>>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.

(electronic music) >> Welcome back to our studios in Palo Alto, California. My name is Dave Vellante, I'm here with John Furrier, who is taking a quick break. You know, in one of the early examples that we used of so called super cloud was Snowflake. We called it a super data cloud. We had, really, a lot of fun with that. And we've started to evolve our thinking. Years ago, we said that data was going to form in the cloud around industries and ecosystems. And Benoit Dogeville is a many time guest of theCube. He's the co-founder and president of products at Snowflake. Benoit, thanks for spending some time with us, at Supercloud 22, good to see you. >> Thank you, thank you, Dave. >> So, you know, like I said, we've had some fun with this meme. But it really is, we heard on the previous panel, everybody's using Snowflake as an example. Somebody how builds on top of hyper scale infrastructure. You're not building your own data centers. And, so, are you building a super data cloud? >> We don't call it exactly that way. We don't like the super word, it's a bit dismissive. >> That's our term. >> About our friends, cloud provider friends. But we call it a data cloud. And the vision, really, for the data cloud is, indeed, it's a cloud which overlays the hyper scaler cloud. But there is a big difference, right? There are several ways to do this super cloud, as you name them. The way we picked is to create one single system, and that's very important, right? There are several ways, right. You can instantiate your solution in every region of the cloud and, you know, potentially that region could be AWS, that region could be GCP. So, you are, indeed, a multi-cloud solution. But Snowflake, we did it differently. We are really creating cloud regions, which are superimposed on top of the cloud provider region, infrastructure region. So, we are building our regions. But where it's very different is that each region of Snowflake is not one instantiation of our service. Our service is global, by nature. We can move data from one region to the other. When you land in Snowflake, you land into one region. But you can grow from there and you can, you know, exist in multiple cloud at the same time. And that's very important, right? It's not different instantiation of a system, it's one single instantiation which covers many cloud regions and many cloud provider. >> So, we used Snowflake as an example. And we're trying to understand what the salient aspects are of your data cloud, what we call super cloud. In fact, you've used the word instantiate. Kit Colbert, just earlier today, laid out, he said, there's sort of three levels. You can run it on one cloud and communicate with the other cloud, you can instantiate on the clouds, or you can have the same service running 24/7 across clouds, that's the hardest example. >> Yeah. >> The most mature. You just described, essentially, doing that. How do you enable that? What are the technical enablers? >> Yeah, so, as I said, first we start by building, you know, Snowflake regions, we have today 30 regions that span the world, so it's a world wide system, with many regions. But all these regions are connected together. They are meshed together with our technology, we name it Snow Grid, and that makes it hard because, you know, Azure region can talk to a WS region, or GCP regions, and as a user for our cloud, you don't see, really, these regional differences, that regions are in different potentially cloud. When you use Snowflake, you can exist, your presence as an organization can be in several regions, several clouds, if you want, geographic, both geographic and cloud provider. >> So, I can share data irrespective of the cloud. And I'm in the Snowflake data cloud, is that correct? I can do that today? >> Exactly, and that's very critical, right? What we wanted is to remove data silos. And when you insociate a system in one single region, and that system is locked in that region, you cannot communicate with other parts of the world, you are locking data in one region. Right, and we didn't want to do that. We wanted data to be distributed the way customer wants it to be distributed across the world. And potentially sharing data at world scales. >> Does that mean if I'm in one region and I want to run a query, if I'm in AWS in one region, and I want to run a query on data that happens to be in an Azure cloud, I can actually execute that? >> So, yes and no. The way we do it is very expensive to do that. Because, generally, if you want to join data which are in different region and different cloud, it's going to be very expensive because you need to move data every time you join it. So, the way we do it is that you replicate the subset of data that you want to access from one region from other region. So, you can create this data mesh, but data is replicated to make it very cheap and very performing too. >> And is the Snow Grid, does that have the metadata intelligence to actually? >> Yes, yes. >> Can you describe that a little? >> Yeah, Snow Grid is both a way to exchange metadata. So, each region of Snowflake knows about all the other regions of Snowflake. Every time we create a new region, the metadata is distributed over our data cloud, not only region knows all the region, but knows every organization that exists in our cloud, where this organization is, where data can be replicated by this organization. And then, of course, it's also used as a way to exchange data, right? So, you can exchange data by scale of data size. And I was just receiving an email from one of our customers who moved more than four petabytes of data, cross region, cross cloud providers in, you know, few days. And it's a lot of data, so it takes some time to move. But they were able to do that online, completely online, and switch over to the other region, which is very important also. >> So, one of the hardest parts about super cloud that I'm still trying to struggling through is the security model. Because you've got the cloud as your sort of first line of defense. And now we've got multiple clouds, with multiple first lines of defense, I've got a shared responsibility model across those clouds, I've got different tools in each of those clouds. Do you take care of that? Where do you pick up from the cloud providers? Do you abstract that security layer? Do you bring in partners? It's a very complicated. >> No, this is a great question. Security has always been the most important aspect of Snowflake sense day one, right? This is the question that every customer of ours has. You know, how can you guarantee the security of my data? And, so, we secure data really tightly in region. We have several layers of security. It starts by creating every data at rest. And that's very important. A lot of customers are not doing that, right? You hear of these attacks, for example, on cloud, where someone left their buckets. And then, you know, you can access the data because it's a non-encrypted. So, we are encrypting everything at rest. We are encrypting everything in transit. So, a region is very secure. Now, you know, from one region, you never access data from another region in Snowflake. That's why, also, we replicate data. Now the replication of that data across region, or the metadata, for that matter, is really our least secure, so Snow Grid ensures that everything is encrypted, everything is, we have multiple encryption keys, and it's stored in hardware secure modules, so, we bit Snow Grid such that it's secure and it allows very secure movement of data. >> Okay, so, I know we kind of, getting into the technology here a lot today, but because super cloud is the future, we actually have to have an architectural foundation on which to build. So, you mentioned a bucket, like an S3 bucket. Okay, that's storage, but you also, for instance, taking advantage of new semi-conductor technology. Like Graviton, as an example, that drives efficiency. You guys talk about how you pass that on to your customers. Even if it means less revenue for you, so, awesome, we love that, you'll make it up in volume. And, so. >> Exactly. >> How do you deal with the lowest common denominator problem? I was talking to somebody the other day and this individual brought up what I thought was a really good point. What if we, let's say, AWS, have the best, silicon. And we can run the fastest and the least expensive, and the lowest power. But another cloud provider hasn't caught up yet. How do you deal with that delta? Do you just take the best of and try to respect that? >> No, it's a great question. I mean, of course, our software is extracting all the cloud providers infrastructure so that when you run in one region, let's say AWS, or Azure, it doesn't make any difference, as far as the applications are concerned. And this abstraction, of course, is a lot of work. I mean, really, a lot of work. Because it needs to be secure, it needs to be performance, and every cloud, and it has to expose APIs which are uniform. And, you know, cloud providers, even though they have potentially the same concept, let's say block storage, APIs are completely different. The way these systems are secure, it's completely different. There errors that you can get. And the retry mechanism is very different from one cloud to the other. The performance is also different. We discovered that when we starting to port our software. And we had to completely rethink how to leverage block storage in that cloud versus that cloud, because just off performance too. And, so, we had, for example, to stripe data. So, all this work is work that you don't need as an application because our vision, really, is that application, which are running in our data cloud, can be abstracted for this difference. And we provide all the services, all the workload that this application need. Whether it's transactional access to data, analytical access to data, managing logs, managing metrics, all of this is abstracted too, so that they are not tied to one particular service of one cloud. And distributing this application across many region, many cloud, is very seamless. >> So, Snowflake has built, your team has built a true abstraction layer across those clouds that's available today? It's actually shipping? >> Yes, and we are still developing it. You know, transactional, Unistore, as we call it, was announced last summit. So, they are still, you know, work in progress. >> You're not done yet. >> But that's the vision, right? And that's important, because we talk about the infrastructure, right. You mention a lot about storage and compute. But it's not only that, right. When you think about application, they need to use the transactional database. They need to use an analytical system. They need to use machine learning. So, you need to provide, also, all these services which are consistent across all the cloud providers. >> So, let's talk developers. Because, you know, you think Snowpark, you guys announced a big application development push at the Snowflake summit recently. And we have said that a criterion of super cloud is a super paz layer, people wince when I say that, but okay, we're just going to go with it. But the point is, it's a purpose built application development layer, specific to your particular agenda, that supports your vision. >> Yes. >> Have you essentially built a purpose built paz layer? Or do you just take them off the shelf, standard paz, and cobble it together? >> No, we build it a custom build. Because, as you said, what exist in one cloud might not exist in another cloud provider, right. So, we have to build in this, all these components that a multi-application need. And that goes to machine learning, as I said, transactional analytical system, and the entire thing. So that it can run in isolation physically. >> And the objective is the developer experience will be identical across those clouds? >> Yes, the developers doesn't need to worry about cloud provider. And, actually, our system will have, we didn't talk about it, but a marketplace that we have, which allows, actually, to deliver. >> We're getting there. >> Yeah, okay. (both laughing) I won't divert. >> No, no, let's go there, because the other aspect of super cloud that we've talked about is the ecosystem. You have to enable an ecosystem to add incremental value, it's not the power of many versus the capabilities of one. So, talk about the challenges of doing that. Not just the business challenges but, again, I'm interested in the technical and architectural challenges. >> Yeah, yeah, so, it's really about, I mean, the way we enable our ecosystem and our partners to create value on top of our data cloud, is via the marketplace. Where you can put shared data on the marketplace. Provide listing on this marketplace, which are data sets. But it goes way beyond data. It's all the way to application. So, you can think of it as the iPhone. A little bit more, all right. Your iPhone is great. Not so much because the hardware is great, or because of the iOS, but because of all the applications that you have. And all these applications are not necessarily developed by Apple, basically. So, we are, it's the same model with our marketplace. We foresee an environment where providers and partners are going to build these applications. We call it native application. And we are going to help them distribute these applications across cloud, everywhere in the world, potentially. And they don't need to worry about that. They don't need to worry about how these applications are going to be instantiated. We are going to help them to monetize these applications. So, that unlocks, you know, really, all the partner ecosystem that you have seen, you know, with something like the iPhone, right? It has created so many new companies that have developed these applications. >> Your detractors have criticized you for being a walled garden. I've actually used that term. I used terms like defacto standard, which are maybe less sensitive to you, but, nonetheless, we've seen defacto standards actually deliver value. I've talked to Frank Slootman about this, and he said, Dave, we deliver value, that's what we're all about. At the same time, he even said to me, and I want your thoughts on this, is, look, we have to embrace open source where it makes sense. You guys announced Apache Iceberg. So, what are your thoughts on that? Is that to enable a developer ecosystem? Why did you do Iceberg? >> Yeah, Iceberg is very important. So, just to give some context, Iceberg is an open table format. >> Right. >> Which was first developed by Netflix. And Netflix put it open source in the Apache community. So, we embraced that open source standard because it's widely used by many companies. And, also, many companies have really invested a lot of effort in building big data, Hadoop Solutions, or DataX Solution, and they want to use Snowflake. And they couldn't really use Snowflake, because all their data were in open format. So, we are embracing Iceberg to help these companies move through the cloud. But why we have been reluctant with direct access to data, direct access to data is a little bit of a problem for us. And the reason is when you direct access to data, now you have direct access to storage. Now you have to understand, for example, the specificity of one cloud versus the other. So, as soon as you start to have direct access to data, you lose your cloud data sync layer. You don't access data with API. When you have direct access to data, it's very hard to sync your data. Because you need to grant access, direct access to tools which are not protected. And you see a lot of hacking of data because of that. So, direct access to data is not serving well our customers, and that's why we have been reluctant to do that. Because it is not cloud diagnostic. You have to code that, you need a lot of intelligence, why APIs access, so we want open APIs. That's, I guess, the way we embrace openness, is by open API versus you access, directly, data. >> iPhone. >> Yeah, yeah, iPhone, APIs, you know. We define a set of APIs because APIs, you know, the implementation of the APIs can change, can improve. You can improve compression of data, for example. If you open direct access to data now, you cannot evolve. >> My point is, you made a promise, from governed, security, data sharing ecosystem. It works the same way, so that's the path that you've chosen. Benoit Dogeville, thank you so much for coming on theCube and participating in Supercloud 22, really appreciate that. >> Thank you, Dave. It was a great pleasure. >> All right, keep it right there, we'll be right back with our next segment, right after this short break. (electronic music)

>> Live from Las Vegas, it's theCUBE covering Magento Imagine 2019, brought to you by Adobe. >> Welcome back to theCUBE Lisa Martin with Jeff Frick and we are here live at Magento Imagine 2019, our second time being back here with theCUBE and we're very excited to welcome Brittany Hodak to theCUBE, entrepreneur, customer engagement speaker, writer, co-founder of the Superfan Company. Brittany it's so exciting to have you on theCUBE. >> Thank you so much for having me. I'm so excited to be here. >> So, you have an incredibly impressive background and I'm like where do we start? >> Thank you. >> So, here we are talking about customer experiences and how Magento and Adobe empower a lot of customer experiences. But you've written a ton of articles, over 350, you've been published in the Huff Post, Wall Street Journal, talk to us about your experiences with customer engagement, some of the things that you as a co-founder of the Superfan have discovered working with a variety of brands from Walmart to Katy Perry? >> Well, thank you so much for saying that. I always say that the biggest problem brands and entertainers have is often one that's not even on their radar at all. I talked to a lot of small and medium sized business owners and they say, You know, my big problem is people don't know who I am. I've got an awareness problem. I'm struggling to let people know who I am. And I really think my business would change if more people knew. And I said, You know, that's not the problem. You can always fix awareness. You can always spend money to get your message out there. Your big problem is apathy. Your problem is there are people who know and don't care. And you've got to figure out how to make people care. You've got to figure out how to connect your story with their story in a way that's meaningful, and in a way that's going to mean something in their lives because that's how you really start the fan engagement process. That's how you lay the groundwork for creating a culture of super fandom amongst your customers, that's really going to help you grow not just the business but a brand. >> Is it about having a more relevant messages or is it just finding those people that have a propensity to be a fan to the services that you provide? >> Well, it's understanding your uniqueness in a way that really makes your value proposition different from anybody else is. Once you understand your uniqueness and you're able to turn it into service of others, that's when you really you position yourself to be able to make the kind of difference that makes somebody want to be a super fan. And I always say, we've had the fortune of working with tons of celebrities, some of the biggest recording artists and superstars on the planet, and a lot of times people say to me, Oh, you know, it's easy when you're talking about being a super fan of Taylor Swift or being a super fan of Katy Perry, but, you know, I'm a plumber or I'm an electrician, how can I have super fans? And I say, By providing people the kindness service that changes their lives. I have an exterminator who I am a super fan of. His name is Scott and the reason I am a super fan of him is because he makes sure there are no brown recluse spiders in my house and I am absolutely terrified about recluse spiders. They are super evil creatures if you're not familiar with them, I encourage you not to google it. They're like nastiest little bug in the world. But you know to me that's super important because he's not just killing bugs, he's helping me feel safe in my home. So that's absolutely a vital service and finding the right guy to do that and the right guy to put my mind at ease and let me know there aren't going to be brown recluse spiders in my house is invaluable and because of that, like there's no way I would ever switch exterminators because Scott's my guy. And I know you know, I can text him 50 different pictures of critters and say, Is this okay, Is this okay? And he's going to get back to me and let me know. So, it's all about points of connection and finding ways to make your audience feel really valued, and connecting your story with their story. >> So, if you look at an exterminator versus a Taylor Swift or Katy Perry or Walmart, are there similarities and what they need to do to deliver this service that's impacting lives? Or are there fundamental differences? >> There are some fundamental differences, but there's more overlap than you would think. And I always say, if you think about it like a Venn diagram, you've got your brand or your business, your service, your product, whatever it is that you're providing, and you've got your customers over here. Where the magic happens is that point of intersection, where your story overlaps with their story, that intersection, that's where super fandom