out to the edge. I mean, everybody talks about the edge in IoT. Do you actually think that there's like a whole new stove pipe that's going to get created. And does that concern you, or do you think it actually is going to be, you know, connective tissue with all these clouds? >> I honestly don't know. I live in a practical world of like, how does it help me right now? How does it, you know, help me in the next five years? And mind you, in five years, things can change a lot. Because if you think back five years ago, things weren't as they are right now. I mean, I really hope that somebody out there challenges things 'cause, you know, the whole cloud promise was crazy. It was insane. Like, who came up with it? Why would I do that, right? And now I can't imagine the world without it. >> Yeah, I mean a lot of it is same wine, new bottle. You know, but a lot of it is different, right? I mean, technology keeps moving us forward, doesn't it? >> Absolutely. >> Veronika, it was great to have you. Thank you so much for your perspectives. If there was one thing that the industry could do for your data life that would make your world better, what would it be? >> I think standards for like data sharing, data marketplace. I would love, love, love nothing else to have some agreed upon standards. >> I had one other question for you, actually. I forgot to ask you this. 'Cause you were saying every company's a data company. Every company's a software company. We're already seeing it, but how prevalent do you think it will be that companies, you've seen some of it in financial services, but companies begin to now take their own data, their own tooling, their own software, which they've developed internally, and point that to the outside world? Kind of do what AWS did. You know, working backwards from the customer and saying, "Hey, we did this for ourselves. We can now do this for the rest of the world." Do you see that as a real trend, or is that Dave's pie in the sky? >> I think it's a real trend. Every company's trying to reinvent themselves and come up with new products. And every company is a data company. Every company collects data, and they're trying to figure out what to do with it. And again, it's not necessarily to sell it. Like, you don't have to sell data to monetize it. You can use it with your partners. You can exchange data. You know, you can create products. Capital One I think created a product for Snowflake pricing. I don't recall, but it just, you know, they built it for themselves, and they decided to kind of like, monetize on it. And I'm absolutely 100% on board with that. I think it's an amazing idea. >> Yeah, Goldman is another example. Nasdaq is basically taking their exchange stack and selling it around the world. And the cloud is available to do that. You don't have to build your own data center. >> Absolutely. Or for good, right? Like, we're talking about, again, we live in a capitalist country, but use data for good. We're collecting data. We're, you know, analyzing it, we're aggregating it. How can we use it for greater good for the planet? >> Veronika, thanks so much for coming to our Marlborough studios. Always a pleasure talking to you. >> Thank you so much for having me. >> You're really welcome. All right, stay tuned for more great content. From Supercloud 2, this is Dave Vellante. We'll be right back. (upbeat music)

>>How do y'all and welcome back to the cube where we're live from Dallas at a Supercomputing 2022. My name is Savannah Peterson. Joined with L AED today, as well as some very exciting guests talking about one of my favorite and most complex topics out there, talking about quantum a bit today. Please welcome Ken and Matthew. Thank you so much for reading here. Matthew. Everyone's gonna be able to see your shirt. What's going on with hybrid quantum? I have >>To ask. Wait, what is hybrid quantum? Yeah, let's not pretend that. >>Let's not >>Pretend that everybody knows, Everyone already knows what quantum computing is if we goes straight to highway. Yeah. Okay. So with the brief tour detour took qu regular quantum computing. Yeah, >>No, no. Yeah. Let's start with quantum start before. >>So you know, like regular computers made of transistors gives us ones and zeros, right? Binary, like you were talking about just like half of the Cheerios, right? The joke, it turns out there's some problems that even if we could build a computer as big as the whole universe, which would be pretty expensive, >>That might not be a bad thing, but >>Yeah. Yeah. Good for Dell Got mill. >>Yeah. >>Yeah. We wouldn't be able to solve them cuz they scale exponentially. And it turns out some of those problems have efficient solutions in quantum computing where we take any two state quantum system, which I'll explain in a sec and turn it into what we call a quantum bit or qubit. And those qubits can actually solve some problems that are just infeasible on even these world's largest computers by offering exponential advantage. And it turns out that today's quantum computers are a little too small and a little too noisy to do that alone. So by pairing a quantum computer with a classical computer, hence the partnership between IQ and Dell, you allow each kind of compute to do what it's best at and thereby get answers you can't get with either one alone. >>Okay. So the concept of introducing hybridity, I love that word bridge. I dunno if I made it up, but it's it for it. Let's about it. Abri, ding ding. So does this include simulating the quantum world within the, what was the opposite? The classical quantum world? Classical. Classical, classical computer. Yeah. So does it include the concept of simulating quantum in classical compute? >>Absolutely. >>Okay. How, how, how do, how do you do that? >>So there's simulators and emulators that effectively are programmed in exactly the same way that a physical quantum machine is through circuits translated into chasm or quantum assembly language. And those are the exact same ways that you would program either a physical q p or a simulated >>Q p. So, so access to quantum computing today is scarce, right? I mean it's, it's, it's, it's limited. So having the ability to have the world at large or a greater segment of society be able to access this through simulation is probably a good idea. >>Fair. It's absolutely a wonderful one. And so I often talk to customers and I tell them about the journey, which is hands on keyboard, learning, experimentation, building proof of concepts, and then finally productization. And you could do much of that first two steps anyway very robustly with simulation. >>It's much like classical computing where if you imagine back in the fifties, if, if the cube was at some conference in 1955, you know, we wouldn't have possibly been able to predict what we'd be doing with computing 70 years later, right? Yeah. That teenagers be making apps on their phones that changed the world, right? And so by democratizing access this way, suddenly we can open up all sorts of new use cases. We sort of like to joke, there's only a couple hundred people in the world who really know how to program quantum computers today. And so how are we gonna make thousands, tens of thousands, millions of quantum programmers? The answer is access and simulators are an amazingly accessible way for everyone to start playing around with the >>Fields. Very powerful tool. >>Wow. Yeah. I'm just thinking about how many, there's, are there really only hundreds of people who can program quantum computing? >>I kind of generally throw it out there and I say, you know, if you looked at a matrix of a thousand operations with hundreds of qubits, there's probably, I don't know, 2000 people worldwide that could program that type of a circuit. I mean it's a fairly complex circuit at that point and >>I, I mean it's pretty phenomenal When you think about how early we are in adoption and, and the rollout of this technology as a whole, can you see quite a bit as, as you look across your customer portfolio, what are some of the other trends you're seeing? >>Well, non quantum related trends or just any type you give us >>Both. >>Yeah. So >>We're a thought leader. This is >>Your moment. Yeah, so we do quite a bit. We see quite a bit actually. There's a lot of work happening at the edge as you're probably well aware of. And we see a lot of autonomous mobile robots. I actually lead the, the research office. So I get to see all the cool stuff that's really kind of emerging before it really regrets >>What's coming next. >>Let's see, Oh, I can't tell you what's coming next, but we see edge applications. Yes, we see a lot of, of AI applications and artificial intelligence is morphing dramatically through the number of frameworks and through the, the types and places you would place ai, even places I, I personally never thought we would go like manufacturing environments. Some places that were traditionally not very early adopters. We're seeing AI move very quickly in some of those areas. One of the areas that I'm really excited about is digital twins and the ability to eventually do, let's come up on acceleration with quantum technologies on, on things like computational fluid dynamics. And I think it's gonna be a wonderful, wonderful area for us moving forward. >>So, So I can hear the people screaming at the screen right now. Wait a minute, You said it was hybrid, you're only talking the front half. That's, that's cat. What about the back half? That's dog. What about the quantum part of it? So I, on Q and, and I apologize. Ion Q >>Ion >>Q, Yeah Ion Q cuz you never know. You never never know. Yeah. Where does the actual quantum come in? >>That's a great >>Question. So you guys have one of these things. >>Yeah, we've built, we currently have the world's best quantum computer by, by sub measures I drop there. Yeah, no big deal. Give me some snaps for that. Yeah, Ken knows how to pick em. Yeah, so right. Our, our approach, which is actually based on technology that's 50 years old, so it's quite, quite has a long history. The way we build atomic clocks is the basis for trapped eye quantum computing. And in fact the first quantum logic gate ever made in 1995 was at NIST where they modified their atomic clock experiment to do quantum gates. And that launched really the hardware experimentalist quantum Peter Revolution. And that was by Chris Monroe, our co-founder. So you know that history has flown directly into us. So to simplify, we start with an ion trap. Imagine a gold block with a bunch of electrodes that allow you to make precisely shaped electromagnetic fields, sort of like a rotating saddle. >>Then take a source of atoms. Now obviously we're all sources of atoms. We have a highly purified source of metal atium. We heat it up, we get a nice hot plume of atoms, we ionize those atoms with an ionizing later laser. Now they're hot and heavy and charged. So we can trap them in one of these fields. And now our electromagnetic field that's spitting rapidly holds the, the ions like balls in a bowl if you can imagine them. And they line up in a nice straight line and we hold them in place with these fields and with cooling laser beams. And up to now, that's how an atomic clock works. Trap an item and shine it with a laser beam. Count the oscillations, that's your clock. Now if you got 32 of those and you can manipulate their energy states, in our case we use the hyper fine energy states of the atom. >>But you can basically think of your high school chemistry where you have like an unexcited electron, an excited electron. Take your unexcited state as a zero, your excited state as a one. And it turns out with commercially available lasers, you can drive anywhere between a zero, a one or a super position of zero and one. And so that is our quantum bit, the hyper fine energy state of the atrium atom. And we just line up a bunch of them and through there access the magical powers of supervision entanglement, as we were talking about before, they don't really make sense to us here in the regular world, but >>They do exist. But what you just described is one cubit. That's right. And the way that you do it isn't exactly the same way that others who are doing quantum computing do it. That's right. Is that okay? >>And there's a lot of advantages to the trapped iron approach. So for example, you can also build a super conducting qubit where you, where you basically cool a chip to 47 mil kelvin and coerce millions of atoms to work together as a single system. The problem is that's not naturally quantum. So it's inherently noisy and it wants to deco here does not want to be a quantum bit. Whereas an atom is very happy to be by itself a qubit because we don't have to do anything to it. It's naturally quantum, if that makes sense. And so atomic qubits, like we use feature a few things. One the longest coherence times in the industry, meaning you can run very deep circuits, the most accurate operations, very low noise operations. And we don't have any wires. Our atoms are connected by laser light. That means you can connect any pair. So with some other technologies, the qubits are connected by wires. That means you can only run operations between physically connected qubits. It's like programming. If you could only use, for example, bits that are adjacent with an i untrapped approach, you can connect any pair so that all to all connectivity means your compilation is much more efficient and you can do much wider and deeper circuits. >>So what's the, what is the closest thing to a practical application that we've been able to achieve at this point? Question. And when I say practical, it doesn't have to be super practical. I mean, what is the, what is the sort of demonstration, the least esoteric demonstration of this at this point? >>To tie into what Ken was saying earlier, I think there's at least two areas that are very exciting. One is chemistry. Chemistry. So for example, you know, we have water in our cup and we understand water pretty well, but there's lots of molecules that in order to study them, we actually have to make them in a lab and do lots of experiments. And to give you a sense of the order of magnitude, if you wanted to understand the ground state of the caffeine molecule, which we all know and has 200 electrons, you would need to build a computer bigger than the moon. So, which is, you know, again, would be good profit for Dell, but probably not gonna happen time soon. That's >>Kind of fun to think about though. Yeah, that's a great analogy. That >>Was, yeah. And in fact it'd be like 10 moons of compute. Okay. So build 10 moons of >>Computer. I >>Love the sci-fi issue. Exactly. And now you can calculate caffeine, it's crazy or it just fits in a quantum computer the size of this table. And so we're using hybrid quantum computing now to start proving out these algorithms not for molecules as complex as caffeine or what we want in the future. Like biologics, you know, new cancer medications, new materials and so forth. But we are able to show, for example, the ground state of smaller molecules and prove a path to where, you know, decision maker could see in a few years from now, Oh, we'll be able to actually simulate not molecules we already understand, but molecules we've never been able to study a prayer, if that makes sense. And then, >>Yeah, I think there's a key point underneath that, and I think goes back to the question that you asked earlier about the why hybrid applications inherently run on the classical infrastructure and algorithms are accelerated through qs, the quantum processing units. >>And so are you sort of time sharing in the sense that this environment that you set up starts with classical, with simulation and then you get to a point where you say, okay, we're ready, you pick up the bat phone and you say I wanna, >>I would say it's more like a partnership, really. Yeah, >>Yeah. And I think, I think it's kind of the, the way I normally describe it is, you know, we've taken a look at it it from a really kind of a software development life cycle type of perspective where again, if you follow that learn experiment, pro proof of concept, and then finally productize, we, we can cover and allow for a developer to start prototyping and proofing on simulators and when they're ready all they do is flip a switch and a manifest and they can automatically engage a qu a real quantum physical quantum system. And so we've made it super simple and very accessible in a democratizing access for developers. >>Yeah. Makes such big difference. Go ahead. >>A good analogy is to like GPUs, right? Where it's not really like, you know, you send it away, but rather the GPU accelerates certain operations. The q p. Yeah, because quantum mechanics, it turns out the universe runs on linear algebra. So one way to think about the q p is the most efficient way of doing linear algebra that exists. So lots of problems that can be expressed in that form. Combinatorial optimization problems in general, certain kinds of machine learning, et cetera, get an exponential speed up by running a section of the algorithm on the quantum computer. But of course you wouldn't like port Microsoft Word. Yeah, exactly. You know, you're not gonna do that in your product. It would be a waste of your quantum computer. >>Not just that you wanna know exactly how much money is in your bank account, not probabilistically how much might be ballpark. Yeah. Realm 10, moon ballpark, right? >>10 moon ballpark. Be using that for the rest of the show. Yeah. Oh, I love that. Ken, tell me a little bit about how you identify companies and like I n Q and and end up working with Matthew. What, what's that like, >>What's it like or how do you >>Find it's the process? Like, so, you know, let's say I've got the the >>We're not going there though. Yeah. We're not >>Personal relationship. >>Well, >>You can answer these questions however you want, you know. No, but, but what does that look like for Dell? How do you, how do you curate and figure out who you're gonna bring into this partnership nest? >>Yeah, you know, I, I think it was a, it's, it was a, a very long drawn out learning opportunity. We started actually our working quantum back in 2016. So we've been at it for a long time. And only >>In quantum would we say six years is a long time. I love >>That. Exactly. >>By the way, that was like, we've been doing this for age for a >>Long time. Yeah. Very long time before >>You were born. Yes. >>Feels like it actually, believe it or not. But, so we've been at it for a long time and you know, we went down some very specific learning paths. We took a lot of different time to, to learn about different types of qubits available, different companies, what their approaches were, et cetera. Yeah. And, and we ended up meeting up with, with I N Q and, and we also have other partners as well, like ibm, but I N q you know, we, there is a nice symbiotic relationship. We're actually doing some really cool technologies that are even much, much further ahead than the, you know, strict classical does this, quantum does that where there's significant amount of interplay between the simulation systems and between the real physical QS. And so it's, it's turning out to be a great relationship. They're, they're very easy to work with and, and a lot of fun too, as you could probably tell. Yeah. >>Clearly. So before we wrap, I've got it. Okay. Okay. So get it. Let's get, let's get, yeah, let's get deep. Let's get deep for a second or a little deeper than we've been. So our current, our current understanding of all this, of the universe, it's pretty limited. It's down to the point where we effectively have it assigned to witchcraft. It's all dark energy and dark matter. Right. What does that mean exactly? Nobody knows. But if you're in the quantum computing space and you're living this every day, do you believe that it represents the key to us understanding things that currently we just can't understand classical models, including classical computing, our brains as they're constructed aren't capable of understanding the real real that's out there. Yeah. If you're in the quantum computing space, do you possess that level of hubris? Do you think that you are gonna deliver the answers? >>I'm just like, I think the more you're in the space, the more mysterious and amazing it all seems. There's a, but there is a great quote by Richard Feinman that sort of kicked off the quantum exploration. So he gave a lecture in 1981, so, you know, long before any of this began, truly ages ago, right? Yeah. And in this lecture he said, you know, kind of wild at that time, right? We had to build these giant supercomputers to simulate just a couple atoms interacting, right? And it's kind of crazy that you need all this compute to simulate what nature does with just a handful >>Particles. Yeah. >>Really small. So, and, and famously he said, you know, nature just isn't classical. Damn it. And so you need to build a computer that works with nature to understand nature. I think, you know, the, the quantum revolution has only just begun. There's so many new things to learn, and I'm sure the quantum computers of 40 years from now are not gonna look like the, you know, the computers of today, just as the classical computers of 40 years ago look quite different to us now, >>And we're a bunch of apes. But you think we'll get there? >>I, yeah, I, I mean, I, I have, I think we have, I feel incredibly optimistic that this tool, quantum computing as a tool represents a sea change in what's possible for humans to compute. >>Yeah. I think it's that possibility. You know, I, when I tell people right now in the quantum era, we're in the inac stage of the quantum era, and so we have a long way to go, but the potential is absolutely enormous. In fact, incomprehensibly enormous, I >>Was just gonna say, I don't even think we could grasp >>In the, from the inac is they had no idea of computers inside of your hand, right? Yeah. >>They're calculating, you know, trajectories, right? Yeah. If you told them, like, we'd all be video chatting, you >>Know, >>Like, and kids would be doing synchronized dances, you know, you'd be like, What? >>I love that. Well, well, on that note, Ken Matthew, really great to have you both, everyone now will be pondering the scale and scope of the universe with their 10 moon computer, 10 moons. That's right. And, and you've given me my, my new favorite bumper sticker since we've been on a, on a roll here, David and I, which is just naturally quantum. Yeah, that's, that's, that's, that's one of my new favorite phrases from the show. Thank you both for being here. David, thank you for hanging out and thank all of you for tuning in to our cube footage live here in Dallas. We are at Supercomputing. This is our last show for the day, but we look forward to seeing you tomorrow morning. My name's Savannah Peterson. Y'all have a lovely night.

