(upbeat music) >> Welcome back, everyone, to theCUBE's coverage here live on the floor at AWS re:MARS 2022. I'm John Furrier, host for theCUBE. Great event, machine learning, automation, robotics, space, that's MARS. It's part of the re-series of events, re:Invent's the big event at the end of the year, re:Inforce, security, re:MARS, really intersection of the future of space, industrial, automation, which is very heavily DevOps machine learning, of course, machine learning, which is AI. We have Luis Ceze here, who's the CEO co-founder of OctoML. Welcome to theCUBE. >> Thank you very much for having me in the show, John. >> So we've been following you guys. You guys are a growing startup funded by Madrona Venture Capital, one of your backers. You guys are here at the show. This is a, I would say small show relative what it's going to be, but a lot of robotics, a lot of space, a lot of industrial kind of edge, but machine learning is the centerpiece of this trend. You guys are in the middle of it. Tell us your story. >> Absolutely, yeah. So our mission is to make machine learning sustainable and accessible to everyone. So I say sustainable because it means we're going to make it faster and more efficient. You know, use less human effort, and accessible to everyone, accessible to as many developers as possible, and also accessible in any device. So, we started from an open source project that began at University of Washington, where I'm a professor there. And several of the co-founders were PhD students there. We started with this open source project called Apache TVM that had actually contributions and collaborations from Amazon and a bunch of other big tech companies. And that allows you to get a machine learning model and run on any hardware, like run on CPUs, GPUs, various GPUs, accelerators, and so on. It was the kernel of our company and the project's been around for about six years or so. Company is about three years old. And we grew from Apache TVM into a whole platform that essentially supports any model on any hardware cloud and edge. >> So is the thesis that, when it first started, that you want to be agnostic on platform? >> Agnostic on hardware, that's right. >> Hardware, hardware. >> Yeah. >> What was it like back then? What kind of hardware were you talking about back then? Cause a lot's changed, certainly on the silicon side. >> Luis: Absolutely, yeah. >> So take me through the journey, 'cause I could see the progression. I'm connecting the dots here. >> So once upon a time, yeah, no... (both chuckling) >> I walked in the snow with my bare feet. >> You have to be careful because if you wake up the professor in me, then you're going to be here for two hours, you know. >> Fast forward. >> The average version here is that, clearly machine learning has shown to actually solve real interesting, high value problems. And where machine learning runs in the end, it becomes code that runs on different hardware, right? And when we started Apache TVM, which stands for tensor virtual machine, at that time it was just beginning to start using GPUs for machine learning, we already saw that, with a bunch of machine learning models popping up and CPUs and GPU's starting to be used for machine learning, it was clear that it come opportunity to run on everywhere. >> And GPU's were coming fast. >> GPUs were coming and huge diversity of CPUs, of GPU's and accelerators now, and the ecosystem and the system software that maps models to hardware is still very fragmented today. So hardware vendors have their own specific stacks. So Nvidia has its own software stack, and so does Intel, AMD. And honestly, I mean, I hope I'm not being, you know, too controversial here to say that it kind of of looks like the mainframe era. We had tight coupling between hardware and software. You know, if you bought IBM hardware, you had to buy IBM OS and IBM database, IBM applications, it all tightly coupled. And if you want to use IBM software, you had to buy IBM hardware. So that's kind of like what machine learning systems look like today. If you buy a certain big name GPU, you've got to use their software. Even if you use their software, which is pretty good, you have to buy their GPUs, right? So, but you know, we wanted to help peel away the model and the software infrastructure from the hardware to give people choice, ability to run the models where it best suit them. Right? So that includes picking the best instance in the cloud, that's going to give you the right, you know, cost properties, performance properties, or might want to run it on the edge. You might run it on an accelerator. >> What year was that roughly, when you were going this? >> We started that project in 2015, 2016 >> Yeah. So that was pre-conventional wisdom. I think TensorFlow wasn't even around yet. >> Luis: No, it wasn't. >> It was, I'm thinking like 2017 or so. >> Luis: Right. So that was the beginning of, okay, this is opportunity. AWS, I don't think they had released some of the nitro stuff that the Hamilton was working on. So, they were already kind of going that way. It's kind of like converging. >> Luis: Yeah. >> The space was happening, exploding. >> Right. And the way that was dealt with, and to this day, you know, to a large extent as well is by backing machine learning models with a bunch of hardware specific libraries. And we were some of the first ones to say, like, know what, let's take a compilation approach, take a model and compile it to very efficient code for that specific hardware. And what underpins all of that is using machine learning for machine learning code optimization. Right? But it was way back when. We can talk about where we are today. >> No, let's fast forward. >> That's the beginning of the open source project. >> But that was a fundamental belief, worldview there. I mean, you have a world real view that was logical when you compare to the mainframe, but not obvious to the machine learning community. Okay, good call, check. Now let's fast forward, okay. Evolution, we'll go through the speed of the years. More chips are coming, you got GPUs, and seeing what's going on in AWS. Wow! Now it's booming. Now I got unlimited processors, I got silicon on chips, I got, everywhere >> Yeah. And what's interesting is that the ecosystem got even more complex, in fact. Because now you have, there's a cross product between machine learning models, frameworks like TensorFlow, PyTorch, Keras, and like that and so on, and then hardware targets. So how do you navigate that? What we want here, our vision is to say, folks should focus, people should focus on making the machine learning models do what they want to do that solves a value, like solves a problem of high value to them. Right? So another deployment should be completely automatic. Today, it's very, very manual to a large extent. So once you're serious about deploying machine learning model, you got a good understanding where you're going to deploy it, how you're going to deploy it, and then, you know, pick out the right libraries and compilers, and we automated the whole thing in our platform. This is why you see the tagline, the booth is right there, like bringing DevOps agility for machine learning, because our mission is to make that fully transparent. >> Well, I think that, first of all, I use that line here, cause I'm looking at it here on live on camera. People can't see, but it's like, I use it on a couple couple of my interviews because the word agility is very interesting because that's kind of the test on any kind of approach these days. Agility could be, and I talked to the robotics guys, just having their product be more agile. I talked to Pepsi here just before you came on, they had this large scale data environment because they built an architecture, but that fostered agility. So again, this is an architectural concept, it's a systems' view of agility being the output, and removing dependencies, which I think what you guys were trying to do. >> Only part of what we do. Right? So agility means a bunch of things. First, you know-- >> Yeah explain. >> Today it takes a couple months to get a model from, when the model's ready, to production, why not turn that in two hours. Agile, literally, physically agile, in terms of walk off time. Right? And then the other thing is give you flexibility to choose where your model should run. So, in our deployment, between the demo and the platform expansion that we announced yesterday, you know, we give the ability of getting your model and, you know, get it compiled, get it optimized for any instance in the cloud and automatically move it around. Today, that's not the case. You have to pick one instance and that's what you do. And then you might auto scale with that one instance. So we give the agility of actually running and scaling the model the way you want, and the way it gives you the right SLAs. >> Yeah, I think Swami was mentioning that, not specifically that use case for you, but that use case generally, that scale being moving things around, making them faster, not having to do that integration work. >> Scale, and run the models where they need to run. Like some day you want to have a large scale deployment in the cloud. You're going to have models in the edge for various reasons because speed of light is limited. We cannot make lights faster. So, you know, got to have some, that's a physics there you cannot change. There's privacy reasons. You want to keep data locally, not send it around to run the model locally. So anyways, and giving the flexibility. >> Let me jump in real quick. I want to ask this specific question because you made me think of something. So we're just having a data mesh conversation. And one of the comments that's come out of a few of these data as code conversations is data's the product now. So if you can move data to the edge, which everyone's talking about, you know, why move data if you don't have to, but I can move a machine learning algorithm to the edge. Cause it's costly to move data. I can move computer, everyone knows that. But now I can move machine learning to anywhere else and not worry about integrating on the fly. So the model is the code. >> It is the product. >> Yeah. And since you said, the model is the code, okay, now we're talking even more here. So machine learning models today are not treated as code, by the way. So do not have any of the typical properties of code that you can, whenever you write a piece of code, you run a code, you don't know, you don't even think what is a CPU, we don't think where it runs, what kind of CPU it runs, what kind of instance it runs. But with machine learning model, you do. So what we are doing and created this fully transparent automated way of allowing you to treat your machine learning models if you were a regular function that you call and then a function could run anywhere. >> Yeah. >> Right. >> That's why-- >> That's better. >> Bringing DevOps agility-- >> That's better. >> Yeah. And you can use existing-- >> That's better, because I can run it on the Artemis too, in space. >> You could, yeah. >> If they have the hardware. (both laugh) >> And that allows you to run your existing, continue to use your existing DevOps infrastructure and your existing people. >> So I have to ask you, cause since you're a professor, this is like a masterclass on theCube. Thank you for coming on. Professor. (Luis laughing) I'm a hardware guy. I'm building hardware for Boston Dynamics, Spot, the dog, that's the diversity in hardware, it's tends to be purpose driven. I got a spaceship, I'm going to have hardware on there. >> Luis: Right. >> It's generally viewed in the community here, that everyone I talk to and other communities, open source is going to drive all software. That's a check. But the scale and integration is super important. And they're also recognizing that hardware is really about the software. And they even said on stage, here. Hardware is not about the hardware, it's about the software. So if you believe that to be true, then your model checks all the boxes. Are people getting this? >> I think they're starting to. Here is why, right. A lot of companies that were hardware first, that thought about software too late, aren't making it. Right? There's a large number of hardware companies, AI chip companies that aren't making it. Probably some of them that won't make it, unfortunately just because they started thinking about software too late. I'm so glad to see a lot of the early, I hope I'm not just doing our own horn here, but Apache TVM, the infrastructure that we built to map models to different hardware, it's very flexible. So we see a lot of emerging chip companies like SiMa.ai's been doing fantastic work, and they use Apache TVM to map algorithms to their hardware. And there's a bunch of others that are also using Apache TVM. That's because you have, you know, an opening infrastructure that keeps it up to date with all the machine learning frameworks and models and allows you to extend to the chips that you want. So these companies pay attention that early, gives them a much higher fighting chance, I'd say. >> Well, first of all, not only are you backable by the VCs cause you have pedigree, you're a professor, you're smart, and you get good recruiting-- >> Luis: I don't know about the smart part. >> And you get good recruiting for PhDs out of University of Washington, which is not too shabby computer science department. But they want to make money. The VCs want to make money. >> Right. >> So you have to make money. So what's the pitch? What's the business model? >> Yeah. Absolutely. >> Share us what you're thinking there. >> Yeah. The value of using our solution is shorter time to value for your model from months to hours. Second, you shrink operator, op-packs, because you don't need a specialized expensive team. Talk about expensive, expensive engineers who can understand machine learning hardware and software engineering to deploy models. You don't need those teams if you use this automated solution, right? Then you reduce that. And also, in the process of actually getting a model and getting specialized to the hardware, making hardware aware, we're talking about a very significant performance improvement that leads to lower cost of deployment in the cloud. We're talking about very significant reduction in costs in cloud deployment. And also enabling new applications on the edge that weren't possible before. It creates, you know, latent value opportunities. Right? So, that's the high level value pitch. But how do we make money? Well, we charge for access to the platform. Right? >> Usage. Consumption. >> Yeah, and value based. Yeah, so it's consumption and value based. So depends on the scale of the deployment. If you're going to deploy machine learning model at a larger scale, chances are that it produces a lot of value. So then we'll capture some of that value in our pricing scale. >> So, you have direct sales force then to work those deals. >> Exactly. >> Got it. How many customers do you have? Just curious. >> So we started, the SaaS platform just launched now. So we started onboarding customers. We've been building this for a while. We have a bunch of, you know, partners that we can talk about openly, like, you know, revenue generating partners, that's fair to say. We work closely with Qualcomm to enable Snapdragon on TVM and hence our platform. We're close with AMD as well, enabling AMD hardware on the platform. We've been working closely with two hyperscaler cloud providers that-- >> I wonder who they are. >> I don't know who they are, right. >> Both start with the letter A. >> And they're both here, right. What is that? >> They both start with the letter A. >> Oh, that's right. >> I won't give it away. (laughing) >> Don't give it away. >> One has three, one has four. (both laugh) >> I'm guessing, by the way. >> Then we have customers in the, actually, early customers have been using the platform from the beginning in the consumer electronics space, in Japan, you know, self driving car technology, as well. As well as some AI first companies that actually, whose core value, the core business come from AI models. >> So, serious, serious customers. They got deep tech chops. They're integrating, they see this as a strategic part of their architecture. >> That's what I call AI native, exactly. But now there's, we have several enterprise customers in line now, we've been talking to. Of course, because now we launched the platform, now we started onboarding and exploring how we're going to serve it to these customers. But it's pretty clear that our technology can solve a lot of other pain points right now. And we're going to work with them as early customers to go and refine them. >> So, do you sell to the little guys, like us? Will we be customers if we wanted to be? >> You could, absolutely, yeah. >> What we have to do, have machine learning folks on staff? >> So, here's what you're going to have to do. Since you can see the booth, others can't. No, but they can certainly, you can try our demo. >> OctoML. >> And you should look at the transparent AI app that's compiled and optimized with our flow, and deployed and built with our flow. That allows you to get your image and do style transfer. You know, you can get you and a pineapple and see how you look like with a pineapple texture. >> We got a lot of transcript and video data. >> Right. Yeah. Right, exactly. So, you can use that. Then there's a very clear-- >> But I could use it. You're not blocking me from using it. Everyone's, it's pretty much democratized. >> You can try the demo, and then you can request access to the platform. >> But you get a lot of more serious deeper customers. But you can serve anybody, what you're saying. >> Luis: We can serve anybody, yeah. >> All right, so what's the vision going forward? Let me ask this. When did people start getting the epiphany of removing the machine learning from the hardware? Was it recently, a couple years ago? >> Well, on the research side, we helped start that trend a while ago. I don't need to repeat that. But I think the vision that's important here, I want the audience here to take away is that, there's a lot of progress being made in creating machine learning models. So, there's fantastic tools to deal with training data, and creating the models, and so on. And now there's a bunch of models that can solve real problems there. The question is, how do you very easily integrate that into your intelligent applications? Madrona Venture Group has been very vocal and investing heavily in intelligent applications both and user applications as well as enablers. So we say an enable of that because it's so easy to use our flow to get a model integrated into your application. Now, any regular software developer can integrate that. And that's just the beginning, right? Because, you know, now we have CI/CD integration to keep your models up to date, to continue to integrate, and then there's more downstream support for other features that you normally have in regular software development. >> I've been thinking about this for a long, long, time. And I think this whole code, no one thinks about code. Like, I write code, I'm deploying it. I think this idea of machine learning as code independent of other dependencies is really amazing. It's so obvious now that you say it. What's the choices now? Let's just say that, I buy it, I love it, I'm using it. Now what do I got to do if I want to deploy it? Do I have to pick processors? Are there verified platforms that you support? Is there a short list? Is there every piece of hardware? >> We actually can help you. I hope we're not saying we can do everything in the world here, but we can help you with that. So, here's how. When you have them all in the platform you can actually see how this model runs on any instance of any cloud, by the way. So we support all the three major cloud providers. And then you can make decisions. For example, if you care about latency, your model has to run on, at most 50 milliseconds, because you're going to have interactivity. And then, after that, you don't care if it's faster. All you care is that, is it going to run cheap enough. So we can help you navigate. And also going to make it automatic. >> It's like tire kicking in the dealer showroom. >> Right. >> You can test everything out, you can see the simulation. Are they simulations, or are they real tests? >> Oh, no, we run all in real hardware. So, we have, as I said, we support any instances of any of the major clouds. We actually run on the cloud. But we also support a select number of edge devices today, like ARMs and Nvidia Jetsons. And we have the OctoML cloud, which is a bunch of racks with a bunch Raspberry Pis and Nvidia Jetsons, and very soon, a bunch of mobile phones there too that can actually run the real hardware, and validate it, and test it out, so you can see that your model runs performant and economically enough in the cloud. And it can run on the edge devices-- >> You're a machine learning as a service. Would that be an accurate? >> That's part of it, because we're not doing the machine learning model itself. You come with a model and we make it deployable and make it ready to deploy. So, here's why it's important. Let me try. There's a large number of really interesting companies that do API models, as in API as a service. You have an NLP model, you have computer vision models, where you call an API and then point in the cloud. You send an image and you got a description, for example. But it is using a third party. Now, if you want to have your model on your infrastructure but having the same convenience as an API you can use our service. So, today, chances are that, if you have a model that you know that you want to do, there might not be an API for it, we actually automatically create the API for you. >> Okay, so that's why I get the DevOps agility for machine learning is a better description. Cause it's not, you're not providing the service. You're providing the service of deploying it like DevOps infrastructure as code. You're now ML as code. >> It's your model, your API, your infrastructure, but all of the convenience of having it ready to go, fully automatic, hands off. >> Cause I think what's interesting about this is that it brings the craftsmanship back to machine learning. Cause it's a craft. I mean, let's face it. >> Yeah. I want human brains, which are very precious resources, to focus on building those models, that is going to solve business problems. I don't want these very smart human brains figuring out how to scrub this into actually getting run the right way. This should be automatic. That's why we use machine learning, for machine learning to solve that. >> Here's an idea for you. We should write a book called, The Lean Machine Learning. Cause the lean startup was all about DevOps. >> Luis: We call machine leaning. No, that's not it going to work. (laughs) >> Remember when iteration was the big mantra. Oh, yeah, iterate. You know, that was from DevOps. >> Yeah, that's right. >> This code allowed for standing up stuff fast, double down, we all know the history, what it turned out. That was a good value for developers. >> I could really agree. If you don't mind me building on that point. You know, something we see as OctoML, but we also see at Madrona as well. Seeing that there's a trend towards best in breed for each one of the stages of getting a model deployed. From the data aspect of creating the data, and then to the model creation aspect, to the model deployment, and even model monitoring. Right? We develop integrations with all the major pieces of the ecosystem, such that you can integrate, say with model monitoring to go and monitor how a model is doing. Just like you monitor how code is doing in deployment in the cloud. >> It's evolution. I think it's a great step. And again, I love the analogy to the mainstream. I lived during those days. I remember the monolithic propriety, and then, you know, OSI model kind of blew it. But that OSI stack never went full stack, and it only stopped at TCP/IP. So, I think the same thing's going on here. You see some scalability around it to try to uncouple it, free it. >> Absolutely. And sustainability and accessibility to make it run faster and make it run on any deice that you want by any developer. So, that's the tagline. >> Luis Ceze, thanks for coming on. Professor. >> Thank you. >> I didn't know you were a professor. That's great to have you on. It was a masterclass in DevOps agility for machine learning. Thanks for coming on. Appreciate it. >> Thank you very much. Thank you. >> Congratulations, again. All right. OctoML here on theCube. Really important. Uncoupling the machine learning from the hardware specifically. That's only going to make space faster and safer, and more reliable. And that's where the whole theme of re:MARS is. Let's see how they fit in. I'm John for theCube. Thanks for watching. More coverage after this short break. >> Luis: Thank you. (gentle music)

>>Hey everyone. Welcome to the cubes coverage of PagerDuty summit 22. I'm Lisa Martin, and I'm on the ground with Michael cooky, the VP of product and marketing at PagerDuty. Michael. It's great to have you on the program. There is great momentum right now at PagerDuty. The company's fourth quarter fiscal 22 financials showed a revenue rise of 34% year over year with figures of 85.4 million for the quarter, for the first time ever. Awesome stuff. Let's talk Michael, about what some of the great things are that, um, attendees can expect from this year's summit. You know, automation has been always at the forefront of PagerDuty's focus on managing critical work, but it's a big focus for this year's summit. Let's unpack why that is. >>Sure, absolutely. Thanks so much for having me, Lisa. It's great to be here. Um, we did just finish a grade quarter. We're super excited about it. I think Summit's a good example. It kind of is aligned around the areas that we've been seeing a lot of success and momentum with our customer base. Um, and automation is definitely one of those pillars without a doubt. Um, you know what we've seen, uh, we've been at this now, uh, for over well over a decade, uh, and we've been investing in automation in kind of two major areas and I'll, and I'll explain why, um, we study our customers and what they need. And I think we can all talk about the limited time that everybody has to get their jobs done today, limited people, right? The, you know, the great rotation or the great resignation is definitely hit hitting, you know, every single industry. >>And so it results in limited skills, uh, and a lot of strain on the people that are trying to get their jobs done every day. Um, we also saw that the more you interrupt someone, so you have a very skilled worker, let's say it's a developer for example, and you're constantly interrupting them to try and get them to help you fix something. Uh, they get super unhappy and we actually on our platform prove they quit their jobs more often when they are interrupted more often. Uh, so you know, that is an area where we think automation can have huge impacts and huge returns to take limited resources and really stretch them a lot further, um, by taking care of repeat work, but also taking some of those higher skilled capabilities and handing them to more people across the enterprise. So the work could be shared across the enterprise. >>That's critical to share that work, but I also find it fascinating that you studied that and actually saw direct correlation of, of developers actually resigning from their jobs. And as you mentioned, the great resignation, something that many companies in every industry are dealing with. Let's talk a little bit about some of the things that we're announced recently. I know you guys are weaving automation actions everywhere to empower more users, to be able to, to be, to take action, to resolve issues faster, which is critical for the customer experience. It's critical for revenue. Talk a little bit about automation actions. What are some of the key things that, that delivers and enables PagerDuty to do for its customers? >>Yeah, great. So, you know, two years ago we acquired an automation company named Rundeck and we got right to work integrating their technology across the PagerDuty operations cloud and automation actions is, is the ability to execute automation from wherever you are. And so that is, um, you know, I think there's two directions to talk about automation. One is kind of what can we automate inside of an incident response? So when something's going wrong, what can we automate? What can we automate inside of our own platform? And then there's, what can we automate out in the customer's environment? So whether that's fixing something that's going wrong on a cloud or in a data center, or, uh, provisioning new resources out on the cloud so that, uh, people can scale their applications more rapidly. Um, all of that is done with automation actions, which you just mentioned. >>And so it's not enough to just be able to send work, to be done somewhere else. You have to kind of do it E everywhere. And so at summit this year, we announced that you'll be able to fire off that automation in real time using event intelligence, which is our machine learning product. So as machine learning learns something, it can then run off and try and take action based on it. And then we're delivering it to all of our users. So inside of, you know, for a responder, who's responding to a problem for a customer service representative who might be working with a customer who's having a problem, giving them automation can totally change the customer experience because now the customer service person is actually empowered, uh, to do diagnostics and try and solve problems. So, so that's right. Automation actions being delivered both in real time and to every different, uh, type of user that that leverages PagerDuty today, >>That's really quite transformative. Michael, it sounds like getting the first line responders, the corrective information that in an automated fashion, because as we know, one of the things that's been in short supply the last couple of years is patients. And one of the risks, several of the risks associated with that are customer churn, you know, poor customer experience, brand reputation, et cetera. What are some of the expected outcomes, um, with, with, uh, automation