happens. And I like to talk about something I call the four A's of super fandom. So, you can, I see a lot of people make the mistake of trying to talk to everybody the same way. So, whether somebody is encountering your brand for the very first time or has been your customer for a long time, using the same messaging for those people and that doesn't work. So, I talk a lot about the four A's. So, the first day is awareness. That's when somebody is first uncovering your brand, first interacting with your brand. The second a is action, that's when somebody is actually interacting with your brand for the first time. The third a is affinity. Those are the people who are fans of your brand. They've sort of bought into your why, these are the satisfied customers, I would say. And a lot of businesses stop there. They say, These are the people who are satisfied. These are the people who liked what I'm doing, they're buying from me. And that's a mistake that a lot of especially small and medium sized businesses make they sort of feel like, I've got these customers, I don't have to do anything else. They're not over delivering or over serving them which is a huge missed opportunity because if you do, you're able to convert people from that third A to the fourth a which is advocacy. And advocacy is where you want to get the majority of the people because those are your superfans so to speak, those are the ones who are out there sharing your story and your why with other people, helping refer new customers and new clients to you. So, I always say if you can get past the affinity, the people who are happy with you but not really talking about it and really make them feel valued. That's how you create advocates and advocacy is really the super secret sauce when you're talking about super fandom. >> So where should people get started to try to build super fandom within their client base? Is that really with the good customers that they already have, they try to get them to be advocates or I think most people spend so much time focusing on the fat end of the funnel as opposed to on the narrow end of the funnel and converting that transaction into a fan which is what it sounds like you're suggesting? >> Yeah, well, it's important to to focus on all parts of the funnel man, like I said that that awareness, that that fat of the top, you certainly need to be dealing with those people to get them further down. But the skinny part of the funnel is really where you want to make sure that people are continuing to drip out to the other side to make those referrals for you. So, absolutely focusing on everybody. One thing that I am always shocked I when I do consulting and work with small businesses and medium sized businesses, when I asked how much referral business they get, a lot of people don't know that number off the top of your head. So, if you're not tracking the amount of referrals, you absolutely need to know that as a metric, and the number one thing that you can do to increase the amount of referral business that you're getting is by asking your customers for referrals. It's so funny the amount of people who say, I hardly get any referral business at all. And I say, Well, when's the last time you asked? When's the last time that you went to one of your clients or your customers and said, I so appreciate your business. And I wonder if you know anybody in your network who could benefit from our product or service. And they say, oh I've never done that. But yeah, they wonder why they don't have any referrals so-- >> It seems like such an easy step but to your point, you're saying they're focusing on awareness, getting my brand, my service, my name out there, getting people to take action? >> Yes. >> And building that affinity and then I'm good, but that simply asking to make it a referral whether it's a yelp or something as simple as that seems like a pretty easy step. Strategically, how do you advise customers to get from that, take that if you look at it like a funnel like Jeff saying, take that group of affinity customers and convert some percentage to advocates, what's your strategy for helping a consumer brand or even a service provider, like an exterminator for actually making those conversions and then and then having that be a really kind of engine to drive referrals, to drive more leads to the top of that funnel? >> That's a great question. So, I like to talk about something I call the high five which is knowing the five most important people that have the potential to drive your business forward for the next quarter, the next year and the next five years. So, this is an actual list of five people. And any business owner hopefully can sit down and say, Here are the people that I need to really super serve in order to move my business forward. So knowing who those five people are, it could be an advisor, it could be an investor, it could be somebody you've never even met, maybe a thought leader whose thought that you really enjoy, that you think this person could really help me and open me up to a lot of people in their network if they knew who I was. Make a list of those five people, and then figure out how often you need to be doing something staying top of mind for those people. So for me, I like to make sure it's at least once every two weeks. So, sometimes it's as simple as sending an article and saying, Hey, I came across this article, I thought you would really love it, wanted to send it your way. Now and reality, did I just come across that article? No, I spent maybe an hour looking for the right article to forward that person. It's taking the time out to show them that they matter to you, so whether that's sending them a nice gift in the mail for no reason or a handwritten thank you note after they made an introduction for you. It's checking in on things, I always say, you should know what is important to the people who are important to you. You should know the teams that they follow, you should know their spouse, their children, the things that are happening in their lives so you can check in with them. And we live in an age where it's so easy to get information about anyone because all of us are putting content out there on the internet all the time about ourselves. So take the time to figure out what matters to those people who matter to you, and then stay top of mind, letting them know that they matter to you. So, like I said, for me, it's once every two weeks and I look at my list of five about every six months in terms of adding a couple of new people on maybe cycling some people off. But I've been doing this for four years. So, I have a list of 20 people. And I those are like my alums, some of the alumni of my high five, and I'm still extremely close with all of them. I still make sure that I'm trying to add value to them because having one person who's going to advocate for you could open the door for millions of dollars of revenue for you. So, it's just identifying who those people are, because to your point, it's impossible to sort of make everyone the most important person, it's impossible to take everyone at that third step and take them to the fourth step. So, rather than holistically thinking about it. I like to really drill in and say let's start with five. And if you've got 50 employees and you assign five people to each of those 50 employees to say make sure this vendor or make sure this customer, or make sure this partner feels very appreciated by you on a regular basis. You're going to, you really start to see the ROI very, very quickly in your business. >> So some of the trends, if we look at this we're all consumers of any kind of product service, we have this expectation, this growing expectation that we're going to be able to get whatever we want whenever we want it, have it delivered in an hour or a day, or so, we want to be able to have this experience on mobile, maybe started there, maybe finish it in the store, what are some of the trends that you're seeing that you recommend that the company with any product or service needs to get on board with, for example, this morning they were talking about progressive web apps and being able to deliver an experience where the person doesn't have to leave the app, or they can transact something like through Instagram. What are some of those top tools that you recommend to your broad client base. You got to get on board with like mobile, for example, right away. >> Yes, I was going to say the PWAs are absolutely critical, because I think we've all as consumers been in the situation of trying to load something on our phone, and it's five seconds goes by six seconds, I'm like forget about it. >> We're done. >> Yeah, I'm done, I'm over it. So PWAs is super important because it's all about putting your customer first and making things simple for them. The other thing is making sure that whatever system process you're using, everything needs to be connected. You can't be managing stuff across eight different platforms and expect for things not to fall through the cracks which is I'm learning so much here at Imagine and listening to all the best practices of people who are using Magento to manage every part of their business because something is seemingly minor as sending a confirmation email twice instead of once or having eight hours go by before the customer gets that, those types of things, say to a customer on a subliminal level, I'm not important, I don't matter, they're not putting me first. >> So just fan comes from fanatic. And there's great things about fans, and some times there's less great things about fans and we've seen a little bit of that here in terms of this really passionate community around Magento. And it was independent. And then it went to eBay and then it went back out of eBay. And now it's back in Adobe. And it's funny seeing the people that have been here for the whole journey. Part of that responsibility, if you're going to invite someone to be a fan is you have to let them participate, you have to let them contribute. And often which we're seeing, I guess, in Game of Thrones, I'm not a big fan, but if you get outside of kind of the realm of where the fans want things to go, it can also cause some conflict. So, how to people manage encouraging fans, really supporting fans, but at the same time not letting them completely knock their business off or hold the business back probably from places where the entrepreneur needs to still go? >> That's a great question. There was a really fascinating study that Viacom did a couple of years ago about fans. And especially in the under 35 sets, so millennials, gen Z. And the vast majority of people felt like fans have some ownership of the thing that they're a fan of. And that's a really interesting study in psychology to think about these people who feel the ownership. But you know, it's true. You mentioned Game of Thrones, that's a great example of seeing these fan bases who come up with names for themselves, and who are tweeting in real time about things that are happening. Magento a great example because open source has been such an important part of the culture and the history of the platform. These people feel in a very real sense this ownership. And you're right, I think sometimes that scares small business owners, medium sized business owners. They say, Well, we don't want to relinquish control. We don't want to put ourselves in a situation where we're upsetting people. And I would say, You're right, fan comes from the word fanatic. And that fanaticism, that passion is something you absolutely want. Because I would argue that a greater threat than that is what I was talking about earlier, which is apathy. You don't want people to be like, I don't care. And passion is of course, the opposite of apathy. And that's what you're looking for. So I would say, are you going to put yourself in a position where sometimes there could be a disagreement, you could upset somebody? Absolutely, but you those are the people, it's like if you're in a relationship with somebody and you have a fight that passion that's there is because there's care on both sides. You're both super engaged, you're both very passionate about your position. So, having a system in place to defuse that by saying, I hear you I understand where you're coming from, let's figure this out together, is part of the customer service staff that you've just got to prepare for. >> Can you using, sorry Brittany, using all this data that's available that Magento, Adobe et cetera can deliver and enable organizations to understand that and maybe even kind of marry those behaviors with apathy on one hand passion on the other and how do we get to that happy medium? >> Exactly, how do we get to the happy medium, what are the data points that matter? How are we, the idea of super fan means something different to every organization. So, part of it is uncovering what it is that really matters to you. I always say a super fan is somebody who over indexes and their affinity for a product, service, brand, entertainer, therefore increasing the chance that they're going to advocate on its behalf. So, thinking about, there could be people who are spending a lot of money with your brand who just aren't really that passionate about it. They're not going to tell people and that's fine. But those aren't the people who would be a quote unquote superfan, even though they may be spending a lot of money with you. So, it's figuring out what the markers are that are important to your brand or service. I work with a lot of brands on this because it really is different for everyone. But figuring out who those people are and then talking to them because this is something that, there's so much psychology around the why. Like why people behave the way we do that the consumer behavior, the internal and philosophical drives that are making us make the decisions that we make and the best way to uncover that is to talk to your customers because a lot of times you'll learn so much about your brand, you'll find so many things. I always love talking to recording artists about this, they put out a new song or a new album and in the fans find all these hidden messages >> Taylor is known for that. >> Always some-- >> Taylor is one of the best in the world. And a lot of times artists will say, Oh, yeah, like, I didn't do that on purpose but I'm totally going to take credit for it because these fans found it. And oh, yeah, of course, I meant to do that. So, you'll find that some of these customers understand your brand oftentimes better than you do which is a really fun thing. >> It's also just the ecosystem. You my favorite one always reference is Harley Davidson, guess how many brands get tattooed on people's arms, and just the whole ecosystem of other products that were built up around the motorcycle, and to support kind of that community they weren't getting any nickels necessarily if somebody sold a saddle bag or a leather jacket, or whatever but it was such and it still is, I think such a vibrant community again, and as evidence by you put a tattoo on your arm that it's something to strive for, not easy to get. >> Why we always say build a brand not a business because the brand are those things that people are connecting to. We were talking about NASA before we started filming. I'm a huge space geek and Lisa loves space having worked for NASA in the past and that's one of those things, I don't know this to be true but I got to believe NASA way outpaces like every other combined government agency in licensing. I mean, people walk around wearing NASA logos on everything >> I saw at least three of them this morning. >> Yeah, I mean, I bought in the last month, probably three different NASA licensed products. So I mean that's the passion that if you can connect to somebody on an emotional level and make your story part of their story. They want to represent it, they want to get that Harley tattooed on their arm. >> That emotional connection but also that personalization that's key? >> Yes. >> What's difference in from your perspective on a superfan versus an influencer? Are they one in the same? >> It's a great question. So, they a lot of times are one in the same and that same Viacom study that I mentioned earlier. Something like two thirds of people said that they consider themselves to be pop culture influencers which sounds like a lot. But if you think about it, pretty much everyone is an influencer and that's because for Nielsen, the most trusted recommendation is or the most trusted form advertising is a recommendation from a friend or a family member, 92% of people trust a recommendation from a friend or family member, which far outpaces every other form of advertising. So in a lot of ways, these micro influencers are the next wave of advertising. These advocates or these super fans are, I think in many ways an untapped well of resources for the fans who drill in and you mentioned Taylor Swift before. How many people listen to Taylor Swift for the first time because a friend suggested they listen to Taylor Swift. I would argue that lots and lots of people and Taylor said something to me years ago that like a former manager, or someone said to her, and that was, if you want to sell half a million albums, you're going to have to meet half a million people. That was said to her when she was like, 15, 16 years old and she thought, okay, yeah, I'm going to go meet half a million people. I'm going to be befriend them, I'm going to listen to their stories, I'm going to let them know what they say matters to me. And here we are, she sold, I don't know, 50, 60 million albums, however many she sold worldwide. And but that's really where it starts, that one to one connection. >> Seems to just kind of all go back to referral. And isn't that sort of the basic human connection? It's like, are we trying to over-complicate this with all these different tools that simply, even with hiring and tech or whatever industry, referrals are so much more important because you've got some sort of connection to a brand or a person or a product or service. >> You've got that connection, you've got somebody who's already very well qualified. And I like to talk about something that I call the wave method which the wave is a ritual hello, goodbye. How many times a day do you wave at people, countless. And virtually you say hello to tons of people everyday. People who are coming to one of your social pages, people who are engaging with your website. So I say, I encourage people to think about that hello and goodbye, that interaction. Think of a wave as an acronym and ask yourself, are you making everybody who's going to come into contact with you today feel welcomed? Is there something on your virtual site or in your real storefront. If you're a brick and mortar business that's going to make people feel welcomed? How are you making them feel like they belong? The A is appreciated, how are you letting those people know that they are appreciated by your business? I think I know I have often felt like I'm a number or I don't matter. Utility companies are notorious for this for making you feel like they don't really care if they have your business or not. Or they know perhaps that they're going to because there's not like a different water company you can you can use it your home. And that sucks, like we've all been made to feel like we weren't appreciated by somebody that we were doing a financial transaction with. So ask yourself, how can you make your potential and current customers feel appreciated? The V stands for validated, and one of the best quotes that I've ever come across is from Oprah. On her last episode, she was imparting some of the lessons that she had learned over the years of hosting her shows and she said she'd interviewed something like 30,000 people over the years, and they all wanted the same thing. And that was validation. They all want it to feel like they were important and their feelings mattered. I see you, I hear you what you're saying is important to me. So, validate your customers. One big mistake that I see people make all the time in customer service is when somebody has a complaint, having your rebuttal be like, Oh, I've never heard that before. Or it's 10,000 people haven't have had great experiences. That's absolutely the worst thing that you can ever say to somebody because you're bringing in other experiences that don't matter to them. It's a one to one conversation. It's a one to one relationship. So bringing in, that's like having a fight with your significant other and saying like, Well none of the women I dated before you ever had a problem with this, like how well is that going to go over? Like you don't want to bring in other experiences. So that V and wave validated >> And the E? >> and then the E is excited, making people feel excited because that passion, having people feel like you know you're excited that they're a customer of yours and you can bring something that's going to make their lives better is the most important key. >> Brittany, thank you so much. I could keep talking to ya. I wish we didn't end but we do, for sharing your experiences, your expertise, your recommendations on becoming any kind of brand with any product or service, generating the super fans. We appreciate your time. >> Thank you so much. It was so great speaking with you guys today. >> Ditto. >> Thanks. >> For Jeff Frick, I'm Lisa Martin. You're watching this on theCUBE live from Magento Imagine 2019 from Vegas, thanks for watching.

