>>Machine intelligence is everywhere. AI is being embedded into our everyday lives, through applications, process automation, social media, ad tech, and it's permeating virtually every industry and touching everyone. Now, a major issue with machine learning and deep learning is trust in the outcome. That is the black box problem. What is that? Well, the black box issue arises when we can see the input and the output of the data, but we don't know what happens in the middle. Take a simple example of a picture of a cat or a hotdog for you. Silicon valley fans, the machine analyzes the picture and determines it's a cat, but we really don't know exactly how the machine determined that. Why is it a problem? Well, if it's a cat on social media, maybe it isn't so onerous, but what if it's a medical diagnosis facilitated by a machine? And what if that diagnosis is wrong? >>Or what if the machine is using deep learning to qualify an individual for a home loan and that person applying for the loan gets rejected. Was that decision based on bias? If the technology to produce that result is opaque. Well, you get the point. There are serious implications of not understanding how decisions are made with AI. So we're going to dig into the issue and the topic of how to make AI explainable and operationalize AI. And with me are two guests today, Shea speaky, who's the co-founder and COO of cognitive scale and long time friend of the cube and newly minted CEO of cognitive scale. Bob pitchy, Yano, gents. Welcome to the cube, Bob. Good to see you again. Welcome back on. >>Thanks for having us >>Say, let me start with you. Why did you start the company? I think you started the company in 2013. Give us a little history and the why behind cognitive scale. >>Sure. David. So, um, look, I spent some time, um, you know, through multiple startups, but I ended up at IBM, which is where I met Bob. And one of the things that we did was the commercialization of IBM Watson initially. And that led to, uh, uh, thinking about how do you operationalize this because of the, a lot of people thinking about data science and machine learning in isolation, building models, you know, trying to come up with better ways to deliver some kind of a prediction, but if you truly want to operationalize it, you need to think about scale that enterprises need. So, you know, we were in the early days, enamored by ways, I'm still in landed by ways. The application that takes me from point a to point B and our view is look as you go from point a to point B, but if you happen to be, um, let's say a patient or a financial services customer, imagine if you could have a raise like application giving you all the insights that you needed telling you at the right moment, you know, what was needed, the right explanation so that it could guide you through the journey. >>So that was really the sort of the thesis behind cognitive scale is how do you apply AI, uh, to solve problems like that in regulated industries like health care management services, but do it in a way that it's done at scale where you can get, bring the output of the data scientists, application developers, and then those insights that can be powered into those end applications like CRM systems, mobile applications, web applications, applications that consumers like us, whether it be in a healthcare setting or a financial services setting can get the benefit of those insights, but have the appropriate sort of evidence and transparency behind it. So that was the, that was the thesis for. >>Got it. Thank you for that. Now, Bob, I got to ask you, I knew you couldn't stay in the sidelines, my friend. So, uh, so what was it that you saw in the marketplace that Lord you back in to, to take on the CEO role? >>Yeah, so David is an exciting space and, uh, you're right. I couldn't stay on the sideline stuff. So look, I always felt that, uh, enterprise AI had a promise to keep. Um, and I don't think that many enterprises would say, you know, with their experience that yeah, we're getting the value that we wanted out of it. We're getting the scale that we wanted out of it. Um, and we're really satisfied with what it's delivered to us so far. So I felt there was a gap in keeping that promise and I saw cognitive scale as an important company and being able to fill that gap. And the reason that that gap exists is that, you know, enterprise AI, unlike AI, that relates to one particular conversational service or one particular small narrow domain application is really a team sport. You know, it involves all sorts of roles, um, and all sorts of aspects of a working enterprise. >>That's already scaled with systems of engagement, um, and, and systems of record. And we show up in the, with the ability to actually help put all of that together. It's a brown field, so to speak, not a Greenfield, um, and where Shea and Matt and Minosh and the team really focused was on what are the important last mile problems, uh, that an enterprise needs to address that aren't necessarily addressed with any one tool that might serve some members of that team? Because there are a lot of great tools out there in the space of AI or machine learning or deep learning, but they don't necessarily help come together to, to deliver the outcomes that an enterprise wants. So what are those important aspects? And then also, where do we apply AI inside of our platform and our capabilities to kind of take that operationalization to the next level, uh, with, you know, very specific insights and to take that journey and make it highly personalized while also making it more transparent and explainable. >>So what's the ICP, the ideal customer profile, is it, is it highly regulated industries? Is it, is it developers? Uh, maybe you could parse that a little bit. >>Yeah. So we do focus in healthcare and in financial services. And part of the reason for that is the problem is very difficult for them. You know, you're, you're working in a space where, you know, you have rules and regulations about when and how you need to engage with that client. So the bar for trust is very, very high and everything that we do is around trusted AI, which means, you know, thinking about using the data platforms and the model platforms in a way to create marketplaces, where being able to utilize that data is something that's provisioned in permission before we go out and do that assembly so that the target customer really is somebody who's driving digital transformation in those regulated industries. It might be a chief digital officer. It might be a chief client officer, customer officer, somebody who's really trying to understand. I have a very fragmented view of my member or of my patient or my client. And I want to be able to utilize AI to help that client get better outcomes or to make sure that they're not lost in the system by understanding and more holistically understanding them in a more personalized way, but while always maintaining, you know, that that chain of trust >>Got it. So can we get into the product like a little bit more about what the product is and maybe share, you can give us a census to kind of where you started and the evolution of the portfolio >>Look where we started there is, um, the application of AI, right? So look, the product and the platform was all being developed, but our biggest sort of view from the start had been, how do you get into the trenches and apply this to solve problems? And as well, pointed out, one of the areas we picked was healthcare because it is a tough industry. There's a lot of data, but there's a lot of regulation. And it's truly where you need the notion of being able to explain your decision at a really granular level, because those decisions have some serious consequences. So, you know, he started building a platform out and, um, a core product is called cortex. It's the, it's a software platform on top of this. These applications are built, but to our engagements over the last six, seven years, working with customers in healthcare, in financial services, some of the largest banks, the largest healthcare organizations, we have developed a software product to essentially help you scale enterprise AI, but it starts with how do you build these systems? >>Building the systems requires us to provide tooling that can help developers take models, data that exists within the enterprise, bring it together, rapidly, assemble this, orchestrate these different components, stand up. These systems, deploy these systems again in a very complex environment that includes, you know, on-prem systems as well as on the cloud, and then be able to done on APIs that can plug into an application. So we had to essentially think of this entire problem end to end, and that's poor cortex does, but extremely important part of cortex that didn't start off. Initially. We certainly had all the, you know, the, the makings of a trusted AI would be founded the industry wasn't quite ready over time. We've developed capabilities around explainability being able to detect bias. So not only are you building these end to end systems, assembling them and deploying them, you have as a first-class citizen built into this product, the notion of being able to understand bias, being able to detect whether there's the appropriate level of explainability to make a decision and all of that's embedded within the cortex platform. So that's what the platform does. And it's now in its sixth generation as we >>Speak. Yeah. So Dave, if you think about the platform, it really has three primary components. One is this, uh, uh, application development or assembly platform that fits between existing AI tools and models and data and systems of engagement. And that allows for those AI developers to rapidly visualize and orchestrate those aspects. And in that regard were tremendous partners with people like IBM, Microsoft H2O people that provide aspects that are helping develop the data platform, the data fabric, things like the, uh, data science tools to be able to then feed this platform. And then on the front end, really helping transform those systems of engagement into things that are more personalized with better recommendations in a more targeted space with explainable decisions. So that's one element that's called cortex fabric. There's another component called cortex certify. And that capability is largely around the model intelligence model introspection. >>It works, uh, across things that are of cost model driven, but other things that are based on deterministic algorithms, as well as rule-based algorithms to provide that explainability of decisions that are made upstream before they get to the black box model, because organizations are discovering that many times the data has, you know, aspects of dimensions to it and, and, and biases to it before it gets to the model. So they want to understand that entire chain of, of, uh, of decisioning before it gets there. And then there's the notion of some pew, preacher rated applications and blueprints to rapidly deliver outcomes in some key repeating areas like customer experience or like lead generation. Um, those elements where almost every customer we engage with, who is thinking about digital transformation wants to start by providing better client experience. They want to reduce costs. They want to have operational savings while driving up things like NPS and improving the outcomes for the people they're serving. So we have those sets of applications that we built over time that imagine that being that first use application, that starter set, that also trains the customer on how to you utilize this operational platform. And then they're off to the races building out those next use cases. So what we see as one typical insertion place play that returns value, and then they're scaling rapidly. Now I want to cover some secret sauce inside of the platform. >>Yeah. So before you do, I think, I just want to clarify, so the cortex fabric, cause that's really where I wanted to go next, but the cortex fabric, it seems like that's the way in which you're helping people operationalize inject use familiar tooling. It sounds like, am I correct? That the cortex certify is where you're kind of peeling the onion of that complicated, whether it's deep learning or neural networks, which is that's where the black box exists. Maybe you could tell us, you know, is that where the secret sauce lives, if not, where is it? And if >>It actually is in all places right though. So there's some really important, uh, introductions of capabilities, because like I mentioned, many times these, uh, regulated industries have been developed and highly fragmented pillars. Just think about the insurance companies between property casualty and personal lines. Um, many times they have grown through acquisition. So they have these systems of record that are, that are really delivering the operational aspects of the company's products, but the customers are sometimes lost in the scenes. And so they've built master data management capabilities and data warehouse capabilities to try to serve that. But they find that when they then go to apply AI across some of those curated data environments, it's still not sufficient. So we developed an element of being able to rapidly assemble what we call a profile of one. It's a very, very intimate profile around declared data sources, uh, that relate to a key business entity. >>In most cases, it's a person, it's a member, it's a patient, it's a client, but it can be a product for some of our clients. It's real estate. Uh, it's a listing. Um, you know, it can be someone who's enjoying a theme park. It can be someone who's a shopper in a grocery store. Um, it can be a region. So it's any key business entity. And one of the places where we applied our AI knowledge is by being able to extract key information out of these declared systems and then start to make longitudinal observations about those systems and to learn about them. And then line those up with prediction engines that both we supply as well as third parties and the customers themselves supply them. So in this theme of operationalization, they're constantly coming up with new innovations or a new model that they might want to interject into that engagement application. Our platform with this profile of one allows them to align that model directly into that profile, get the benefits of what we've already done, but then also continue to enhance, differentiate and provide even greater, uh, greater value to that client. IBM is providing aspects of those models that we can plug in. And many of our clients are that's really >>Well. That's interesting. So that profile of one is kind of the instantiation of that secret sauce, but you mentioned like master data management data warehouse, and, you know, as well as I do Bob we've we've we've decades of failures trying to get a 360 degree view for example of the customer. Uh, it's just, just not real time. It's not as current as we would want it to be. The quality is not necessarily there. It's a very asynchronous process. Things have changed the processing power. You and I have talked about this a lot. We have much more data now. So it's that, that, that profile one. So, but also you mentioned curated apps, customer experience, and lead gen. You mentioned those two, uh, and you've also talked about digital transformation. So it sounds like you're supporting, and maybe this is not necessarily the case, but I'm curious as to what's going on here, maybe supporting more revenue generation in the early phases than say privacy or compliance, or is it actually, do you have use cases for both? >>It's all, it's all of it. Um, and, and shake and, you know, really talk passionately about some of the things we've helped clients do, like for instance, uh, J money. Why don't you talk about the, the hospital, um, uh, uh, you know, discharge processes. >>Absolutely. So, so, you know, just to make this a bit more real, they, you know, when you talk about a profile on one, it's about understanding of patient, as I said earlier, but it's trying to bring this notion of not just the things that you know about the patient you call that declared information. You can find the system in, you can find this information in traditional EMR systems, right? But imagine bringing in, uh, observed information, things that you observed an interaction with the patient, uh, and then bring in inferences that you can then start drawing on top of that. So to bring this to a live example, imagine at the point of care, knowing when all the conditions are right for the patient to be discharged after surgery. And oftentimes as you know, those, if all the different evidence of the different elements that don't come together, you can make some really serious mistakes in terms of patient discharge, bad things can happen. >>Patient could be readmitted or even worse. That could be a serious outcome. Now, how do you bring that information at the point of care for the person making a decision, but not just looking at the information, you know, but also understanding not just the clinical information, but the social, the socioeconomic information, and then making sure that that decision has the appropriate evidence behind it. So then when you do make that decision, you have the appropriate sort of, uh, you know, the guidance behind it for audit reasons, but also for ensuring that you don't have a bad outcome. So that's the example Bob's talking about, where we have a flight this in real settings, in, in healthcare, but also in financial services and other industries where you can make these decisions based on the machine, telling you with a lot of detail behind it, whether this is the right decision to be made, we call this explainability and the evidence that's needed. >>You know, that's interesting. I, I, I'm imagining a use case in my mind where after a patient leaves, so often there's just a complete disconnect with the patient, unless that patient has problems and goes back, but that patient might have some problems, but they forget it's too much of a pain in the neck to go back, but, but the system can now track this and we could get much more accurate information and that could help in future diagnoses and, and also decision-making for a patient in terms of, of outcomes and probability of success. Um, question, what do you actually sell? So it's a middleware product. It's a, how do I license it? >>It's a, it's a, uh, it's a software platform. So we sell software, um, and it is deployed in the customer's cloud environment of choice. Uh, of course we support complete hybrid cloud capabilities. Um, we support native cloud deployments on top of Microsoft and Amazon and Google. And we support IBM's hybrid cloud initiative with red hat OpenShift as well, which also puts us in a position to both support those public cloud environments, as well as the customer's private cloud environments. So constructed with Kubernetes in that environment, um, which helps the customer also re you know, realize the value of that operational appar operationalization, because they can modify those applications and then redeploy them directly into their cloud environment and start to see those as struck to see those spaces. Now, I want to cover a couple of the other components of the secret sauce, if I could date to make sure that you've got a couple other elements where some real breakthroughs are occurring, uh, in these spaces. >>Um, so Dave, you and I, you know, we're passionate about the semiconductor industry, uh, and you know, we know what is, you know, happening with regard to innovation and broadening the people who are now siliconized their intellectual property and a lot of that's happening because those companies who have been able to figure out how to manufacture or how to design those semiconductors are operationalizing those platforms with our customers. So you have people like apple who are able to really break out of the scene and do things by utilizing utilities and macros their own knowledge about how things need to work. And it's just, it's very similar to what we're talking about doing here for enterprise AI, they're operationalizing that construction, but none of those companies would actually start creating the actual devices until they go through simulation and design. Correct. Well, when you think about most enterprises and how they develop software, they just immediately start to develop the code and they're going through AB testing, but they're all writing code. >>They're developing those assets. They're creating many, many models. You know, some organizations say 90% of the models they create. They never use some say 50, and they think that's good. But when you think about that in terms of, you know, the capital that's being deployed, both on the resources, as well as the infrastructure, that's potentially a lot of waste as well. So one of the breakthroughs is, uh, the creation of what we call synthetic data and simulations inside of our, of our operational platform. So cortex fabric allows someone to actually say, look, this is my data pattern. And because it's sensitive data, it might be, you know, PII. Um, we can help them by saying, okay, what is the pattern of that data? And then we can create synthetic data off of that pattern for someone to experiment with how a model might function or how that might work in the application context. >>And then to run that through a set of simulations, if they want to bring a new model into an application and say, what will the outcomes of this model be before I deployed into production, we allow them to drive simulations across millions or billions of interactions to understand what is that model going to be effective. Was it going to make a difference for that individual or for this application or for the cost savings goal and outcomes that I'm trying to drive? So just think about what that means in terms of that digital transformation officers, having the great idea, being in the C-suite and saying, I want to do this with my business. Oftentimes they have to turn around to the CIO or the chief data officer and say, when can you get me that data? And we all know the answer to that question. They go like this, like the, yeah, I've got a couple other things on the plate and I'll get to that as soon as I can. >>Now we're able to liberate that. Now we're able to say, look, you know, what's the concept that you're trying to develop. Let's create the synthetic data off of that environment. We have a Corpus of data that we have collected through various client directions that many times gets that bootstrapped and then drive that through simulation. So we're able to drive from imagination of what could be the outcome to really getting high confidence that this initiative is going to have a meaningful value for the enterprise. And then that stimulates the right kind of following and the right kind of endorsement, uh, throughout really driving that change to the enterprise and that aspect of the simulations, the ability to plan out what that looks like and develop those synthetic aspects is another important element that the secret sauce inside of cortex fabric, >>Back to the semiconductor innovation, I can do that very cheaply. I think, I think I I'm thinking AWS cloud, I could experiment using graviton or maybe do a little bit of training with some, you know, new processors and, and then containerize it, bring it back to my on-premise state and apply it. Uh, and so, uh, just a as you say, a much more agile environment, um, yeah, >>Speed efficiency, um, and the ability to validate the hypothesis that, that started the process. >>Guys, think about the Tam, the total available market. Can we have that discussion? How big is that? >>I mean, if you think about the spend across, uh, the healthcare space and financial services, we're talking about hundreds of billions, uh, in that, in terms of what the enterprise AI opportunity, as in just those spaces. And remember financial services is a broad spectrum. So one of the things that we're actually starting to roll out today in fact, is a SAS service that we developed. That's based on top of our offerings called trust star trust star.ai, and trust star is a set of personalized insights that get delivered directly to the loan officer inside of, uh, an institution who's trying to, uh, really match, uh, lending to someone who wants to buy a property. Um, and when you think about many of those organizations, they have very, very high demand. They've got a lot of information, they've got a lot of regulation they need to adhere to. >>But many times they're very analytically challenged in terms of the tools they have to be able to serve those needs. So what's happening with new listings, what's happening with my competitors, what's happening. As people move from high tax states, where they want to potentially leave into new, more attractive toxin and opportunity-based environments where they're not known to those lending institutions that maybe, you know, they're, they're trying to be married up with. So we've developed a set of insights that are, is, this is a subscription service trust r.ai, um, which goes directly to the loan officer. And then we use our platform behind the scenes to use things like the home disclosure act, data, MLS data, other data that is typically Isagenix to those sources and providing very customized insights to help that buyer journey. And of course, along the way, we can identify things like are some of the decisions more difficult to explain, are there potential biases that might be involved in that environment as people are applying for mortgages, and we can really drive growth through inclusion for those lending institutions, because they might just not understand that potential client well enough, that we can identify the kind of things that they can do to know them better. >>And the benefit is really to hold there, right? And shale, I'll let you jump in, but to me, it's twofold. There. One is, you know, you want to have accurate decisions. You want to have low risk decisions. And if you want to be able to explain that to an individual that may get rejected, here's why, um, and, and it wasn't because of bias. It was because of XYZ and you need to work on these things, but go ahead shape. >>Now, this is going to add that point here, Dave, which is a double-faced point on the dam. One of the things that, and the reason why, you know, industries like healthcare, financial services spending billions, it's not because they look at AI in isolation, they actually looking at the existing processes. So, you know, established disciplines like CRM or supply chain procurement, whether it is contact center and so on. And the examples that we gave you earlier, it's about infusing AI into those existing applications, existing systems. And that's, what's creating the left because what's been missing so far is the silos of data and you traditional traditional transaction systems, but this notion of intelligence that can be infused into the systems and that's, what's creating this massive market opportunity for us. >>Yeah. And I think, um, I think a lot of people just misunderstood in the, or in the early, early days of the AI, you know, new AI when we came out of the AI winter, if you will, people thought, okay, the incumbents are in big trouble now because they are not, they're not AI developers, but really what you guys are showing is it's not about building your own AI. It's about applying AI and having the tools to do so. The incumbents actually have a huge advantage because they've got the systems in place. They can, if they, if they're smart, they can infuse AI and then extract value out of that for their customers. >>And that's why, you know, companies like, uh, like IBM are an investor in a great partner in this space. Anthem is an investor, uh, you know, of the company, but also, you know, someone who can utilize the capabilities, Microsoft, uh, Intel, um, you know, we've been, we've been, uh, you know, really blessed with a great backing Norwest venture partners, um, obviously is, uh, an investor in us as well. So, you know, we've seen the ability to really help those organizations think about, um, you know, where that future lies. But one of the things that is also, you know, one of the gaps in the promises when a C-suite executive like a digital transformation officer, chief digital chief customer officer, they're having their idea, they want to be accountable to that idea. They're having that idea in the boardroom. And they're saying, look, I think I can improve my customer satisfaction and, uh, by 20 points and decrease the cost of my call center by 20 or 30 or 50 points. >>Um, but they need to be able to measure that. So one of the other things that, uh, we've done a cognitive scale is help them understand the progress that they're making across those business goals. Um, now when you think about this people like Andrew Nang, or just really talking about this aspect of goal oriented AI, don't start with the problem, start with what your business goal is, start with, what outcome you're trying to drive, and then think about how AI helps you along that goal. We're delivering this now in our product, our version six product. So while some people are saying, yeah, this is really the right way to potentially do it. We have those capabilities in the product. And what we do is we identify this notion of the campaign, an AI campaign. So when the case that I just gave you where the chief digital officer is saying, I want to drive customer satisfaction up. >>I want to have more explainable decisions, and I want to drive cost down. Maybe I want to drive, call avoidance. Um, you know, and I want to be able to reduce a handling time, um, to drive those costs down, that is a campaign. And then underneath that campaign, there's all sorts of missions that support that campaign. Some of them are very long running. Some of them are very ephemeral. Some of them are cyclical, and we have this notion of the campaign and then admission planner that supports the goals of that campaign, showing that a leader, how they're doing against that goal by measuring the outcomes of every interaction against that mission and all the missions against the campaign. So, you know, we think accountability is an important part of that process as well. And we've never engaged an executive that says, I want to do this, but I don't want to be accountable to the result, but they're having a hard time identifying I'm spending this money. >>How do I ensure that I'm getting the return? And so we've put our, you know, our secret sauce into that space as well. And that includes, you know, the information around the trustworthiness of those, uh, capabilities. Um, and I should mention as well, you know, when we think about that aspect of the responsible AI capabilities, it's really important. The partnerships that we're driving across that space, no one company is going to have the perfect model intelligence tool to be able to address an enterprise's needs. It's much like cybersecurity, right? People thought initially, well, I'll do it myself. I'll just turn up my firewall. You know, I'll make my applications, you know, uh, you know, roll access much more granular. I'll turn down the permissions on the database and I'll be safe from cybersecurity. And then they realized, no, that's not how it was going to work. >>And by the way, the threats already inside and there's, long-term persistent code running, and you have to be able to scan it, have intelligence around it. And there are different capabilities that are specialized for different components of that problem. The same is going to be turnaround responsible and trustworthy AI. So we're partnered with people like IBM, people like Microsoft and others to really understand how we take the best of what it is that they're doing partner with the best, uh, that they're doing and make those outcomes better for clients. And then there's also leaders like the responsible AI Institute, which is a non-profit independent organization who were thinking about a new rating systems for, um, the space of responsible and trusted AI, thinking about things like certifications for professionals that really drive that notion of education, which is an important component of addressing the problem. And we're providing the integration of our tools directly with those assessments and those certifications. So if someone gets started with our platform, they're already using an ecosystem that includes independent thinkers from across the entire industry, um, including public sector, as well as the private sector, to be able to be on the cutting edge of what it's going to take to really step up to the challenge in that space. >>Yeah. You guys got a lot going on. I mean, you're eight years in now and you've got now an executive to really drive the next scale. You mentioned Bob, some of your investors, uh, Anthem, IBM Norwest, uh, I it's Crunchbase, right? It says you've raised 40 million. Is that the right number? Where are you in fundraising? What can you tell? >>Um, they're a little behind where we are, but, uh, you know, we're staged B and, uh, you know, we're looking forward to now really driving that growth. We're past that startup phase, and now we're into the growth phase. Um, and we're seeing, you know, the focus that we've applied in the industries, um, really starting to pay off, you know, initially it would be a couple of months as a customer was starting to understand what to be able to do with our capabilities to address their challenges. Now we're seeing that happen in weeks. So now is the right time to be able to drive that scalability. So we'll be, you know, looking in the market of how we assemble that, uh, you know, necessary capability to grow. Um, Shay and I have worked, uh, in the past year of, uh, with the board support of building out our go to market around that space. >>Um, and in the first hundred days, it's all about alignment because when you're going to go through that growth phase growth phase, you really have to make sure that things were pointed in the right direction and pointed together in the right direction, simplifying what it is that we're doing for the market. So people could really understand, you know, how unique we are in this space, um, and what they can expect out of an engagement with us. Um, and then, you know, really driving that aspect of designing to go to market. Um, and then scaling that. >>Yeah, I think I, it sounds like you've got, you got, if you're, if you're in down to days or weeks in terms of the ROI, it sounds like you've got product market fit nailed. Now it's about sort of the next phase is you really driving your go to market and the science behind how your dimension and your, your sales productivity, and you can now codify what you've learned in that first phase. I like the approach. A lot of, a lot of times you see companies, of course, this comes out of the west coast, east coast guy, but you see the double, double, triple, triple grow, grow, grow, grow, grow, and then, and then churn becomes that silent killer of the S the software company. I think you guys, it sounds you've, you've taken a much, much more adult-like approach, and now you're ready to really drive that scale. I think it's the new formula really for success for hitting escape velocity. Guys, we got to go, but thanks so much. Uh, uh, Bob, I'll give you the last word, w w w what you mentioned some of your a hundred day priorities. Maybe you can summarize that and what should we be looking for as Martin? >>I mean, I, I think, I think the, you know, the, our measures of success are our clients measure success and the same for our partners. So we're not doing this alone, we're doing it with system integrator partners, and we're doing it with a great technology partners in the market as well. So this is a part about keeping that promise for enterprise AI. And one of the things that I'll say just in the last couple of minutes is, you know, this is not just a company with a great vision and great engineers to develop out this great portfolio, but it's a company with great values, great commitments to its employees and the marketplace and the communities we serve. So I was attracted to the culture of this company, as well as I was, uh, to the, uh, innovation and what they mean to the, to the space of a, >>And I said, I said, I'll give you last word. Actually, I got a question for Shea you Austin based, is that correct? >>But we have a global presence, obviously I'm operating out of Austin, other parts of the U S but, uh, offices in, in, uh, in the UK, as well as in India, >>You're not moving to tax-free Texas. Like everybody else. >>I've got to, I've got an important home, uh, and life in Connecticut cell. I'll be traveling back and forth between Connecticut and Austin, but keeping my home there. >>Thanks for coming on and best of luck, we want to follow your progress and really appreciate your time today. Good luck. >>Thank you, Dave. All right. >>Thank you for watching this cube conversation. This is Dave Volante. We'll see you next time.