(upbeat music) >> Welcome back to San Francisco, "theCUBE" is live, our day two coverage of VMware Explore 2022 continues. Lisa Martin with Dave Nicholson. Dave and I are pleased to welcome Jason Collier, principal member of technical staff at AMD to the program. Jason, it's great to have you. >> Thank you, it's great to be here. >> So what's going on at AMD? I hear you have some juicy stuff to talk about. >> Oh, we've got a ton of juicy stuff to talk about. Clearly the Project Monterey announcement was big for us, so we've got that to talk about. Another thing that I really wanted to talk about was a tool that we created and we call it, it's the VMware Architecture Migration Tool, call it VAMT for short. It's a tool that we created and we worked together with VMware and some of their professional services crew to actually develop this tool. And it is also an open source based tool. And really the primary purpose is to easily enable you to move from one CPU architecture to another CPU architecture, and do that in a cold migration fashion. >> So we're probably not talking about CPUs from Tandy, Radio Shack systems, likely this would be what we might refer to as other X86 systems. >> Other X86 systems is a good way to refer to it. >> So it's interesting timing for the development and the release of a tool like this, because in this sort of X86 universe, there are players who have been delayed in terms of delivering their next gen stuff. My understanding is AMD has been public with the idea that they're on track for by the end of the year, Genoa, next gen architecture. So can you imagine a situation where someone has an existing set of infrastructure and they're like, hey, you know what I want to get on board, the AMD train, is this something they can use from the VMware environment? >> Absolutely, and when you think about- >> Tell us exactly what that would look like, walk us through 100 servers, VMware, 1000 VMs, just to make the math easy. What do you do? How does it work? >> So one, there's several things that the tool can do, we actually went through, the design process was quite extensive on this. And we went through all of the planning phases that you need to go through to do these VM migrations. Now this has to be a cold migration, it's not a live migration. You can't do that between the CPU architectures. But what we do is you create a list of all of the virtual machines that you want to migrate. So we take this CSV file, we import this CSV file, and we ask for things like, okay, what's the name? Where do you want to migrate it to? So from one cluster to another, what do you want to migrate it to? What are the networks that you want to move it to? And then the storage platform. So we can move storage, it could either be shared storage, or we could move say from VSAN to VSAN, however you want to set it up. So it will do those storage migrations as well. And then what happens is it's actually going to go through, it's going to shut down the VM, it's going to take a snapshot, it is going to then basically move the compute and/or storage resources over. And once it does that, it's going to power 'em back up. And it's going to check, we've got some validation tools, where it's going to make sure VM Tools comes back up where everything is copacetic, it didn't blue screen or anything like that. And once it comes back up, then everything's good, it moves onto the next one. Now a couple of things that we've got feature wise, we built into it. You can parallelize these tasks. So you can say, how many of these machines do you want to do at any given time? So it could be, say 10 machines, 50 machines, 100 machines at a time, that you want to go through and do this move. Now, if it did blue screen, it will actually roll it back to that snapshot on the origin cluster. So that there is some protection on that. A couple other things that are actually in there are things like audit tracking. So we do full audit logging on this stuff, we take a snapshot, there's basically kind of an audit trail of what happens. There's also full logging, SYS logging, and then also we'll do email reporting. So you can say, run this and then shoot me a report when this is over. Now, one other cool thing is you can also actually define a change window. So I don't want to do this in the middle of the afternoon on a Tuesday. So I want to do this later at night, over the weekend, you can actually just queue this up, set it, schedule it, it'll run. You can also define how long you want that change window to be. And what it'll do, it'll do as many as it can, then it'll effectively stop, finish up, clean up the tasks and then send you a report on what all was successfully moved. >> Okay, I'm going to go down the rabbit hole a little bit on this, 'cause I think it's important. And if I say something incorrect, you correct me. >> No problem. >> In terms of my technical understanding. >> I got you. >> So you've got a VM, essentially a virtual machine typically will consist of an entire operating system within that virtual machine. So there's a construct that containerizes, if you will, the operating system, what is the difference, where is the difference in the instruction set? Where does it lie? Is it in the OS' interaction with the CPU or is it between the construct that is the sort of wrapper around the VM that is the difference? >> It's really primarily the OS, right? And we've not really had too many issues doing this and most of the time, what is going to happen, that OS is going to boot up, it's going to recognize the architecture that it's on, it's going to see the underlying architecture, and boot up. All the major operating systems that we test worked fine. I mean, typically they're going to work on all the X86 platforms. But there might be instruction sets that are kind of enabled in one architecture that may not be in another architecture. >> And you're looking for that during this process. >> Well usually the OS itself is going to kind of detect that. So if it pops up, the one thing that is kind of a caution that you need to look for. If you've got an application that's explicitly using an instruction set that's on one CPU vendor and not the other CPU vendor. That's the one thing where you're probably going to see some application differences. That said, it'll probably be compatible, but you may not get that instruction set advantage in it. >> But this tool remediates against that. >> Yeah, and what we do, we're actually using VM Tools itself to go through and validate a lot of those components. So we'll look and make sure VM Tools is enabled in the first place, on the source system. And then when it gets to the destination system, we also look at VM Tools to see what is and what is not enabled. >> Okay, I'm going to put you on the spot here. What's the zinger, where doesn't it work? You already said cold, we understand, you can schedule for cold migrations, that's not a zinger. What's the zinger, where doesn't it work? >> It doesn't work like, live migrations just don't work. >> No live, okay, okay, no live. What about something else? What's the oh, you've got that version, you've got that version of X86 architecture, it-won't work, anything? >> A majority of those cases work, where it would fail, where it's going to kick back and say, hey, VM Tools is not installed. So where you would see this is if you're running a virtual appliance from some vendor, like insert vendor here that say, got a firewall, or got something like that, and they don't have VM Tools enabled. It's going to fail it out of the gate, and say, hey, VM Tools is not on this, you might want to manually do it. >> But you can figure out how to fix that? >> You can figure out how to do that. You can also, and there's a flag in there, so in kind of the options that you give it, you say, ignore VM Tools, don't care, move it anyway. So if you've got less, some VMs that are in there, but they're not a priority VM, then it's going to migrate just fine. >> Got It. >> Can you elaborate a little bit on the joint development work that AMD and VMware are doing together and the value in it for customers? >> Yeah, so it's one of those things we worked with VMware to basically produce this open source tool. So we did a lot of the core component and design and we actually engaged VMware Professional Services. And a big shout out to Austin Browder. He helped us a ton in this project specifically. And we basically worked, we created this, kind of co-designed, what it was going to look like. And then jointly worked together on the coding, of pulling this thing together. And then after that, and this is actually posted up on VMware's public repos now in GitHub. So you can go to GitHub, you can go to the VMware samples code, and you can download this thing that we've created. And it's really built to help ease migrations from one architecture to another. So if you're looking for a big data center move and you got a bunch of VMs to move. I mean, even if it's same architecture to same architecture, it's definitely going to ease the pain of going through and doing a migration of, it's one thing when you're doing 10 machines, but when you're doing 10,000 virtual machines, that's a different story. It gets to be quite operationally inefficient. >> I lose track after three. >> Yeah. >> So I'm good for three, not four. >> I was going to ask you what your target market segment is here. Expand on that a little bit and talk to me about who you're working with and those organizations. >> So really this is targeted toward organizations that have large deployments in enterprise, but also I think this is a big play with channel partners as well. So folks out there in the channel that are doing these migrations and they do a lot of these, when you're thinking about the small and mid-size organizations, it's a great fit for that. Especially if they're kind of doing that upgrade, the lift and shift upgrade, from here's where you've been five to seven years on an architecture and you want to move to a new architecture. This is really going to help. And this is not a point and click GUI kind of thing. It's command line driven, it's using PowerShell, we're using PowerCLI to do the majority of this work. And for channel partners, this is an excellent opportunity to put the value and the value add and VAR, And there's a lot of opportunity for, I think, channel partners to really go and take this. And once again, being open source. We expect this to be extensible, we want the community to contribute and put back into this to basically help grow it and make it a more useful tool for doing these cold migrations between CPU architectures. >> Have you seen any in the last couple of years of dynamics, obviously across the world, any industries in particular that are really leading edge for what you guys are doing? >> Yeah, that's really, really interesting. I mean, we've seen it, it's honestly been a very horizontal problem, pretty much across all vertical markets. I mean, we've seen it in financial services, we've seen it in, honestly, pretty much across the board. Manufacturing, financial services, healthcare, we have seen kind of a strong interest in that. And then also we we've actually taken this and presented this to some of our channel partners as well. And there's been a lot of interest in it. I think we presented it to about 30 different channel partners, a couple of weeks back about this. And I got contact from 30 different channel partners that said they're interested in basically helping us work on it. >> Tagging on to Lisa's question, do you have visibility into the AMD thought process around the timing of your next gen release versus others that are competitors in the marketplace? How you might leverage that in terms of programs where partners are going out and saying, hey, perfect time, you need a refresh, perfect time to look at AMD, if you haven't looked at them recently. Do you have any insight into that in what's going on? I know you're focused on this area. But what are your thoughts on, well, what's the buzz? What's the buzz inside AMD on that? >> Well, when you look overall, if you look at the Gartner Hype Cycle, when VMware was being broadly adopted, when VMware was being broadly adopted, I'm going to be blunt, and I'm going to be honest right here, AMD didn't have a horse in the race. And the majority of those VMware deployments we see are not running on AMD. Now that said, there's an extreme interest in the fact that we've got these very cored in systems that are now coming up on, now you're at that five to seven year refresh window of pulling in new hardware. And we have extremely attractive hardware when it comes to running virtualized workloads. The test cluster that I'm running at home, I've got that five to seven year old gear, and I've got some of the, even just the Milan systems that we've got. And I've got three nodes of another architecture going onto AMD. And when I got these three nodes completely maxed to the number of VMs that I can run on 'em, I'm at a quarter of the capacity of what I'm putting on the new stuff. So what you get is, I mean, we worked the numbers, and it's definitely, it's like a 30% decrease in the amount of resources that you need. >> That's a compelling number. >> It's a compelling number. >> 5%, 10%, nobody's going to do anything for that. You talk 30%. >> 30%. It's meaningful, it's meaningful. Now you you're out of Austin, right? >> Yes. >> So first thing I thought of when you talk about running clusters in your home is the cost of electricity, but you're okay. >> I'm okay. >> You don't live here, you don't live here, you don't need to worry about that. >> I'm okay. >> Do you have a favorite customer example that you think really articulates the value of AMD when you're in customer conversations and they go, why AMD and you hit back with this? >> Yeah. Actually it's funny because I had a conversation like that last night, kind of random person I met later on in the evening. We were going through this discussion and they were facing exactly this problem. They had that five to seven year infrastructure. It's funny, because the guy was a gamer too, and he's like, man, I've always been a big AMD fan, I love the CPUs all the way since back in basically the Opterons and Athlons right. He's like, I've always loved the AMD systems, loved the graphics cards. And now with what we're doing with Ryzen and all that stuff. He's always been a big AMD fan. He's like, and I'm going through doing my infrastructure refresh. And I told him, I'm just like, well, hey, talk to your VAR and have 'em plug some AMD SKUs in there from the Dells, HPs and Lenovos. And then we've got this tool to basically help make that migration easier on you. And so once we had that discussion and it was great, then he swung by the booth today and I was able to just go over, hey, this is the tool, this is how you use it, here's all the info. Call me if you need any help. >> Yeah, when we were talking earlier, we learned that you were at Scale. So what are you liking about AMD? How does that relate? >> The funny thing is this is actually the first time in my career that I've actually had a job where I didn't work for myself. I've been doing venture backed startups the last 25 years and we've raised couple hundred million dollars worth of investment over the years. And so one, I figured, here I am going to AMD, a larger corporation. I'm just like, am I going to be able to make it a year? And I have been here longer than a year and I absolutely love it. The culture at AMD is amazing. We still have that really, I mean, almost it's like that underdog mentality within the organization. And the team that I'm working with is a phenomenal team. And it's actually, our EVP and our Corp VP, were actually my executive sponsors, we were at a prior company. They were one of my executive sponsors when I was at Scale. And so my now VP boss calls me up and says, hey, I'm putting a band together, are you interested? And I was kind of enjoying a semi-retirement lifestyle. And then I'm just like, man, because it's you, yes, I am interested. And the group that we're in, the work that we're doing, the way that we're really focusing on forward looking things that are affecting the data center, what's going to be the data center like three to five years from now. It's exciting, and I am having a blast, I'm having the time of my life. I absolutely love it. >> Well, that relationship and the trust that you will have with each other, that bleeds into the customer conversations, the partner conversations, the employee conversations, it's all inextricably linked. >> Yes it is. >> And we want to know, you said three to five years out, like what? Like what? Just general futurist stuff, where do you think this is going. >> Well, it's interesting. >> So moon collides with the earth in 2025, we already know that. >> So we dialed this back to the Pensando acquisition. When you look at the Pensando acquisition and you look at basically where data centers are today, but then you look at where basically the big hyperscalers are. You look at an AWS, you look at their architecture, you specifically wrap Nitro around that, that's a very different architecture than what's being run in the data center. And when you look at what Pensando does, that's a lot of starting to bring what these real clouds out there, what these big hyperscalers are running into the grasps of the data center. And so I think you're going to see a fundamental shift. The next 10 years are going to be exciting because the way you look at a data center now, when you think of what CPUs do, what shared storage, how the networking is all set up, it ain't going to look the same. >> Okay, so the competing vision with that, to play devil's advocate, would be DPUs are kind of expensive. Why don't we just use NICs, give 'em some more bandwidth, and use the cheapest stuff. That's the competing vision. >> That could be. >> Or the alternative vision, and I imagine everything else we've experienced in our careers, they will run in parallel paths, fit for function. >> Well, parallel paths always exist, right? Otherwise, 'cause you know how many times you've heard mainframe's dead, tape's dead, spinning disk is dead. None of 'em dead, right? The reality is you get to a point within an industry where it basically goes from instead of a growth curve like that, it goes to a growth curve of like that, it's pretty flat. So from a revenue growth perspective, I don't think you're going to see the revenue growth there. I think you're going to see the revenue growth in DPUs. And when you actually take, they may be expensive now, but you look at what Monterey's doing and you look at the way that those DPUs are getting integrated in at the OEM level. It's going to be a part of it. You're going to order your VxRail and VSAN style boxes, they're going to come with them. It's going to be an integrated component. Because when you start to offload things off the CPU, you've driven your overall utilization up. When you don't have to process NSX on basically the X86, you've just freed up cores and a considerable amount of them. And you've also moved that to where there's a more intelligent place for that pack to be processed right, out here on this edge. 'Cause you know what, that might not need to go into the host bus at all. So you have just alleviated any transfers over a PCI bus, over the PCI lanes, into DRAM, all of these components, when you're like, but all to come with, oh, that bit needs to be on this other machine. So now it's coming in and it's making that decision there. And then you take and integrate that into things like the Aruba Smart Switch, that's running the Pensando technology. So now you got top of rack that is already making those intelligent routing decisions on where packets really need to go. >> Jason, thank you so much for joining us. I know you guys could keep talking. >> No, I was going to say, you're going to have to come back. You're going to have to come back. >> We've just started to peel the layers of the onion, but we really appreciate you coming by the show, talking about what AMD and VMware are doing, what you're enabling customers to achieve. Sounds like there's a lot of tailwind behind you. That's awesome. >> Yeah. >> Great stuff, thank you. >> It's a great time to be at AMD, I can tell you that. >> Oh, that's good to hear, we like it. Well, thank you again for joining us, we appreciate it. For our guest and Dave Nicholson, I'm Lisa Martin. You're watching "theCUBE Live" from San Francisco, VMware Explore 2022. We'll be back with our next guest in just a minute. (upbeat music)

>>Okay, welcome back everyone to the Cube's live coverage here in Monaco for the MoCo crypto summit. I'm John fur. You're host of the cube. We got a great guest Dustin plant Boltz who is a crypto advisor, but also the crypto editor for Forbes Monaco here. Seeing the official event, the AAL event of the Monaco crypto summit in Monaco, your coverage area for Forbes, your MCing. Welcome to the >>Cube. Thank you for having me. And it's, it's always fun when I get to have an event in our backyard, cuz I get to hear what others know. And to me I'm very curious. Yeah. Always >>Learning. So you're on the MC on the stage here, you know, queue in the program online great program. So it's innovative event, inaugural event, great name by the way. Crypto summit and mono crypto >>Summit. Yeah, the MoCo crypto summit. >>That sounds like I want to attend every year. >>You're you're more than welcome to attend next year. >>Well, I hope so. Either way. I'm at the Al event with you. So gimme the take on what's on stage. What's been the program, like what's your observations going on here at the event today? >>So what we're starting to see globally is this digitization of things and the people that are part of the innovation side. And so that's what we've been able to see this morning. We're we're now at the break is what sort of companies are out there, the good ones and what are they building? Is this innovation? Is it even innovative and figuring out how they're gonna do it and the roadmaps to getting there from the metaverses to NFTs and even to decentralized finance. >>Yeah, it's the number one question I get is what's legit. What's not legit. And then you're starting to see the, the, the wheat and the shaft separating here and you know, something called crypto winter. But I don't see it. I mean, I see correction for some of the bad things going on in terms of not having the right underpinning infrastructure, the creative ideas are amazing. We're also seeing like digital bits and other platforms kind of coming together to enable the creators and, and the NFT side for instance has been huge. What has been your observation on that enablement? Because you have two schools of thoughts. You have the total nerds we're up and down building everything. Then you have artists and creators, whether it's music, tech apps building, they don't necessarily want to get 'em to the covers. They don't want to deal with all that. Yeah. Have you seen, what's your, what's your take on that? >>So I I'm seeing that a lot of these major brands, you know, they they're striving for excellence. You know, they're being more careful of who they partner with and the types of companies and you know, they, they look at it from reality and a little tough love to figure out should they align their brand. So what we're seeing here is is that there is so much inertia moving forward. That we're just at the beginning of this thing. Yeah. McKinsey recently said that the ecosystem will be over $30 trillion. So when you recognize that we are so early and it's those right now, or some might say are the risk takers. But to me there, aren't taking risk. They're being a part of making history. >>Yeah. You get the pioneers and you get the financial. So as they come together, how do you see the market? Cause what I've noticed with crypto and here in, in this, this market is international. One lot of international finance us is kind of lag behind. You got all kinds of rules, but you got the, the combination of the, the future billionaires. Sure. Okay. The pioneers and then the financeers yeah. Coming the money, the money and the power coming together. What's your reporting show you that's going on right now? What should people know about on how this is evolving? What they shouldn't >>Expect? Well, so you have a group that wants to become cryers they're seeing these individuals globally. They're making lots and lots of money, but what they don't realize is that not everybody is gonna have that outcome, but looking at the technology aspect of it and how it's going to improve a system that many can agree is collectively broken legacy just can't move beyond. It was never designed to you'll see people take shots at certain card companies and I go, but you recognize they developed the assembly line. And so I'm seeing that the smart money they got in long ago, believe it or not. And those now they're looking out for their errors are the ones that saying, I will not have an excuse when my, my grandkids or my, my nieces or my nephews, when they come and ask, where were you when the greatest transformational shift in human history, from both education to jobs, to careers and even wealth was being shifted to a digital world, why were you on the sideline waiting? And so I think what we're gonna see is this tsunami coming, and it's gonna start with one big player and then two and five, you go, go alone. You go far, go together. You go further. And that's what we're seeing is that this collective is moving forward >>And the community, we just had Beth Kaiser on, I've known Beth for many, many years. And she's what she's her journey has done. She's had a great mission and then gets she's a data scientist and came to Analytica. Now she's doing work with Ukraine and the rallying support around it has been impressive. And it's a community vibe, but the community's not just like sympathetic they're hands on together to your point. >>Yeah. It, but it also takes courage. I mean, you look at Britney Kaiser and what she had, and to me, courage is not, not having fear. Courage is not allowing the fear to stop. You, you know, recently asked my executive coach, who's 85 and I'm turning 39. This question of, do you let fear stop you? How do you decide? And he said, you know, you can either let, you can either ride the dragon. And I said, or let the dragon chase you. And Brittany has been one of these that made a decision to do what was right. And it came down to integrity. Yeah. >>So what are you have to these days what's going on in your world? >>What is going on in my world? So I moderate events all over and I connect and I like to ask people questions. So I'm gonna ask you, I'm gonna turn at the interviewer on the >>Interview. It's good. Natural. >>What are you learning? >>I mean, I'm learning, I mean today or this week or this month or this year. Well, I was just talking with Brittany about this. The security world is converging cloud technology, cloud computing. That revolution has just been amazing. Amazon posted their earnings yesterday. They blew it away as far as I'm concerned. So they kind of show there's no tech recession. I've learned that this recession, that we're so called in is the first downturn in tech where there's been cloud players as hyperscalers as an economic engine. Okay. So from a, from a business perspective, Amazon web services, Microsoft Azure now Google cloud, Alibaba's now in, in international version. This is the first time at downturns ever happened with cloud computing as an economic engine. And so therefore what I'm seeing is the digital transformation that's happening across the world for enterprises and entrepreneurs is not stopping. >>It's actually accelerating. So although the GDPs down in inflation is down, you're seeing a massive shift continuing to accelerate, spending and transformation with cloud technologies and decentralized. So you can almost see it kind of in the, this event and other events, even some of the bigger events, the best smartest people are working on it. The applications in all the categories are transforming. If cloud is step one, decentralized gonna be step two. So I see that kind of bridge going from cloud computing, cloud native to decentralized native. And I think a D DAPP market's gonna just explode. I think NFTs are just scratched on the surface. I think that's kind of, I won't say gimmicky, but I think no, but you're right, much more of a much more of a, an illustration that there's more coming. >>There is a lot more coming because people are seeing that there's more to an NFT than an ugly luck and J you know, ugly and JP image that there's, that there's data in there. And that your avatar will be stored as just that as an NFT. And I learned today from go of sing, that decentralization is, is the key to innovation. And I agree with that statement. Holy. >>Yeah. I mean, I think access to stuff is gonna be multidimensional. Like you think about the NFT as, as an ID, whether it's him or UN unstoppable domains is that company just got financing another round where the billion dollars, their concept is like, Hey, one NFT is your access for all of your potential identities in context. >>And isn't that exciting that we're now gonna be at this stage where you travel with you. Yeah. Instead of someone else traveling with you, you get to decide who you will be. And to me, everything you're doing in this world, this reality is now becoming part of your digital asset as a whole. >>I remember when I started my podcasting company in 20 2004, early pioneers, Evan Williams was there with Odo and you had, you know, the blogging revolution going on that whole democratization wave actually didn't happen right then. But all the people that were involved in that web two oh, kind of CRAs was all about democratization. It's kind of happening now. I mean, 15, 20 years later at web services is transformed cloud the democratization for own your own data, putting users in control. And I think in the middle of that, the Facebook's the world, the world garden data, you know, manipulation kind of took it off track a little bit. So I think now I'm, I psych to see that it's back on track to where it was. I mean, Facebook made billions of dollars. Now you got LinkedIn. I mean, LinkedIn's great for your resume, but it's also become a wall's garden with no data export. >>Yeah. And then >>No APIs keep >>Changing. Think about this. That if you wanna apply for a job, just change something quickly. Yeah. Ah, now you're the senior VP. Yeah. Before you were, you're an office manager >>Like to see the immutable block change, >>You don't get to see when did the record change. Yeah. >>Reputation data. You're a digital exhaust people gonna wanna reign that in. And I think the user in charge message that Brit Kaiser was talks about is hugely a mess under, under, under amplified concept. Digital assets are key, but the data ownership is something that I think is, is >>Powerful. So I'm gonna be launching a brand new company in and around September called cryptos. And it's a crypto career center. Think of it like the, the crypto for LinkedIn, that it's an aggregator becoming the industry standard for education, becoming the industry standard for crypto ships, with partners like ledger and moon pay and Casper labs. >>Look at this, we got an exclusive scoop on the cube. This >>Is the first time I will tell you this the first time in, in an environment like this. Yeah. That I'm excited to, I'm excited to talk about, right. Because it's time to be part of the change. Yeah, exactly. You know, as a father, I look at, I know where it's headed in the world of business. I know in the world of this, that we're gonna call the internet of connected things. Yeah. That it's gonna require you to have a certain talent skill or a certain certification. And to me, it's important to have an industry that supports one >>Staff and also, and also history on misinformation, smear campaigns can happen and ruin a career >>Overnight. Can you imagine that one little thing and because the internet never forgets. Yeah. It stays around indefinitely. >>The truth has to come out. Dustin. Great to have you on the queue. Thank you so much. Final question. What have you learned in there is MC what's your takeaway real quick? >>What I've learned is I never tire of learning. Thank you again, to learn more. Dustin plan.com. >>All right. Thanks for coming. Thank you. Cube coverage here at Monaco. I'm Shawn furry. We'll back with more coverage after this short break.