>> Announcer: From Denver, Colorado, it's theCUBE, covering Super Computing 17. Brought to you by Intel. (soft electronic music) Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're gettin' towards the end of the day here at Super Computing 2017 in Denver, Colorado. 12,000 people talkin' really about the outer limits of what you can do with compute power and lookin' out into the universe and black holes and all kinds of exciting stuff. We're kind of bringin' it back, right? We're all about democratization of technology for people to solve real problems. We're really excited to have our last guest of the day, bringin' the energy, Armughan Ahmad. He's SVP and GM, Hybrid Cloud and Ready Solutions for Dell EMC, and a many-time CUBE alumni. Armughan, great to see you. >> Yeah, good to see you, Jeff. So, first off, just impressions of the show. 12,000 people, we had no idea. We've never been to this show before. This is great. >> This is a show that has been around. If you know the history of the show, this was an IEEE engineering show, that actually turned into high-performance computing around research-based analytics and other things that came out of it. But, it's just grown. We're seeing now, yesterday the super computing top petaflops were released here. So, it's fascinating. You have some of the brightest minds in the world that actually come to this event. 12,000 of them. >> Yeah, and Dell EMC is here in force, so a lot of announcements, a lot of excitement. What are you guys excited about participating in this type of show? >> Yeah, Jeff, so when we come to an event like this, HBC-- We know that HBC is also evolved from your traditional HBC, which was around modeling and simulation, and how it started from engineering to then clusters. It's now evolving more towards machine learning, deep learning, and artificial intelligence. So, what we announced here-- Yesterday, our press release went out. It was really related to how our strategy of advancing HBC, but also democratizing HBC's working. So, on the advancing, on the HBC side, the top 500 super computing list came out. We're powering some of the top 500 of those. One big one is TAC, which is Texas Institute out of UT, University of Texas. They now have, I believe, the number 12 spot in the top 500 super computers in the world, running an 8.2 petaflops off computing. >> So, a lot of zeros. I have no idea what a petaflop is. >> It's very, very big. It's very big. It's available for machine learning, but also eventually going to be available for deep learning. But, more importantly, we're also moving towards democratizing HBC because we feel that democratizing is also very important, where HBC should not only be for the research and the academia, but it should also be focused towards the manufacturing customers, the financial customers, our commercial customers, so that they can actually take the complexity of HBC out, and that's where our-- We call it our HBC 2.0 strategy, off learning from the advancements that we continue to drive, to then also democratizing it for our customers. >> It's interesting, I think, back to the old days of Intel microprocessors getting better and better and better, and you had Spark and you had Silicon Graphics, and these things that were way better. This huge differentiation. But, the Intel I32 just kept pluggin' along and it really begs the question, where is the distinction now? You have huge clusters of computers you can put together with virtualization. Where is the difference between just a really big cluster and HBC and super computing? >> So, I think, if you look at HBC, HBC is also evolving, so let's look at the customer view, right? So, the other part of our announcement here was artificial intelligence, which is really, what is artificial intelligence? It's, if you look at a customer retailer, a retailer has-- They start with data, for example. You buy beer and chips at J's Retailer, for example. You come in and do that, you usually used to run a SEQUEL database or you used to run a RDBMS database, and then that would basically tell you, these are the people who can purchase from me. You know their purchase history. But, then you evolved into BI, and then if that data got really, very large, you then had an HBC cluster, would which basically analyze a lot of that data for you, and show you trends and things. That would then tell you, you know what, these are my customers, this is how many times they are frequent. But, now it's moving more towards machine learning and deep learning as well. So, as the data gets larger and larger, we're seeing datas becoming larger, not just by social media, but your traditional computational frameworks, your traditional applications and others. We're finding that data is also growing at the edge, so by 2020, about 20 billion devices are going to wake up at the edge and start generating data. So, now, Internet data is going to look very small over the next three, four years, as the edge data comes up. So, you actually need to now start thinking of machine learning and deep learning a lot more. So, you asked the question, how do you see that evolving? So, you see an RDBMS traditional SQL evolving to BI. BI then evolves into either an HBC or hadoop. Then, from HBC and hadoop, what do you do next? What you do next is you start to now feed predictive analytics into machine learning kind of solutions, and then once those predictive analytics are there, then you really, truly start thinking about the full deep learning frameworks. >> Right, well and clearly like the data in motion. I think it's funny, we used to make decisions on a sample of data in the past. Now, we have the opportunity to take all the data in real time and make those decisions with Kafka and Spark and Flink and all these crazy systems that are comin' to play. Makes Hadoop look ancient, tired, and yesterday, right? But, it's still valid, right? >> A lot of customers are still paying. Customers are using it, and that's where we feel we need to simplify the complex for our customers. That's why we announced our Machine Learning Ready Bundle and our Deep Learning Ready Bundle. We announced it with Intel and Nvidia together, because we feel like our customers either go to the GPU route, which is your accelerator's route. We announced-- You were talking to Ravi, from our server team, earlier, where he talked about the C4140, which has the quad GPU power, and it's perfect for deep learning. But, with Intel, we've also worked on the same, where we worked on the AI software with Intel. Why are we doing all of this? We're saying that if you thought that RDBMS was difficult, and if you thought that building a hadoop cluster or HBC was a little challenging and time consuming, as the customers move to machine learning and deep learning, you now have to think about the whole stack. So, let me explain the stack to you. You think of a compute storage and network stack, then you think of-- The whole eternity. Yeah, that's right, the whole eternity of our data center. Then you talk about our-- These frameworks, like Theano, Caffe, TensorFlow, right? These are new frameworks. They are machine learning and deep learning frameworks. They're open source and others. Then you go to libraries. Then you go to accelerators, which accelerators you choose, then you go to your operating systems. Now, you haven't even talked about your use case. Retail use case or genomic sequencing use case. All you're trying to do is now figure out TensorFlow works with this accelerator or does not work with this accelerator. Or, does Caffe and Theano work with this operating system or not? And, that is a complexity that is way more complex. So, that's where we felt that we really needed to launch these new solutions, and we prelaunched them here at Super Computing, because we feel the evolution of HBC towards AI is happening. We're going to start shipping these Ready Bundles for machine learning and deep learning in first half of 2018. >> So, that's what the Ready Solutions are? You're basically putting the solution together for the client, then they can start-- You work together to build the application to fix whatever it is they're trying to do. >> That's exactly it. But, not just fix it. It's an outcome. So, I'm going to go back to the retailer. So, if you are the CEO of the biggest retailer and you are saying, hey, I just don't want to know who buys from me, I want to now do predictive analytics, which is who buys chips and beer, but who can I sell more things to, right? So, you now start thinking about demographic data. You start thinking about payroll data and other datas that surround-- You start feeding that data into it, so your machine now starts to learn a lot more of those frameworks, and then can actually give you predictive analytics. But, imagine a day where you actually-- The machine or the deep learning AI actually tells you that it's not just who you want to sell chips and beer to, it's who's going to buy the 4k TV? You're makin' a lot of presumptions. Well, there you go, and the 4k-- But, I'm glad you're doin' the 4k TV. So, that's important, right? That is where our customers need to understand how predictive analytics are going to move towards cognitive analytics. So, this is complex but we're trying to make that complex simple with these Ready Solutions from machine learning and deep learning. >> So, I want to just get your take on-- You've kind of talked about these three things a couple times, how you delineate between AI, machine learning, and deep learning. >> So, as I said, there is an evolution. I don't think a customer can achieve artificial intelligence unless they go through the whole crawl walk around space. There's no shortcuts there, right? What do you do? So, if you think about, Mastercard is a great customer of ours. They do an incredible amount of transactions per day, (laughs) as you can think, right? In millions. They want to do facial recognitions at kiosks, or they're looking at different policies based on your buying behavior-- That, hey, Jeff doesn't buy $20,000 Rolexes every year. Maybe once every week, you know, (laughs) it just depends how your mood is. I was in the Emirates. Exactly, you were in Dubai (laughs). Then, you think about his credit card is being used where? And, based on your behaviors that's important. Now, think about, even for Mastercard, they have traditional RDBMS databases. They went to BI. They have high-performance computing clusters. Then, they developed the hadoop cluster. So, what we did with them, we said okay. All that is good. That data that has been generated for you through customers and through internal IT organizations, those things are all very important. But, at the same time, now you need to start going through this data and start analyzing this data for predictive analytics. So, they had 1.2 million policies, for example, that they had to crunch. Now, think about 1.2 million policies that they had to say-- In which they had to take decisions on. That they had to take decisions on. One of the policies could be, hey, does Jeff go to Dubai to buy a Rolex or not? Or, does Jeff do these other patterns, or is Armughan taking his card and having a field day with it? So, those are policies that they feed into machine learning frameworks, and then machine learning actually gives you patterns that they can now see what your behavior is. Then, based on that, eventually deep learning is when they move to next. Deep learning now not only you actually talk about your behavior patterns on the credit card, but your entire other life data starts to-- Starts to also come into that. Then, now, you're actually talking about something before, that's for catching a fraud, you can actually be a lot more predictive about it and cognitive about it. So, that's where we feel that our Ready Solutions around machine learning and deep learning are really geared towards, so taking HBC to then democratizing it, advancing it, and then now helping our customers move towards machine learning and deep learning, 'cause these buzzwords of AIs are out there. If you're a financial institution and you're trying to figure out, who is that customer who's going to buy the next mortgage from you? Or, who are you going to lend to next? You want the machine and others to tell you this, not to take over your life, but to actually help you make these decisions so that your bottom line can go up along with your top line. Revenue and margins are important to every customer. >> It's amazing on the credit card example, because people get so pissed if there's a false positive. With the amount of effort that they've put into keep you from making fraudulent transactions, and if your credit card ever gets denied, people go bananas, right? The behavior just is amazing. But, I want to ask you-- We're comin' to the end of 2017, which is hard to believe. Things are rolling at Dell EMC. Michael Dell, ever since he took that thing private, you could see the sparkle in his eye. We got him on a CUBE interview a few years back. A year from now, 2018. What are we going to talk about? What are your top priorities for 2018? >> So, number one, Michael continues to talk about that our vision is advancing human progress through technology, right? That's our vision. We want to get there. But, at the same time we know that we have to drive IT transformation, we have to drive workforce transformation, we have to drive digital transformation, and we have to drive security transformation. All those things are important because lots of customers-- I mean, Jeff, do you know like 75% of the S&P 500 companies will not exist by 2027 because they're either not going to be able to make that shift from Blockbuster to Netflix, or Uber taxi-- It's happened to our friends at GE over the last little while. >> You can think about any customer-- That's what Michael did. Michael actually disrupted Dell with Dell technologies and the acquisition of EMC and Pivotal and VMWare. In a year from now, our strategy is really about edge to core to the cloud. We think the world is going to be all three, because the rise of 20 billion devices at the edge is going to require new computational frameworks. But, at the same time, people are going to bring them into the core, and then cloud will still exist. But, a lot of times-- Let me ask you, if you were driving an autonomous vehicle, do you want that data-- I'm an Edge guy. I know where you're going with this. It's not going to go, right? You want it at the edge, because data gravity is important. That's where we're going, so it's going to be huge. We feel data gravity is going to be big. We think core is going to be big. We think cloud's going to be big. And we really want to play in all three of those areas. >> That's when the speed of light is just too damn slow, in the car example. You don't want to send it to the data center and back. You don't want to send it to the data center, you want those decisions to be made at the edge. Your manufacturing floor needs to make the decision at the edge as well. You don't want a lot of that data going back to the cloud. All right, Armughan, thanks for bringing the energy to wrap up our day, and it's great to see you as always. Always good to see you guys, thank you. >> All right, this is Armughan, I'm Jeff Frick. You're watching theCUBE from Super Computing Summit 2017. Thanks for watching. We'll see you next time. (soft electronic music)

>> Announcer: From Denver, Colorado it's theCUBE. Covering Super Computing 17, brought to you by Intel. Welcome back everybody, Jeff Frick here with theCUBE. We're at Super Computing 2017 in Denver, Colorado talking about big big iron, we're talking about space and new frontiers, black holes, mapping the brain. That's all fine and dandy, but we're going to have a little bit more fun this next segment. We're excited to have our next guest Bernie Spang. He's a VP Software Defined Infrastructure for IBM. And his buddy and guest Wayne Glanfield HPC Manager for Red Bull Racing. And for those of you that don't know, that's not the pickup trucks, it's not the guy jumping out of space, this is the Formula One racing team. The fastest, most advanced race cars in the world. So gentlemen, first off welcome. Thank you. Thank you Jeff. So what is a race car company doing here for a super computing conference? Obviously we're very interested in high performance computing so traditionally we've used a wind tunnel to do our external aerodynamics. HPC allows us to do many many more iterations, design iterations of the car. So we can actually kind of get more iterations of the designs out there and make the car go faster very quicker. So that's great, you're not limited to how many times you can get it in the wind tunnel. The time you have in the wind tunnel. I'm sure there's all types of restrictions, cost and otherwise. There's lots of restrictions and both the wind tunnel and in HPC usage. So with HPC we're limited to 25 teraflops, which isn't many teraflops. 25 teraflops. >> Wayne: That's all. And Bernie, how did IBM get involved in Formula One racing? Well I mean our spectrum computing offerings are about virtualizing clusters to optimize efficiency, and the performance of the workloads. So our Spectrum LSF offering is used by manufacturers, designers to get ultimate efficiency out of the infrastructure. So with the Formula One restrictions on the teraflops you want to get as much work through that system as efficiently as you can. And that's where Spectrum computing comes in. That's great. And so again, back to the simulations. So not only can you just do simulations 'cause you got the capacity, but then you can customize it as you said I think before we turned on the cameras for specific tracks, specific race conditions. All types of variables that you couldn't do very easily in a traditional wind tunnel. Yes obviously it takes a lot longer to actually kind of develop, create, and rapid prototype the models and get them in the wind tunnel, and actually test them. And it's obviously much more expensive. So by having a HPC facility we can actually kind of do the design simulations in a virtual environment. So what's been kind of the ahah from that? Is it just simply more better faster data? Is there some other kind of transformational thing that you observed as a team when you started doing this type of simulation versus just physical simulation in a wind tunnel? We started using HPC and computational fluid dynamics about 12 years ago in anger. Traditionally it started out as a complementary tool to the wind tunnel. But now with the advances in HPC technology and software from IBM, it's actually beginning to overtake the wind tunnel. So it's actually kind of driving the way we design the car these days. That's great. So Bernie, working with super high end performance, right, where everything is really optimized to get that car to go a little bit faster, just a little bit faster. Right. Pretty exciting space to work in, you know, there's a lot of other great applications, aerospace, genomics, this and that. But this is kind of a fun thing you can actually put your hands on. Oh it's definitely fun, it's definitely fun being with the Red Bull Racing team, and with our clients when we brief them there. But we have commercial clients in automotive design, aeronautics, semiconductor manufacturing, where getting every bit of efficiency and performance out of their infrastructure is also important. Maybe they're not limited by rules, but they're limited by money, you know and the ability to investment. And their ability to get more out of the environment gives them a competitive advantage as well. And really what's interesting about racing, and a lot of sports is you get to witness the competition. We don't get to witness the competition between big companies day to day. You're not kind of watching it in those little micro instances. So the good thing is you get to learn a lot from such a focused, relatively small team as Red Bull Racing that you can apply to other things. So what are some of the learnings as you've got work with them that you've taken back? Well certainly they push the performance of the environment, and they push us, which is a great thing for us, and for our other clients who benefit. But one of the things I think that really stands out is the culture there of the entire team no matter what their role and function. From the driver on down to everybody else are focused on winning races and winning championships. And that team view of getting every bit of performance out of everything everybody does all the time really opened our thinking to being broader than just the scheduling of the IT infrastructure, it's also about making the design team more productive and taking steps out of the process, and anything we can do there. Inclusive of the storage management, and the data management over time. So it's not just the compute environment it's also the virtualized storage environment. Right, and just massive amounts of storage. You said not only are you running and generating, I'm just going to use boatloads 'cause I'm not sure which version of the flops you're going to use. But also you got historical data, and you have result data, and you have models that need to be tweaked, and continually upgraded so that you do better the following race. Exactly, I mean we're generating petabytes of data a year and I think one of the issues which is probably different from most industries is our workflows are incredibly complex. So we have up to 200 discrete job steps for each workflow to actually kind of produce a simulation. This is where the kind of IBM Spectrum product range actually helps us do that efficiently. If you imagine an aerospace engineer, or aerodynamics engineer trying to manually manage 200 individual job steps, it just wouldn't happen very efficiently. So this is where Spectrum scale actually kind of helps us do that. So you mentioned it briefly Bernie, but just a little bit more specifically. What are some of the other industries that you guys are showcasing that are leveraging the power of Spectrum to basically win their races. Yeah so and we talked about the infrastructure and manufacturing, but they're industrial clients. But also in financial services. So think in terms of risk analytics and financial models being an important area. Also healthcare life sciences. So molecular biology, finding new drugs. When you talk about the competition and who wins right. Genomics research and advances there. Again, you need a system and an infrastructure that can chew through vast amounts of data. Both the performance and the compute, as well as the longterm management with cost efficiency of huge volumes of data. And then you need that virtualized cluster so that you can run multiple workloads many times with an infrastructure that's running in 80%, 90% efficiency. You can't afford to have silos of clusters. Right we're seeing clients that have problems where they don't have this cluster virtualization software, have cluster creep, just like in the early days we had server sprawl, right? With a different app on a different server, and we needed to virtualize the servers. Well now we're seeing cluster creep. Right the Hadoop clusters and Spark clusters, and machine learning and deep learning clusters. As well as the traditional HPC workload. So what Spectrum computing does is virtualizes that shared cluster environment so that you can run all these different kind of workloads and drive up the efficiency of the environment. 'Cause efficiency is really the key right. You got to have efficiency that's what, that's really where cloud got its start, you know, kind of eating into the traditional space, right. There's a lot of inefficient stuff out there so you got to use your resources efficiently it's way too competitive. Correct well we're also seeing inefficiencies in the use of cloud, right. >> Jeff: Absolutely. So one of the features that we've added to the Spectrum computing recently is automated dynamic cloud bursting. So we have clients who say that they've got their scientists or their design engineers spinning up clusters in the cloud to run workloads, and then leaving the servers running, and they're paying the bill. So we built in automation where we push the workload and the data over the cloud, start the servers, run the workload. When the workload's done, spin down the servers and bring the data back to the user. And it's very cost effective that way. It's pretty fun everyone talks often about the spin up, but they forget to talk about the spin down. Well that's where the cost savings is, exactly. Alright so final words, Wayne, you know as you look forward, it's super a lot of technology in Formula One racing. You know kind of what's next, where do you guys go next in terms of trying to get another edge in Formula One racing for Red Bull specifically. I mean I'm hoping they reduce the restrictions on HPC so it can actually start using CFD and the software IBM provides in a serious manner. So it can actually start pushing the technologies way beyond where they are at the moment. It's really interesting that they, that as a restriction right, you think of like plates and size of the engine, and these types of things as the rule restrictions. But they're actually restricting based on data size, your use of high performance computing. They're trying to save money basically, but. It's crazy. So whether it's a rule or you know you're share holders, everybody's trying to save money. Alright so Bernie what are you looking at, sort of 2017 is coming to an end, it's hard for me to say that as you look forward to 2018 what are some of your priorities for 2018. Well the really important thing and we're hearing it at this conference, I'm talking with the analysts and with the clients. The next generation of HPC in analytics is what we're calling machine learning, deep learning, cognitive AI, whatever you want to call it. That's just the new generation of this workload. And our Spectrum conductor offering and our new deep learning impact capability to automate the training of deep learning models, so that you can more quickly get to an accurate model like in hours or minutes, not days or weeks. That's going to a huge break through. And based on our early client experience this year, I think 2018 is going to be a breakout year for putting that to work in commercial enterprise use cases. Alright well I look forward to the briefing a year from now at Super Computing 2018. Absolutely. Alright Bernie, Wayne, thanks for taking a few minutes out of your day, appreciate it. You're welcome, thank you. Alright he's Bernie, he's Wayne, I'm Jeff Frick we're talking Formula One Red Bull Racing here at Super Computing 2017. Thanks for watching.