>>from around the globe. It's the Cube presenting Cuban Cloud brought to you by Silicon Angle. Welcome back to the live segment of the Cuban cloud. I'm Dave, along with my co host, John Ferrier. John Rose is here. He's the global C T o Dell Technologies. John, great to see you as always, Really appreciate >>it. Absolutely good to know. >>Hey, so we're gonna talk edge, you know, the the edge, it's it's estimated. It's a multi multi trillion dollar opportunity, but it's a highly fragmented, very complex. I mean, it comprises from autonomous vehicles and windmills, even retail stores outer space. And it's so it brings in a lot of really gnarly technical issues that we want to pick your brain on. Let me start with just what to you is edge. How do you think about >>it? Yeah, I think I mean, I've been saying for a while that edges the when you reconstitute Ike back out in the real world. You know, for 10 years we've been sucking it out of the real world, taking it out of factories, you know, nobody has an email server under their desk anymore. On that was because we could put it in data centers and cloud public clouds, and you know that that's been a a good journey. And then we realized, Wait a minute, all the data actually was being created out in the real world. And a lot of the actions that have to come from that data have to happen in real time in the real world. And so we realized we actually had toe reconstitute a nightie capacity out near where the data is created, consumed and utilized. And, you know, that turns out to be smart cities, smart factories. You know, uh, we're dealing with military apparatus. What you're saying, how do you put, you know, edges in tow, warfighting theaters or first responder environments? It's really anywhere that data exists that needs to be processed and understood and acted on. That isn't in a data center. So it's kind of one of these things. Defining edge is easier to find. What it isn't. It's anywhere that you're going to have. I t capacity that isn't aggregated into a public or private cloud data center. That seems to be the answer. So >>follow. Follow that. Follow the data. And so you've got these big issue, of course, is late and see people saying, Well, some applications or some use cases like autonomous vehicles. You have to make the decision locally. Others you can you can send back. And you, Kamal, is there some kind of magic algorithm the technical people used to figure out? You know what, the right approaches? Yeah, >>the good news is math still works and way spent a lot of time thinking about why you build on edge. You know, not all things belong at the edge. Let's just get that out of the way. And so we started thinking about what does belong at the edge, and it turns out there's four things you need. You know, if you have a real time responsiveness in the full closed loop of processing data, you might want to put it in an edge. But then you have to define real time, and real time varies. You know, real time might be one millisecond. It might be 30 milliseconds. It might be 50 milliseconds. It turns out that it's 50 milliseconds. You probably could do that in a co located data center pretty far away from those devices. One millisecond you better be doing it on the device itself. And so so the Leighton see around real time processing matters. And, you know, the other reasons interesting enough to do edge actually don't have to do with real time crossing they have to do with. There's so much data being created at the edge that if you just blow it all the way across the Internet, you'll overwhelm the Internets. We have need toe pre process and post process data and control the flow across the world. The third one is the I T. O T boundary that we all know. That was the I O t. Thing that we were dealing with for a long time. And the fourth, which is the fascinating one, is it's actually a place where you might want to inject your security boundaries, because security tends to be a huge problem and connected things because they're kind of dumb and kind of simple and kind of exposed. And if you protect them on the other end of the Internet, the surface area of protecting is enormous, so there's a big shift basically move security functions to the average. I think Gardner made up a term for called Sassy. You know, it's a pretty enabled edge, but these are the four big ones. We've actually tested that for probably about a year with customers. And it turns out that, you know, seems to hold If it's one of those four things you might want to think about an edge of it isn't it probably doesn't belong in >>it. John. I want to get your thoughts on that point. The security things huge. We talked about that last time at Del Tech World when we did an interview with the Cube. But now look at what's happened. Over the past few months, we've been having a lot of investigative reporting here at Silicon angle on the notion of misinformation, not just fake news. Everyone talks about that with the election, but misinformation as a vulnerability because you have now edge devices that need to be secured. But I can send misinformation to devices. So, you know, faking news could be fake data say, Hey, Tesla, drive off the road or, you know, do this on the other thing. So you gotta have the vulnerabilities looked at and it could be everything. Data is one of them. Leighton. See secure. Is there a chip on the device? Could you share your vision on how you see that being handled? Cause it's a huge >>problem. Yeah, this is this is a big deal because, you know, what you're describing is the fact that if data is everything, the flow of data ultimately turns into the flow of information that knowledge and wisdom and action. And if you pollute the data, if you could compromise it the most rudimentary levels by I don't know, putting bad data into a sensor or tricking the sensor which lots of people can dio or simulating a sensor, you can actually distort things like a I algorithms. You can introduce bias into them and then that's a That's a real problem. The solution to it isn't making the sensors smarter. There's this weird Catch 22 when you sense arise the world, you know you have ah, you know, finite amount of power and budget and the making sensors fatter and more complex is actually the wrong direction. So edges have materialized from that security dimension is an interesting augment to those connected things. And so imagine a world where you know your sensor is creating data and maybe have hundreds or thousands of sensors that air flowing into an edge compute layer and the edge compute layer isn't just aggregating it. It's putting context on it. It's metadata that it's adding to the system saying, Hey, that particular stream of telemetry came from this device, and I'm watching that device and Aiken score it and understand whether it's been compromised or whether it's trustworthy or whether it's a risky device and is that all flows into the metadata world the the overall understanding of not just the data itself, but where did it come from? Is it likely to be trustworthy? Should you score it higher or lower in your neural net to basically manipulate your algorithm? These kind of things were really sophisticated and powerful tools to protect against this kind of injection of false information at the sensor, but you could never do that at a sensor. You have to do it in a place that has more compute capacity and is more able to kind of enriched the data and enhance it. So that's why we think edges are important in that fourth characteristic of they aren't the security system of the sensor itself. But they're the way to make sure that there's integrity in the sense arised world before it reaches the Internet before it reaches the cloud data centers. >>So access to that metadata is access to the metadata is critical, and it's gonna be it's gonna be near real time, if not real time, right? >>Yeah, absolutely. And, you know, the important thing is, Well, I'll tell you this. You know, if you haven't figured this out by looking at cybersecurity issues, you know, compromising from the authoritative metadata is a really good compromise. If you could get that, you can manipulate things that a scale you've never imagined. Well, in this case, if the metadata is actually authoritatively controlled by the edge note the edge note is processing is determining whether or not this is trustworthy or not. Those edge nodes are not $5 parts, their servers, their higher end systems. And you can inject a lot more sophisticated security technology and you can have hardware root of trust. You can have, you know, mawr advanced. PK I in it, you can have a I engines watching the behavior of it, and again, you'd never do that in a sensor. But if you do it at the first step into the overall data pipeline, which is really where the edges materializing, you can do much more sophisticated things to the data. But you can also protect that thing at a level that you'd never be able to do to protect a smart lightbulb. A thermostat in your house? >>Uh, yes. So give us the playbook on how you see the evolution of the this mark. I'll see these air key foundational things, a distributed network and it's a you know I o t trends into industrial i o t vice versa. As a software becomes critical, what is the programming model to build the modern applications is something that I know. You guys talk to Michael Dell about this in the Cuban, everyone, your companies as well as everyone else. Its software define everything these days, right? So what is the software framework? How did people code on this? What's the application aware viewpoint on this? >>Yeah, this is, uh, that's unfortunately it's a very complex area that's got a lot of dimensions to it. Let me let me walk you through a couple of them in terms of what is the software framework for for For the edge. The first is that we have to separate edge platforms from the actual edge workload today too many of the edge dialogues or this amorphous blob of code running on an appliance. We call that an edge, and the reality is that thing is actually doing two things. It's, ah, platform of compute out in the real world and it's some kind of extension of the cloud data pipeline of the cloud Operating model. Instance, he added, A software probably is containerized code sitting on that edge platform. Our first principle about the software world is we have to separate those two things. You do not build your cloud your edge platform co mingled with the thing that runs on it. That's like building your app into the OS. That's just dumb user space. Colonel, you keep those two things separate. We have Thio start to enforce that discipline in the software model at the edges. The first principle, the second is we have to recognize that the edges are are probably best implemented in ways that don't require a lot of human intervention. You know, humans air bad when it comes to really complex distributed systems. And so what we're finding is that most of the code being pushed into production benefits from using things like kubernetes or container orchestration or even functional frameworks like, you know, the server list fast type models because those low code architectures generally our interface with via AP, eyes through CCD pipelines without a lot of human touch on it. And it turns out that, you know, those actually worked reasonably well because the edges, when you look at them in production, the code actually doesn't change very often, they kind of do singular things relatively well over a period of time. And if you can make that a fully automated function by basically taking all of the human intervention away from it, and if you can program it through low code interfaces or through automated interfaces, you take a lot of the risk out of the human intervention piece of this type environment. We all know that you know most of the errors and conditions that break things are not because the technology fails it because it's because of human being touches it. So in the software paradigm, we're big fans of more modern software paradigms that have a lot less touch from human beings and a lot more automation being applied to the edge. The last thing I'll leave you with, though, is we do have a problem with some of the edge software architectures today because what happened early in the i o t world is people invented kind of new edge software platforms. And we were involved in these, you know, edge X foundry, mobile edge acts, a crane. Oh, and those were very important because they gave you a set of functions and capabilities of the edge that you kind of needed in the early days. Our long term vision, though for edge software, is that it really needs to be the same code base that we're using in data centers and public clouds. It needs to be the same cloud stack the same orchestration level, the same automation level, because what you're really doing at the edge is not something that spoke. You're taking a piece of your data pipeline and you're pushing it to the edge and the other pieces are living in private data centers and public clouds, and you like they all operate under the same framework. So we're big believers in, like pushing kubernetes orchestration all the way to the edge, pushing the same fast layer all the way to the edge. And don't create a bespoke world of the edge making an extension of the multi cloud software framework >>even though the underlying the underlying hardware might change the microprocessor, GPU might change GP or whatever it is. Uh, >>by the way, that that's a really good reason to use these modern framework because the energies compute where it's not always next 86 underneath it, programming down at the OS level and traditional languages has an awful lot of hardware dependencies. We need to separate that because we're gonna have a lot of arm. We're gonna have a lot of accelerators a lot of deep. Use a lot of other stuff out there. And so the software has to be modern and able to support header genius computer, which a lot of these new frameworks do quite well, John. >>Thanks. Thanks so much for for coming on, Really? Spending some time with us and you always a great guest to really appreciate it. >>Going to be a great stuff >>of a technical edge. Ongoing room. Dave, this is gonna be a great topic. It's a clubhouse room for us. Well, technical edge section every time. Really. Thanks >>again, Jon. Jon Rose. Okay, so now we're gonna We're gonna move to the second part of our of our technical edge discussion. Chris Wolf is here. He leads the advanced architecture group at VM Ware. And that really means So Chris's looks >>at I >>think it's three years out is kind of his time. Arise. And so, you know, advanced architecture, Er and yeah. So really excited to have you here. Chris, can you hear us? >>Okay. Uh, >>can Great. Right. Great to see you again. >>Great >>to see you. Thanks for coming on. Really appreciate it. >>So >>we're talking about the edge you're talking about the things that you see way set it up is a multi trillion dollar opportunity. It's It's defined all over the place. Uh, Joey joke. It's Could be a windmill. You know, it could be a retail store. It could be something in outer space. Its's It's it's, you know, whatever is defined A factory, a military installation, etcetera. How do you look at the edge. And And how do you think about the technical evolution? >>Yeah, I think it is. It was interesting listening to John, and I would say we're very well aligned there. You know, we also would see the edge is really the place where data is created, processed and are consumed. And I think what's interesting here is that you have a number off challenges in that edges are different. So, like John was talking about kubernetes. And there's there's multiple different kubernetes open source projects that are trying to address thes different edge use cases, whether it's K three s or Cubbage or open your it or super edge. And I mean the list goes on and on, and the reason that you see this conflict of projects is multiple reasons. You have a platform that's not really designed to supported computing, which kubernetes is designed for data center infrastructure. Uh, first on then you have these different environments where you have some edge sites that have connectivity to the cloud, and you have some websites that just simply don't write whether it's an oil rig or a cruise ship. You have all these different use cases, so What we're seeing is you can't just say this is our edge platform and, you know, go consume it because it won't work. You actually have to have multiple flavors of your edge platform and decide. You know what? You should time first. From a market perspective, I >>was gonna ask you great to have you on. We've had many chest on the Cube during when we actually would go to events and be on the credit. But we appreciate you coming into our virtual editorial event will be doing more of these things is our software will be put in the work to do kind of a clubhouse model. We get these talks going and make them really valuable. But this one is important because one of the things that's come up all day and we kind of introduced earlier to come back every time is the standardization openness of how open source is going to extend out this this interoperability kind of vibe. And then the second theme is and we were kind of like the U S side stack come throwback to the old days. Uh, talk about Cooper days is that next layer, but then also what is going to be the programming model for modern applications? Okay, with the edge being obviously a key part of it. What's your take on that vision? Because that's a complex area certain a lot of a lot of software to be written, still to come, some stuff that need to be written today as well. So what's your view on How do you programs on the edge? >>Yeah, it's a It's a great question, John and I would say, with Cove it We have seen some examples of organizations that have been successful when they had already built an edge for the expectation of change. So when you have a truly software to find edge, you can make some of these rapid pivots quite quickly, you know. Example was Vanderbilt University had to put 1000 hospital beds in a parking garage, and they needed dynamic network and security to be able to accommodate that. You know, we had a lab testing company that had to roll out 400 testing sites in a matter of weeks. So when you can start tohave first and foremost, think about the edge as being our edge. Agility is being defined as you know, what is the speed of software? How quickly can I push updates? How quickly can I transform my application posture or my security posture in lieu of these types of events is super important. Now, if then if we walk that back, you know, to your point on open source, you know, we see open source is really, uh you know, the key enabler for driving edge innovation and driving in I S V ecosystem around that edge Innovation. You know, we mentioned kubernetes, but there's other really important projects that we're already seeing strong traction in the edge. You know, projects such as edge X foundry is seeing significant growth in China. That is, the core ejects foundry was about giving you ah, pass for some of your I o T aps and services. Another one that's quite interesting is the open source faith project in the Linux Foundation. And fate is really addressing a melody edge through a Federated M L model, which we think is the going to be the long term dominant model for localized machine learning training as we continue to see massive scale out to these edge sites, >>right? So I wonder if you could You could pick up on that. I mean, in in thinking about ai influencing at the edge. Um, how do you see that? That evolving? Uh, maybe You know what, Z? Maybe you could We could double click on the architecture that you guys see. Uh, progressing. >>Yeah, Yeah. Right now we're doing some really good work. A zai mentioned with the Fate project. We're one of the key contributors to the project. Today. We see that you need to expand the breath of contributors to these types of projects. For starters, uh, some of these, what we've seen is sometimes the early momentum starts in China because there is a lot of innovation associated with the edge there, and now it starts to be pulled a bit further West. So when you look at Federated Learning, we do believe that the emergence of five g I's not doesn't really help you to centralized data. It really creates the more opportunity to create, put more data and more places. So that's, you know, that's the first challenge that you have. But then when you look at Federated learning in general, I'd say there's two challenges that we still have to overcome organizations that have very sophisticated data. Science practices are really well versed here, and I'd say they're at the forefront of some of these innovations. But that's 1% of enterprises today. We have to start looking at about solutions for the 99% of enterprises. And I'd say even VM Ware partners such as Microsoft Azure Cognitive Services as an example. They've been addressing ML for the 99%. I say That's a That's a positive development. When you look in the open source community, it's one thing to build a platform, right? Look, we love to talk about platforms. That's the easy part. But it's the APS that run on that platform in the services that run on that platform that drive adoption. So the work that we're incubating in the VM, or CTO office is not just about building platforms, but it's about building the applications that are needed by say that 99% of enterprises to drive that adoption. >>So if you if you carry that through that, I infer from that Chris that the developers are ultimately gonna kind of win the edge or define the edge Um, How do you see that From their >>perspective? Yeah, >>I think its way. I like to look at this. I like to call a pragmatic Dev ops where the winning formula is actually giving the developer the core services that they need using the native tools and the native AP eyes that they prefer and that is predominantly open source. It would some cloud services as they start to come to the edge as well. But then, beyond that, there's no reason that I t operations can't have the tools that they prefer to use. A swell. So we see this coming together of two worlds where I t operations has to think even for differently about edge computing, where it's not enough to assume that I t has full control of all of these different devices and sensors and things that exists at the edge. It doesn't happen. Often times it's the lines of business that air directly. Deploying these types of infrastructure solutions or application services is a better phrase and connecting them to the networks at the edge. So what does this mean From a nightie operations perspective? We need tohave, dynamic discovery capabilities and more policy and automation that can allow the developers to have the velocity they want but still have that consistency of security, agility, networking and all of the other hard stuff that somebody has to solve. And you can have the best of both worlds here. >>So if Amazon turned the data center into an A P I and then the traditional, you know, vendors sort of caught up or catching up and trying to do in the same premise is the edge one big happy I Is it coming from the cloud? Is it coming from the on Prem World? How do you see that evolving? >>Yes, that's the question and races on. Yeah, but it doesn't. It doesn't have to be exclusive in one way or another. The VM Ware perspective is that, you know, we can have a consistent platform for open source, a consistent platform for cloud services. And I think the key here is this. If you look at the partnerships we've been driving, you know, we've on boarded Amazon rds onto our platform. We announced the tech preview of Azure Arc sequel database as a service on our platform as well. In addition, toe everything we're doing with open source. So the way that we're looking at this is you don't wanna make a bet on an edge appliance with one cloud provider. Because what happens if you have a business partner that says I am a line to Google or on the line to AWS? So I want to use this open source. Our philosophy is to virtualized the edge so that software can dictate, you know, organizations velocity at the end of the day. >>Yeah. So, Chris, you come on, you're you're an analyst at Gartner. You know us. Everything is a zero sum game, but it's but But life is not like that, right? I mean, there's so much of an incremental opportunity, especially at the edge. I mean, the numbers are mind boggling when when you look at it, >>I I agree wholeheartedly. And I think you're seeing a maturity in the vendor landscape to where we know we can't solve all the problems ourselves and nobody can. So we have to partner, and we have to to your earlier point on a P. I s. We have to build external interfaces in tow, our platforms to make it very easy for customers have choice around ice vendors, partners and so on. >>So, Chris, I gotta ask you since you run the advanced technology group in charge of what's going on there, will there be a ship and focus on mawr ships at the edge with that girl singer going over to intel? Um, good to see Oh, shit, so to speak. Um, all kidding aside, but, you know, patch leaving big news around bm where I saw some of your tweets and you laid out there was a nice tribute, pat, but that's gonna be cool. That's gonna be a didn't tell. Maybe it's more more advanced stuff there. >>Yeah, I think >>for people pats staying on the VMRO board and to me it's it's really think about it. I mean, Pat was part of the team that brought us the X 86 right and to come back to Intel as the CEO. It's really the perfect book end to his career. So we're really sad to see him go. Can't blame him. Of course it's it's a It's a nice chapter for Pat, so totally understand that. And we prior to pack going to Intel, we announced major partnerships within video last year, where we've been doing a lot of work with >>arm. So >>thio us again. We see all of this is opportunity, and a lot of the advanced development projects were running right now in the CTO office is about expanding that ecosystem in terms of how vendors can participate, whether you're running an application on arm, whether it's running on X 86 or whatever, it's running on what comes next, including a variety of hardware accelerators. >>So is it really? Is that really irrelevant to you? I mean, you heard John Rose talk about that because it's all containerized is it is. It is a technologies. Is it truly irrelevant? What processor is underneath? And what underlying hardware architectures there are? >>No, it's not. You know it's funny, right? Because we always want to say these things like, Well, it's just a commodity, but it's not. You didn't then be asking the hardware vendors Thio pack up their balls and go home because there's just nothing nothing left to do, and we're seeing actually quite the opposite where there's this emergence and variety of so many hardware accelerators. So even from an innovation perspective, for us. We're looking at ways to increase the velocity by which organizations can take advantage of these different specialized hardware components, because that's that's going to continue to be a race. But the real key is to make it seamless that an application could take advantage of these benefits without having to go out and buy all of this different hardware on a per application basis. >>But if you do make bets, you can optimize for that architecture, true or not, I mean, our estimate is that the you know the number of wafer is coming out of arm based, you know, platforms is 10 x x 86. And so it appears that, you know, from a cost standpoint, that's that's got some real hard decisions to make. Or maybe maybe they're easy decisions, I don't know. But so you have to make bets, Do you not as a technologist and try to optimize for one of those architectures, even though you have to hedge those bets? >>Yeah, >>we do. It really boils down to use cases and seeing, you know, what do you need for a particular use case like, you know, you mentioned arm, you know, There's a lot of arm out at the edge and on smaller form factor devices. Not so much in the traditional enterprise data center today. So our bets and a lot of the focus there has been on those types of devices. And again, it's it's really the It's about timing, right? The customer demand versus when we need to make a particular move from an innovation >>perspective. It's my final question for you as we wrap up our day here with Great Cuban Cloud Day. What is the most important stories in in the cloud tech world, edge and or cloud? And you think people should be paying attention to that will matter most of them over the next few years. >>Wow, that's a huge question. How much time do we have? Not not enough. A >>architect. Architectural things. They gotta focus on a lot of people looking at this cove it saying I got to come out with a growth strategy obvious and clear, obvious things to see Cloud >>Yeah, yeah, let me let me break it down this way. I think the most important thing that people have to focus on >>is deciding How >>do they when they build architectures. What does the reliance on cloud services Native Cloud Services so far more proprietary services versus open source technologies such as kubernetes and the SV ecosystem around kubernetes. You know, one is an investment in flexibility and control, lots of management and for your intellectual property, right where Maybe I'm building this application in the cloud today. But tomorrow I have to run it out at the edge. Or I do an acquisition that I just wasn't expecting, or I just simply don't know. Sure way. Sure hope that cova doesn't come around again or something like it, right as we get past this and navigate this today. But architect ng for the expectation of change is really important and having flexibility of round your intellectual property, including flexibility to be able to deploy and run on different clouds, especially as you build up your different partnerships. That's really key. So building a discipline to say you know what >>this is >>database as a service, it's never going to define who I am is a business. It's something I have to do is an I T organization. I'm consuming that from the cloud This part of the application sacked that defines who I am is a business. My active team is building this with kubernetes. And I'm gonna maintain more flexibility around that intellectual property. The strategic discipline to operate this way among many of >>enterprise customers >>just hasn't gotten there yet. But I think that's going to be a key inflection point as we start to see. You know, these hybrid architectures continue to mature. >>Hey, Chris. Great stuff, man. Really appreciate you coming on the cube and participate in the Cuban cloud. Thank you for your perspectives. >>Great. Thank you very much. Always a pleasure >>to see you. >>Thank you, everybody for watching this ends the Cuban Cloud Day. Volonte and John Furry. All these sessions gonna be available on demand. All the write ups will hit silicon angle calm. So check that out. We'll have links to this site up there and really appreciate you know, you attending our our first virtual editorial >>event again? >>There's day Volonte for John Ferrier in the entire Cube and Cuba and Cloud Team >>Q 3 65. Thanks >>for watching. Mhm