(upbeat music) >> Okay, welcome back everyone. CUBE's coverage of re:MARS, here in Las Vegas, in person. I'm John Furrier, host of theCUBE. This is the analyst panel wrap up analysis of the keynote, the show, past one and a half days. We got two great guests here. We got Andy Thurai, Vice President, Principal Consultant, Constellation Research. Larry Carvalho, Principal Consultant at RobustCloud LLC. Congratulations going out on your own. >> Thank you. >> Andy, great to see you. >> Great to see you as well. >> Guys, thanks for coming out. So this is the session where we break down and analyze, you guys are analysts, industry analysts, you go to all the shows, we see each other. You guys are analyzing the landscape. What does this show mean to you guys? 'Cause this is not obvious to the normal tech follower. The insiders see the confluence of robotics, space, automation and machine learning. Obviously, it's IoTs, industrials, it's a bunch of things. But there's some dots to connect. Let's start with you, Larry. What do you see here happening at this show? >> So you got to see how Amazon started, right? When AWS started. When AWS started, it primarily took the compute storage, networking of Amazon.com and put it as a cloud service, as a service, and started selling the heck out of it. This is a stage later now that Amazon.com has done a lot of physical activity, and using AIML and the robotics, et cetera, it's now the second phase of innovation, which is beyond digital transformation of back office processes, to the transformation of physical processes where people are now actually delivering remotely and it's an amazing area. >> So back office's IT data center kind of vibe. >> Yeah. >> You're saying front end, industrial life. >> Yes. >> Life as we know it. >> Right, right. I mean, I just stopped at a booth here and they have something that helps anybody who's stuck in the house who cannot move around. But with Alexa, order some water to bring them wherever they are in the house where they're stuck in their bed. But look at the innovation that's going on there right at the edge. So I think those are... >> John: And you got the Lunar, got the sex appeal of the space, Lunar Outpost interview, >> Yes. >> those guys. They got Rover on Mars. They're going to have be colonizing the moon. >> Yes. >> I made a joke, I'm like, "Well, I left a part back on earth, I'll be right back." (Larry and Andy laugh) >> You can't drive back to the office. So a lot of challenges. Andy, what's your take of the show? Take us your analysis. What's the vibe, what's your analysis so far? >> It's a great show. So, as Larry was saying, one of the thing was that when Amazon started, right? So they were more about cloud computing. So, which means is they try to commoditize more of data center components or compute components. So that was working really well for what I call it as a compute economy, right? >> John: Mm hmm. >> And I call the newer economy as more of a AIML-based data economy. So when you move from a compute economy into a data economy, there are things that come into the forefront that never existed before, never popular before. Things like your AIML model creation, model training, model movement, model influencing, all of the above, right? And then of course the robotics has come long way since then. And then some of what they do at the store, or the charging, the whole nine yards. So, the whole concept of all of these components, when you put them on re:Invent, such a big show, it was getting lost. So that's why they don't have it for a couple of years. They had it one year. And now all of a sudden they woke up and say, "You know what? We got to do this!" >> John: Yeah. >> To bring out this critical components that we have, that's ripe, mature for the world to next component. So that's why- I think they're pretty good stuff. And some of the robotics things I saw in there, like one of them I posted on my Twitter, it's about the robot dog, sniffing out the robot rover, which I thought was pretty hilarious. (All laugh) >> Yeah, this is the thing. You're seeing like the pandemic put everything on hold on the last re:Mars, and then the whole world was upside down. But a lot of stuff pulled forward. You saw the call center stuff booming. You saw the Zoomification of our workplace. And I think a lot of people got to the realization that this hybrid, steady-state's here. And so, okay. That settles that. But the digital transformation of actually physical work? >> Andy: Yeah. >> Location, the walk in and out store right over here we've seen that's the ghost store in Seattle. We've all been there. In fact, I was kind of challenged, try to steal something. I'm like, okay- (Larry laughs) I'm pulling all my best New Jersey moves on everyone. You know? >> Andy: You'll get charged for it. >> I couldn't get away with it. Two double packs, drop it, it's smart as hell. Can't beat the system. But, you bring that to where the AI machine learning, and the robotics meet, robots. I mean, we had robots here on theCUBE. So, I think this robotics piece is a huge IoT, 'cause we've been covering industrial IoT for how many years, guys? And you could know what's going on there. Huge cyber threats. >> Mm hmm. >> Huge challenges, old antiquated OT technology. So I see a confluence in the collision between that OT getting decimated, to your point. And so, do you guys see that? I mean, am I just kind of seeing mirage? >> I don't see it'll get decimated, it'll get replaced with a newer- >> John: Dave would call me out on that. (Larry laughs) >> Decimated- >> Microsoft's going to get killed. >> I think it's going to have to be reworked. And just right now, you want do anything in a shop floor, you have to have a physical wire connected to it. Now you think about 5G coming in, and without a wire, you get minute details, you get low latency, high bandwidth. And the possibilities are endless at the edge. And I think with AWS, they got Outposts, they got Snowcone. >> John: There's a threat to them at the edge. Outpost is not doing well. You talk to anyone out there, it's like, you can't find success stories. >> Larry: Yeah. >> I'm going to get hammered by Amazon people, "Oh, what're you're saying that?" You know, EKS for example, with serverless is kicking ass too. So, I mean I'm not saying Outpost was wrong answer, it was a right at the time, what, four years ago that came out? >> Yeah. >> Okay, so, but that doesn't mean it's just theirs. You got Dell Technologies want some edge action. >> Yeah. >> So does HPE. >> Yes. >> So you got a competitive edge situation. >> I agree with that and I think that's definitely not Amazon's strong point, but like everything, they try to make it easy to use. >> John: Yeah. >> You know, you look at the AIML and they got Canvas. So Canvas says, hey, anybody can do AIML. If they can do that for the physical robotic processes, or even like with Outpost and Snowcone, that'll be good. I don't think they're there yet, and they don't have the presence in the market, >> John: Yeah. >> like HPE and, >> John: Well, let me ask you guys this question, because I think this brings up the next point. Will the best technology win or will the best solution win? Because if cloud's a platform and all software's open source, which you can make those assumptions, you then say, hey, they got this killer robotics thing going on with Artemis and Moonshot, they're trying to colonize the moon, but oh, they discovered a killer way to solve a big problem. Does something fall out of this kind of re:Mars environment, that cracks the code and radically changes and disrupts the IoT game? That's my open question. I don't know the answer. I'd love to get your take on what might be possible, what wild card's out there around, disrupting the edge. >> So one thing I see the way, so when IoT came into the world of play, it's when you're digitizing the physical world, it's IoT that does digitalization part of that actually, right? >> But then it has its own set of problems. >> John: Yeah. >> You're talking about you installing sensor everywhere, right? And not only installing your own sensor, but also you're installing competitor sensors. So in a given square feet how many sensors can you accommodate? So there are physical limitations on liabilities of bandwidth and networking all of that. >> John: And integration. >> As well. >> John: Your point. >> Right? So when that became an issue, this is where I was talking to the robotic guys here, a couple of companies, and one of the use cases they were talking about, which I thought was pretty cool, is, rather than going the sensor route, you go the robot route. So if you have either a factor that you want to map out, you put as many sensors on your robot, whatever that is, and then you make it go around, map the whole thing, and then you also do a surveillance in the whole nine yards. So, you can either have a fixed sensors or you can have moving sensors. So you can have three or four robots. So initially, when I was asking them about the price of it, when they were saying about a hundred thousand dollars, I was like, "Who would buy that?" (John and Larry laugh) >> When they then explained that, this is the use case, oh, that makes sense, because if you had to install, entire factory floor sensors, you're talking about millions of dollars. >> John: Yeah. >> But if you do the moveable sensors in this way, it's a lot cheaper. >> John: Yeah, yeah. >> So it's based on your use case, what are your use cases? What are you trying to achieve? >> The general purpose is over. >> Yeah. >> Which you're getting at, and that the enablement, this is again, this is the cloud scale open question- >> Yep. >> it's, okay, the differentiations isn't going to be open source software. That's open. >> It's going to be in the, how you configure it. >> Yes. >> What workflows you might have, the data streams. >> I think, John, you're bringing up a very good point about general purpose versus special purpose. Yesterday Zoox was on the stage and when they talked about their vehicle, it's made just for self-driving. You walk around in Vegas, over here, you see a bunch of old fashioned cars, whether they're Ford or GM- >> and they put all these devices around it, but you're still driving the same car. >> John: Yeah, exactly. >> You can retrofit those, but I don't think that kind of IoT is going to work. But if you redo the whole thing, we are going to see a significant change in how IoT delivers value all the way from the industrial to home, to healthcare, mining, agriculture, it's going to have to redo. I'll go back to the OT question. There are some OT guys, I know Rockwell and Siemens, some of them are innovating faster. The ones who innovate faster to keep up with the IT side, as well as the MLAI model are going to be the winners on that one. >> John: Yeah, I agree. Andy, your thoughts on manufacturing, you brought up the sensor thing. Robotics ultimately is, end of the day, an opportunity there. Obviously machine learning, we know what that does. As we move into these more autonomous builds, what does that look like? And is Amazon positioned well there? Obviously they have big manufacturers. Some are saying that they might want to get out of that business too, that Jassy's evaluating that some are saying. So, where does this all lead for that robotics manufacturing lifestyle, walk in, grab my food? 'Cause it's all robotics and AI at the end of the day, I got sensors, I got cameras, I got non-humans moving heavy lifting stuff, fixing the moon will be done by robots, not humans. So it's all coming. What's your analysis? >> Well, so, the point about robotics is on how far it has come, it is unbelievable, right? Couple of examples. One was that I was just talking to somebody, was explaining to them, to see that robot dog over there at the Boston Dynamics one- >> John: Yeah. >> climbing up and down the stairs. >> Larry: Yeah. >> That's more like the dinosaur movie opening the doors scene. (John and Larry laugh) It's like that for me, because the coordinated things, it is able to go walk up and down, that's unbelievable. But okay, it does that, and then there was also another video which is going on viral on the internet. This guy kicks the dog, robot dog, and then it falls down and it gets back up, and the sentiment that people were feeling for the dog, (Larry laughs) >> you can't, it's a robot, but people, it just comes at that level- >> John: Empathy, for a non-human. >> Yeah. >> But you see him, hey you, get off my lawn, you know? It's like, where are we? >> It has come to that level that people are able to kind of not look at that as a robot, but as more like a functioning, almost like a pet-level, human-level being. >> John: Yeah. >> And you saw that the human-like walking robot there as well. But to an extent, in my view, they are all still in an experimentation, innovation phase. It doesn't made it in the industrial terms yet. >> John: Yeah, not yet, it's coming. >> But, the problem- >> John: It's coming fast. That's what I'm trying to figure out is where you guys see Amazon and the industry relative to what from the fantasy coming reality- >> Right. >> of space in Mars, which is, it's intoxicating, let's face it. People love this. The nerds are all here. The geeks are all here. It's a celebration. James Hamilton's here- >> Yep. >> trying to get him on theCUBE. And he's here as a civilian. Jeff Barr, same thing. I'm here, not for Amazon, I bought a ticket. No, you didn't buy a ticket. (Larry laughs) >> I'm going to check on that. But, he's geeking out. >> Yeah. >> They're there because they want to be here. >> Yeah. >> Not because they have to work here. >> Well, I mean, the thing is, the innovation velocity has increased, because, in the past, remember, the smaller companies couldn't innovate because they don't have the platform. Now Compute is a platform available at the scale you want, AI is available at the scale. Every one of them is available at the scale you want. So if you have an idea, it's easy to innovate. The innovation velocity is high. But where I see most of the companies failing, whether startup or big company, is that you don't find the appropriate use case to solve, and then don't sell it to the right people to buy that. So if you don't find the right use case or don't sell the right value proposition to the actual buyer, >> John: Mm hmm. >> then why are you here? What are you doing? (John laughs) I mean, you're not just an invention, >> John: Eh, yeah. >> like a telephone kind of thing. >> Now, let's get into next talk track. I want to get your thoughts on the experience here at re:Mars. Obviously AWS and the Amazon people kind of combined effort between their teams. The event team does a great job. I thought the event, personally, was first class. The coffee didn't come in late today, I was complaining about that, (Larry laughs) >> people complaining out there, at CUBE reviews. But world class, high bar on the quality of the event. But you guys were involved in the analyst program. You've been through the walkthrough, some of the briefings. I couldn't do that 'cause I'm doing theCUBE interviews. What would you guys learn? What were some of the key walkaways, impressions? Amazon's putting all new teams together, seems on the analyst relations. >> Larry: Yeah. >> They got their mojo booming. They got three shows now, re:Mars, re:inforce, re:invent. >> Andy: Yeah. >> Which will be at theCUBE at all three. Now we got that coverage going, what's it like? What was the experience like? Did you feel it was good? Where do they need to improve? How would you grade the Amazon team? >> I think they did a great job over here in just bringing all the physical elements of the show. Even on the stage, where they had robots in there. It made it real and it's not just fake stuff. And every, or most of the booths out there are actually having- >> John: High quality demos. >> high quality demos. (John laughs) >> John: Not vaporware. >> Yeah, exactly. Not vaporware. >> John: I won't say the name of the company. (all laugh) >> And even the sessions were very good. They went through details. One thing that stood out, which is good, and I cover Low Code/No Code, and Low Code/No Code goes across everything. You know, you got DevOps No Low-Code Low-Code. You got AI Low Code/No Code. You got application development Low Code/No Code. What they have done with AI with Low Code/No Code is very powerful with Canvas. And I think that has really grown the adoption of AI. Because you don't have to go and train people what to do. And then, people are just saying, Hey, let me kick the tires, let me use it. Let me try it. >> John: It's going to be very interesting to see how Amazon, on that point, handles this, AWS handles this data tsunami. It's cause of Snowflake. Snowflake especially running the table >> Larry: Yeah. >> on the old Hadoop world. I think Dave had a great analysis with other colleagues last week at Snowflake Summit. But still, just scratching the surface. >> Larry: Yeah. >> The question is, how shared that ecosystem, how will that morph? 'Cause right now you've got Data Bricks, you've got Snowflake and a handful of others. Teradata's got some new chops going on there and a bunch of other folks. Some are going to win and lose in this downturn, but still, the scale that's needed is massive. >> So you got data growing so much, you were talking earlier about the growth of data and they were talking about the growth. That is a big pie and the pie can be shared by a lot of folks. I don't think- >> John: And snowflake pays AWS, remember that? >> Right, I get it. (John laughs) >> I get it. But they got very unique capabilities, just like Netflix has very unique capabilities. >> John: Yeah. >> They also pay AWS. >> John: Yeah. >> Right? But they're competing on prime. So I really think the cooperation is going to be there. >> John: Yeah. >> The pie is so big >> John: Yeah. >> that there's not going to be losers, but everybody could be winners. >> John: I'd be interested to follow up with you guys after next time we have an event together, we'll get you back on and figure out how do you measure this transitions? You went to IDC, so they had all kinds of ways to measure shipments. >> Larry: Yep. >> Even Gartner had fumbled for years, the Magic Quadrant on IaaS and PaaS when they had the market share. (Larry laughs) And then they finally bundled PaaS and IaaS together after years of my suggesting, thank you very much Gartner. (Larry laughs) But that just performs as the landscape changes so does the scoreboard. >> Yep. >> Right so, how do you measure who's winning and who's losing? How can we be critical of Amazon so they can get better? I mean, Andy Jassy always said to me, and Adam Salassi same way, we want to hear how bad we're doing so we can get better. >> Yeah. >> So they're open-minded to feedback. I mean, not (beep) posting on them, but they're open to critical feedback. What do you guys, what feedback would you give Amazon? Are they winning? I see them number one clearly over Azure, by miles. And even though Azure's kicking ass and taking names, getting back in the game, Microsoft's still behind, by a long ways, in some areas. >> Andy: Yes. In some ways. >> So, the scoreboard's changing. What's your thoughts on that? >> So, look, I mean, at the end of the day, when it comes to compute, right, Amazon is a clear winner. I mean, there are others who are catching up to it, but still, they are the established leader. And it comes with its own advantages because when you're trying to do innovation, when you're trying to do anything else, whether it's a data collection, we were talking about the data sensors, the amount of data they are collecting, whether it's the store, that self-serving store or other innovation projects, what they have going on. The storage compute and process of that requires a ton of compute. And they have that advantage with them. And, as I mentioned in my last article, one of my articles, when it comes to AIML and data programs, there is a rich and there is a poor. And the rich always gets richer because they, they have one leg up already. >> John: Yeah. >> I mean the amount of model training they have done, the billion or trillion dollar trillion parametrization, fine tuning of the model training and everything. They could do it faster. >> John: Yeah. >> Which means they have a leg up to begin with. So unless you are given an opportunity as a smaller, mid-size company to compete at them at the same level, you're going to start at the negative level to begin with. You have a lot of catch up to do. So, the other thing about Amazon is that they, when it comes to a lot of areas, they admit that they have to improve in certain areas and they're open and willing and listen to the people. >> Where are you, let's get critical. Let's do some critical analysis. Where does Amazon Websters need to get better? In your opinion, what criticism would you, in a constructive way, share? >> I think on the open source side, they need to be more proactive in, they are already, but they got to get even better than what they are. They got to engage with the community. They got to be able to talk on the open source side, hey, what are we doing? Maybe on the hardware side, can they do some open-sourcing of that? They got graviton. They got a lot of stuff. Will they be able to share the wealth with other folks, other than just being on an Amazon site, on the edge with their partners. >> John: Got it. >> If they can now take that, like you said, compute with what they have with a very end-to-end solution, the full stack. And if they can extend it, that's going to be really beneficial for them. >> Awesome. Andy, final word here. >> So one area where I think they could improve, which would be a game changer would be, right now, if you look at all of their solutions, if you look at the way they suggest implementation, the innovations, everything that comes out, comes out across very techy-oriented. The persona is very techy-oriented. Very rarely their solutions are built to the business audience or to the decision makers. So if I'm, say, an analyst, if I want to build, a business analyst rather, if I want to build a model, and then I want to deploy that or do some sort of application, mobile application, or what have you, it's a little bit hard. It's more techy-oriented. >> John: Yeah, yeah. >> So, if they could appeal or build a higher level abstraction of how to build and deploy applications for business users, or even build something industry specific, that's where a lot of the legacy companies succeeded. >> John: Yeah. >> Go after manufacturing specific or education. >> Well, we coined the term 'Supercloud' last re:Invent, and that's what we see. And Jerry Chen at Greylock calls it Castles in the Cloud, you can create these moats >> Yep. >> on top of the CapEx >> Yep. >> of Amazon. >> Exactly. >> And ride their back. >> Yep. >> And the difference in what you're paying and what you're charging, if you're good, like a Snowflake or a Mongo. I mean, Mongo's, they're just as big as Snow, if not bigger on Amazon than Snowflake is. 'Cause they use a lot of compute. No one turns off their database. (John laughs) >> Snowflake a little bit different, a little nuanced point, but, this is the new thing. You see Goldman Sachs, you got Capital One. They're building their own kind of, I call them sub clouds, but Dave Vellante says it's a Supercloud. And that essentially is the model. And then once you have a Supercloud, you say, great, I'm going to make sure it works on Azure and Google. >> Andy: Yep. >> And Alibaba if I have to. So, we're kind of seeing a playbook. >> Andy: Mm hmm. >> But you can't get it wrong 'cause it scales. >> Larry: Yeah, yeah. >> You can't scale the wrong answer. >> Andy: Yeah. >> So that seems to be what I'm watching is, who gets it right? Product market fit. Then if they roll it out to the cloud, then it becomes a Supercloud, and that's pure product market fit. So I think that's something that I've seen some people trying to figure out. And then, are you a supplier to the Superclouds? Like a Dell? Or you become an enabler? >> Andy: Yeah. >> You know, what's Dell Technologies do? >> Larry: Yeah. >> I mean, how do the box movers compete? >> Larry: I, the whole thing is now hybrid and you're going to have to see just, you said. (Larry laughs) >> John: Hybrid's a steady-state. I don't need to. >> Andy: I mean, >> By the way we're (indistinct), we can't get the chips, cause Broadcom and Apple bought 'em all. (Larry laughs) I mean there's a huge chip problem going on. >> Yes. I agree. >> Right now. >> I agree. >> I mean all these problems when you attract to a much higher level, a lot of those problems go away because you don't care about what they're using underlying as long as you deliver my solution. >> Larry: Yes. >> Yeah, it could be significantly, a little bit faster than what it used to be. But at the end of the day, are you solving my specific use case? >> John: Yeah. >> Then I'm willing to wait a little bit longer. >> John: Yeah. Time's on our side and now they're getting the right answers. Larry, Andy, thanks for coming on. This great analyst session turned into more of a podcast vibe, but you know what? (Larry laughs) To chill here at re:Mars, thanks for coming on, and we unpacked a lot. Thanks for sharing. >> Both: Thank you. >> Appreciate it. We'll get you back on. We'll get you in the rotation. We'll take it virtual. Do a panel. Do a panel, do some panels around this. >> Larry: Absolutely. >> Andy: Oh this not virtual, this physical. >> No we're live right now! (all laugh) We get back to Palo Alto. You guys are influencers. Thanks for coming on. You guys are moving the market, congratulations. Take a minute, quick minute each to plug any work you're doing for the people watching. Larry, what are you working on? Andy? You go after Larry, what you're working on. >> Yeah. So since I started my company, RobustCloud, since I left IDC about a year ago, I'm focused on edge computing, cloud-native technologies, and Low Code/No Code. And basically I help companies put their business value together. >> All right, Andy, what are you working on? >> I do a lot of work on the AIML areas. Particularly, last few of my reports are in the AI Ops incident management and ML Ops areas of how to generally improve your operations. >> John: Got it, yeah. >> In other words, how do you use the AIML to improve your IT operations? How do you use IT Ops to improve your AIML efficiency? So those are the- >> John: The real hardcore business transformation. >> Yep. >> All right. Guys, thanks so much for coming on the analyst session. We do keynote review, breaking down re:Mars after day two. We got a full day tomorrow. I'm John Furrier with theCUBE. See you next time. (pleasant music)

(upbeat music) >> Okay, welcome back everyone to theCUBE's coverage here at re:MARS. I'm John Furrier, host of theCUBE. It's the event where it's part of the "re:" series: re:MARS, re:Inforce, re:Invent. MARS stands for machine learning, automation, robotics, and space. And a lot of conversation is all about AI machine learning. This one's about AI and business transformation. We've got Stepan Pushkarev CTO, CEO, Co-Founder of Provectus. Welcome to theCUBE. And Russ Lamb, eCommerce Retail Data Engineering Lead at PepsiCo, customer story. Gentlemen, thanks for coming on theCUBE. >> Great to be here, John. >> Yeah, thanks for having us. >> I love the practical customer stories because it brings everything to life. This show is about the future, but it's got all the things we want, we love: machine learning, robotics, automation. If you're in DevOps, or you're in data engineering, this is the world of automation. So what's the relationship? You guys, you're a customer. Talk about the relationship between you guys. >> Sure, sure. Provectus as a whole is a professional services firm, premier, a AWS partner, specializing in machine learning, data, DevOps. PepsiCo is our customer, our marquee customer, lovely customer. So happy to jointly present at this re:Invent, sorry, re:MARS. Anyway, Russ... >> I made that mistake earlier, by the way, 'cause re:Invent's always on the tip of my tongue and re:MARS is just, I'm not used to it yet, but I'm getting there. Talk about what are you guys working together on? >> Well, I mean, we work with Provectus in a lot of ways. They really helped us get started within our e-commerce division with AWS, provided a lot of expertise in that regard and, you know, just hands-on experience. >> We were talking before we came on camera, you guys just had another talk and how it's all future and kind of get back to reality, Earth. >> Russ: Get back to Earth. >> If we're on earth still. We're not on Mars yet, or the moon. You know, AI's kind of got a future, but it does give a tell sign to what's coming, industrial change, full transformation, 'cause cloud does the back office. You got data centers. Now you've got cloud going to the edge with industrial spaces, the ultimate poster child of edge and automation safety. But at the end of the day, we're still in the real world. Now people got to run businesses. And I think, you know, having you here is interesting. So I have to ask you, you know, as you look at the technology, you got to see AI everywhere. And the theme here, to me, that I see is the inflection point driving all this future robotics change, that everyone's been waiting for by the way, but it's like been in movies and in novels, is the machine learning and AI as the tipping point. This is key. And now you're here integrating AI into your company. Tell us your story. >> Well, I think that every enterprise is going to need more machine learning, more, you know, AI or data science. And that's the journey that we're on right now. And we've come a long way in the past six years, particularly with our e-commerce division, it's a really data rich environment. So, you know, going from brick and mortar, you know, delivering to restaurants, vending machines and stuff, it's a whole different world when you're, people are ordering on Amazon every couple minutes, or seconds even, our products. But they, being able to track all that... >> Can you scope the problem statement and the opportunity? Because if I just kind of just, again, I'm not, you're in, it's your company, you're in the weeds, you're at the data, you're everything, But it just seems me, the world's now more integration, more different data sources. You've got suppliers, they have their different IT back ends. Some are in the cloud, some aren't in the cloud. This is, like, a hard problem when you want to bring data together. I mean, API certainly help, but can you scope the problem, and, like, what we're talking about here? >> Well, we've got so many different sources of data now, right? So we used to be relying on a couple of aggregators who would pull all this data for us and hand us an aggregated view of things. But now we're able to partner with different retailers and get detail, granular information about transactions, orders. And it's just changed the game, changed the landscape from just, like, getting a rough view, to seeing the nuts and bolts and, like, all the moving parts. >> Yeah, and you see in data engineering much more tied into like cloud scale. Then you got the data scientists, more the democratization application and enablement. So I got to ask, how did you guys connect? What was the problem statement? How did you guys, did you have smoke and fire? You came in solved the problem? Was it a growth thing? How did this, how did you guys connect as a customer with Provectus? >> Yeah, I can elaborate on that. So we were in the very beginning of that journey when there was, like, just a few people in this new startup, let's call it startup within PepsiCo. >> John: Yeah. >> Calling like a, it's not only e-commerce, it was a huge belief from the top management that it's going to bring tremendous value to the enterprise. So there was no single use case, "Hey, do this and you're going to get that." So it's a huge belief that e-commerce is the future. Some industry trends like from brand-centric to consumer-centric. So brand, product-centric. Amazon has the mission to build the most customer-centric customer company. And I believe that success, it gets a lot of enterprises are being influenced by that success. So I remember that time, PepsiCo had a huge belief. We started building just from scratch, figuring out what does the business need? What are the business use cases? We have not started with the IT. We have not started with this very complicated migrations, modernizations. >> John: So clean sheet of paper. >> Yeah. >> From scratch. >> From scratch. >> And so you got the green light. >> Yeah. >> And the leadership threw the holy water on that and said, "Hey, we'll do this."