actions and one obviously speeding, mean time to repair, lowering interruptions, getting problems fixed faster, but from a customer's perspective, what are some of the outcomes that they can expect? >>Awesome. Um, great question. The there's a lot of different ways you can leverage automation, right? You just mentioned a bunch of super high return ones when something's broken and your company's actually losing customer experience or, or revenue, uh, or you're unable to deliver a service to your employees or your users. That is obviously a moment of massive return for automation in those cases. Like you said, you're gonna see a reduction in the requirements to escalate, which means that the first responder can actually solve the problem themselves. Uh, and they're not gonna have to go interrupt that more higher skilled employee. Like we talked about, uh, we see that over 50% of the time, we're actually reducing escalations by using this technology. That also means the problems are getting solved a lot faster, which you also mentioned. Um, so using automation actions to both diagnose what's going wrong, but then actually try and remediate it. >>Um, and as I mentioned earlier, we can do that before you even have to get a human being at all. We can do that with machine learning in real time, which is, uh, super powerful. And then there's a long tail of other ways to leverage, uh, automation in an environment from service provisioning and redundant tasks that are used, that are done for maintenance across an environment or provisioning, uh, provisioning services to developers so that they can get to work faster. So there's a lot to do there. Um, and, and then we're also exploring ways to, to automate, uh, outside of just technical use cases, um, which we talked a little bit about in the product keynote as well. >>One of the things that, that you mentioned earlier is that the, the data that PagerDuty has that demonstrates, um, from a resignation perspective, what happens when developers are, are really taken away from their core job? Is there any data that shows that auto, uh, automation actions, you mentioned, um, a big reduction, 50% reduction in time to respond there is that, is there a direct correlation in actually helping the folks on the front lines stay in the front lines? >>That's right. So, um, and, and also those that are coding coding, right? So, um, the, that 50% reduction means 50% time given back for them to do their primary function, which in this case is building amazing new digital services, whether that's a new customer experience, uh, or a piece of, uh, uh, digital service to drive the business and business efficiency. And so driving this automation access kind of a shock absorber for your business and for the people in your business that are, that are super taxed. And we actually release something called the state, uh, state of digital operations. And, uh, we are updating all that data actually, and announced, uh, today that that is now available on our website as well. So you can hop on there and actually see live statistics off of our platform that we culminate, uh, along with some survey statistics that are trending all of this information you're mentioning in terms of people being interrupted and then, uh, you know, churning actually from their job because they've been interrupted so many times. And so that's right, this will directly impact that. Um, and, and as we bring automation out from just developers, we hope to have an impact across the rest of the business in a very similar way, >>Absolutely transformative. I mean, you know, we, when we think about churn, it impacts to revenue. I always think the customer experience and the employee experience are inextricably linked. And, and I think what you're talking about really demonstrates that you need to be able to empower the right employees to resolve incidents, to absorb that shock as you talked about. And that's really something that for any organization in any industry globally, is no longer a nice to have. It's really something that I think sounds like a competitive differentiator that PagerDuty can help organizations really uncover and bring to the surface. >>Yeah, you're, you're hitting on one of my favorite topics, I think in, in the service of the customer in service of like customer delight and customer obsession, all of the business is now centered on the customer, which, which means that the back office is the front office they're coming together. And, um, and with the pandemic and kind of the transition that we all took into dependency on digital services, it's all starting to look very similar. And so, um, because of that, we're able to now expand our impact at PagerDuty across so much more of a business, uh, out to, uh, everything, including employee experience, um, and also accelerating the time to productivity for your, for your business, so that you can serve your customer faster. Um, we, we acquired a company recently, uh, named catalytic and, uh, their help, their technology helped us kind of accelerate a couple of pieces to market that are just the tip of the iceberg, uh, for kind of being able to rapidly automate and configure workflows for anyone at the enterprise, whether that's for a customer, uh, experience or whether it's, uh, it's to keep your business productive or efficient, uh, for business users. >>So unpack those incident workflows, you talked about the, the catalytic acquisition that was just from March. Talk to me about the incident workflows and what were customers asking for that really kind of generated this new capability that PagerDuty recently announced. >>So, you know, people lean on PagerDuty at, at all types of times, but as we've already kind of talked about the most critical time is when something is broken for the business that is vital to their business. And so when those moments happen, you know, we call those major incidents and when you're responding to a major incident across a business, you really have to do everything you can because every second really matters. And so, um, we, you know, Catalytics technology enables flexible, automated workflows of behavior when certain conditions exist. And so the first thing you're seeing from that technology is called incident workflows, which when something's going wrong, enables you to kind of automate steps of processes very, very quickly that can be carried out company wide. So this could be something like when we see that, uh, critical service is impacted, we wanna automatically send out updates across the business. >>We wanna automatically create a, an area to go troubleshoot on a, on a collaborative, you know, collab, ops platform. We wanna automatically invite the right people into that room and automatically deliver diagnostics to them and automation to them. So they can troubleshoot faster instead of a human having to take those steps in terms of firefighting and trying to re, trying to pull those coordination steps together. Now we can configure that quickly and have it, you know, happen automatically and it, and it can actually happen without a human having to trigger it. So again, this is about something's broken, we're responding. We need to be as fast as possible. You can't rely on a human anymore. You really need, you know, the, what the earlier automation we talked about was automating off our platform. Incident workflows is automating on the operations cloud. So taking steps to solve the problem when it goes wrong without needing a human being to take those steps, >>When you're in customer conversations, Michael, and you, you talk about these capabilities. What are some of the things that, that you talk to the customers about, about why automation is going to be, I wouldn't even say critical for, or, I mean, business critical table stakes for organizations it's no longer okay. To just default to depend on humans. You know, the, the customers on the other end don't want to, you know, a couple seconds delay is hugely impactful. >>Yeah. We, we call that the abandonment threshold, but that's absolutely right. So we've already talked a lot about why you have, why the, why our businesses and our employees depend on digital. I think we've covered that what's important to understand is what is digital. So contemporary applications and digital services, there, there are tens and hundreds of microservices that are powering these things. And then there's thousands of different dependencies between those services. Um, and so supporting these and understanding these is difficult. So, so being able to interpret are they operating correct correctly? And if not, what do we do about it? It's actually a problem that humans can't calculate. Um, then you throw into change, right? So everybody's now competing with the digital service. So they want to innovate as fast as possible, get new capabilities out, keep that customer excited and happy with your offering. >>And so we need to push change on that complex environment. Very often, it's a pretty hairy mess to try and solve and to do that in real time. So we, we use two arms of an area that, that we call AIOps. One is using machine learning to interpret all of those signals and figure out is what is going on? Is it happening correctly? Is something going wrong? Is, is something looking like it's going wrong. And also to determine how to fix it, if it is going wrong, do we need a person to do this or not? And then that other side is, is what we've talked about today, which is you can't bring a human in to do all the work. So you have to know how to solve the problem. So the combination of is, is what we call AIOps it's it's event intelligence, which is machine learning to understand the situation. And then it's automation to actually go out and react to it and solve the problem. That's that's this branch of our, of our platform. >>Got it. You guys have PagerDuty has 19,000 customers, including 60% of the fortune 100. Is there a customer example that, that jumps to mind to you that really articulates the value of AI ops for example, and what it is at PagerDuty is able to allow its customers to do >>Sure. Um, and, and now a million users on this platform, which is just phenomenal. And so that, that actually helps us design better machine learning, because we have so many people using this platform. Um, you know, there's, there's a great example that was just shown on in our kickoff. So if you haven't seen the product, uh, keynote, you really have to see it. We run what are called, uh, day in the life demos. And in this case, this kind of hit close to home for us, because a lot of us have been sitting in delays in airports around the globe, as we get back to our travel, uh, and, and get back to seeing people face to face. Um, but, but what we showed there is, is, uh, very, very, uh, close to real world example where, um, you know, a, a ticketing, uh, service goes down for a travel agency and it impacts everything from directly their end users, customer satisfaction, but also partner engagements and employee behaviors. >>And whether they can get the right people booked to staff, that flight, et cetera, it really throws logistical chaos on the entire business. And it's all based on digital systems. And in that you can see our, our platform helps them react and manage customers at the customer service layer. It gets the developers and the infrastructure, and it teams reacting to solve the problem instantly. They use automation to solve the problem, and they actually learn some new things in that situation. And they bring that back to the flexible workflows. So it's a, it's basically what I call a virtuous loop as they solve a problem, and they realize they could do it faster, better, quicker, or automate more of it. You're now able to bake that back into the platform so that you're basically getting better and better and better every single time you are called to solve a problem. And so over time, we like to bring our customers up. We what we call the operational maturity model. And, uh, it, in, in, in, at the end of that journey, you should really be focused on critical work for you and for your business. And the rest of it should really be handled by our platform. >>An operational flywheel that is constantly learning is impactful. As you described in that example across an entire enterprise. So many different facets there, last question, Michael, as we're running out of time, here, you, as I mentioned in the very beginning, PagerDuty is coming off amazing momentum from FY 22. What are some of the things that you're seeing, uh, for the year ahead that, that you're excited about or that we can expect? >>Uh, great question. Um, so you just saw us release automation in every area for every user. Um, I think what you're gonna see us do across automation is bring faster and more powerful value out of the box with our automation capability. Some of that will be, for example, finding homogeneous, what we call runbooks or automation calls that you can make shared across all platforms. One of our recent announcements was the ability to host process automation, either in the PagerDuty operations cloud or behind your own firewall. We also have a hosted SAS offering for process automation. And what we're gonna do is enable the very common set of automation capabilities across all of those. So it's a homogeneous environment, no matter how you are hosting or scaling your automation. So that's one, and I think number two is this workflow stuff we touched on very, very much just the tip of the iceberg, uh, leveraging kind of a no code rapid interface to build workflows, to solve the highest ROI problem, but then we're gonna take that technology. We're gonna apply it to every downstream, repetitive service in your environment. So everything from employee onboarding to critical sales processes, or legal contract management, um, you know, anything that is time critical, you're gonna be able to build these rapid workflows around, um, and PagerDuty's gonna help you keep your business, uh, you know, healthy and, and operating around them. And so that's, that's where we're gonna be focused, uh, is for the, for the next 12, uh, months I would say. And, uh, it's gonna be an exciting run. >>It is gonna be exciting run. I better let you get back to work as VP of product and marketing. You got a lot to do Michael >>That's right. Well, I'll get back to it. I appreciate the time though. Thanks for so much for the chat, Lisa, >>Thank you so much for Michael Cook. I'm Lisa Martin. You're watching the cubes on the ground coverage of PagerDuty summit 22.

(upbeat music) >> Hey, welcome back everyone to theCube's coverage of AWS re:Invent 2021. I'm John Furrier, host of theCube, wall-to-wall coverage in-person and hybrid. The two great guests here, Manu Parbhakar, worldwide Leader, Linux and IBM Software Partnership at AWS, and Mike Evans, Vice President of Technical Business Development at Red Hat. Gentlemen, thanks for coming on theCube. Love this conversation, bringing Red Hat and AWS together. Two great companies, great technologies. It really is about software in the cloud, Cloud-Scale. Thanks for coming on. >> Thanks John. >> So get us into the partnership. Okay. This is super important. Red Hat, well known open source as cloud needs to become clear, doing an amazing work. Amazon, Cloud-Scale, Data is a big part of it. Modern software. Tell us about the partnership. >> Thanks John. Super excited to share about our partnership. As we have been partnering for almost 14 years together. We started in the very early days of AWS. And now we have tens of thousands of customers that are running RHEL on EC2. If you look at over the last three years, the pace of innovation for our joint partnership has only increased. It has manifested in three key formats. The first one is the pace at which RHEL supports new EC2 instances like Arm, Graviton. You know, think a lot of features like Nitro. The second is just the portfolio of new RHEL offerings that we have launched over the last three years. We started with RHEL for sequel, RHEL high availability, RHEL for SAP, and then only last month, we've launched the support for knowledge base for RHEL customers. Mike, you want to talk about what you're doing with OpenShift and Ansible as well? >> Yeah, it's good to be here. It's fascinating to me cause I've been at Red Hat for 21 years now. And vividly remember the start of working with AWS back in 2008, when the cloud was kind of a wild idea with a whole bunch of doubters. And it's been an interesting time, but I feel the next 14 years are going to be exciting in a different way. We now have a very large customer base from almost every industry in the world built on RHEL, and running on AWS. And our goal now is to continue to add additional elements to our offerings, to build upon that and extend it. The largest addition which we're going to be talking a lot about here at the re:Invent show was the partnership in April this year when we launched the Red Hat OpenShift service on AWS as a managed version of OpenShift for containers based workloads. And we're seeing a lot of the customers that have standardized on RHEL on EC2, or ones that are using OpenShift on-premise deployments, as the early adopters of ROSA, but we're also seeing a huge number of new customers who never purchased anything from Red Hat. So, in addition to the customers, we're getting great feedback from systems integrators and ISV partners who are looking to have a software application run both on-premise and in AWS, and with OpenShift being one of the pioneers in enabling both container and harnessing Kubernetes where ROSA is just a really exciting area for us to track and continue to advance together with AWS. >> It's very interesting. Before I get to ROSA, I want to just get the update on Red Hat and IBM, obviously the acquisition part of IBM, how is that impacting the partnership? You can just quickly touch on that. >> Sure. I'll start off and, I mean, Red Hat went from a company that was about 15,000 employees competing with a lot of really large technology companies and we added more than 100,000 field oriented people when IBM acquired Red Hat to help magnify the Red Hat solutions, and the global scale and coverage of IBM is incredible. I like to give two simple examples of people. One is, I remember our salesforce in EMEA telling me they got a $4 million order from a country in Africa theydidn't even know existed. And IBM had 100 people in it, or AT&T is one of Red Hat's largest accounts, and I think at one point we had seven full-time people on it and AT&T is one of IBM's largest accounts and they had two seven storey buildings full of people working with AT&T. So RHELative to AWS, we now also see IBM embracing AWS more with both software, and services, in the magnification of Red Hat based solutions, combined with that embrace should be, create some great growth. And I think IBM is pretty excited about being able to sell Red Hat software as well. >> Yeah, go ahead. >> And Manu I think you have, yeah. >> Yeah. I think there's also, it is definitely very positive John. >> Yeah. >> You know, just the joint work that Red Hat and AWS have done for the last 14 years, working in the trenches supporting our end customers is now also providing lot of Tailwinds for the IBM software partnership. We have done some incredible work over the last 12 months around three broad categories. The first one is around product, what we're doing around customer success, and then what we're doing around sales and marketing. So on the product side, we have listed about 15 products on Marketplace over the course of the last 12 to 15 months. And our goal is to launch all of the IBM Cloud Paks. These are containerized versions of IBM software on Marketplace by the first half of next year. The other feedback that we are getting from our customers is that, hey, we love IBM software running at Amazon, but we like to have a cloud native SaaS version of the software. So there's a lot of work that's going on right now, to make sure that many of these offerings are available in a cloud-native manner. And you're not talking with Db2 Cognos, Maximo, (indistinct), on EC2. The second thing that we're doing is making sure that many of these large enterprise customers are running IBM software, are successful. So our technical teams are attached to the hip, working on the ground floor in making customers like Delta successful in running IBM software on them. I think the third piece around sales and marketing just filing up a vibrant ecosystem, rather how do we modernize and migrate this IBM software on Cloud Paks on AWS? So there's a huge push going on here. So (indistinct), you know, the Red Hat partnership is providing a lot of Tailwinds to accelerate our partnership with IBM software. >> You know, I always, I've been saying all this year in Red Hat summit, as well as Ansible Fest that, distributed computing is coming to large scale. And that's really the, what's happening. I mean, you looking at what you guys are doing cause it's amazing. ROSA Red Hat OpenShift on AWS, very notable to use the term on AWS, which actually means something in the partnership as we learned over the years. How is that going Mike because you launched on theCube in April, ROSA, it had great traction going in. It's in the Marketplace. You've got some integration. It's really a hand in glove situation with Cloud-Scale. Take us through what's the update? >> Yeah, let me, let me let Manu speak first to his AWS view and then I'll add the Red Hat picture. >> Thanks Mike. John for ROSA is part of an entire container portfolio. So if you look at it, so we have ECS, EKS, the managed Kubernetes service. We have the serverless containers with Fargate. We launched ECS case anywhere. And then ROSA is part of an entire portfolio of container services. As you know, two thirds of all container workloads run on AWS. And a big function of that is because we (indistinct) from our customer and then sold them what the requirements are. There are two sets of key customers that are driving the demand and the early adoption of ROSA. The first set of customers that have standardized on OpenShift on-premises. They love the fact that everything that comes out of the box and they would love to use it on Arm. So that's the first (indistinct). The second set of customers are, you know, the large RHEL users on EC2. The tens of thousands of customers that we've talked about that want to move from VM to containers, and want to do DevOps. So it's this set of two customers that are informing our roadmap, as well as our investments around ROSA. We are seeing solid adoption, both in terms of adoption by a customer, as well as the partners and helping, and how our partners are helping our customers in modernizing from VMs to containers. So it's a, it's a huge, it's a huge priority for our container service. And over the next few years, we continue to see, to increase our investment on the product road map here. >> Yeah, from my perspective, first off at the high level in mind, my one of the most interesting parts of ROSA is being integrated in the AWS console and not just for the, you know, where it shows up on the screen, but also all the work behind what that took to get there and why we did it. And we did it because customers were asking both of us, we're saying, look, OpenShift is a platform. We're going to be building and deploying serious applications at incredible scale on it. And it's really got to have joint high-quality support, joint high-quality engineering. It's got to be rock solid. And so we came to agreement with AWS. That was the best way to do that, was to build it in the console, you know, integrated in, into the core of an AWS engineering team with Red Hat engineers, Arm and Arms. So that's, that's a very unique service and it's not like a high level SaaS application that runs above everything, it's down in the bowels and, and really is, needs to be rock solid. So we're seeing, we're seeing great interest, both from end users, as I mentioned, existing customers, new customers, the partner base, you know, how the systems integrators are coming on board. There's lots of business and money to be made in modernizing applications as well as building new cloud native applications. People can, you know, between Red Hat and AWS, we've got some, some models around supporting POCs and customer migrations. We've got some joint investments. it's a really ripe area. >> Yeah. That's good stuff. Real quick. what do you think of ROSA versus EKS and ECS? What's, how should people think about that Mike? (indistinct) >> You got to go for it Manu. Your job is to position all these (indistinct). (indistinct) >> John, ROSA is part of our container portfolio services along with EKS, ECS, Fargate, and any (indistinct) services that we just launched earlier this year. There are, you know, set of customers both that are running OpenShift on-premises that are standardized on ROSA. And then there are large set of RHEL customers that are running RHEL on EC2, that want to use the ROSA service. So, you know, both AWS and Red Hat are now continuing to invest in accelerating the roadmap of the service on our platform. You know, we are working on improving the console experience. Also one of the things we just launched recently is the Amazon controller to Kubernetes, or what , you know, service operators for S3. So over the next few years you will see, you know, significant investment from both Red Hat and AWS in this joint service. And this is an integral part of our overall container portfolio. >> And great stuff to get in the console. That's great, great integration. That's the future. I got to ask about the graviton instances. It's been one of the most biggest success stories, I think we believe in Amazon history in the acquisition of Annapurna, has really created great differentiation. And anyone who's in the software knows if you have good chips powering apps, they go faster. And if the chips are good, they're less expensive. And that's the innovation. We saw that RHEL now supports graviton instances. Tell us more about the Red Hat strategy with graviton and Arms specifically, has that impact your (indistinct) development, and what does it mean for customers? >> Sure. Yeah, it's pretty, it's a pretty fascinating area for me. As I said, I've been a Red Hat for 21 years and my job is actually looking at new markets and new technologies now for Red Hat and work with our largest partners. So, I've been tracking the Arm dynamics for awhile, and we've been working with AWS for over two years, supporting graviton. And it's, I'm seeing more enthusiasm now in terms of developers and, especially for very horizontal, large scale applications. And we're excited to be working with AWS directly on it. And I think it's going to be a fascinating next two years on Arm, personally. >> Many of the specialized processors for training and instances, all that stuff, can be applied to web services and automation like cloud native services, right? Is that, it sounds like a good direction. Take us through that. >> John, on our partnership with Red Hat, we are continuing to iterate, as Mike mentioned, the stuff that we've done around graviton, both the last two years is pretty incredible. And the pace at which we are innovating is improving. Around the (indistinct) and the inferential instances, we are continuing to work with Red Hat and, you know, the support for RHEL should come shortly, very soon. >> Well, my prediction is that the graviton success was going to be applied to every single category. You can get that kind of innovation with this on the software side, just really kind of just, that's the magical, that's the, that's the proven form of software, right? We've been there. Good software powering with some great performance. Manu, Mike, thank you for coming on and sharing the, the news and the partnership update. Congratulations on the partnership. Really good. Thank you. >> Excellent John. Incredible (indistinct). >> Yeah, this is the future software as we see, it's all coming together. Here on theCube, we're bringing all the action, software being powered by chips, is theCube coverage of AWS re:invent 2021. I'm John Furrier, your host. Thanks for watching. (upbeat music)