(upbeat music) >> Hello everyone and welcome to theCUBE. In this special power panel we're going to dig into and take a peek at the future of cloud. You know a lot has transpired in the last decade. The cloud itself, we've seen a data explosion. The AI winter turned into machine intelligence going mainstream. We've seen the emergence of As-a-Service models. And as we look forward to the next 10 years we see the whole idea of cloud expanding, new definitions occurring. Yes, the world is hybrid but the situation is more nuanced than that. You've got remote locations, smaller data centers, clandestine facilities, oil rigs, autonomous vehicles, windmills, you name it. Technology is connecting our world, data is flowing through the pipes like water, and AI is helping us make sense of the noise. All of this, and more is driving a new digital economy. And with me to talk about these topics are three great guests from IBM. Maia Sisk is the Director of SaaS Offering Management, at IBM Data and AI. And she's within the IBM Cloud and Cognitive Software Group. Stephanie Walter is the Program Director for data and AI Offering Management, same group IBM Cloud and Cognitive Software. And Daniel Berg is a Distinguished Engineer. He's focused on IBM Cloud Kubernetes Service. He's in the Cloud Organization. And he's going to talk today a lot about IBM's cloud Satellite and of course Containers. Wow, two girls, two boys on a panel, we did it. Folks welcome to theCUBE. (chuckles) >> Thank you. >> Thank you. >> Glad to be here. >> So Maia, I want to start with you and have some other folks chime in here. And really want to dig into the problem statement and what you're seeing with customers and you know, what are some of the challenges that you're hearing from customers? >> Yeah, I think a big challenge that we face is, (indistinct) talked about it earlier just data is everywhere. And when we look at opportunities to apply the cloud and apply an As-a-Service model, one of the challenges that we typically face is that the data isn't all nice cleanly package where you can bring it all together, and you know, one AI models on it, run analytics on it, get it in an easy and clean way. It's messy. And what we're finding is that customers are challenged with the problem of having to bring all of the data together on a single cloud in order to leverage it. So we're now looking at IBM and how we flip that paradigm around. And instead of bringing the data to the cloud bring the cloud to the data , in order to help clients manage that challenge and really harness the value of the data, regardless of where you live. >> I love that because data is distributed by its very nature it's silo, Daniel, anything you'd add? >> Yeah, I mean, I would definitely echo that, what Maia was saying, because we're seeing this with a number of customers that they have certain amount of data that while they're strategically looking that moving to the cloud, there's data that for various reasons they can not move itself into the cloud. And in order to reduce latency and get the fastest amount of processing time, they going to move the processing closer to that data. And that's something that we're looking at providing for our customers as well. The other services within IBM Cloud, through our notion of IBM Cloud Satellite. How to help teams and organizations get processing power manage them to service, but closer to where their data may reside. >> And just to play off of that with one other comment. Then the other thing I think we see a lot today is heightened concerned about risks, about data security, about data privacy. And you're trying to figure out how to manage that challenge of especially when you start sending data over the wire, wanting to make sure that it is still safe, it is still secure and it is still resident in the appropriate places. And that kind of need to manage the governance of the data kind of adds an additional layer of complexity. >> Right, if it's not secure, it's a, non-starter, Stephanie let's bring you into the conversation and talk about, you know, some of the waves that you're seeing. Maybe some of the trends, we've certainly seen digital accelerate as a result of the pandemic. It's no longer I'll get to that someday. It's really, it become a mandate you're out of business, if you don't have a digital business. What are some of the markets shifts that you're seeing? >> Well, I mean, really at the end of the day our clients want to infuse AI into their organizations. And so, you know, really the goal is to achieve ambient AI, AI that's just running in the background unchoosibly helping our clients make these really important business decisions. They're also really focused on trust. That's a big issue here. They're really focused on, you know, being able to explain how their AI is making these decisions and also being able to feel confident that they're not introducing harmful biases into their decision-making. So I say that because when you think about, you know digital organization going digital, that's what our customers want to focus on. They don't want to focus on managing IT. They don't want to focus on managing software. They don't want to to have to focus on, you know, patching and upgrading. And so we're seeing more of a move to manage services As-a-Service technologies, where the clients can really focus on their business problems and using The technologies like AI, to help improve their businesses. And not have to worry so much about building them from the ground up. >> So let's stay on that for a minute. And maybe Maia, Daniel, you can comment. So you, Stephanie, you said that customers want to infuse AI and kind of gave some reasons why, but I want to stay on that for a minute. That, what is that really that main outcome that they're looking for? Maybe there are several, they're trying to get to insight. You mentioned that trynna be more efficient it sounds like they're trynna automate governance and compliance, Maia, Daniel can you sort of add anything to this conversation? >> Yeah, well, I would, I would definitely say that, you know at the end of the day, customers are looking to use the data that they have to make smarter decisions. And in order to make smarter decisions it's not enough to just have the insight. The insight has to, you know, meet the business person that needs it, you know in the context, you know, in the application, in the customer interaction. So I think that that's really important. And then everything else becomes like the the superstructure that helps power, that decision and the decision being embedded in the business process. So we at IBM talk a lot about a concept we call the Ladder to AI. And the the short tagline is there is no AI without IA. You know, there is no Artificial Intelligence without Information Architecture. It is so critical, you know, Maia's version this is the garbage in garbage out. You have to have high quality data. You have to have that data be well-organized and well-managed so that you're using it appropriately. And all of that is just, you know then becomes the fuel that powers your AI. But if you have the AI without having that super structure, you know, you're going to end up making, get bad decisions. And ultimately, you know our customers making their customers experience less than it could and should be. And in a digital world, that's, you know, at the end of the day, it's all about digitizing that interaction with whoever the end customer whoever the end consumer is and making that experience the best it can be, because that's what fuels innovation and growth. >> Okay. So we've heard Arvind Krishna talk about, he actually made this statement IBM has to win the architectural battle for cloud. And I'm wondering maybe Daniel you can comment, on what that architectural framework looks like. I mean Maia just talked about the Information Architecture. You can't have AI without that foundation but we know what does Arvind mean by that? How is IBM thinking about that? >> Yeah, I mean, this is where we're really striving to allow our customers really focusing on their business and focusing on the goals that they're trying to achieve without forcing them to worry as much about the IT and the infrastructure and the platform for which they're going to run. Typically, if you're anchored by your data and the data is not able to move into the cloud, generally we would say that you don't have access to cloud services. You must go and install and run and operate your own software to perform the duties or the processing that you require. And that's a huge burden to push onto a customer because they couldn't move their data to your cloud. Now you're pushing a lot of responsibilities back onto them. So what we're really striving for here is, how can we give them that cloud experience where they can process their data? They can run their run book. They can have all of that managed As-a-Service so that they could focus on their business but get that closer to where the data actually resides. And that's what we're really striving for as far as the architecture is concerned. So with IBM Cloud Satellite, we're pushing the core platform and the platform services that we support in IBM Cloud outside of our data centers and into locations where it's closer to your data. And all of that is underpinned by Containerizations, Containers, Kubernetes and OpenShift. Is fundamentally the platform for which we're building upon. >> Okay. So that, so really it's still it's always a data problem, right? Data is you don't want to move it if you don't have to. Right. So it's, so Stephanie, should we think about this as a new emergent data architecture I guess that's what IA is all about. How do you see that evolving? >> Well I mean, I see it evolving as, I mean, first of all our clients, you know, we know that data is the lifeblood of AI. We know the vast majority of our clients are using more than one cloud. And we know that the client's data may be located in different clouds, and that could be due to costs, that could be due to location. So we have to ask the question, how are our clients supposed to deal with this? This is incredibly complex environments they're are incredibly complex reasons sometimes for the data to be where it is. It can include anything from costs to laws, that our clients have to abide by. So what we need to do, is we need to adapt to these different environments and provide clients with the consistent experience and lower complexity to be able to handle data and be able to use AI in these complex environments. And so, you know, we know data, we also know data science talent is scarce. And if each one of these environments have their own tools that need to be used, depending on where the data is located, that's a huge time sink, for these data scientist and our clients don't want to waste their talents time on problems like this. So what we're seeing is, we're seeing more of a acceptance and realization that this is what our clients are dealing with. We have to make it easier. We have to do Innovative things like figure out how to bring the AI to the data, how to bring the AI to where the clients need it and make it much easier and accessible for them to take advantage of. >> And I think there's an additional point to make on this one, which is it's not just easy and accessible but it's also unified. I mean, one of the challenges that customers face in this multicloud environment and many customers are multicloud, you know, not necessarily by intent but just because of how, you know, businesses have adopted as a service. But to then have all of that experience be fragmented and have different tools not just of data, but different pools of, again catalog, different pools of data science it's extremely complex to manage. So I think one of the powerful things that we're doing here, is we're kind of bringing those multiple clouds together, into more of an integrated or a unified, you know window into the client's data in AI state. So not only does the end-user not have to worry about you know, the technologies of dealing with multiple individual clouds, but also, you know it all comes together in one place. So it can be give managed in a more unified way so that assets can be shared across, and it becomes more of a unified approach. The way I like to think of it is, you know, it's true hybrid multicloud, in that it is all connected as opposed to multi-cloud, but it's pools of multiple clouds, one cloud at a time. >> So it can we stay on that for a second because it's, you're saying it's unified but the data stays where it is. The data is distributed by nature. So it's unified logically, but it's decentralized. Is that, am I getting that right? Correct. Okay. Correct. All right. I'm really interested in how you do this. And maybe we can talk about maybe the approach that you take for some of your offerings and maybe get specific on that. So maybe Stephanie, why don't you start, you know, Yes so, what do you have in your basket? Like Cloud Pak So what we have in our basket I mean lets talk about that. >> We have, so Cloud Pak for Data as a Service. This is our premier data and AI platform. It's offered as a service, its fully managed, and there's roughly, there's 30 services integrated services in our services catalog and growing. So we have services to help you through the entire AI life cycle from preparing your data, which is Maia was saying it's very, very, very important. It's critical to any successful AI project. From building your models, from running the models and then monitoring them to make sure that as I was saying before, you can trust them. You don't have to make sure that, you need to make sure that there's not biased. You need to be able to manage these models and then the life cycle them retrain them if needed. So our platform handles all of that. It's hosted on IBM Cloud. And what we're doing now, which is really exciting, is we're going to use, and you mentioned before IBM Cloud Satellite, as a way for us to send our AI to data that perhaps is located on another cloud or another environment. So how this would work is that the services that are integrated with Cloud Pak for Data as a Service they'll be able to use satellite locations to send their AI workloads, to run next to the data. And this means that the data doesn't need to be moved. You don't have to worry about high egress charges. You can see, you can reduce latency and see much stronger performance by running these AI workloads where it counts. We're really excited to to add this capability to our platform. Because, you know, we spent a lot of time talking about earlier all of these challenges that our clients have and this is going to make a big difference in helping them overcome them. Okay. So Daniel, how to Containers fit in? I mean, obviously the Red Hat acquisition was so strategic. We're seeing the real, the ascendancy of OpenShift in particular. Talk about Containers and where it fits into the IBM Cloud Satellite strategy. >> Yeah. So a lot of this builds on top of how we run our cloud business today. Today the vast majority of the services that are available in IBM cloud catalog, actually runs as Containers, runs in a Kubernetes based environment and runs on top of the services that we provide to our customers. So the Container Platform that we provide to our customers is the same one that we're using to run our own cloud services. And those are underpinned with Containers, Kubernetes, and OpenShift. And IBM cloud satellite, based on the way that the designed our Container Platform using Kubernetes and Containers and OpenShift, allows us to take that same design and the same principles and extended outside of our data centers with user provided infrastructure. And this, this goes back to what Stephanie was saying is a satellite location. So using that technology, that same technology and the fact that we've already containerized many of our services and run them on our own platform, we are now distributing our platform outside of IBM Cloud Data Centers using satellite locations and making those available for our cloud service teams, to make their services available in those locations. >> I see and Maia, this, it is as a service. It's a OPEX. Is that right? Absolutely Okay. Absolutely >> Yeah, it's with the two different options on how we can run. One is we can leverage IBM Cloud Satellite and reach into a customer's operating environment. They provide the infrastructure, but we've provide the As-a-Service experience for the Container on up. The other option that we have is for some of our capabilities like our data science capability, where, you know customer might need something a little bit more turnkey because it's, you know, more of a business person or somebody in the CTO's office consuming the As-a-Service. We'll also offer select workloads in an IBM own satellite and environment. I, you know, so that it kind of soup to nuts managed by us. But that is the key is that other than, you know providing the operating environment and then connecting what we do to, you know, their data sources, really the rest is up to us. We're responsible for, you know everything that you would expect in an As-a-Service environment. That things are running, that they're updated, that they're secure, that they're compliant, that's all part of our responsibility. >> Yeah. So a lot of options for customers and it's kind of the way they want to consume. Let's talk about the business impact. You know, you guys, IBM, very consultative selling, you know, tight relationships with customers. What's the business case look like when you go into a client? What's the conversation like? What's possible? What can you share? Stephanie, can you maybe start things off there? Any examples, use-cases, business case, help us understand the metrics. >> Yeah. I mean, so let's talk about a couple of use cases here. So let's say I'm an investment firm, and I'm using data points from all kinds of data sources right? To use AI, to create models to inform my investment decisions. So I'm going to be using, I may be using data sources you know, like regulatory filings, newspaper articles that are pretty standard. I may also be using things like satellite data that monitors parking lots or maybe even weather data, weather forecast data. And all of this data is coming together and being, it needs to be used for models to predict, you know when to buy, sell, trade, however, due to costs, due to just availability of the data they may be located on completely different clouds. You know, and we know that especially capital markets things are fast, fast, fast. So I need to bring my AI to my data, and need to do it quickly so that I can build these models where the data resides, and then be able to make my investment decisions, very fast. And these models get updated often because conditions change, markets change. And this is one way to provide a unified set of AI tools that my data scientists can use. We don't have to be trained on I'm told depending on what cloud the data is stored on. And they can actually build these models much faster and even cheaper. If you would take into egress charges into consideration, you know, moving all the all this data around. Another use case that we're seeing is you know, something like let's say, a multinational telecommunications company that has locations in multiple countries and maybe they want to reduce their customer churn. So they have say customer data that it's stored in different countries and different countries may have different regulations, or the company may have policies that, that data can't be moved out to those country. So what can we do? Again, what we can do is we can send our AI to this data. We can make a customer churn prediction model, that when my customer service representative is on the phone with a customer, and put their information, and see how likely they are to stop using my service and tailor my phone interaction and the offers that I would offer them as this customer service representative to them. If there's a high likelihood that they're going to churn I will probably sweeten the deal. And I can do all that while I'm being fast, right. Because we know that these interactions need to happen quickly. But also while complying with whatever policies or even regulations that are in place for my multinational company. So you know, if you think back to the use cases that I was just talking about you know, latency, performance, reducing costs and also being able to comply with any policy or regulations that our customers might have are really, are really the key pieces of the use cases that we've been seeing. >> Yeah. So Maia there's a theme here. I bring five megabytes of code to a petabyte of data kind of thing. And so Stephanie was talking about speed. There's a an inherent compliance and governance piece. It's it sounds like it's not a bolt on, it's not an afterthought, it's fundamental. So maybe you could add to the conversation, just specifically interested in, you know, what should a client expect? I mean, you're putting data in the hands of you know domain experts in the line of business. There's a self-serve component here, presumably. So there's cross selling is what I heard in some of what Stephanie was just talking about. So it was revenue, there's cost cutting, there's risk reduction, that I'm seeing the business case form. What can you add? >> Yeah, absolutely. I think that the only other thing I would add, is going back to the conversation that we had about, Oh you know, a lot of this is being driven by, you know the digitization of business and you know even moreso this year. You know, at the end of the day there's a lot of costs benefits to leveraging and As-a-Service model, you know, to leveraging that experience in economies of scale from a service provider, as well as, you know leveraging satellite kind of takes that to the next level of, you know, reducing some other costs. But I always go back to, you know at the end of the day, this is about customer experience. It's about revenue creation, and it's about, you know, creating, you know enhanced customer satisfaction and loyalty. So there's a top-line benefits here, you know, of having the best possible AI, you know plugging that into the customer experience, the application where that application resides. So it's not just about where the data resides. You can also put it on the other side and say, you know, we're bringing the AI, we're bringing the machine learning model to the application so that the experiences at excellent the application is responsive there's less latency and that can help clients then leverage AI to create those revenue benefits, you know, of having the the satisfied customer and of having the, you know the right decision at the right time in order to, you know propel them to, to spend and spend more. >> So Daniel bring us home. I mean, there's a lot of engineering going on here. There's the technology, the people in the process if I'm a client, I'm going to say, okay, I'm going to rely on IBM R&D to cut my labor costs, to drive automation, to help me, you know, automate governance and reduce my risks, you know, take care of the technology. You know, I'll focus my efforts on my process, my people but it's a journey. So how do you see that shaping out in the next, you know several years or, or the coming decade, bring us home. >> Yeah. I mean what we're seeing here is that there's a realization that customers have highly skilled individuals. And we're not saying that these highly skilled individuals couldn't run and operate these platforms and the software themselves, they absolutely could. In some cases, maybe they can't but in many cases they could. But we're also talking about these are they're highly skilled individuals that are focusing on platform and platform services and not their business. And the realization here is that companies want their best and brightest focused on their business, not the platform. If they can get that platform from another vendor that they rely on and can provide the necessary compute services, in a timely and available fashion. The other aspect of this is, people have grown to appreciate those cloud services. They like that on demand experience. And they want that in almost every aspect of what they're working on. And the problem is, sometimes you have to have that experience in localities that are remote. They're very difficult. There's no cloud in some of these remote parts of the world. You might think that clouds everywhere, but it's not. It's actually in very specific locations across the world, but there are many remote locations that they want and need these services from the cloud that they can get. Something like IBM Cloud Satellite. That is what we're pursuing here, is being able to bring that cloud experience into these remote locations where you can't get it today. And that's where you can run your AI workloads. You don't have to run it yourself, we will run it and you can put it in those remote locations. And remote locations don't actually have to be like in the middle of a jungle, they could be in your, on your plant floor or within a port that you have across the world, right? It could be in a warehouse. I mean, there's lots of areas where there's data that needs to be processed quickly, and you want to have that cloud experience, that usage pay model for that processing. And that's exactly what we're trying to achieve with IBM Cloud Satellite and what we're trying to achieve with the IBM Cloud Pak for Data as a Service as well. Running on satellite is to give you those cloud experiences. Those services managed as a service in those remote locations that you absolutely need them and want them. >> Well, you guys are making a lot of progress in the next decade is not going to look like the last decade. I can pretty confident in that prediction. Guys thanks so much for coming on the cube and sharing your insights, really great conversation. >> Absolutely. Thank you, Dave. >> Thank you. >> You're welcome, and thank you for watching everybody. This is Dave Vellante from the cube. We'll see you next time. (upbeat music)