? >> That's exactly what happened. It was from the top down. The CEO kind of set aside the e-commerce vision as kind of being able to, in a rapidly evolving business place like e-commerce, it's a growing field. Not everybody's figured it out yet, but to be able to change quickly, right? The business needs to change quickly. The technology needs to change quickly. And that's what we're doing here. >> So this is interesting. A lot of companies don't have that, actually, luxury. I mean, it's still more fun because the tools are available now that all the hyper scales built on their own. I mean, back in the day, 10 years ago, they had to build it all, Facebook. You didn't know, I had people on here from Pinterest and other companies. They had to build all of that from scratch. Now cloud's here. So how did you guys do this? What was the playbook? Take us through the AI because it sounds like the AI is core, you know, belief principle of the whole entire system. What did you guys do? Take me through the journey there. >> Yeah. Beyond management decisions, strategic decisions that has been made as a separate startup, whatever- >> John: That's great. >> So some practical, tactical. So it may sound like a cliche, but it's a huge thing because I work with many enterprises and this, like, "center of excellence" that does a nice technology stuff and then looks for the budget on the different business units. It just doesn't go anywhere. It could take you forever to modernize. >> We call that the Game of Thrones environment. >> Yes. >> Yeah. Nothing ever gets done 'till it blows up at the end. >> Here, these guys, and I have to admit, I don't want to steal their thunder. I just want to emphasize it as an external person. These guys just made it so differently. >> John: Yeah. >> They even physically sat in a different office in a WeWork co-working and built that business from scratch. >> That's what Andy Jackson talked about two years ago. And if you look at some of the big successes on AWS, Capital One, all the big, Goldman Sachs. The leadership, real commitment, not like BS, like total commitment says, "Go." But enough rope to give you some room, right? >> Yeah. I think that's the thing is, there was always an IT presence, right, overseeing what we were doing within e-commerce, but we had a lot of freedoms to make design choices, technology choices, and really accelerate the business, focus on those use cases where we could make a big impact with a technology choice. >> Take me through the stages of the AI transformation. What are some of the use cases and specific tactics you guys executed on? >> Well, I think that the supply chain, which I think is a hot topic right now, but that was one use case where we're using, like, data real time, real time data to inform our sales projections and delivery logistics. But also our marketing return on investment, I feel like that was a really interesting, complex problem to solve using machine learning, Because there's so much data that we needed to process in terms of countries, territories, products, like where do you spend your limited marketing budget when you have so many choices, and, using machine learning, boil that all down to, you know, this is the optimal choice, right now. >> What were some of the challenges and how did you overcome them in the early days to get things set up, 'cause it takes a lot of energy to get it going, to get the models. What were some of the challenges and how did you overcome them? >> Well, I think some of it was expertise, right? Like having a partner like Provectus and Stepan really helped because they could guide us, Stepan could guide us, give his expertise and what he knows in terms of what he's seen to our budding and growing business. >> And what were the things that you guys saw that you contributed on? And was there anything new that you had to do together? >> Yeah, so yeah. First of all, just a very practical tip. Yes, start with the use cases. Clearly talk to the business and say, "Hey, these are the list of the use cases" and prioritize them. So not with IT, not with technology, not with the migration thing. Don't touch anything on legacy systems. Second, get data in. So you may have your legacy systems or some other third party systems that you work with. There's no AI without data. Get all the pipelines, get data. Quickly boat strap the data lake house. Put all the pipelines, all the governance in place. And yeah, literally took us three months to get up and running. And we started delivering first analytical reports. It's just to have something back to business and keep going. >> By the way, that's huge, speed. I mean, this is speed. You go back and had that baggage of IT and the old antiquated systems, you'd be dragging probably months. Right? >> It's years, years. Imagine you should migrate SAP to the cloud first. No, you don't do don't need to do that. >> Pipeline. >> Just get data. I need data. >> Stream that data. All right, where are we now? When did you guys start? I want to get just going to timeline my head 'cause I heard three months. Where are we now? You guys threw it. Now you have impact. You have, you have results. >> Yeah. I mean that for our marketing ROI engine, we've built it and it's developed within e-commerce, but we've started to spread it throughout the organization now. So it's not just about the digital and the e-commerce space. We're deploying it to, you know, regionally to other, to Europe, to Latin America, other divisions within PepsiCo. And it's just grown exponentially. >> So you have scale to it right now? >> Yeah. Well- >> How far are you in now? What, how many years, months, days? >> E-commerce, the division was created six years ago, which is, so we've had some time to develop this, our machine learning capabilities and this use case particular, but it's increasingly relevant and expansion is happening as we speak. >> What are you most proud of? You look back at the impact. What are you most proud of? >> I think the relationship we built with the people, you know, who use our technology, right. Just seeing the impact is what makes me proud. >> Can you give an example without revealing any confidential information? >> Yeah. Yeah. I mean, there was an example from my talk about, I was approached recently by our sales team. They were having difficulty with supply chain, monitoring our fill rate of our top brands with these retailers. And they come up to me, they have this problem. They're like, "How do we solve it?" So we work together to find a data source, just start getting that data in the hands of people who can use it within days. You know, not talking like a long time. Bring that data into our data warehouse, and then surface the data in a tool they can use, you know, within a matter of a week or two. >> I mean, the transformation is just incredible. In fact, we were talking on theCUBE earlier today around, you know, data warehouses in the cloud, data meshes of different pros and cons. And the theme that came out of that conversation was data's a product now. >> Yes. >> Yes. >> And what you're kind of describing is, just gimme the product or find it. >> Russ: Right. >> And bring it in with everything else. And there's some, you know, cleaning and stuff people do if they have issues with that. But, if not, it's just bring it in, right? It's a product. >> Well, especially with the data exchanges now. AWS has a data exchange and this, I think, is the future of data and what's possible with data because you don't have to start from, okay, I've got this Excel file somebody's been working with on their desktop. This is a, someone's taken that file, put it into a warehouse or a data model, and then they can share it with you. >> John: So are you happy with these guys? >> Absolutely, yeah. >> You're actually telling the story. What was the biggest impact that they did? Was it partnering? Was it writing code, bringing development in, counseling, all the above, managed services? What? >> I think the biggest impact was the idea, you know, like being able to bring ideas to the table and not just, you know, ask us what we want, right? Like I think Provectus is a true partner and was able to share that sort of expertise with us. >> You know, Andy Jackson, whenever I interview on theCUBE, he's now in charge of all Amazon. But when he was at (inaudible). He always had to use their learnings, get the learnings out. What was the learnings you look back now and say, Hey, those were tough times. We overcome them. We stopped, we started, we iterated, we kept moving forward. What was the big learning as you look back, some of the key success points, maybe some failures that you overcome. What was the big learnings that you could share with folks out there now that are in the same situation where they're saying, "Hey, I'd rather start from scratch and do a reset." >> Yeah. So with that in particular, yes, we started this like sort of startup within the enterprise, but now we've got to integrate, right? It's been six years and e-commerce is now sharing our data with the rest of the organization. How do we do that, right? There's an enterprise solution, and we've got this scrappy or, I mean, not scrappy anymore, but we've got our own, you know, way of doing. >> Kind of boot strap. I mean, you were kind of given charter. It's a start up within a big company, I mean- >> But our data platform now is robust, and it's one of the best I've seen. But how do we now get those systems to talk? And I think Provectus has came to us with, "Here, there's this idea called data mesh, where you can, you know, have these two independent platforms, but share the data in a centralized way. >> So you guys are obviously have a data mesh in place, big part of the architecture? >> So it is in progress, but we know the next step. So we know the next step. We know the next two steps, what we're going to do, what we need to do to make it really, to have that common method, data layer. between different data products within organization, different locations, different business units. So they can start talking to each other through the data and have specific escalates on the data. And yeah. >> It's smart because I think one of the things that people, I think, I'd love to get your reaction to this is that we've been telling the story for many, many years, you have horizontally scalable cloud and vertically specialized domain solutions, you need machine learning that's smart, but you need a lot of data to help it. And that's not, a new architecture, that's a data plane, it's control plane, but now everyone goes, "Okay, let's do silos." And they forget the scale side. And then they go, "Wait a minute." You know, "I'm not going to share it." And so you have this new debate of, and I want to own my own data. So the data layer becomes an interesting conversation. >> Yeah, yes. Meta data. >> Yeah. So what, how do you guys see that? Because this becomes a super important kind of decision point architecturally. >> I mean, my take is that there has to be some, there will always be domains, right? Everyone, like there's only so much that you can find commonality across, like in industry, for example. But there will always be a data owner. And, you know, kind of like what happened with rush to APIs, how that enabled microservices within applications and being sharing in a standardized way, I think something like that has to happen in the data space. So it's not a monolithic data warehouse, it's- >> You know, the other thing I want to ask you guys both, if you don't mind commenting while I got you here, 'cause you're both experts. >> We just did a showcase on data programmability. Kind of a radical idea, but like data as code, we called it. >> Oh yeah. >> And so if data's a product and you're acting on, you've got an architecture and system set up, you got to might code it's programmable. You need you're coding with data. Data becomes like a part of the development process. What do you guys think of when you hear data as code and data being programmable? >> Yeah, it's a interesting, so yeah, first of all, I think Russ can elaborate on that, Data engineering is also software engineering. Machine learning engineering is a software. At the end of the day, it's all product. So we can use different terms and buzz words for that but this is what we have at the end of the day. So having the data, well I will use another buzz word, but in terms of the headless architecture- >> Yes. >> When you have a nice SDK, nice API, but you can manipulate with the data as your programming object to build reach applications for your users, and give it, and share not as just a table in Redshift or a bunch of CSV files in S3 bucket, but share it as a programmable thing that you can work with. >> Data as code. >> Yeah. This is- >> Infrastructure code was a revolution for DevOps, but it's not AI Ops so it's something different. It's really it's data engineering. It's programming. >> Yeah. This is the way to deliver data to your consumers. So there are different ways you can show it on a dashboard. You can show it, you can expose it as an API, or you can give it as an object, programmable interface. >> So now you're set up with a data architecture that's extensible 'cause that's the goal. You don't want to foreclose. You must think about that must keep you up at night. What's going to foreclose that benefit? 'Cause there's more coming. Right? >> Absolutely. There's always more coming. And I think that's why it's important to have that robust data platform to work from. And yeah, as Stepan mentioned, I'm a big believer in data engineering as software engineering. It's not some like it's not completely separate. You have to follow the best practices software engineers practice. And, you know, really think about maintainability and scalability. >> You know, we were riffing about how cloud had the SRE managing all those servers. One person, data engineering has a many, a one to many relationships too. You got a lot going on. It's not managing a database. It's millions of data points and data opportunity. So gentlemen, thanks for coming on theCUBE. I really appreciate it. And thanks for telling the story of Pepsi. >> Of course, >> And great conversation. Congratulations on this great customer. And thanks for >> coming on theCUBE. >> Thanks, thank you. Thanks, Russ, would you like to wrap it up with the pantry shops story? >> Oh, yeah! I think it will just be a super relevant evidence of the agility and speed and some real world applicable >> Let's go. Close us out. >> So when, when the pandemic happened and there were lockdowns everywhere, people started buying things online. And we noticed this and got a challenge from our direct to consumer team saying, "Look, we need a storefront to be able to sell to our consumers, and we've got 30 days to do it." We need to be able to work fast. And so we built not just a website, but like everything that behind it, the logistics of supply chain aspects, the data platform. And we didn't just build one. We built two. We got pantry shop.com and snacks.com, within 30 days. >> Good domains! >> The domain broker was happy on that one. Well continue the story. >> Yeah, yeah. So I feel like that the agility that's required for that kind of thing and the like the planning to be able to scale from just, you know, an idea to something that people can use every day. And, and that's, I think.- >> And you know, that's a great point too, that shows if you're in the cloud, you're doing the work you're prepared for anything. The pandemic was the true test for who was ready because it was unforeseen force majeure. It was just like here it comes and the people who were in the cloud had that set up, could move quickly. The ones that couldn't. >> Exactly. >> We know what happened. >> And I would like to echo this. So they have built not just a website, they have built the whole business line within, and launched that successfully to production. That includes sales, marketing, supply chain, e-commerce, aside within 30 days. And that's just a role model that could be used by other enterprises. >> Yeah. And it was not possible without, first of all, right culture. And second, without cloud Amazon elasticity and all the tools that we have in place. >> Well, the right architecture allows for scale. That's the whole, I mean, you did everything right at the architecture that's scale. I mean, you're scaling. >> And we empower our engineers to make those choices, right. We're not, like, super bureaucratic where every decision has to be approved by the manager or the managers manager. The engineers have the power to just make good decisions, and that's how we move fast. >> That's exactly the future right there. And this is what it's all about. Reliability, scale agility, the ability to react and have applications roll out on top of it without long timeframes. Congratulations. Thanks for being on theCUBE. Appreciate it. All right. >> Thank you. >> Okay, you're watching theCUBE here at re:MARS 2020, I'm John Furrier. Stay tuned. We've got more coverage coming after this short break. (upbeat music)

(inspiring music) >> Welcome back to The Cube's coverage here in Las Vegas. I'm John Furrier for re:Mars coverage. Two days of live action, a lot of things happening in space, robotics, automation, and machine learning. That's Mars spelled backwards, but that's machine learning, automation, robotics and space. Got a great guest, Jens Ortmann, associate director at Boston Consulting Group, also known as BCG. Jens, welcome to The Cube. >> Thank you very much. >> So tell me what you're working on. You've got a very cool project you're working on, 'Involved'. Take us through what it is, explain what the project is. >> Yeah, so I'm part of the data science unit within BCG Gamma and I'm focusing on solving business problems for the automotive industry. What I would like to talk about is actually a small internal site project that we were building. It's a conversion rate engine, where we built an advanced analytics tool that computes the conversion rate for car dealerships, at scale. So for every single car dealer in a market, we can compute the conversion rate. >> John: What is a conversion rate? Can you explain that? >> So a conversion rate is very simple. It's actually out of the people that come into your car dealership, how many do you, as a car dealer, manage to sell a car to? >> So, what's your sell, through monthly kind of- >> Per visitor that come into, so your walk-ins. >> So, physical? >> Physical, yeah. So this was for physical stores. It's actually a key metric for sales performance for car dealerships, or for the automotive manufacturers to be aware of. >> So I'm watching here in the show floor at re:Mars, you've got the 'Just Walk Through', which is Amazon's 'take whatever you want and go', are you seeing you're getting analytics on like people coming in, you can see them, there's a drop off rate? Take me through how it works, the challenges because I don't envision like, "Oh, so they walked in and they left but they didn't leave with a car." It's not take and walk out, it's not grab and go. But the concept of using computer vision, I can imagine it being a popular thing. So how do you measure this, people coming in? >> It's actually a big challenge that we learned when we were doing this project. Traditionally, people were measuring it with like these laser sensors but the signal is very, very messy. Now when we wanted to do it at scale, we partnered with an Israeli startup called Play Sense, who aggregate mobile phone data. So we used mobile footfall data to measure how many people visit a store. So it actually is a combination of three main data sources to get to the conversion rate. One, as I mentioned, the mobile footfall data, the second one is building footprints, actual outlines of buildings that we source from the cadastral agency that we need to use it to cut out the footfall data to get the visitors. And the third one, of course, is sales that we get from the official car registration data. Then we combine those to have the key numbers. >> Is there a facial recognition involved in this? >> There's no facial recognition involved. >> So the tire kickers that come in and kick the tires and leave, but might come back. Is that factored in too, or? >> So there is a lot of pre-processing going on to really only get the signals from visitors. So filtering out people that maybe come into the store after hours, cleaning crews, people that come into the store every day, people that work there, they would be in the footfall data. So we applied some logic to identify exactly those people that are most likely actually visitors interested in buying a car >> Well everyone can relate to buying a car, obviously. I wanted you to step back and you mentioned scale. Can you scope the scale of the problem for us? How big is this observation space? What systems are involved? 'Cause when you say scale, I'm thinking all the dealerships in the aggregate. Or, is it by franchise or is it anonymous data? Can you scale the scope of this thing, or scope the scale? >> So we built this as a prototype for the German market and we used the top 10 car brands in Germany. They have around 10,000 car dealerships, for which we all have data. The actual mobile phone footprint data, it's a lot more. I think it was 30 million data points. >> Are you triangulating that? How does that mobile data work? Signal? >> So the mobile data is coming through apps. So mobile apps where you allow the app to track your location. >> Got it, okay. >> That gets anonymized and then you have these mobile data aggregators, like Play Sense. >> Got it, okay. >> That sell the data on. >> So you have to plug into a lot of systems? >> Yes. >> To make all this work. >> Yes and a lot of different data sources. >> And how easy is that? What's the challenge there? Is it cloud integration? How are you guys pulling this together? >> So we build it as a prototype initially, based on our own internal infrastructure, using basic Python and regular cloud infrastructure to process the data. >> All right, so I'm looking at my notes here. Data sets, you have a lot of data sets. What kind of analytics are you running on that? Can you share some examples? >> So I have to be careful since we filed a patent on this but a lot of the thing is actually in data processing, making sure that the data points we get are accurate and usable for this, and then differentiating between the different types of businesses that people are running. So there is on the one hand, you have the problem of outliers, basically filtering out when numbers don't make sense. On the other hand, there is a lot going on in the business itself. Like what do you do when a car dealership sells cars of multiple brands? You see only one visitor seeing cars of different brands but you see sales for two different types of brands. So this is just two examples of some of the processing that we had to implement to make this happen. >> So where can people find out information on this project? Or is it pretty much not public? Are you sharing anything publicly? >> So currently, we have held off the publication on this because we filed a patent on it. We're now about to go to market, building out a solution for the US as well, to then bring this to clients. >> What do you think about this show here at re:Mars? What's your assessment of the vibe? What's it like? Share with the folks who aren't here, what's your takeaway? >> It's really fun. It's really impressive. And it has a great, really inspiring vibe of cool innovative solutions. >> Yeah, you get the creative geniuses, you got the industrial geniuses, you got the software geniuses, all kind of coming together, and they're real people and they're here as a community. To me, the positive future vibe of this show, really is resonating in the keynotes and the energy. It's a forward thinking, positive message. And it's not marketing, this is the vibe. >> Exactly, I think it's something we really need at the moment. >> Yeah, we can solve all of the global problems by going to the moon and Mars. First the moon, then Mars. Who knows, maybe the breakthrough is there. >> People solve a lot of fundamental issues along the way that'll help in a lot of different areas as well. >> I wonder if I'll be alive when there's tourism in the moon. I was just joking with the folks earlier, "Oh yeah, I left that part on Earth, I have to go get it." Cause there's going to be a whole infrastructure there. Construction, all in good time. Okay, what's next for you guys? Tell me what's next on the project. You got a patent pending, so you're a little bit tight lipped and quiet on the secret sauce, I get that. What's next for the vision of the project? >> So this is just one example of how we can use this. Especially this footfall data set in an innovative way in the automotive industry. What we would like to look into is getting more details. Currently, we only see a single data point for a visitor. What would be interesting to understand, also, like the journey of visitors. Did they visit other car dealerships? Or, where are they from? What demographics do they come from? If you can tie that to a geographic location. And then on the business side as well, linking this for example, for companies to marketing campaigns. Does advertisement catch on? Do discounts catch on? Do they drive more people into the stores? Do they drive more sales? How does it affect conversion rate? Also, benchmark within the network, how different car dealerships are performing, how different brands are performing. And then eventually, everything is going to online. This can also be a foundation to set a baseline for online sales, which is still at the very early stages in the automotive industry. >> Yeah, I think there's a lot of reference implementations here for other applications, not just dealerships, all footfall traffic. That's interesting. The question I have for you, and the final question really before we wrap up, is the convergence of online, offline, physical, virtual. It's pretty clear we're living in a hybrid steady state right now, with all the post pandemic and the innovations pulled forward. So, having a device on me, IOT device or phone, will be a big part of things. So I'm buying online and I'm walking in, I'm one presence, virtually and physical. How do you guys see that around the corner? What's next there? Because I can see that coming together in my mind. >> It is. I mean, we can see it happen at Tesla. Tesla barely has any physical dealerships anymore, they have showrooms and do all the sales online. And I think that has a large impact on the industry at the moment. Driving the more traditional manufacturers also to think about what can be and what can be in a digital and online first world. >> Yeah, well this is happening. Well, Jens, thanks for coming on. I appreciate the commentary on re:Mars. Thanks for sharing your perspective and sharing about your project at Boston Consulting Group, also known as BCG. >> Thank you very much. >> Very reputable firm. Okay, that's the Cube coverage here at re:Mars. I'm John Furrier, your host. Two days of wall to wall coverage here. It's a great show. Machine learning, automation, robotics, and space, Mars. Of course, you got Reinvent, the big show, and at Reinforce, the security show. You got the space-software-robotics show, security. And then of course Reinvent is the big show. The Cube covers it, all three will be here. So keep watching here for more coverage. We'll be right back. (gentle inspiring music)