(upbeat music) >> Hi, I'm Joe Rodriguez, Managing Director of Financial Services at Cloudera. Welcome to the Fight Fraud with Data session. At Cloudera we believe that fighting fraud begins with data. So financial services is Cloudera's largest industry vertical. We have approximately 425 global financial services customers, which consists of 82 out of a hundred of the largest global banks of which we have 27 that are globally systemic banks. Four out of the five top stock exchanges, eight out of the top 10 wealth management firms and all four of the top credit card networks. So as you can see, most financial services institutions utilize Cloudera for data analytics and machine learning. We also have over 20 central banks and a dozen or so financial regulators. So it's an incredible footprint which gives Cloudera lots of insight into the many innovations that our customers are coming up with. Criminals can steal thousands of dollars before a fraudulent transaction is detected. So the cost to purchase your account data is well worth the price to fraudsters. According to Experian, credit and a debit card account information sells on the dark web for a mere $5 with the CVV number and up to $110 if it comes with all the bank information, including your name, social security number, date of birth, complete account numbers, and other personal data. Our customers have several key data and analytics challenges when it comes to fighting financial crime. The volume of data that they need to deal with is huge and growing exponentially. All this data needs to be evaluated in real time. There are new sources of streaming data that need to be integrated with existing legacy data sources. This includes biometrics data and enhanced authentication video surveillance, call center data, and of course all that needs to be integrated with existing legacy data sources. There is an analytics Arms Race between the banks and the criminals, and the criminal networks never stop innovating. They also have to deal with disjointed security and governance. Security and governance policies are often set per data source or application requiring redundant work across workloads. And they have to deal with siloed environments. The specialized nature of platforms and people results in disparate data sources and data management processes. This duplicates efforts and divides the business risk and crime teams, limiting collaboration opportunities between them. CDP enhances financial crime solutions to be holistic by eliminating data gaps between siloed solutions, with an enterprise data approach, advanced data analytics and machine learning. By deploying an enterprise wide data platform, you reduce siloed divisions between business risk and crime teams and enable better collaboration through industrialized machine learning, you tighten up the loop between detection and new fraud patterns. Cloudera provides the data platform on which a best of breed applications can run and leverage integrated machine learning. Cloudera stands rather than replaces your existing fraud modeling applications. So Oracle, SAS, Actimize, to name a few, integrate with an enterprise data hub to scale the data, increase speed and flexibility and improve efficacy of your entire fraud system. It also centralizes the fraud workload on data that can be used for other use cases in applications like Enhanced KYC and Customer 360 for example. I just wanted to highlight a couple of our partners in financial crime prevention, Simudyne and Quantexa. So Simudyne provides fraud simulation using agent-based modeling machine learning techniques to generate synthetic transaction data. This data simulates potential fraud scenarios in a cost-effective GDPR-compliant virtual environment to significantly improve financial crime detection systems. Simudyne identifies future fraud topologies for millions of simulations that can be used to dynamically train new machine learning algorithms for enhanced identification. And Quantexa connects the dots within your data using dynamic entity resolution, and advanced network analytics to create context around your customers. This enables you to see the bigger picture and automatically assesses potential criminal behavior. Now let's go over some of our customers and how they're using Cloudera. First, we'll talk about United Overseas Bank or UOB. UOB is a leading full service bank in Asia with a network of more than 500 offices in 19 countries and territories, in Asia Pacific, Western Europe and North America. UOB built a modern data platform on Cloudera that gives it the flexibility and speed to develop new AI and machine learning solutions and to create a data-driven enterprise. UOB set up it's big data analytics center in 2017. It was Singapore's first centralized big data unit within a bank to deepen the bank's data analytic capabilities and to use data insights to enhance the bank's performance. Essential to this work was implementing a platform that could cost efficiently bring together data from dozens of separate systems and incorporate a range of unstructured data, including voice and text. Using Cloudera CDP and machine learning, UOB gained a richer understanding of its customer preferences to help make their banking experience simpler, safer, and more reliable. Working with Cloudera, UOB has a big data platform that gives business staff and data scientists, faster access to relevant and quality data for self-service analytics, machine learning and emerging artificial intelligence solutions. With new self-service analytics and machine learning driven insights, UOB has realized improvements in digital banking, asset management, compliance, AML, and more. Advanced AML detection capabilities, help analysts detect suspicious transactions either based on hidden relationships of shell companies and high risk individuals with Cloudera and machine learning technologies, UOB was able to enhance AML detection and reduce the time to identify new links from months to three weeks. Next, let's speak about MasterCard. So MasterCard's principle business is to process payments between banks and merchants and the credit issuing banks and credit unions of the purchasers who use the MasterCard brand debit and credit cards to make purchases. MasterCard chose Cloudera Enterprise for fraud detection and to optimize their DW infrastructure, delivering deep insights and best practices and big data security and compliance. Next, let's speak about Bank Rakyat in Indonesia or BRI. BRI is one of the largest and oldest banks in Indonesia and engages in the provision of general banking services. It's headquartered in Jakarta, Indonesia. BRI is well-known for its focus on microfinancing initiatives and serves over 75 million customers through its more than 11,000 offices and rural service outposts. BRI required better insight to understand customer activity and identify fraudulent transactions. The bank needed a solid foundation that allowed it to leverage the power of advanced analytics, artificial intelligence, and machine learning to gain better understanding of customers and the market. BRI used Cloudera Enterprise data platform to build an agile and reliable, predictive augmented intelligence solution to enhance its credit scoring system. And to address the rising concern around data security from regulators and customers, BRI developed a real-time fraud detection service powered by Cloudera and Kafka, BRI's data scientists developed a machine learning model for fraud detection by creating a behavioral scoring model based on customer savings, loan transactions, deposits, payroll and other financial real-time data. This led to improvements in its fraud detection and credit scoring capabilities, as well as the development of a new digital microfinancing product. With the enablement of real-time fraud detection, BRI was able to reduce the rate of fraud by 40%. It improved relationship manager productivity by two and a half fold. It improved the credit scoring system to cut down on micro-financing loan processing times from two weeks to two days to now two minutes. So fraud prevention is a good area to start with data focus if you haven't already. It offers a quick return on investment and it's a focused area that's not too entrenched across the company. To learn more about fraud prevention, go to www.cloudera.com, and you should schedule a meeting with Cloudera to learn even more. And with that, thank you for listening and thank you for your time. (upbeat music)

>>Welcome back to HPD discovered 2021. The virtual edition. My name is Dave Volonte and you're watching the cubes continuous coverage of H. P. S. Big customer event. Patrick Moorehead is here of moor insights and strategy is the number one analyst in the research analyst. Business. Patrick. Always a pleasure. Great to see you, >>David. Great to see you too. And I know you're you're up there fighting for that number one spot to. It's great to see you and it's great to see you in the meetings that were in. But it's even more fun to be here on the cube. I love to be on the cube and every once in a while you'll even call me a friend of the cube, >>unquestionably my friend and so and I can't wait second half. I mean you're traveling right now. We're headed to Barcelona to mobile World Congress later on this month. So so we're gonna we're gonna see each other face to face this year. 100%. So looking forward to that. So, you know, let's get into it. Um you know, before we get into H. P. E. Let's talk a little bit about what you're seeing in the market. We've got, you know, we we we finally, it feels like the on prem guys are finally getting their cloud act together. Um, it's maybe taken a while, but we're seeing as a service models emerge. I think it's resonating with customers. The clearly not everything is moving to the cloud. There's this hybrid model emerging. Multi cloud is real despite what, you know, >>some some >>cloud players want to say. And then there's this edges like jump ball, what are you seeing in the marketplace? >>Yeah. Davis, as exciting as ever in. Just to put in perspective, I mean, the public cloud has been around for about 10 years and still only 20%. Around 20% of the data in 20% of the applications are there now, albeit very important ones. And I'm certainly not a public cloud denier, I never have been, but there are some missing pieces that need to come together. And you know, even five years ago we were debating dave the hybrid cloud and I feel like when Amazon brought out outposts, the conversation was over right now, what you have is cloud native folks building out hybrid and on prem capabilities, you have the classic on prem folks building out hybrid and as a service capabilities. And I really think it boils down 22 things. I mean it's wanting to have more flexibility and you know, I hate to use it because it sounds like a marketing word, but agility, the ability to spin up things and spin down things in a very quick way. And uh, you know what they've learned. The veterans also know, hey, let's do this in a way that doesn't lock us in too much into a certain vendor. And I've been around for a long time. David and I'm a realist too. Well, you have to lock yourself into something. It just depends on what do you want to lock yourself into, but super exciting. And what H. P. E. When they threw the acts in the sea with Green Lake, I think it was four years ago, I think really started to stir the pot. >>You know, you mentioned the term cloud denial, but you know, and I feel like the narrative from, I like to determine is I think you should use the term veteran. You know, it's very, they're ours is the only industry patrick where legacy is a pejorative, but but but so but the point I want to make is I feel like there's been a lot of sort of fear from the veteran players, but I look at it differently. I wonder what you're taking. I think, I think, I think I calculated that the Capex spending by the big four public clouds including Alibaba last year was $100 billion. That's like a gift to the world. Here, we're going to spend $100 billion like the internet here you go build. And and so I, and I feel like companies like HP are finally saying, yeah, we're gonna build, we're gonna build a layer and we're gonna hide the complexity and we're gonna add value on top. What do you think about that? >>Yeah. So I think it's now, I wish, I wish the on prem folks like HP, you would have done it 10 years ago, but I don't think anybody expected the cloud to be as big as it's become over the last 10 years. I think we saw companies like salesforce with sas taking off, but I think it is the right direction because there are advantages to having workloads on prem and if you add an as a service capability on top of the top of that, and let's say even do a Coehlo or a managed service, it's pretty close to being similar to the public cloud with the exception, that you can't necessarily swipe a credit card for a bespoke workload if you're a developer and it is a little harder to scale out. But that is the next step in the equation day, which is having, having these folks make capital expenditures, make them in a polo facility and then put a layer to swipe a credit card and you literally have the public cloud. >>Yeah. So that's, that's a great point and that's where it's headed, isn't it? Um, so let's, let's talk about the horses on the track. Hp. As you mentioned, I didn't realize it was four years ago. I thought it was, wow, That's amazing. So everybody's followed suit. You see, Dallas announced, Cisco has announced, uh, Lenovo was announced, I think IBM as well. So we, so everybody started following suit there. The reality is, is it's taken some time to get this stuff standardized. What are you seeing from, from HP? They've made some additional announcements, discover what's your take on all this. >>Yeah. So HPD was definitely the rabbit here and they were first in the market. It was good to see, first off some of their, Um, announcements on, on how it's going. And they talked about 4, $28 billion 1200 customers over 900 partners and 95% retention. And I think that's important anybody who's in the lead and remember what Aws used to do with the slide with the amount of customers would just get bigger and bigger and bigger and that's a good way to show momentum. I like the retention part two which is 95%. And I think that that says a lot uh probably the more important announcements that they made is they talked about the G. A. Of some of their solutions on Green Lake and whether it was S. A. P. Hana Ml apps HPC with Francis V. I was Citrus in video but they also brought more of what I would call a vertical layer and I'm sure you've seen the vertical ization of all of these cloud and as a service workloads. But what they're doing with Epic with EMR and looseness, with financial payments and Splunk and intel with data and risk analysis and finally, a full stack for telco five G. One of the biggest secrets and I covered this about five years ago is HPV actually has a full stack that western european carriers use and they're now extending that to five G. And um, so more horizontal uh and and more vertical. That was the one of the big swipes uh that I saw that there was a second though, but maybe we can talk about these. >>Yeah. Okay, Okay. So, so the other piece of that of course is standardization right there there because there was a, there was, there was a lot of customization leading up to this and everybody sort of, everybody always had some kind of financial game they can play and say, hey, there's an adversary as a service model, but this is definitely more of a standardized scalable move that H P E. Is making with what they call Lighthouse, Right? >>Yeah, that's exactly right. And I've talked to some Green Lake customers and they obviously gave it kudos or they wouldn't have HP wouldn't have served them up and they wouldn't have been buying it. But they did say, um, it took, it took a while, took some paperwork to get it going. It's not 100% of push button, but that's partially because hp allows you to customize the hardware. You want a one off network adapter. Hp says yes, right. You want to integrate a different type of storage? They said yes. But with Green Lake Lighthouse, it's more of a, what you see is what you get, which by the way is very much like the public cloud or you go to a public cloud product sheet or order sheet. You're picking from a list and you really don't know everything that's underneath the covers, aside from, let's say the speed of the network, the type of the storage and the amount of the storage you get. You do get to pick between, let's say, an intel processor, Graviton two or an M. D processor. You get to pick your own GPU. But that's pretty much it. And HP Lighthouse, sorry, Green Lake Lighthouse uh, is bringing, I think a simplification to Green Lake that it needs to truly scale beyond, let's say, the white house customers at HP. Yeah, >>Well done. So, you know, and I hear your point about 10 years in, you know, plus and to me this is like a mandate. I mean, this is okay. Good, good job guys about time. But if I had a, you know, sort of look at the big players, like, can we have an oligopoly here in this, in this business? It's HP, Cisco, you got Dell Lenovo, you've got, you know, IBM, they're all doing this and they all have a different little difference, you know, waste of skin of catch. And your point about simplicity, it seems like HP HP is all in Antonio's like, okay, here's what we're going to announce that, you know, while ago, so, and they seem to have done a good job with Wall Street and they get a simple model, you know, Dell's obviously bigger portfolio, much more complicated. IBM is even more complicated than that. I don't know so much about Lenovo and in Cisco of course, has acquired a ton of SAAS companies and sort of they've got a lot of bespoke products that they're trying to put together, so they've got, but they do have SAS models. So each of them is coming at it from a different perspective. How do you think? And so and the other point we got lighthouse, which is sort of Phase one, get product market fit. Phase two now is scale codify standardized and then phase three is the moat build your unique advantage that protects your business. What do you see as HP? Es sort of unique value proposition and moat that they can build longer term. >>That's a great, great question. And let me rattle off kind of what I'm seeing that some of these these players here. So Cisco, ironically, has sells the most software of any of those players that you mentioned, uh with the exception of IBM. Um, and yeah, C >>ICSDB two. Yeah, >>yeah, they're the they're the number two security player, uh, Microsoft, number one. So and I think the evaluation on the street uh indicate that shows that I feel like uh Deltek is a is a very broad play because not only do they have servers, storage, networking and security, but they also have Pcs and devices, so it's a it's a scale and end play with a focus on VM ware solutions, not exclusively, of course. Uh And um then you've got Lenovo who is just getting into the as a service game and are gosh, they're doing great in hyper scale, they've got scale there vertically integrated. I don't know if if too many people talk about that, but Lenovo does a lot of their own manufacturing and they actually manufacture Netapp storage solutions as well. So yeah, each of these folks brings a different game to the table, I think with h P E, what your bring to the table is nimble. When HP and HP split, the number one thing that I said was that uh huh H P E is going to have to be so much faster than it offsets the scale that Dell technology has and the HBs credit, although there, I don't think we're getting credit for this in the stock market yet. Um, and I know you and I are both industry folks, not financial folks, but I think their biggest thing is speed and the ability to move faster and that is what I've seen as it relates to the moat, which is a unique uh, competitive advantage. Quite frankly, I'm still looking for that day in, in, in what that is and I think in this industry it's nearly impossible and I would posit that that any, even the cloud folks, if you say, is there something that AWS can do that Azure can't, if it put it put its mind to it or G C P. I don't think so. I think it's more of a kind of land and expand and I think for H P E, when it comes to high performance computing and I'm not just talking about government installations, I'm talking about product development, drug development, I think that is a landing place where H P E already does pretty well can come in and expand its footprint, >>you know, that's really interesting um, observations. So, and I would agree with you, it's kind of like, this is a copycat industry, it's like the west coast offense, like the NFL >>and >>so, so the moat comes from, you know, brand execution and your other point about when HP and HP split, that was a game changer, because all of a sudden you saw companies like them, you always had a long term relationship with H P E but or HP, but then they came out of the woodworks and started to explode. And so it really opened up opportunities. So it really >>is an execution, >>isn't it? But go ahead, please >>Dave if I had to pick something that I think HP HPV needs to always be ahead and as a service and listen, you know, I both know announcements don't mean delivery, but there is correlation between if you start four years ahead of somebody that other company is going to have to put just, I mean they're gonna have to turn that ship and many of its competitors really big ships to be able to get there. So I think what Antonio needs to do is run like hell, right, Because it, it, I think it is in the lead and as a service holistically doesn't mean they're going to be there forever, but they have to stay ahead. They have to add more horizontal solutions. They have to add more vertical solutions. And I believe that at some point it does need to invest in some Capex at somebody like ANna Quinn x play credit card swiper on top of that. And Dave, you have the public, you have the public cloud, you don't have all the availability zones, but you have a public cloud. >>Yeah, that's going to happen. I think you're right on. So we see this notion of cloud expanding. It's no longer just remote set of services. Somewhere out in the cloud. It's as you said, outpost was the sort of signal. Okay, We're coming on prem clearly the on prem, uh, guys are connecting to the cloud. Multi cloud exists, we know this and then there's the edge but but but that brings me to that sort of vision and everybody's laying out of this this this seamless integration hiding the complexity log into my cloud and then life will be good. But the edge is different. Right? It's not just, you know, retail store or a race track. I mean there's the far edge, there's the Tesla car, there's gonna be compute everywhere. And that sort of ties into the data. The data flows, you know the real time influencing at the edge ai new semiconductor models. You you came out of the semiconductor industry, you know it inside and out arm is exploding is dominating in the edge with with with apple and amazon Alexa and things like that. That's really where the action is. So this is a really interesting cocktail and soup that we have going on. How do you >>say? Well, you know, Dave if the data most data, I think one thing most everybody agrees on is that most of the data will be created on the edge. Whether that's a moving edge a car, a smartphone or what I call an edge data center without tile flooring. Like that server that's bolted to the wall of Mcdonald's. When you drive through, you can see it versus the walmart. Every walmart has a raised tile floor. It's the edge to economically and performance wise, it doesn't make any sense to send all that data to the mother ships. Okay. And whether that's unproven data center or the giant public cloud, more efficient way is to do the compute at the closest way possible. But what it does, it does bring up challenges. The first challenge is security. If I wanted to, I could walk in and I could take that server off the Mcdonald's or the Shell gas station wall. So I can't do that in a big data center. Okay, so security, Physical security is a challenge. The second is you don't have the people to go in there and fix stuff that are qualified. If you have a networking problem that goes wrong and Mcdonald's, there's nobody there that can help uh, they can they can help you fix that. So this notion of autonomy and management and not keeping hyper critical data sitting out there and it becomes it becomes a security issue becomes a management issue. Let me talk about the benefits though. The benefits are lower latency. You want you want answers more quickly when that car is driving down the road and it has a five G V two X communication cameras can't see around corners, but that car communicating ahead, that ran into the stop sign, can I through vi to X. Talk to the car behind it and say, hey, something is going on there, you can't go to, you can't go to the big data center in the sky to make that happen, that is to be in near real time and that computer has to happen on the edge. So I think this is a tremendous opportunity and ironically the classic on prem guys, they own this, they own this space aside from smartphones of course, but if you look at compute on a light pole, companies like Intel have built Complete architectures to do that, putting compute into 5G base stations. Heck, I just, there was an announcement this week of google cloud in its gaming solution putting compute in a carrier edge to give lower latency to deliver a better experience. >>Yeah, so there, of course there is no one edge, it's highly fragmented, but I'm interested in your thoughts on kind of who's stack actually can play at the edge. And I've been sort of poking uh H P E about this. And the one thing that comes back consistently is Aruba, we we can take a room but not only to the, to the near edge, but to the far edge. And and that, do you see that as a competitive advantage? >>Oh gosh, yes. I mean, I would say the best acquisition That hp has made in 10 years has been aruba it's fantastic. And they also managed it in the right way. I mean, it was part of HB but it was it was managed a lot more loosely then, you know, a company that might get sucked into the board. And I think that paid off tremendously. They're giving Cisco on the edge a absolute run for their money, their first with new technologies. But it's about the solution. What I love about what a ruble looks at is it's looking at entertainment solutions inside of a stadium, um a information solution inside of an airport as opposed to just pushing the technology forward. And then when you integrate compute with with with Aruba, I think that's where the real magic happens. Most of the data on a permanent basis is actually video data. And a lot of it's for security uh for surveillance. And quite frankly, people taking videos off, they're off their smartphones and downloading video. I I just interviewed the chief network officer of T mobile and their number one bit of data is video, video uploaded, video download. But that's where the magic happens when you put that connectivity and the compute together and you can manage it in a, in an orderly and secure fashion >>while I have you, we have a ton of time here, but I I don't pick your brain about intel, the future of intel. I know you've been following it quite closely, you always have Intel's fighting a forefront war. You got there, battling A. M. D. There, battling your arm slash and video. They're they're taking on TSMC now and in foundry and, and I'll add china for the looming threat there. So what's your prognosis for for intel? >>Yeah, I liked bob the previous Ceo and I think he was doing a lot of of the right things, but I really think that customers and investors and even their ecosystem wanted somebody leading the company with a high degree of technical aptitude and Pat coming, I mean, Pat had a great job at VM or, I mean, he had a great run there and I think it is a very positive move. I've never seen the energy At Intel probably in the last 10 years that I've seen today. I actually got a chance to talk with pat. I visited pat uhh last month and and talk to him about pretty much everything and where he wanted to take the company the way you looked at technology, what was important, what's not important. But I think first off in the world of semiconductors, there are no quick fixes. Okay. Intel has a another two years Before we see what the results are. And I think 2023 for them is gonna be a huge year. But even with all this competition though, Dave they still have close to 85% market share in servers and revenue share for client computing around 90%. Okay. So and they've built out there networking business, they build out a storage business um with with obtain they have the leading Aid as provider with Mobileye. And and listen I was I was one of Intel's biggest, I was into one of Intel's biggest, I was Intel's biggest customer when I was a compact. I was their biggest competitor at AMG. So um I'm not obviously not overly pushing or there's just got to wait and see. They're doing the right things. They have the right strategy. They need to execute. One of the most important things That Intel did is extend their alliance with TSMC. So in 2023 we're going to see Intel compute units these tiles, they integrate into the larger chips called S. O. C S B. Manufactured by TSMC. Not exclusively, but we could see that. So literally we could have AMG three nanometer on TSMC CPU blocks, competing with intel chips with TSMC three nanometer CPU blocks and it's on with regard to video. I mean in video is one of these companies that just keeps going charging, charging hard and I'm actually meeting with Jensen wang this week and Arms Ceo Simon Segers to talk about this opportunity and that's a company that keeps on moving interestingly enough in video. If the arm deal does go through will be the largest chip license, see CPU licensee and have the largest CPU footprint on the planet. So here we have AMG who's CPU and Gpu and buying an F. P. G. A company called Xilinx, you have Intel, Cpus, Gpus machine learning accelerators and F. P. G. S. And then you've got arms slashing video bit with everything as well. We have three massive ecosystems. They're gonna be colliding here and I think it's gonna be great for competition. Date. Competition is great. You know, when there's not competition in CPUs and Gpus, we know what happens right. Uh, the beach just does not go on and we start to stagnate. And I did, I do feel like the industry on CPU started to stagnate when intel had no competition. So bring it on. This is gonna be great for for enterprises then customers to and then, oh, by the way, you have the custom Chip providers. WS has created no less than 15 custom semiconductors started with networking and nitro and building out an edge that surrounded the general computer. And then it moved to Inferential for inference trainee um, is about to come out for training Graviton and Gravitas to for general purpose CPU and then you've got apple. So innovation is huge and I love to always make fun of the software is eating the world. I always say yeah but has to run on something. And so I think the combination of semiconductors software and cloud is just really a magical combination. >>Real quick handicap the video arm acquisition. What what are the odds that that they will be successful? They say it's on track. You got a 2 to 13 to 1 10 to 1. >>I say 75%. Yes 25%. No China is always the has been the odd odd man out for the last three years. They scuttled the Qualcomm NXp deal. You just don't know what china is going to do. I think the EU with some conditions is going to let this fly. I think the U. S. Is absolutely going to let this fly. And even though the I. P. Will still stay over in the UK, I think the U. S. Wants to see wants to see this happen, Japan and Korea I think we'll allow this china is the odd man out. >>In a word, the future of h p. E is blank >>as a service >>patrick Moorehead. Always a pleasure. My friend. Great to see you. Thanks so much for coming back in the cube. >>Yeah, Thanks for having me on. I appreciate that. >>Everybody stay tuned for more great coverage from HP discover 21 this is day Volonte for the cube. The leader and enterprise tech coverage. We'll be right back.