>>from around the globe. >>It's the Cube with >>coverage of Coop Khan and Cloud Native Con Europe 2020 Virtual brought to you by Red Hat Cloud, >>Native Computing Foundation and >>Ecosystem Partners. Hi. And welcome back. I'm stew Minuteman. And this is the cube coverage of que con cognitive con 2020. The Europe virtual addition Course kubernetes won the container wars as we went from managing a few containers that managing clusters, too many customers managing multiple clusters and that and get more complicated. So to help understand those challenges and how solutions are being put out to solve them, having a welcome back to the from one of our cube alumni do if it Gerald is the vice president and general manager of the management business unit at Red Hat. Joe, good to see you again. Thanks so much for joining us >>two. Thanks for having me back. >>All right, so at Red Hat Summit, one of the interesting conversation do you and I add, was talking about advanced cluster management or a CME course. That was some people and some technology that came over to Red hat from IBM post acquisition. So it was tech preview give us the update. What's the news? And, you know, just level set for the audience. You know what cluster management is? >>Sure, So advanced Cluster manager or a CMS, We actually falling, basically, is a way to manage multiple clusters. Ross, even different environments, right? As people have adopted communities and you know, we have at several 1000 customers running open shift on their starting to push it in some very, very big ways. And so what they run into is a stay scale. They need better ways to manage. It would make those environments, and a CMS is a huge way to help manage those environments. It was early availability back at Summit end of April, and in just a few months now it's generally available. We're super excited about that. >>Well, that that Congratulations on moving that from technical preview to general availability so fast. What can you tell us? How many customers have you had used this? What have you learned in talking to them about this solution? >>So, first of all, we're really pleasantly surprised by the amount of people that were interested in the tech preview. Integrity is not a product that's ready to use in production yet so a lot of times accounts are not interested in. They want to wait for the production version. We had over 100 customers in our tech review across. Not only geography is all over the world Asia, America, Europe, us across all different verticals. There's a tremendous amount of interest in it. I think that just shows you know, how applicable it is to these environments of people trying to manage. So tremendous had update. We got great feedback from that. And in just a few months, we incorporate that feedback into the now generally available product. So great uptick during the tech created >>Excellent Bring assigned side a little bit, you know, When would I use this solution? If I just have a single cluster, Does it make sense for May eyes? Is it only for multi clusters? You know, what's the applicability of the offering? Yes, sir, even for >>single clusters that the things that ACM really does fall into three major areas right allows closer lifecycle management. Of course, that would mean that you have more than one cluster ondas people grow. They do for a number of reasons. Also, policy based management the ability to enforced and fig policies and enforce compliance across even your single cluster to make sure that stays perfect in terms of settings and configuration and things like that. Any other application. Lifecycle management The ability to deploy applications in more advanced way, even if you're on a single cluster, gets even better for multi cluster. But you can deploy your APS to just the clusters that are tagged a certainly, but lots of capabilities, even for application, even a single cluster. So we find even people that are running a single cluster need it askew, deployed more more clusters. You're definitely >>that's great. Any you mentioned you had feedback from customers. What are the things that I guess would be the biggest pain points that this solves for them that they were struggling with in the past? Well, >>first of being able to sort of Federated Management multiple clusters, right, as opposed to having to manage each cluster individually, but the ability to do policy based configuration management to just express the way you want things to stay, have them stay that way to adopt a more of a getups ethnology in terms of how they're managing their your open ships environments. There's lots more feedback, but those were some of the ones that seem to be fairly common, repetitive across the country. >>Yeah, and you know, Joe, you've also gotten automation in the management suite. How do I think about this? How does this fit into the broader management automation that customers were using? Well, >>I think as people in employees environments. And it was a long conversation about platform right? But there's a lot of things that have to go with the platform and red hats actually in very good about that, in terms of providing all the things you necessary that you would find necessary to make the five form successful in your environment. Right? So I was seen by four. We need storage, then development environments management, the automation ability to train on it. We have our open innovation labs. There's lots of things that are beyond the platform that people acquire in order to be successful. In the case of management automation, ACM was a huge advancement. Terms had managed these environments, but we're not done. We're gonna continue to ADM or automation integration with things like answerable mawr, integration with observe ability and analytics so far from done. But we want to make sure that open ship stays the best managed environment that's out there. I also do want to make a call out to the fact that you know, this team has been working on this technology for the past couple of years. And so, you know, it's only been a red hat for five months. This technology is actually very mature, but it is quite an accomplishment for any company to take a new team in a new technology. And in five months, do what Red Hat does to it in terms of making it consumable for the enterprise. So then kudos continue. Really not >>well. And I know a piece of that is, you know, moving that along to be open source. So, you know, where are we with the solution? Now that is be a How does that fit in tow being open? Source. >>Eso supports that are open source Already. When the process of open sourcing the rest of it, as you've seen over time read, it has a perfect record here of acquiring technologies that were either completely closed Source Open core in some cases where part it was open. It was closed. But that was the case with Ansell a few years ago. But basically our strategy is everything has to be open source. That takes time in the process of going through all of the processes necessary to open source parts of ACM on. We think that will find lots of interest in the community around the different projects inside of >>Yeah. How about what? One of the bigger concerns talking to customers in general about kubernetes even Mawr in 2020 is. What about security? How does a CME help customers make sure that their environment to secure? >>Yeah, so you know, configuration policies and forcing you can actually sent with ACM that you want things to be a certain way that somebody changes them that automatically either warn you about them or enforcement would set them back. So it's got some very strong security chops in terms of keeping the configurations just the way you want. That gets harder as you get more and more clusters. Imagine trying to keep everything but the same levels, settings, software, all the parts and pieces so affected you have ACM that can do this across any and all of your clusters really took the burden off people trying to maintain secure environments, >>okay, and so generally available. Now, anything you can share about how this solution is priced, how it fits in tow. The broader open shift offerings, >>Yes. Oh, so it's an add on for open shift is priced very similarly to open shift in terms of the, you know, core pricing. One thing I do want to mention about ACM, which maybe doesn't come out just by a description product is the fact that a scene was built from scratch for communities, environments and optimize for open shift. We're seeing a lot of competition out there that's taking products that were built for other environments, trying to sort of been member coerce them into managing kubernetes environments. We don't think people are going to be successful at that. Haven't been successful to date. So one things that we find as sort of a competitive differentiator for ACM and market is the fact that it was built from scratch designed for communities environments. So it is really well designed for the environment it's trying to manage, and we think that's gonna keep your competitive edge? >>Well, always. Joe. When you have a new architecture, you advantage of things. Any examples that you have is what, what a new architecture like this can do that that an older architecture might struggle with or not believe. Be able to do even though when you look at the product sheet, the words sound similar. But when you get underneath the covers, it's just not a good architect well fit. >>Yeah, so it's very similar sort of the shift from physical to virtual. You can't have a paradigm shift in the infrastructure and not have a sort of a corresponding paradigm shift in management tool. So the way you monitor these environments, where you secure them the way they scale and expand, we do resource management, security. All those things are vastly different in this environment compared to, let's say, a virtual more physical environment. So this has improved many times in the past. You know, paradigm shift in the infrastructure or the application environment will drive a commensurate paradigm shift in management. That's what you're seeing here. So that's why we thought it was super important to have management that was built for these environments. by design. So it's not trying to do sort of unnatural things north manage the environment. >>Yeah, I wondered. I love to hear just a little bit your philosophy as to what's needed in this space. You know, I look back to previous generations, look at virtualization. You know, Microsoft did very well at managing their environment, the M where did the same for their environments. But, you know, we've had generations of times where solutions have tried to be management of everything, and that could be challenging. So, you know, what's Red Hat in a CM's position and what do we need in the community space, you know, today and for the next couple of years. >>So kubernetes itself is the automation platform you talked about, you know, early on in the second. So you know, Cooper navies itself provides, you know, a lot of automation around container management. What a CME does is build a top it out and then capture, you know, data and events and configuration items in the environment and then allows you to define policies. People want to move away from manual processes. Certainly, but they wanna be able to get to a more state full expression of the way things should be. You want to be able to use more about, you know, sort of get up, you know, kind of philosophy where they say, this is how I want things today. Check the version in, keep it at that level. If it changes, put it back. Tell me about it. But sort of the era of chasing. You know, management with people is changing. You're seeing a huge premium now on probation. So automation at all levels. And I think this is where a cm's automation on top of open shift automation on down the road, combined with things like ansell, will provide the most automated environment you can have for these container platforms. Um, so it's definitely changing your seeing observe ability, ai ops getups type of philosophies Coming in these air very different manager in the past helps you seeing innovation across the whole management landscape in the communities environment because they are so different. The physics of them are different than the previous environments. We think with ACM answerable or insights product and some over analytics that we've got the right thing for this environment >>and can give us a little bit of a look forward, you know? How often should we expect to see updates on this? Of course. You mentioned getting feedback from the community from the technical preview to G A. So give us a little bit. Look, you know, what should we be expecting to see from a CME down the right the So >>the ACM team is far from done, right? So they're going to continue to rev, you know, just like we read open shift, that very, very fast base we're gonna be reading ACM and fast face. Also, you see a lot of integration between ACM. A lot of the partners were already working with in the application monitoring space and the analytics space security automation I would expect to see in the uncivil fest time frame, which is mid October, will cease, um, integration with danceable on ACM around things. That insult does very well combined with what ACM does. A sand will continue to push out on Mawr cluster management, more policy based management and certainly advancing the application life cycles that people are very interested in ruined faster. They want to move faster with a higher degree of certainty in their application. Employments on ACM is right there. >>It just final question for you, Joe, is, you know, just in the broader space, looking at management in this kind of cube con cloud, native con ecosystem final words, you want customers to understand where we are today and where we need to go down the road. >>So I think the you know, the market and industry has decided communities is the platform of future right? And certainly we were one of the earliest to invest in container management platforms with open shift were one of the first to invest in communities. We have thousands of customers running open shift back Russell Industries on geography is so we bet on that a long time ago. Now we're betting on the management automation of those environments and bringing them to scale. And the other thing I think that redhead is unique on is that we think that people gonna want to run their kubernetes environments across all different kinds of environments, whether it's on premise visible in virtual multiple public clouds, where we have offerings as well as at the edge. Right. So this is gonna be an environment that's going to be very, very ubiquitous. Pervasive, deported scale. And so the management of a nation has become a necessity. And so but had investing in the right areas to make sure that enterprises continues communities particularly open shift in all the environments that they want at the scale. >>All right. Excellent. Well, Joe, I know we'll be catching up with you and your team for answerable fest. Ah, coming in the fall. Thanks so much for the update. Congratulations to you in the team on the rapid progression of ACM now being G A. >>Thanks to appreciate it, we'll see you soon. >>All right, Stay tuned for more coverage from que con club native con 2020 in Europe, the virtual addition on still minimum and thanks, as always, for watching the Cube.

>>live from Barcelona, Spain. It's the Cube covering Cisco Live 2020 right to you by Cisco and its ecosystem partners. >>Welcome back to the Cisco Live 2020 show in Barcelona, Spain. It's the Cube's live coverage. Four days of action. I'm John Furrier with my co host, Dave Vellante. Stew Minimum is in the house. We've been really interview all the thought. Leads all the action here in the DEV. Net zone of Cisco. We're here with Susie Wee, who's the senior vice president, chief technology officer and general manager of Cisco's DEV. Net and C X ecosystem success. Susie, great to see you again, Thanks to you. With our third year we've been we've been watching the growth of definite explode and definite create a separate event for developers. Great to see you. >>Great to see you. Great to be here. >>So how does it feel to be on a wave of success? You've had quite an impact in the industry, and I think the biggest story that's going on in the industry is the role of developers. You guys have embraced that four years ago, brought it all together and really just been marching to the cadence of just humble training, education and programming, all the Cisco products enabling what it looks like to be the future of Cisco. >>Yeah, I mean, it's it's humbling, t So you know what's been really great? It's really all about our community. And, you know, I mean, you guys have jumped in, been with us on this journey. You've seen it like all around us in terms of how it's progressed. But what's interesting is that, you know, networkers the software developers, the Dev Ops pros that people who are coming into definite really progressing, they're getting to the next level. And then we have more and more new people coming in. And what happens is the technology keeps advancing right. So networking, security going toe intent based networking, multi domain. How do you integrate these things? Cognitive collaboration. I o t an edge, you know, edge computing. As all of this comes together, you get to a really interesting place. But what happens is we have to think about I t department networking departments like how do people use this to their advantage? Right. So there's actually users of people who install and run these things and how do they make that available and actually get a business advantage out of that infrastructure? That's what this is all about. >>And the big scene. Wendy on the opening keynote, kicked off before David came on. She had a slide that I thought encapsulate what I think the future of all business and you guys have been on and on and on, a reference that it was people in communities, business model and business operations almost like a three legged stool. You've been on this because your team Michael was on the Cube just now said people have been in their careers on Cisco. But Cisco is betting the business on the people, that ecosystem, it's developers. CC III is the certifications. This dynamic of the role of the people is critical, >>and they're driving >>the change >>it is. And you know what was tremendous about Cisco's business model and how Cisco was founded. So this was pre me, you know, and it's just the brilliance of the early folks is like Cisco made this router, you know? It was a little start up. It was like five people, right? And then it started flying off the shelf in the mid eighties in late eighties when the Internet started taking off, and then the way they scale that out was by growing the community, they didn't say We're going to hire people around the world to install these networks. We're gonna create a community of professionals who can go around and install these networks. And then we're going to create a partner ecosystem of partners who are going to build businesses around this, installing networks for customers. And so really, Cisco very early on, learn that we had to be very customer focused and build with an ecosystem of partners. And then we created Cisco Certification Program, and that started to take the people who are getting trained to do networking and give them certifications. And then they were able to get jobs in customers and partners and build their careers. And so now we move that to today, and we're continuing with that philosophy and doubling down. It's about them, except there's a shift in technology. So the network has changed. It's not the same old network like now. There's new capabilities that require software. It requires dev ops. It requires applications to hit the infrastructure it requires. I T and Infrastructure to solve business problems. But we need to bring the people along and doing that, and that is absolutely what we've been about. >>I said in my breaking analysis there were there were many things that helped Cisco rise with the three things like pointed out where the bet on I p, the M and A and then I was too narrow. I liked how you describe it as the community, but really talking about the Army of trained engineers that were advocates. And you're extending that to the partner ecosystem. What's interesting about watching this rise over the faster uses? Not only transformation of Cisco from hardware to software and now even business transformation is you see, I t go from a cost center to a profit center, but you're sort of following that track. I don't know if you're leading it are following it sort of incompetent what's going on. And, >>you know, I would say >>that we're doing both because, uh, obviously we're listening to customers and partners all the time to see what do you need? So we're listening, and that would make us leading as we're sorry. Following is >>we're >>listening and yet we're creating technology to enable them to do these new things right? So there's a reason that you can think about the network to be solving business problems. It's because we made the networking programmable and based on software. If we didn't make it software, it would still be running the old way. And it wouldn't be able to play in a Dev ops loop or be automated or anything there. So I would say that it's very combined. But Cisco takes a holistic approach right back there. We have an I T managers forum where there are people who are trying to say, Hey, you know, I've been leading technology teams in I T. But I need to learn how to talk to the business, right? So there's a transformation that needs to happen, which is okay, The technologist networkers I t folks themselves need to learn about software. But then also, these folks and their managers need to be able to talk to the business and think differently. So take some design thinking. Think about what are the business stakeholders problems where customers problems, how can I make my technology work for them? So we really have a lot going on Teoh building the kind of success of our ecosystem. >>Yeah, it's interesting you mentioned technology shift, and that's causing a lot of change is actually how people are certified business models. And it's interesting. When we were chatting years ago, Dev. Ops was actually out there. The hyper scales around you saw it evolving was pretty clear to a lot of the insiders. That's Dev Ops. Infrastructure is code. Then you kicked on something where programmable networks I heard this week, and this is kind of again goes to the next level and kind of connect the dots. Biz Dev. Ops. So the AB dynamics guys, look at this as OK. So this agile attitude yes, has been on for a while. Could you comment? I think >>a lot of people that >>are looking at Cisco trying to understand its evolution where it's gonna go >>yeah, >>is rooted in years ago. A shift in thinking, yes, and it's an agile It's a dev ops mindset, >>yes, but >>the Dev ops notion from whether it's pure Dev ops, cloud native or Dev ops or Biz Dev ops >>for what's next? So this is a It's been around for a while. You just share your Yeah, Absolutely. So >>I have a slide and we don't show slides here, which is a good thing. But it was called it the hamburger slide. So the hamburger slide, where there would be infrastructure and the applications. And then there's this other layer appear business, you know. And basically, what happens is the infrastructure became programmable, so as opposed to the infrastructure and the applications being separate, the I T teams did the infrastructure of the app Dev did. The businesses did the APS. Then now that the infrastructure's family can get into a dev ops workflow. So for cloud applications, the APS and the infrastructure can really mix. And now the network is programmable. So there's Net Dev ops. And it's not just compute that can get into Dev ops. But you know, the network can too. But then, now that business layer can flow into this. And so what happens is once again, you could say that cloud enables business, right? And so, if you know, a business is trying to say, how do I compete like a retail store? How my completing with a cloud competitors. Well, you have to embrace it. Take your traditional infrastructure, your customer data your stores, but then mix that with cloud offerings. That's a huge transformation that needs to happen. But now there's even more capabilities. As you're saying, Hey, I'm like a coffee shop and I'm rolling out all of these stores. How do I make sure my business applications get there? How do I get customer intelligence and business intelligence together so my workers can serve my customers with the right knowledge and information they need so you can actually use the infrastructure and APS as an advantage in how you serve your business? And you wouldn't even be able to do those things if you didn't know about the technology. So I would say that there's like a workforce trend where technology is enabling business and it can grow your business in different ways. But we need to make sure that we can express that because the technologist doesn't usually talk in terms of the business. But that's where all the value >>on the application has always been. That point of business value in connection to the business when the APP is the infrastructure has been removed from that now that the infrastructure's becoming programmable. It's embedded into that application, and developers can now add value on top of it. I mean, the striking thing to me was just behind us, to seeing a number of your customers lining up to learn how to code in Python. And then I o t was off the charts. And I've always been saying that Look at the edge is going to be one by developers E. I think you really got that right. I'm curious as to why you think just really is the one company in a large, established player. That is, I think, figured it out that I've said that many have tried throwing money at the problem, reaching out to developers fallen flat. I mean, even very successful software companies were struggling. Why do you think Cisco has had successes? Is a culture is at the leverage of that certification and community that you talked about earlier? >>Yeah, it's while it's really hard to say, like one reason why, because these air tricky things, like so taking on a new business strategy, getting everybody aligned in a big company, even in a little company, is hard, but it takes like everybody pushing towards the direction and what happens is different. People get it at different times. So obviously with Dev net, we're trying to push something along. The CEO Chuck Robbins. He got it and he was pushing it. And then the businesses and product teams. Some of them had a P I first, and some of them did not. But now more and more on almost all of them do. Now. All the products have AP eyes and they're getting more AP I first and now what we're doing is aligning AP eyes across the portfolio. You need to get your sales teams to understand and to engage. Like the regions. We have people in Italy who are engaging with the Italian community. We have our seas around the world that are basically engaging the people in each of their countries to evangelize it in tow, work with customers and partners in their local language is using this material to get them on board. So, you know, when we started, Definite Way had different ways we could take it. No one defined a developer program for a company like Cisco before, like a networking company, but we actually didn't do it by saying, Oh, we're only gonna talk to application developers and ignore those old networkers We said we're going to make them core and bring them along and bring in the captives and bring them together. I wouldn't say we're gonna, like, forget about the old Cisco products We said we're gonna work with them as they add AP eyes and make that better. We're gonna ignore our sales guys and the ones that we're going to bring them along and make them our evangelists and advocates to work with the region. So we kind of use the whole fabric along with it and just I kind of gained. The community >>recognized the appetite for building, and some people are like, >>I'm going to jump in and give this a try because I think it's important and something like, I'm gonna wait and see and they're like, Oh, it's something now, Okay, now I'll jump in and we're like, >>That's right, >>you're totally We do a lot of Cuban. It is many different events here at Cisco over the years. It's interesting to see when people get in and you can see it when their eyes pop up. Oh, I get it. It is a progression of whether they're orientation, what their background is. But it seems to me the early people who click it on it is our systems thinkers. Most of the techies, they're systems systems, folks. Yeah, they see as a system not as one thing. Yes, As you said, it's not just absent infrastructure. So a lot of the system guys get it first. And then on the business side, they see it from more of the making money. So you see the impact of the application changing the business model. It's a retail app or whatever they get it. That that's gonna be the future. Yeah, it depends on where you're coming from. >>It does. It does. And what's interesting is to >>see how this community has evolved and actually, how we've evolved to be able to support people along the way. So as you remember, when you were first year, it was really some techies who realized they needed to learn something new. So is about learning about software and AP eyes. And then we evolved. It became about coding. So how do we use a definite automation exchange in code exchange to use a software based model to build community code around networking use cases because they wanted to use it and get it into use cases. And then now we have people are like, Okay, I'm doing it, I get it. But I can't get my business leaders to understand. So now we're actually helping them express the business case and create use cases that solve business problems more directly, so >>your access to customer success >>and customer success. So now explain that piece. What is that? How >>to be successful at training is everything >>customer direction. What is that piece? So s >>o me and my team were Cisco employees, and sometimes I mean, this doesn't get represented, but we move around the York, so you know, as different things change. And so there's a recent move where it has been in the engineering team. I've now moved into the customer experience organization. We're doing a transformation like a customer experience, customer success, transformation for Cisco and so you know, as we think about that. Well, first of all, Cisco's always been customer oriented, But what does this mean in a world of software in world of partners? ecosystems with the products and opportunities we have now. And so, as we're gearing towards this kind of customer success and customer experience model, is that, you know, they're trying to do a transformation, and it's actually very similar to what Dev Net has already done, which is specifically, let's see. So when you engage with a company on new technology, we can say Okay, come here to the DEV Net Zone and learn about the AP eyes, you know. But as you're working with a customer and you say, Hey, you know