>> Narrator: From Denver, Colorado, it's theCUBE. Covering Super Computing '17, brought to you by Intel. Hey welcome back everybody, Jeff Frick here with theCUBE. We're at Super Computing 2017, Denver, Colorado, 12,000 people talking about big iron, big questions, big challenges. It's really an interesting take on computing, really out on the edge. The key note was, literally, light years out in space, talking about predicting the future with quirks and all kinds of things, a little over my head for sure. But we're excited to kind of get back to the ground and we have Ravi Pendekanti. He's the Senior Vice President of Product Management and Marketing, Server Platforms, Dell EMC. It's a mouthful, Ravi great to see you. Great to see you too Jeff and thanks for having me here. Absolutely, so we were talking before we turned the cameras on. One of your big themes, which I love, is kind of democratizing this whole concept of high performance computing, so it's not just the academics answering the really, really, really big questions. You're absolutely right. I mean think about it Jeff, 20 years ago, even 10 years ago, when people talk about high performance computing, it was what I call as being in the back alleys of research and development. There were a few research scientists working on it, but we're at a time in our journey towards helping humanity in a bigger way. The HPC has found it's way into almost every single mainstream industry you can think of. Whether it is fraud detection, you see MasterCard is using it for ensuring that they can see and detect any of the fraud that can be committed earlier than the perpetrators come in and actually hack the system. Or if you get into life sciences, if you talk about genomics. I mean this is what might be good for our next set of generations, where they can probably go out and tweak some of the things in a genome sequence so that we don't have the same issues that we have had in the past. Right. Right? So, likewise, you can pick any favorite industry. I mean we are coming up to the holiday seasons soon. I know a lot of our customers are looking at how do they come up with the right schema to ensure that they can stock the right product and ensure that it is available for everyone at the right time? 'Cause timing is important. I don't think any kid wants to go with no toy and have the product ship later. So bottom line is, yes, we are looking at ensuring the HPC reaches every single industry you can think of. So how do you guys parse HPC verses a really big virtualized cluster? I mean there's so many ways that compute and store has evolved, right? So now, with cloud and virtual cloud and private cloud and virtualization, you know, I can pull quite a bit of horsepower together to attack a problem. So how do you kind of cut the line between Navigate, yeah. big, big compute, verses true HPC? HPC. It's interesting you ask. I'm actually glad you asked because people think that it's just feeding CPU or additional CPU will do the trick, it doesn't. The simple fact is, if you look at the amount of data that is being created. I'll give you a simple example. I mean, we are talking to one of the airlines right now, and they're interested in capturing all the data that comes through their flights. And one of the things they're doing is capturing all the data from their engines. 'Cause end of the day, you want to make sure that your engines are pristine as they're flying. And every hour that an engine flies out, I mean as an airplane flies out, it creates about 20 terabytes of data. So, if you have a dual engine, which is what most flights are. In one hour they create about 40 terabytes of data. And there are supposedly about 38,000 flights taking off at any given time around the world. I mean, it's one huge data collection problem. Right? I mean, I'm told it's like a real Godzilla number, so I'll let you do the computation. My point is if you really look at the data, data has no value, right? What really is important is getting information out of it. The CPU on the other side has gone to a time and a phase where it is hitting the, what I call as the threshold of the Moore's law. Moore's law was all about performance doubles every two years. But today, that performance is not sufficient. Which is where auxiliary technologies need to be brought in. This is where the GPUs, the FBGAs. Right, right. Right. So when you think about these, that's where the HPC world takes off, is you're augmenting your CPUs and your processors with additional auxiliary technology such as the GPUs and FBGAs to ensure that you have more juice to go do this kind of analytics and the massive amounts of data that you and I and the rest of the humanity is creating. It's funny that you talk about that. We were just at a Western Digital event a little while ago, talking about the next generation of drives and it was the same thing where now it's this energy assist method to change really the molecular way that it saves information to get more out of it. So that's kind of how you parse it. If you've got to juice the CPU, and kind of juice the traditional standard architecture, then you're moving into the realm of high performance computing. Absolutely, I mean this is why, Jeff, yesterday we launched a new PowerEdge C4140, right? The first of it's kind in terms of the fact that it's got two Intel Xeon processors, but beyond that, it also can support four Nvidia GPUs. So now you're looking at a server that's got both the CPUs, to your earlier comment on processors, but is augmented by four of the GPUs, that gives immense capacity to do this kind of high performance computing. But as you said, it's not just compute, it's store, it's networking, it's services, and then hopefully you package something together in a solution so I don't have to build the whole thing from scratch, you guys are making moves, right? Oh, this is a perfect lead in, perfect lead in. I know, my colleague, Armagon will be talking to you guys shortly. What his team does, is it takes all the building blocks we provide, such as the servers, obviously looks at the networking, the storage elements, and then puts them together to create what are called solutions. So if you've got solutions, which enable our customers to go back in and easily deploy a machine-learning or a deep-learning solution. Where now our customers don't have to do what I call as the heavy lift. In trying to make sure that they understand how the different pieces integrate together. So the goal behind what we are doing at Dell EMC is to remove the guess work out so that our customers and partners can go out and spend their time deploying the solution. Whether it is for machine learning, deep learning or pick your favorite industry, we can also verticalize it. So that's the beauty of what we are doing at Dell EMC. So the other thing we were talking about before we turned turned the cameras on is, I call them the itys from my old Intel days, reliability, sustainability, serviceability, and you had a different phrase for it. >> Ravi: Oh yes, I know you're talking about the RAS. The RAS, right. Which is the reliability, availability, and serviceability. >> Jeff: But you've got a new twist on it. Oh we do. Adding something very important, and we were just at a security show early this week, CyberConnect, and security now cuts through everything. Because it's no longer a walled garden, 'cause there are no walls. There are no walls. It's really got to be baked in every layer of the solution. Absolutely right. The reason is, if you really look at security, it's not about, you know till a few years ago, people used to think it's all about protecting yourself from external forces, but today we know that 40% of the hacks happen because of the internal, you know, system processes that we don't have in place. Or we could have a person with an intent to break in for whatever reason, so the integrated security becomes part and parcel of what we do. This is where, with in part of a 14G family, one of the things we said is we need to have integrated security built in. And along with that, we want to have the scalability because no two workloads are the same and we all know that the amount of data that's being created today is twice what it was the last year for each of us. Forget about everything else we are collecting. So when you think about it, we need integrated security. We need to have the scalability feature set, also we want to make sure there is automation built in. These three main tenets that we talked about feed into what we call internally, the monic of a user's PARIS. And that's what I think, Jeff, to our earlier conversation, PARIS is all about, P is for best price performance. Anybody can choose to get the right performance or the best performance, but you don't want to shell out a ton of dollars. Likewise, you don't want to pay minimal dollars and try and get the best performance, that's not going to happen. I think there's a healthy balance between price performance, that's important. Availability is important. Interoperability, as much as everybody thinks that they can act on their own, it's nearly impossible, or it's impossible that you can do it on your own. >> Jeff: These are big customers, they've got a lot of systems. You are. You need to have an ecosystem of partners and technologies that come together and then, end of the day, you have to go out and have availability and serviceability, or security, to your point, security is important. So PARIS is about price performance, availability, interoperability, reliability, availability and security. I like it. That's the way we design it. It's much sexier than that. We drop in, like an Eiffel Tower picture right now. There you go, you should. So Ravi, hard to believe we're at the end of 2017, if we get together a year from now at Super Computing 2018, what are some of your goals, what are your some objectives for 2018? What are we going to be talking about a year from today? Oh, well looking into a crystal ball, as much as I can look into that, I thin that-- >> Jeff: As much as you can disclose. And as much as we can disclose, a few things I think are going to happen. >> Jeff: Okay. Number one, I think you will see people talk about to where we started this conversation. HPC has become mainstream, we talked about it, but the adoption of high performance computing, in my personal belief, is not still at a level that it needs to be. So, if you go down next 12 to 18 months, lets say, I do think the adoption rates will be much higher than where we are. And we talk about security now, because it's a very topical subject, but as much as we are trying to emphasize to our partners and customers that you've got to think about security from ground zero. We still see a number of customers who are not ready. You know, some of the analysis show there are nearly 40% of the CIOs are not ready in helping and they truly understand, I should say, what it takes to have a secure system and a secure infrastructure. It's my humble belief that people will pay attention to it and move the needle on it. And we talked about, you know, four GPUs in our C4140, do anticipate that there will be a lot more of auxiliary technology packed into it. Sure, sure. So that's essentially what I can say without spilling the beans too much. Okay, all right, super. Ravi, thanks for taking a couple of minutes out of your day, appreciate it. = Thank you. All right, he's Ravi, I'm Jeff Frick, you're watching theCUBE from Super Computing 2017 in Denver, Colorado. Thanks for watching. (techno music)

>> [Announcer] From Denver, Colorado, it's the Cube covering Super Computing 17, brought to you by Intel. (techno music) >> Welcome back, everybody, Jeff Frick with the Cube. We are at Super Computing 2017 here in Denver, Colorado. 12,000 people talking about big iron, heavy lifting, stars, future mapping the brain, all kinds of big applications. We're here, first time ever for the Cube, great to be here. We're excited for our next guest. She's Susan Bobholtz, she's the Fabric Alliance Manager for Omni-Path at Intel, Susan, welcome. >> Thank you. >> So what is Omni-Path, for those that don't know? >> Omni-Path is Intel's high performance fabric. What it does is it allows you to connect systems and make big huge supercomputers. >> Okay, so for the royal three-headed horsemen of compute, store, and networking, you're really into data center networking, connecting the compute and the store. >> Exactly, correct, yes. >> Okay. How long has this product been around? >> We started shipping 18 months ago. >> Oh, so pretty new? >> Very new. >> Great, okay and target market, I'm guessing has something to do with high performance computing. >> (laughing) Yes, our target market is high performance computing, but we're also seeing a lot of deployments in artificial intelligence now. >> Okay and so what's different? Why did Intel feel compelled that they needed to come out with a new connectivity solution? >> We were getting people telling us they were concerned that the existing solutions were becoming too expensive and weren't going to scale into the future, so they said Intel, can you do something about it, so we did. We made a couple of strategic acquisitions, we combined that with some of our own IP and came up with Omni-Path. Omni-Path is very much a proprietary protocol, but we use all the same software interfaces as InfiniBand, so your software applications just run. >> Okay, so to the machines it looks like InfiniBand? >> Yes. >> Just plug and play and run. >> Very much so, it's very similar. >> Okay what are some of the attributes that make it so special? >> The reason it's really going very well is that it's the price performance benefits, so we have equal to, or better, performance than InfiniBand today, but we also have our switch technology is 48 ports verses InfiniBand is 36 ports. So that means you can build denser clusters in less space and less cables, lower power, total cost of ownership goes down, and that's why people are buying it. >> Really fits into the data center strategy that Intel's executing very aggressively right now. >> Fits very nicely, absolutely, yes, very much so. >> Okay, awesome, so what are your thoughts here at the show? Any announcements, anything that you've seen that's of interest? >> Oh yeah, so, a couple things. We've had really had good luck on the Top 500 list. 60% of the servers that are running a 100 gigabyte fabrics in the Top 500 list are running connected via Omni-Path. >> What percentage again? >> 60% >> 60? >> Yes. >> You've only been at it for 18 months? >> Yes, exactly. >> Impressive. >> Very, very good. We've got systems in the Top 10 already. Some of the Top 10 systems in the world are using Omni-Path. >> Is it rip and replace, do you find, or these are new systems that people are putting in. >> Yeah, these are new systems. Usually when somebody's got a system they like and run, they don't want to touch it. >> Right. >> These are people saying I need a new system. I need more power, I need more oompf. They have the money, the budget, they want to put in something new, and that's when they look to Omni-Path. >> Okay, so what are you working on now, what's kind of next for Omni-Path? >> What's next for us is we are announcing a new higher, denser switch technology, so that will allow you to go for your director class switches, which is the really big ones, is now rather than having 768 ports, you can go to 1152, and that means, again, denser topologies, lower power, less cabling, it reduces your total cost of ownership. >> Right, I think you just answered my question, but I'm going to ask you anyway. >> (laughs) Okay. >> We talked a little bit before we turned the camera on about AI and some of the really unique challenges of AI, and that was part of the motivation behind this product. So what are some of the special attributes of AI that really require this type of connectivity? >> It's very much what you see even with high performance computing. You need low latency, you need high bandwidth. It's the same technologies, and in fact, in a lot of cases, it's the same systems, or sometimes they can be running software load that is HPC focused, and sometimes they're running a software load that is artificial intelligence focused. But they have the same exact needs. >> Okay. >> Do it fast, do it quick. >> Right, right, that's why I said you already answered the question. Higher density, more computing, more storing, faster. >> Exactly, right, exactly. >> And price performance. All right, good, so if we come back a year from now for Super Computing 2018, which I guess is in Dallas in November, they just announced. What are we going to be talking about, what are some of your priorities and the team's priorities as you look ahead to 2018? >> Oh we're continuing to advance the Omni-Path technology with software and additional capabilities moving forward, so we're hoping to have some really cool announcements next year. >> All right, well, we'll look forward to it, and we'll see you in Dallas in a year. >> Thanks, Cube. >> All right, she's Susan, and I'm Jeff. You're watching the Cube from Super Computing 2017. Thanks for watching, see ya next time. (techno music)

>> Announcer: From Denver, Colorado, it's theCUBE covering Super Computing '17. Brought to you by Intel. (upbeat techno music) Hey welcome back, everybody. Jeff Frick here with theCUBE. We're at Super Computing 2017 in Denver, Colorado. It's our first trip to the show, 12,000 people, a lot of exciting stuff going on, big iron, big lifting, heavy duty compute. We're excited to have our next guest on. He's Jim Wu, he's the Director of Customer Experience for Falcon Computing. Jim, welcome. Thank you. Good to see you. So, what does Falcon do for people that aren't familiar with the company? Yeah, Falcon Company is in our early stages startup, focus on AVA-based acceleration development. Our vision is to allow software engineers to develop a FPGA-based accelerators, accelerators without FPGA expertise. Right, you just said you closed your B round. So, congratulations on that. >> Jim: Thank you. Yeah, very exciting. So, it's a pretty interesting concept. To really bring the capability to traditional software engineers to program for hardware. That's kind of a new concept. What do you think? 'Cause it brings the power of a hardware system. but the flexibility of a software system. Yeah, so today, to develop FPGA accelerators is very challenging. So, today for the accelerations-based people use very low level language, like a Verilog and the VHDL to develop FPGA accelerators. Which was very time consuming, very labor-intensive. So, our goal is to liberate them to use, C/C++ space design flow to give them an environment that they are familiar with in C/C++. So now not only can they improve their productivity, we also do a lot of automatic organization under the hood, to give them the highest accelerator results. Right, so that really opens up the ecosystem well beyond the relatively small ecosystem that knows how to program their hardware. Definitely, that's what we are hoping to see. We want to the tool in the hands of all software programmers. They can use it in the Cloud. They can use it on premises. Okay. So what's the name of your product? And how does it fit within the stack? I know we've got the Intel microprocessor under the covers, we've got the accelerator, we've got the cards. There's a lot of pieces to the puzzle. >> Jim: Yeah. So where does Falcon fit? So our main product is a compiler, called the Merlin Compiler. >> Jeff: Okay. It's a pure C and the C++ flow that enables software programmers to design APGA-based accelerators without any knowledge of APGA. And it's highly integrated with Intel development tools. So users don't even need to learn anything about the Intel development environment. They can just use their C++ development environment. Then in the end, we give them the host code as well as APGA binaries so they can round on APGA to see a accelerated applications. Okay, and how long has Merlin been GA? Actually, we'll be GA early next year. Early next year. So finishing, doing the final polish here and there. Yes. So in this quarter, we are heavily investing a lot of ease-of-use features. Okay. We have most of the features we want to be in the tool, but we're still lacking a bit in terms of ease-of-use. >> Jeff: Okay. So we are enhancing our report capabilities, we are enhancing our profiling of capabilities. We want to really truly like a traditional C++-based development environment for software application engineers. Okay, that's fine. You want to get it done, right, before you ship it out the door? So you have some Alpha programs going on? Some Beta programs of some really early adopters? Yeah, exactly. So today we provide a 14 day free trial to any customers who are interested. We have it, you can set up your enterprise or you can set up on Cloud. Okay. We provide to where you want your work done. Okay. And so you'll support all the cloud service providers, the big public clouds, all the private clouds. All the traditional data servers as well. Right. So, we are twice already on Aduplas as well as Alibaba Cloud. So we are working on bringing the tool to other public cloud providers as well. Right. So what is some of the early feedback you're getting from some of the people you're talking to? As to where this is going to make the biggest impact. What type of application space has just been waiting for this solution? So our Merlin Compiler is a productivity tool, so any space that FPGA can traditionally play well that's where we want to be there. So like encryption, decryption, video codec, compression, decompression. Those kind of applications are very stable for APGA. Now traditionally they can only be developed by hardware engineers. Now with the Merlin Compiler, all of these software engineers can use the Merlin Compiler to do all of these applications. Okay. And when is the GA getting out, I know it's coming? When is it coming? Approximately So probably first quarter of 2018. Okay, that's just right around the corner. Exactly. Alright, super. And again, a little bit about the company, how many people are you? A little bit of the background on the founders. So we have about 30 employees, at the moment, so we have offices in Santa Clara which is our headquarters. We also have an office in Los Angeles. As well as a Beijing, China. Okay, great. Alright well Jim, thanks for taking a few minutes. We'll be looking for GA in a couple of months and wish you nothing but the best success. Okay, thank you so much, Jeff. Alright, he's Jim Lu I'm Jeff Frick. You're watching theCUBE from supering computing 2017. Thanks for watching. (upbeat techno music)