>> From theCUBE studios in Palo Alto, in Boston bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante >> The latest Arm Neoverse announcement further cements our opinion that it's architecture business model and ecosystem execution are defining a new era of computing and leaving Intel in it's dust. We believe the company and its partners have at least a two year lead on Intel and are currently in a far better position to capitalize on a major waves that are driving the technology industry and its innovation. To compete our view is that Intel needs a new strategy. Now, Pat Gelsinger is bringing that but they also need financial support from the US and the EU governments. Pat Gelsinger was just noted as asking or requesting from the EU government $9 billion, sorry, 8 billion euros in financial support. And very importantly, Intel needs a volume for its new Foundry business. And that is where Apple could be a key. Hello, everyone. And welcome to this week's weekly bond Cube insights powered by ETR. In this breaking analysis will explain why Apple could be the key to saving Intel and America's semiconductor industry leadership. We'll also further explore our scenario of the evolution of computing and what will happen to Intel if it can't catch up. Here's a hint it's not pretty. Let's start by looking at some of the key assumptions that we've made that are informing our scenarios. We've pointed out many times that we believe Arm wafer volumes are approaching 10 times those of x86 wafers. This means that manufacturers of Arm chips have a significant cost advantage over Intel. We've covered that extensively, but we repeat it because when we see news reports and analysis and print it's not a factor that anybody's highlighting. And this is probably the most important issue that Intel faces. And it's why we feel that Apple could be Intel's savior. We'll come back to that. We've projected that the chip shortage will last no less than three years, perhaps even longer. As we reported in a recent breaking analysis. Well, Moore's law is waning. The result of Moore's law, I.e the doubling of processor performance every 18 to 24 months is actually accelerating. We've observed and continue to project a quadrupling of performance every two years, breaking historical norms. Arm is attacking the enterprise and the data center. We see hyperscalers as the tip of their entry spear. AWS's graviton chip is the best example. Amazon and other cloud vendors that have engineering and software capabilities are making Arm-based chips capable of running general purpose applications. This is a huge threat to x86. And if Intel doesn't quickly we believe Arm will gain a 50% share of an enterprise semiconductor spend by 2030. We see the definition of Cloud expanding. Cloud is no longer a remote set of services, in the cloud, rather it's expanding to the edge where the edge could be a data center, a data closet, or a true edge device or system. And Arm is by far in our view in the best position to support the new workloads and computing models that are emerging as a result. Finally geopolitical forces are at play here. We believe the U S government will do, or at least should do everything possible to ensure that Intel and the U S chip industry regain its leadership position in the semiconductor business. If they don't the U S and Intel could fade to irrelevance. Let's look at this last point and make some comments on that. Here's a map of the South China sea in a way off in the Pacific we've superimposed a little pie chart. And we asked ourselves if you had a hundred points of strategic value to allocate, how much would you put in the semiconductor manufacturing bucket and how much would go to design? And our conclusion was 50, 50. Now it used to be because of Intel's dominance with x86 and its volume that the United States was number one in both strategic areas. But today that orange slice of the pie is dominated by TSMC. Thanks to Arm volumes. Now we've reported extensively on this and we don't want to dwell on it for too long but on all accounts cost, technology, volume. TSMC is the clear leader here. China's president Xi has a stated goal of unifying Taiwan by China's Centennial in 2049, will this tiny Island nation which dominates a critical part of the strategic semiconductor pie, go the way of Hong Kong and be subsumed into China. Well, military experts say it was very hard for China to take Taiwan by force, without heavy losses and some serious international repercussions. The US's military presence in the Philippines and Okinawa and Guam combined with support from Japan and South Korea would make it even more difficult. And certainly the Taiwanese people you would think would prefer their independence. But Taiwanese leadership, it ebbs and flows between those hardliners who really want to separate and want independence and those that are more sympathetic to China. Could China for example, use cyber warfare to over time control the narrative in Taiwan. Remember if you control the narrative you can control the meme. If you can crawl the meme you control the idea. If you control the idea, you control the belief system. And if you control the belief system you control the population without firing a shot. So is it possible that over the next 25 years China could weaponize propaganda and social media to reach its objectives with Taiwan? Maybe it's a long shot but if you're a senior strategist in the U S government would you want to leave that to chance? We don't think so. Let's park that for now and double click on one of our key findings. And that is the pace of semiconductor performance gains. As we first reported a few weeks ago. Well, Moore's law is moderating the outlook for cheap dense and efficient processing power has never been better. This slideshows two simple log lines. One is the traditional Moore's law curve. That's the one at the bottom. And the other is the current pace of system performance improvement that we're seeing measured in trillions of operations per second. Now, if you calculate the historical annual rate of processor performance improvement that we saw with x86, the math comes out to around 40% improvement per year. Now that rate is slowing. It's now down to around 30% annually. So we're not quite doubling every 24 months anymore with x86 and that's why people say Moore's law is dead. But if you look at the (indistinct) effects of packaging CPU's, GPU's, NPUs accelerators, DSPs and all the alternative processing power you can find in SOC system on chip and eventually system on package it's growing at more than a hundred percent per annum. And this means that the processing power is now quadrupling every 24 months. That's impressive. And the reason we're here is Arm. Arm has redefined the core process of model for a new era of computing. Arm made an announcement last week which really recycle some old content from last September, but it also put forth new proof points on adoption and performance. Arm laid out three components and its announcement. The first was Neoverse version one which is all about extending vector performance. This is critical for high performance computing HPC which at one point you thought that was a niche but it is the AI platform. AI workloads are not a niche. Second Arm announced the Neoverse and two platform based on the recently introduced Arm V9. We talked about that a lot in one of our earlier Breaking Analysis. This is going to performance boost of around 40%. Now the third was, it was called CMN-700 Arm maybe needs to work on some of its names, but Arm said this is the industry's most advanced mesh interconnect. This is the glue for the V1 and the N2 platforms. The importance is it allows for more efficient use and sharing of memory resources across components of the system package. We talked about this extensively in previous episodes the importance of that capability. Now let's share with you this wheel diagram underscores the completeness of the Arm platform. Arms approach is to enable flexibility across an open ecosystem, allowing for value add at many levels. Arm has built the architecture in design and allows an open ecosystem to provide the value added software. Now, very importantly, Arm has created the standards and specifications by which they can with certainty, certify that the Foundry can make the chips to a high quality standard, and importantly that all the applications are going to run properly. In other words, if you design an application, it will work across the ecosystem and maintain backwards compatibility with previous generations, like Intel has done for years but Arm as we'll see next is positioning not only for existing workloads but also the emerging high growth applications. To (indistinct) here's the Arm total available market as we see it, we think the end market spending value of just the chips going into these areas is $600 billion today. And it's going to grow to 1 trillion by 2030. In other words, we're allocating the value of the end market spend in these sectors to the marked up value of the Silicon as a percentage of the total spend. It's enormous. So the big areas are Hyperscale Clouds which we think is around 20% of this TAM and the HPC and AI workloads, which account for about 35% and the Edge will ultimately be the largest of all probably capturing 45%. And these are rough estimates and they'll ebb and flow and there's obviously some overlap but the bottom line is the market is huge and growing very rapidly. And you see that little red highlighted area that's enterprise IT. Traditional IT and that's the x86 market in context. So it's relatively small. What's happening is we're seeing a number of traditional IT vendors, packaging x86 boxes throwing them over the fence and saying, we're going after the Edge. And what they're doing is saying, okay the edge is this aggregation point for all these end point devices. We think the real opportunity at the Edge is for AI inferencing. That, that is where most of the activity and most of the spending is going to be. And we think Arm is going to dominate that market. And this brings up another challenge for Intel. So we've made the point a zillion times that PC volumes peaked in 2011. And we saw that as problematic for Intel for the cost reasons that we've beat into your head. And lo and behold PC volumes, they actually grew last year thanks to COVID and we'll continue to grow it seems for a year or so. Here's some ETR data that underscores that fact. This chart shows the net score. Remember that's spending momentum it's the breakdown for Dell's laptop business. The green means spending is accelerating and the red is decelerating. And the blue line is net score that spending momentum. And the trend is up and to the right now, as we've said this is great news for Dell and HP and Lenovo and Apple for its laptops, all the laptops sellers but it's not necessarily great news for Intel. Why? I mean, it's okay. But what it does is it shifts Intel's product mix toward lower margin, PC chips and it squeezes Intel's gross margins. So the CFO has to explain that margin contraction to wall street. Imagine that the business that got Intel to its monopoly status is growing faster than the high margin server business. And that's pulling margins down. So as we said, Intel is fighting a war on multiple fronts. It's battling AMD in the core x86 business both PCs and servers. It's watching Arm mop up in mobile. It's trying to figure out how to reinvent itself and change its culture to allow more flexibility into its designs. And it's spinning up a Foundry business to compete with TSMC. So it's got to fund all this while at the same time propping up at stock with buybacks Intel last summer announced that it was accelerating it's $10 billion stock buyback program, $10 billion. Buy stock back, or build a Foundry which do you think is more important for the future of Intel and the us semiconductor industry? So Intel, it's got to protect its past while building his future and placating wall street all at the same time. And here's where it gets even more dicey. Intel's got to protect its high-end x86 business. It is the cash cow and funds their operation. Who's Intel's biggest customer Dell, HP, Facebook, Google Amazon? Well, let's just say Amazon is a big customer. Can we agree on that? And we know AWS is biggest revenue generator is EC2. And EC2 was powered by microprocessors made from Intel and others. We found this slide in the Arm Neoverse deck and it caught our attention. The data comes from a data platform called lifter insights. The charts show, the rapid growth of AWS is graviton chips which are they're custom designed chips based on Arm of course. The blue is that graviton and the black vendor A presumably is Intel and the gray is assumed to be AMD. The eye popper is the 2020 pie chart. The instant deployments, nearly 50% are graviton. So if you're Pat Gelsinger, you better be all over AWS. You don't want to lose this customer and you're going to do everything in your power to keep them. But the trend is not your friend in this account. Now the story gets even gnarlier and here's the killer chart. It shows the ISV ecosystem platforms that run on graviton too, because AWS has such good engineering and controls its own stack. It can build Arm-based chips that run software designed to run on general purpose x86 systems. Yes, it's true. The ISV, they got to do some work, but large ISV they have a huge incentives because they want to ride the AWS wave. Certainly the user doesn't know or care but AWS cares because it's driving costs and energy consumption down and performance up. Lower cost, higher performance. Sounds like something Amazon wants to consistently deliver, right? And the ISV portfolio that runs on our base graviton and it's just going to continue to grow. And by the way, it's not just Amazon. It's Alibaba, it's Oracle, it's Marvell. It's 10 cents. The list keeps growing Arm, trotted out a number of names. And I would expect over time it's going to be Facebook and Google and Microsoft. If they're not, are you there? Now the last piece of the Arm architecture story that we want to share is the progress that they're making and compare that to x86. This chart shows how Arm is innovating and let's start with the first line under platform capabilities. Number of cores supported per die or, or system. Now die is what ends up as a chip on a small piece of Silicon. Think of the die as circuit diagram of the chip if you will, and these circuits they're fabricated on wafers using photo lithography. The wafers then cut up into many pieces each one, having a chip. Each of these pieces is the chip. And two chips make up a system. The key here is that Arm is quadrupling the number of cores instead of increasing thread counts. It's giving you cores. Cores are better than threads because threads are shared and cores are independent and much easier to virtualize. This is particularly important in situations where you want to be as efficient as possible sharing massive resources like the Cloud. Now, as you can see in the right hand side of the chart under the orange Arm is dramatically increasing the amount of capabilities compared to previous generations. And one of the other highlights to us is that last line that CCIX and CXL support again Arm maybe needs to name these better. These refer to Arms and memory sharing capabilities within and between processors. This allows CPU's GPU's NPS, et cetera to share resources very often efficiently especially compared to the way x86 works where everything is currently controlled by the x86 processor. CCIX and CXL support on the other hand will allow designers to program the system and share memory wherever they want within the system directly and not have to go through the overhead of a central processor, which owns the memory. So for example, if there's a CPU, GPU, NPU the CPU can say to the GPU, give me your results at a specified location and signal me when you're done. So when the GPU is finished calculating and sending the results, the GPU just signals the operation is complete. Versus having to ping the CPU constantly, which is overhead intensive. Now composability in that chart means the system it's a fixed. Rather you can programmatically change the characteristics of the system on the fly. For example, if the NPU is idle you can allocate more resources to other parts of the system. Now, Intel is doing this too in the future but we think Arm is way ahead. At least by two years this is also huge for Nvidia, which today relies on x86. A major problem for Nvidia has been coherent memory management because the utilization of its GPU is appallingly low and it can't be easily optimized. Last week, Nvidia announced it's intent to provide an AI capability for the data center without x86 I.e using Arm-based processors. So Nvidia another big Intel customer is also moving to Arm. And if it's successful acquiring Arm which is still a long shot this trend is only going to accelerate. But the bottom line is if Intel can't move fast enough to stem the momentum of Arm we believe Arm will capture 50% of the enterprise semiconductor spending by 2030. So how does Intel continue to lead? Well, it's not going to be easy. Remember we said, Intel, can't go it alone. And we posited that the company would have to initiate a joint venture structure. We propose a triumvirate of Intel, IBM with its power of 10 and memory aggregation and memory architecture And Samsung with its volume manufacturing expertise on the premise that it coveted in on US soil presence. Now upon further review we're not sure the Samsung is willing to give up and contribute its IP to this venture. It's put a lot of money and a lot of emphasis on infrastructure in South Korea. And furthermore, we're not convinced that Arvind Krishna who we believe ultimately made the call to Jettisons. Jettison IBM's micro electronics business wants to put his efforts back into manufacturing semi-conductors. So we have this conundrum. Intel is fighting AMD, which is already at seven nanometer. Intel has a fall behind in process manufacturing which is strategically important to the United States it's military and the nation's competitiveness. Intel's behind the curve on cost and architecture and is losing key customers in the most important market segments. And it's way behind on volume. The critical piece of the pie that nobody ever talks about. Intel must become more price and performance competitive with x86 and bring in new composable designs that maintain x86 competitive. And give the ability to allow customers and designers to add and customize GPU's, NPUs, accelerators et cetera. All while launching a successful Foundry business. So we think there's another possibility to this thought exercise. Apple is currently reliant on TSMC and is pushing them hard toward five nanometer, in fact sucking up a lot of that volume and TSMC is maybe not servicing some other customers as well as it's servicing Apple because it's a bit destructive, it is distracted and you have this chip shortage. So Apple because of its size gets the lion's share of the attention but Apple needs a trusted onshore supplier. Sure TSMC is adding manufacturing capacity in the US and Arizona. But back to our precarious scenario in the South China sea. Will the U S government and Apple sit back and hope for the best or will they hope for the best and plan for the worst? Let's face it. If China gains control of TSMC, it could block access to the latest and greatest process technology. Apple just announced that it's investing billions of dollars in semiconductor technology across the US. The US government is pressuring big tech. What about an Apple Intel joint venture? Apple brings the volume, it's Cloud, it's Cloud, sorry. It's money it's design leadership, all that to the table. And they could partner with Intel. It gives Intel the Foundry business and a guaranteed volume stream. And maybe the U S government gives Apple a little bit of breathing room and the whole big up big breakup, big tech narrative. And even though it's not necessarily specifically targeting Apple but maybe the US government needs to think twice before it attacks big tech and thinks about the long-term strategic ramifications. Wouldn't that be ironic? Apple dumps Intel in favor of Arm for the M1 and then incubates, and essentially saves Intel with a pipeline of Foundry business. Now back to IBM in this scenario, we've put a question mark on the slide because maybe IBM just gets in the way and why not? A nice clean partnership between Intel and Apple? Who knows? Maybe Gelsinger can even negotiate this without giving up any equity to Apple, but Apple could be a key ingredient to a cocktail of a new strategy under Pat Gelsinger leadership. Gobs of cash from the US and EU governments and volume from Apple. Wow, still a long shot, but one worth pursuing because as we've written, Intel is too strategic to fail. Okay, well, what do you think? You can DM me @dvellante or email me at david.vellante@siliconangle.com or comment on my LinkedIn post. Remember, these episodes are all available as podcasts so please subscribe wherever you listen. I publish weekly on wikibon.com and siliconangle.com. And don't forget to check out etr.plus for all the survey analysis. And I want to thank my colleague, David Floyer for his collaboration on this and other related episodes. This is Dave Vellante for theCUBE insights powered by ETR. Thanks for watching, be well, and we'll see you next time. (upbeat music)

>> Announcer: From the Cube's studios in Palo Alto in Boston, bringing you data-driven insights from The Cube and ETR. This is "Breaking Analysis" with Dave Vellante. >> Exactly one week after Pat Gelsinger's announcement of his plans to reinvent Intel. Arm announced version nine of its architecture and laid out its vision for the next decade. We believe this vision is extremely strong as it combines an end-to-end capability from Edge to Cloud, to the data center, to the home and everything in between. Arms aspirations are ambitious and powerful. Leveraging its business model, ecosystem and software compatibility with previous generations. Hello every one and welcome to this week's Wikibon Cube Insights powered by ETR. And this breaking analysis will explain why we think this announcement is so important and what it means for Intel and the broader technology landscape. We'll also share with you some feedback that we received from the Cube Community on last week's episode and a little inside baseball on how Intel, IBM, Samsung, TSMC and the U.S. government might be thinking about the shifting landscape of semiconductor technology. Now, there were two notable announcements this week that were directly related to Intel's announcement of March 23rd. The Armv9 news and TSMC's plans to invest a $100 billion in chip manufacturing and development over the next three years. That is a big number. It appears to tramp Intel's plan $20 billion investment to launch two new fabs in the U.S. starting in 2024. You may remember back in 2019, Samsung pledged to invest a $116 billion to diversify its production beyond memory trip, memory chips. Why are all these companies getting so aggressive? And won't this cause a glut in chips? Well, first, China looms large and aims to dominate its local markets, which in turn is going to confer advantages globally. The second, there's a huge chip shortage right now. And the belief is that it's going to continue through the decade and possibly beyond. We are seeing a new inflection point in the demand as we discussed last week. Stemming from digital, IOT, cloud, autos in new use cases in the home as so well presented by Sarjeet Johal in our community. As to the glut, these manufacturers believe that demand will outstrip supply indefinitely. And I understand that a lack of manufacturing capacity is actually more deadly than an oversupply. Look, if there's a glut, manufacturers can cut production and take the financial hit. Whereas capacity constraints mean you can miss entire cycles of growth and really miss out on the demand and the cost reductions. So, all these manufacturers are going for it. Now let's talk about Arm, its approach and the announcements that it made this week. Now last week, we talked about how Pat Gelsinger his vision of a system on package was an attempt to leapfrog system on chip SOC, while Arm is taking a similar system approach. But in our view, it's even broader than the vision laid out by Pat at Intel. Arm is targeting a wide variety of use cases that are shown here. Arm's fundamental philosophy is that the future will require highly specialized chips and Intel as you recall from Pat's announcement, would agree. But Arm historically takes an ecosystem approach that is different from Intel's model. Arm is all about enabling the production of specialized chips to really fit a specific application. For example, think about the amount of AI going on iPhones. They move if I remember from fingerprint to face recognition. This requires specialized neural processing units, NPUs that are designed by Apple for that particular use case. Arm is facilitating the creation of these specialized chips to be designed and produced by the ecosystem. Intel on the other hand has historically taken a one size fits all approach. Built around the x86. The Intel's design has always been about improving the processor. For example, in terms of speed, density, adding vector processing to accommodate AI, et cetera. And Intel does all the design and the manufacturing in any specialization for the ecosystem is done by Intel. Much of the value, that's added from the ecosystem is frankly been bending metal or adding displays or other features at the margin. But, the advantage is that the x86 architecture is well understood. It's consistent, reliable, and let's face it. Most enterprise software runs on x86. So, but very, very different models historically, which we heard from Gelsinger last week they're going to change with a new trusted foundry strategy. Now let's go through an example that might help explain the power of Arm's model. Let's say, your AWS and you're doing graviton. Designing graviton and graviton2. Or Apple, designing the M1 chip, or Tesla designing its own chip, or any other company in in any one of these use cases that are shown here. Tesla is a really good example. In order to optimize for video processing, Tesla needed to add specialized code firmware in the NPU for it's specific use case within autos. It was happy to take off the shelf CPU or GPU or whatever, and leverage Arm's standards there. And then it added its own value in the NPU. So the advantage of this model is Tesla could go from tape out in less or, or, or or in less than a year versus get the tape out in less than a year versus what would normally take many years. Arm is, think of Arm is like customize a Lego blocks that enable unique value add by the ecosystem with a much faster time to market. So like I say, the Tesla goes from logical tape out if you will, to Samsung and then says, okay run this against your manufacturing process. And it should all work as advertised by Arm. Tesla, interestingly, just as an aside chose the 14 nanometer process to keep its costs down. It didn't need the latest and greatest density. Okay, so you can see big difference in philosophies historically between Arm and Intel. And you can see Intel vectoring toward the Arm model based on what Gelsinger said last week for its foundry business. Essentially it has to. Now, Arm announced a new Arm architecture, Armv9. v9 is backwards compatible with previous generations. Perhaps Arm learned from Intel's failed, Itanium effort for those remember that word. Had no backward compatibility and it really floundered. As well, Arm adds some additional capabilities. And today we're going to focus on the two areas that have highlighted, machine learning piece and