you're from the customer, let's go on this journey together. Did you know that we have AP eyes? Let's learn about AP eyes. >>And did you know that >>this product performs this function? But it also has AP eyes. So let's teach you about those. Then you learn a different aspect of the product that you might not have thought about before because you're like, Oh, it can be a platform and then you say, Hey, and you know you need to solve automation. This can be used to solve automation, and so then you're like, Oh, I'm thinking about automation, but how do I do it? so you can't have just one product. That's >>that's a progression that depends on what the customer's orientation is, whether environment looks like >>so it >>means, like start to evolve and think about their problem. Actually, their problem is automation. Their problem is not using this product right. They're trying to solve a bigger product and hopefully this is a bigger business problem or an automation problem. And this product is a piece of the puzzle into it. So we want to kind of engage in the full discussion from what is your need, an automation and then work backwards toe like, How can this product help? And so it's kind of like turning things upside down and ensuring the customer uses. And, you know, we understand their business problem. We're helping them solve it. And this is how these products can play a role in helping you achieve that >>in every business is looking at that from the corner office. They all want to drive automation into their business. They're looking at okay, if the economy turns out more automation, whether it's you know, you see an R P. A takeoff is the cloud is supporting that, Yeah, it's a big trend >>is huge, and it's, you know, and actually moving to an automation infrastructure. It's not like buy a new product and you've automated and you're done. It's actually very hard, and it requires an architectural shift. It means, like I'm going to start to build telemetry, analyze data and get insights from it. Well, if you don't have that implemented somewhere, then you need to architect for them. And then once you start building into that and seeing dashboards and then connecting that into other business APS, then you start to go further and further so every step along the way, we want to get them closer to an automation architecture. But that takes work, >>and it's cultural as well as people hear automation. If it well, that's my job and so >>little >>education. And then once they see it, Oh, you mean I could get rid of all these things I don't like to do, and I can do this instead. Then they really lean in and create new value. >>Yeah, So what we're getting at is this, like, really interesting. I'll call it a new technology trend of looking at kind of automation, plus Ai together, right? And so I've been talking about it out here in some places, which is now we've been talking about automation. We've been talking about AI. You look at these together. There's a set of people who are like, Let's think about what automation means. It could mean Oh my gosh, someone's going to take my job away. I don't need people anymore That would be called like autonomous. And there's some things that you do want to make autonomous and work themselves. But then you can also look at kind of assisting humans. Right? So assisting like, what are you trying to do? Roll out configurations across different places and get them set up where we can automate that and you can assist a human? And being able to do it on this next age is augmenting humans. What is there that a person really couldn't do that they can do now in a night? Example of that is, you know, you take a look at threat intelligence and security going around the world. Cisco has products around the world that are looking for security threats. You put those together, you can see a threat before it comes to a customer environment and say, Hey, we found this threat. We better shut it down over in your system to make sure you're blocked and protected from it. You've augmented human capability, you know, using automation and AI. >>You know, one of the things a lot of companies do is they focus on a big wave and they focus on it. They get on that new wave. Cisco's on a lot of different ways. You got I, O. T. And Security, which you were talking about. This kubernetes and Cloud native is like all these collaboration. They're all their own big waves coming. So I have to ask you because you've been so successful, definite and then a great leader in the industry with all your experience. What's your vision as this comes in? Because Cisco is that one of the benefits uniquely positioned with all the complexity, all the opportunities to the Dev ops, like across the board up and down the stack, these waves are coming. It's not just one. You have a focus on kubernetes. You got a focus on security. There's all these different big things that you guys are working on. What's your vision >>on how >>this all plays out >>like so while there's different, there's different things going on kubernetes and cloud. You know, we're doing networking. What's going on in I O. T and Edge Computing and the Future of Cognitive Collaboration and AI and ML, And you know all of this kind of thing a security I don't actually view them as separate. Actually view them is all part of a bigger system, right? They're part of a platform that's trying to solve a bigger problem, >>and the secret is AP ice. So it's actually a >>combination of architecture in AP eyes and how this works is a fabric together and you know there's benefit. Like if you're trying to do security, sure, you can use security products to do security. But why don't you also use network segmentation to do security, like literally segment out pieces of the network and, you know, data and APS that should not be talking to other places and use that for security? So, you know, I kind of view it is all working together towards a bigger architecture because you're using Ap eyes. You can start to put these things together and start to apply policies across these different domains. So this kind of whole new area, another new technology trend, is looking at multi domain opportunities and cross architecture. So that's really key >>in the data that you get out of that as well, right? Data and metadata that you can analyze and then act upon. Yes, Dr. Inside >>multi domain, multi clouds Having >>data models, right? Look at how do you take, you know, so that all these different systems are adding up to a everything you need to create data models that these different applications can kind of pour into >>that used to be locked inside of a box. Sitting in >>these types of application would have its own >>kind of model, But we're really all working towards the bigger thing in software that lets you down in >>the silicon is a great thing to get so looking One coming, Yes, moving from the box of the chip. Yeah, not a bad strategy. >>Super interesting. So, yeah, >>if you look at, you know, where are the bottlenecks in this? And this is where you need to rethink what your business strategy is. And it's just like you down in the optics down at that layer is where the big opportunities are. And if we can differentiate and provide value in that space, then that's what we've done. We >>were riffing the other night in the taxi came in I said, The day of Digital and digital, which is the Internet's all digital. Now the business model is the killer app, and we're just more of a provocative statement like, What are you trying to dio with that? What all this is? What's the purpose of all this? >>Yeah, I >>have a business model that actually works. >>It is, But it is, Yeah, >>and what's interesting about the business model? Also, to think about that? It's not just your own business model. It's again. That's where that's why I called our new group ecosystem success. It's what you do, you know. And there's this whole model of success, meaning you your customer, your supply chain up above you and then how you deliver. But it's east west now, too, right? It's like, How does your innovation work with your partner's innovation? Another area that and how did this all happen together? Like, how do you take trends in security and advances there and, you know, in workforce and people. And as you take a look at, you know, everything that's happening in cloud and then intersect so that we're all successful >>and it's enabled by what you're saying before automation and AI obviously supported by Cloud AP eyes and data across that system that you guys were talking >>about, I think that I think the bumper sticker for Cisco's Cisco connects businesses because that's really what you're doing. >>There we go way >>shut up for the 1st 500 >>Yes, yes, yes. So yes. So some of the big news over here is that well, in this >>world of where the infrastructure becomes programmable. So what Cisco's had a long time is Cisco's sort of certification program. So we have ccn a Cisco Certified Network Associates. Si Si n Pi's CC III is the expert level, and that's been an industry standard for the last 26 years, and people have job roles. They've gotten promotions, they get recognized, their certified for delivering quality, and what we've introduced is the definite certifications. So, in addition to the engineering certifications or the software certifications and Devon, it's kind of growing to the next level. By so far, everybody who's been in here has been into definite because of their hearts and because they knew they had to learn anything. But now we're giving them a certification so they can be recognized as their efforts, and we're expanding Cisco certification to cover it. Now. This represents the move of engineering plus software together in your I T teams and together for your technology teams and the new certifications. The definite set of Cisco Live February 24th the 1st 500 people to earn a definite certification. We're going to call the definite 500. And >>so they want to be the first >>ones who are really stepping forward in this new industrial shift towards combining engineering and software, making the world of the infrastructure talking to business and driving business happen. >>Well, we'd love to be First, get a list of >>thousands of people 500 seats that will take. We'll take the 501st 10,000 in the 1,000,000 I dive >>Heard Susie. Some Cisco VP's want to get into that 500. >>They yes, Gamification. >>Always a good strategy, Susie. Great to watch your successes with folks watching, seeing definitely come from an idea execution and now core to the business model's been quite an evolution. Congratulations. Always success. >>Thank you. And thank you for joining us on this journey. >>So we've been working together on it. >>We've learned a lot. It's been so much fun. We're in the DEV Net zone. I'm John Furrier Dave Vellante with Susie Wee, the chief of the definite team and the big zones gets bigger every year. And the cube's getting big air thanks to you and the team. Appreciate it is to keep more live coverage from Barcelona. Cisco live 2020 after this short break. >>Yeah, yeah, yeah.

>> Live from San Francisco, California. It's theCUBE, covering the IBM Chief Data Officer Summit. Brought to you by IBM. >> We're back at Fisherman's Wharf at the IBM CDO conference. You're watching theCUBE, the leader in live tech coverage. My name is Dave Volante, Joe Selle is here. He's the Global Advanced Analytics and Cognitive Lead at IBM, Boston base. Joe, good to see you again. >> You to Dave. >> And Jerry Gupta, the Senior Vice President and Digital Catalyst at Swiss Re Institute at Swiss Re, great to see you. Thanks for coming on. >> Thank you for having me Dave. >> You're very welcome. So Jerry, you've been at this event now a couple of years, we've been here I think the last four or five years and in the early, now this goes back 10 years this event, now 10 years ago, it was kind of before the whole big data meme took off. It was a lot of focus I'm sure on data quality and data compliance and all of a sudden data became the new source of value. And then we rolled into digital transformation. But how from your perspective, how have things changed? Maybe the themes over the last couple of years, how have they changed? >> I think, from a theme perspective, I would frame the question a little bit differently, right? For me, this conference is a must have on my calendar, because it's very relevant. The topics are very current. So two years ago, when I first attended this conference, it was about cyber and when we went out in the market, they were not too many companies talking about cyber. And so you come to a place like this and you're not and you're sort of blown away by the depth of knowledge that IBM has, the statistics that you guys did a great job presenting. And that really helped us inform ourselves about the cyber risk that we're going on in cyber and so evolve a little bit the consistent theme is it's relevant, it's topical. The other thing that's very consistent is that you always learn something new. The struggle with large conferences like this is sometimes it becomes a lot of me too environment. But in conference that IBM organizes the CDO, in particular, I always learn something new because the practitioners, they do a really good job curating the practitioners. >> And Joe, this has always been an intimate event. You do 'em in San Francisco and Boston, it's, a couple hundred people, kind of belly to belly interactions. So that's kind of nice. But how do you scale this globally? >> Well, I would say that is the key question 'cause I think the AI algorithms and the machine learning has been proven to work. And we've infiltrated that into all of the business processes at IBM, and in many of our client companies. But we've been doing proof of concepts and small applications, and maybe there's a dozen or 50 people using it. But the the themes now are around scale AI at scale. How do you do that? Like we have a remit at IBM to get 100,000 IBMers that's the real number. On our Cognitive Enterprise Data Platform by the end of this calendar year, and we're making great progress there. But that's the key question, how do you do that? and it involves cultural issues of teams and business process owners being willing to share the data, which is really key. And it also involves technical issues around cloud computing models, hybrid public and private clouds, multi cloud environments where we know we're not the only game in town. So there's a Microsoft Cloud, there's an IBM Cloud, there's another cloud. And all of those clouds have to be woven together in some sort of a multi-cloud management model. So that's the techie geek part. But the cultural change part is equally as challenging and important and you need both to get to 100,000 users at IBM. >> You know guys what this conversation brings into focus for me is that for decades, we've marched to the cadence of Moore's laws, as the innovation engine for our industry, that feels like just so yesterday. Today, it's like you've got this data bedrock that we built up over the last decade. You've got machine intelligence or AI, that you now can apply to that data. And then for scale, you've got cloud. And there's all kinds of innovation coming in. Does that sort of innovation cocktail or sandwich makes sense in your business? >> So there's the innovation piece of it, which is new and exciting, the shiny, new toy. And that's definitely exciting and we definitely tried that. But from my perspective and the perspective of my company, it's not the shiny, new toy that's attractive, or that really moves the needle for us. It is the underlying risk. So if you have the shiny new toy of an autonomous vehicle, what mayhem is it going to cause?, right? What are the underlying risks that's what we are focused on. And Joe alluded to, to AI and algorithms and stuff. And it clearly is a very, it's starting to become a very big topic globally. Even people are starting to talk about the risks and dangers inherent in algorithms and AI. And for us, that's an opportunity that we need to study more, look into deeply to see if this is something that we can help address and solve. >> So you're looking for blind spots, essentially. And then and one of them is this sort of algorithmic risk. Is that the right way to look at it? I mean, how do you think about risk of algorithms? >> So yeah, so algorithmic risk would be I would call blind spot I think that's really good way of saying it. We look at not just blind spots, so risks that we don't even know about that we are facing. We also look at known risks, right? >> So we are one of the largest reinsurers in the world. And we insure just you name a risk, we reinsure it, right? so your auto risk, your catastrophe risk, you name it, we probably have some exposure to it. The blind spot as you call it are, anytime you create something new, there are pros and cons. The shiny, new toy is the pro. What risks, what damage, what liability can result there in that's the piece that we're starting to look at. >> So you got the potentially Joe these unintended consequences of algorithms. So how do you address that? Is there a way in which you've thought through, some kind of oversight of the algorithms? Maybe you could talk about IBM's point of view there. >> Well we have >> Yeah and that's a fantastic and interesting conversation that Jerry and I are having together on behalf of our organizations. IBM knowing in great detail about how these AI algorithms work and are built and are deployed, Jerry and his organization, knowing the bigger risk picture and how you understand, predict, remediate and protect against the risk so that companies can happily adopt these new technologies and put them everywhere in their business. So the name of the game is really understanding how as we all move towards a digital enterprise with big data streaming in, in every format, so we use AI to modify the data to a train the models and then we set some of the models up as self training. So they're learning on their own. They're enhancing data sets. And once we turn them on, we can go to sleep, so they do their own thing, then what? We need a way to understand how these models are producing results. Are they results that we agree with? Are these self training algorithms making these, like railroad trains going off the track? Or are they still on the track? So we want to monitor understand and remediate, but it's at scale again, my earlier comments. So you might be an organization, you might have 10,000 not models at work. You can't watch those. >> So you're looking at the intersection of risk and machine intelligence and then you're, if I understand it correctly applying AI, what I call machine intelligence to oversee the algorithms, is that correct? >> Well yes and you could think of it as an AI, watching over the other AI. That's really what we have 'cause we're using AI in as we envision what might or might not be the future. It's an AI and it's watching other AI. >> That's kind of mind blowing. Jerry, you mentioned autonomous vehicles before that's obviously a potential disruptor to your business. What can you share about how you guys are thinking about that? I mean, a lot of people are skeptical. Like there's not enough data, every time there's a another accident, they'll point to that. What's your point of view on that? From your corporation standpoint are you guys thinking is near term, mid term, very long term or it's sort of this journey, that there's quasi-autonomous that sort of gets us there. >> So on autonomous vehicles or algorithmic risk? >> On autonomous vehicles. >> So, the journey towards full automation is a series of continuous steps, right? So it's a continuum and to a certain extent, we are in a space now, where even though we may not have full autonomy while we're driving, there is significant feedback and signals that a car provides and acts or not in an automated manner that eventually move us towards full autonomy, right? So for example, the anti-lock braking system. That's a component of that, right? which is it prevents the car from skidding out of control. So if you're asking for a time horizon when it might have happened, yeah, at our previous firm, we had done some analysis and the horizons were as sort of aggressive as 15 years to as conservative as 50 years. But the component that we all agreed to where there was not such a wide range was that the cars are becoming more sophisticated because the cars are not just cars, any automobile or truck vehicles, they're becoming more automated. Where does risk lie at each piece? Or each piece of the value chain, right? And the answer is different. If you look at commercial versus personal. If you look at commercial space, autonomous fleets are already on the road. >> Right >> Right? And so the question then becomes where does liability lie? Owner, manufacturer, driver >> Shared model >> Shared, manual versus automated mode, conditions of driving, what decisions algorithm is making, which is when you know, the physics don't allow you to avoid an accident? Who do you end up hitting? (crosstalk) >> Again, not just the technology problem. Now, last thing is you guys are doing a panel, on wowing customers making customers the king, I think, is what the title of it is. What's that all about? And get into that a little bit? >> Sure. Well, we focus as IBM mostly on a B2B framework. So the example that I that I'll share to you is, somewhere between like making a customer or making a client the king, the example is that we're using some of our AI to create an alert system that we call Operations Risks Insights. And so the example that I wanted to share was that, we've been giving this away to nonprofit relief agencies who can deploy it around a geo-fenced area like say, North Carolina and South Carolina. And if you're a relief agency providing flood relief or services to people affected by floods, you can use our solution to understand the magnitude and the potential damage impact from a storm. We can layer up a map with not only normal geospatial information, but socio-economic data. So I can say find the relief agency and I've got a huge storm coming in and I can't cover the entire two-state area. I can say okay, well show me the area where there's greater population density than 1000 per square kilometer and the socio-economic level is, lower than a certain point and those are the people that don't have a lot of resources can't move, are going to shelter in place. So I want to know that because they need my help. >> That's where the risk is. Yeah, right they can't get out >> And we use AI to do to use that those are happy customers, and I've delivered wow to them. >> That's pretty wow, that's right. Jerry, anything you would add to that sort of wow customer experience? Yeah, absolutely, So we are a B2B company as well. >> Yeah. >> And so the span of interaction is dictated by that piece of our business. And so we tried to create wow, by either making our customers' life easier, providing tools and technologies that make them do their jobs better, cheaper, faster, more efficiently, or by helping create, goal create, modify products, such that, it accomplishes the former, right? So, Joe mentioned about the product that you launched. So we have what we call parametric insurance and we are one of the pioneers in the field. And so we've launched three products in that area. For earthquake, for hurricanes and for flight delay. And so, for example, our flight delay product is really unique in the market, where we are able to insure a traveler for flight delays. And then if there is a flight delay event that exceeds a pre established threshold, the customer gets paid without even having to file a claim. >> I love that product, I want to learn more about that. You can say (mumbles) but then it's like then it's not a wow experience for the customer, nobody's happy. So that's for Jerry. Guys, we're out of time. We're going to leave it there but Jerry, Joe, thanks so much for. >> We could go on Dave but thank you Let's do that down the road. Maybe have you guys in Boston in the fall? it'll be great. Thanks again for coming on. >> Thanks Dave. >> All right, keep it right there everybody. We'll back with our next guest. You're watching theCUBE live from IBM CDO in San Francisco. We'll be right back. (upbeat music)

>> Announcer: Live from Boston, Massachusetts. It's theCUBE, covering Red Hat Summit 2019. Brought to you by Red Hat. >> And welcome back to Boston. Here on theCUBE we continue our coverage of Red Hat Summit 2019. We just had Jim Whitehurst on, President and CEO, along with Stu Miniman, I'm John Walls. And now, we turn to the IBM side of the equation. Arvind Krishna is with us, the SVP of Cloud and Cognitive Software at IBM. Arvind, good to see you this morning. >> My pleasure to be here, what a great show. >> Yeah, absolutely, it has been. I was telling Jim he couldn't have a better week, right? Monday had good news, Tuesday great kick off, today again following through great key notes. We were talking briefly, a year ago you were with us on theCUBE and talking about IBM and its forward plans, so on and so forth. What a difference a year makes, right? (laughs) >> We couldn't predict that you'd be in the position that you are in now, so just if you can summarize the last year and maybe the last six months for you. >> Sure, and I think it's more building on what I talked to you about a year ago, I remember last May, May of 2018, in San Francisco. So I was exposing very heavily, look the world's going to move towards containers, the world has already embraced Linux, this is the time to have a new architecture that enables hybrid, much along the lines that Jim and all of the clients as well as Ginni and Satya were talking about on stage yesterday. So you put all that together and you say that is what we mentioned last year and we were clear, that is where the world is gonna go. Now you step forward a few months from there into October of 2018 and on the 29th October we announced that IBM intends to acquire Red Hat, so then you say wow, we put actually our money where our mouth was. We were talking about the strategy, we were talking about Linux containers, OpenShift, the partnership we announced last May was IBM software products together with OpenShift. We already believed in that. But now this allows us coming together, it's more like a marriage than sort of loose partners passing each other in the middle of the night. >> Right. >> And that then goes forward, you mention the news on Monday so for our viewers that don't know it, that's the news that the United States Department of Justice approved merger with no conditions. So now we've got to wait on a few other jurisdictions and then hopefully we can get together really soon. >> John: Right, right. >> So, I think back to looking at IBM over my career. I think the first time I heard the word coopetition it was related to IBM because IBM, big ego system, lots of innovation over its long history but as we know the bigger you get, the more chance that your partners are also going to overlap with you. Seeing Ginni up on stage and a little bit later seeing Satya up on stage is really interesting. You look at the public, multicloud environment, everybody doesn't need to work together, you talk to your customers, and I'm sure you find today it's not the future is hybrid and multicloud, that's where they are today even if they're trying to get their arms around all of it. So I'd love to hear your, with the mega trend of Cloud, what you're seeing that competitive but partnering dynamic. >> Look, I want to step back to just give it a little bit of context. So when you talk about companies, let's go back to the beginning of computing, of PC. The PC came from IBM operating system, DOS came from Microsoft. Then you had Windows setting up the IBM PC. So that's coopetition or is that pure partnership? Right, I mean you can take your pick of those words. Our value has always been that we, IBM, come to clients and we try to service problems that actually help them in their business outcomes. Then whoever they have inside their IT shops, that they depend upon, has to be a part of that answer. You cannot say oh, so and so is bad, they're out. So it always had to be coopetition from the lengths that we came to with our clients. We always build originally computers, other people's software are on those computers, other people provided services around it. As we went into certain software space, ISVs and so on came together. So now that you come to the world of Cloud, we hold a very fundamental belief and I think we heard a number of the clients talk about this. They are going to be on multiple public Clouds. If they are going to be on multiple public Clouds, they are also going to have traditional IT and they are also going to have private Clouds. That's the world to live in if I look at it from the viewpoint of that infrastructure. To now come to your direct question, so if that's the world they're going to live in hopefully one of those public Clouds is ours but the others are from other people. The private Cloud, we believe the standard for that should be OpenShift and should be containers. So as we go down that path, then you say if you want to take that environment and also run it on the other publics. That's good for the client, that's good for the publics, that's good for us. It's really a win, win, win. And so I think the ability to go do this and to make that play out, it really goes back to my thesis from more than a year ago where we talk about this is a new set of standards and a new set of technical protocols emerging. >> I want you to take us inside the conversations you're having with CIOs when you talk about Cloud because when Cloud first came out, it was well, the sins of IT is this heterogeneous mess and it's complex and expensive. Cloud's going to be simple, homogeneous and cheap. I look at Cloud of 2019 and I don't think I would use any of those adjectives to define what most people have for Cloud. Where are they today? Where do we need to go as an industry? >> Glass house computing, all centralized, all homogeneous, not all at heterogeneous. Oops, 15 flavors of Unix, all different, none of them really talk to each other. Oops let's go to desktop computing, we begin with a pure architecture, maybe Novell which doesn't exist, maybe it does, I don't even know. Oops, back to this complete sprawl of client server. Okay let's go to Cloud back to centralized glass house. >> You're making me dizzy. >> Oops, let's go to-- (laughing) >> Let's go to lots of public, lots of SaaS, lots of private, back to this thing. So, in each of these a different answer came on how to unite them. I think when we look at that Unix and client server sprawl, I think TCP/IP and the internet came together so that you could have all these islands talk to each other and be able to communicate. All right, great, we've got 20 years of victory on that. Now you're getting these things, how do you begin to workload across because that becomes the next level of values. Not enough to communicate. Can I really take a workload? A workload is not just a VM or just one container, it's a collection of these things integrated together in a pretty tight and complex way. And can we take it from one place and move it to the other? Because that goes to the write once, run anywhere mantra which by the way also we come to about every 20 years. I think that's the magic of this moment and if we succeed in making that happen, which I have complete conviction we will, especially together, then I think we give a huge value back and we give freedom to every CTO and every CIO. >> You paint this really interesting whoops picture, I love that, it's really a back and forth, right, we're swinging and almost there's a cyclical nature to this is what you're I think implying. What's to say in your mind that this isn't just another whoops as opposed to this being a permanent shift in the paradigm? >> I think it's, the reason I think that it's going to be cyclical is we tend to, you know whether you go to construction and real estate, you talk about capacity and factories. You see an opportunity and people tend to go one way. The only way to correct culture if you're sitting in one place is to sort of over-correct the other way, now you're over-corrected. Now you have to come back. And always when you over-correct one way, then suddenly all those other benefits you've lost, so then you've got to come back to get those benefits. After about 10 years, probably, you can debate 10 or 15, you're done. You've exploited all those benefits, now you need to go get those benefits. Because the technologies have changed, it's not just that you're going back to what was. We're going very conceptually from centralized to distributed, to centralized to distributed. And by the way, another one that's getting out from pure centralized is also Edge. Edge in effect is another distributed, so you put those together and you say I went there, but then I lost all this stuff, now I need to get back to that stuff. If you've got too much there, you'll say, no, no, no, I need to get some of this back. So it's going to go that way I think for every, if you look at it, the big arcs are back, the pendulum, what do you call it, the pendulum swing, is I think about 20 years it looks like, right? 