>> Narrator: From Denver, Colorado, it's theCUBE. Covering Super Computing '17, brought to you by Intel. (electronic music) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at Denver, Colorado at Super Computing 2017. 12,000 people, our first trip to the show. We've been trying to come for awhile, it's pretty amazing. A lot of heavy science in terms of the keynotes. All about space and looking into brain mapping and it's heavy lifting, academics all around. We're excited to have our next guest, who's an expert, all about speed and that's John Lockwood. He's the CEO of Algo-Logic. First off, John, great to see you. >> Yeah, thanks Jeff, glad to be here. >> Absolutely, so for folks that aren't familiar with the company, give them kind of the quick overview of Algo. >> Yes, Algo-Logic puts algorithms into logic. So our main focus is taking things are typically done in software and putting them into FPGAs and by doing that we make them go faster. >> So it's a pretty interesting phenomenon. We've heard a lot from some of the Intel execs about kind of the software overlay that now, kind of I guess, a broader ecosystem of programmers into hardware, but then still leveraging the speed that you get in hardware. So it's a pretty interesting combination to get those latencies down, down, down. >> Right, right, I mean Intel certainly made a shift to go on into heterogeneous compute. And so in this heterogeneous world, we've got software running on Xeons, Xeon Phis. And we've also got the need though, to use new compute in more than just the traditional microprocessor. And so with the acquisition of Altera, is that now Intel customers can use FPGAs in order to get the benefit in speed. And so Algo-Logic, we typically provide applications with software APIs, so it makes it really easy for end customers to deploy FPGAs into their data center, into their hosts, into their network and start using them right away. >> And you said one of your big customer sets is financial services and trading desk. So low latency there is critical as millions and millions and millions if not billions of dollars. >> Right, so Algo-Logic we have a whole product line of high-frequency trading systems. And so our Tick-To-Trade system is unique in the fact that it has a sub-microsecond trading latency and this means going from market data that comes in, for example on CME for options and futures trading, to time that we can place a fix order back out to the market. All of that happens in an FPGA. That happens in under a microsecond. So under a millionth of second and that beats every other software system that's being used. >> Right, which is a game change, right? Wins or losses can be made on those time frames. >> It's become a must have is that if you're trading on Wall Street or trading in Chicago and you're not trading with an FPGA, you're trading at a severe disadvantage. And so we make a product that enables all the trading firms to be playing on a fair, level playing field against the big firms. >> Right, so it's interesting because the adoption of Flash and some of these other kind of speed accelerator technologies that have been happening over the last several years, people are kind of getting accustomed to the fact that speed is better, but often it was kind of put aside in this kind of high-value applications like financial services and not really proliferating to a broader use of applications. I wonder if you're seeing that kind of change a little bit, where people are seeing the benefits of real time and speed beyond kind of the classic high-value applications? >> Well, I think the big change that's happened is that it's become machine-to-machine now. And so humans, for example in trading, are not part of the loop anymore and so it's not a matter of am I faster than another person? It's am I faster than the other person's machine? And so this notion of having compute that goes fast has become suddenly dramatically much more important because everything now is going to machine versus machine. And so if you're an ad tech advertiser, is that how quickly you can do an auction to place an ad matters and if you can get a higher value ad placed because you're able to do a couple rounds of an auction, that's worth a lot. And so, again, with Algo-Logic we make things go faster and that time benefit means, that all thing else being the same, you're the first to come to a decision. >> Right, right and then of course the machine-to-machine obviously brings up the hottest topic that everybody loves to talk about is autonomous vehicles and networked autonomous vehicles and just the whole IOT space with the compute moving out to the edge. So this machine-to-machine systems are only growing in importance and really percentage of the total compute consumption by far. >> That's right, yeah. So last year at Super Computing, we demonstrated a drone, bringing in realtime data from a drone. So doing realtime data collection and doing processing with our Key Value Store. So this year, we have a machine learning application, a Markov Decision Process where we show that we can scale-out a machine learning process and teach cars how to drive in a few minutes. >> Teach them how to drive in a few minutes? >> Right. >> So that's their learning. That's not somebody programming the commands. They're actually going through a process of learning? >> Right, well so the Key Value Store is just a part of this. We're just the part of the system that makes the scale-outs that runs well in a data center. And so we're still running the Markov Decision Process in simulations in software. So we have a couple Xeon servers that we brought with us to do the machine learning and a data center would scale-out to be dozens of racks, but even with a few machines though, for simple highway driving, what we can show is we start off with, the system's untrained and that in the Markov Decision Process, we reward the final state of not having accidents. And so at first, the cars drive and they're bouncing into each other. It's like bumper cars, but within a few minutes and after about 15 million simulations, which can be run that quickly, is that the cars start driving better than humans. And so I think that's a really phenomenal step, is the fact that you're able to get to a point where you can train a system how to drive and give them 15 man years of experience in a matter of minutes by the scale-out compute systems. >> Right, 'cause then you can put in new variables, right? You can change that training and modify it over time as conditions change, throw in snow or throw in urban environments and other things. >> Absolutely, right. And so we're not pretending that our machine learning, that application we're showing here is an end-all solution. But as you bring in other factors like pedestrians, deer, other cars running different algorithms or crazy drivers, is that you want to expose the system to those conditions as well. And so one of the questions that came up to us was, "What machine learning application are you running?" So we're showing all 25 cars running one machine learned application and that's incrementally getting better as they learn to drive, but we could also have every car running a different machine learning application and see how different AIs interact with each other. And I think that's what you're going to see on the highway as we have more self-driving cars running different algorithms, we have to make sure they all place nice with each other. >> Right, but it's really a different way of looking at the world, right, using machine learning, machine-to-machine versus single person or a team of people writing a piece of software to instruct something to do something and then you got to go back and change it. This is a much more dynamic realtime environment that we're entering into with IOT. >> Right, I mean the machine-to-human, which was kind of last year and years before, were, "How do you make interactions "between the computers better than humans?" But now it's about machine-to-machine and it's,"How do you make machines interact better "with other machines?" And that's where it gets really competitive. I mean, you can imagine with drones for example, for applications where you have drones against drones, the drones that are faster are going to be the ones that win. >> Right, right, it's funny, we were just here last week at the commercial drone show and it's pretty interesting how they're designing the drones now into a three-part platform. So there's the platform that flies around. There's the payload, which can be different sensors or whatever it's carrying, could be herbicide if it's an agricultural drone. And then they've opened up the STKs, both on the control side as well as the mobile side, in terms of the controls. So it's a very interesting way that all these things now, via software could tie together, but as you say, using machine learning you can train them to work together even better, quicker, faster. >> Right, I mean having a swarm or a cluster of these machines that work with each other, you could really do interesting things. >> Yeah, that's the whole next thing, right? Instead of one-to-one it's many-to-many. >> And then when swarms interact with other swarms, then I think that's really fascinating. >> So alright, is that what we're going to be talking about? So if we connect in 2018, what are we going to be talking about? The year's almost over. What are your top priorities for next year? >> Our top priorities are to see. We think that FPGA is just playing this important part. A GPU for example, became a very big part of the super computing systems here at this conference. But the other side of heterogeneous is the FPGA and the FPGA has seen almost, just very minimal adoption so far. But the FPGA has the capability of providing, especially when it comes to doing network IO transactions, it's speeding up realtime interactions, it has an ability to change the world again for HPC. And so I'm expecting that in a couple years, at this HPC conference, that what we'll be talking about, is the biggest top 500 super computers, is that how big of FPGAs do they have. Not how big of GPUs do they have. >> All right, time will tell. Well, John, thanks for taking a few minutes out of your day and stopping by. >> Okay, thanks Jeff, great to talk to you. >> All right, he's John Lockwood, I'm Jeff Frick. You're watching theCUBE from Super Computing 2017. Thanks for watching. >> Bye. (electronic music)

>> Voiceover: From Denver, Colorado, it's theCUBE covering Super Computing '17, brought to you by Intel. Hey, welcome back, everybody. Jeff Frick, here, with theCUBE. We're in Denver, Colorado at Super Computing 2017. It's all things heavy lifting, big iron, 12,000 people. I think it's the 20th anniversary of the conference. A lot of academics, really talking about big iron, doin' big computing. And we're excited to have our next guest, talking about speed, he's Stephane Monoboisset. Did I get that right? That's right. He's a director of marketing and partnerships for Accelize. Welcome. Thank you. So, for folks that aren't familiar with Accelize, give them kind of the quick overview. Okay, so Accelize is a French startup. Actually, a spinoff for a company called PLDA that has been around for 20 years, doing PCI express IP. And about a few years ago, we started initiative to basically bring FPGA acceleration to the cloud industry. So what we say is, we basically enable FPGA acceleration as a service. So did it not exist in cloud service providers before that, or what was kind of the opportunity that you saw there? So, FPGAs have been used in data centers in many different ways. They're starting to make their way into, as a service type of approach. But one of the thing that the industry, one of the buzzword that the industry's using, is FPGA as a service. And the industry usually refers to it as the way to bring FPGA to the end users. But when you think about it, end users don't really want FPGA as a service. Most of the cloud end users are not FPGA experts. So they couldn't care less whether it's an FPGA or something else. What they really want is the acceleration benefits. Hence the term, FPGA acceleration as a service. So, in order to do that, instead of just going and offering an FPGA platform, and giving them the tools, even if they are easy to use and develop the FPGAs, our objective is to propose to provide a marketplace of accelerators that they can use as a service, without even thinking that it's an FPGA on the background. So that's a really interesting concept. Because that also leverages an ecosystem. And one thing we know that's important, if you have any kind of a platform playing, you need an ecosystem that brings a much broader breadth of applications, and solution suites, and there's a lot of talk about solutions. So that was pretty insightful, 'cause now you open it up to this much broader set of applications. Well, absolutely. The ecosystem is the essential part of the offering because obviously, as a company, we cannot be expert in every single domain. And to a certain extent, even FPGA designers, they are what, about maybe 10, 15,000 FPGA designers in the world. They are not really expert in the end application. So one of the challenges that we're trying to address is how do we make application developers, the people who are already playing in the cloud, the ISVs, for example, who have the expertise of what the end user wants, being able to develop something that is efficient to the end user in FPGAs. And this is why we've created a tool called Quick Play, which basically enables what we call the accelerator function developers, the guys who have the application expertise, to leverage an ecosystem of IP providers in the FPGA space that have built efficient building blocks, like encryption, compression, video transcoding. Right. These sort of things. So what you have is an ecosystem of cloud service providers. You have an ecosystem of IP providers. And we have this growing ecosystem of accelerator developers that develop all these accelerators that are sold as a service. And that really opens up the number of people that are qualified to play in the space. 'Cause you're kind of hiding the complexity into the hardcore, harder engineers and really making it more kind of a traditional software application space. Is that right? Yeah, you're absolutely right. And we're doing that on the technical front, but we're also doing that on the business model front. Because one thing with FPGAs is that FPGAs has relied heavily over the years on the IP industry. And the IP industry for FPGAs, and it's the same for ASIGs, have been also relying on the business model, which is based on very high up-front cost. So let me give you an example. Let's say I want to develop an accelerator, right, for database. And what I need to do is to get the stream of data coming in. It's most likely encrypted, so I need to decrypt this data, then I want to do some search algorithm on it to extract certain functions. I'm going to do some processing on it, and maybe the last thing I want to do is, I want to compress because I want to store the result of that data. If I'm doing that with a traditional IP business model, what I need to do is basically go and talk to every single one of those IP providers and ask them to sell me the IP. In the traditional IP business model, I'm looking at somewhere between 200,000 to 500,000 up front cost. And I want to sell this accelerator for maybe a couple of dollars on one of the marketplace. There's something that doesn't play out. So what we've done, also, is we've introduced a pay-per-use business model that allows us to track those IPs that are being used by the accelerators so we can propagate the as-a-service business model throughout the industry, the supply chain. Which is huge, right? 'Cause as much as cloud is about flexibility and extensibility, it's about the business model as well. About paying what you use when you use it, turning it on, turning it off. So that's a pretty critical success factor. Absolutely, I mean, you can imagine that there's, I don't know, millions of users in the cloud. There's maybe hundreds of thousands of different type of ways they're processing their data. So we also need a very agile ecosystem that can develop very quickly. And we also need them to do it in a way that doesn't cost too much money, right? Think about it, and think about the app store when it was launched, right? Right. When Apple launched the iPhone back about 10 years ago, right, they didn't have much application. And they didn't, I don't think they quite knew, exactly, how it was going to be used. But what they did, which completely changed the industry, is they opened up the SDK that they sold for very small amount of money and enabled a huge community to come up with a lot of a lot of application. And now you go there and you can find application that really meats your need. That's kind of the similar concept that we're trying to develop here. Right. So how's been the uptake? I mean, so where are you, kind of, in the life cycle of this project? 'Cause it's a relatively new spinout of the larger company? Yes, so it's relatively new. We did the spinout because we really want to give that product its own life. Right, right. Right? But we are still at the beginning. So we started a developing partnership with cloud service providers. The two ones that we've announced is Amazon Web Services and OVH, the cloud service provider in France. And we have recruited, I think, about a dozen IP partners. And now we're also working with accelerator developer, accelerator functions developers. Okay. So it's a work in progress. And our main goal right now is to, really to evangelize, and to show them how much money they can do and how they can serve this market of FPGA acceleration as a service. The cloud providers, or the application providers? Who do you really have to convince the most? So the one we have to convince today are really the application developers. Okay, okay. Because without content, your marketplace doesn't mean much. So this is the main thing we're focusing on right now. Okay, great. So, 2017's coming to an end, which is hard to believe. So as you look forward to 2018, of those things you just outlined, kind of what are some of the top priorities for 2018? So, top priorities will be to strengthen our relationship with the key cloud service providers we work with. We have a couple of other discussions ongoing to try to offer a platform on more cloud service providers. We also want to strengthen our relationship with Intel. And we'll continue the evangelization to really onboard all the IP providers and the accelerator developers so that the marketplace becomes filled with valuable accelerators that people can use. And that's going to be a long process, but we are focusing right now on key application space that we know people can leverage in the application. Exciting times. Oh yeah, it is. You know, it's 10 years since the app store launched, I think, so I look at acceleration as a service in cloud service providers, this sounds like a terrific opportunity. It is, it is a huge opportunity. Everybody's talking about it. We just need to materialize it now. All right, well, congratulations and thanks for taking a couple minutes out of your day. Oh, thanks for your time. All right, he's Stephane, I'm Jeff Frick. You're watching theCUBE from Super Computing 2017. Thanks for watching. (upbeat music)

>> Announcer: On Denver, Colorado, it's theCUBE, covering SuperComputing '17, brought to you by Intel. >> Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're in Denver, Colorado at this SuperComputing conference 2017. I think there's 12,000 people. Our first time being here is pretty amazing. A lot of academics, a lot of conversations about space and genomes and you know, heavy-lifting computing stuff. It's fun to be here, and we're really excited. Our next guest, Karsten Ronner. He's the CEO of Swarm64. So Karsten, great to see you. >> Yeah, thank you very much for this opportunity. >> Absolutely. So for people that aren't familiar with Swarm64, give us kind of the quick eye-level. >> Yeah. Well, in a nutshell, Swarm64 is accelerating relational databases, and we allow them to ingest data so much faster, 50 times faster than a relational database. And we can also then query that data 10, 20 times faster than relational database. And that is very important for many new applications in IoT and in netbanking and in finance, and so on. >> So you're in a good space. So beyond just general or better performance, faster, faster, faster, you know, we're seeing all these movements now in real-time analytics and real-time applications, which is only going to get crazier with IoT and Internet of Things. So how do you do this? Where do you do this? What are some of the examples you could share with us? >> Yeah, so all our solution is a combination of a software wrapper that attaches our solution to existing databases. And inside, there's an FPGA from Intel, the Arria 10. And we are combining both, such that they actually plug into standard interfaces of existing databases, like in PostgreSQL, Foreign Data Wrappers, the storage engine in MySQL, and MariaDB and so on. And with that mechanism, we ensure that the database, the application doesn't see us. For the application, there's just fast database but we're invisible and also the functionality of the database remains what it was. That's the net of what we're doing. >> So that's so important because we talked a little bit about offline, you said you had a banking customer that said they have every database that's ever been created. They've been buying them all along so they've got embedded systems, you can't just rip and replace. You have to work with existing infrastructure. At the same time, they want to go faster. >> Yeah, absolutely right. Absolutely right. And there's a huge code base, which has been verified, which has been debugged, and in banking, it's also about compliance so you can't just rip out your old code base and do something new, because again, you would have to go through compliance. Therefore, customers really, really, really want their existing databases faster. >> Right. Now the other interesting part, and we've talked to some of the other Intel execs, is kind of this combination hybrid of the Hardware Software Solution in the FPGA, and you're really opening up an ecosystem for people to build more software-based solutions that leverage that combination of the hardware software power. Where do you see that kind of evolving? How's that going to help your company? >> Yeah. We are a little bit unique in that we are hiding that FPGA from the user, and we're not exposing it. Many people, actually, many applications expose it to the user, but apart from that, we are benefiting a lot from what Intel is doing. Intel is providing the entire environment, including virtualization, all those things that help us then to be able to get into Cloud service providers or into proprietary virtualized environments and things like that. So it is really a very close cooperation with Intel that helps us and enables us to do what we're doing. >> Okay. And I'm curious because you spend a lot of time with customers, you said a lot of legacy customers. So as they see the challenges of this new real-time environment, what are some of their concerns, what are some of the things that they're excited that they can do now with real-time, versus bash and data lake. And I think it's always funny, right? We used to make decisions based on stuff that happened in the past. And we're kind of querying now really the desires just to make action on stuff that's happening now, it's a fundamentally different way to address a problem. >> Yeah, absolutely. And a very, very key element of our solution is that we can not only insert these very, very large amounts of data that also other solutions can do, massively parallel solutions, streaming solutions, you know them all. They can do that too. However, the difference is that we can make that data available within less than 10 microseconds. >> Jeff: 10 microseconds? >> So dataset arrives within less than 10 microseconds, that dataset is part of the next query and that is a game changer. That allows you to do controlled loop processing of data in machine-to-machine environments, and autonomous, for autonomous applications and all those solutions where you just can't wait. If your car is driving down the street, you better know what has happened, right? And you can react to it. As an example, it could be a robot in a plant or things like that, where you really want to react immediately. >> I'm curious as to the kind of value unlocking that that provides to those old applications that were working with what they think is an old database. Now, you said, you know, you're accelerating it. To the application, it looks just the same as it looked before. How does that change those performances of those applications? I would imagine there's a whole other layer of value unlocking in those entrenched applications with this vast data. >> Yeah. That is actually true, and it's on a business level, the applications enable customers to do things they were not capable of doing before, and look for example in finance. If you can analyze the market data much quicker, if you can analyze past trades much quicker, then obviously you're generating value for the firm because you can react to market trends more accurately, you can mirror them in a more tighter fashion, and if you can do that, then you can reduce the margin of error with which you're estimating what's happening, and all of that is money. It's really pure money in the bank account of the customer, so to speak. >> Right. And the other big trend we talked about, besides faster, is you know, sampling versus not sampling. In the old days, we sampled old data and made decisions. Now we don't want to sample, we want all of the data, we want to make decisions on all the data, so again that's opening up another level of application performance because it's all the data, not a sample. >> For sure. Because before, you were aggregating. When you aggregate, you reduce the amount of information available. Now, of course, when you have the full set of information available, your decision-making is just so much smarter. And that's what we're enabling. >> And it's funny because in finance, you mentioned a couple of times, they've been doing that forever, right. The value of a few units of time, however small, is tremendous, but now we're seeing it in other industries as well that realize the value of real-time, aggregated, streaming data versus a sampling of old. Really opens up new types of opportunities. >> Absolutely, yes, yes. Yeah, finance, as I mentioned is an example, but then also IoT, machine-to-machine communication, everything which is real-time, logging, data logging, security and network monitoring. If you want to really understand what's flowing through your network, is there anything malicious, is there any actor on my network that should not be there? And you want to react so quickly that you can prevent that bad actor from doing anything to your data, this is where we come in. >> Right. And security's so big, right? It in everywhere. Especially with IoT and machine learning. >> Absolutely. >> All right, Karsten, I'm going to put you on the spot. So we're November 2017, hard to believe. As you look forward to 2018, what are some of your priorities? If we're standing here next year, at SuperComputing 2018, what are we going to be talking about? >> Okay, what we're going to talk about really is that we will, right now we're accelerating single-server solutions and we are working very, very hard on massively parallel systems, while retaining the real-time components. So we will not only then accelerate a single server, by then, allowing horizontal scaling, we will then bring a completely new level of analytics performance to customers. So that's what I'm happy to talk to you about next year. >> All right, we'll see you next year, I think it's in Texas. >> Wonderful, yeah, great. >> So thanks for stopping by. >> Thank you. >> He's Karsten, I'm Jeff. You're watching TheCUBE, from SuperComputing 2017. Thanks for watching.