security. I'll take note of the call out, 300 billion chips. That's Arm's vision. That's a lot. And we've said, before, Arm's way for volumes are 10X those of x86. Volume, we sound like a broken record. Volume equals cost reduction. We'll come back to that a little bit later. Now let's have a word on AI and machine learning. Arm is betting on AI and ML. Big as are many others. And this chart really shows why, it's a graphic that shows ETR data and spending momentum and pervasiveness in the dataset across all the different sectors that ETR tracks within its taxonomy. Note that ML/AI gets the top spot on the vertical axis, which represents net score. That's a measure of spending momentum or spending velocity. The horizontal axis is market share presence in the dataset. And we give this sector four stars to signify it's consistent lead in the data. So pretty reasonable bet by Arm. But the other area that we're going to talk about is security. And its vision day, Arm talked about confidential compute architecture and these things called realms. Note in the left-hand side, showing data traveling all over the different use cases and around the world and the call-out from the CISO below, it's a large public airline CISO that spoke at an ETR Venn round table. And this individual noted that the shifting end points increase the threat vectors. We all know that. Arm said something that really resonated. Specifically, they said today, there's far too much trust on the OS and the hypervisor that are running these applications. And their broad access to data is a weakness. Arm's concept of realms as shown in the right-hand side, underscores the company strategy to remove the assumption that privileged software. Like the hypervisor needs to be able to see the data. So by creating realms, in a virtualized multi-tenant environment, data can be more protected from memory leaks which of course is a major opportunity for hackers that they exploit. So it's a nice concept in a way for the system to isolate attendance data from other users. Okay, we want, we want to share some feedback that we got last week from the community on our analysis of Intel. A tech exec from city pointed out that, Intel really didn't miss a mobile, as we said, it really missed smartphones. In fact, whell, this is a kind of a minor distinction, it's important to recognize we think. Because Intel facilitated WIFI with Centrino, under the direction of Paul Alini. Who by the way, was not an engineer. I think he was the first non-engineer to be the CEO of Intel. He was a marketing person by background. Ironically, Intel's work in wifi connectivity enabled, actually enabled the smartphone revolution. And maybe that makes the smartphone missed by Intel all that more egregious, I don't know. Now the other piece of feedback we received related to our IBM scenario and our three-way joint venture prediction bringing together Intel, IBM, and Samsung in a triumvirate where Intel brings the foundry and it's process manufacturing. IBM brings its dis-aggregated memory technology and Samsung brings its its volume and its knowledge of of volume down the learning curve. Let's start with IBM. Remember we said that IBM with power 10 has the best technology in terms of this notion of dis-aggregating compute from memory and sharing memory in a pool across different processor types. So a few things in this regard, IBM when it restructured its micro electronics business under Ginni Rometty, catalyzed the partnership with global foundries and you know, this picture in the upper right it shows the global foundries facility outside of Albany, New York in Malta. And the partnership included AMD and Samsung. But we believe that global foundries is backed away from some of its contractual commitments with IBM causing a bit of a rift between the companies and leaving a hole in your original strategy. And evidently AMD hasn't really leaned in to move the needle in any way and so the New York foundry, is it a bit of a state of limbo with respect to its original vision. Now, well, Arvind Krishna was the face of the Intel announcement. It clearly has deep knowledge of IBM semiconductor strategy. Dario Gill, we think is a key player in the mix. He's the senior vice president director of IBM research. And it is in a position to affect some knowledge sharing and maybe even knowledge transfer with Intel possibly as it relates to disaggregated architecture. His questions remain as to how open IBM will be. And how protected it will be with its IP. It's got, as we said, last week, it's got to have an incentive to do so. Now why would IBM do that? Well, it wants to compete more effectively with VMware who has done a great job leveraging x86 and that's the biggest competitor in threat to open shift. So Arvind needs Intel chips to really execute on IBM's cloud strategy. Because almost all of IBM's customers are running apps on x86. So IBM's cloud and hybrid cloud. Strategy really need to leverage that Intel partnership. Now Intel for its part has great FinFET technology. FinFET is a tactic goes beyond CMOs. You all mainframes might remember when IBM burned the boat on ECL, Emitter-coupled Logic. And then moved to CMOs for its mainframes. Well, this is the next gen beyond, and it could give Intel a leg up on AMD's chiplet intellectual properties. Especially as it relates to latency. And there could be some benefits there for IBM. So maybe there's a quid pro quo going on. Now, where it really gets interesting is New York Senator, Chuck Schumer, is keen on building up an alternative to Silicon Valley in New York now it is Silicon Alley. So it's possible that Intel, who by the way has really good process technology. This is an aside, it really allowed TSMC to run the table with the whole seven nanometers versus 10 minute nanometer narrative. TSMC was at seven nanometer. Intel was at 10 nanometer. And really, we've said in the past that Intel's 10 nanometer tech is pretty close to TSMC seven. So Intel's ahead in that regard, even though in terms of, you know, the intervener thickness density, it's it's not, you know. These are sort of games that the semiconductor companies play, but you know it's possible that Intel with the U.S. government and IBM and Samsung could make a play for that New York foundry as part of Intel's trusted foundry strategy and kind of reshuffle that deck in Albany. Sounds like a "Game of Thrones," doesn't it? By the way, TSMC has been so consumed servicing Apple for five nanometer and eventually four nanometer that it's dropped the ball on some of its other's customers, namely Nvidia. And remember, a long-term competitiveness and cost reductions, they all come down to volume. And we think that Intel can't get to volume without an Arm strategy. Okay, so maybe the JV, the Joint Venture that we talked about, maybe we're out on a limb there and that's a stretch. And perhaps Samsung's not willing to play ball, given it's made huge investments in fabs and infrastructure and other resources, locally, but we think it's still viable scenario because we think Samsung definitely would covet a presence in the United States. No good to do that directly but maybe a partnership makes more sense in terms of gaining ground on TSMC. But anyway, let's say Intel can become a trusted foundry with the help of IBM and the U.S. government. Maybe then it could compete on volume. Well, how would that work? Well, let's say Nvidia, let's say they're not too happy with TSMC. Maybe with entertain Intel as a second source. Would that do it? In and of itself, no. But what about AWS and Google and Facebook? Maybe this is a way to placate the U.S. government and call off the antitrust dogs. Hey, we'll give Intel Foundry our business to secure America's semiconductor leadership and future and pay U.S. government. Why don't you chill out, back off a little bit. Microsoft even though, you know, it's not getting as much scrutiny from the U.S. government, it's anti trustee is maybe perhaps are behind it, who knows. But I think Microsoft would be happy to play ball as well. Now, would this give Intel a competitive volume posture? Yes, we think it would, for sure. If it can gain the trust of these companies and the volume we think would be there. But as we've said, currently, this is a very, very long shot because of the, the, the new strategy, the distance the difference in the Foundry business all those challenges that we laid out last week, it's going to take years to play out. But the dots are starting to connect in this scenario and the stakes are exceedingly high hence the importance of the U.S. government. Okay, that's it for now. Thanks to the community for your comments and insights. And thanks again to David Floyer whose analysis around Arm and semiconductors. And this work that he's done for the past decade is of tremendous help. Remember I publish each week on wikibon.com and siliconangle.com. And these episodes are all available as podcasts, just search for braking analysis podcast and you can always connect on Twitter. You can hit the chat right here or this live event or email me at david.vellante@siliconangle.com. Look, I always appreciate the comments on LinkedIn and Clubhouse. You can follow me so you're notified when we start a room and riff on these topics as well as others. And don't forget to check out etr.plus where all the survey data. This is Dave Vellante for the Cube Insights powered by ETR. Be well, and we'll see you next time. (cheerful music) (cheerful music)

>> From the Cube's studios in Palo Alto in Boston, bringing you data-driven insights from The Cube and ETR. This is "Breaking Analysis" with Dave Vellante. >> Exactly one week after Pat Gelsinger's announcement of his plans to reinvent Intel. Arm announced version nine of its architecture and laid out its vision for the next decade. We believe this vision is extremely strong as it combines an end-to-end capability from Edge to Cloud, to the data center, to the home and everything in between. Arms aspirations are ambitious and powerful. Leveraging its business model, ecosystem and software compatibility with previous generations. Hello every one and welcome to this week's Wikibon Cube Insights powered by ETR. And this breaking analysis will explain why we think this announcement is so important and what it means for Intel and the broader technology landscape. We'll also share with you some feedback that we received from the Cube Community on last week's episode and a little inside baseball on how Intel, IBM, Samsung, TSMC and the U.S. government might be thinking about the shifting landscape of semiconductor technology. Now, there were two notable announcements this week that were directly related to Intel's announcement of March 23rd. The Armv9 news and TSMC's plans to invest a $100 billion in chip manufacturing and development over the next three years. That is a big number. It appears to tramp Intel's plan $20 billion investment to launch two new fabs in the U.S. starting in 2024. You may remember back in 2019, Samsung pledged to invest a $116 billion to diversify its production beyond memory trip, memory chips. Why are all these companies getting so aggressive? And won't this cause a glut in chips? Well, first, China looms large and aims to dominate its local markets, which in turn is going to confer advantages globally. The second, there's a huge chip shortage right now. And the belief is that it's going to continue through the decade and possibly beyond. We are seeing a new inflection point in the demand as we discussed last week. Stemming from digital, IOT, cloud, autos in new use cases in the home as so well presented by Sarjeet Johal in our community. As to the glut, these manufacturers believe that demand will outstrip supply indefinitely. And I understand that a lack of manufacturing capacity is actually more deadly than an oversupply. Look, if there's a glut, manufacturers can cut production and take the financial hit. Whereas capacity constraints mean you can miss entire cycles of growth and really miss out on the demand and the cost reductions. So, all these manufacturers are going for it. Now let's talk about Arm, its approach and the announcements that it made this week. Now last week, we talked about how Pat Gelsinger his vision of a system on package was an attempt to leapfrog system on chip SOC, while Arm is taking a similar system approach. But in our view, it's even broader than the vision laid out by Pat at Intel. Arm is targeting a wide variety of use cases that are shown here. Arm's fundamental philosophy is that the future will require highly specialized chips and Intel as you recall from Pat's announcement, would agree. But Arm historically takes an ecosystem approach that is different from Intel's model. Arm is all about enabling the production of specialized chips to really fit a specific application. For example, think about the amount of AI going on iPhones. They move if I remember from fingerprint to face recognition. This requires specialized neural processing units, NPUs that are designed by Apple for that particular use case. Arm is facilitating the creation of these specialized chips to be designed and produced by the ecosystem. Intel on the other hand has historically taken a one size fits all approach. Built around the x86. The Intel's design has always been about improving the processor. For example, in terms of speed, density, adding vector processing to accommodate AI, et cetera. And Intel does all the design and the manufacturing in any specialization for the ecosystem is done by Intel. Much of the value, that's added from the ecosystem is frankly been bending metal or adding displays or other features at the margin. But, the advantage is that the x86 architecture is well understood. It's consistent, reliable, and let's face it. Most enterprise software runs on x86. So, but very, very different models historically, which we heard from Gelsinger last week they're going to change with a new trusted foundry strategy. Now let's go through an example that might help explain the power of Arm's model. Let's say, your AWS and you're doing graviton. Designing graviton and graviton2. Or Apple, designing the M1 chip, or Tesla designing its own chip, or any other company in in any one of these use cases that are shown here. Tesla is a really good example. In order to optimize for video processing, Tesla needed to add specialized code firmware in the NPU for it's specific use case within autos. It was happy to take off the shelf CPU or GPU or whatever, and leverage Arm's standards there. And then it added its own value in the NPU. So the advantage of this model is Tesla could go from tape out in less or, or, or or in less than a year versus get the tape out in less than a year versus what would normally take many years. Arm is, think of Arm is like customize a Lego blocks that enable unique value add by the ecosystem with a much faster time to market. So like I say, the Tesla goes from logical tape out if you will, to Samsung and then says, okay run this against your manufacturing process. And it should all work as advertised by Arm. Tesla, interestingly, just as an aside chose the 14 nanometer process to keep its costs down. It didn't need the latest and greatest density. Okay, so you can see big difference in philosophies historically between Arm and Intel. And you can see Intel vectoring toward the Arm model based on what Gelsinger said last week for its foundry business. Essentially it has to. Now, Arm announced a new Arm architecture, Armv9. v9 is backwards compatible with previous generations. Perhaps Arm learned from Intel's failed, Itanium effort for those remember that word. Had no backward compatibility and it really floundered. As well, Arm adds some additional capabilities. And today we're going to focus on the two areas that have highlighted, machine learning piece and security. I'll take note of the call out, 300 billion chips. That's Arm's vision. That's a lot. And we've said, before, Arm's way for volumes are 10X those of x86. Volume, we sound like a broken record. Volume equals cost reduction. We'll come back to that a little bit later. Now let's have a word on AI and machine learning. Arm is betting on AI and ML. Big as are many others. And this chart really shows why, it's a graphic that shows ETR data and spending momentum and pervasiveness in the dataset across all the different sectors that ETR tracks within its taxonomy. Note that ML/AI gets the top spot on the vertical axis, which represents net score. That's a measure of spending momentum or spending velocity. The horizontal axis is market share presence in the dataset. And we give this sector four stars to signify it's consistent lead in the data. So pretty reasonable bet by Arm. But the other area that we're going to talk about is security. And its vision day, Arm talked about confidential compute architecture and these things called realms. Note in the left-hand side, showing data traveling all over the different use cases and around the world and the call-out from the CISO below, it's a large public airline CISO that spoke at an ETR Venn round table. And this individual noted that the shifting end points increase the threat vectors. We all know that. Arm said something that really resonated. Specifically, they said today, there's far too much trust on the OS and the hypervisor that are running these applications. And their broad access to data is a weakness. Arm's concept of realms as shown in the right-hand side, underscores the company strategy to remove the assumption that privileged software. Like the hypervisor needs to be able to see the data. So by creating realms, in a virtualized multi-tenant environment, data can be more protected from memory leaks which of course is a major opportunity for hackers that they exploit. So it's a nice concept in a way for the system to isolate attendance data from other users. Okay, we want, we want to share some feedback that we got last week from the community on our analysis of Intel. A tech exec from city pointed out that, Intel really didn't miss a mobile, as we said, it really missed smartphones. In fact, whell, this is a kind of a minor distinction, it's important to recognize we think. Because Intel facilitated WIFI with Centrino, under the direction of Paul Alini. Who by the way, was not an engineer. I think he was the first non-engineer to be the CEO of Intel. He was a marketing person by background. Ironically, Intel's work in wifi connectivity enabled, actually enabled the smartphone revolution. And maybe that makes the smartphone missed by Intel all that more egregious, I don't know. Now the other piece of feedback we received related to our IBM scenario and our three-way joint venture prediction bringing together Intel, IBM, and Samsung in a triumvirate where Intel brings the foundry and it's process manufacturing. IBM brings its dis-aggregated memory technology and Samsung brings its its volume and its knowledge of of volume down the learning curve. Let's start with IBM. Remember we said that IBM with power 10 has the best technology in terms of this notion of dis-aggregating compute from memory and sharing memory in a pool across different processor types. So a few things in this regard, IBM when it restructured its micro electronics business under Ginni Rometty, catalyzed the partnership with global foundries and you know, this picture in the upper right it shows the global foundries facility outside of Albany, New York in Malta. And the partnership included AMD and Samsung. But we believe that global foundries is backed away from some of its contractual commitments with IBM causing a bit of a rift between the companies and leaving a hole in your original strategy. And evidently AMD hasn't really leaned in to move the needle in any way and so the New York foundry, is it a bit of a state of limbo with respect to its original vision. Now, well, Arvind Krishna was the face of the Intel announcement. It clearly has deep knowledge of IBM semiconductor strategy. Dario Gill, we think is a key player in the mix. He's the senior vice president director of IBM research. And it is in a position to affect some knowledge sharing and maybe even knowledge transfer with Intel possibly as it relates to disaggregated architecture. His questions remain as to how open IBM will be. And how protected it will be with its IP. It's got, as we said, last week, it's got to have an incentive to do so. Now why would IBM do that? Well, it wants to compete more effectively with VMware who has done a great job leveraging x86 and that's the biggest competitor in threat to open shift. So Arvind needs Intel chips to really execute on IBM's cloud strategy. Because almost all of IBM's customers are running apps on x86. So IBM's cloud and hybrid cloud. Strategy really need to leverage that Intel partnership. Now Intel for its part has great FinFET technology. FinFET is a tactic goes beyond CMOs. You all mainframes might remember when IBM burned the boat on ECL, Emitter-coupled Logic. And then moved to CMOs for its mainframes. Well, this is the next gen beyond, and it could give Intel a leg up on AMD's chiplet intellectual properties. Especially as it relates to latency. And there could be some benefits there for IBM. So maybe there's a quid pro quo going on. Now, where it really gets interesting is New York Senator, Chuck Schumer, is keen on building up an alternative to Silicon Valley in New York now it is Silicon Alley. So it's possible that Intel, who by the way has really good process technology. This is an aside, it really allowed TSMC to run the table with the whole seven nanometers versus 10 minute nanometer narrative. TSMC was at seven nanometer. Intel was at 10 nanometer. And really, we've said in the past that Intel's 10 nanometer tech is pretty close to TSMC seven. So Intel's ahead in that regard, even though in terms of, you know, the intervener thickness density, it's it's not, you know. These are sort of games that the semiconductor companies play, but you know it's possible that Intel with the U.S. government and IBM and Samsung could make a play for that New York foundry as part of Intel's trusted foundry strategy and kind of reshuffle that deck in Albany. Sounds like a "Game of Thrones," doesn't it? By the way, TSMC has been so consumed servicing Apple for five nanometer and eventually four nanometer that it's dropped the ball on some of its other's customers, namely Nvidia. And remember, a long-term competitiveness and cost reductions, they all come down to volume. And we think that Intel can't get to volume without an Arm strategy. Okay, so maybe the JV, the Joint Venture that we talked about, maybe we're out on a limb there and that's a stretch. And perhaps Samsung's not willing to play ball, given it's made huge investments in fabs and infrastructure and other resources, locally, but we think it's still viable scenario because we think Samsung definitely would covet a presence in the United States. No good to do that directly but maybe a partnership makes more sense in terms of gaining ground on TSMC. But anyway, let's say Intel can become a trusted foundry with the help of IBM and the U.S. government. Maybe then it could compete on volume. Well, how would that work? Well, let's say Nvidia, let's say they're not too happy with TSMC. Maybe with entertain Intel as a second source. Would that do it? In and of itself, no. But what about AWS and Google and Facebook? Maybe this is a way to placate the U.S. government and call off the antitrust dogs. Hey, we'll give Intel Foundry our business to secure America's semiconductor leadership and future and pay U.S. government. Why don't you chill out, back off a little bit. Microsoft even though, you know, it's not getting as much scrutiny from the U.S. government, it's anti trustee is maybe perhaps are behind it, who knows. But I think Microsoft would be happy to play ball as well. Now, would this give Intel a competitive volume posture? Yes, we think it would, for sure. If it can gain the trust of these companies and the volume we think would be there. But as we've said, currently, this is a very, very long shot because of the, the, the new strategy, the distance the difference in the Foundry business all those challenges that we laid out last week, it's going to take years to play out. But the dots are starting to connect in this scenario and the stakes are exceedingly high hence the importance of the U.S. government. Okay, that's it for now. Thanks to the community for your comments and insights. And thanks again to David Floyer whose analysis around Arm and semiconductors. And this work that he's done for the past decade is of tremendous help. Remember I publish each week on wikibon.com and siliconangle.com. And these episodes are all available as podcasts, just search for braking analysis podcast and you can always connect on Twitter. You can hit the chat right here or this live event or email me at david.vellante@siliconangle.com. Look, I always appreciate the comments on LinkedIn and Clubhouse. You can follow me so you're notified when we start a room and riff on these topics as well as others. And don't forget to check out etr.plus where all the survey data. This is Dave Vellante for the Cube Insights powered by ETR. Be well, and we'll see you next time. (cheerful music) (cheerful music)