>>Okay, welcome back. Everyone's Cube's coverage here in Las Vegas for Amazon re Mars machine learning, automation, robotics, and space out. John fir host of the queue. Got a great set of guests here talking about AI, Jason Montgomery CTO and co-founder man and Ryans CEO, founder guys. Thanks for coming on. We're just chatting, lost my train of thought. Cuz we were chatting about something else, your history with DataRobot and, and your backgrounds entrepreneurs. Welcome to the queue. Thanks >>Tur. Thanks for having >>Us. So first, before we get into the conversation, tell me about the company. You guys have a history together, multiple startups, multiple exits. What are you guys working on? Obviously AI is hot here as part of the show. M is Mars machine learning, which we all know is the basis for AI. What's the story. >>Yeah, really. We're we're here for two of the letters and Mars. We're here for the machine learning and the automation part. So at the high level, man is a no code AI application development platform. And basically anybody could log in and start making AI applications. It could be anything from just texting it with the Twilio integration to tell you that you're doing great or that you need to exercise more to integrating with zenes to get support tickets classified. >>So Jason, we were talking too about before he came on camera about the cloud and how you can spin up resources. The data world is coming together and I, and I like to see two flash points. The, I call it the 2010 big data era that began and then failed Hadoop crashed and burned. Yeah. Then out of the, out of the woodwork came data robots and the data stacks and the snowflakes >>Data break snowflake. >>And now you have that world coming back at scale. So we're now seeing a huge era of, I need to stand up infrastructure and platform to do all this heavy lifting. I don't have time to do. Right. That sounds like what you guys are doing. Is that kind of the case? >>That's absolutely correct. Yeah. Typically you would have to hire a whole team. It would take you months to sort of get the infrastructure automation in place, the dev ops DevOps pipelines together. And to do the automation to spin up, spin down, scale up scale down requires a lot of special expertise with, you know, Kubernetes. Yeah. And a lot of the other data pipelines and a lot of the AWS technologies. So we automate a lot of that. So >>If, if DevOps did what they did, infrastructure has code. Yeah. Data has code. This is kind of like that. It's not data ops per se. Is there a category? How do you see this? Cuz it's you could say data ops, but that's also it's DevOps dev. It's a lot going on. Oh yeah. It's not just seeing AI ops, right? There's a lot more, what, what would you call this? >>It's a good question. I don't know if we've quite come up with the name. I know >>It's not data ops. It's not >>Like we call it AI process automation >>SSPA instead of RPA, >>What RPA promised to be. Yes, >>Exactly. But what's the challenge. The number one problem is it's I would say not, not so much all on ever on undifferent heavy lifting. It's a lot of heavy lifting that for sure. Yes. What's involved. What's the consequences of not going this way. If I want to do it myself, can you take me through the, the pros and cons of what the scale scope, the scale of without you guys? >>Yeah. Historically you needed to curate all your data, bring it together and have some sort of data lake or something like that. And then you had to do really a lot of feature engineering and a lot of other sort of data science on the back end and automate the whole thing and deploy it and get it out there. It's a, it's a pretty rigorous and, and challenging problem that, you know, we there's a lot of automation platforms for, but they typically focus on data scientists with these large language models we're using they're pre-trained. So you've sort of taken out that whole first step of all that data collection to start out and you can basically start prototyping almost instantly because they've already got like 6 billion parameters, 10 billion parameters in them. They understand the human language really well. And a lot of other problems. I dunno if you have anything you wanna add to that, Ryan, but >>Yeah, I think the other part is we deal with a lot of organizations that don't have big it teams. Yeah. And it would be impossible quite frankly, for them to ever do something like deploy text, track as an example. Yeah. They're just not gonna do it, but now they can come to us. They know the problem they want solved. They know that they have all these invoices as an example and they wanna run it through a text track. And now with us they can just drag and drop and say, yeah, we want tech extract. Then we wanted to go through this. This is what we >>Want. Expertise is a huge problem. And the fact that it's changing too, right? Yeah. Put that out there. You guys say, you know, cybersecurity challenges. We guys do have a background on that. So you know, all the cutting edge. So this just seems to be this it, I hate to say transformation. Cause I not the word I'm looking for, I'd say stuck in the mud kind of scenario where they can't, they have to get bigger, faster. Yeah. And the scale is bigger and they don't have the people to do it. So you're seeing the rise of managed service. You mentioned Kubernetes, right? I know this young 21 year old kid, he's got a great business. He runs a managed service. Yep. Just for Kubernetes. Why? Because no, one's there to stand up the clusters. >>Yeah. >>It's a big gap. >>So this, you have these sets of services coming in now, where, where do you guys fit into that conversation? If I'm the customer? My problem is what, what is my, what is my problem that I need you guys for? What does it look like to describe my problem? >>Typically you actually, you, you kind of know that your employees are spending a lot of time, a lot of hours. So I'll just give you a real example. We have a customer that they were spending 60 hours a week just reviewing these accounts, payable, invoices, 60 hours a week on that. And they knew there had to be a better way. So manual review manual, like when we got their data, they were showing us these invoices and they had to have their people circle the total on the invoice, highlight the customer name, the >>Person who quit the next day. Right? >>No like they, they, Hey, you know, they had four people doing this, I think. And the point is, is they come to us and we say, well, you know, AI can, can just basically using something like text track can just do this. And then we can enrich those outputs from text track with the AI. So that's where the transformers come in. And when we showed them that and got them up and running in about 30 minutes, they were mind blown. Yeah. And now this is a company that doesn't have a big it department. So the >>Kind, and they had the ability to quantify the problem >>They knew. And, and in this case it was actually a business user. It was not a technical >>In is our she consequence technical it's hours. She consequences that's wasted. Manual, labor wasted. >>Exactly. Yeah. And, and to their point, it was look, we have way more high, valuable tasks that our people could be doing yeah. Than doing this AP thing. It takes 60 hours. And I think that's really important to remember about AI. What're I don't think it's gonna automate away people's jobs. Yeah. What it's going to do is it's going to free us up to focus on what really matters and focus on the high value stuff. And that's what people should >>Be doing. I know it's a cliche. I'm gonna say it again. Cause I keep saying, cause I keep saying for people to listen, the bank teller argument always was the big thing. Oh yeah. They're gonna get killed by the ATM machine. No, they're opening up more branches. That's right. That's right. So it's like, come on. People let's get, get over that. So I, I definitely agree with that. Then the question, next question is what's your secret sauce? I'm the customer I'm gonna like that value proposition. You make something go away. It's a pain relief. Then there's the growth side. Okay. You can solve from problems. Now I want this, the, the vitamin you got aspirin. And I want the vitamin. What's the growth angle for you guys with your customers. What's the big learnings. Once they get the beach head with problem solving. >>I think it, it, it it's the big one is let's say that we start with the account payable thing because it's so our platform's so approachable. They go in and then they start tinkering with the initial, we'll call it a template. So they might say, Hey, you know what, actually, in this edge case, I'm gonna play with this. And not only do I want it to go to our accounting system, but if it's this edge case, I want it to email me. So they'll just drag and drop an email block into our canvas. And now they're making it >>Their own. There is the no code, low code's situation. They're essentially building a notification engine under the covers. They have no idea what they're doing. That's >>Right. They get the, they just know that, Hey, you know what? When, when like the amount's over $10,000, I want an email. They know that's what they want. They don't, they don't know that's the notification engine. Of >>Course that's value email. Exactly. I get what I wanted. All right. So tell me about the secret sauce. What's under the covers. What's the big, big, big scale, valuable, valuable, secret sauce. >>I would say part of it. And, and honestly, the reason that we're able to do this now is transformer architecture. When the transformer papers came out and then of course the attention is all you need paper, those kind of unlocked it and made this all possible. Beyond that. I think the other secret sauce we've been doing this a long time. >>So we kind of, we know we're in the paid points. We went to those band points. Cause we weren't data scientists or ML people. >>Yeah. >>Yeah. You, you walked the snow and no shoes on in the winter. That's right. These kids now got boots on. They're all happy. You've installed machines. You've loaded OSS on, on top of rack switches. Yeah. I mean, it's unbelievable how awesome it's right now to be a developer and now a business user's doing the low code. Yep. If you have the system architecture set up, so back to the data engineering side, you guys had the experience got you here. This is a big discussion right now. We're having in, in, on the cube and many conversations like the server market, you had that go away through Amazon and Google was one of the first, obviously the board, but the idea that servers could be everywhere. So the SRE role came out the site reliability engineer, right. Which was one guy or gal and zillions of servers. Now you're seeing the same kind of role with data engineering. And then there's not a lot of people that fit the requirement of being a data engineer. It's like, yeah, it's very unique. Cause you're dealing with a system architecture, not data science. So start to see the role of this, this, this new persona, because they're taking on all the manual challenges of doing that. You guys are kind of replaced that I think. Well, do you agree with it about the data engineer? First of all? >>I think, yeah. Well and it's different cuz there's the older data engineer and then there's sort of the newer cloud aware one who knows how to use all the cloud technologies. And so when you're trying, we've tried to hire some of those and it's like, okay, you're really familiar with old database technology, but can you orchestrate that in a serverless environment with a lot of AWS technology for instance. And it's, and that's hard though. They don't, they don't, there's not a lot of people who know that space, >>So there's no real curriculum out there. That's gonna teach you how to handle, you know, ETL. And also like I got I'm on stream data from this source. Right. I'm using sequel I'm I got put all together. >>Yeah. So it's yeah, it's a lot of just not >>Data science. It's >>Figure that out. So its a large language models too. We don't have to worry about some of the data there too. It's it's already, you know, codified in the model. And then as we collect data, as people use our platform, they can then curate data. They want to annotate or enrich the model with so that it works better as it goes. So we're kind of curating, collecting the data as it's used. So as it evolves, it just gets better. >>Well, you guys obviously have a lot of experience together and congratulations on the venture. Thank you. What's going on here at re Mars. Why are you here? What's the pitch. What's the story. Where's your, you got two letters. You got the, you got the M for the machine learning and AI and you got the, a for automation. What's the ecosystem here for you? What are you doing? >>Well, I mean, I think you, you kind of said it right. We're here because the machine learning and the automation part, >>But >>More, more widely than that. I mean we work very, very closely with Amazon on a number of front things like text track, transcribe Alexa, basically all these AWS services are just integrations within our system. So you might want to hook up your AI to an Alexa so that you could say, Hey Alexa, tell me updates about my LinkedIn feed. I don't know, whatever, whatever your hearts content >>Is. Well what about this cube transcription? >>Yeah, exactly. A hundred percent. >>Yeah. We could do that. You know, feed all this in there and then we could do summarization of everything >>Here, >>Q and a extraction >>And say, Hey, these guys are >>Technicals. Yeah, >>There you go. No, they mentioned Kubernetes. We didn't say serverless chef puppet. Those are words straight, you know, and no linguistics matters right into that's a service that no one's ever gonna build. >>Well, and actually on that point, really interesting. We work with some healthcare companies and when you're basically, when people call in and they call into the insurance, they have a question about their, what like is this gonna be covered? And what they want to key in on are things like I just went to my doctor and got a cancer diagnosis. So the, the, the relevant thing here is they just got this diagnosis. And why is that important? Well, because if you just got a diagnosis, they want to start a certain triage to make you successful with your treatments. Because obviously there's an >>Incentive to do time. That time series matters and, and data exactly. And machine learning reacts to it. But also it could be fed back old data. It used to be time series to store it. Yeah. But now you could reuse it to see how to make the machine learning better. Are you guys doing anything, anything around that, how to make that machine learning smarter, look doing look backs or maybe not the right word, but because you have data, I might as well look back at it's happened. >>So part of, part of our platform and part of what we do is as people use these applications, to your point, there's lots of data that's getting generated, but we capture all that. And that becomes now a labeled data set within our platform. And you can take that label data set and do something called fine tuning, which just makes the underlying model more and more yours. It's proprietary. The more you do it. And it's more accurate. Usually the more you do it. >>So yeah, we keep all that. I wanna ask your reaction on this is a good point. The competitive advantage in the intellectual property is gonna be the workflows. And so the data is the IP. If this refinement happens, that becomes intellectual property. Yeah. That's kind of not software. It's the data modeling. It's the data itself is worth something. Are you guys seeing that? >>Yeah. And actually how we position the company is man team is a control plane and you retain ownership of the data plane. So it is your intellectual property. Yeah. It's in your system, it's in your AWS environment. >>That's not what everyone else is doing. Everyone wants to be the control plane and the data plan. We >>Don't wanna own your data. We don't, it's a compliance and security nightmare. Yeah. >>Let's be, Real's the question. What do you optimize for? Great. And I think that's a fair, a fair bet. Given the fact that clients want to be more agile with their data anyway, and the more restrictions you put on them, why would that this only gets you in trouble? Yeah. I could see that being a and plus lock. In's gonna be a huge factor. Yeah. I think this is coming fast and no one's talking about it in the press, but everyone's like run to silos, be a silo and that's not how data works. No. So the question is how do you create siloing of data for say domain specific applications while maintaining a horizontally scalable data plan or control plan that seems to be kind of disconnected everyone to lock in their data. What do you guys think about that? This industry transition we're in now because it seems people are reverting back to fourth grade, right. And to, you know, back to silos. >>Yeah. I think, well, I think the companies probably want their silo of data, their IP. And so as they refine their models and, and we give them the ability to deploy it in their own stage maker and their own VPC, they, they retain and own it. They can actually get rid of us and they still have that model. Now they may have to build, you know, a lot of pipelines and other technology to support it. But well, >>Your lock in is usability. Exactly. And value. Yeah. Value proposition is the lock in bingo. That's not counterintuitive. Exactly. Yeah. You say, Hey, more value. How do I wanna get rid of it? Valuable. I'll pay for it. Right. As long as you have multiple value, step up. And that's what cloud does. I mean, think that's the thing about cloud. That's gonna make all this work. In my opinion, the value enablement is much higher. Yeah. So good business model. Anything else here at the show that you observed that you like, that you think people would be interested in? What's the most important story coming out of the, the holistic, if you zoom up and look at re Mars, what's, what's coming out of the vibe. >>You know, one thing that I think about a lot is we're, you know, we have Artis here, humanity hopefully soon gonna be going to Mars. And I think that's really, really exciting. And I also think when we go to Mars, we're probably not gonna send a bunch of software engineers up there. >>Right. So like robots will do break fix now. So, you know, we're good. It's gone. So services are gonna be easy. >>Yeah. But I, oh, >>I left that device back at earth. I just think that's not gonna be good. Just >>Replicated it in one. I think there's like an eight >>Minute, the first monopoly on next day delivery in space. >>They'll just have a spaceship that sends out drones to Barss. Yeah. But I think that when we start going back to the moon and we go to Mars, people are gonna think, Hey, I need this application now to solve this problem that I didn't anticipate having. And in science fiction, we kind of saw this with like how, right? Like you had this AI on this computer or this, on this spaceship that could do all this stuff. We need that. And I haven't seen that here yet. >>No, it's not >>Here yet. And >>It's right now I think getting the hardware right first. Yep. But we did a lot of reporting on this with the D O D and the tactile edge, you know, military applications. It's a fundamental, I won't say it's a tech, religious argument. Like, do you believe in agile realtime data or do you believe in democratizing multi-vendor, you know, capability? I think, I think the interesting needs to sort itself out because sometimes multi vendor multi-cloud might not work for an application that needs this database or this application at the edge. >>Right. >>You know, so if you're in space, the back haul, it matters. >>It really does. Yeah. >>Yeah. Not a good time to go back and get that highly available data. You mean highly, is it highly available or there's two terms highly available, which means real time and available. Yeah. Available means it's on a dis, right? >>Yeah. >>So that's a big challenge. Well guys, thanks for coming on. Plug for the company. What are you guys up to? How much funding do you have? How old are you staff hiring? What's some of the details. >>We're about 45 people right now. We are a globally distributed team. So we hire every like from every country, pretty much we are fully remote. So if you're looking for that, hit us up, definitely always look for engineers, looking for more data scientists. We're very, very well funded as well. And yeah. So >>You guys headquarters out, you guys headquartered. >>So a lot of us live in Columbus, Ohio that's technically HQ, but like I said, we we're in pretty much every continent except in Antarctica. So >>You're for all virtual. >>Yeah. A hundred percent virtual, a hundred percent. >>Got it. Well, congratulations and love to hear that Datadog story at another time >>Or DataBot >>Yeah. I mean data, DataBot sorry. Let's get, get all confused >>Data dog data company. >>Well, thanks for coming on and congratulations for your success and thanks for sharing. Yeah. >>Thanks for having us for having >>Pleasure to be here. It's a cube here at rebars. I'm John furier host. Thanks for watching more coming back after this short break.

>>Okay, welcome back everyone. This is the Cube's coverage here in Las Vegas. Back at events re Mars, Amazon re Mars. I'm your host, John fur with the cube. Mars stands for machine learning, automation, robotics, and space. It's great event brings together a lot of the industrial space machine learning and all the new changes in scaling up from going on the moon to, you know, doing great machine learning. And we've got two great guests here with kinda called lunar outpost, Justin Sears, CEO, Lauren, man. He's the co-founder and chief strategy officer lunar outpost. They're right next to us, watching their booth. Love the name, gentlemen. Welcome to the cube. >>Yeah. Thanks for having us, John. >>All right. So lunar outpost, I get the clues here. Tell us what you guys do. Start with that. >>Absolutely. So lunar outpost, we're a company based outta Colorado that has two missions headed to the moon over the course of the next 24 months. We're currently operating on Mars, which forest will tell you a little bit more about here in a second. And we're really pushing out towards expanding the infrastructure on the lunar surface. And then we're gonna utilize that to provide sustainable access to other planetary bodies. >>All right, far as teeing it up for you. Go, how cool is this? We don't, we wanna use every minute. What's the lunar surface look like? What's the infrastructure roads. You gonna pave it down. You what's going on. Well, >>Where we're going. No one has ever been. So, um, our first mission is going to Shackleton connecting Ridge on the south pole, the moon, and that's ripe to add infrastructure such as landing pads and other things. But our first Rover will be primarily driving across the surface, uh, exploring, uh, what the material looks like, prospecting for resources and testing new technologies. >>And you have a lot of technology involved. You're getting data in, you're just doing surveillance. What's the tech involved there. >>Yeah. So the primary technology that we're demonstrating is a 4g network for NOK. Um, we're providing them mobility services, which is basically like the old Verizon commercial. Can you hear me now? Uh, where the Rover drives farther and farther away from the Lander to test their signal strength, and then we're gonna have some other payloads ride sharing along with us for the ride >>Reminds me the old days of wifi. We used to call it war drive and you go around and try to find someone's wifi hotspot <laugh> inside the thing, but no, this is kind of cool. It brings up the whole thing. Now on lunar outpost, how big is the company? What's how what's to some of the stats heres some of the stats. >>Absolutely. So lunar outpost, 58 people, uh, growing quite quickly on track to double. So any of you watching, you want a job, please apply <laugh>. But with lunar outpost, uh, very similar to how launch companies provide people access to different parts of space. Lunar outpost provides people access to different spots on planetary bodies, whether it's the moon, Mars or beyond. So that's really where we're starting. >>So it's kinda like a managed service for all kinds of space utilities. If you kind of think about it, you're gonna provide services. Yeah, >>Absolutely. Yeah. It, it's definitely starting there and, and we're pushing towards building that infrastructure and that long term vision of utilizing space resources. But I can talk about that a little bit more here in a sec. >>Let's get into that. Let's talk about Mars first. You guys said what's going on with >>Mars. Absolutely. >>Yeah. So right now, uh, lunar outpost is part of the science team for, uh, Moxi, which is an instrument on the perseverance Rover. Yeah. Moxi is the first demonstration of space resource utilization on another planet. And what space resource utilization is basically taking resources on another planet, turning them into something useful. What Moxi does is it takes the CO2 from the atmosphere of Mars and atmosphere of Mars is mostly CO2 and it uses a process called solid oxide electrolysis to basically strip oxygen off of that CO2 to produce oh two and carbon monoxide. >>So it's what you need to self sustain on the surface. >>Exactly. It's not just sustaining, um, the astronauts, but also for producing oxygen for propellant. So it'll actually produce, um, it's a, it's a technology that'll produce a propellant for return rockets, um, to come back for Mars. So >>This is the real wildcard and all this, this, this exploration is how fast can the discoveries invent the new science to provide the life and the habitat on the surface. And that seems to be the real focus in the, in the conversations I heard on the keynote as well, get the infrastructure up so you can kinda land and, and we'll pull back and forth. Um, where are we on progress? You guys have the peg from one zero to 10, 10 being we're going, my grandmother's going, everyone's going to zero. Nothing's moving. >>We're making pretty rapid >>Progress. A three six, >>You know, I'll, I'll put it on an eight, John an >>Eight, I'll put it on >>Eight. This is why the mission force was just talking about that's launching within the next 12 months. This is no longer 10 years out. This is no longer 20 years away, 12 months. And then we have mission two shortly after, and that's just the beginning. We have over a dozen Landers that are headed to line surface this decade alone and heavy lift Landers and launchers, uh, start going to the moon and coming back by 2025. >>So, and you guys are from Colorado. You mentioned before you came on camera, right with the swap offices. So you got some space in Colorado, then the rovers to move around. You get, you get weird looks when people drive by and see the space gear. >>Oh yeah, definitely. So we have, um, you know, we have our facility in golden and our Nevada Colorado, and we'll take the vehicles out for strolls and you'll see construction workers, building stuff, and looking over and saying, what's >>Good place to work too. So you're, you're hiring great. You're doubling on the business model side. I can see a lot of demand. It's cheaper to launch stuff now in space. Is there becoming any rules of engagement relative to space? I don't wanna say verified, but like, you know, yet somehow get to the point where, I mean, I could launch a satellite, I could launch something for a couple hundred grand that might interfere with something legitimate. Do you see that on the radar because you guys are having ease of use so smaller, faster, cheaper to get out there. Now you gotta refine the infrastructure, get the services going. Is there threats from just random launches? >>It's a, it's a really interesting question. I mean, current state of the art people who have put rovers on other planetary bodies, you're talking like $3 billion, uh, for the March perseverance Rover. So historically there hasn't been that threat, but when you start talking about lowering the cost and the access to some of these different locations, I do think we'll get to the point where there might be folks that interfere with large scale operations. And that's something that's not very well defined in international law and something you won't really probably get any of the major space powers to agree to. So it's gonna be up to commercial companies to operate responsibly so we can make that space sustainable. And if there is a bad actor, I think it they'll weed themselves out over time. >>Yeah. It's gonna be of self govern, I think in the short term. Good point. Yeah. What about the technology? Where are we in the technology? What are some of the big, uh, challenges that we're overcoming now and what's that next 20 M stare in terms of the next milestone? Yeah, a tech perspective. >>Yeah. So the big technology technological hurdle that has been identified by many is the ability to survive the LUN night. Um, it gets exceptionally cold, uh, when the sun on the moon and that happens every 14 days for another, for, you know, for 14 days. So these long, cold lunar nights, uh, can destroy circuit boards and batteries and different components. So lunar outpost has invested in developing thermal technologies to overcome this, um, both in our offices, in the United States, but we also have opened a new office in, uh, Luxembourg in Europe. That's focusing specifically on thermal technologies to survive the lunar night, not just for rovers, but all sorts of space assets. >>Yeah. Huge. That's a hardware, you know, five, nine kind of like meantime between failure conversation, right. >><laugh> and it's, it gets fun, right? Because you talk five nines and it's such like, uh, you know, ingrained part of the aerospace community. But what we're pitching is we can send a dozen rovers for the cost of one of these historical rovers. So even if 25% of 'em fail, you still have eight rovers for the cost of one of the old rovers. And that's just the, economy's a scale. >>I saw James Hamilton here walking around. He's one of the legendary Amazonians who built out the data center. You might come by the cube. That's just like what they did with servers. Hey, if one breaks throw it away. Yeah. Why buy the big mainframe? Yeah. That's the new model. All right. So now about, uh, space space, that's a not space space, but like room to move around when you start getting some of these habitats going, um, how does space factor into the size of the location? Um, cuz you got the, to live there, solve some of the thermal problems. How do I live on space? I gotta have, you know, how many people gonna be there? What's your forecast? You think from a mission standpoint where there'll be dozens of people or is it still gonna be small teams? >>Yeah. >>Uh, what's that look like? >>I mean you >>Can guess it's okay. >>I mean, my vision's thousands of people. Yep. Uh, living and working in space because it's gonna be, especially the moon I think is a destination that's gonna grow, uh, for tourism. There's an insane drive from people to go visit a new destination. And the moon is one of the most unique experiences you could imagine. Yep. Um, in the near term for Artis, we're gonna start by supporting the Artis astronauts, which are gonna be small crews of astronauts. Um, you know, two to six in the near term. >>And to answer your question, uh, you know, in a different way, the habitat that we're actually gonna build, it's gonna take dozens of these robotic systems to build and maintain over time. And when we're actually talking, timelines, force talks, thousands of people living and working in space, I think that's gonna happen within the next 10 to 15 years. The first few folks are gonna be on the moon by 2025. And we're pushing towards having dozens of people living and working in space and by 2030. >>Yeah. I think it's an awesome goal. And I think it's doable question I'll have for you is the role of software in all this. I had a conversation with, uh, space nerd and we were talking and, and I said open sources everywhere now in the software. Yeah. How do you repair in space? Does you know, you don't want to have a firmware be down. So send down backhoe back to the United States. The us, wait a minute, it's the planet. I gotta go back to earth. Yeah. To get apart. So how does break fix work in space? How, how do you guys see that problem? >>So this one's actually quite fun. I mean, currently we don't have astronauts that can pick up a or change a tire. Uh, so you have to make robots that are really reliable, right. That can continuously operate for years at a time. But when you're talking about long-term repairs, there's some really cool ideas and concepts about standardization of some of these parts, you know, just like Lu knots on your car, right? Yeah. If everyone has the same Lu knots on their wheel, great. Now I can go change it out. I can switch off different parts that are available on the line surface. So I think we're moving towards, uh, that in the long >>Term you guys got a great company. Love the mission. Final question for both of you is I noticed that there's a huge community development around Mars, living on Mars, living on the moon. I mean, there's not a chat group that clubhouse app used, used to be around just kind of dying. But now it's when the Twitter spaces Reddit, you name it, there's a fanatical fan base that loves to talk about an engineer and kind of a collective intelligence, not, may not be official engineering, but they just love to talk about it. So there's a huge fan base for space. How does someone get involved if they really want to dive in and then how do you nurture that audience? How does that, is it developing? What's your take on this whole movement? It's it's beyond just being interested. It's it's become, I won't say cult-like but it's been, there's very, a lot of people in young people interested in space. >>Yeah. >>Yeah. There's, there's a whole, lots of places to get involved. There's, you know, societies, right? Like the Mar society there's technical committees, um, there's, you know, even potentially learning about these, you know, taking a space, resources master program and getting into the field and, and joining the company. So, um, we really, uh, thrive on that energy from the community and it really helps press us forward. And we hope to, uh, have a way to take everyone with us on the mission. And so stay tuned, follow our website. We'll be announcing some of that stuff soon. >>Awesome. And just one last, uh, quick pitch for you, John, I'll leave you with one thought. There are two things that space has an infinite amount of the first is power and the second is resources. And if we can find a way to access either of those, we can fundamentally change the way humanity operates. Yeah. So when you're talking about living on Mars long term, we're gonna need to access the resource from Mars. And then long term, once we get the transportation infrastructure in place, we can start bringing those resources back here to earth. So of course there are gonna be those people that sign up for that first mission out to Mars with SpaceX. But, uh, we'd love for folks to join on with us at lunar outpost and be a part of that kind of next leap accessing those resources. >>I love the mission, as always said, once in the cube, everything in star Trek will be invented someday. <laugh>, we're almost there except for the, the, uh, the transporter room. We don't have that done yet, but almost soon be there. All right. Well, thanks for coming. I, I really appreciate Justin for us for sharing. Great story. Final minute. Give a plug for the company. What are you guys looking for? You said hiring. Yep. Anything else you'd like to share? Put a plug in for lunar outpost. >>Absolutely. So we're hiring across the board, aerospace engineering, robotics engineering, sales marketing. Doesn't really matter. Uh, we're doubling as a company currently around 58 people, as we said, and we're looking for the top people that want to make an impact in aerospace. This is truly a unique moment. First time we've ever had continuous reliable operations. First time NASA is pushing really hard on the public private partnerships for commercial companies like ours to go out and create this sustainable presence on the moon. So whether you wanna work with us, our partner with us, we'd be excited to talk to you and, uh, yeah. Please contact us at info. Lunar outpost.com. >>We'll certainly follow up. Thanks for coming. I love the mission we're behind you and everyone else is too. You can see the energy it's gonna happen. It's the cube coverage from re Mars new actions happening in space on the ground, in the, on the moon you name it's happening right here in Vegas. I'm John furrier. Thanks for watching.