1960, centralized, 1980, PC, 2000, you could say was the peak of the internet. Hey, 2020, we're in Cloud. So looks like about 20 years, looks like. >> All right, so, I like what you were saying when you talk about that multicloud environment, the application is really central there. IBM, of course, has a strong history, not just in middleware but in applications. What do you think will differentiate this kind of next wave of multicloud, how will the leaders emerge? >> Right, so if you look at it today, you run infrastructure. I think OpenShift has done a great job of how you help run their infrastructure. The value in our eyes in putting the services on top, both coming from open source as well as other companies that are running like an integrated package. This is all about taking the cost out of how do you deploy and develop. And if we can take the cost out of that, you're not talking about that five to 10 X as we heard a couple of the clients up on stage yesterday with Jim talk about. If we give that to everybody, you can sort of say that 70% which goes into managing your current and only 30% on innovation. Can you shift that paradigm completely? That's the big business outcome that you get. As you begin to deliver these towers of function on top of the base. You need to start at base, without one base, you don't know how to say, I can't deal with these towers of function on thirty different things underneath. That engineering answer is a terrible one. >> In terms of the infrastructure market, things keep changing, right? Consolidating, EMC doing what they're, you know what happened there. How do you see your play in that market? First off, how do you see infrastructure evolving? And then how do you see your play in that going forward? >> Infrastructure has always been big, in the end all the stuff you talk about has to run on infrastructure. I'd say the consumption model of how you get infrastructure is changing. So it used to be that many years ago, people bought all their own infrastructures. They bought boxes, they put in boxes, they did all the integration. And what came from the vendor was just a box. Then you went to, all right you can get it as a managed service or you can get it in Cloud which is also a pay by the drink but you can now turn it up and down also. So it's not a either or, people want all of these models. And so our role in infrastructure, certain things we will provide. When it comes to running really high mission critical workloads, think mainframe, think big Unix, think storage, of that ilk; we'll keep providing that. We believe there's a lot of value in that. We see the value, our clients appreciate that value. That workload turns up, but it's the mission critical part of the workload. Then in turn we also provide the more commodity infrastructure but as a service. We supply a large amount of it to our clients. It comes sometimes wrapped in a managed service, it sometimes comes wrapped as a Cloud. And we will also consume infrastructure from other Cloud providers because if people are providing base computer, network and storage, there is no reason to presume that our capabilities wouldn't run on top. If I go back to just February, we announced that Watson will now run. We said we used the moniker Watson Anywhere to make the assertion that we will run Watson anywhere that we can run the correct containerized infrastructure. >> So, Arvind, what's the single most pressing issue that you hear from organizations with respect to their technology strategy and how's IBM helping there? >> I think modernizing applications is the biggest one. So people have, typically a large enterprise will have anywhere from 3,000 to 15,000 applications. That's what runs the enterprise. We talk about everyone's becoming a software company, right, I mean that was one of the quotes and everybody is becoming a tech company that was I think what one of the clients said, hey, we think you're a bank, you're actually a tech company. What that says is that you're capturing the essence of all the business processes. You're capturing the essence of the experiences. The essence of what regulators need, the essence of how you maintain customer and customer of our clients, trust, back to them. It's maintained through this collection of applications. Now if you say I want to go change, I want to become even more client centric, I want to insert AI into the middle of my business process, I want to become more digital. All of that is modernizing applications. The big pinpoint they all have is how do I modernize them? What becomes that fabric in which I modernize? How do I know I'm not locked into yet another spaghetti mess if I go down this path? Because we've seen that movie also. So they're interested in, hey, I want to be clean at the end of this. I want freedom to be able to move it. And that is why I'm so passionate about, the fabric is based on open source, the fabric's got to be based on open standards. If you go there, there is no lock-in, and it's not a spaghetti mess, it is actually clean. Much cleaner than any other option that we can dream of is going to be. And so if we go down this path, now you can open yourself up to a much faster velocity of how you deliver innovation and value back to the business. >> Okay, so, I'd agree first of all when you talk about modernization, the applications that they have, that's the long pole in the tent. We understand compared to all the other digitization, modernization, this is the toughest challenge here. I'm a little surprised though that I didn't hear the word data because they don't necessarily articulate it but the biggest opportunity that they have has to be tied to data. >> Well to me, when I use the word application here, and you heard me use the word AI, can I insert AI in the context of an application? Now, why is it not being done today? To get the value out of AI, the data that powers the AI is stuck in all the silos, all over the place. So you've got to have, as you do this modernization, it's imperative to put the correct data architecture so that now you can do the governance, so that you can choose to unlock the appropriate parts of the data. It's really important to say the appropriate parts because neither do you want data sort of free floating around the globe, because that is the value of a company at the end of the day. And so that unlocking of that value is a huge part of this. So you're absolutely right to ask me to express it more strongly when I use the word application, I'm inclusive of not just runtime but always of the data that powers that application. >> Arvind, it was again a year ago that we were talking to you out in San Francisco and you made some rather strong thematic predictions that turned out well. I'm not going to put you on the spot here, but I can't wait to see next year. And see how this turns out. >> I can't let him go before, we had the CIO of Delta who we had on our program. >> Oh, right, right. >> In the key note, made a question about licensing, of course Jim Whitehurst said we don't have licensing but what's your answer? >> I'm willing to offer a deal to Samant. So I think that both IBM and Red Hat do a fair amount of air travel. We'll give him a common license if he can just include Red Hat for whatever IBM pays, just include all the Red Hat travel that is needed on Delta. (laughing) You know just so that the business models become clear and we can go have a robust discussion. >> Out of Raleigh that's a good deal. >> For us. >> That's what I'm saying. That is a good deal. All right, the ball is in your court, or on your runway. Whatever the case may be. Arvind, thanks for being with us. >> My pleasure. >> We appreciate it. And we'll let you know if we hear back from Rahul on that good deal. TheCUBE continues live from Boston right after this. (upbeat music)

(cheerful music) >> Live from San Francisco. It's theCUBE. Covering IBM Think 2019. Brought to you by IBM. >> Welcome to theCUBE, I'm Lisa Martin. We are at day one of IBM Think 2019, I'm with Dave Vellante. Hey Dave! Hey Lisa, good to see you. The new improved Moscone. >> Exactly, and Stu Miniman, yeah. >> Shiny. >> Yeah, this is the new, it is shiny, The carpets smells new. This is the second annual IBM Think, gentleman where there's this conglomeration of five to six previous events. Doesn't really kick off yet today. I think Partner World starts today but here we are in San Francisco. Moscone North, I think south, and west they have here expecting about 25,000 people. No news yet today, Dave, so let's kind of talk about where IBM is right now with the early part of Q1 of 2019. Red Hat acquisition just approved by shareholders last month. What are your thoughts on the status of Big Blue? >> Well, I think you're right, Lisa, that the Red Hat news is the big news for IBM. We're now entering the next chapter but if you look back for the last five years IBM had to go out and pay two billion dollars for a soft layer to get into the cloud business. That was precipitated by the big, high profile loss of the CIA deal against Amazon. So that was a wake up call for IBM. So they got into the public cloud game. So that's the good news. The bad news is the public cloud's not easy when you're going up against the likes of Google and Microsoft and of course, Amazon. But the linchpin of IBM's cloud strategy is it's SAS portfolio. Over the last 20 years Steve Mills and his organization built a very large software business which they now have migrated into their cloud and so they've got that advantage much like Oracle. They're not a big, dominant cloud infrastructure as a service player but they have a platform where they can put things like Cognitive Solutions and Watson and offer those SAS services to clients. So you'll check on that but when you'll peel through the numbers IBM beat it's numbers last quarter. Stock was up. You know, when it announced the Red Hat acquisition the stock actually got crushed because when you spend 34 billion dollars on a company, you know the shareholders don't necessarily love that but we'll talk about the merits of that move. But they beat in the fourth quarter. They beat on the strength of services. So IBM remains largely a services company, about 60% plus of it's revenues comes from services. It's a somewhat lower margin business, even though IBM margins have been ticking up. As I say, you go back the last five, six years IBM Genesys did Mike's it's microelectronics business, which was a, you know, lost business. It got rid of it's x86 business which is a x86 server business, which is a low margin business. So again, like Oracle, it's focusing on high margin software and services and now we enter the era, Stu, of hybrid cloud with the Red Hat acquisition. A lot of money to pay, but it gets IBM into the next generation of multi cloud. >> Yeah, Dave, the knock I've had against IBM is in many ways they always try to be all things to all people and of course we know you can be good at some things but, you know, it's really tough to be great at everything. And, you know, you talked about cloud, Dave, you know, the SoftLayer acquisition to kind of get into public cloud but, you know, IBM is not one of the big players in public cloud. It's easy. It's Amazon and then followed by you know, Azure, Google, and let's talk Alibaba if we're talking globally. In a multi cloud world IBM has a strong play. As you said, they've got a lot of application assets, they have public cloud, they partner with a lot of the different cloud players out there and with Red Hat they get a key asset to be able to play across all of these multi cloud environments whether we're talking public cloud, private cloud, across all these environments. IBM's been pushing hard into the Kubernetes space, doing a lot with Istio. You know, where they play there, in Red Hat is a key piece of this puzzle. Red Hat running at about three billion dollars of revenue and paying 34 billion dollars but, you know, this is a linchpin as to say how does IBM stay relevant in this cloud world going forward? It's really a you know, a key moment for IBM as to what this means. A lot of discussion as to you know, it's not just the revenue piece but what will Red Hat do to the culture of IBM? IBM has a strong history in open source but you know, you got to, you have a large bench of Red Hat's strong executive team. We're going to see some of them here at the show. We're even going to have one Red Hat executive on our program here and so what will happen once this deal finally closes, which is expected later this year, probably October if you read, you know everything right. But what will it look like as to how will, you know, relatively small Red Hat impact the larger IBM going forward? >> Well, I think it's a big lever, right? I mean we were, Lisa, we were at Cisco Live in Barcelona last week kind of laying out the horses on the track for this multi cloud. Cisco doesn't own it's own public cloud. VMware and Dell don't own it's own public cloud. They both tried to get into the public cloud in the early days and IBM does own it's own public cloud as does Oracle but they're also going hard after this notion of multi cloud as is Cisco, as is VMware. So it sort of sets up the sort of Cisco, IBM Red Hat, VMware, Dell, sort of competing to get after that multi cloud revenue and then HPE fits in there somewhere. We can talk about that. >> So I saw a stat the other day that said in 2018, 80% of companies moved data or apps from public cloud. Reasons being security, control, cost, performance. So to some of the things I've read, Dave, that you've covered recently, if IBM isn't able to really go head to head against the Azures and the AWS, what is their differentiator in this new, hybrid multi cloud world? Is it being able to bring AI, Watson, Cognitive Solutions, better than their competitors in that space that you just mentioned? >> Yeah, IBM does complicate it. You know and cloud and hybrid cloud is complicated and so that's IBM's wheelhouse. And so it tends not to do commodity. So if it's complicated and sophisticated and requires a lot of services and a lot of business processing happening and things like that, IBM tends to excel. So, you know, if you do the SWOT analysis it's big opportunity is to be that multi-cloud provider for it's largest customers. And the larger customers are running, you know, transaction systems on mainframe. They're running cognitive systems on things like power. They've got a giant portfolio, at IBM that is, and they can cobble things together with their services and solve problems and that's kind of how IBM approaches the marketplace. Much different than say, Stu, Cisco or VMware. >> Yeah, Dave, you're absolutely right. You know one of the things I look at is you know, in this multi-cloud space we've see the SI's that are very important there. Companies like Accenture and KPMG and the like. IBM partners with them but IBM also has a large services business. So, you know who's going to be able to help customers get in there and figure out this rather complicated environment. So we are definitely one of the things I want to dig into this week is understand where IBM is at the Cisco Show, Dave. We've talked about their messaging was the bridge to you know what's possible. You know meet the customers where they are, show them how to reach into the future and from Cisco's standpoint, it's strong partnerships with AWS and Google at the forefront. So IBM has just one of the broadest portfolios in the industry. They absolutely play in every single piece but you know customers need good consulting as to Okay, what's going to be the fit for my business. How do I modernize, how do I go forward? And IBM's been down this trip for a number of years. >> Well the in the legacy of Ginni Rometty, in my opinion is going to be determined by the pace at which it can integrate Red Hat and use Red Hat as a lever. Ginni Rometty, when she was doing the roadshow with Jim Whitehurst kept saying it's not a backend loaded deal, and the reason it's not a backend loaded deal is because IBM is a 20 plus billion dollar outsourcing business and they're going to plug Red Hat right into that business to modernize applications. So there's a captive revenue source for IBM. In my view they have to really move fast, faster than typically IBM moves. We've been hearing about strategic initiatives and cloud, and Watson and it's been moving too slow in my opinion. The Red Hat acquisition has to move very very quickly. It's got to move at the speed of cloud and that's going to determine in my opinion-- >> So, actually, so a couple of weeks after the acquisition Red Hat had brought in an analyst to hear what was going on, and while the discussion is Red Hat will stay a distinct brand, there's going to be no lay offs were >> Yeah absolutely. >> Going to keep them separate, what they will get is IBM can really help them scale so >> Yep. Red Hat is getting into some new environments, you know that whole services organization, Red Hat doesn't have that. So IBM absolutely can plug in there and we think really accelerate, the old goal for Red Hat was okay how do we get from that three billion dollars to five billion dollars in the next couple of years. IBM thinks that they can accelerate that even faster. >> And Lisa I think the good news is IBM has always had an affinity toward open source. IBM was really the first, really to make a big investment you know they poured a billion dollars into Linux as a means of competing with Microsoft back in the day, and so they've got open source chops. So for those large IBM customers that might not want to go it alone on open source and you know Red Hat's kind of the cool kid on the block. But at the same time, you know there's some risks there. Now IBM can take that big blue blanket wrap it around it's largest customers and say okay, we've got you covered in open source, we've got the Red Hat asset, and we've got the services organization to help you modernize your application portfolio. >> One of the things too that Stu, you brought up a couple minutes ago is culture. And so looking at what, Red Hat estimates that it's got about eight million developers world wide using their technologies and this is an area that IBM had historically not been really focused on. What are some of the things that you're expecting to hear this week or see this week with respect to the developer community embracing IMB? >> Yeah and Lisa it's not like IBM hasn't been trying to get into the developer community. I remember back at some of the previous shows Edge and Pulse and the like, they would have you know Dev at and try to do a nice little piece of it but it really didn't gain as much traction as you might like. Compare and contrast that with cisco, we've been watching over the last five years the DevNet community. They've got over half a million developers on that platform. So you know, developer engagement usually requires that ground level activity where I've seen good work from IBM has been getting into that cloud native space. So absolutely seen them at the Kubernetes shows working in the container space very heavily and of course that's an area that Red Hat exceeds. So the Linux developers are absolutely there. Now you mentioned how many developers Red Hat has and in that multi cloud, cloud native space, you know Red Hat one of the leaders if not kind of the leader in that space and therefore it should help super charge what IBM is doing, give them some credibility. I'd love to see how many developers we see at this show, you know, you've been to this show Dave and you've been to this show before, it looked more enterprisey to me from the outside-- >> Well, I'm glad you brought up developers because that is the lynch pin of the Red Hat acquisition. If you look at the companies that actually have in the cloud that have a strong developer affinity obviously Microsoft does and always had AWS clearly does Google has you know it's developer community. Stu you mentioned Sisco. Sisco came at it from a networking standpoint and opened up it's network for infrastructure's code. One of the few legacy hardware companies that's done a good job there. VMware, you know not so much. Right? Not really a big developer world and IBM has tried as you pointed out. When they announced Bluemix but that really didn't take off in the developer world. Now with Red Hat IBM, it's your point eight million developers. That is a huge asset for IBM and one that as I said before it absolutely has to leverage and leverage fast. >> And what are you expectations in terms of any sort of industry deeper penetration? There's been some big cloud deals, cloud wins that IBM has made is recent history. One of them being really big in the energy sector. Are you guys kind of expecting to see any sort of industry deeper penetration as a result of what the Red Hat Acquisition will bring? >> Well thats IBM's strength. Stu you pointed out before, it's Accenture, you know Ernie Young, to a lesser extend maybe KPNG but those big SI's and IBM. When IBM bought PWC Gerstner transformed the company and it became a global leader with deep deep industry expertise. That is IBM's you know, savior frankly over these past many many years. So it can compete with virtually anybody on that front and so yes absolutely every industry is being transformed because of digital transformation. IBM understands this as well as anybody. It's a boon for services, it's a good margin business and so that's their competitive advantage. >> Yeah I mean it ties back into their services. I think back when I lived on the vendor side I learned a lot of the industry off of watching IBM. I see how many companies are talking about smarter cities. IBM had you know a long history of working In those environment's. Energy, industrial, IBM is very good at digging into the needed requirements of specific industries and driving that forward. >> So we're going to be here for four days as we mentioned, today is day one. We're going to be talking a lot about this hybrid multi-cloud world. But some of the double clicks we're going to do is talking about data protection, modern data protection, you know a lot of the statistics say that there's eighty percent of the worlds data isn't searchable yet. We all hear every event we do guys, data is the new oil. If companies can actually harness that, extract insights faster than their competition. Create new business models, new services, new products. What are your expectations about how, I hear a lot get your data AI ready. As a marketer I go, what does that mean? What are your thoughts Stu on, and we're sitting in a lot of signage here. How is IBM going to help companies get AI, Data rather AI ready and what does that actually mean? >> So IBM really educated a lot of the world and the broader world as to what some of this AI is. I mean I know we all watched many years ago when Watson was on Jeopardy and we kind of hit through the past the peak and have been trying to sort out okay well how can IBM monetize this? They're taking Watson and getting it into healthcare, they're getting it into all these other environments. So IBM is well known in the AI space. Really well known in the data space but there's a lot of competition and we're still relatively early in the sorting how this new machine learning and AI are going to fit in there. You know we spent a lot of time looking at things like RPA was kind of the gateway drug of AI if you will robotic process automation. And I'm not sure where IBM fit's into that environment. So once again IBM has always had a broad portfolio they do a lot of acquisitions in the space. So you know how can they take all those pieces, pull them together, get after the multicloud world, enable developers to be able to really leverage data even more that's possible and as you said you know more than eighty percent of data today isn't used, you know from an infrastructure stand point I'm looking at how do things like edge computing all get pulled into this environment and lot of questions still. >> IBM is going after hard problems like I said before. You don't expect IBM to be doing things like ad serving with Alexa. You know that's not IBM's game, they're not going to appropriate to sell ad's they're going to take really hard complex problems and charge a lot of money for big services engagements to transform companies. That's their game and that's a data game for sure. >> It's a data game and one of the pieces too that I'm excited to learn about this week is what they're doing about security. We all know you can throw a ton of technology at security and infrastructure but there's the people piece. So we're going to be having a lot of conversations about that as well. Alright guys looking forward to a full week with you and with John joining us at IBM Think I'm Lisa Martin for Dave Vellante and Stu Miniman. You're watching theCUBE live day one IBM Think 2019. Stick around we'll be right back with our next guest. (energetic electronic music)