>> Announcer: Live from San Diego, California, it's theCUBE, covering Cisco Live US 2019. Brought to you by Cisco and its ecosystem partners. >> Hey, welcome back to theCUBE's coverage. Day two of Cisco Live from San Diego. I'm Lisa Martin. Dave Vellante is my esteemed cohost. And we're pleased to welcome one of Cisco and Cohesity's customers from Quantium, Craig Tayler, Executive Manager at Business Technology and Platforms. Craig, welcome to theCUBE. >> Thank you. It's great to be here. >> Great seeing you. >> So, we love talking with customers. We love talking about data. Tell our audience a little bit about Quantium. I know you guys have expertise in two core domains, data science, AI, two really sexy topics that we talk about on theCUBE at every event. But give our audience a little bit of the flavor of who you guys are. >> Yeah, so Quantium's been around for 16 years, founded and headquartered in Sydney, Australia. And really, they are like you mentioned, the two main aspects of our business. So when you think of data science more as human intelligence, and then the AI side is how we can augment that with computers as much as possible. So, on the human intelligence side, we're looking at things like data curation, how can we work with a company to understand their data, perhaps monetize their data. And then on the AI side, we're more looking at things like, how do we do predictive modeling or predictive analytics, and how can we get that in front of maybe say a supply chain solution, or working with grocery stores around actually predicting how much fresh food they need. So we think of these things like, wouldn't it be great if we had a better idea of how much we needed? Less waste, less cost, everything else. So that's really how we kind of split the two sides of the company. >> You guys provide this as a service, is that right? >> Yeah, that's correct. So, with those two arms we focus on, whether it be a consulting engagement with a company, where that's a one-off, or an ongoing thing, and we have a range of products that we sell as well, with the idea that any of these companies, whether it be a bank or a retailer, can plug these tools into their existing solutions to give them some real data, some real impact, as opposed to the thoughts, or the feels, or the gut instincts, that we've been working on for so long, all right. >> So paint a picture of your environment. I mean, what does it look like? Cloud, not cloud, apps. >> Yeah. It's certainly a variety. So, if we think, on-premise is really where we do a lot of our work. And this is around, a lot of companies still feel a little bit sensitive around where their data is going, and they like that security of knowing physically where it's located. So on-premise stack we have a bit over 300 servers running a Hadoop cluster, that's where we do the majority of our AI work. And then what we augment that with is, and what we use the cloud a lot for, as we're doing work globally, we're doing a lot of work in North America, it's not feasible to bring all that data back to Sydney, process it, and send it all back, so then really, what we use the cloud for is to take our technology, take our analytics, to the data. So if we're working with a customer, West Coast, East Coast, and they're in Azure we'll deploy in Azure. If they're in GCP, we can deploy in GCP. And that's really how we use cloud is to offer our service, as much as we can, around the world. >> So you said, you got 300 servers, did I hear you right, in a Hadoop cluster, right? >> Yeah, correct. >> What's your distribution? >> We use MapR at the moment. I know there's certainly been a bit of news about them. >> I was going to ask you, well, all three of them. (Craig laughs) Well I guess Hortonworks now folded in, but-- >> Yeah, correct. Cloud has certainly shaken up that marketplace quite a bit. >> Dave: I'm sure, yeah. >> It's been something that we've been keeping a close eye on for quite a while. What's the future there? Is it another distribution? Will someone pick up MapR? Will they get through it? So it is interesting, it's certainly a challenge, but when you're playing in a more emerging space, these are some of the risks you take, but we've always felt that they're worth it. We've had many great years of that and we don't really see any reason that we're not going to get more great years out of that Hadoop environment. >> Yeah, I mean, the IP's going to survive, and it sounds like you guys were early on into it, you got a lot of value out of it. If you had to do it again, you'd probably do the same thing. >> Yeah, that's certainly true. I think, what we've built, there are cloud options on the hyperscale providers that you can use, but look, out of the box, they're not really capable of what we were trying to do. So if we had our time again, we probably would still build the same solution. We'd build it a little bit quicker, obviously, because it's a little bit more in the marketplace, it's not such an emerging technology, but I think we would do the same thing again. >> Dave: Right, and MapR was always ahead of the game with their approach. >> Correct. >> So, obvious question is, how do you protect that data? You're a Cohesity customer, but talk about the data protection aspect of that. >> Yeah, so this is where Cohesity really had a lot of synergies with us, was centralizing a whole raft of datasets into one location. And that's what we do with Hadoop. We take a lot of different datasets and we put it all there. We aggregate it there. So on the secondary data side we had the same problem. Silo datasets all over the environment. Things like, the protection aspect, the compliance aspect, it's not impossible, but it's very hard to manage. So what we really wanted to do was, what do we do with the data when we're not using it anymore? So we might still want to use it in the future, we have to hold onto it. And we needed a better solution for how we manage that. So, having Cohesity, which, to us, being a hyper-converged solution, it's very similar to how Hadoop works. It's a lot of data, a lot of compute, and that's how you deploy it. So we found that actually having all of that, the secondary kind of data that we still needed to keep, combined into one location, for us, it matched on a technology level. And then being able to have all that data in one space, you can do some analytics on it. How often are we using it? What is the data? How many copies of it do we have? So there are a lot of synergies from the data science aspect, and also the technology aspect, which has worked really well for us. >> So what was profound about Hadoop was the idea of bringing five megabytes of code to a petabyte of data, leaving the data where it is, highly distributed environment, obviously challenge protecting that. Help us understand. You're saying that Cohesity architecture is well-suited for that type of environment? >> Yeah, it certainly is. I mean, it augments it quite well, is how I'd say. So at the moment we keep the environments quite separate, but the way we manage them is very similar. So there's great audit login, great security controls that you can place on both environments. So the way that we structure Hadoop with role-based access, who can perform what action, the same thing applies in Cohesity. So now we sort of see that the way that we manage primary is the same way that we can manage secondary. So, it's easier for the staff, when we come to things like compliance or legislation, or, we value data, it's our lifeblood, so we have to be very careful with it. So if we want to do any audit reports or anything like this, we can do 'em the same way. Who has access, what they've done. >> So, Hadoop's been around a lot longer than Cohesity. So, what were you doing before Cohesity, and what were some of those challenges? >> Yeah, what we were doing was a lot. And that was really the only option we had. So we had four or five different solutions that had kind of organically grown over time, whether that was some secondary storage, multiple different backup products, throw a couple of NASes in there, just for good measure. >> Just in case. >> Yeah, just in case. And then really, what we were doing, and how we managed that, is we had close to one FTE dedicated to that environment. It's not great for that person, it's not really the funnest of jobs. And then obviously, the management of it becomes quite difficult. And so that was how we did it. We got by. But it certainly could have been a lot better. >> So that was one FTE dedicated to the backup? >> Just dedicated to the backup. >> Dedicated to data protection? >> Yeah, yeah, yeah. >> Okay. So then you bring in Cohesity, you do the business case, say oh wow, and part of that was we can free up this person to do other things, I presume, right? >> Yeah, yeah, definitely. That was actually certainly one of the key business cases. So, IT is a cost center. We certainly, we work for the business, we support the business, there's no doubt about that. But we are, at the end of the day, a cost center. So getting extra headcount or getting equipment, there has to be a really good business case behind this. And so we found that, so we freed up about 80% of time that we're spending on this, and so actually the two biggest things that we've seen as a benefit of that, staff engagement is actually a lot higher, right, because we don't have someone just dedicated to turning the screws on this old solution all the time. So they get to spend more time on newer tech, which is great, and obviously, if their time's freed-up, value-added activities. What can they be focusing on. >> So how's it work? Is it a self-service platform now? Or somebody, this individual, sets the overall policy, and then people apply it as they see fit, the application guys? >> Yeah, so we have a range. So our infrastructure team holds the overall management of it, and we have that one person who kind of, say rules it, so to speak, but the way we've done with this role-based access, we can give the service desk permission to search backups, so if someone needs a restore, or maybe legal and the compliance team want to know who was accessing what, we can give a lot more self-service to these teams. So the service desk, if they're dealing with an end-user that wants a restore, within 30 seconds, we can tell them, okay, here is the backup we have. Here are the dates that we have it. Which one do you want? Previously, that's a week-and-a-half turnaround. Escalate a ticket, spend three days doing restores and searching through it-- >> Dave: Working weekends. >> Right. Working weekends, and if you even do have the data. Typically what happens, by the time you've restored it, the customer has said, "Look, well I don't need it anymore." It's too late. >> So let's talk about some of the customer benefits. You've only deployed this about six months ago. >> Yeah, correct. >> You talked about a number of the benefits from a time perspective, allowing valuable FTEs to not only be reallocated for other projects, but also from a job satisfaction perspective-- >> Yeah definitely. >> Which is all the way up to the top end of the business. But in terms of helping customers extract more value from their data, monetizing their data, that example that you just gave of where it took too long to recover data before and the customer, the time has passed, what are some of the impacts that your customers are achieving so far? >> Yeah, so I think the biggest area of this that I think we actually look at the most, is that, like I mentioned earlier, we will do, say a piece of work with a customer, and then we'll keep that data. We might need it in the future, but there's not an ongoing engagement. What are we going to do with that? And so we tend to sort of put it aside. If a customer wants any further work done, or perhaps they want to come back with clarification, or anything like this, it then takes us quite a bit of time to find that data, get it back into production, get it back to the state that we were previously using it in. So, one of the biggest things that we've seen is actually now having all of that data always available on Cohesity, and being a hyper-converged platform, it has a lot of compute on it as well, so we can actually run some simple analytics on that data. So if a customer comes back and wants to query just a couple of small items, or perhaps we want to recheck a couple of things, super easy now for us to do that. And so we talk about time to market, or anything like this, is really big for us, and customer responsiveness. So if a customer is asking us a question and the answer is a five-minute answer, they don't want it in four days. So if we can turn that answer around a lot quicker, then obviously everyone's happier. >> And you've already been able to start achieving that? >> Yeah, we have been able to start achieving that already. Whether that be from a customer perspective, and certainly from a compliance perspective, if we have a customer that actually wants to know, where is our data, who has accessed it, everything else, we can turn that around straightaway. So obviously, when we talk about customer satisfaction, or that relationship, they feel a lot more comfortable that we're doing the right thing with their data, and that is obviously hugely invaluable for us as a business. >> And just another infrastructure question. These 300 servers, it's mostly UCS, is that right? Or a lot of UCS? >> Yeah, so we use Cisco for pretty much everything. We certainly are heavy, heavy users of UCS, and so, when we are looking at, I mean, implementing anything to the environment, you don't want it to be a lengthy process, because your return on investment is going to be hit. If you're spending three months installing something, you've already paid, you're getting no benefit out if it, it's now three months old before it's even implemented. So having this kit on Cisco UCS has been great for us, and we were having issues with our previous backup solution and we actually managed to implement the Cohesity solution on UCS and start using it before repairing our existing solution. So it's phenomenal how quickly, through UCS, we were able to bring it in. >> Dave: What kind of issues were you having? Just integration issues, or? >> Yeah, so with our previous backup solution, being a fragmented solution that we had stitched together, we had something as simple as a RAID controller failure caused a whole bunch of data corruption across multiple areas, and so, how the NAS saw the data corruption was different to how the SANDS saw it, and trying to re-index everything, we were struggling to understand what was going on. And whilst we were working through that, we actually had some other members of the team implement Cohesity and get it into the environment quicker than we could repair our existing solution. That's the power of Cisco UCS, really. >> Looking at this massive transformation that Cisco has been undergoing for a while, from a traditional network appliance vendor to now hardware, software, what are your thoughts on how that transformation, which is, in part, you could say, accelerated by DevNet, how is it going to enable businesses like yours to be able to start getting value even faster from the technology? >> Yeah, that's a very good question, and that's something, I think, a few of us in the industry, if we go back two, three, four, five years, was Cisco going to reinvent itself? What was that place? With hyperscale cloud, all these kind of things. I think quite a few people had some questions around what was going to happen in that space. They weren't always the quickest to market. They had great products, but there was a bit of speed issues there. And what we've seen as they've reinvented themselves is, Cisco has this great name for really being ahead of the curve, or leading industry, and this is, I think, what they were built on, really. And so it's been great from our perspective to see them, say, almost getting back to their roots a little bit, in this regard, and so for us, we are a technology business, we are fast-moving, our customers want things to be fast-moving, and so being able to rely on a technology partner like Cisco, and knowing that they're looking for the latest and greatest even quicker than ourselves, I think that's probably where we start to see the biggest impact. In the past, we might have a challenge that we need to solve, you talk to some vendors, and you might hear something like, oh, we're working on that. Maybe in 12 to 18 months we'll have it in the marketplace. Well we need it now. We don't need it in 18 months, it's a today problem. And that's not what we're seeing anymore with Cisco. Typically, any conversation we have with our account reps around here are some of the challenges, here are what our customers want to do, more frequently than not, our Cisco account reps will say, I think we have a solution for that. And that really, being able to partner with players like that in the industry, that makes some of the biggest differences for us as a company, because we need to partner with all these people to do what we do. >> Exactly. So, with all the momentum that you guys have achieved in just six short months, what's next? >> Yeah, Quantium is certainly a fast-moving company, like I mentioned, and what we wanted, we always like to run close to the leading edge, we're similar with Hadoop, we like to be early adopters. We like technology to grow with us. And this is what we saw in Cohesity. So, they haven't been around for long, and they're already doing everything we need. So we think, well this is a great mix. If we've got someone who's already solving everything that we need, this question of what next is great. And so as we move more towards your hyperscale cloud, being able to run Cohesity across all those environments to manage all of that data across all of it, that's certainly a big one that we're investigating. Like I mentioned, we keep pretty much all of our data, and so actually being able to use cloud as an archive solution, it sounds great, but then it's another silo to manage, it's another solution that you need to implement, but Cohesity will manage all that for us. So, the what next, I think, is we'll see the scale out of the solution as our data requirement grows, we will see it expand into the cloud environments that we're going to start building, so we really see it growing with us from that aspect. And then we see a great idea of being able to repurpose a lot of our on-premise hardware by archiving out to the cloud as well. >> What about SaaS? Do you see a need to use a Cohesity to protect your SaaS data, or are you kind of not there yet? >> Yeah, I think it certainly has a play there, it's still something that I think we're exploring a little bit more to make sure that it's a right fit. But certainly, there is an opportunity there to be explored, yeah. >> Always opportunities. Well Craig, we appreciate you stopping by theCUBE-- >> Thank you for having me. >> And sharing how Quantium is leveraging your partnerships with Cisco, with Cohesity, to drive those core business drivers of data science and AI. >> Thank you. >> Our pleasure. For Dave Vellante, I'm Lisa Martin. You're watching theCUBE Live from Cisco Live, in San Diego. (light music)

>> from our studios in the heart of Silicon Valley. Holloway ALTO, California It is a cute conversation. >> Welcome to the special Keep conversation here in Palo Alto, California. Here, two cubes Studios. I'm John for the host of the Cube. We're talking WiFi six. If you, uh, have use the Internet anywhere outside inside Cos you know why Fiza lifeblood connectivity and hear Expert in WiFi Todd Nightingale, senior vice president general manager at Cisco Muraki. It's been around the block around y fight knows a lot about wireless. Great to see you again. Welcome back. >> Thanks so much. Love the Cube. >> Last time we chatted, we were at definite create, which is advance. Cisco runs around bringing developers cloud native developers into the definite community and programming the infrastructure houses key part of the Cisco. You've been doing a lot of great work. They're making things programmable, switches, wireless, and you got to be big success of Iraqi. But now you're involved in something that I'm super excited about, Which is WiFi. WiFi. Six is upon us. Love the name. It's simple. It's not some acronym letter. Tell us what WiFi six. What is it? What's the new innovation around WiFi six. >> Actually, I've spent practically my whole career in WiFi and we've had just this alphabet soup of WiFi for years, not eleven A and B G and and and A C. And, um, Finally, we're putting that behind us and getting out of the alphabet soup. So there was a new standard called X uh, which is just about to launch around the world. And as a as an industry wide change, we've decided no longer to call that woman dot eight x, but instead WiFi six, which will be hopefully just dramatically easier for people to kind of relate to and understand. And now we have a shortcut. So I'll take it >> on. We want seven eight nights. We innovation run wireless is happening. Seeing a lot of discussion for G five g. Anyone who has a smartphone knows the importance of connectivity. How many bars do you have? How much battery left you So the world has been indoctrinated. Now it's pretty standard that we kind of get this kind, understand the value of having connectivity. What is the innovation on WiFi? Because it's become the critical needed people's lives has been joke. That's that one of the Masters hierarchy of needs. You goto a sporting event, you can see the band with getting choked away. You go to a spotty office. You know the limitations of WiFi. People have experienced that firsthand. What's the new innovations for this next generation? WiFi? >> Yeah. Look, I think wireless has become a basic need. And where that comes from the cellular side and for G. And we hope soon five g or or for comes from the WiFi side. The future. While she's probably looks more and more like outdoor with cellular and five indoor really WiFi and WiFi six and WiFi sixes Justin enormous step forward for that. WiFi technology has far better performance, especially when it comes to ban with client density on blatant See, that could give us just much more immersive experiences, much cleaner video, Much, uh, better, you know, density and performance. It also has a really unique performance optimization, something I think that has a lot of power in the mystery, which is a very sophisticated change to power save power state mode, which means that a wife I will be able to stay to support a whole new generation of Io ti devices operating on batteries for for months or years on this Khun, just open up the door. Tow new IO to use cases we really never thought possible before. >> So the next generation higher band with better power sounds like to market or trends or user trends that we see on the consumption side are immersive experiences. Video people are streaming more than ever now, whether it's in the office or at home or on the go. You have a R N V are more pressure tohave real time, rendering more band with. So this is the band with pressure device pressure on the power. These are the two big ones and I oh, Ty's been enterprise now emerging cloud space. But you know, I ot use cases, but really, it's about the new experiences are really kind of jamming up the highway of Digital highway, if you will. What's going? What's the new things is gonna help that goat better, >> We'LL tell you. We're seeing just a larger and larger percentage of the band with on the Internet and on all networks is as video me. That is the way people want to consume content. ATM, Iraqi We actually launched Ah, whole line of smart cameras just just a couple of years ago, and we see this enormous surge in people deploying cameras and wanting to see real time truly rial time video feeds from around the world. They want to consume content that way, and video is driving just and these immersive experiences, whether it's V. R. It's just driving this enormous need for >> true >> you know, High Band with connectivity. The wireless office in WiFi six the wires office feature. It has to feel like a wired connection. It has to be better than a wired connection. Mohr Band with Lower Late and Seymour efficient. And that's That's the promise of life. I said, >> Just kneel you down on this. I want to get out of the company in the spec sheet in my mind. So why five six has what better than WiFi current version? What's the last version? New version, One of the key bullet points. If you could just go down, stack rank the features that you think are >> important, I think, look, it quadruples the band with scruples. The capacity of these channels that lowers the latent see significantly both of those are important has a technology embedded and called off Oh FDA, which will help us increase the client density per channel, and especially for highly dense deployments that Khun Stadiums. We'LL be able to support MAWR clients on more channels, which is more clients on each channel, which is the key to making those deployments work. Um, and this this power save change for I have T devices for battery powered devices. That's that's really remarkable. And that power save change will affect everyone's mobile phone to I mean, I'm a person who worries about the battery life on my phone almost every day, and I'm hoping WiFi sex will really change that. There's other changes going on in the life I spaces. Well, there's more spectrum opening up. We're starting to see the six gigahertz band being opened up, which will be right, have a unique type of, uh, partially license, regionally licensed model. And by opening up more channels again, we can gain better, better dancer. >> So good density that on the modulation in the multiplex inside that that's for large stadiums. We've all been there offices. What's the impact would like, say, an enterprise who have been, you know, architect ing elaborate wireless networks Because this channel and all the configuration that goes on has been had to be done. What happens there? Is it easier to manage or what's the improvements with WiFi six over in an office space example? >> You know, I think what we'LL see is in high density spaces in conference rooms and our times immediately. See this benefit was higher density. This better performance. Uh, many of the WiFi platforms being built for WiFi six. They have twice as many antennas as the last generations of the high end of life. I five, uh, which was called a Hell of a C that was a four antenna system of what we call four by four radio. The high end of life high sex will be an eight by eight, and what that means is far better response to multi path, meaning these air radios that can see through walls that Khun see around corners. It's remarkable the performance, the thie R F sensitivities device, >> and that solves that people called the Dead Zone areas where, you know, like okay, the bars are down, or why's the why's the video stopping and kind of buffering. >> Exactly. Also solves issues were on interference, so places that of interference. Extra antennas could help see through that as well. And we sometimes call it the line of sight problem. If I could see the AP, it works. But if it's around a wall, I can't lie. Five, six and especially eight by eight antenna. >> Any mission concrete earlier before getting Karen also bounces around a lot of thistle environment where the are wrecked houses around that solves that problem helps that. >> Actually, that's called multi path in the industry. And, yeah, this eight, this eighty antenna ate our chain system really makes a difference >> because that change the form factories, they're still getting faster, smaller, cheaper, kind of thing. Going on boards law, um, or is it same size radios or chipsets? And >> that's a good question. The A. P s, uh, that we're building ATM Iraqi. Uh, they're about the same size, maybe, maybe a little bigger, but we've just built them in a slightly different shape. Um, but I think generally speaking, the technology has hit a point where the size of these devices similar toward the where they were in the last generation our eight by eight, uh, appeal. Maybe about things I >> think, General, if you pulled anyone who's in the WiFi business, whether deploying and rolling out our users, they really don't care what you think it the best performance is also not like, massively, like a tower of his small form factor. It's not going to change much. >> Do you really care? That's everything. There's some people who really care, and the aesthetic of the device really matters. They either wanted to look like physical plant like maybe it should look like it's kind of part of the building, or it should be really aesthetically pleasing and mixing and in your right. Of course, there's some people who really don't care. It's above a ceiling tile or something. All >> right, so let's talk about like the good point about the word matters. Size wise, also kind of footprint. A wind tower and I ot device. This does matter because size is important, whether it's a physical factory floor or somewhere out in the wild. Out in the open, rugged, durable Can it fit in with something? How does y five six save that? Is there any changes there? >> I think we're going to see pretty similar kind of idea will have. In turn, we will see the industry building internal antennas that we call it. Integrate antenna system an external ones for people who want to put custom and tennis solutions. And we'LL see indoor and outdoor e peas and the ruggedized after ones. I don't think that'LL change too much from life by six, but we will see perhaps just higher density deployment because these Raiders, they're so much more power. >> Tell what the impact to the industry isn't going to change the chipsets. How is WiFi? Signal Rollout is R O E EMS who manufactures it? Standards can use at some commentary around the industry coalition around it and impact. >> Yes, a WiFi six will become the new standard wife I will. Over time it will. It will replace not just the consumer at home. Ah, WiFi standard, but also the business and enterprise life I standard what it means is today we're starting to roll out the very first deployments of life I six in enterprise in enterprise B to B use cases on the access point side, and the client devices are just starting to come out. And so we're really right at