>>We're here live in Las Vegas with a cubes coverage of Amazon re Mars. It's a reinvent re Mars reinforced. The big three shows called the res. This is Mars machine learning, automation, robotic and space. It's a program about the future it and the future innovation around industrial cloud scale climate change the moon, a lot of great topics, really connecting all the dots together here in Las Vegas with Amazon re Mars I'm John ER, host of the cube. Our first guest is Howard Hughes program manager, necess Ryan program. Howard is involved with all the action and space and the moon project, which we'll get into Howard. Thanks for coming on the cube. >>Well, Hey, thanks for having me here this morning. Appreciate you guys inviting me here. >>So this show is not obvious to the normal tech observer, the insiders in, in the industry. It's the confluence of a lot of things coming together. It's gonna be obvious very soon because the stuff they're showing here is pretty impressive. It's motivating, it's positive and it's a force for change in good. All of it coming together, space, machine learning, robotics, industrial, you have one of the coolest areas, the space what's going on with your Orion program. You guys got the big moon project statement to >>Explain. Well, let me tell you, I'll start with Orion. Orion is our next human space craft. That's gonna take humans beyond low earth orbit and we're part of the broader Artis campaign. So Artis is our plan, our NASA plan to return the first person of color, first woman, back to the moon. And we're very excited to do that. We have several missions that I could talk to you about starting with in a very few months, Artis one. So Artis one is going to fly on the space launch system, which is gonna be the biggest rocket we call the mega rocket has been built since the Saturn five on top of the SLS is the Ryan spacecraft and that Ryan spacecraft houses four crew members for up to 21 days in deep space. And we'll have an unru test in a few months launch on the S SLS. And Orion's gonna go around the moon for up to 40 days on Aus two, we will have the first test of the humans on board Orion. So four people will fly on Aus two. We will also circle the moon for about 10 to 12 days. And then our third mission will be our landing. >>So the moon is back in play, obviously it's close to the earth. So it's a short flight, relatively speaking the Mars a little bit further out. I'll see everyone as know what's going on in Mars. A lot of people are interested in Mars. Moon's closer. Yes, but there's also new things going on around discovery. Can you share the big story around why the moon what's? Why is the moon so important and why is everyone so excited about it? >>Yeah. You, you know, you know, coming to this conference and talking about sustainability, you know, I mean it is exploration is I think ingrained in our DNA, but it's more than just exploration is about, you know, projecting human presence beyond our earth. And these are the stepping stones. You know, we talk about Amazon talked about day one, and I think about, we are on those very early days where we're building the infrastructure Ryans of transportation infrastructure, and we're gonna build infrastructure on the moon to learn how to live on a surface and how to utilize the assets. And then that's very important because you know, it's very expensive to carry fuel, to carry water and all the necessities that you need to survive as a human being and outer space. If you can generate that on the surface or on the planet you go to, and this is a perfect way to do it because it's very in your backyard, as I told you earlier. So for future mission, when you want to go to Mars, you're nine months out, you really wanna make sure you have the technologies and you're able to utilize those technologies robustly and in a sustainable way. >>Yeah, we were talking before you came on, came camera camping in your backyard is a good practice round. Before you go out into the, to the wilderness, this is kind of what's going on here, but there's also the discovery angle. I mean, I just see so much science going on there. So if you can get to the moon, get a base camp there, get set up, then things could come out of that. What are some of the things that you guys are talking about that you see as possible exploration upside? >>Yeah. Well, several things. One is power generation recently. We just released some contracts that from vision power, so long, sustainable power capability is very, very important. You know, the other technologies that you need utilize is regenerative, you know, air, water, things that are, you need for that, but then there's a science aspect of it, which is, you know, we're going to the south pole where we think there's a lot of water potentially, or, or available water that we can extract and utilize that to generate fuel. So liquid hydrogen liquid oxygen is one of the areas that are very interesting. And of course, lunar minerals are very exciting, very interesting to bring and, and, and be able to mine potentially in the future, depending on what is there. >>Well, a lot of cool stuff happening. What's your take on this show here, obviously NASA's reputation as innovators and deep technologists, you know, big moonshot missions, pun intended here. You got a lot of other explorations. What's this show bring together, share your perspective because I think the story here to me is you got walkout retail, like the Amazon technology, you got Watson dynamics, the dog, everyone loves that's walking on. Then you got supply chain, robotics, machine learning, and space. It all points to one thing, innovation around industrial. I think what, what, what's your, what's your, what's your take? >>You know, I think one of the things is, is, you know, normally we are innovating in a, in our aerospace industry. You know, I think there's so much to learn from innovation across all these areas you described and trying to pull some of that into the spacecraft. You know, when, when you're a human being sitting in spacecraft is more than just flying the spacecraft. You know, you have interaction with displays, you have a lot of technologies that you normally would want to interact with on the ground that you could apply in space to help you and make your tasks easier. And I think those are things that are really important as we look across, you know, the whole entire innovative infrastructure that I see here in this show, how can we extract some that and apply it in the space program? I think there is a very significant leveraging that you could do off of that. >>What are some of the look at what's going on in donors? What are some of the cool people who aren't following the day to day? Anything? >>Well, well, certainly, you know, the Artman's mission Artis campaign is one of the, the, the coolest things I could think of. That's why I came into, you know, I think wrapping around that where we are not only just going to a destination, but we're exploring, and we're trying to establish a very clear, long term presence that will allow us to engage. What I think is the next step, which is science, you know, and science and the, and the things that can, can come out of that in terms of scientific discoveries. And I think the cool, coolest thing would be, Hey, could we take the things that we are in the labs and the innovation relative to power generation, relative to energy development of energy technologies, robotics, to utilize, to help explore the surface. And of course the science that comes out of just naturally, when you go somewhere, you don't know what to expect. And I think that's what the exciting thing. And for NASA, we're putting a program, an infrastructure around that. I think that's really exciting. Of course, the other parts of NASA is science. Yeah. And so the partnering those two pieces together to accomplish a very important mission for everybody on planet earth is, is really important. >>And also it's a curiosity. People are being curious about what's going on now in space, cuz the costs are down and you got universities here and you got the, of robotics and industrial. This is gonna provide a, a new ground for education, younger, younger generation coming up. What would you share to teachers and potential students, people who wanna learn what's different about now than the old generation and what's the same, what what's the same and what's new. What's how does someone get their arms around this, their mind around it? Where can they jump in? This is gonna open up the aperture for, for, for talent. I mean with all the technology, it's not one dimensional. >>Yeah. I think what is still true is core sciences, math, you know, engineering, the hard science, chemistry, biology. I mean, I think those are really also very important, but what we're we're getting today is the amount of collaboration we're able to do against organically. And I think the innovation that's driven by a lot of this collaboration where you have these tools and your ability to engage and then you're able to, to get, I would say the best out of people in lots of different areas. And that's what I think one of the things we're learning at NASA is, you know, we have a broad spectrum of people that come to work for us and we're pulling that. And now we're coming to these kinds of things where we're kind getting even more innovation ideas and partnerships so that we are not just off on our own thinking about the problem we're branching out and allowing a lot of other people to help us solve the problems that >>We need. You know, I've noticed with space force too. I had the same kind of conversations around those with those guys as well. Collaboration and public private partnerships are huge. You've seen a lot more kind of cross pollination of funding, col technology software. I mean, how do you do break, fix and space at software, right? So you gotta have, I mean, it's gotta work. So you got security challenges. Yeah. This is a new frontier. It is the cybersecurity, the usability, the operationalizing for humans, not just, you know, put atypical, you know, scientists and, and, and astronauts who are, you know, in peak shape, we're talking about humans. Yeah. What's the big problem to solve? Is it security? Is it, what, what would you say the big challenges >>Are? Yeah. You know, I think information and access to information and how we interact with information is probably our biggest challenge because we have very limited space in terms of not only mass, but just volume. Yeah. You know, you want to reserve the space for the people and they, they need to, you know, you want maximize your space that you're having in spacecraft. And so I think having access to information, being able to, to utilize information and quickly access systems so you can solve problems cuz you don't know when you're in deep space, you're several months out to Mars, what problems you might encounter and what kind of systems and access to information you need to help you solve the problems. You know, both, both, both from a just unplanned kind of contingencies or even planned contingencies where you wanna make sure you have that information to do it. So information is gonna be very vital as we go out into deep >>Space and the infrastructure's changed. How has the infrastructure changed in terms of support services? I mean see, in the United States, just the growth of a aerospace you mentioned earlier is, is just phenomenal. You've got smaller, faster, cheaper equipment density, it solved the technology. Where's there gonna be the, the big game changing move movement. Where do you see it go? Is it AIST three? It kind of kicks in AIST ones, obviously the first one unmanned one. But where do in your mind, do you see key milestones that are gonna be super important to >>Watch? I think, I think, I think, you know, we've already, you know, pushed the boundaries of what we, we are, you know, in terms of applying our aerospace technologies for AIST one and certainly two, we've got those in, in work already. And so we've got that those vehicles already in work and built yeah. One already at the, at the Kennedy space center ready for launch, but starting with three because you have a lot more interaction, you gotta take the crew down with a Lander, a human landing system. You gotta build rovers. You've gotta build a, a capability which they could explore. So starting with three and then four we're building the gateway gateways orbiting platform around the moon. So for all future missions after Rist three, we're gonna take Aion to the gateway. The crew gets into the orbiting platform. They get on a human landing system and they go down. >>So all that interaction, all that infrastructure and all the support equipment you need, not only in the orbit of the moon, but also down the ground is gonna drive a lot of innovation. You're gonna have to realize, oh, Hey, I needed this. Now I need to figure out how to get something there. You know? And, and how much of the robotics and how much AI you need will be very interesting because you'll need these assistance to help you do your daily routine or lessen your daily routine. So you can focus on the science and you can focus on doing the advancing those technologies that you're gonna >>Need. And you gotta have the infrastructure. It's like a road. Yeah. You know, you wanna go pop down to the moon, you just pop down, it's already built. It's ready for you. Yep. Come back up. So just ease of use from a deployment standpoint is, >>And, and the infrastructure, the things that you're gonna need, you know, what is a have gonna look like? What are you gonna need in a habitat? You know, are, are you gonna be able to have the power that you're gonna have? How many station power stations are you gonna need? Right. So all these things are gonna be really, things are gonna be driven by what you need to do the mission. And that drives, I think a lot of innovation, you know, it's very much like the end goal. What are you trying to solve? And then you go, okay, here's what I need to solve to build things, to solve that >>Problem. There's so many things involved in the mission. I can imagine. Safety's huge. Number one, gotta be up safe. Yep. Space is dangerous game. Yes. Yeah. It's not pleasant there. Not for the faint of heart. As you say, >>It's not for the faint >>Heart. That's correct. What's the big safety concerns obviously besides blowing up and oxygen and water and the basic needs. >>I think, I think, you know, I think you, you said it very well, you know, it is not for the faint of heart. We try to minimize risk. You know, asset is one of the big, you're sitting under 8.8 million pounds of thrust on the launch vehicle. So it is going very fast and you're flying and you, and, and it's it's light cuz we got solid rocket motors too as well. Once they're lit. They're lit. Yeah. So we have a escape system on Orion that allows a crew to be safe. And of course we build in redundancy. That's the other thing I think that will drive innovation. You know, you build redundancy in the system, but you also think about the kind of issues that you would run into potentially from a safety perspective, you know, how you gonna get outta situation if you get hit by a meteor, right? Right. You, you, you are going through the band, Ellen belt, you have radiation. So you know, some of these things that are harsh on your vehicle and on, on the human side of this shop too. And so when you have to do these things, you have to think about what are you gonna protect for and how do you go protect for that? And we have to find innovations for >>That. Yeah. And it's also gonna be a really exciting air for engineering work. And you mentioned the data, data's huge simulations, running scenarios. This is where the AI comes in. And that seems to me where the dots connect from me when you start thinking about how to have, how to run those simulations, to identify what's possible. >>I think that's a great point, you know, because we have all this computing capability and because we can run simulations and because we can collect data, we have terabytes of data, but it's very challenging for humans to analyze at that level. So AI is one of the things we're looking at, which is trying to systematically have a process by which data is called through so that the engineering mind is only looking at the things and focus on things that are problematic. So we repeat tests, every flight, you don't have to look at all the terabytes of data of each test. You have a computer AI do that. And you allow yourself to look at just the pieces that don't look right, have anomalies in the data. Then you're going to do that digging, right. That's where the power of those kinds of technologies can really help us because we have that capability to do a lot of computing. >>And I think that's why this show to me is important because it, it, it shows for the first time, at least from my coverage of the industry where technology's not the bottleneck anymore, it's human mind. And we wanna live in a peaceful world with climate. We wanna have the earth around for a while. So climate change was a huge topic yesterday and how the force for good, what could come outta the moon shots is to, is to help for earth. >>Yeah. >>Yeah. Better understanding there all good. What's your take on the show. If you had to summarize this show, re Mars from the NASA perspective. So you, the essence space, what's the what's going on here? What's the big, big story. >>Yeah. For, for me, I think it's eyeopening in terms of how much innovation is happening across a spectrum of areas. And I look at various things like bossy, scientific robots that the dog that's walking around. I mean to think, you know, people are applying it in different ways and then those applications in a lot of ways are very similar to what we need for exploration going forward. And how do you apply some of these technologies to the space program and how do we leverage that? How do we leverage that innovation and how we take the innovations already happening organically for other reasons and how would those help us solve those problems that we're gonna encounter going forward as we try to live on another planet? >>Well, congratulations on a great assignment. You got a great job. I do super fun. I love being an observer and I love space. Love how at the innovations there. And plus space space is cool. I mean, how many millions of live views do you see? Everyone's stopping work to watch SpaceX land and NASA do their work. It's just, it's bringing back the tech vibe. You know what I'm saying? It's just, it's just, things are going you a good tailwind. Yeah. >>Congratulations. Thank you very much. >>Appreciate it on the, okay. This cube coverage. I'm John fur. You're here for the cube here. Live in Las Vegas back at reinvent reinforce re Mars, the reser coverage here at re Mars. We'll be back with more coverage after this short break.

>>no real filter and that kind of stuff. But you're also an entrepreneur, right? And you know the business, you've been in software, you detect business. I'm instructing you get a lot of pictures, this entertainment business on our show, we're a bubble. We don't do a lot of tech deals that were talking because it's boring tv tech people love tech consumers love the benefit of text. No consumer opens up their iphone and says, oh my gosh, I love the technology behind my, what's it been like being on the shark tank? You know, filming is fun and hang out just fun and it's fun to be a celebrity at first your head gets really big and you get a really good tables at restaurants and who says texas has got a little possessed more skin in the game today in charge of his destiny. Great robert Herjavec. No, these two stars cube alumni >>welcome back to the cubes coverage of A W. S. Public sector seven. I'm john for your host of the cube got a great segment here on healthcare startup accelerators of course. Sandy carter is co hosting media. This one Vice President Aws. She's awesome on the cuBA and jennifer Blumenthal co founder and C of one record entrepreneur, very successful. Thanks for coming on jennifer. Thank good to see you. Sandy thanks for joining me again. You >>are most welcome, >>jennifer. Before we get into the whole accelerated dynamic, just take a minute to explain what you guys do. One record. >>Sure. So one record is a digital health company that enables users to access aggregate and share their healthcare information. So what that means is we help you as a person get your data and then we also help companies who would like to have workflows were consumers in the loop to get their data. So whether they're sharing it with a provider, researcher payer. >>So, Sandy, we've talked about this amazon web services, healthcare accelerator cohort batches. What do you call cohort batches? Cohorts explain what's going on with the healthcare accelerator? >>Yeah. So, um, we decided that we would launch and partner an accelerator program and accelerator program just provides to a start up a little bit extra technical help. A little bit extra subject matter expertise and introductions to funders. And so we decided we were going to start one for health care. It's one of the biggest disruptive industries in public sector. Um, and so we weren't sure how it's gonna go. We partnered with Kids X. Kids X is part of the Los Angeles system for medical. And so we put out a call for startups and we had 427 startups, we were told on average and accelerating it's 50-100. So we were blown away 31 different countries. So it was really amazing. And then what we've been doing is down selecting and selecting that Top 10 for our first cohort. So we're going from 427 down to 10. And so obviously we looked at the founders themselves to see the quality of the leadership of the company, um the strength of their technology and the fit of the technology into the broader overall healthcare and healthcare ecosystem. And so we were thrilled that jennifer and one record was one of the top 10 start ups in this space that we chose to be in the, in the cohort. And so now we're going to take it to the six weeks intensive where we'll do training, helping them with AWS, provide them A W. S. Credits and then Kid X will also provide some of the health care uh subject matter >>expertise as well. Can I get some of those credits over here to maybe? >>Yes, you can actually, you can talk to me don you can't >>Talk to me, Jennifer, I gotta ask you. So you're an entrepreneur. So doing start doing cos it's like a roller coaster. So now to make the top 10 but also be in the area of his accelerator, it's a partnership, right? You're making a bet. What's your take on all this? >>Well, we've always been partners with a W. S. We started building on AWS in the very beginning. So when I was setting up the company a huge decision early on with infrastructure and when I saw the launch of the accelerator, I had to apply because we're at the point in the company that we're growing and part of growing is growing with the VW. So I was really excited to take advantage of that opportunity and now in the accelerator, it's more of thinking about things that we weren't thinking about the services that we can leverage to fill in the gaps within our platform so we can meet our customers where they are >>using award winning MSP cloud status city, your partners, great relationship with the ecosystem. So congratulations Sandi. What's the disruption for the healthcare? Because right now education and health care, the two top areas we're seeing and we're reporting on where cloud scale developed two point or whatever buzzword digital transformation you want to use is impacting heavily healthcare industry. There's some new realities. What's your, what's your vision, what's your view? >>Hey john before she does that, I have to give a plug to Claudius city because they just made premier partner as well, which is a huge deal. Uh and they're also serving public sector. So I just wanted to make sure that you knew that too. So you can congratulate. Go ahead, jennifer >>Well, so if I zoom in, I think about a P. I. S. Every day, that's what I think about and I think about microservices. So for me and for one record, what we think about is legislation. So 21st century Cures act says that you as a consumer have to be able to access your healthcare data from both your providers and from your players and not just your providers, but also the underlying technology vendors and H. I. E. S. H. I am and it's probably gonna extend to really anyone who plays within the healthcare ecosystem. So you're just going to see this explosion of A. P. I. S. And we're just your one of that. I mean for the payers that we went into effect on july 1st. So I mean when you think about the decentralization of healthcare where healthcare is being delivered plus an api economy, you're just going to have a whole new model developing and then throwing price transparency and you've got a whole new cake. >>I'm smiling because I love the peacocks. In fact, last night I shouldn't have tweeted this but there's a little tweet flames going on around A. P. Is being brittle and all this stuff and I said, hey developer experience about building great software apps are there for you. It's not a glue layer by itself. You got to build software around the so kind of a little preaching to the younger generation. But this health care thing is huge because think about like old school health care, it was anti ap I was also siloed. So what's your take on has the culture is changing health care because the user experience, I want my records, I want my privacy, I want to maintain everything confidential but access. That's hard. >>I think well health care to be used to just be paper was forget about a. P. I. Is it was just paper records. I think uh to me you think about uh patient journey, like a patient journey starts with booking an appointment and then everything after that is essentially an api call. So that's how I think about it is to all these micro transactions that are happening all the time and you want your data to go to your health care provider so they can give you the proper care, you want your data to go to your pair so they can pay for your care and then those two stakeholders want your data so that they can provide the right services at the right time to the right channel. And that is just a series of api calls that literally sits on a platform. >>What's interesting, I'd love to get your take on the where you think the progress bar is in the industry because Fintech has shown the way you got defy now behind a decentralized finance, health care seems to be moving on in a very accelerated rate towards that kind of concept of cloud, scale, decentralization, privacy. >>Yeah, I mean, that's a big question, what's interesting to me around that is how healthcare stakeholders are thinking about where they're providing care. So as they're buying up practices primary care specialty care and they're moving more and more outside of the brick and mortar of the health care system or partnering with your startups. That's really where I think you're going to see a larger ecosystem development, you could just look at CVS and walmart or the dollar store if they're going to be moving into health care, what does that look like? And then if you're seeking care in those settings, but then you're going to Mayo clinic or Kaiser permanente, there's so many new relationships that are part of your hair circle >>delivery is just what does that even mean now, delivery of health >>care. It's wherever you it's like the app economy you want to ride right now, you want a doctor right now, that's where we're heading its ease of use. >>This is this exciting startups, changing the game. Yes, I love it. I mean, this is what it's all about this health >>Care, this is what it's all about. And if you look at the funding right now from VCS, we're seeing so much funding pour into health care, we were just looking at some numbers and in the second quarter alone, the funding went up almost 700%. And the amount of funding that is pouring into companies like jennifer's company to really transform healthcare, 30% of it is going into telehealth. So when you talked about, you know, kind of ai at the edge, getting the right doctor the right expert at the right time, we're seeing that as a big trend in healthcare to >>well jennifer, I think the funding dynamics aside the opportunity for market total addressable market is massive when the application is being decomposed, you got front end, whether it's telemedicine, you got the different building blocks of healthcare being radically reconfigured. It's a re factoring of healthcare. Yeah, >>I think if you just think about where we're sitting today, you had to use an app to prove proof of vaccination. So this is not just national, this is a global thing to have that covid wallet. We at one record have a covid wallet. But just a couple years from now, I need more than just by covid vaccination. I need all my vaccinations. I need all my lab results. I need all my beds. It's opening the door for a new consumer behavior pattern, which is the first step to adoption for any technology. >>So somebody else covid wallet. So I need >>that was California. Did the, did a version of we just have a pen and it's pretty cool. Very handy. I should save it to my drive. But my phone, but I don't jennifer, what's the coolest thing you're working on right now because you're in the middle of all the action. >>I get very excited about the payer app is that we're working on. So I think by the end of the month we will be connected to almost to all the blues in the United States. So I'm very excited when a user comes into the one record and they're able to get their clinical data from the provider organization and then their clinical financial and formulary data from their payers because then you're getting a complete view, You're getting the records for someone who gave you care and you're getting the records from someone who paid for your care. And that's an interesting thing that's really moving towards a complete picture. So from a personal perspective that gets exciting. And then from a professional perspective, it's really working with our partners as they're using our API s to build out workflows and their applications. >>It's an api economy. I'd like to ask you to on the impact side to the patient. I hear a lot of people complaining that hey, I want to bring my records to the doctor and I want to have my own control of my own stuff. A lot of times, some doctors don't even know other historical data points about a patient that could open up a diagnosis and, or care >>or they can't even refer you to a doctor. Most doctors really only refer within a network of people that they know having a provider directory that allows doctors refer, having the data from different doctors outside of their, you know, I didn't really allows people to start thinking beyond just their little box. >>Cool. Well, great to have you on and congratulations on being in the top 10 saying this is a wonderful example of how the ecosystem where you got cloud city, your MSP. You mentioned the shout out to them jerry Miller and his team by working together the cloud gives you advantages. So I have to ask, we look at amazon cloud as an entrepreneur. It's kind of a loaded question, but I'm going to ask it. I love it. >>You always do it >>when you look at amazon, what do you see as opportunities as an entrepreneur? Because I'll see the easy ones. They have computing everything else. But like what's the, what does cloud do for you as an entrepreneur? What does it, what does it make you do? >>Yeah. So for been working with jerry since the beginning for me when I think about it, it's really the growth of our company. So when we start building, we really just thinking about it from a monolithic build and we move to microservices and amazon has been there every step of the way to support us as that. And now, you know, the things that I'm interested in are specifically health lake and anything that's NLP related that we could plug into our solution for when we get data from different sources that are coming in really unstructured formats and making it structured so that it's searchable for people and amazon does that for us with their services that we can add into the applications. >>Yeah, we announced that data health like and july it has a whole set of templates for analytics, focused on health care as well as hip hop compliance out of the box as well. >>The I think I think that's what's important is people used to think application first. Now it's creating essentially a data lake, then analytics and then what applications you build on top of that. And that's how our partners think about it and that's how we try and service them using amazon as our problem. So >>you're honing in on the value of the data and how that conflicts and then work within the whatever application requests might come >>in. Yes, >>it's interesting. You know, we had an event last month and jerry Chen from Greylock partners came on and gave a talk called castles in the cloud. He's gonna be cute before. He's a, he's a veces, they talk about moats and competitive manage so having a moat, The old school perimeter moz how cloud destroyed that. He's like, no, now the castles are in the cloud, he pointed snowflake basically data warehouse in the cloud red shifts there too. But they can be successful. And that's how the cloud, you could actually build value, sustainable value in the cloud. If you think that way of re factoring not just hosting a huge, huge, huge thing. >>I think the only thing he, this was customer service because health care is still very personal. So it's always about how you interact with the end user and how you can help me get to where they need to be going >>and what do you see that going? Because that's, that's a good point. >>I think that is a huge opportunity for any new company that wants to enter healthcare, customer service as a service in health care for all the different places that health care is going to be delivered. Maybe there's a company that I don't know about, but when they come out, I'd like to meet them. >>Yeah, I mean, I can't think of one cover that can think of right now. This is what I would say is great customer service for health care. >>And if there is one out there contacted me because I want to talk to you about AWS. >>Yeah. And you need the app from one record that make it all >>happen. That's where Omni channel customer service across all health care entities. Yeah, that's >>a great billion dollar idea for someone listening to our show right now. >>Right, alright. So saying they had to give you the opportunity to talk more because this is a great example of how the world's very agile. What's the next step for the AWS Healthcare accelerator? Are there more accelerators? Do you do it by vertical? >>What happens next? So, with the healthcare accelerator, this was our first go at the accelerator. So, this is our first set of cohorts, Of course, all 427 companies are going to get some help from a W. S. as well. We also you'll love this john We also did a space accelerator. Make sure you ask Clint about that. So we have startups that are synthesizing oxygen on mars to sending an outpost box to the moon. I mean, it's crazy what these startups are doing. Um, and then the third accelerator we started was around clean energy. So sustainability, we sold that one out to, we had folks from 66 different countries participate in that one. So these have been really successful for us. So it reinvent. When we talk again, we'll be announcing a couple of others. So right now we've got healthcare, space, clean energy and we'll be announcing a couple other accelerators moving forward. >>You know, it's interesting, jennifer the pandemic has changed even our ability to get stories. Just more stories out there now. So you're seeing kind of remote hybrid connections, ap ideas, whether it's software or remote interviews or remote connections. There's more stories being told out there with digital transformation. I mean there wasn't that many before pandemic has changed the landscape because let's face it, people were hiding some really bad projects behind metrics. But when you pull the pandemic back and you go, hey, everyone's kind of emperors got no clothes on. Those are bad projects. Those are good projects that cloud investment worked or I didn't have a cloud investment. They were pretty much screwed at that point. So this is now a new reality of like value, you can't show me value. >>It's crazy to me when I meet people who tell me like we want to move to the cloud of like, why are you not on the cloud? Like this really just blows my life. Like I don't understand why you have on prem or while you did start on the cloud, this is more for larger organizations, but younger organizations, you know, the first thing you have to do, it's set up that environment. >>Yeah. And then now with the migration plans and seeing here, uh whereas education or health care or other verticals, you've got, now you've got containers to give you that compatibility and then you've got kubernetes and you've got microservices, you've got land. Uh I mean, come on, that's the perfect storm innovation. There's no excuses in my opinion. So, you know, if you're out there and you're not leveraging it, then you're probably gonna be out of business. That's my philosophy. Thank you for coming up. Okay. Sandy, thank you. Thank you, john Okay. Any of his coverage here, summit here in D. C. I'm john ferrier. Thanks for watching. Mm >>mm mm mhm. I have been in the software and technology industry for over 12 years now, so I've had >>the opportunity