>> Narrator: From Denver, Colorado, it's theCUBE. Covering Super Computing 17. Brought to you by Intel. (techno music) Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're in Denver, Colorado at the Super Computing Conference 2017. About 12 thousand people, talking about the outer edges of computing. It's pretty amazing. The keynote was huge. The square kilometer array, a new vocabulary word I learned today. It's pretty exciting times, and we're excited to have our next guest. He's Bill Jenkins. He's a Product Line Manager for AI on FPGAs at Intel. Bill, welcome. Thank you very much for having me. Nice to meet you, and nice to talk to you today. So you're right in the middle of this machine-learning AI storm, which we keep hearing more and more about. Kind of the next generation of big data, if you will. That's right. It's the most dynamic industry I've seen since the telecom industry back in the 90s. It's evolving every day, every month. Intel's been making some announcements. Using this combination of software programming and FPGAs on the acceleration stack to get more performance out of the data center. Did I get that right? Sure, yeah, yeah. Pretty exciting. The use of both hardware, as well as software on top of it, to open up the solution stack, open up the ecosystem. What of those things are you working on specifically? I really build first the enabling technology that brings the FPGA into that Intel ecosystem. Where Intel is trying to provide that solution from top to bottom to deliver AI products. >> Jeff: Right. Into that market. FPGAs are a key piece of that because we provide a different way to accelerate those machine-learning and AI workloads. Where we can be an offload engine to a CPU. We can be inline analytics to offload the system, and get higher performance that way. We tie into that overall Intel ecosystem of tools and products. Right. So that's a pretty interesting piece because the real-time streaming data is all the rage now, right? Not in batch. You want to get it now. So how do you get it in? How do you get it written to the database? How do you get it into the micro-processor? That's a really, really important piece. That's different than even two years ago. You didn't really hear much about real-time. I think it's, like I said, it's evolving quite a bit. Now, a lot of people deal with training. It's the science behind it. The data scientists work to figure out what topologies they want to deploy and how they want to deploy 'em. But now, people are building products around it. >> Jeff: Right. And once they start deploying these technologies into products, they realize that they don't want to compensate for limitations in hardware. They want to work around them. A lot of this evolution that we're building is to try to find ways to more efficiently do that compute. What we call inferencing, the actual deployed machine-learning scoring, as they will. >> Jeff: Right. In a product, it's all about how quickly can I get the data out. It's not about waiting two seconds to start the processing. You know, in an autonomous-driven car where someone's crossing the road, I'm not waiting two seconds to figure out it's a person. Right, right. I need it right away. So I need to be able to do that with video feeds, right off a disk drive, from the ethernet data coming in. I want to do that directly in line, so that my processor can do what it's good at, and we offload that processor to get better system performance. Right. And then on the machine-learning specifically, 'cause that is all the rage. And it is learning. So there is a real-time aspect to it. You talked about autonomous vehicles. But there's also continuous learning over time, that's not necessarily dependent on learning immediately. Right. But continuous improvement over time. What are some of the unique challenges in machine-learning? And what are some of the ways that you guys are trying to address those? Once you've trained the network, people always have to go back and retrain. They say okay, I've got a good accuracy, but I want better performance. Then they start lowering the precision, and they say well, today we're at 32-bit, maybe 16-bit. Then they start looking into eight. But the problem is, their accuracy drops. So they retrain that into eight topology, that network, to get the performance benefit, but with the higher accuracy. The flexibility of FPGA actually allows people to take that network at 32-bit, with the 32-bit trained weights, but deploy it in lower precision. So we can abstract away the fact that the hardware's so flexible, we can do what we call floating point 11-bit floating point. Or even 8-bit floating point. Even here today at the show, we've got a binary and ternary demo, showcasing the flexibility that the FPGA can provide today with that building block piece of hardware that the FPGA can be. And really provide, not only the topologies that people are trying to build today, but tomorrow. >> Jeff: Right. Future proofing their hardware. But then the precisions that they may want to do. So that they don't have to retrain. They can get less than a 1% accuracy loss, but they can lower that precision to get all the performance benefits of that data scientist's work to come up with a new architecture. Right. But it's interesting 'cause there's trade-offs, right? >> Bill: Sure. There's no optimum solution. It's optimum as to what you're trying to optimize for. >> Bill: Right. So really, the ability to change the ability to continue to work on those learning algorithms, to be able to change your priority, is pretty key. Yeah, a lot of times today, you want this. So this has been the mantra of the FPGA for 30 plus years. You deploy it today, and it works fine. Maybe you build an ASIC out of it. But what you want tomorrow is going to be different. So maybe if it's changing so rapidly, you build the ASIC because there's runway to that. But if there isn't, you may just say, I have the FPGA, I can just reprogram it to do what's the next architecture, the next methodology. Right. So it gives you that future proofing. That capability to sustain different topologies. Different architectures, different precisions. To kind of keep people going with the same piece of hardware. Without having to say, spin up a new ASIC every year. >> Jeff: Right, right. Which, even then, it's so dynamic it's probably faster then, every year, the way things are going today. So the other thing you mentioned is topography, and it's not the same topography you mentioned, but this whole idea of edge. Sure. So moving more and more compute, and store, and smarts to the edge. 'Cause there's just not going to be time, you mentioned autonomous vehicles, a lot of applications to get everything back up into the cloud. Back into the data center. You guys are pushing this technology, not only in the data center, but progressively closer and closer to the edge. Absolutely. The data center has a need. It's always going to be there, but they're getting big. The amount of data that we're trying to process every day is growing. I always say that the telecom industry started the Information Age. Well, the Information Age has done a great job of collecting a lot of data. We have to process that. If you think about where, maybe I'll allude back to autonomous vehicles. You're talking about thousands of gigabytes, per day, of data generated. Smart factories. Exabytes of data generated a day. What are you going to do with all that? It has to be processed. We need that compute in the data center. But we have to start pushing it out into the edge, where I start thinking, well even a show like this, I want security. So, I want to do real-time weapons detection, right? Security prevention. I want to do smart city applications. Just monitoring how traffic moves through a mall, so that I can control lighting and heating. All of these things at the edge, in the camera, that's deployed on the street. In the camera that's deployed in a mall. All of that, we want to make those smarter, so that we can do more compute. To offload the amount of data that needs to be sent back to the data center. >> Jeff: Right. As much as possible. Relevant data gets sent back. No shortage of demand for compute store networking, is there? No, no. It's really a heterogeneous world, right? We need all the different compute. We need all the different aspects of transmission of the data with 5G. We need disk space to store it. >> Jeff: Right. We need cooling to cool it. It's really becoming a heterogeneous world. All right, well, I'm going to give you the last word. I can't believe we're in November of 2017. Yeah. Which is bananas. What are you working on for 2018? What are some of your priorities? If we talk a year from now, what are we going to be talking about? Intel's acquired a lot of companies over the past couple years now on AI. You're seeing a lot of merging of the FPGA into that ecosystem. We've got the Nervana. We've got Movidius. We've got Mobileye acquisitions. Saffron Technologies. All of these things, when the FPGA is kind of a key piece of that because it gives you that flexibility of the hardware, to extend those pieces. You're going to see a lot more stuff in the cloud. A lot more stuff with partners next year. And really enabling that edge to data center compute, with things like binary neural networks, ternary neural networks. All the different next generation of topologies to kind of keep that leading edge flexibility that the FPGA can provide for people's products tomorrow. >> Jeff: Exciting times. Yeah, great. All right, Bill Jenkins. There's a lot going on in computing. If you're not getting your computer science degree, kids, think about it again. He's Bill Jenkins. I'm Jeff Frick. You're watching theCUBE from Super Computing 2017. Thanks for watching. Thank you. (techno music)

>> Announcer: From Denver, Colorado, it's theCUBE. Covering Super Computing 2017 brought to you by Intel. (upbeat music) >> Hey, welcome back everybody. Jeffrey Frick here with theCube. We're in Denver, Colorado at Super Computing 17. I think it's the 20th year of the convention. 12,000 people. We've never been here before. It's pretty amazing. Amazing keynote, really talking about space, and really big, big, big computing projects, so, excited to be here, and we've got our first guest of the day. He's Bernard Friebe, he is the Senior Director of FPGA, I'll get that good by the end of the day, Software Solutions for Intel Programmable group. First off, welcome, Bernard. >> Thank you. I'm glad to be here. >> Absolutely. So, have you been to this conference before? >> Yeah, a couple of times before. It's always a big event. Always a big show for us, so I'm excited. >> Yeah, and it's different, too, cuz it's got a lot of academic influence, as well, as you walk around the outside. It's pretty hardcore. >> Yes, it's wonderful, and you see a lot of innovation going on, and we need to move fast. We need to move faster. That's what it is. And accelerate. >> And that's what you're all about, acceleration, so, Intel's making a lot of announcements, really, about acceleration at FPGA. For acceleration and in data centers and in big data, and all these big applications. So, explain just a little bit how that seed is evolving and what some of the recent announcements are all about. >> The world of computing must accelerate. I think we all agree on that. We all see that that's a key requirement. And FPGA's are a truly versatile, multi-function accelerator. It accelerates so many workloads in the high-performance computing space, may it be financial, genomics, oil and gas, data analytics, and the list goes on. Machine learning is a very big one. The list goes on and on. And, so, we're investing heavily in providing solutions which makes it much easier for our users to develop and deploy FPGA in a high-performance computing environment. >> You guys are taking a lot of steps to make the software programming at FPGA a lot easier, so you don't have to be a hardcore hardware engineer, so you can open it up to a broader ecosystem and get a broader solution set. Is that right? >> That's right, and it's not just the hardware. How do you unlock the benefits of FPGA as a versatile accelerator, so their parallelism, their ability to do real-time, low-latency, acceleration of many different workloads, and how do you enable that in an environment which is truly dynamic and multi-function, like a data center. And so, the product we've recently announced is the acceleration stack for xeon with FPGA, which enables that use more. >> So, what are the components for that stack? >> It starts with hardware. So, we are building a hardware accelerator card, it's a pc express plugin card, it's called programmable accelerator card. We have integrated solutions where you have everything on an FPGA in package, but what's common is a software framework solution stack, which sits on top of these different hardware implementation, which really makes it easy for a developer to develop an accelerator, for a user to then deploy that accelerator and run it in their environment, and it also enables a data center operator to basically enable the FPGA like any other computer resources by integrating it into their orchestration framework. So, multiple levels taking care of all those needs. >> It's interesting, because there's a lot of big trends that you guys are taking advantage of. Obviously, we're at Super Computing, but big data, streaming analytics, is all the rage now, so more data faster, reading it in real time, pumping it into the database in real time, and then, right around the corner, we have IoT and internet of things and all these connected devices. So the demand for increased speed, to get that data in, get that data processed, get the analytics back out, is only growing exponentially. >> That's right, and FPGAs, due to their flexibility, have distinct advantages there. The traditional model is look aside of offload, where you have a processor, and then you offload your tasks to your accelerator. The FPGA, with their flexible I/Os and flexible core can actually run directly in the data path, so that's what we call in-line processing. And what that allows people to do is, whatever the source is, may it be cameras, may it be storage, may it be through the network, through ethernet, can stream directly into the FPGA and do your acceleration as the data comes in in a streaming way. And FPGAs provide really unique advantages there versus other types of accelerators. Low-latency, very high band-width, and they're flexible in a sense that our customers can build different interfaces, different connectivity around those FPGAs. So, it's really amazing how versatile the usage of FPGA has become. >> It is pretty interesting, because you're using all the benefits that come from hardware, hardware-based solutions, which you just get a lot of benefits when things are hardwired, with the software component and enabling a broader ecosystem to write ready-made solutions and integrations to their existing solutions that they already have. Great approach. >> The acceleration stack provides a consistent interface to the developer and the user of the FPGA. What that allows our ecosystem and our customers to do is to define these accelerators based on this framework, and then they can easily migrate those between different hardware platforms, so we're building in future improvements of the solution, and the consistent interfaces then allow our customers and partners to build their software stacks on top of it. So, their investment, once they do it and we target our Arria 10 programmable accelerator card can easily be leveraged and moved forward into the next generation strategy, and beyond. We enable, really, and encourage a broad ecosystem, to build solutions. You'll see that here at the show, many partners now have demos, and they show their solutions built on Intel FPGA hardware and the acceleration stack. >> OK, so I'm going to put you on the spot. So, these are announced, what's the current state of the general availability? >> We're sampling now on the cards, the acceleration stack is available for delivery to customers. A lot of it is open source, by the way, so it can already be downloaded from GitHub And the partners are developing the solutions they are demonstrating today. The product will go into volume production in the first half of next year. So, we're very close. >> All right, very good. Well, Bernard, thanks for taking a few minutes to stop by. >> Oh, it's my pleasure. >> All right. He's Bernard, I'm Jeff. You're watching theCUBE from Super Computing 17. Thanks for watching. (upbeat music)

(bright upbeat music) >> Hello and welcome to this Cube Conversation. I'm John Furrier, host of theCUBE. We've got a great conversation featuring Arthur AI. I'm your host. I'm excited to have Adam Wenchel who's the Co-Founder and CEO. Thanks for joining us today, appreciate it. >> Yeah, thanks for having me on, John, looking forward to the conversation. >> I got to say, it's been an exciting world in AI or artificial intelligence. Just an explosion of interest kind of in the mainstream with the language models, which people don't really get, but they're seeing the benefits of some of the hype around OpenAI. Which kind of wakes everyone up to, "Oh, I get it now." And then of course the pessimism comes in, all the skeptics are out there. But this breakthrough in generative AI field is just awesome, it's really a shift, it's a wave. We've been calling it probably the biggest inflection point, then the others combined of what this can do from a surge standpoint, applications. I mean, all aspects of what we used to know is the computing industry, software industry, hardware, is completely going to get turbo. So we're totally obviously bullish on this thing. So, this is really interesting. So my first question is, I got to ask you, what's you guys taking? 'Cause you've been doing this, you're in it, and now all of a sudden you're at the beach where the big waves are. What's the explosion of interest is there? What are you seeing right now? >> Yeah, I mean, it's amazing, so for starters, I've been in AI for over 20 years and just seeing this amount of excitement and the growth, and like you said, the inflection point we've hit in the last six months has just been amazing. And, you know, what we're seeing is like people are getting applications into production using LLMs. I mean, really all this excitement just started a few months ago, with ChatGPT and other breakthroughs and the amount of activity and the amount of new systems that we're seeing hitting production already so soon after that is just unlike anything we've ever seen. So it's pretty awesome. And, you know, these language models are just, they could be applied in so many different business contexts and that it's just the amount of value that's being created is again, like unprecedented compared to anything. >> Adam, you know, you've been in this for a while, so it's an interesting point you're bringing up, and this is a good point. I was talking with my friend John Markoff, former New York Times journalist and he was talking about, there's been a lot of work been done on ethics. So there's been, it's not like it's new. It's like been, there's a lot of stuff that's been baking over many, many years and, you know, decades. So now everyone wakes up in the season, so I think that is a key point I want to get into some of your observations. But before we get into it, I want you to explain for the folks watching, just so we can kind of get a definition on the record. What's an LLM, what's a foundational model and what's generative ai? Can you just quickly explain the three things there? >> Yeah, absolutely. So an LLM or a large language model, it's just a large, they would imply a large language model that's been trained on a huge amount of data typically pulled from the internet. And it's a general purpose language model that can be built on top for all sorts of different things, that includes traditional NLP tasks like document classification and sentiment understanding. But the thing that's gotten people really excited is it's used for generative tasks. So, you know, asking it to summarize documents or asking it to answer questions. And these aren't new techniques, they've been around for a while, but what's changed is just this new class of models that's based on new architectures. They're just so much more capable that they've gone from sort of science projects to something that's actually incredibly useful in the real world. And there's a number of companies that are making them accessible to everyone so that you can build on top of them. So that's the other big thing is, this kind of access to these models that can power generative tasks has been democratized in the last few months and it's just opening up all these new possibilities. And then the third one you mentioned foundation models is sort of a broader term for the category that includes LLMs, but it's not just language models that are included. So we've actually seen this for a while in the computer vision world. So people have been building on top of computer vision models, pre-trained computer vision models for a while for image classification, object detection, that's something we've had customers doing for three or four years already. And so, you know, like you said, there are antecedents to like, everything that's happened, it's not entirely new, but it does feel like a step change. >> Yeah, I did ask ChatGPT to give me a riveting introduction to you and it gave me an interesting read. If we have time, I'll read it. It's kind of, it's fun, you get a kick out of it. "Ladies and gentlemen, today we're a privileged "to have Adam Wenchel, Founder of Arthur who's going to talk "about the exciting world of artificial intelligence." And then it goes on with some really riveting sentences. So if we have time, I'll share that, it's kind of funny. It was good. >> Okay. >> So anyway, this is what people see and this is why I think it's exciting 'cause I think people are going to start refactoring what they do. And I've been saying this