the beginning of this transition of this curve, and over the next couple of years, we'LL see more and more devices move overto life by six until essentially all devices a couple years are launching on that. >> Iran has been the wireless because you've been in for a long time. They all kind of have this, you know, you know, Comrade of Arms can think is why, if I became so revolutionary that it just grew so fast. But there's been trouble spots has been hard thinking frequency physics, laws of physics, security, security all kind of coming. What's your personal take on where we are now mentioned? Five. G great back haul potential. The network's getting better and faster. Your thoughts just in the industry. Your personal perspective >> give you something I think is really important about life. I is, um, as an industry wife, I sort of developed together as part of a consortium called the WiFi Alliance, and what that means is these air truly standardised protocols and we run interoperability testing with our partners at Cisco. We work closely with the Intel and Samsung, and we run tons and tons of interrupt really testing. So the day this equipment ships it is operating at old, ultra high quality and interrupt. Inter operates with all types of devices made by all types of different vendors. Many other standards don't have that type of strong consortium, that kind of strong ecosystem of partners and that that that's a really powerful for why find? I think that's why it has become such a strong standard. >> You know, I know you're really humble Todd's, but I'LL give the plugs haven't fallen Cisco for many, many decades, So I've been following you. Guys have done a lot of wireless early days, you know. Misfires. Stop start acquisitions, airspace one of the notable acquisition, the WiFi space. Think a bunch of memo based acquisitions also have come in. You could have a lot of experience almost twenty years, plus experienced fifteen that I can point to direct wireless experience that Cisco you guys also care about. You're involved. You're part of the Alliance group ecosystem. What's the vibe internally at Cisco and why? Because it's packets or packets and we went with the air. They movement through cables. It's the same kind of philosophy right >> packets are packets, but it's how you care for your packets that really matters. That's why Cisco is different. >> No, I, uh I >> think the Cisco teams are all super excited. I'm of course, part of the Iraqi acquisition and our team is is just like I know we're pumped. WiFi six is going to be the new standard of WiFi across all of Cisco across all of our regions were starting to roll out education about it and getting ready for a big WiFi six product launch in the coming weeks. And >> what pumps you most? My wife, I six just is that attack? Is that the program ability? What if some of the key things that get you excited? >> I just think we will put the era of wiring desks behind us, and that is an enormous step forward. The life I six enables truly, ah, wireless work space and what we call the true digital work space. And we just won't be wiring offices anymore. After the life I six roll out and that is that's exciting. Wireless has arrived. >> I mean, I want one of my friends built this big house, and he was so meticulous. He's a nerd. He wired fiber to every port, every room. And I'm like, I don't think you need that anymore. He's out. I just going to have the highest band with. So now again, to the tear point that kind of becomes obsolete as long as you've got an access point to some back Hall with its Comcast or two networks. Three. >> Realistically, actually, the wireless devices for the enterprise, especially wireless capacity, is driving switch capacity. At this point, Um, we're building M gig switches to connect our access points primarily, and the purpose of the performance on that on that WiFi access points. Really, What's driving the wired performance on DH? That's, I think, just a telltale sign that this is a wireless digital work spaces. >> So I totally agree with that thing. It's a great vision. Nothing. It's pretty plausible. What would be your advice to your friend if I was your CEO, buddy? And I said, Hey, Todd, how should I be thinking about our protecting my network for the next ten years? OK, bye. Bye bye. The wireless thing I got What should I be thinking about? How shall be architect ing the big holistic plan. >> Yeah, I think right now we're really talking about building for the future. Most CEOs air thinking about rolling something out today or of the next twelve months, and they wantto be using that. Now we're deployment for five years, seven years. And in order to do that, you really need to look. The two technologies really need to look at our WiFi six and EM gig in the access layer, and you have to find a solution that provides a holistic, secure access. And don't think about any of your network deployment without bringing security into that thought process and decide how you're going to secure these sites. Because the band with goes up as the capacity goes up. All of our security concerns, of course. Just increase with that. And we have to be meticulous about that. My number one piece of advice to CEOs is planned for the future of life by six and m gig and plan plan for security. Because even if it's not top of mind >> today, in >> six months and twelve months and eighteen months, it will. >> The reality for them is the surface area is just now the world Todd Nightingale here breaking it down. WiFi six. Next generation Wireless Ethernet wireless connectivity. We all know WiFi wireless six going next generation secu bringing you all the coverage in tech here inside a studio. John Fergus. Thanks for watching.

live from Las Vegas it's the cube covering IBM think 2018 brought to you by IBM hello everyone welcome back to the cube coverage here at IBM think 2018 in Las Vegas Nevada the Mandalay Bay it's a cubes exclusive covers three days of wall-to-wall interviews thought leaders experts entrepreneurs people making an impact and our next guest artists ani who's the co-founder of hosh io h OS h o de Ojo kayo advisor at the pink sky capital he's a cube alumni a walk in off the streets cuz he lives in Las Vegas but very instrumentals are connected to this community because of his pioneering work in in crypto blockchain and the future of money architects great to see you thanks for coming by thank you for having me it's good to be back on the cube great second time and second time yeah only couple we just saw each other the Bahamas the first security token conference yeah I bike on I I be on IBM's really big on supply chain this is their visitor old school you know generations of providing software for businesses b2b and now blockchains their big thing but blockchains yeah pretty straightforward yeah you know you get efficiencies but they're not talking about token economics because they talk about something execs here they're like well that implies the general public in their world thinks cryptocurrency they think Bitcoin so I want to connect the networks together our network IBM's network your network because the melting pot of this trend is really about blockchain cryptocurrency in the sense of the value around tokens and how tokens can be harnessed to capture the values I want to get your perspective as these worlds collide so I think that IBM is doing a great job by spearheading a blockchain movement and they're very they're focusing on the fortune 500 and the key with Fortune 500 companies right now is that they have rooms full of Java developers Java engineers and aetherium is the protocol right now that is most commonly found the majority of icos and token generation events that have occurred to date have all been on etherium x' network and etherium is the most and they found in blockchain however the etherium blockchain the language to build and launch token generation events on aetherium you have to write in a language called solidity and solidity is a new language and iBM has made a smart move by doing everything in Java and JavaScript similar to a lot of the new block chains that are aiming to compete with aetherium and the key distinction just to kind of put it out there when I get your reaction to and get some commentary around is IBM is not competing with public block chains they're looking at a in a different way they're saying hey you know you can have I guess private blockchains I mean it's not a really a dirty word they because they have a different use case correct I think it's very important especially when it comes to things like healthcare you look at the health care industry healthcare records will not be going on public block chains and so the hyper ledger fabric framework may make sense for things that need to be HIPAA compliant for example so reliability is key so what's their jannat like say hashed crafts got a lot of traction in their performance and their speed they got time stamps that's not a native blockchain yet that's kind of getting some traction IBM's got something similar for those markets that require the reliability the performance and the security so help the audience understand IBM's moves here because IBM's conservative so they don't really want to throw the word cryptocurrency out there because it might be misunderstood but this is gonna end up in token economics how are you explaining what the moves ibm's making to the average person that might not know the inside nuances in baseball for say the crypto market I think what's interesting is that iBM has a more mature focus on this space and you know they have direct ties historically to the fortune 500 companies the way others do not and so they've taken a much more sophisticated and a much much more conservative approach you don't see IBM throwing around the word cryptocurrency and that's a smart move because it's about the cryptography that secures block chains in a decentralized ecosystem and it's that the discussion of just tokens and token sales and leveraging tokens as a currency it's a premature time in this entire industry to be having that discussion so although it's going on it's a distraction for IBM you saying yeah because we but it's more interesting for smart contracts to be written that our functional smart contracts that for the first time ever white collared middlemen are being cut out of the picture in a new trustless decentralized ecosystem so talk about where IBM could take this with token economists obviously do you think that it's all leads to some sort of tokenization is that gonna be where the value capture is gonna be how does IBM get there in your mind I think they get there by having fortune 500 companies launch legitimate decentralized applications on their blockchain and that's just what Java JavaScript it's because most fortune 500 companies already have a plethora of Engineers globally that they can simply have start working on IBM's blockchain whereas you don't see that as a risk for IBM no that's that's IBM's advantage because today if the fortune 500 companies aren't building on aetherium whose blockchain mainly because of the learning curve it takes for a current full stack engineer to become a solidity engineer what's the etherium future now obviously they have they're working on lightning seeing some things going on that area the Lightning is Bitcoin is plasma yeah plasma sorry I got them confused so they got to go through the work this and some real work that's got to get done the theory of childs a big developer community the biggest the biggest so do those merge with the IBM communities downstream at some point or is it okay to be separate and does it matter I think they'll remain separate and in this case I I highly doubt that a theory IBM hyper ledger will go down the route that route stock has gone root stock essentially is the etherium virtual machine sidechain to the Bitcoin blockchain enabling smart contracts on the Bitcoin blockchain for the first time and rootstock is a very interesting project however IBM is its own ecosystem and the way in which they're catering to the fortune 500 is extremely intriguing and from Hojo's perspective we want to be auditing smart contracts that are functional that are written by more sophisticated players in the industry centralized ecosystem our main focus is just security auditing and there's gonna be a lot more smart contracts written by more sophisticated players on IBM's blockchain then possibly the other ones right now we have we have not seen a plethora of Fortune 500 companies by any means launching smart contracts on the theorems blockchain and blue chips banks or whatever as they try to disrupt themselves need to get us to partner governments okay so how about for a minute I just take a second to talk about your business I know we covered this at polycon and the Bahamas but for the sake of the context to IBM yeah talk about what you guys are doing we're specifically in the marketplace of partners would you sit if you were parting with IBM and and your role that you could possibly take with IBM so to take a step back quick host show the word itself hosho and means security in japanese and we started this company eight months ago my co-founder yo Kwon and I and our laser focus is blockchain security and being the global leader within the blockchain security space so as far as we see there's new blockchains being made new protocol tokens being launched as well as new tokens being launched as well as do smart contracts being written that are functional for the entire business and no matter what blockchain a smart contract is written on that smart contract is code that has been written and that code has to be audited by a professional third party and we are that professional third party that there's a line-by-line code review of the smart contract and finds all security vulnerabilities and we've been building proprietary tooling to find vulnerabilities faster and faster and faster we do a gas analysis to make sure that that blockchain is not being clogged we conduct the static analysis to find any hidden functionality within the framework of the smart contract and the last part which is very crucial is that yeah very uniquely qualified full stack engineer with a unique QA mindset and a security background who knows the language in which this is coded which currently most projects that were auditing our aetherium ERC 20 tokens written in solidity someone has to marry the source of truth which in the case of an ICO is a white paper and marry the white paper to the smart contract and make sure is the smart contract doing what the white paper says codify the white paper basically this process of auditing is gonna be ever more crucial within the the business that IBM does with Fortune 500 businesses because when a publicly traded company launches a smart contract for a decentralized application security is the highest priority and abilities is where the hackers could come in just be on a time to market getting those smart contract codes written it was fully baked it's irreversible once the smart contract is launched and millions of dollars are gonna go through this smart contract it's been regular practice in the cybersecurity world to type up code and to have it reviewed by a third party auditor we're simply applying the exact same logic to the blockchain space and it's exciting to see more blockchains by sophisticated players like IBM come to fruition and we're looking forward to actual projects from big players around the world launch on IBM's blockchain and hosho is looking to be a preferred partner of IBM's to do all their security work whether it's smart contract auditing or penetration testing and real quick on penetration testing that's our other core service that we provide and penetration testing is both for websites and for crypto currency exchanges in which we're making sure there's no security vulnerabilities within your website and finding every way possible to penetrate your website or your exchange and every time code is changed you open up the doors to more vulnerabilities and so in the crypto currency exchange space right now we're seeing that new exchanges are being made but sophisticated investors don't know if this is a safe place to trade hundreds of millions of dollars or not yeah and so when you got commerce being John I mean IBM as folk as you mentioned is legit and they're doing a great job by the way props to IBM for doing what they're doing they've been in for multiple years now and they're supporting the Apache project they're putting their their weight behind it but these are real-world examples granted supply chain might be boring to some audiences but not to others I mean you're moving real product around this is commerce with digital fingerprints and code and potentially tokens that's a highly gonna accelerate the payment process I mean the notion of clearing goes away it's instant yes this is a highly accelerated money transfer value capture value Tran for environment you can't take me chances yes and security is primal concern and we're excited that companies like IBM value security and this space is one that the dust has yet to settle and what's gonna help the dust settle within the blockchain ecosystem is more priority on security so what's your take if you are gonna give a talk here we're doing talking here in the cube so it's awesome it's gonna be alive and on-demand as well your advice to people saying you know we got a tokenizer our business I need to start with blockchain I can see some areas to create some efficiencies around some inefficient processes and create new business models I got to get started your thoughts my thoughts are take a step back and first evaluate do you have a business what problem are you solving once your business is actually generating some revenue and you've evaluated why the concept of a blockchain could be interesting for your business then pick a blockchain and stick to it and then when you start building on that block chain you've figured out that a token could actually be leveraged within this decentralized application that you're building then you can start figuring out what the token economics of it would actually be I think what people are doing nowadays is rushing to create a token because of their excitement about the fundraising mechanism that an ICO is and an ICO is democratizing to some extent at least global capital raising and I think that fundraising mechanism is not going anywhere that that fundraising mechanism is here to stay however the majority of ICO projects that we're seeing occurring today I don't think these companies will be around in the next couple of years which shows how immature to some extent the industry actually is whereas maybe the projects that are built and launched on IBM's block chains that they develop maybe they're more sophisticated and will be companies that have gone through a more rigorous process of making sure security was a primary concern and they wrote quality code for quality businesses that are actually leveraging decentralization in the appropriate way not the other way around of we want to raise capital so let's invent a reason to have a token or you have a big case right now in Silicon Valley at least is you have companies that are very serious a and B and decided let's do an icy overseer you see and that that's tricky it's not always the right solution when you're saying is don't confuse the ico crazy fundraising arbitrage and new new model to applying supply chain tokens and blockchain to a durable business agreed and on the same token we have people in the space whether they're investors they're lawyers PR firms exchanges they all need to mitigate their risk by keeping security as a concern for them both in-house and for the companies that they're working with yeah lawyers don't want to be doing lawyer work for a company that will turn out to be a scam coin and someone has to do a security audit of that token the same goes for a PR firm a marketer and in exchange exchanges should not be listing tokens that have not gone through a smart contract audit well it's good to know we got a cube alumni here in the cube to help us with our security audit yeah well the answers the life were in a cube interview so do we got one right here I want to just get into in topic you and I were talking of dinner the other night when we had we saw each other a few nights ago about the problem of picks and shovels and tools and maturity in this new emerging area can you um can you just take a minute to explain what that we were talking about there and I thought you had a good point I mean maturity of the space is not mature it's growing it's embryonic but moving fast and there's need for tools let's unpack that just share your thoughts vision so I think that a lot of people have been more excited to join in an IC o---- a token generation event and do more quick money grabs but to me what's more exciting is the infrastructure that's needed for this industry to actually grow and mature an infrastructure is infamously known as picks and shovels because when the gold rush happened the people who made the real money or the people selling the shovels to the gold diggers and what's included in that is businesses like our own hosho which is selling security audits of smart contracts doing penetration testing bringing maturity and making making things less risky for for everybody in this space so we start we see ourselves as selling picks and shovels on the other hand I'll give you an example Goldman Sachs has a trading desk today it's not 24/7 stock market Oh at 9:00 closes at 5:00 what happens tomorrow when a 24/7 crypto currency trading desk is turned on at Goldman Sachs do the traders that are now 24/7 have the appropriate tools and the governance built into software to manage a team of 24/7 traders at Goldman Sachs today when you have traders trading in the stock market they have a plethora of tools that make them snipers and you have certified market technicians telling hedge funds that this isn't gonna go up two points here in three points they're reading candlesticks in the cryptocurrency space it's like poking a stick you go from being a sniper to having a stick find by Wars like a blind man so companies there's a dozen companies that can be made building the infrastructure for just what I just said the governance with four trading floors this is a really good point and I wanted to bring it up because in these emerging markets these white spaces for tools and technology to help the overall trend grow faster has always been a successful man however you mentioned something about the goldman sachs trading this and that is it literally could be turned down overnight right so that's the problem you can accelerate things too fast and not be prepared that seems Oldman honest I'd know Goldman's done a great job at being very much forward-thinking they've been at every money 20/20 since the beginning of that FinTech conference and they're definitely in this pickle this exercise the analogy is a company can turn on a new model fairly quickly faster than the old days which we're taking months and then now you can do it really on a much shorter timeframe that means they potentially could be exposed if they go too fast yeah this is where the ecosystem has to help yeah I think the bulge bracket banks are treading the water very carefully JPMorgan is doing things they're involved with aetherium Z cash - JPMorgan has a lot going on on this front but these are publicly traded monster banks they're not going to take any risks these are they have a they have stockholders to to answer to they have the US government we have over 150 regulatory arms just regulating the finance industry in the United States and so I think it the American citizen citizen is quick to point fingers to bulge bracket banks and those banks are answering to too many regulatory arms this is one of the downsides of the United States right now in general is the increasingly coercive environment of the government ybm certainly got the blue chip company got the the fortune 500 but they also have a marketplace and that's where they could really kind of change the game feeling in those white spaces yeah IBM's marketplace sounds very exciting and my mind just goes to who's handling the security for everything to do with IBM blockchain we're hoping at Osho arch edge thanks for joining us thanks for sharing your your insight here in the queue with an IBM think conference breaking down all the top news that's really around blockchain at AI would date at the sin of the value proposition all being disrupted by new decentralized technologies blockchain being the beginning and a lot more is happening and certainly we're in and bring it to you on the cube no matter where it is will be there I'm John furrow here and Las Vegas for IBM think coverage we'll be back with more coverage after this short break [Music]