>>Yeah, >>approximately two years after HP split into two separate companies, antonioni Ranieri was named president and Ceo of Hewlett Packard Enterprise. Under his tenure, the company has streamlined its operations, sharpened his priorities, simplified the product portfolio and strategically aligned its human capital with key growth initiatives. He's made a number of smaller but high leverage acquisitions and return the company to growth while affecting a massive company wide pivot to an as a service model. Welcome back to HPD discovered 2021. This is Dave Volonte for the cube and it's my pleasure to welcome back Antonio Neary to the program. Antonio it's been a while. Great to see you again. >>Hi, Dave. Thanks for having me. >>That's really our pleasure. It was just gonna start off with the big picture. Let's talk about trends. You're a trend spotter. What do you see today? Everybody talks about digital transformation. We had to force marks to digital last year. Now it's really come into focus. But what are the big trends that you're seeing that are affecting your customers transformations? >>Well, Dave, I mean obviously we have been talking about digital transformation for some time uh in our view is no longer a priority is a strategic imperative. And through the last 15 months or so since we have been going through the pandemic, we have seen that accelerated to a level we haven't never seen before. And so what's going on is that we live in a digital economy and through the pandemic now we are more connected than ever. We are much more distributed than ever before and an enormous amount of data is being created and that data has tremendous value. And so what we see in our customer's name, more connectivity, they need a platform from the edge to the cloud to manage all the data and most important they need to move faster and extracting that inside that value from the data and this is where HP is uniquely positioned to deliver against those experiences and way we haven't imagined before. >>Yeah, we're gonna dig into that now, of course you and I have been talking about data and how much data for decades, but I feel like we're gonna look back at 2030 and say, wow, we never, we're not gonna do anything like that. So we're really living in a data centric era as the curves are going exponential, What do you see? How do you see customers handling this? How are they thinking about the opportunities? >>Well, I think, you know, customer realized now that they need to move faster, they need to absolutely be uh much more agile and everything. They do, they need to deploy a cloud experience for all the work clothes and data that they manage and they need to deliver business outcomes to stay ahead of the competition. And so we believe technology now plays even a bigger role and every industry is a technology industry in many ways, every company, right, is a technology company, whether your health care, your manufacturer, your transportation company, you are an education, everybody needs more. It no less. It but at the same time they want the way they want to consumer dave is very different than ever before, right? They want an elastic consumption model and they want to be able to scale up and down based on the needs of their enterprise. But if you recall three years ago, I knew and I had this conversation, I predicted that enterprise of the future will be edge century, cloud enable and data driven. The edge is the next frontier, we said in 2018 and think about it, you know, people now are working remotely and that age now is much more distribute than we imagined before. Cloud is no longer a destination, it is an experience for all your apps and data, but now we are entering what we call the edge of insight, which is all about that data driven approach and this is where all three have to come together in ways that customer did envision before and that's why they need help. >>So I see that, I see the definition of cloud changing, it's no longer a set of remote services, you know, somewhere up there in the cloud, it's expanding on prem cross clouds, you mentioned the edge and so that brings complexity. Every every company is a technology company but they may not be great at technology. So it seems that there are some challenges around there, partly my senses, some of some of what you're trying to do is simplify that for your customers. But what are the challenges that your customers are asking you to solve? >>Well, the first they want a consistent and seamless experience, whatever that application and data lives, so, you know, for them, you know, they want to move away from running it to innovate in our 90 and then obviously they need to move much faster. As I said earlier about this data driven approaches. So they need help because obviously they need to digitize every every aspect of the company, but at the same time they need to do it in a much more cost effective way. So they're asking for subject matter expertise on process engineering. They're asking for the fighting the right mix of hybrid experiences from the edge to cloud and they need to move much faster at scale in deploying technologies like Ai deep learning and machine learning and Hewlett Packard Enterprise uh is extremely well positioned because we have been building an age to club platform where you provide connectivity where you bring computing and storage uh in a softer, define scalable way that you can consume as a service. And so we have great capabilities without HP Point next technology services and advice and run inside. But we have a portfolio with HP Green Lake, our cloud services, the cloud that comes to you that are addressing the most critical data driven warlords. >>Probably about 24 months ago you announced that HP was was going to basically go all in on as a service and get there by by 2022 for all your solutions. I gotta get, I gotta say you've done a good job communicating the Wall Street, I think, I think culturally you've really done a good job of emphasizing that to your, to the workforce. Uh, but but how should we measure the progress that you've made toward that goal? How our customers responding? I I know how the markets responding, you know, three or four year big competitors have now announced. But how should we measure, you know, how you're tracking to that goal? >>Well, I think, you know, the fact that our competitors are entering the other service market is a validation that our vision was right. And that's that's that's good because in the end, you know, it tells us we are on the right track. However, we have to move much faster than than ever before. And that's why we constantly looking for ways to go further and faster. You're right. The court of this is a cultural transformation. Engineering wise, once you state, once you state the North Star, we need to learn our internal processes to think Cloud first and data first versus infrastructure. And we have made great progress. The way we measure ourselves. Dave is very simple is by giving a consistent and transparent report on our pivot in that financial aspect of it, which is what we call the annualized revenue run rate, which we have been disclosed enough for more than a year and a half. And this past quarter grew 30% year over year. So we are on track to deliver a 30-40% Kegel that we committed two years ago And this business going to triple more than uh more than one year from now. So it's gonna be three times as bigger as we enter 2022 and 2023. But in the end, it's all about the experience you deliver and that's why architecturally uh while we made great progress. I know there is way more work to be done, but I'm really excited because what we just announced here this week is just simply remarkable. And you will see more as we become more a cloud operating driven company in the in the next months and years to come. >>I want to ask you kind of a personal question. I mean, COVID-19 is you know, sharpened our sensitivity and empathy to to a lot of different things. Uh and I think uh ceos in your position of a large tech company or any large company, they really can't just give lip service to things like E. S. G. Or or ethical uh digital transformation, which is something that you've talked about in other words, making sure that it's inclusive. Everybody is able to participate in this economy and not get left behind. What does this mean to you personally? >>Well, they remember I'm in a privileged position, right? Leading a company like Hewlett Packard Enterprise that has Hewlett and Packard on the brand is an honor, but it's also a big responsibility. Let's remember what this company stands for and what our purpose is, which is to advance the way people live and work, and in that we have to be able to create a more equitable society and use this technology to solve some of the biggest societal challenge you have been facing The last 18 months has been really hard on a number of dimensions, not just for the business but for their communities. Uh, we saw disruption, we saw hardships on the financial side, we saw acts of violence and hatred. Those are completely unacceptable. But if we work together, we can use these technologies to bring the community together and to make it equitable. And that's one is one of my passion because as we move into this digital economy, I keep saying that connecting people is the first step and if you are not connected, you're not going to participate. Therefore we cannot afford to create a digital economy for only few. And this is why connectivity has to become an essential service, not different than water and electricity. And that's why I have passion and invest my own personal time working with entities like World Economic Forum, educating our government, right, Which is very important because both the public sector and the private sector have to come together. And then from the technology standpoint, we have to architect these things that are commercially accessible and viable to everyone. And so it's uh it's I will say that it's not just my mission. Uh this is top of mind for many of my colleagues ceos that talked all the time and you can see of movement, but at the same time it's good for business because shareholders now want to invest in companies that take care about this, how we make, not just a word more inclusive and equitable, but also how we make a more sustainable and we with our technologies, we can make the world way more sustainable with circular economy, power, efficiency and so forth. So a lot of work to be done dave but I'm encouraged by the progress but we need to do way way more. >>Thank you for that Antonio. I want to ask you about the future and I want to ask you a couple of different angles. So I want to start with the edge. So it seems to me that you're you're building this vision of what I call a layer that abstracts the underlying complexity of the whether it's the public cloud across clouds on prem and and and the edge and it's your job to simplify that. So I as the customer can focus on more strategic initiatives and that's clearly the vision that you guys are setting forth on. My question is is how far do you go on the edge? In other words, it seems to me that Aruba for example, for example, awesome acquisition could go really, really deep into the far edge, maybe other parts of your portfolio, you're kind of more looking at horizontal. How should we think about HP. Es, positioning and participation in that edge opportunity? >>Well, we believe we are becoming one of the merger leaders at the intelligent edge. Right? These edges becoming way more intelligent. We live in a hyper connected world and that will continue to grow at an exponential pace. Right? So today we we may have billions of people and devices pursue. We're entering trillions of things that will be connected to the network. Uh, so you need a platform to be able to do with the scale. So there is a horizontal view of that to create these vertical experiences which are industry driven. Right? So one thing is to deliver a vertical experience in healthcare versus manufacturer transportation. And so we take a really far dave I mean, to the point that we just, you know, put into space 256 miles above the Earth, a supercomputer that tells you we take a really far, but in the end, it's about acting where the data is created and bringing that knowledge and that inside to the people who can make a difference real time as much as possible. And that's why I start by connecting things by bringing a cloud experience to that data, whatever it lives because it's cheaper and it's way more economical and obviously there's aspects of latest in security and compliance. They have to deal with it and then ultimately accelerate that inside into some sort of outcome. And we have many, many use cases were driving today and Aruba is the platform by the way, which we have been using now to extend from the edge all the way to the core into the cloud business. And that's why you HP has unique set of assets to deliver against that opportunity. >>Yes, I want to talk about some of the weapons you have in your arsenal. You know, some people talk about, hey, well we have to win the architectural battle for hybrid cloud. I've heard that statement made, certainly HP is in that battle. It's not a zero sum game, but you're a player there. And so when I, when I look at as a service, great, you're making progress there. But I feel like there's more, there's, there's architecture there, you're making acquisitions, you're building out as moral, which is kind of an interesting data platform. Uh, and so I want to ask you how you see the architecture emerging and where H. P. S sort of value add, I. P. Is your big player and compute you've got actually, you've got chops and memory disaggregate asian, you've done custom silicon over the years. How how should we think about your contribution to the next decade of innovation? >>Well, I think it's gonna come different layers of what we call the stock, right? Obviously, uh, we have been known for an infrastructure company, but the reality is what customers are looking for. Our integrated solutions that are optimized for the given world or application. So they don't have to spend time bringing things together. Right? And and spend weeks sometimes months when they can do it in just in a matter of minutes a day so they can move forward innovative on I. T. And so we were really focused on that connectivity as the first step. And Aruba give us an enormous rich uh through the cloud provisioning of a port or a wifi or a one. As you know, as we move to more cloud native applications. Much of the traffic through the connectivity will go into the internet, not through the traditional fixed networks. And that's what we did acquisitions like Silver Peak because now we can connect all your ages and all your clouds in an autonomous softer. The final way as we go to the other spectrum. Right? We talk about one load optimization and uh for us H. P. S my role is the recipe by which we bring the infrastructure and the software in through that integrated solution that can run autonomously that eventually can consume as a service. And that's why we made the introduction here of HP Green like Lighthouse, which is actually a fully optimised stack. They with the push of a bottom from HP Green Lake cloud platform, we can deploy whatever that that is required and then be able to Federated so we can also address other aspects like disaster recovery and be able to share all the knowledge real time. Swarm learning is another thing that people don't understand. I mean if you think about it. So I'm learning is a distributed Ai learning ecosystem and think about what we did with the D. C. Any in order to find cures for Alzheimer's or dementia. But so I'm learning is going to be the next platform sitting on this age to cloud architecture. So that instead of people worrying about sharing data, what we're doing is actually sharing insights And be able to learn through these millions of data points that they can connect with each other in a secure way. Security is another example, right? So today on an average takes 28 days to find a bridge in your enterprise with project Aurora, which we're going to make available at the end of the year by the end of the year. We actually can address zero day attacks within seconds. And then we're work in other areas like disaster recovery when you get attacked. Think about the ransom ramp somewhere that we have seen in the last few weeks, right? You know, God forbid you have to pay for it. But at the same time, recovery takes days and weeks. Sometimes we are working on technology to do it within 23 seconds. So this is where HP can place across all spectrums of the stack And at the same time of course people expect us to innovate in infrastructure layer. That's why we also partner with companies like Intel were with the push of a bottom. If you need more capacity of the court, you don't have to order anything. She's pushed the bottle, we make more calls available so that that warlord can perform and when you don't need it, shut it off so you don't have to pay for it. And last finalist, you know, I will say for us is all about the consumption availability of our solutions And that's what I said, you know, in 2019 we will make available everything as a service by 2022. You know, we have to say as you know, there is no need to build the church for Easter Sunday when you can rent it for that day. The point here is to grow elastically. And the fact that you don't need to move the data is already a cost savings because cost of aggression data back and forth is enormous and customers also don't want to be locked in. So we have an open approach and we have a true age to cloud architecture and we are focusing on what is most valuable aspect for the customer, which is ultimately the data. >>Thank you for that. One of the other things I wanted to ask you about, again, another weapon in your arsenal is you mentioned supercomputing before. Up in space, we're on the cusp of exa scale and that's the importance of high performance computing. You know, it used to be viewed as just a niche. I've had some great conversations with DR go about this, but that really is the big data platform, if you will. Uh can I wonder if you could talk a little bit about how that fits into the future. Your expertise in HPC, you're obviously a leader in that space. What's the fit with this new vision you're laying out? >>Well, HPC, high performance computing in memory computer are the backbone to be able to manage large data sets at massive scale. Um, and, you know, deployed technologies like deep learning or artificial intelligence for this massive amount of data. If we talked about the explosion of data all around us and uh, you know, and the algorithms and the parameters to be able to extract inside from the day is getting way more complex. And so the ability to co locate data and computed a massive scale is becoming a necessity, whether it's in academia, whether it's in the government obviously to protect your, your most valuable assets or whether it is in the traditional enterprise. But that's why with the acquisition of cray as G. I. And our organic business, we are absolutely the undisputed leader to provide the level of capabilities. And that's why we are going to build five of the top six exa scale systems, which is basically be able to process the billion billion, meaning billion square transactions per second. Can you imagine what you can do with that? Right. What type of problems you can go solve climate problems? Right. Um you know, obviously be able to put someone back into the moon and eventually in mars, you know, the first step to put that supercomputer as an edge computer into the international space station. It's about being able to process data from the images that take from the ice caps of the of the earth to understand climate changes. But eventually, if you want to put somebody in in into the Marks planet, you have to be able to communicate with those astronauts as they go and you know, you can't afford the latency. Right? So this is what the type of problems we are really focused on. But HPC is something that we are absolutely super committed and it's something that honestly, we have the full stack from silicon to software to the system performance that nobody else has in the industry. >>Well, I think it's a real tailwind for you because the industry is moving in that direction and everybody talks about the data and workloads are shifting. We used to be uh I got O. L. T. P. And I got reporting. Now you look at the workloads, there's so much diversity so I'll give you the last word. What what really is the most exciting to you about the future of HPV? >>Well, I'm excited about the innovation will bring it to the market and honestly as the Ceo I care about the culture of the company. For me, the last almost 3.5 years have been truly remarkable. As you said at the beginning, we are transforming every aspect of this company. When I became Ceo I had three priorities for myself. One is our customers and partners. That's why we do these events right to communicate, communicate, communicate. They are our North Star, that's why we exist. Second is our innovation right? We compete and win with the best innovation, solving the most complex problems in a sustainable and equitable way. And third is the culture of the company, which are the core is how we do things in our Team members and employees. You know, I represent my colleagues here, the 60,000 strong team members that had incredible passion for our customers and to make a contribution every single day. And so for me, I'm very optimistic about what we see the recovery of the economy and the possibilities of technology. Uh, but ultimately, you know, we have to work together hand in hand and I believe this company now is absolutely on the right track to not just be relevant, but really to make a difference. And remember That in the end we we have to be a force for good. And let's not forget that while we do all of this, we have some farm with technology. We have to also help some, uh, to address some of the challenges we have seen in the last 18 months and H. P. E. is a whole different company uh, that you knew 3.5 years ago. >>And as you said, knowledge is the right thing to do. It's good. It's good for business Antonio. Neary, thanks so much for coming back to the cube is always a pleasure to see you. >>Thanks for having me. Dave and >>thank you for watching this version of HP discover 2021 on the cube. This is David want to keep it right there for more great coverage. Mm

(upbeat music) >> Welcome to the special CUBE presentation of the AWS Global Public Sector Partner Awards Program. I'm here with the leader of the partner program, Sandy Carter, Vice President, AWS, Amazon Web Services @Sandy_Carter on Twitter, prolific on social and great leader. Sandy, great to see you again. And congratulations on this great program we're having here. In fact, thanks for coming out for this keynote. Well, thank you, John, for having me. You guys always talk about the coolest thing. So we had to be part of it. >> Well, one of the things that I've been really loving about this success of public sector we talked to us before is that as we start coming out of the pandemic, is becoming very clear that the cloud has helped a lot of people and your team has done amazing work, just want to give you props for that and say, congratulations, and what a great time to talk about the winners. Because everyone's been working really hard in public sector, because of the pandemic. The internet didn't break. And everyone stepped up with cloud scale and solve some problems. So take us through the award winners and talk about them. Give us an overview of what it is. The criteria and all the specifics. >> Yeah, you got it. So we've been doing this annually, and it's for our public sector partners overall, to really recognize the very best of the best. Now, we love all of our partners, John, as you know, but every year we'd like to really hone in on a couple who really leverage their skills and their ability to deliver a great customer solution. They demonstrate those Amazon leadership principles like working backwards from the customer, having a bias for action, they've engaged with AWS and very unique ways. And as well, they've contributed to our customer success, which is so very important to us and to our customers as well. >> That's awesome. Hey, can we put up a slide, I know we have slide on the winners, I want to look at them, with the tiles here. So here's a list of some of the winners. I see a nice little stars on there. Look at the gold star. I knows IronNet, CrowdStrike. That's General Keith Alexander's company, I mean, super relevant. Presidio, we've interviewed them before many times, got Palantir in there. And is there another one, I want to take a look at some of the other names here. >> In overall we had 21 categories. You know, we have over 1900 public sector partners today. So you'll notice that the awards we did, a big focus on mission. So things like government, education, health care, we spotlighted some of the brand new technologies like Containers, Artificial Intelligence, Amazon Connect. And we also this year added in awards for innovative use of our programs, like think big for small business and PTP as well. >> Yeah, well, great roundup, they're looking forward to hearing more about those companies. I have to ask you, because this always comes up, we're seeing more and more ecosystem discussions when we talk about the future of cloud. And obviously, we're going to, you know, be at Mobile World Congress, theCUBE, back in physical form, again, (indistinct) will continue to go on. The notion of ecosystem is becoming a key competitive advantage for companies and missions. So I have to ask you, why are partners so important to your public sector team? Talk about the importance of partners in context to your mission? >> Yeah, you know, our partners are critical. We drive most of our business and public sector through partners. They have great relationships, they've got great skills, and they have, you know, that really unique ability to meet the customer needs. If I just highlighted a couple of things, even using some of our partners who won awards, the first is, you know, migrations are so critical. Andy talked at Reinvent about still 96% of applications still sitting on premises. So anybody who can help us with the velocity of migrations is really critical. And I don't know if you knew John, but 80% of our migrations are led by partners. So for example, we gave awards to Collibra and Databricks as best lead migration for data as well as Datacom for best data lead migration as well. And that's because they increase the velocity of migrations, which increases customer satisfaction. They also bring great subject matter expertise, in particular around that mission that you're talking about. So for instance, GDIT won best Mission Solution For Federal, and they had just an amazing solution that was a secure virtual desktop that reduced a federal agencies deployment process, from months to days. And then finally, you know, our partners drive new opportunities and innovate on behalf of our customers. So we did award this year for P to P, Partnering to Partner which is a really big element of ecosystems, but it was won by four points and in quizon, and they were able to work together to implement a data, implement a data lake and an AI, ML solution, and then you just did the startup showcase, we have a best startup delivering innovation too, and that was EduTech (indistinct) Central America. And they won for implementing an amazing student registration and early warning system to alert and risks that may impact a student's educational achievement. So those are just some of the reasons why partners are important. I could go on and on. As you know, I'm so passionate about my partners, >> I know you're going to talk for an hour, we have to cut you off a little there. (indistinct) love your partners so much. You have to focus on this mission thing. It was a strong mission focus in the awards this year. Why are customers requiring much more of a mission focused? Is it because, is it a part of the criteria? I mean, we're seeing a mission being big. Why is that the case? >> Well, you know, IDC, said that IT spend for a mission or something with a purpose or line of business was five times greater than IT. We also recently did our CTO study where we surveyed thousands of CTOs. And the biggest and most changing elements today is really not around the technology. But it's around the industry, healthcare, space that we talked about earlier, or government. So those are really important. So for instance, New Reburial, they won Best Emission for Healthcare. And they did that because of their new smart diagnostic system. And then we had a partner when PA consulting for Best Amazon Connect solution around a mission for providing support for those most at risk, the elderly population, those who already had pre existing conditions, and really making sure they were doing what they called risk shielding during COVID. Really exciting and big, strong focus on mission. >> Yeah, and it's also, you know, we've been covering a lot on this, people want to work for a company that has purpose, and that has missions. I think that's going to be part of the table stakes going forward. I got to ask you on the secrets of success when this came up, I love asking this question, because, you know, we're starting to see the playbooks of what I call post COVID and cloud scale 2.0, whatever you want to call it, as you're starting to see this new modern era of success formulas, obviously, large scale value creation mission. These are points we're hearing and keep conversations across the board. What do you see as the secret of success for these parties? I mean, obviously, it's indirect for Amazon, I get that, but they're also have their customers, they're your customers, customers. That's been around for a while. But there's a new model emerging. What are the secrets from your standpoint of success? you know, it's so interesting, John, that you asked me this, because this is the number one question that I get from partners too. I would say the first secret is being able to work backwards from your customer, not just technology. So take one of our award winners Cognizant. They won for their digital tolling solution. And they work backwards from the customer and how to modernize that, or Pariveda, who is one of our best energy solution winners. And again, they looked at some of these major capital projects that oil companies were doing, working backwards from what the customer needed. I think that's number one, working backwards from the customer. Two, is having that mission expertise. So given that you have to have technology, but you also got to have that expertise in the area. We see that as a big secret of our public sector partners. So education cloud, (indistinct) one for education, effectual one for government and not for profit, Accenture won, really leveraging and showcasing their global expansion around public safety and disaster response. Very important as well. And then I would say the last secret of success is building repeatable solutions using those strong skills. So Deloitte, they have a great solution for migration, including mainframes. And then you mentioned early on, CloudStrike and IronNet, just think about the skill sets that they have there for repeatable solutions around security. So I think it's really around working backwards from the customer, having that mission expertise, and then building a repeatable solution, leveraging your skill sets. >> That's a great formula for success. I got you mentioned IronNet, and cybersecurity. One of things that's coming up is, in addition to having those best practices, there's also like real problems to solve, like, ransomware is now becoming a government and commercial problem, right. So (indistinct) seeing that happen a lot in DC, that's a front burner. That's a societal impact issue. That's like a cybersecurity kind of national security defense issue, but also, it's a technical one. And also public sector, through my interviews, I can tell you the past year and a half, there's been a lot of creativity of new solutions, new problems or new opportunities that are not yet identified as problems and I'd love to get your thoughts on my concern is with Jeff Bar yesterday from AWS, who's been blogging all the the news and he is a leader in the community. He was saying that he sees like 5G in the edge as new opportunities where it's creative. It's like he compared to the going to the home improvement store where he just goes to buy one thing. He does other things. And so there's a builder culture. And I think this is something that's coming out of your group more, because the pandemic forced these problems, and they forced new opportunities to be creative, and to build. What's your thoughts? >> Yeah, so I see that too. So if you think about builders, you know, we had a partner, Executive Council yesterday, we had 900, executives sign up from all of our partners. And we asked some survey questions like, what are you building with today? And the number one thing was artificial intelligence and machine learning. And I think that's such a new builders tool today, John, and, you know, one of our partners who won an award for the most innovative AI&ML was Kablamo And what they did was they use AI&ML to do a risk assessment on bushfires or wildfires in Australia. But I think it goes beyond that. I think it's building for that need. And this goes back to, we always talk about #techforgood. Presidio, I love this award that they won for best nonprofit, the Cherokee Nation, which is one of our, you know, Native American heritage, they were worried about their language going out, like completely out like no one being able to speak yet. And so they came to Presidio, and they asked how could we have a virtual classroom platform for the Cherokee Nation? And they created this game that's available on your phone, so innovative, so much of a builder's culture to