>> From the SiliconANGLE Media office in Boston, Massachusetts, it's theCUBE. Now here are your hosts, Dave Vellante and Stu Miniman. >> Hi everybody, Dave Vellante here with Stu Miniman. We're here to unpack the recent acquisition that IBM announced of Red Hat. $34 billon acquisition financed with cash and debt. And Stu, let me get us started. Why would IBM spend $34 billion on Red Hat? Its largest acquisition to date of a software company had been Cognos at $5 billion. This is a massive move. IBM's Ginni Rometty called this a game changer. And essentially, my take is that they're pivoting. Their public cloud strategy was not living up to expectations. They're pivoting to hybrid cloud. Their hybrid cloud strategy was limited because they didn't really have strong developer mojo, their Bluemix PaaS layer had really failed. And so they really needed to make a big move here, and this is a big move. And so IBM's intent, and Ginni Rometty laid out the strategy, is to become number one in hybrid cloud, the undisputed leader. And so we'll talk about that. But Stu, from Red Hat's perspective, it's a company you're very close to and you've observed for a number of years, Red Hat was on a path touting a $5 billion revenue plan, what happened? Why would they capitulate? >> Yeah Dave, on the face of it, Red Hat says that IBM will help it further its mission. We just listened to Arvin Krishna from IBM talking with Paul Cormier at Red Hat, and they talked about how they were gonna keep the Red Hat brand alive. IBM has a long history with open source. As you mentioned, I've been working with Red Hat, gosh, almost 20 years now, and we all think back to two decades ago, when IBM put a billion dollars into Linux and really pushed on open source. So these are not strangers, they know each other really well. Part of me looks at these from a cynicism standpoint. Somebody on Twitter said that Red Hat is hitting it at the peak of Kubernetes hype. And therefore, they're gonna get maximum valuation for where the stock is. Red Hat has positioned itself rather well in the hybrid cloud world, really the multicloud world, when you go to AWS, when you go to the Microsoft Azure environment, you talk to Google. Open source fits into that environment and Red Hat products specifically tie into those environments. Remember last year, in Boston, there's a video of Andy Jassy talking about a partnership with Red Hat. This year, up on stage, Microsoft with Azure partnering deeply with Red Hat. So Red Hat has done a nice job of moving beyond Linux. But Linux is still at its core. There definitely is concern that the operating system is less important today than it was in the past. It was actually Red Hat's acquisition of CoreOS for about $250 million earlier this year that really put a fine point on it. CoreOS was launched to be just enough Linux to live in this kind of container and Kubernetes world. And Red Hat, of course, like we've seen often, the company that is saying, "We're going to kill you", well you go and you buy them. So Red Hat wasn't looking to kill IBM, but definitely we've seen this trend of softwares eating the world, and open sources eating software. So IBM, hopefully, is a embracing that open source ethos. I have to say, Dave, for myself, a little sad to see the news. Red Hat being the paragon of open source. The one that we always go to for winning in this space. So we hope that they will be able to keep their culture. We've had a chance, many times, to interview Jim Whitehurst, really respected CEO. One that we think should stay involved in IBM deeply for this. But if they can keep and grow the culture, then it's a win for Red Hat. But still sorting through everything, and it feels like a little bit of a capitulation that Red Hat decides to sell off rather than keep its mission of getting to five billion and beyond, and be the leading company in the space. >> Well I think it is a bit of a capitulation. Because look, Red Hat is roughly a $3 billion company, growing at 20% a year, had that vision of five billion Its stock, in June, had hit $175. So while IBM's paying a 60% premium off of its current price, it's really only about 8 or 9% higher than where Red Hat was just a few months ago. And so I think, there's an old saying on Wall Street, the first disappointment is never the last. And so I think that Red Hat was looking at a long slog. They reduced expectations, they guided lower, and they were looking at the 90-day shot clock. And this probably wasn't going to be a good 'nother couple of years for Red Hat. And they're selling at the peak of the market, or roughly the peak of the market. They probably figured, hey, the window is closing, potentially, to do this deal. Maybe not such a bad time to get out, as opposed to trying to slog it out. Your thoughts. >> Yeah, Dave, I think you're absolutely right. When you look at where Red Hat is winning, they've done great in OpenStack but there's not a lot of excitement around OpenStack. Kubernetes was talked about lots in the announcement, in the briefings, and everything like that. I was actually surprised you didn't hear as much about just the core business. You would think you would be hearing about all the companies using Red Hat Enterprise Linux around the world. That ratable model that Red Hat really has a nice base of their environment. It was talking more about the future and where Kubernetes, and cloud-native, and all of that development will go. IBM has done middling okay with developers. They have a strong history in middleware, which is where a lot of the Red Hat development activity has been heading. It was interesting to hear, on the call, it's like, oh well, what about the customers that are using IBM too say, "Oh well, if customers want that, we'll still do it." What about IBM with Cloud Foundry? Well absolutely, if customers wanna still be doing it, they'll do that. So you don't hear the typical, "Oh well, we're going to take Red Hat technology "and push it through all of IBM's channel." This is in the IBM cloud group, and that's really their focus, as it is. I feel like they're almost limiting the potential for growth for Red Hat. >> Well so IBM's gonna pay for this, as I said, it's an all cash deal. IBM's got about 14 and a half billion dollars on the balance sheet. And so they gotta take out some debt. S&P downgraded IBM's rating from an A+ to an A. And so the ratings agency is going to be watching IBM's growth. IBM said this will add 200 basis points of revenue growth over the five year CAGR. But that means we're really not gonna see that for six, seven years. And Ginni Rometty stressed this is not a backend loaded thing. We're gonna find revenue opportunities through cross-selling and go-to-market. But we have a lot of questions on this deal, Stu. And I wanna sorta get into that. So first of all, again, I think it's the right move for IBM. It's a big move for IBM. Rumors were that Cisco might have been interested. I'm not sure if Microsoft was in the mix. So IBM went for it and, as I said, didn't pay a huge premium over where their stock was back in June. Now of course, back in June, the market was kind of inflated. But nonetheless, the strategy now is to go multi-cloud. The number one in the multi-cloud world. What is that multi-cloud leadership? How are we gonna measure multi-cloud? Is IBM, now, the steward of open source for the industry? To your point earlier, you're sad, Stu, I know. >> You bring up a great point. So I think back to three years ago, with the Wikibon we put together, our true private cloud forecast. And when we built that, we said, "Okay, here's the hardware, and software, "and services in private cloud." And we said, "Well let's try to measure hybrid cloud." And we spent like, six months looking at this. And it's like, well what is hybrid cloud? I've got my public cloud pieces, and I've got my private cloud pieces. Well there's some management layers and things that go in between. Do I count things like PaaS? So do you save people like Pivotal and Red Hat's OpenShift? Are those hybrid cloud? Well but they live either here or there. They're not usually necessarily helping with the migration and moving around. I can live in multiple environments. So Linux and containers live in the public, they live in the private, they don't just fly around in the ether. So measuring hybrid cloud, I think is really tough. Does IBM plus Red Hat make them a top leader in this hybrid multi-cloud world? Absolutely, they should be mentioned a lot more. When I go to the cloud shows, the public cloud shows, IBM isn't one of the first peak companies you think about. Red Hat absolutely is in the conversation. It actually should raise the profile of Red Hat because, while Red Hat plays in a lot of the conversations, they're also not the first company that comes to mind when you talk about them. Microsoft, middle of hybrid cloud. Oracle, positioning their applications in this multi-cloud world. Of course you can't talk about cloud, any cloud, without talking about Amazon's position in the marketplace. And SAS is the real place that it plays. So IBM, one of their biggest strengths is that they have applications. Dave, you know the space really well. What does this mean vis-à-vis Oracle? >> Well let's see, so Oracle, I think, is looking at this, saying, alright. I would say IBM is Oracle's number one competitor in the enterprise. You got SAP, and Amazon obviously in cloud, et cetera, et cetera. But let me put it this way, I think Oracle is IBM's number one competitor. Whether Oracle sees it that way or not. But they're clearly similar companies, in terms of their vertical integration. I think Oracle's looking at this, saying, hey. There's no way Oracle was gonna spend $34 billion on Red Hat. And I don't think they were interested in really spending any money on the alternatives. But does this put Canonical and SUSE in play? I think Oracle's gonna look at this and sort of message to its customers, "We're already number one in our world in hybrid cloud." But I wanna come back to the deal. I'm actually optimistic on the deal, from the standpoint of, I think IBM had to make a big move like this. Because it was largely just bumping along. But I'm not buying the narrative from Jim Whitehurst that, "Well we had to do this to scale." Why couldn't they scale with partners? I just don't understand that. They're open. This is largely, to me, a services deal. This is a big boon for IBM Services business. In fact, Jim Whitehurst, and Ginni even said that today on the financial analyst call, Jim said, "Our big constraint was "services scale and the industry expertise there." So what was that constraint? Why couldn't they partner with Accenture, and Ernie Young, and PwC, and the likes of Deloitte, to scale and preserve greater independence? And I think that the reason is, IBM sees an opportunity and they're going hard after it. So how will, or will, IBM change its posture relative to some of those big services plays? >> Yeah, Dave, I think you're absolutely right there. Because Red Hat should've been able to scale there. I wonder if it's just that all of those big service system integrators, they're working really closely with the public cloud providers. And while Red Hat was a piece of it, it wasn't the big piece of it. And therefore, I'm worried on the application migration. I'm worried about the adoption of infrastructure as a service. And Red Hat might be a piece in the puzzle, but it wasn't the driver for that change, and the move, and the modernization activities that were going on. That being said, OpenShift was a great opportunity. It plays in a lot of these environments. It'll be really interesting to see. And a huge opportunity for IBM to take and accelerate that business. From a services standpoint, do you think it'll change their position with regard to the SIs? >> I don't. I think IBM's gonna try to present, preserve Red Hat as an independent company. I would love to see IBM do what EMC did years ago with VMware, and float some portion of the company, and truly have it at least be quasi-independent. With an independent operating structure, and reporting structure from the standpoint of a public company. That would really signal to the partners that IBM's serious about maintaining independence. >> Yeah now, look Dave, IBM has said they will keep the brand, they will keep the products. Of all the companies that would buy Red Hat, I'm not super worried about kinda polluting open source. It was kinda nice that Jim Whitehurst would say, if it's a Red Hat thing, it is 100% open source. And IBM plays in a lot of these environments. A friend of mine on Twitter was like, "Oh hey, IBM's coming back to OpenDaylight or things like that." Because they'd been part of Cloud Foundry, they'd been part of OpenDaylight. There's certain ones that they are part of it and then they step back. So IBM, credibly open source space, if they can let Red Hat people still do their thing. But the concern is that lots of other companies are gonna be calling up project leads, and contributors in the open source community that might've felt that Red Hat was ideal place to live, and now they might go get their paycheck somewhere else. >> There's rumors that Jim Whitehurst eventually will take over IBM. I don't see it, I just don't think Jim Whitehurst wants to run Z mainframes and Services. That doesn't make any sense to me. Ginni's getting to the age where IBM CEOs typically retire, within the next couple of years. And so I think that it's more likely they'll bring in somebody from internally. Whether it's Arvin or, more likely, Jim Kavanaugh 'cause he's got the relationship with Wall Street. Let's talk about winners and losers. It's just, again, a huge strategic move for IBM. Frankly, I see the big winners is IBM and Red Hat. Because as we described before, IBM was struggling with its execution, and Red Hat was just basically, finally hitting a wall after 60-plus quarters of growth. And so the question is, will its customers win? The big concern I have for the customers is, IBM has this nasty habit of raising prices when it does acquisitions. We've seen it a number of times. And so you keep an eye on it, if I were a Red Hat customer, I'd be locking in some attractive pricing, longterm. And I would also be calling Mark Shuttleworth, and get his take, and get that Amdahl coffee cup on my desk, as it were. Other winners and losers, your thoughts on some of the partners, and the ecosystem. >> Yeah, when I look at this and say, compare it to Microsoft buying GitHub. We're all wondering, is this a real game changer for IBM? And if they embrace the direction. It's not like Red Hat culture is going to just take over IBM. In the Q&A with IBM, they said, "Will there be influence? Absolutely. "Is this a marriage of equals? No. "We're buying Red Hat and we will be "communicating and working together on this" But you can see how this can help IBM, as to the direction. Open source and the multi-cloud world is a huge, important piece. Cisco, I think, could've made a move like this. I would've been a little bit more worried about maintaining open source purity, if it was somebody like Cisco. There's other acquisitions, you mentioned Canonical and SUSE are out there. If somebody wanted to do this, the role of the operating system is much less important than it is today. You wouldn't have seen Microsoft up on stage at Red Hat Summit this year if Windows was the driver for Microsoft going forward. The cloud companies out there, to be honest, it really cements their presence out there. I don't think AWS is sitting there saying, "Oh jeez, we need to worry." They're saying, "Well IBM's capitulated." Realizing that, "Sure they have their own cloud, "and their environment, but they're going to be "successful only when they live in, "and around, and amongst our platform of Amazon." And Azure's gonna feel the same way, and same about Google. So there's that dynamic there. >> What about VMware? >> So I think VMware absolutely is a loser here. When I went back to say one of the biggest strengths of IBM is that they have applications. When you talk about Red Hat, they're really working, not only at the infrastructure layer, but working with developers, and working in that environment. The biggest weakness of VMware, is they don't own the applications. I'm paying licenses to VMware. And in a multi-cloud world, why do I need VMware? As opposed to Red Hat and IBM, or Amazon, or Microsoft, have a much more natural affinity for the applications and the data in the future. >> And what about the arms dealers? HPE and Dell, in particular, and of course, Lenovo. Wouldn't they prefer Red Hat being independent? >> Absolutely, they would prefer that they're gonna stay independent. As long as it doesn't seem to customers that IBM is trying to twist everybody's arms, and get you on to Z, or Power, or something like that. And continues to allow partnerships with the HPEs, Dells, Lenovos of the world. I think they'll be okay. So I'd say middling to impact. But absolutely, Red Hat, as an independent, was really the Switzerland of the marketplace. >> Ginni Rometty had sited three growth areas. One was Red Hat scale and go-to-market. I think there's no question about that. IBM could help with Red Hat's go-to-market. The other growth vector was IBM's products and software on the Red Hat stack. I'm less optimistic there, because I think that it's the strength of IBM's products, in and of themselves, that are largely gonna determine that success. And then the third was Services. I think IBM Services is a huge winner here. Having the bat phone into Red Hat is a big win for IBM Services. They can now differentiate. And this is where I think it's gonna be really interesting to see the posture of Accenture and those other big guys. I think IBM can now somewhat differentiate from those guys, saying, "Well wait, "we have exclusive, or not exclusive, "but inside baseball access to Red Hat." So that's gonna be an interesting dynamic to watch. Your final thoughts here. >> Yeah, yeah, Dave, absolutely. On the product integration piece, the question would be, you're gonna have OpenAPIs. This is all gonna work with the entire ecosystem. Couldn't IBM have done more of this without having to pay $34 billion and put things together? Services, absolutely, will be the measurement as to whether this is successful or not. That's probably gonna be the line out of them in financials, that we're gonna have to look at. Because, Dave, going back to, what is hybrid, and how do we measure it? What is success for this whole acquisition down the line? Any final pieces to what we should watch and how we measure that? >> So I think that, first of all, IBM's really good with acquisitions, so keep an eye on that. I'm not so concerned about the debt. IBM's got strong free cash flow. Red Hat throws off a billion dollars a year in free cash flow. This should be an accretive acquisition. In terms of operating profits, it might take a couple of years. But certainly from a standpoint of free cash flow and revenue growth, I think it's gonna help near-term. If it doesn't, that's something that's really important to watch. And then the last thing is culture. You know a lot of people at these companies. I know a lot of people at these companies. Look, the Red Hat culture drinks the Kool-Aid of open. You know this. Do they see IBM as the steward of open, and are they gonna face a brain drain? That's why it's no coincidence that Whitehurst and Rometty were down in North Carolina today. And Arvin and Paul Cormier were in Boston today. This is where a lot of employees are for Red Hat. And they're messaging. And so that's very, very important. IBM's not foolish. So that, to me, Stu, is a huge thing, is the culture. Dave, IBM is no longer the navy suit with the red tie, and everybody buttoned down. People are concerned about like, oh, IBM's gonna give the Red Hat people a dress code. Sure, the typical IBMer is not in a graphic tee and a hoodie. But, Dave, you've seen such a transformation in IBM over the last couple of decades. >> Yeah, definitely. And I think this really does, in my view, cement, now, the legacy of Ginny Rometty, which was kinda hanging on Watson, and Cognitive, and this sort of bespoke set of capabilities, and the SoftLayer acquisition. It, now, all comes together. This is a major pivot by IBM. I think, strategically, it's the right move for IBM. And I think, if in fact, IBM can maintain Red Hat's independence and that posture, and maintain its culture and employee base, I think it does change the game for IBM. So I would say, smart move, good move. Expensive but probably worth it. >> Yeah, where else would they have put their money, Dave? >> Yeah, right. Alright, Stu, thank you very much for unpacking this announcement. And thank you for watching. We'll see you next time. (mellow electronic music)

>> Live from Miami Beach, Florida, it's theCUBE. Covering UiPathForward Americas. Brought to you by UiPath. >> Welcome back to Miami everybody. You're watching theCUBE, the leader in live tech coverage. We go out to events, we extract the signal from the noise. A lot of noise here but the signal's all around automation and robotic process automation. I'm Dave Vellante, he's Stu Miniman, my co-host. Guy Kirkwood's here he's the UiPath chief evangelist otherwise known as the chief injector of Kool-Aid. Welcome. (guests chuckling) And Craig LeClair, the Vice President at Forrester. Covers this market, wrote the seminal document on this space. Knows it inside out. Craig, great to see you again. >> Yeah, nice to see you again. It's great to be back at theCUBE. >> So let's start with the analyst perspective. Take us back to when you first discovered RPA, why you got excited about it, and what Forrester Research is all about in that space. >> Yeah, it's been a very a interesting ride. Most of these companies, at least that are the higher value ones in the category they've been around for a long time. They've been around for over a decade, and no one ever heard of them three years ago. So I had covered at Forrester, business process management and some of the business rules engines, and I've always been in process. I just got this sense that there was a way that companies could make progress and digital transformation and overcome the technical debt that they had. A lot of the progress has been tepid in digital transformation because it takes tremendous amount of time and tons of consultants to modernize that core system that really runs the company. So along comes this RPA technology that allows you to build human equivalence that patch up the inefficiencies without touching. I came in on American Airlines and the system that cut my ticket was designed in 1960. It's the same Sabre reservation system. That's the big obstacle that a lot of companies have been struggling to really take advantage of AI in general. A lot of the more moonshot and more sophisticated promises haven't been realized. RPA is a very practical form of automation that companies can get a handle on right now, and move the dial for digital transformation. >> So Guy we heard a vision set forth by Daniel this morning. Basically a chicken in every pot, I call it, a robot for every person. Now what Craig was just saying about essentially cutting the line on technical debt, do you have clear evidence of that in your customer base? Maybe you could give some examples. >> What we're really seeing is that as organizations have to deal with the stresses, what Leslie Wilcox professor at LSE describes as the stresses within organizations and particularly in environments where the demographics are changing. What we're seeing is that organizations have to automate. So the best example of that is in Japan where the Japanese population peaked in 2010. It's now falling as a whole, plus all the baby boomers, people of Craig's and my age are now retiring. So we're now in a position where they measure levels of dangerous overwork as being more that 106 hours a week. That isn't 106 hour a week in total, that's 106 hours a week in addition to the 60 hours a week the Japanese people normally work. And there is a word in Japanese, which is (speaking in foreign language), which means to work oneself to death. So there really is no choice. So what we're seeing happening in Japan will be replicated in Western Europe and certainly in the US over the next few years. So what's driving that is the rise of the ecosystems of technologies of which RPA and AI are part, and that's really what we're seeing within the market. >> Craig, sometimes these big waves particularly in infrastructure, you kind of saw it with virtualization and some other wonky techs, like data reduction. They could be a one-time step function, and not an ongoing business value creator. Where does RPA fit in there? How can organizations make sure that this is a continuous business value generator as opposed to a one time hit? >> Good question. >> Well, I like the concept of RPA as a platform that can lead to more intelligence and more integration with AI components. It allows companies to build an automation center or a center of excellence focused on automation. But the next thing they're going to do after building some simple robots that are doing repetitive tasks, is they're going to say "Oh well wouldn't it be better "if my employee could have a textual chat with a chatbot "that then was interacting with the digital worker "that I built with the bot." Or they're going to say "You know what? I really want to use that machine learning algorithm "for my underwriting process, but I can use these bots "to go out and collect all the data from the core systems "and elsewhere and from the web and feed the algorithms "so that I could make a better decision." So again it goes back to that backing off the moonshot approach that we've been talking about that AI has been taking because of the tremendous amount of money spent by the major players to lay out the promise of AI has really been a little dysfunctional in getting organizations' eye off the ball in terms of what could be done with slightly more intelligent automation. So RPA will be a flash in the pan unless it starts to embed these more learning-capable AI modules. But I think it has a very good chance of doing that particularly now with so much investment coming into the category right. >> Craig, it's really interesting. When I heard you describe that it reminds me of the home automation. The Cortanas and Alexas and consumer side where you're seeing this. You've got the consumer side where you can build skills yourself, you know teenagers people can do that. One of the challenges always on the business side is how do you get the momentum when you don't have the consumer side. How do those interact? >> It's the technical debt issue and it's just like the mobile peak in 2011. Consumers in their hands had much better mobility right away than businesses. It took businesses five, they're still not there in building a great mobile environment. So these Alexa in our kitchen snooping on our conversation and to some extent Netflix that observes our behavior. That's a light form of AI. There is a learning from that behavior that's updating an algorithm autonomously in Netflix to understand what you want to watch. There's no one with a spreadsheet back there right. So this has given us in a sense a false sense of progress with all of AI. The reality is business is just getting started. Business is nowhere with AI. RPA is an initial foray on that path. We're in Miami so I'll call it a gateway drug. >> In fact there's also an element that the Siris, the Cortanas, the Alexas, are very poor at understanding specific ontologies that are required for industry, and that's where the limitation is right now. We're working with an organization called Humly, they're focused on those ontologies for specific industries. So if the robot doesn't understand something, then you could say to the robot Okay sit that in the Wells account, if you're in a bank, and it understands that Wells in that case means Wells Fargo it doesn't mean a hole in the ground with water at the bottom or a town in Somerset in the UK, 'cause they're all wells. So it's getting that understanding correct. >> I wonder if you guys could comment on this. Stu and I were at Splunk earlier this week and they were talking up NLP and we were saying one of the problems is that NLP is sometimes not that great. And they made a comment that I thought was very interesting. They said frankly a lot of the stuff that we're ingesting is text and it's actually pretty good. I would imagine the same is true for RPA. Is that what you see? >> You were talking about that on stage. With regards to the text analytics. >> Yes. So RPA doesn't handle unstructured content the way that NLP does. So NLP can handle voice, it can handle text. For the bots to work in RPA today you have to have a layer of analytics that understands those documents, understands those emails and creates a nice clean file that the bots can then work with. But what's happening is the text analytics layer is slowly merging with the RPA bots platforms so it's going to be viewed as one solution. But it's more about categories of use cases that deal with forms and documents and emails rather than natural language, which is where it's at. >> So known business processes really is the starting point. >> Known business-- >> One example we've got live is an insurance company in South Africa called Hollard, and they've used a combination of Microsoft Cognitive Toolkit, plus IBM Watson and it's orchestrated doing NLP and orchestrated by UiPath. So that's dealing with utterly unstructured data. That's the 1.5 million emails that that organization gets in a year. They've managed to automate 98% of that, so it never sees a human. And their reduction in cost is 91% cost in reduction per transaction. And that's done by one of our implementation partners, a company called LarcAI down there. It's superb. >> Yeah, so text analytics is hard. Last several years we have that sentiment out of it, but if I understand it correctly Craig, you're saying if you apply it to a known process it actually could have outcomes that can save money. >> Yes, absolutely yes. >> As Guy was just saying. >> I think it's moving from that rules-based activity to more experience-based activity as more of these technologies become merged. >> Will the technology in your view advance to the point, because the known processes. okay, there's probably a lot of work to be done there, but today there's so many unknown processes. It's like this messy, unpredictable