>> Announcer: Live from Las Vegas, it's The Cube, covering IBM Think 2018, brought to you by IBM. >> Welcome back to The Cube. We are live in sunny Las Vegas at the inaugural IBM Think 2018 event. I'm Lisa Martin with Dave Vellante. Dave, this weather has got to beat Boston hands down, right? >> It was beautiful yesterday, about 15 degrees in Boston, snowy. >> So you thawed out since you've gotten here? >> I took the snowshoes out, actually. Life makes lemons. >> Exactly, and we have another cold-weather guest who's probably thawing out as well, Ranjana Young, the senior vice president of Enterprise Data Services from Northern Trust, welcome. >> Thank you, thanks for having me. >> We're excited to chat with you. You have a role at Northern Trust, and your mission is all-around data, five-core competencies, including data governance and stewardship, data quality, master data management, enterprise integration with data platforms. Tell us a little bit about your role, how long you've been doing that, and really what this focus on data is enabling for Northern Trust. >> Sure, I want to talk first about our mission as you had mentioned. I think it was critical to establish a broad mission for Northern Trust. We wanted to make sure that we establishing an enterprise data program that enabled our customer needs and overall our customer experience, but also truly helped support our regulatory needs that we had, and it was critical to establish those two as the main goals, not just one or the other. And then the role, I call myself a change agent because establishing capabilities that you talked about, it is difficult to do, with a lot of legacy that we have. The firm has been in existence for 128 years To establish a data-driven culture was very different. I think we were known to do provide good business solutions, but a lot with the gut, given that we were good at it, but how do you make sure that you change that culture and have a relationship managers and others really think differently and use data to provide those solutions to our clients. >> I remember when I met Inderpal Bhandari, I'm sure you know him, and he said that he has a framework for a data leader, and he said there are five things a data leader has to do to get started, and three are in parallel, or sorry, three are linear, two are in parallel. I don't know if you've heard this rap, but I'd like to sort of explore them and see how your three are generally. He said you start with understanding how the organization monetizes data, not directly, maybe selling data, but how it contributes, and then the next one was sort of data access and then data quality. Those are the sort of sequential activities, and then the parallel ones were form relationships with a line of business and then re-skill. So those are his five. How did you approach it, what was different, what was similar, what were some of the challenges that you had in doing that? >> Sure. If I had to think about kind of, to correlate some of the components of the strategy, skills is an important thing. When I started establishing the team three years ago, it was critical that we had to bring some of the core skills within the firm because they had the business capabilities, they understood the systems, they understood kind of the skeletons that were in the closets and knew the culture and also embraced the challenges and still could find solutions. And then you had to bring external folks that really had the capability to drive that change, had the mastery of management skills to really support and set up an account domain and a party domain, a reference data domain, especially an asset domain, et cetera. So we had to look at kind of a conglomerate of individuals to do that. And then if you look at kind of where was the starting point in terms of really establishing the program was, we were going through a transformation to really re-platform a lot of our legacy, whether it was our valuation system or our cash platform, others, and data was a thread throughout all of those programs, so it was critical to establish and think and take bite-sized chunks, it was important to think about, okay, throughout all the programs, what is the important data that we could kind of understand, so we focused quite a bit on initially looking at critical data and looking at critical data from a master data perspective, so asset data, which is very critical to the work that we do on the institutional side. As you know, we had a management asset servicing company. Data is an asset for us, we enrich the data. We provide services around that today, and have been, and so embedding data governance through that process was important, and also our clients were really looking for the enriched data but also were looking for clean information but also were looking for where did that data come from? Where does the definition of this data? So kind of giving them that external catalog of here's the data, but here's the enriched data and here's the metrics for data quality around it, and then here's the definitions for it. So to some extent, that drove change because of customers were looking for it, and a lot of the capabilities that were foundational to the firm, we're starting to externalize, especially the meta-data catalog, et cetera. >> So if I could play that back, so you started the team, all right, you said, okay, I need to build a team. I think I heard that, and then the data quality, and then presumably, okay, who has access to this data? Is that about right? >> So I started with the mission to say, we have to do this for both arms, the left arm being our customer experience and making sure that we change the way we're doing our work there, or enhance the work so that our customer experience was better, and then obviously the regulatory, make sure that we need the regulatory. So for that, we needed five core competencies. We knew that we had to establish a role of the steward, a role of the custodian, so the team started to become very critical then, and then we knew that we had some gaps in our master data management capability, a complete gap in having integrated data platforms. I notice I've talked a little bit about we established a whole strategy and architecture for ING. I totally relate to how we had to do the same. Each silo did their own particular thing. The management did their own thing. >> David: By data. >> The institutional side did their own thing. Asset management was, I would say, a lot more mature. So I would say if you were to think about it, it's establishing the mission and establishing the team. >> And then, just one last follow-up. The services that you're providing, data services, those are delivered through your organization, the IT organization, what's the practice? >> We have a partnership, a very collaborative partnership that we work together. The technology team does all the build for the work, we work collaboratively to kind of build a strategy of what solutions need to be first versus later, given the client priorities and our institutional side, our business unit priorities, so that's a collaborative effort, working together. >> So speaking of collaboration, you mentioned earlier that it was really key to have both the veterans within Northern Trust and their expertise that you said kind of the skeletons, that they know where things are buried, as well as that maybe external, you might say more fresh perspective. You also talked about, we chatted before we went live, about governance. Seems like what you guys have done is kind of flipped governance from being viewed as potentially an inhibitor to really empowering, being an empowering capability. Can you tell us how you've leveraged data governance to empower a data-driven culture within a business that is 128, I think, years old, you said? >> Yes, that's right. So, for us, I think that while we were establishing the program, it was very critical to understand kind of the challenges on the institutional side first because they had the maximum number of challenges with data. Again, because we're an asset servicing company, a data is an asset, we enrich that information and provide that information, but what was happening was it was taking us so much longer to provide these solutions to our clients, so we've embedded, now, the data governance framework as a part of that solution, and our clients are seeing the value, so if you look at one of the customers that we're working with, we actually have externalized our catalog where they understand now what data that they're receiving, and you're speaking the same language, and that was not the case before. But again, as I said, if we didn't do the foundational work of cataloging the information, understanding what the data is, where the data is, what the data assets are, we just couldn't have done that, so it's really paying off because of that. >> How has that affected your ability to be prepared for GDPR, which obviously went into effect last year, the fines go into effect in May of this year? What was the relationship there? >> So we have worked very, very closely with our chief privacy officer, and we've really done a phenomenal job of identifying where our highly sensitive data assets are. We're in the process of cataloging all of them through the unified governance framework that we've established, so we leverage IBM's IGC NIA to do all that work, and the lineage all the way to the authentic source, which is something the regulators definitely are looking for, so are we fully, completely done yet? No, so we're in that journey, and with unstructured data, we're looking at discovery tools to kind of provide that. We have a solution that's a little manual at this point, but we hope to kind of make more progress on that side. >> I got to ask you, so around 17%, the data suggests, 17% of the IT, technology industry is women, but I was at an IBM, it was a Data Divas breakfast that I crashed, I snuck in, one of the few guys there. >> Oh, very cool. And there was a stat that around 30% of data leaders are women, I don't know, it was a sort of a small sample, who knows? Sounded a little high. Somebody said it's because it's a thankless job and women have to take it on, so thoughts on women in tech, women in this role, perspectives. >> So I am excited to meet a few here at the conference. That statistic is pretty high that you're stating. I don't see that. >> David: It's outside that. >> In the industry, I do find myself sometimes as a lone warrior, at least in the industry forums, but I think it's growing. I think especially women in technology, women in leadership on the line of business side is growing, and Northern Trust, I'm very proud to say, is big around diversity and providing opportunities to women, so from that perspective, I think I'm excited that women are taking interest in data, yes, it is a very hard job, so I think, I feel like we are organized, we get a lot done at the same time, so I think it's really helped. >> Other than it's the right thing to do, are there other sort of business dimensions? Is it Mars versus Venus? Are there sort of enrichments that a woman leader brings to the equation, or is it just because it's the right thing to do? >> I've seen tenacity women have. No offense to anyone, I think the higher tenacity to be persistent. >> I don't take offense. >> To be methodical, to be methodical, and also to have the hard discussions in a very factual way sometimes, but also in, yes, this is the right thing to do, but is there ways we could make this change happen in a systematic, bite-size chunk way. Sometimes I think those coercive conversations help a lot more than the others, and I think, to me, I would say tenacity, tenacity. >> I love that word. I have to say, that's a word that's oftentimes associated with males. A lot of times a tenacious woman, it's a different adjective, right? It's a term, I don't know, Lisa, what your experience has been, so that's good, a good choice of words in my view. >> I've heard pushy before, and I think what they really meant >> David: There you go, okay. >> Is persistence. (laughs) >> That's right. >> A man is tenacious, a woman is pushy. You hear that a lot. >> Right, I think it's persistence. So last question for you. Here we are at the inaugural IBM Think 2018. You guys are an IBM Analytics Global Elite Partner. Can you talk to us a little bit about that strategic partnership and what it means for Northern Trust? >> This partnership has really helped us tremendously in the last three years while we were putting the strategy to action while operationalizing data governance, while operationalizing a lot of the capabilities we thought we would have but really kind of bringing that to life. We're also really excited because lot of the feedback that we've provided has gone into kind of redoing some of the products within IBM, so we've definitely partnered and done lot of testing for some of the ones, the beta versions, and it's also helped us, I think, sometimes it's been like a marriage. We've had hard times getting through certain hurdles, but it really has paid off, and I think the other thing is we've really operationalized governance to the core at Northern Trust. I think IBM is also seeing value in sharing that our story with others because others have started the journey but may have taken certain different approaches to making that happen, so all in all, I think that the unified governance framework has really helped us, and I think we really love the partnership. >> As a client, what's on their to-do list? What's on IBM's to-do list for you? >> So I think one of the things that we've been talking quite a bit is we have a new CIO, and he's really interested in the cloud strategy, I know you've been talking about that. Again, we're a bank, so due to regulation there's strategies in terms of private versus public cloud. That's one conversation we'll definitely want to take further. We want more integrated tooling within the unified governance platform. That's something that's been a topic that we've discussed quite a bit with them. AI, machine learning, robotics is huge for us, so how do we leverage Watson much more? We've done a few POCs, how do we really operationalize and make sure that that's something that we do more of, so I think I would say those three. >> So sounds like a very symbiotic relationship. >> Ranjana: It is. >> Slash marriage that you have. Ranjana, we want to thank you for joining us and sharing how really kind of you're exhibiting the term change agent in a tenacious way. >> Okay, thank you. >> I feel like I want to say I'm flanked between two data divas, you don't take offense at that, do you? >> No, not at all. It's a compliment. >> You crashed an event. I'm seeing a new >> I like that. >> Twitter handle come up here. We want to thank you so much again for stopping by and sharing. Congrats on your success, and we hope you have a great time here. Enjoy the sunshine! Maybe bring some back to Chicago. >> Will do, will do, yeah. Thanks again, very much. >> And for Dave Vellante, I'm Lisa Martin. We want to encourage you to check out thecube.net to watch all of the videos that we have done so far and will be doing at IBM Think 2018, and of course on all of the shows that we do. Also, head over to siliconangle.com. That's our media site where you're going to find pretty much in near real time synopsis and stories on not just what we're doing here but everything around the globe. Again, for Dave Vellante, I'm Lisa Martin, live from IBM Think 2018 in Vegas. We'll be right back after a short break with our next guest.

>> Announcer: Live from Orlando, Florida. It's theCUBE, covering Grace Hopper's Celebration of Women in Computing, brought to you by Silicon Angle Media. (bright music) >> Welcome back to the Cube's coverage of the Grace Hopper Conference here in Orlando, Florida. I'm your host Rebecca Knight. I'm joined by Ayanna Howard. She is a professor at the Georgia Institute of Technology and also Chief Technology Officer at Zyrobotics. >> Thank you. >> Thanks so much for joining us. >> Thank you very much for having me. >> So start to tell our viewers a little bit about Zyrobotics. I know it was a spin-off of your research that you were doing at Georgia Tech. >> Yeah, so interesting enough Zyrobotics, so at Georgia Tech I focus on working in technologies, robotics for children with special needs. Primarily children with motor disabilities, cerebral palsy for example, children with autism. And so one of the things as we had developed was the ability to access computing technology because I was running robot programming camp. So I was running camps for all children, so an inclusive camp and I had typical children and children with special needs, and what happened was people kept asking me, "Oh, can we take this home?" It was like, "Yeah, no, (laughing) "that's got to stay in the lab, sorry. "But you can bring your kid back." And so the company really came out of trying to commercialize that special technology that allows inclusiveness for kids in this kind of STEM education. So that's how Zyrobotics came about. >> So talk a little bit about the technology. What does it do? How does it help kids with these different learning needs? >> So imagine you have a child who has motor limitation, and if you look now, so much is on tablets. Tablets, smartphones, even education. And if I have a motor disability, have you ever tried swiping with your fist? Right, or even if you're an older adult, and taking your finger, and if you have a tremor, like moving things around, so this is very difficult. And yet that is the way the technology is made, which isn't a service. It's just not made for everyone. And so what we've done is we've created these devices, very fun, think of it as a stuffed animal, that allows you to, if you want to stomp, if you want to do your finger, if your access point is in your foot, and you just tap your foot, it allows you to interact with the different educational apps. But what we found is that typical kids also like (laughing) playing with the toys. >> Rebecca: Right, right, right. >> So it's like, oh what is this? This is interesting. And so that's why it provided this nice blend of kids of any ability the ability to access these educational apps. So but you also are a full-time professor at Georgia Tech, and you run a traineeship in healthcare robotics. Tell our viewers a little bit more about that. >> Yeah, so I run a program called ARMS, so it's funded by the National Science Foundation. And what I've found is, a long time ago, the way that we were training our computer science students, our engineering students in robotics was typically I would say ad hoc. So I'd have a student, and they were like, "I'm interested in healthcare robotics." And I would call up my clinician friend and say, "hey, can we do an observation?" And my student would go there and basically shadow a therapist or a doctor for the day. And then they go back. And so this is what I was doing. And I found out that most professors who had students in healthcare-related activities were doing the same thing. And I was like, wait, hold it. This sounds like it's more than just me. Maybe we can formalize this a little bit more. And so the trainee-ship program actually takes roboticist students and immerses them in the medical side. And so for example this past summer, they spent the entire summer over in the clinic and the hospital watching surgeries, I mean actually scrubbing up, following patients, understanding what is Parkinson's and how do you do assessments. And so they were fully immersed as if they were medical resident students, or resident person in the clinic. And what happens is, then, and this is all in their first year, they come back into their studies, and now they understand, "okay, if I'm designing "this technology, what does it mean "if I'm designing for someone who's recovering from stroke? "What does that really mean?" And they have a vision of the patients, not just their own, I mean, they have a real vision of Mister Joe, that they've worked with and how he might have struggled with some concept and what they're doing can actually enable. And so it gives engineers, scientists, roboticists that power. >> And the empathy to really understand how it will be used. >> Yes, and understand that and not build or design in a box, which is really unfortunate that sometimes we do that. We design based on our own beliefs, not taking into account that there are other users and you are not the user, necessarily, of your own technology. >> So I want talk a little bit about this conference. This is your third Grace Hopper Conference. What does it mean to you to be here, and what do you get out of it? Are you here for Zyrobotics? Are you here for Georgia Tech? >> I am here for women in computing. And so it's actually not linked to a specific company or an organization. It's the fact that I feel a responsibility, they call me a role model, but- >> Rebecca: We're going to go with it, we're going to go with it. >> We're going to go with it. (laughing) I mean, I had a lot of mentors growing up. Not many were women. It's only at my later age that I've actually met some great, great women mentors. And so I feel a responsibility to come to Grace Hopper and just talk, share my experiences, sometimes be vulnerable and open to the trials and tribulations, but then the pure joy you get from staying in the field and the pure joy you get from actually impacting the world with your mind, with your technology, with your stuff. And I think it's amazing how, to be here and see all these young ladies, both students and older, well-established women leaders, and say, "yeah, we got this. "We can change the world with our power." >> So we're really at this inflection point in technology where problems, the biases, the barriers that have kept women from progressing, from first of all getting into the field and also progressing, are really front-page news. And sort of the problems that women have faced in the industry, the sexism, is really being talked about. But is that a good thing in the sense, I mean, yes, it's one thing to get these problems out there, but are we also discouraging women because it's showing women how tough it is to be in this industry and this bro-grammer culture? >> I think it's a two-edged sword. So in one instance, these things were happening anyway. And if you actually look at retention, which is surprising, retention of women who've been in the computing field for a longer period of time, a lot of them were dropping out. It's like, wait, hold it. You got through the pipeline, what happened? And so we all knew a lot of this stuff was going on. We have first-hand experience with it. And so the conversation now is letting everyone know about it. And I think that's how anything happens. It's that others are like, "I didn't realize." others start empathizing. "I didn't realize that this is what you were "going through. "What can I do to help?" Even if they are not necessarily a woman or a minority. And so I think what happens is by having that conversation, it makes everyone aware of it so that things can start changing. It's a negative, the fact that maybe young women are like, "oh, I don't want to go through that." I think by having role models that are like, "hey, yeah, that's what it's like, "but guess what, I'm running the company. "I'm the CEO, and so imagine what it'd be like "if you come in now that the conversation is open "versus what I was going through "when nobody was talking about it." We didn't have anyone to say, "hey, can you help me? "I just need some assistance, just to talk about something." Now you can, you can be open about it. >> So what is your advice? I mean, we know that the numbers are bleak. Tech is comprised of 25% women, 15% in leadership positions. For black and Latina, it's abysmal. What do you tell your students about this industry? >> So I tell my students, one is, if you want to change the world, and usually students that take my course and work with me are ones that want to have an impact with their minds and their technology, and so my thing is if you want to change the world, computer science, engineering is the only way that you can because the world is based on you and your technology. And in fact, if you don't, I put in the guilt, if you don't get involved in this, then the world is not going to change. And your kids' kids will have to live in this world that you have. So it's really your responsibility (laughing) to get into this space. >> The guilt is good, that's good, yeah. >> It is, for women, guilt is really good. >> I know, it's powerful, so powerful. >> Yeah, yeah. >> I want to talk a little bit about funding because I know that your trainee program, it's partly funded by the National Science Foundation. So funding is such a hot topic here, and whether you're a female entrepreneur who's trying to get money for your idea or you're a scientist trying to fund your research, tell us a little bit about the landscape, what you're seeing, what you're feeling. >> I would say that government funding, so the National Science Foundation, I would say NIH, there is more equality in the representation. >> Rebecca: There is more equality. >> It's not 50-50. But you have a fighting chance, right? I would argue, though, that in the startup world, you need to go for government funding and non-profits that may be angels because honey, VCs are not going to look at you. I truly believe that, and being a startup company, I talked to a lot of women entrepreneurs who have broke in the VC field, and they tell me basically how many frogs they had to kiss, you know? And so I think that landscape has not changed as much. But I think funding as a scientist for government grants, I think it's more, it's not fair, but it's more equal because in government, it's okay for you to say, as a program manager, "hey, something's wrong here." Because the government represents the population. So it's okay as a program manager to say that. I don't know that it's as safe to say that as a VC, like, "hey, our company portfolio doesn't look "like the rest of America." >> Right, right. So your advice there for female entrepreneurs or female researchers trying to get money is to go first to either angels or the government. >> I say that will help you keep your company alive. But you still have to kiss a lot of frogs. You still do. And eventually you will find a frog that turns into a princess and will fund you. But if you think about, how do you survive through this company and how do you keep it to the next levels, you go through any type of funding resource that you can. And so if the angel funding world in terms of government, it's not a guarantee, but it's easier, grab that, non-diluted, by the way, typically, until you go the VC direction. >> Now, in terms of the funding environment, though, NIH and NSF, do you feel they're giving as much money right now? We have an administration that is... >> Yeah, no, so overall the budgets themselves are, so NSF and NIH, this last cycle they kind of weathered a cut. But if you look overall over the last umpteen years, you see that the rate of acceptance has dropped because there's a lot more researchers going for funding, the budget doesn't keep up, necessarily, with the cost of living expenses kind of thing, cost for tuition, cost for grad students. And so overall the funding has declined. But that is not a gender issue. That is a issue just about the value of basic research in general. And the US, a lot of us understand but a lot of us do not. And so we feel that in terms of the funding process. >> So as a professor but then also as someone who's working in industry, how do you make sure that women can see themselves and see potentially rich and rewarding careers? >> So I do a couple of activities. For example, I'm going to talk about one, which CRWA grad cohort. And so what that focuses on is graduate students, women, either PhD, Master's wanting to be a PhD, and what we do is we provide those mechanisms for them to interact with community members. So we bring in these- >> Rebecca: So this is not just at Georgia Tech. This is nationwide. >> This is nationwide. Young women, they come in, like, "oh, what is this?" First off, they get to see other of their peers at other schools. Second is we bring in senior women that are doing exceptionally well, and they do things like one on one mentorship. They share. So we select these women who are open to sharing their experiences, both the good and the bad, and so it provides that network of, "okay, look, it might be hard in grad school, "but we have a peer network, take advantage. "And there are senior women you can take advantage, "to talk to and kind of ping them on different issues "that you have." So I think programs like that, and we're not the only one, but programs like CRWA grad cohort, CRAW URM, undergraduate cohort, are ways to ensure that you don't get discouraged at a younger age. >> So Zyrobotics, it's founded in 2013. What is the future of it? I mean, it's such an exciting technology and one that I think really has a lot of uses because as you said, it's not only for children but it could be for stroke victims, for aging people who are sort of losing some of their mobility. >> So my goal, I always say five years, right? So when I started it was like, five year goal cause that's like the holy grail, you make it for five years. So we're at year four, we just crossed. So we're in that five years. But what I see more as the vision, what I would say the secret magic of Zyrobotics is to make sure that accessibility is an integral part of the conversation. It's not an afterthought, it's not a someone designed technology, oh, let's think about accessibility and inclusiveness after the fact. And so I'm hoping that one, the product of course takes off, but also that it starts changing the conversation a little bit. So for example, I go out, I talk about how do you design technology that is really, really cool, is cutting edge, that's accessible at its core. It's accessible to the different learning ways, different access ways that people have of interacting with technology. How do you get that message across that, "hey, you can so this and you can still make money." So it's not like oh, accessibility, we can't make any money. Like, no, you can actually still make money even if it's a core value. So that's my vision is to have basically, have Zyrobotics lead that but then have other companies adopt it as, "oh, yeah, why haven't we done this? "Yeah, this makes total, total sense." >> Great, Ayanna Howard, thank you so much for joining us. It's been a pleasure having you on theCUBE. >> Thank you, this was fun. Thank you for the invite. >> I'm Rebecca Knight, here in Orlando, Florida at Grace Hopper. We will have more just after this. (bright music)