on theCUBE now for about a couple months is that, you know, there's a scene in "Moneyball" where Billy Beane sits down with the Red Sox owner and the Red Sox owner says, "If people aren't rebuilding their teams on your model, "they're going to be dinosaurs." And it reminds me of what's happening right now. And I think everyone that I talk to in the business sphere is looking at this and they're connecting the dots and just saying, if we don't rebuild our business with this new wave, they're going to be out of business because there's so much efficiency, there's so much automation, not like DevOps automation, but like the generative tasks that will free up the intellect of people. Like just the simple things like do an intro or do this for me, write some code, write a countermeasure to a hack. I mean, this is kind of what people are doing. And you mentioned computer vision, again, another huge field where 5G things are coming on, it's going to accelerate. What do you say to people when they kind of are leaning towards that, I need to rethink my business? >> Yeah, it's 100% accurate and what's been amazing to watch the last few months is the speed at which, and the urgency that companies like Microsoft and Google or others are actually racing to, to do that rethinking of their business. And you know, those teams, those companies which are large and haven't always been the fastest moving companies are working around the clock. And the pace at which they're rolling out LLMs across their suite of products is just phenomenal to watch. And it's not just the big, the large tech companies as well, I mean, we're seeing the number of startups, like we get, every week a couple of new startups get in touch with us for help with their LLMs and you know, there's just a huge amount of venture capital flowing into it right now because everyone realizes the opportunities for transforming like legal and healthcare and content creation in all these different areas is just wide open. And so there's a massive gold rush going on right now, which is amazing. >> And the cloud scale, obviously horizontal scalability of the cloud brings us to another level. We've been seeing data infrastructure since the Hadoop days where big data was coined. Now you're seeing this kind of take fruit, now you have vertical specialization where data shines, large language models all of a set up perfectly for kind of this piece. And you know, as you mentioned, you've been doing it for a long time. Let's take a step back and I want to get into how you started the company, what drove you to start it? Because you know, as an entrepreneur you're probably saw this opportunity before other people like, "Hey, this is finally it, it's here." Can you share the origination story of what you guys came up with, how you started it, what was the motivation and take us through that origination story. >> Yeah, absolutely. So as I mentioned, I've been doing AI for many years. I started my career at DARPA, but it wasn't really until 2015, 2016, my previous company was acquired by Capital One. Then I started working there and shortly after I joined, I was asked to start their AI team and scale it up. And for the first time I was actually doing it, had production models that we were working with, that was at scale, right? And so there was hundreds of millions of dollars of business revenue and certainly a big group of customers who were impacted by the way these models acted. And so it got me hyper-aware of these issues of when you get models into production, it, you know. So I think people who are earlier in the AI maturity look at that as a finish line, but it's really just the beginning and there's this constant drive to make them better, make sure they're not degrading, make sure you can explain what they're doing, if they're impacting people, making sure they're not biased. And so at that time, there really weren't any tools to exist to do this, there wasn't open source, there wasn't anything. And so after a few years there, I really started talking to other people in the industry and there was a really clear theme that this needed to be addressed. And so, I joined with my Co-Founder John Dickerson, who was on the faculty in University of Maryland and he'd been doing a lot of research in these areas. And so we ended up joining up together and starting Arthur. >> Awesome. Well, let's get into what you guys do. Can you explain the value proposition? What are people using you for now? Where's the action? What's the customers look like? What do prospects look like? Obviously you mentioned production, this has been the theme. It's not like people woke up one day and said, "Hey, I'm going to put stuff into production." This has kind of been happening. There's been companies that have been doing this at scale and then yet there's a whole follower model coming on mainstream enterprise and businesses. So there's kind of the early adopters are there now in production. What do you guys do? I mean, 'cause I think about just driving the car off the lot is not, you got to manage operations. I mean, that's a big thing. So what do you guys do? Talk about the value proposition and how you guys make money? >> Yeah, so what we do is, listen, when you go to validate ahead of deploying these models in production, starts at that point, right? So you want to make sure that if you're going to be upgrading a model, if you're going to replacing one that's currently in production, that you've proven that it's going to perform well, that it's going to be perform ethically and that you can explain what it's doing. And then when you launch it into production, traditionally data scientists would spend 25, 30% of their time just manually checking in on their model day-to-day babysitting as we call it, just to make sure that the data hasn't drifted, the model performance hasn't degraded, that a programmer did make a change in an upstream data system. You know, there's all sorts of reasons why the world changes and that can have a real adverse effect on these models. And so what we do is bring the same kind of automation that you have for other kinds of, let's say infrastructure monitoring, application monitoring, we bring that to your AI systems. And that way if there ever is an issue, it's not like weeks or months till you find it and you find it before it has an effect on your P&L and your balance sheet, which is too often before they had tools like Arthur, that was the way they were detected. >> You know, I was talking to Swami at Amazon who I've known for a long time for 13 years and been on theCUBE multiple times and you know, I watched Amazon try to pick up that sting with stage maker about six years ago and so much has happened since then. And he and I were talking about this wave, and I kind of brought up this analogy to how when cloud started, it was, Hey, I don't need a data center. 'Cause when I did my startup that time when Amazon, one of my startups at that time, my choice was put a box in the colo, get all the configuration before I could write over the line of code. So the cloud became the benefit for that and you can stand up stuff quickly and then it grew from there. Here it's kind of the same dynamic, you don't want to have to provision a large language model or do all this heavy lifting. So that seeing companies coming out there saying, you can get started faster, there's like a new way to get it going. So it's kind of like the same vibe of limiting that heavy lifting. >> Absolutely. >> How do you look at that because this seems to be a wave that's going to be coming in and how do you guys help companies who are going to move quickly and start developing? >> Yeah, so I think in the race to this kind of gold rush mentality, race to get these models into production, there's starting to see more sort of examples and evidence that there are a lot of risks that go along with it. Either your model says things, your system says things that are just wrong, you know, whether it's hallucination or just making things up, there's lots of examples. If you go on Twitter and the news, you can read about those, as well as sort of times when there could be toxic content coming out of things like that. And so there's a lot of risks there that you need to think about and be thoughtful about when you're deploying these systems. But you know, you need to balance that with the business imperative of getting these things into production and really transforming your business. And so that's where we help people, we say go ahead, put them in production, but just make sure you have the right guardrails in place so that you can do it in a smart way that's going to reflect well on you and your company. >> Let's frame the challenge for the companies now that you have, obviously there's the people who doing large scale production and then you have companies maybe like as small as us who have large linguistic databases or transcripts for example, right? So what are customers doing and why are they deploying AI right now? And is it a speed game, is it a cost game? Why have some companies been able to deploy AI at such faster rates than others? And what's a best practice to onboard new customers? >> Yeah, absolutely. So I mean, we're seeing across a bunch of different verticals, there are leaders who have really kind of started to solve this puzzle about getting AI models into production quickly and being able to iterate on them quickly. And I think those are the ones that realize that imperative that you mentioned earlier about how transformational this technology is. And you know, a lot of times, even like the CEOs or the boards are very personally kind of driving this sense of urgency around it. And so, you know, that creates a lot of movement, right? And so those companies have put in place really smart infrastructure and rails so that people can, data scientists aren't encumbered by having to like hunt down data, get access to it. They're not encumbered by having to stand up new platforms every time they want to deploy an AI system, but that stuff is already in place. There's a really nice ecosystem of products out there, including Arthur, that you can tap into. Compared to five or six years ago when I was building at a top 10 US bank, at that point you really had to build almost everything yourself and that's not the case now. And so it's really nice to have things like, you know, you mentioned AWS SageMaker and a whole host of other tools that can really accelerate things. >> What's your profile customer? Is it someone who already has a team or can people who are learning just dial into the service? What's the persona? What's the pitch, if you will, how do you align with that customer value proposition? Do people have to be built out with a team and in play or is it pre-production or can you start with people who are just getting going? >> Yeah, people do start using it pre-production for validation, but I think a lot of our customers do have a team going and they're starting to put, either close to putting something into production or about to, it's everything from large enterprises that have really sort of complicated, they have dozens of models running all over doing all sorts of use cases to tech startups that are very focused on a single problem, but that's like the lifeblood of the company and so they need to guarantee that it works well. And you know, we make it really easy to get started, especially if you're using one of the common model development platforms, you can just kind of turn key, get going and make sure that you have a nice feedback loop. So then when your models are out there, it's pointing out, areas where it's performing well, areas where it's performing less well, giving you that feedback so that you can make improvements, whether it's in training data or futurization work or algorithm selection. There's a number of, you know, depending on the symptoms, there's a number of things you can do to increase performance over time and we help guide people on that journey. >> So Adam, I have to ask, since you have such a great customer base and they're smart and they got teams and you're on the front end, I mean, early adopters is kind of an overused word, but they're killing it. They're putting stuff in the production's, not like it's a test, it's not like it's early. So as the next wave comes of fast followers, how do you see that coming online? What's your vision for that? How do you see companies that are like just waking up out of the frozen, you know, freeze of like old IT to like, okay, they got cloud, but they're not yet there. What do you see in the market? I see you're in the front end now with the top people really nailing AI and working hard. What's the- >> Yeah, I think a lot of these tools are becoming, or every year they get easier, more accessible, easier to use. And so, you know, even for that kind of like, as the market broadens, it takes less and less of a lift to put these systems in place. And the thing is, every business is unique, they have their own kind of data and so you can use these foundation models which have just been trained on generic data. They're a great starting point, a great accelerant, but then, in most cases you're either going to want to create a model or fine tune a model using data that's really kind of comes from your particular customers, the people you serve and so that it really reflects that and takes that into account. And so I do think that these, like the size of that market is expanding and its broadening as these tools just become easier to use and also the knowledge about how to build these systems becomes more widespread. >> Talk about your customer base you have now, what's the makeup, what size are they? Give a taste a little bit of a customer base you got there, what's they look like? I'll say Capital One, we know very well while you were at there, they were large scale, lot of data from fraud detection to all kinds of cool stuff. What do your customers now look like? >> Yeah, so we have a variety, but I would say one area we're really strong, we have several of the top 10 US banks, that's not surprising, that's a strength for us, but we also have Fortune 100 customers in healthcare, in manufacturing, in retail, in semiconductor and electronics. So what we find is like in any sort of these major verticals, there's typically, you know, one, two, three kind of companies that are really leading the charge and are the ones that, you know, in our opinion, those are the ones that for the next multiple decades are going to be the leaders, the ones that really kind of lead the charge on this AI transformation. And so we're very fortunate to be working with some of those. And then we have a number of startups as well who we love working with just because they're really pushing the boundaries technologically and so they provide great feedback and make sure that we're continuing to innovate and staying abreast of everything that's going on. >> You know, these early markups, even when the hyperscalers were coming online, they had to build everything themselves. That's the new, they're like the alphas out there building it. This is going to be a big wave again as that fast follower comes in. And so when you look at the scale, what advice would you give folks out there right now who want to tee it up and what's your secret sauce that will help them get there? >> Yeah, I think that the secret to teeing it up is just dive in and start like the, I think these are, there's not really a secret. I think it's amazing how accessible these are. I mean, there's all sorts of ways to access LLMs either via either API access or downloadable in some cases. And so, you know, go ahead and get started. And then our secret sauce really is the way that we provide that performance analysis of what's going on, right? So we can tell you in a very actionable way, like, hey, here's where your model is doing good things, here's where it's doing bad things. Here's something you want to take a look at, here's some potential remedies for it. We can help guide you through that. And that way when you're putting it out there, A, you're avoiding a lot of the common pitfalls that people see and B, you're able to really kind of make it better in a much faster way with that tight feedback loop. >> It's interesting, we've been kind of riffing on this supercloud idea because it was just different name than multicloud and you see apps like Snowflake built on top of AWS without even spending any CapEx, you just ride that cloud wave. This next AI, super AI wave is coming. I don't want to call AIOps because I think there's a different distinction. If you, MLOps and AIOps seem a little bit old, almost a few years back, how do you view that because everyone's is like, "Is this AIOps?" And like, "No, not kind of, but not really." How would you, you know, when someone says, just shoots off the hip, "Hey Adam, aren't you doing AIOps?" Do you say, yes we are, do you say, yes, but we do differently because it's doesn't seem like it's the same old AIOps. What's your- >> Yeah, it's a good question. AIOps has been a term that was co-opted for other things and MLOps also has people have used it for different meanings. So I like the term just AI infrastructure, I think it kind of like describes it really well and succinctly. >> But you guys are doing the ops. I mean that's the kind of ironic thing, it's like the next level, it's like NextGen ops, but it's not, you don't want to be put in that bucket. >> Yeah, no, it's very operationally focused platform that we have, I mean, it fires alerts, people can action off them. If you're familiar with like the way people run security operations centers or network operations centers, we do that for data science, right? So think of it as a DSOC, a Data Science Operations Center where all your models, you might have hundreds of models running across your organization, you may have five, but as problems are detected, alerts can be fired and you can actually work the case, make sure they're resolved, escalate them as necessary. And so there is a very strong operational aspect to it, you're right. >> You know, one of the things I think is interesting is, is that, if you don't mind commenting on it, is that the aspect of scale is huge and it feels like that was made up and now you have scale and production. What's your reaction to that when people say, how does scale impact this? >> Yeah, scale is huge for some of, you know, I think, I think look, the highest leverage business areas to apply these to, are generally going to be the ones at the biggest scale, right? And I think that's one of the advantages we have. Several of us come from enterprise backgrounds and we're used to doing things enterprise grade at scale and so, you know, we're seeing more and more companies, I think they started out deploying AI and sort of, you know, important but not necessarily like the crown jewel area of their business, but now they're deploying AI right in the heart of things and yeah, the scale that some of our companies are operating at is pretty impressive. >> John: Well, super exciting, great to have you on and congratulations. I got a final question for you, just random. What are you most excited about right now? Because I mean, you got to be pretty pumped right now with the way the world is going and again, I think this is just the beginning. What's your personal view? How do you feel right now? >> Yeah, the thing I'm really excited about for the next couple years now, you touched on it a little bit earlier, but is a sort of convergence of AI and AI systems with sort of turning into AI native businesses. And so, as you sort of do more, get good further along this transformation curve with AI, it turns out that like the better the performance of your AI systems, the better the performance of your business. Because these models are really starting to underpin all these key areas that cumulatively drive your P&L. And so one of the things that we work a lot with our customers is to do is just understand, you know, take these really esoteric data science notions and performance and tie them to all their business KPIs so that way you really are, it's kind of like the operating system for running your AI native business. And we're starting to see more and more companies get farther along that maturity curve and starting to think that way, which is really exciting. >> I love the AI native. I haven't heard any startup yet say AI first, although we kind of use the term, but I guarantee that's going to come in all the pitch decks, we're an AI first company, it's going to be great run. Adam, congratulations on your success to you and the team. Hey, if we do a few more interviews, we'll get the linguistics down. We can have bots just interact with you directly and ask you, have an interview directly. >> That sounds good, I'm going to go hang out on the beach, right? So, sounds good. >> Thanks for coming on, really appreciate the conversation. Super exciting, really important area and you guys doing great work. Thanks for coming on. >> Adam: Yeah, thanks John. >> Again, this is Cube Conversation. I'm John Furrier here in Palo Alto, AI going next gen. This is legit, this is going to a whole nother level that's going to open up huge opportunities for startups, that's going to use opportunities for investors and the value to the users and the experience will come in, in ways I think no one will ever see. So keep an eye out for more coverage on siliconangle.com and theCUBE.net, thanks for watching. (bright upbeat music)