capture that young generation, so they don't you lose their language. So I do agree. I mean, we're seeing builders everywhere, we're seeing them use artificial intelligence, Container, security. And we're even starting with quantum, so it is pretty powerful of what you can do as a public sector partner. >> I think the partner equation is just so wide open, because it's always been based on value, adding value, right? So adding value is just what they do. And by the way, you make money doing it if you do a good job of adding value. And, again, I just love riffing on this, because Dave and I talked about this on theCUBE all the time, and it comes up all the time in cloud conversations. The lock in isn't proprietary technology anymore, its value, and scale. So you starting to see builders thrive in that environment. So really good points. Great best practice. And I think I'm very bullish on the partner ecosystems in general, and people do it right, flat upside. I got to ask you, though, going forward, because this is the big post COVID kind of conversation. And last time we talked on theCUBE about this, you know, people want to have a growth strategy coming out of COVID. They want to be, they want to have a tail win, they want to be on the right side of history. No one wants to be in the losing end of all this. So last year in 2021 your goals were very clear, mission, migrations, modernization. What's the focus for the partners beyond 2021? What are you guys thinking to enable them, 21 is going to be a nice on ramp to this post COVID growth strategy? What's the focus beyond 2021 for you and your partners? >> Yeah, it's really interesting, we're going to actually continue to focus on those three M's mission, migration and modernization. But we'll bring in different elements of it. So for example, on mission, we see a couple of new areas that are really rising to the top, Smart Cities now that everybody's going back to work and (indistinct) down, operations and maintenance and global defense and using gaming and simulation. I mean, think about that digital twin strategy and how you're doing that. For migration, one of the big ones we see emerging today is data-lead migration. You know, we have been focused on applications and mainframes, but data has gravity. And so we are seeing so many partners and our customers demanding to get their data from on premises to the cloud so that now they can make real time business decisions. And then on modernization. You know, we talked a lot about artificial intelligence and machine learning. Containers are wicked hot right now, provides you portability and performance. I was with a startup last night that just moved everything they're doing to ECS our Container strategy. And then we're also seeing, you know, crippin, quantum blockchain, no code, low code. So the same big focus, mission migration, modernization, but the underpinnings are going to shift a little bit beyond 2021. >> That's great stuff. And you know, you have first of all people don't might not know that your group partners and Amazon Web Services public sector, has a big surface area. You talking about government, health care, space. So I have to ask you, you guys announced in March the space accelerator and you recently announced that you selected 10 companies to participate in the accelerated program. So, I mean, this is this is a space centric, you know, targeting, you know, low earth orbiting satellites to exploring the surface of the Moon and Mars, which people love. And because the space is cool, let's say the tech and space, they kind of go together, right? So take us through, what's this all about? How's that going? What's the selection, give us a quick update, while you're here on this space accelerated selection, because (indistinct) will have had a big blog post that went out (indistinct). >> Yeah, I would be thrilled to do that. So I don't know if you know this. But when I was young, I wanted to be an astronaut. We just helped through (indistinct), one of our partners reach Mars. So Clint, who is a retired general and myself got together, and we decided we needed to do something to help startups accelerate in their space mission. And so we decided to announce a competition for 10 startups to get extra help both from us, as well as a partner Sarafem on space. And so we announced it, everybody expected the companies to come from the US, John, they came from 44 different countries. We had hundreds of startups enter, and we took them through this six week, classroom education. So we had our General Clint, you know, helping and teaching them in space, which he's done his whole life, we provided them with AWS credits, they had mentoring by our partner, Sarafem. And we just down selected to 10 startups, that was what Vernors blog post was. If you haven't read it, you should look at some of the amazing things that they're going to do, from, you know, farming asteroids to, you know, helping with some of the, you know, using small vehicles to connect to larger vehicles, when we all get to space. It's very exciting. Very exciting, indeed, >> You have so much good content areas and partners, exploring, it's a very wide vertical or sector that you're managing. Is there any pattern? Well, I want to get your thoughts on post COVID success again, is there any patterns that you're seeing in terms of the partner ecosystem? You know, whether its business model, or team makeup, or more mindset, or just how they're organizing that that's been successful? Is there like a, do you see a trend? Is there a certain thing, then I've got the working backwards thing, I get that. But like, is there any other observations? Because I think people really want to know, am I doing it right? Am I being a good manager, when you know, people are going to be working remotely more? We're seeing more of that. And there's going to be now virtual events, hybrid events, physical events, the world's coming back to normal, but it's never going to be the same. Do you see any patterns? >> Yeah, you know, we're seeing a lot of small partners that are making an entrance and solving some really difficult problems. And because they're so focused on a niche, it's really having an impact. So I really believe that that's going to be one of the things that we see, I focus on individual creators and companies who are really tightly aligned and not trying to do everything, if you will. I think that's one of the big trends. I think the second we talked about it a little bit, John, I think you're going to see a lot of focus on mission. Because of that purpose. You know, we've talked about #techforgood, with everything going on in the world. As people have been working from home, they've been reevaluating who they are, and what do they stand for, and people want to work for a company that cares about people. I just posted my human footer on LinkedIn. And I got my first over a million hits on LinkedIn, just by posting this human footer, saying, you know what, reply to me at a time that's convenient for you, not necessarily for me. So I think we're going to see a lot of this purpose driven mission, that's going to come out as well. >> Yeah, and I also noticed that, and I was on LinkedIn, I got a similar reaction when I started trying to create more of a community model, not so much have people attend our events, and we need butts in the seats. It was much more personal, like we wanted you to join us, not attend and be like a number. You know, people want to be part of something. This seem to be the new mission. >> Yeah, I completely agree with that. I think that, you know, people do want to be part of something and they want, they want to be part of the meaning of something too, right. Not just be part of something overall, but to have an impact themselves, personally and individually, not just as a company. And I think, you know, one of the other trends that we saw coming up too, was the focus on technology. And I think low code, no code is giving a lot of people entry into doing things I never thought they could do. So I do think that technology, artificial intelligence Containers, low code, no code blockchain, those are going to enable us to even do greater mission-based solutions. >> Low code, no code reduces the friction to create more value, again, back to the value proposition. Adding value is the key to success, your partners are doing it. And of course, being part of something great, like the Global Public Sector Partner Awards list is a good one. And that's what we're talking about here. Sandy, great to see you. Thank you for coming on and sharing your insights and an update and talking more about the 2021, Global Public Sector partner Awards. Thanks for coming on. >> Thank you, John, always a pleasure. >> Okay, the Global Leaders here presented on theCUBE, again, award winners doing great work in mission, modernization, again, adding value. That's what it's all about. That's the new competitive advantage. This is theCUBE. I'm John Furrier, your host, thanks for watching. (upbeat music)

>>from around the globe. It's the Q. With digital coverage >>of exa scale day made possible by Hewlett Packard Enterprise. We're back at the celebration of Exa Scale Day. This is Dave Volant, and I'm pleased to welcome to great guests Brian Dance Berries Here. Here's what The ISS Program Science office at the Johnson Space Center. And Dr Mark Fernandez is back. He's the Americas HPC technology officer at Hewlett Packard Enterprise. Gentlemen, welcome. >>Thank you. Yeah, >>well, thanks for coming on. And, Mark, Good to see you again. And, Brian, I wonder if we could start with you and talk a little bit about your role. A T. I s s program Science office as a scientist. What's happening these days? What are you working on? >>Well, it's been my privilege the last few years to be working in the, uh, research integration area of of the space station office. And that's where we're looking at all of the different sponsors NASA, the other international partners, all the sponsors within NASA, and, uh, prioritizing what research gets to go up to station. What research gets conducted in that regard. And to give you a feel for the magnitude of the task, but we're coming up now on November 2nd for the 20th anniversary of continuous human presence on station. So we've been a space faring society now for coming up on 20 years, and I would like to point out because, you know, as an old guy myself, it impresses me. That's, you know, that's 25% of the US population. Everybody under the age of 20 has never had a moment when they were alive and we didn't have people living and working in space. So Okay, I got off on a tangent there. We'll move on in that 20 years we've done 3000 experiments on station and the station has really made ah, miraculously sort of evolution from, ah, basic platform, what is now really fully functioning national lab up there with, um, commercially run research facilities all the time. I think you can think of it as the world's largest satellite bus. We have, you know, four or five instruments looking down, measuring all kinds of things in the atmosphere during Earth observation data, looking out, doing astrophysics, research, measuring cosmic rays, X ray observatory, all kinds of things, plus inside the station you've got racks and racks of experiments going on typically scores, you know, if not more than 50 experiments going on at any one time. So, you know, the topic of this event is really important. Doesn't NASA, you know, data transmission Up and down, all of the cameras going on on on station the experiments. Um, you know, one of one of those astrophysics observatory's you know, it has collected over 15 billion um uh, impact data of cosmic rays. And so the massive amounts of data that that needs to be collected and transferred for all of these experiments to go on really hits to the core. And I'm glad I'm able toe be here and and speak with you today on this. This topic. >>Well, thank you for that, Bryan. A baby boomer, right? Grew up with the national pride of the moon landing. And of course, we've we've seen we saw the space shuttle. We've seen international collaboration, and it's just always been something, you know, part of our lives. So thank you for the great work that you guys were doing their mark. You and I had a great discussion about exa scale and kind of what it means for society and some of the innovations that we could maybe expect over the coming years. Now I wonder if you could talk about some of the collaboration between what you guys were doing and Brian's team. >>Uh, yeah, so yes, indeed. Thank you for having me early. Appreciate it. That was a great introduction. Brian, Uh, I'm the principal investigator on Space Born computer, too. And as the two implies, where there was one before it. And so we worked with Bryant and his team extensively over the past few years again high performance computing on board the International Space Station. Brian mentioned the thousands of experiments that have been done to date and that there are currently 50 orm or going on at any one time. And those experiments collect data. And up until recently, you've had to transmit that data down to Earth for processing. And that's a significant amount of bandwidth. Yeah, so with baseball and computer to we're inviting hello developers and others to take advantage of that onboard computational capability you mentioned exa scale. We plan to get the extra scale next year. We're currently in the era that's called PETA scale on. We've been in the past scale era since 2000 and seven, so it's taken us a while to make it that next lead. Well, 10 years after Earth had a PETA scale system in 2017 were able to put ah teraflop system on the International space station to prove that we could do a trillion calculations a second in space. That's where the data is originating. That's where it might be best to process it. So we want to be able to take those capabilities with us. And with H. P. E. Acting as a wonderful partner with Brian and NASA and the space station, we think we're able to do that for many of these experiments. >>It's mind boggling you were talking about. I was talking about the moon landing earlier and the limited power of computing power. Now we've got, you know, water, cool supercomputers in space. I'm interested. I'd love to explore this notion of private industry developing space capable computers. I think it's an interesting model where you have computer companies can repurpose technology that they're selling obviously greater scale for space exploration and apply that supercomputing technology instead of having government fund, proprietary purpose built systems that air. Essentially, you use case, if you will. So, Brian, what are the benefits of that model? The perhaps you wouldn't achieve with governments or maybe contractors, you know, kind of building these proprietary systems. >>Well, first of all, you know, any any tool, your using any, any new technology that has, you know, multiple users is going to mature quicker. You're gonna have, you know, greater features, greater capabilities, you know, not even talking about computers. Anything you're doing. So moving from, you know, governor government is a single, um, you know, user to off the shelf type products gives you that opportunity to have things that have been proven, have the technology is fully matured. Now, what had to happen is we had to mature the space station so that we had a platform where we could test these things and make sure they're gonna work in the high radiation environments, you know, And they're gonna be reliable, because first, you've got to make sure that that safety and reliability or taken care of so that that's that's why in the space program you're gonna you're gonna be behind the times in terms of the computing power of the equipment up there because, first of all and foremost, you needed to make sure that it was reliable and say, Now, my undergraduate degree was in aerospace engineering and what we care about is aerospace engineers is how heavy is it, how big and bulky is it because you know it z expensive? You know, every pound I once visited Gulfstream Aerospace, and they would pay their employees $1000 that they could come up with a way saving £1 in building that aircraft. That means you have more capacity for flying. It's on the orders of magnitude. More important to do that when you're taking payloads to space. So you know, particularly with space born computer, the opportunity there to use software and and check the reliability that way, Uh, without having to make the computer, you know, radiation resistance, if you will, with heavy, you know, bulky, um, packaging to protect it from that radiation is a really important thing, and it's gonna be a huge advantage moving forward as we go to the moon and on to Mars. >>Yeah, that's interesting. I mean, your point about cots commercial off the shelf technology. I mean, that's something that obviously governments have wanted to leverage for a long, long time for many, many decades. But but But Mark the issue was always the is. Brian was just saying the very stringent and difficult requirements of space. Well, you're obviously with space Born one. You got to the point where you had visibility of the economics made sense. It made commercial sense for companies like Hewlett Packard Enterprise. And now we've sort of closed that gap to the point where you're sort of now on that innovation curve. What if you could talk about that a little bit? >>Yeah, absolutely. Brian has some excellent points, you know, he said, anything we do today and requires computers, and that's absolutely correct. So I tell people that when you go to the moon and when you go to the Mars, you probably want to go with the iPhone 10 or 11 and not a flip phone. So before space born was sent up, you went with 2000 early two thousands computing technology there which, like you said many of the people born today weren't even around when the space station began and has been occupied so they don't even know how to program or use that type of computing. Power was based on one. We sent the exact same products that we were shipping to customers today, so they are current state of the art, and we had a mandate. Don't touch the hardware, have all the protection that you can via software. So that's what we've done. We've got several philosophical ways to do that. We've implemented those in software. They've been successful improving in the space for one, and now it's space born to. We're going to begin the experiments so that the rest of the community so that the rest of the community can figure out that it is economically viable, and it will accelerate their research and progress in space. I'm most excited about that. Every venture into space as Brian mentioned will require some computational capability, and HP has figured out that the economics air there we need to bring the customers through space ball into in order for them to learn that we are reliable but current state of the art, and that we could benefit them and all of humanity. >>Guys, I wanna ask you kind of a two part question. And, Brian, I'll start with you and it z somewhat philosophical. Uh, I mean, my understanding was and I want to say this was probably around the time of the Bush administration w two on and maybe certainly before that, but as technology progress, there was a debate about all right, Should we put our resource is on moon because of the proximity to Earth? Or should we, you know, go where no man has gone before and or woman and get to Mars? Where What's the thinking today, Brian? On that? That balance between Moon and Mars? >>Well, you know, our plans today are are to get back to the moon by 2024. That's the Artemus program. Uh, it's exciting. It makes sense from, you know, an engineering standpoint. You take, you know, you take baby steps as you continue to move forward. And so you have that opportunity, um, to to learn while you're still, you know, relatively close to home. You can get there in days, not months. If you're going to Mars, for example, toe have everything line up properly. You're looking at a multi year mission you know, it may take you nine months to get there. Then you have to wait for the Earth and Mars to get back in the right position to come back on that same kind of trajectory. So you have toe be there for more than a year before you can turn around and come back. So, you know, he was talking about the computing power. You know, right now that the beautiful thing about the space station is, it's right there. It's it's orbiting above us. It's only 250 miles away. Uh, so you can test out all of these technologies. You can rely on the ground to keep track of systems. There's not that much of a delay in terms of telemetry coming back. But as you get to the moon and then definitely is, you get get out to Mars. You know, there are enough minutes delay out there that you've got to take the computing power with you. You've got to take everything you need to be able to make those decisions you need to make because there's not time to, um, you know, get that information back on the ground, get back get it back to Earth, have people analyze the situation and then tell you what the next step is to do. That may be too late. So you've got to think the computing power with you. >>So extra scale bring some new possibilities. Both both for, you know, the moon and Mars. I know Space Born one did some simulations relative. Tomorrow we'll talk about that. But But, Brian, what are the things that you hope to get out of excess scale computing that maybe you couldn't do with previous generations? >>Well, you know, you know, market on a key point. You know, bandwidth up and down is, of course, always a limitation. In the more computing data analysis you can do on site, the more efficient you could be with parsing out that that bandwidth and to give you ah, feel for just that kind of think about those those observatory's earth observing and an astronomical I was talking about collecting data. Think about the hours of video that are being recorded daily as the astronauts work on various things to document what they're doing. They many of the biological experiments, one of the key key pieces of data that's coming back. Is that video of the the microbes growing or the plants growing or whatever fluid physics experiments going on? We do a lot of colloids research, which is suspended particles inside ah liquid. And that, of course, high speed video. Is he Thio doing that kind of research? Right now? We've got something called the I s s experience going on in there, which is basically recording and will eventually put out a syriza of basically a movie on virtual reality recording. That kind of data is so huge when you have a 360 degree camera up there recording all of that data, great virtual reality, they There's still a lot of times bringing that back on higher hard drives when the space six vehicles come back to the Earth. That's a lot of data going on. We recorded videos all the time, tremendous amount of bandwidth going on. And as you get to the moon and as you get further out, you can a man imagine how much more limiting that bandwidth it. >>Yeah, We used to joke in the old mainframe days that the fastest way to get data from point a to Point B was called C Tam, the Chevy truck access method. Just load >>up a >>truck, whatever it was, tapes or hard drive. So eso and mark, of course space born to was coming on. Spaceport one really was a pilot, but it proved that the commercial computers could actually work for long durations in space, and the economics were feasible. Thinking about, you know, future missions and space born to What are you hoping to accomplish? >>I'm hoping to bring. I'm hoping to bring that success from space born one to the rest of the community with space born to so that they can realize they can do. They're processing at the edge. The purpose of exploration is insight, not data collection. So all of these experiments begin with data collection. Whether that's videos or samples are mold growing, etcetera, collecting that data, we must process it to turn it into information and insight. And the faster we can do that, the faster we get. Our results and the better things are. I often talk Thio College in high school and sometimes grammar school students about this need to process at the edge and how the communication issues can prevent you from doing that. For example, many of us remember the communications with the moon. The moon is about 250,000 miles away, if I remember correctly, and the speed of light is 186,000 miles a second. So even if the speed of light it takes more than a second for the communications to get to the moon and back. So I can remember being stressed out when Houston will to make a statement. And we were wondering if the astronauts could answer Well, they answered as soon as possible. But that 1 to 2 second delay that was natural was what drove us crazy, which made us nervous. We were worried about them in the success of the mission. So Mars is millions of miles away. So flip it around. If you're a Mars explorer and you look out the window and there's a big red cloud coming at you that looks like a tornado and you might want to do some Mars dust storm modeling right then and there to figure out what's the safest thing to do. You don't have the time literally get that back to earth have been processing and get you the answer back. You've got to take those computational capabilities with you. And we're hoping that of these 52 thousands of experiments that are on board, the SS can show that in order to better accomplish their missions on the moon. And Omar, >>I'm so glad you brought that up because I was gonna ask you guys in the commercial world everybody talks about real time. Of course, we talk about the real time edge and AI influencing and and the time value of data I was gonna ask, you know, the real time, Nous, How do you handle that? I think Mark, you just answered that. But at the same time, people will say, you know, the commercial would like, for instance, in advertising. You know, the joke the best. It's not kind of a joke, but the best minds of our generation tryingto get people to click on ads. And it's somewhat true, unfortunately, but at any rate, the value of data diminishes over time. I would imagine in space exploration where where you're dealing and things like light years, that actually there's quite a bit of value in the historical data. But, Mark, you just You just gave a great example of where you need real time, compute capabilities on the ground. But but But, Brian, I wonder if I could ask you the value of this historic historical data, as you just described collecting so much data. Are you? Do you see that the value of that data actually persists over time, you could go back with better modeling and better a i and computing and actually learn from all that data. What are your thoughts on that, Brian? >>Definitely. I think the answer is yes to that. And, you know, as part of the evolution from from basically a platform to a station, we're also learning to make use of the experiments in the data that we have there. NASA has set up. Um, you know, unopened data access sites for some of our physical science experiments that taking place there and and gene lab for looking at some of the biological genomic experiments that have gone on. And I've seen papers already beginning to be generated not from the original experimenters and principal investigators, but from that data set that has been collected. And, you know, when you're sending something up to space and it to the space station and volume for cargo is so limited, you want to get the most you can out of that. So you you want to be is efficient as possible. And one of the ways you do that is you collect. You take these earth observing, uh, instruments. Then you take that data. And, sure, the principal investigators air using it for the key thing that they designed it for. But if that data is available, others will come along and make use of it in different ways. >>Yeah, So I wanna remind the audience and these these these air supercomputers, the space born computers, they're they're solar powered, obviously, and and they're mounted overhead, right? Is that is that correct? >>Yeah. Yes. Space borne computer was mounted in the overhead. I jokingly say that as soon as someone could figure out how to get a data center in orbit, they will have a 50 per cent denser data station that we could have down here instead of two robes side by side. You can also have one overhead on. The power is free. If you can drive it off a solar, and the cooling is free because it's pretty cold out there in space, so it's gonna be very efficient. Uh, space borne computer is the most energy efficient computer in existence. Uh, free electricity and free cooling. And now we're offering free cycles through all the experimenters on goal >>Eso Space born one exceeded its mission timeframe. You were able to run as it was mentioned before some simulations for future Mars missions. And, um and you talked a little bit about what you want to get out of, uh, space born to. I mean, are there other, like, wish list items, bucket bucket list items that people are talking about? >>Yeah, two of them. And these air kind of hypothetical. And Brian kind of alluded to them. Uh, one is having the data on board. So an example that halo developers talk to us about is Hey, I'm on Mars and I see this mold growing on my potatoes. That's not good. So let me let me sample that mold, do a gene sequencing, and then I've got stored all the historical data on space borne computer of all the bad molds out there and let me do a comparison right then and there before I have dinner with my fried potato. So that's that's one. That's very interesting. A second one closely related to it is we have offered up the storage on space borne computer to for all of your raw data that we process. So, Mr Scientist, if if you need the raw data and you need it now, of course, you can have it sent down. But if you don't let us just hold it there as long as they have space. And when we returned to Earth like you mentioned, Patrick will ship that solid state disk back to them so they could have a new person, but again, reserving that network bandwidth, uh, keeping all that raw data available for the entire duration of the mission so that it may have value later on. >>Great. Thank you for that. I want to end on just sort of talking about come back to the collaboration between I S s National Labs and Hewlett Packard Enterprise, and you've got your inviting project ideas using space Bourne to during the upcoming mission. Maybe you could talk about what that's about, and we have A We have a graphic we're gonna put up on DSM information that you can you can access. But please, mark share with us what you're planning there. >>So again, the collaboration has been outstanding. There. There's been a mention off How much savings is, uh, if you can reduce the weight by a pound. Well, our partners ice s national lab and NASA have taken on that cost of delivering baseball in computer to the international space station as part of their collaboration and powering and cooling us and giving us the technical support in return on our side, we're offering up space borne computer to for all the onboard experiments and all those that think they might be wanting doing experiments on space born on the S s in the future to take advantage of that. So we're very, very excited about that. >>Yeah, and you could go toe just email space born at hp dot com on just float some ideas. I'm sure at some point there'll be a website so you can email them or you can email me david dot volonte at at silicon angle dot com and I'll shoot you that that email one or that website once we get it. But, Brian, I wanna end with you. You've been so gracious with your time. Uh, yeah. Give us your final thoughts on on exa scale. Maybe how you're celebrating exa scale day? I was joking with Mark. Maybe we got a special exa scale drink for 10. 18 but, uh, what's your final thoughts, Brian? >>Uh, I'm going to digress just a little bit. I think I think I have a unique perspective to celebrate eggs a scale day because as an undergraduate student, I was interning at Langley Research Center in the wind tunnels and the wind tunnel. I was then, um, they they were very excited that they had a new state of the art giant room size computer to take that data we way worked on unsteady, um, aerodynamic forces. So you need a lot of computation, and you need to be ableto take data at a high bandwidth. To be able to do that, they'd always, you know, run their their wind tunnel for four or five hours. Almost the whole shift. Like that data and maybe a week later, been ableto look at the data to decide if they got what they were looking for? Well, at the time in the in the early eighties, this is definitely the before times that I got there. They had they had that computer in place. Yes, it was a punchcard computer. It was the one time in my life I got to put my hands on the punch cards and was told not to drop them there. Any trouble if I did that. But I was able thio immediately after, uh, actually, during their run, take that data, reduce it down, grabbed my colored pencils and graph paper and graph out coefficient lift coefficient of drag. Other things that they were measuring. Take it back to them. And they were so excited to have data two hours after they had taken it analyzed and looked at it just pickled them. Think that they could make decisions now on what they wanted to do for their next run. Well, we've come a long way since then. You know, extra scale day really, really emphasizes that point, you know? So it really brings it home to me. Yeah. >>Please, no, please carry on. >>Well, I was just gonna say, you know, you talked about the opportunities that that space borne computer provides and and Mark mentioned our colleagues at the I S s national lab. You know, um, the space station has been declared a national laboratory, and so about half of the, uh, capabilities we have for doing research is a portion to the national lab so that commercial entities so that HP can can do these sorts of projects and universities can access station and and other government agencies. And then NASA can focus in on those things we want to do purely to push our exploration programs. So the opportunities to take advantage of that are there marks opening up the door for a lot of opportunities. But others can just Google S s national laboratory and find some information on how to get in the way. Mark did originally using s national lab to maybe get a good experiment up there. >>Well, it's just astounding to see the progress that this industry is made when you go back and look, you know, the early days of supercomputing to imagine that they actually can be space born is just tremendous. Not only the impacts that it can have on Space six exploration, but also society in general. Mark Wayne talked about that. Guys, thanks so much for coming on the Cube and celebrating Exa scale day and helping expand the community. Great work. And, uh, thank you very much for all that you guys dio >>Thank you very much for having me on and everybody out there. Let's get the XO scale as quick as we can. Appreciate everything you all are >>doing. Let's do it. >>I've got a I've got a similar story. Humanity saw the first trillion calculations per second. Like I said in 1997. And it was over 100 racks of computer equipment. Well, space borne one is less than fourth of Iraq in only 20 years. So I'm gonna be celebrating exa scale day in anticipation off exa scale computers on earth and soon following within the national lab that exists in 20 plus years And being on Mars. >>That's awesome. That mark. Thank you for that. And and thank you for watching everybody. We're celebrating Exa scale day with the community. The supercomputing community on the Cube Right back