(soft music) >> Announcer: TheCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (upbeat music) >> Welcome back to Spain, everybody. We're here at the Fira in MWC23. Is just an amazing day. This place is packed. They said 80,000 people. I think it might even be a few more walk-ins. I'm Dave Vellante, Lisa Martin is here, David Nicholson. But right now we have the Analyst Hot Takes with three friends of theCUBE. Chris Lewis is back again with me in the co-host seat. Zeus Kerravala, analyst extraordinaire. Great to see you, Z. and Sarbjeet SJ Johal. Good to see you again, theCUBE contributor. And that's my new name for him. He says that is his nickname. Guys, thanks for coming back on. We got the all male panel, sorry, but it is what it is. So Z, is this the first time you've been on it at MWC. Take aways from the show, Hot Takes. What are you seeing? Same wine, new bottle? >> In a lot of ways, yeah. I mean, I was talking to somebody this earlier that if you had come from like MWC five years ago to this year, a lot of the themes are the same. Telco transformation, cloud. I mean, 5G is a little new. Sustainability is certainly a newer theme here. But I think it highlights just the difficulty I think the telcos have in making this transformation. And I think, in some ways, I've been unfair to them in some degree 'cause I've picked on them in the past for not moving fast enough. These are, you know, I think these kind of big transformations almost take like a perfect storm of things that come together to happen, right? And so, in the past, we had technologies that maybe might have lowered opex, but they're hard to deploy. They're vertically integrated. We didn't have the software stacks. But it appears today that between the cloudification of, you know, going to cloud native, the software stacks, the APIs, the ecosystems, I think we're actually in a position to see this industry finally move forward. >> Yeah, and Chris, I mean, you have served this industry for a long time. And you know, when you, when you do that, you get briefed as an analyst, you actually realize, wow, there's a lot of really smart people here, and they're actually, they have challenges, they're working through it. So Zeus was saying he's been tough on the industry. You know, what do you think about how the telcos have evolved in the last five years? >> I think they've changed enormously. I think the problem we have is we're always looking for the great change, the big step change, and there is no big step change in a way. What telcos deliver to us as individuals, businesses, society, the connectivity piece, that's changed. We get better and better and more reliable connectivity. We're shunting a load more capacity through. What I think has really changed is their attitude to their suppliers, their attitude to their partners, and their attitude to the ecosystem in which they play. Understanding that connectivity is not the end game. Connectivity is part of the emerging end game where it will include storage, compute, connect, and analytics and everything else. So I think the realization that they are not playing their own game anymore, it's a much more open game. And some things they will continue to do, some things they'll stop doing. We've seen them withdraw from moving into adjacent markets as much as we used to see. So a lot of them in the past went off to try and do movies, media, and a lot went way way into business IT stuff. They've mainly pulled back from that, and they're focusing on, and let's face it, it's not just a 5G show. The fixed environment is unbelievably important. We saw that during the pandemic. Having that fixed broadband connection using wifi, combining with cellular. We love it. But the problem as an industry is that the users often don't even know the connectivity's there. They only know when it doesn't work, right? >> If it's not media and it's not business services, what is it? >> Well, in my view, it will be enabling third parties to deliver the services that will include media, that will include business services. So embedding the connectivity all the way into the application that gets delivered or embedding it so the quality mechanism deliver the gaming much more accurately or, I'm not a gamer, so I can't comment on that. But no, the video quality if you want to have a high quality video will come through better. >> And those cohorts will pay for that value? >> Somebody will pay somewhere along the line. >> Seems fuzzy to me. >> Me too. >> I do think it's use case dependent. Like you look at all the work Verizon did at the Super Bowl this year, that's a perfect case where they could have upsold. >> Explain that. I'm not familiar with it. >> So Verizon provided all the 5G in the Super Bowl. They provided a lot of, they provided private connectivity for the coaches to talk to the sidelines. And that's a mission critical application, right? In the NFL, if one side can't talk, the other side gets shut down. You can't communicate with the quarterback or the coaches. There's a lot of risk at that. So, but you know, there's a case there, though, I think where they could have even made that fan facing. Right? And if you're paying 2000 bucks to go to a game, would you pay 50 bucks more to have a higher tier of bandwidth so you can post things on social? People that go there, they want people to know they were there. >> Every football game you go to, you can't use your cell. >> Analyst: Yeah, I know, right? >> All right, let's talk about developers because we saw the eight APIs come out. I think ISVs are going to be a big part of this. But it's like Dee Arthur said. Hey, eight's better than zero, I guess. Okay, so, but so the innovation is going to come from ISVs and developers, but what are your hot takes from this show and now day two, we're a day and a half in, almost two days in. >> Yeah, yeah. There's a thing that we have talked, I mentioned many times is skills gravity, right? Skills have gravity, and also, to outcompete, you have to also educate. That's another theme actually of my talks is, or my research is that to puts your technology out there to the practitioners, you have to educate them. And that's the only way to democratize your technology. What telcos have been doing is they have been stuck to the proprietary software and proprietary hardware for too long, from Nokia's of the world and other vendors like that. So now with the open sourcing of some of the components and a few others, right? And they're open source space and antenna, you know? Antennas are becoming software now. So with the invent of these things, which is open source, it helps us democratize that to the other sort of skirts of the practitioners, if you will. And that will bring in more applications first into the IOT space, and then maybe into the core sort of California, if you will. >> So what does a telco developer look like? I mean, all the blockchain developers and crypto developers are moving into generative AI, right? So maybe those worlds come together. >> You'd like to think though that the developers would understand everything's network centric today. So you'd like to think they'd understand that how the network responds, you know, you'd take a simple app like Zoom or something. If it notices the bandwidth changes, it should knock down the resolution. If it goes up it, then you can add different features and things and you can make apps a lot smarter that way. >> Well, G2 was saying today that they did a deal with Mercedes, you know this probably better than I do, where they're going to embed WebEx in the car. And if you're driving, it'll shut off the camera. >> Of course. >> I'm like, okay. >> I'll give you a better example though. >> But that's my point. Like, isn't there more that we can do? >> You noticed down on the SKT stand the little helicopter. That's a vertical lift helicopter. So it's an electric vertical lift helicopter. Just think of that for a second. And then think of the connectivity to control that, to securely control that. And then I was recently at an event with Zeus actually where we saw an air traffic control system where there was no people manning the tower. It was managed by someone remotely with all the cameras around them. So managing all of those different elements, we call it IOT, but actually it's way more than what we thought of as IOT. All those components connecting, communicating securely and safely. 'Cause I don't want that helicopter to come down on my head, do you? (men laugh) >> Especially if you're in there. (men laugh) >> Okay, so you mentioned sustainability. Everybody's talking about power. I don't know if you guys have a lot of experience around TCO, but I'm trying to get to, well, is this just because energy costs are so high, and then when the energy becomes cheap again, nobody's going to pay any attention to it? Or is this the real deal? >> So one of the issues around the, if we want to experience all that connectivity locally or that helicopter wants to have that connectivity, we have to ultimately build denser, more reliable networks. So there's a CapEx, we're going to put more base stations in place. We need more fiber in the ground to support them. Therefore, the energy consumption will go up. So we need to be more efficient in the use of energy. Simple as that. >> How much of the operating expense is energy? Like what percent of it? Is it 10%? Is it 20%? Is it, does anybody know? >> It depends who you ask and it depends on the- >> I can't get an answer to that. I mean, in the enterprise- >> Analyst: The data centers? >> Yeah, the data centers. >> We have the numbers. I think 10 to 15%. >> It's 10 to 12%, something like that. Is it much higher? >> I've got feeling it's 30%. >> Okay, so if it's 30%, that's pretty good. >> I do think we have to get better at understanding how to measure too. You know, like I was talking with John Davidson at Sysco about this that every rev of silicon they come out with uses more power, but it's a lot more dense. So at the surface, you go, well, that's using a lot more power. But you can consolidate 10 switches down to two switches. >> Well, Intel was on early and talking about how they can intelligently control the cores. >> But it's based off workload, right? That's the thing. So what are you running over it? You know, and so, I don't think our industry measures that very well. I think we look at things kind of boxed by box versus look at total consumption. >> Well, somebody else in theCUBE was saying they go full throttle. That the networks just say just full throttle everything. And that obviously has to change from the power consumption standpoint. >> Obviously sustainability and sensory or sensors from IOT side, they go hand in hand. Just simple examples like, you know, lights in the restrooms, like in public areas. Somebody goes in there and just only then turns. The same concept is being applied to servers and compute and storage and every aspects and to networks as well. >> Cell tower. >> Yeah. >> Cut 'em off, right? >> Like the serverless telco? (crosstalk) >> Cell towers. >> Well, no, I'm saying, right, but like serverless, you're not paying for the compute when you're not using it, you know? >> It is serverless from the economics point of view. Yes, it's like that, you know? It goes to the lowest level almost like sleep on our laptops, sleep level when you need more power, more compute. >> I mean, some of that stuff's been in networking equipment for a long time, it just never really got turned on. >> I want to ask you about private networks. You wrote a piece, Athenet was acquired by HPE right after Dell announced a relationship with Athenet, which was kind of, that was kind of funny. And so a good move, good judo move by by HP. I asked Dell about it, and they said, look, we're open. They said the right things. We'll see, but I think it's up to HP. >> Well, and the network inside Dell is. >> Yeah, okay, so. Okay, cool. So, but you said something in that article you wrote on Silicon Angle that a lot of people feel like P5G is going to basically replace wireless or cannibalize wireless. You said you didn't agree with that. Explain why? >> Analyst: Wifi. >> Wifi, sorry, I said wireless. >> No, that's, I mean that's ridiculous. Pat Gelsinger said that in his last VMware, which I thought was completely irresponsible. >> That it was going to cannibalize? >> Cannibalize wifi globally is what he said, right? Now he had Verizon on stage with him, so. >> Analyst: Wifi's too inexpensive and flexible. >> Wifi's cheap- >> Analyst: It's going to embed really well. Embedded in that. >> It's reached near ubiquity. It's unlicensed. So a lot of businesses don't want to manage their own spectrum, right? And it's great for this, right? >> Analyst: It does the job. >> For casual connectivity. >> Not today. >> Well, it does for the most part. Right now- >> For the most part. But never at these events. >> If it's engineered correctly, it will. Right? Where you need private 5G is when reliability is an absolute must. So, Chris, you and I visited the Port of Rotterdam, right? So they're putting 5G, private 5G there, but there's metal containers everywhere, right? And that's going to disrupt it. And so there are certain use cases where it makes sense. >> I've been in your basement, and you got some pretty intense equipment in there. You have private 5G in there. >> But for carpeted offices, it does not make sense to bring private. The economics don't make any sense. And you know, it runs hot. >> So where's it going to be used? Give us some examples of where we should be looking for. >> The early ones are obviously in mining, and you say in ports, in airports. It broadens cities because you've got so many moving parts in there, and always think about it, very expensive moving parts. The cranes in the port are normally expensive piece of kits. You're moving that, all that logistics around. So managing that over a distance where the wifi won't work over the distance. And in mining, we're going to see enormous expensive trucks moving around trying to- >> I think a great new use case though, so the Cleveland Browns actually the first NFL team to use it for facial recognition to enter the stadium. So instead of having to even pull your phone out, it says, hey Dave Vellante. You've got four tickets, can we check you all in? And you just walk through. You could apply that to airports. You could do put that in a hotel. You could walk up and check in. >> Analyst: Retail. >> Yeah, retail. And so I think video, realtime video analytics, I think it's a perfect use case for that. >> But you don't need 5G to do that. You could do that through another mechanism, couldn't you? >> You could do wire depending on how mobile you want to do it. Like in a stadium, you're pulling those things in and out all the time. You're moving 'em around and things, so. >> Yeah, but you're coming in at a static point. >> I'll take the contrary view here. >> See, we can't even agree on that. (men laugh) >> Yeah, I love it. Let's go. >> I believe the reliability of connection is very important, right? And the moving parts. What are the moving parts in wifi? We have the NIC card, you know, the wifi card in these suckers, right? In a machine, you know? They're bigger in size, and the radios for 5G are smaller in size. So neutralization is important part of the whole sort of progress to future, right? >> I think 5G costs as well. Yes, cost as well. But cost, we know that it goes down with time, right? We're already talking about 60, and the 5G stuff will be good. >> Actually, sorry, so one of the big boom areas at the moment is 4G LTE because the component price has come down so much, so it is affordable, you can afford to bring it all together. People don't, because we're still on 5G, if 5G standalone everywhere, you're not going to get a consistent service. So those components are unbelievably important. The skillsets of the people doing integration to bring them all together, unbelievably important. And the business case within the business. So I was talking to one of the heads of one of the big retail outlets in the UK, and I said, when are you going to do 5G in the stores? He said, well, why would I tear out all the wifi? I've got perfectly functioning wifi. >> Yeah, that's true. It's already there. But I think the technology which disappears in front of you, that's the best technology. Like you don't worry about it. You don't think it's there. Wifi, we think we think about that like it's there. >> And I do think wifi 5G switching's got to get easier too. Like for most users, you don't know which is better. You don't even know how to test it. And to your point, it does need to be invisible where the user doesn't need to think about it, right? >> Invisible. See, we came back to invisible. We talked about that yesterday. Telecom should be invisible. >> And it should be, you know? You don't want to be thinking about telecom, but at the same time, telecoms want to be more visible. They want to be visible like Netflix, don't they? I still don't see the path. It's fuzzy to me the path of how they're not going to repeat what happened with the over the top providers if they're invisible. >> Well, if you think about what telcos delivers to consumers, to businesses, then extending that connectivity into your home to help you support secure and extend your connection into Zeus's basement, whatever it is. Obviously that's- >> His awesome setup down there. >> And then in the business environment, there's a big change going on from the old NPLS networks, the old rigid structures of networks to SD1 where the control point is moved outside, which can be under control of the telco, could be under the control of a third party integrator. So there's a lot changing. I think we obsess about the relative role of the telco. The demand is phenomenal for connectivity. So address that, fulfill that. And if they do that, then they'll start to build trust in other areas. >> But don't you think they're going to address that and fulfill that? I mean, they're good at it. That's their wheelhouse. >> And it's a 1.6 trillion market, right? So it's not to be sniffed at. That's fixed on mobile together, obviously. But no, it's a big market. And do we keep changing? As long as the service is good, we don't move away from it. >> So back to the APIs, the eight APIs, right? >> I mean- >> Eight APIs is a joke actually almost. I think they released it too early. The release release on the main stage, you know? Like, what? What is this, right? But of course they will grow into hundreds and thousands of APIs. But they have to spend a lot of time and effort in that sort of context. >> I'd actually like to see the GSMA work with like AWS and Microsoft and VMware and software companies and create some standardization across their APIs. >> Yeah. >> I spoke to them yes- >> We're trying to reinvent them. >> Is that not what they're doing? >> No, they said we are not in the business of a defining standards. And they used a different term, not standard. I mean, seriously. I was like, are you kidding me? >> Let's face it, there aren't just eight APIs out there. There's so many of them. The TM forum's been defining when it's open data architecture. You know, the telcos themselves are defining them. The standards we talked about too earlier with Danielle. There's a lot of APIs out there, but the consistency of APIs, so we can bring them together, to bring all the different services together that will support us in our different lives is really important. I think telcos will do it, it's in their interest to do it. >> All right, guys, we got to wrap. Let's go around the horn here, starting with Chris, Zeus, and then Sarbjeet, just bring us home. Number one hot take from Mobile World Congress MWC23 day two. >> My favorite hot take is the willingness of all the participants who have been traditional telco players who looked inwardly at the industry looking outside for help for partnerships, and to build an ecosystem, a more open ecosystem, which will address our requirements. >> Zeus? >> Yeah, I was going to talk about ecosystem. I think for the first time ever, when I've met with the telcos here, I think they're actually, I don't think they know how to get there yet, but they're at least aware of the fact that they need to understand how to build a big ecosystem around them. So if you think back like 50 years ago, IBM and compute was the center of everything in your company, and then the ecosystem surrounded it. I think today with digital transformation being network centric, the telcos actually have the opportunity to be that center of excellence, and then build an ecosystem around them. I think the SIs are actually in a really interesting place to help them do that 'cause they understand everything top to bottom that I, you know, pre pandemic, I'm not sure the telcos were really understand. I think they understand it today, I'm just not sure they know how to get there. . >> Sarbjeet? >> I've seen the lot of RN demos and testing companies and I'm amazed by it. Everything is turning into software, almost everything. The parts which are not turned into software. I mean every, they will soon. But everybody says that we need the hardware to run something, right? But that hardware, in my view, is getting miniaturized, and it's becoming smaller and smaller. The antennas are becoming smaller. The equipment is getting smaller. That means the cost on the physicality of the assets is going down. But the cost on the software side will go up for telcos in future. And telco is a messy business. Not everybody can do it. So only few will survive, I believe. So that's what- >> Software defined telco. So I'm on a mission. I'm looking for the monetization path. And what I haven't seen yet is, you know, you want to follow the money, follow the data, I say. So next two days, I'm going to be looking for that data play, that potential, the way in which this industry is going to break down the data silos I think there's potential goldmine there, but I haven't figured out yet. >> That's a subject for another day. >> Guys, thanks so much for coming on. You guys are extraordinary partners of theCUBE friends, and great analysts and congratulations and thank you for all you do. Really appreciate it. >> Analyst: Thank you. >> Thanks a lot. >> All right, this is a wrap on day two MWC 23. Go to siliconangle.com for all the news. Where Rob Hope and team are just covering all the news. John Furrier is in the Palo Alto studio. We're rocking all that news, taking all that news and putting it on video. Go to theCUBE.net, you'll see everything on demand. Thanks for watching. This is a wrap on day two. We'll see you tomorrow. (soft music)