thing. Will machine intelligence combined with robotic process automation get to the point, and if so when, that we can actually be more flexible and adapt to some of these unknown processes or is that just decades off? >> No, no, I think we talk at Forrester about the concept of convergence. Meaning the convergence of the physical world and the digital world. So essentially digital's getting embedded in everything physical that we have right. Think of IoT applications and so forth. But essentially that data coming from those physical devices is unstructured data that the machine learning algorithms are going to make sense of, and make decisions about. So we're very close to seeing that in factory environments. We're seeing that in self-driving cars. The fleet managers that are now understanding where things are based on the signals coming from them. So there's a lot of opportunity that's right here on the horizon. >> Craig, a lot of the technologies you mentioned, we may have had a lot of the technical issues sorted out, but it's the people interactions some things like autonomous vehicles, there's government policies going to be one of the biggest inhibitors out there. When you look at the RPA space, what should workers how do they prepare for this? How do companies, make sure that they can embrace this and be better for it? >> That's a really tough and thoughtful question. The RPA category really attacks what we call the cubicle population. And there are we're estimating four million cubicles will be emptied out in five years by RPA technology specifically. That's how we built the market forecast 'cause each one of the digital workers replacing a cubicle worker will cost $11,000 or what. That's how we built up the market forecast. They're going to be automation deficits. It's not all going to be relocating people. We think that there's going to be a lot of disruption in the outsource community first. So companies are going to look at contractors. They're going to look at the BPO contract. Then they're going to look at their internal staff. Our numbers are pretty clear. We think they're going to be four million automation deficits in five years due to RPA technology specifically. Now there will be better jobs for those that are remaining. But I think it's a big change management issue. When you first talk about robots to employees you can tell them that their jobs are going to get better, they're going to be more human. They're going to have a much more exhilarating experience. And their response to you is, What they're thinking is, "Damn robot's going to take my job." That's what they're thinking. So you have to walk them up the mountain and really understand what their career path is and move them into this motion of adaptive and continual learning and what we call constructive ambition. Which is another whole subject. But there are employees that have a higher level of curiosity and are more willing to adapt to get on the other side of the digital divide. Yep. >> You mentioned the market. You guys did a market forecast. I've seen, read stats, a little over a billion today. I don't know if that's consistent with your numbers? >> Yeah that's about right. >> Is this a 10X market? When does it get to 10 billion? Is it five, seven, 10 years? >> So we go out five years and have it be close to three billion. I think the numbers I presented on stage were 3.2 billion in five years. Now that's just software licenses and it's not the services community that surround that. >> You'd probably triple it if you add in services. >> I think two to three times service license ratio. There's always an issue at this point in emerging markets. Some of the valuations that are there, that market three billion has to be a bit bigger than that in eight or nine years to justify those valuations. That's always the fascinating capital structure questions we create with these sorts of things. >> So you describe this sort of one for one replacement. I'm presuming there's other potential use cases, or maybe not, that you forecast. Is that right? >> Oh no for the cubicles? >> Yes, it's not just cubicle replacement in that three billion right? It's other uplifts. >> No there are use cases that help in factory automation, in supply chain, in guys carrying around clipboards in warehouses. There are a tremendous number of use cases, but the primary focus are back office workers that tend to be in cubicles and contact center employees who are always in cubicles. >> And then we'll see if the non-obvious ones emerge. >> I think ultimately what's going to happen is the number of people doing back office corporate functions, so that's both finance and accounting procurement, HR type roles and indeed the industry specific roles. So claims processing insurance will diminish over time. But I think what we're going to see is an increase in the number of people doing customer experience, because it's the customer intimacy that is really going to differentiate organizations going forward. >> The market's moving very fast. Reading your report, it's like you were saying yesterday's features are now table steaks. Everybody's watching everybody else. You heard Daniel today saying, "Hey our competitors are watching. "We're open they're going to steal from us so be it." The rising tide lifts all boats. What do you advise clients in terms of where they should start, how they should get started? Obviously pick some quick wins. But what do you tell people? >> I always same pretty much the same advice you give almost on any emerging technology. Start with a good solution provider that you trust. Focus on a proof of concept, POC and a pilot. Start small and grow incrementally, and walk people up the mountain as you do that. That's the solution. I also have this report I call The Rule of Fives, that there are certain tasks that are perfect for RPA and they should meet these three rules of five. A relatively small number of decisions, relatively small number of applications involved, and a relatively small number of clicks in the click stream. 500 clicks, five apps, five decisions. Look for those in high volume that have high transaction volume and you'll hit RPA goal. You'll be able to offset 2 1/2 to four FTE's for one bot. And if you follow those rules, follow the proof of concept, good solution partner everyone's winning. >> You have practical advice to get started and actually get to an outcome. Anything you'd add to that? >> In most organizations what they're now doing, is picking one, two, or three different technologies to actually play with to start. And that's a really good way. So we recommend that organizations pick three, four, five processes and do a hackathon and very quickly they work out which organizations they want to work with. It's not necessarily just the technology and in a lot of cases UiPath isn't the right answer. But that is a very good way for them to realize what they want to do and the speed with which they'll want to do it. >> Great, well guys thanks for coming on theCUBE, sharing your knowledge. >> Thank you. >> Pleasure. >> Appreciate your time. >> Thanks very much indeed. >> Alright keep it right there everybody. Stu and I will be back from UiPathForward Americas. This is theCUBE. Be right back. (upbeat music)

>> Live from San Jose, in the heart of Silicon Valley, it's theCUBE, overing DataWorks Summit 2018. Brought to you by Hortonworks. >> Welcome back everyone to day two of theCUBE's live coverage of DataWorks, here in San Jose, California. I'm your host, Rebecca Knight, along with my co-host James Kobielus. We have two guests on this panel today, we have Tim Vincent, he is the VP of Cognitive Systems Software at IBM, and Steve Roberts, who is the Offering Manager for Big Data on IBM Power Systems. Thanks so much for coming on theCUBE. >> Oh thank you very much. >> Thanks for having us. >> So we're now in this new era, this Cognitive Systems era. Can you set the scene for our viewers, and tell our viewers a little bit about what you do and why it's so important >> Okay, I'll give a bit of a background first, because James knows me from my previous role as, and you know I spent a lot of time in the data and analytics space. I was the CTO for Bob running the analytics group up 'til about a year and a half ago, and we spent a lot of time looking at what we needed to do from a data perspective and AI's perspective. And Bob, when he moved over to the Cognitive Systems, Bob Picciano who's my current boss, Bob asked me to move over and really start helping build, help to build out more of a software, and more of an AI focus, and a workload focus on how we thinking of the Power brand. So we spent a lot of time on that. So when you talk about cognitive systems or AI, what we're really trying to do is think about how you actually couple a combination of software, so co-optimize software space and the hardware space specific of what's needed for AI systems. Because the act of processing, the data processing, the algorithmic processing for AI is very, very different then what you would have for traditional data workload. So we're spending a lot of time thinking about how you actually co-optimize those systems so you can actually build a system that's really optimized for the demands of AI. >> And is this driven by customers, is this driven by just a trend that IBM is seeing? I mean how are you, >> It's a combination of both. >> So a lot of this is, you know, there's a lot of thought put into this before I joined the team. So there was a lot of good thinking from the Power brand, but it was really foresight on things like Moore's Law coming to an end of it's lifecycle right, and the ramifications to that. And at the same time as you start getting into things like narrow NATS and the floating point operations that you need to drive a narrow NAT, it was clear that we were hitting the boundaries. And then there's new technologies such as what Nvidia produces with with their GPUs, that are clearly advantageous. So there's a lot of trends that were comin' together the technical team saw, and at the same time we were seeing customers struggling with specific things. You know how to actually build a model if the training time is going to be weeks, and months, or let alone hours. And one of the scenarios I like to think about, I was probably showing my age a bit, but went to a school called University of Waterloo, and when I went to school, and in my early years, they had a batch based system for compilation and a systems run. You sit in the lab at night and you submit a compile job and the compile job will say, okay it's going to take three hours to compile the application, and you think of the productivity hit that has to you. And now you start thinking about, okay you've got this new skill in data scientists, which is really, really hard to find, they're very, very valuable. And you're giving them systems that take hours and weeks to do what the need to do. And you know, so they're trying to drive these models and get a high degree of accuracy in their predictions, and they just can't do it. So there's foresight on the technology side and there's clear demand on the customer side as well. >> Before the cameras were rolling you were talking about how the term data scientists and app developers is used interchangeably, and that's just wrong. >> And actually let's hear, 'cause I'd be in this whole position that I agree with it. I think it's the right framework. Data science is a team sport but application development has an even larger team sport in which data scientists, data engineers play a role. So, yeah we want to hear your ideas on the broader application development ecosystem, and where data scientists, and data engineers, and sort, fall into that broader spectrum. And then how IBM is supporting that entire new paradigm of application development, with your solution portfolio including, you know Power, AI on Power? >> So I think you used the word collaboration and team sport, and data science is a collaborative team sport. But you're 100% correct, there's also a, and I think it's missing to a great degree today, and it's probably limiting the actual value AI in the industry, and that's had to be data scientists and the application developers interact with each other. Because if you think about it, one of the models I like to think about is a consumer-producer model. Who consumes things and who produces things? And basically the data scientists are producing a specific thing, which is you know simply an AI model, >> Machine models, deep-learning models. >> Machine learning and deep learning, and the application developers are consuming those things and then producing something else, which is the application logic which is driving your business processes, and this view. So they got to work together. But there's a lot of confusion about who does what. You know you see people who talk with data scientists, build application logic, and you know the number of people who are data scientists can do that is, you know it exists, but it's not where the value, the value they bring to the equation. And the application developers developing AI models, you know they exist, but it's not the most prevalent form fact. >> But you know it's kind of unbalanced Tim, in the industry discussion of these role definitions. Quite often the traditional, you know definition, our sculpting of data scientist is that they know statistical modeling, plus data management, plus coding right? But you never hear the opposite, that coders somehow need to understand how to build statistical models and so forth. Do you think that the coders of the future will at least on some level need to be conversant with the practices of building,and tuning, or training the machine learning models or no? >> I think it's absolutely happen. And I will actually take it a step further, because again the data scientist skill is hard for a lot of people to find. >> Yeah. >> And as such is a very valuable skill. And what we're seeing, and we are actually one of the offerings that we're pulling out is something called PowerAI Vision, and it takes it up another level above the application developer, which is how do you actually really unlock the capabilities of AI to the business persona, the subject matter expert. So in the case of vision, how do you actually allow somebody to build a model without really knowing what a deep learning algorithm is, what kind of narrow NATS you use, how to do data preparation. So we build a tool set which is, you know effectively a SME tool set, which allows you to automatically label, it actually allows you to tag and label images, and then as you're tagging and labeling images it learns from that and actually it helps automate the labeling of the image. >> Is this distinct from data science experience on the one hand, which is geared towards the data scientists and I think Watson Analytics among your tools, is geared towards the SME, this a third tool, or an overlap. >> Yeah this is a third tool, which is really again one of the co-optimized capabilities that I talked about, is it's a tool that we built out that really is leveraging the combination of what we do in Power, the interconnect which we have with the GPU's, which is the NVLink interconnect, which gives us basically a 10X improvement in bandwidth between the CPU and GPU. That allows you to actually train your models much more quickly, so we're seeing about a 4X improvement over competitive technologies that are also using GPU's. And if we're looking at machine learning algorithms, we've recently come out with some technology we call Snap ML, which allows you to push machine learning, >> Snap ML, >> Yeah, it allows you to push machine learning algorithms down into the GPU's, and this is, we're seeing about a 40 to 50X improvement over traditional processing. So it's coupling all these capabilities, but really allowing a business persona to something specific, which is allow them to build out AI models to do recognition on either images or videos. >> Is there a pre-existing library of models in the solution that they can tap into? >> Basically it allows, it has a, >> Are they pre-trained? >> No they're not pre-trained models that's one of the differences in it. It actually has a set of models that allow, it picks for you, and actually so, >> Oh yes, okay. >> So this is why it helps the business persona because it's helping them with labeling the data. It's also helping select the best model. It's doing things under the covers to optimize things like hyper-parameter tuning, but you know the end-user doesn't have to know about all these things right? So you're tryin' to lift, and it comes back to your point on application developers, it allows you to lift the barrier for people to do these tasks. >> Even for professional data scientists, there may be a vast library of models that they don't necessarily know what is the best fit for the particular task. Ideally you should have, the infrastructure should recommend and choose, under various circumstances, the models, and the algorithms, the libraries, whatever for you for to the task, great. >> One extra feature of PowerAI Enterprises is that it does include a way to do a quick visual inspection of a models accuracy with a small data sample before you invest in scaling over a cluster or large data set. So you can get a visual indicator as to the, whether the models moving towards accuracy or you need to go and test an alternate model. >> So it's like a dashboard, of like Gini coefficients and all that stuff, okay. >> Exactly it gives you a snapshot view. And the other thing I was going to mention, you guys talked about application development, data scientists and of course a big message here at the conference is, you know data science meets big data and the work that Hortonworks is doing involving the notion of container support in YARN, GPU awareness in YARN, bringing data science experience, which you can include the PowerAI capability that Tim was talking about, as a workload tightly coupled with Hadoop. And this is where our Power servers are really built, not for just a monolithic building block that always has the same ratio of compute and storage, but fit for purpose servers that can address either GPU optimized workloads, providing the bandwidth enhancements that Tim talked about with the GPU, but also day-to-day servers, that can now support two terrabytes of memory, double the overall memory bandwidth on the box, 44 cores that can support up to 176 threads for parallelization of Spark workloads, Sequel workloads, distributed data science workloads. So it's really about choosing the combination of servers that can really mix this evolving workload need, 'cause a dupe isn't now just map produced, it's a multitude of workloads that you need to be able to mix and match, and bring various capabilities to the table for a compute, and that's where Power8, now Power9 has really been built for this kind of combination workloads where you can add acceleration where it makes sense, add big data, smaller core, smaller memory, where it makes sense, pick and choose. >> So Steve at this show, at DataWorks 2018 here in San Jose, the prime announcement, partnership announced between IBM and Hortonworks was IHAH, which I believe is IBM Host Analytics on Hortonworks. What I want to know is that solution that runs inside, I mean it runs on top of HDP 3.0 and so forth, is there any tie-in from an offering management standpoint between that and PowerAI so you can build models in the PowerAI environment, and then deploy them out to, in conjunction with the IHAH, is there, going forward, I mean just wanted to get a sense of whether those kinds of integrations. >> Well the same data science capability, data science experience, whether you choose to run it in the public cloud, or run it in private cloud monitor on prem, it's the same data science package. You know PowerAI has a set of optimized deep-learning libraries that can provide advantage on power, apply when you choose to run those deployments on our Power system alright, so we can provide additional value in terms of these optimized libraries, this memory bandwidth improvements. So really it depends upon the customer requirements and whether a Power foundation would make sense in some of those deployment models. I mean for us here with Power9 we've recently announced a whole series of Linux Power9 servers. That's our latest family, including as I mentioned, storage dense servers. The one we're showcasing on the floor here today, along with GPU rich servers. We're releasing fresh reference architecture. It's really to support combinations of clustered models that can as I mentioned, fit for purpose for the workload, to bring data science and big data together in the right combination. And working towards cloud models as well that can support mixing Power in ICP with big data solutions as well. >> And before we wrap, we just wanted to wrap. I think in the reference architecture you describe, I'm excited about the fact that you've commercialized distributed deep-learning for the growing number of instances where you're going to build containerized AI and distributing pieces of it across in this multi-cloud, you need the underlying middleware fabric to allow all those pieces to play together into some larger applications. So I've been following DDL because you've, research lab has been posting information about that, you know for quite a while. So I'm excited that you guys have finally commercialized it. I think there's a really good job of commercializing what comes out of the lab, like with Watson. >> Great well a good note to end on. Thanks so much for joining us. >> Oh thank you. Thank you for the, >> Thank you. >> We will have more from theCUBE's live coverage of DataWorks coming up just after this. (bright electronic music)

>> Narrator: Live from Las Vegas it's theCUBE. Covering Dell Technologies World 2018. Brought to you by Dell EMC and its ecosystem partners. >> And we are indeed in Las Vegas for day two of Dell Technologies World 2018. Some 14,000 strong in attendance and a show with a lot of vibrancy, a lot of energy, and certainly it's reflected in what's happening on the show floor. Along with Stu Minaman, I'm John Walls, and we're now joined by a couple of guests. It's an honor to bring to the set Howard Elias, president of services, digital and IT at Dell EMC. Howard, how are you sir? >> Great, I'm doing fantastic. As you said, the energy's super high. >> John: Absolutely, and also joining us is Jun Sawada, who is the CFO of NTT and the CEO of NTT Security. Sawada-san, nice to have you with us, sir. >> Yeah, nice to meet you, so a very exciting such a time. Thank you very much. >> You bet, thank you for being here. So Howard, let's just kick off, I'm curious. You've thought about the show. Bigger, better than ever. So many people here, so much conversation and dialogue. And how do you feel and what are you hearing from people? >> Well you know, it's our first Dell Technologies World. We continue to believe we're better together, and we're getting great energy and feedback from our customers and partners. And I couldn't be more pleased to be with Sawada-san here today. A great partner, an NTT group of companies, where we're going to talk about some interesting solutions, which is what we've been talking about with all of our customers today. >> Especially right here in Las Vegas. It's certainly no coincidence, right, the show, and then some work that the two companies have done and are announcing. Tell us a little bit about that. What is the project that involves Las Vegas and your perspective involvement? >> Well I'll let Sawada-san, but this is all about an initial POC that we're doing for the city of Las Vegas. Utilizing the IOT technologies combined between NTT group and Dell Technologies. >> Yes, and also, we want to realize the situation awareness of a city of Las Vegas. That's our issue in including three features. One, with the reactive analysis, analyzing and also a site. It may H, H. We are going to realize, such as indicating that some instance has occurred. And the second feature is a proactive analyzing that adds a center, data centers. It's been providing also a trend or investigation, or the predictions. Lastly, third feature is very interesting. It's a deployment automatically all over the ICT lethal seeds, simultaneously. So based on the several technologies. >> So we love this smart cities theme. I've had the opportunity to interview people from different cities and see governments actually getting involved. I wonder if we can get into some of the key technology pieces that are involved here between NTT and the Dell family. >> We are developing ways, actually, at Dell technologies that we call the Cognitive Foundation. It consists of two technologies, one in times for, very focused to multi, much orchestration. It's been a cover up, so March beta, March domain, March layers, lots of the March we can integrate it. The other one is a software defined ICT lethal system, based on the batchilisation technology coming from Dell Technologies, we're in warehouse. >> Howard, sounds like this fits in with a lot of themes we've been hearing at the show. IOT, of course, I would expect to be heavily involved, but maybe explain some of the Dell standpoint, where some of this fits in. >> Well that's exactly right. So as you know, our IOT strategy is a very comprehensive end-to-end one around edge, to the distributed core, to the cloud. And then, working with NTT in terms of bringing that solution to market for a particular use case, like a smart city, in Las Vegas. And we're going to learn a lot together about how all of this comes and comes to fruition. But it really is about that edge to distributed core to cloud. And it's really based on the Dell technologies around our gateways, our hyper conversion infrastructure, some of the VM-ware software foundation capabilities, together with all of the solutions that Sawada-san has talked about. >> Yeah, I would think, it seems like this is a great test case, great test bed if you will. So, where does this go from here? What are your strategic intents in terms of what you plan to learn here and how it will apply elsewhere? >> That's basically, we were starting that this proof of a concept is a form of this coming September. After the two months, we will go into a market offering with two, both companies. But basically, our business plan is a business to business to anything. In that case, the Las Vegas city is a center bean. Dell Technologies and the NTT is a fast bean, as an enabler to support the center bean. Center bean providing a barrier to the anything, anywhere. So those type of package of concepts that we want to deploy to the other United States cities, not only United States, in the world. >> Globally? >> Globally. >> Howard: Yeah, globally, including in Japan, of course. >> Of course. (laughs) I am very familiar Japan. (laughs) >> And it's great because it's not just about the IOT strategy that we've talked about, but it really is about all of the transformation strategies we have. If you think about building a smart city, it's every aspect of digital, IT, workforce, and security transformation, all coming together into a complete, comprehensive solution. >> Alright, where does it go from here? Talk about the vision for the future, as to what we see in the future, Sawada-san. >> Yeah exactly, so not only are we very focused at the time, with public safety. But both company is we can extend those solution to other solution of smart ones. For instance, education, for instance, the retails, or entertainment, including stadium solutions, or other medical. There's no leftover area that we can extend our solution. You're driving a cognitive foundation. >> Yeah, and we're going to learn a lot from the POC. We also have been working on other projects around the world. And we're going to take all of those learnings and roll that into new products and services that we can deliver to our customers. >> Yeah. >> Well, it's a fantastic laboratory, no doubt about that, Las Vegas is, and I'm sure what you learned here will be applicable, as you said, to cities, not only in the United States, in Japan and all over the world. >> All over the world. >> Great project. Gentlemen, thank you for being with us. I appreciate your, and I look forward to hearing back. Check in a year from now. >> We'll do that. >> Let's see where we are. >> Thank you. >> Thank you. >> Thank you very much. >> Thank you very much. >> Back with more from Dell Technologies World 2018. You're watching theCUBE, we're live, and we're in Las Vegas. (electronic music)

(music playing) >> Narrator: Live, from downtown San Francisco It's the Cube. Covering IBM Chief Data Officer Startegy Summit 2018. Brought to you by: IBM >> Welcome back to San Francisco everybody we're at the Parc 55 in Union Square. My name is Dave Vellante, and you're watching the Cube. The leader in live tech coverage and this is our exclusive coverage of IBM's Chief Data Officer Strategy Summit. They hold these both in San Francisco and in Boston. It's an intimate event, about 150 Chief Data Officers really absorbing what IBM has done internally and IBM transferring knowledge to its clients. Steven Eluk is here. He is one of those internal practitioners at IBM. He's the Vice President of Deep Learning and the Global Chief Data Office at IBM. We just heard from him and some of his strategies and used cases. He's joined by Sumit Gupta, a Cube alum. Who is the Vice President of Machine Learning and deep learning within IBM's cognitive systems group. Sumit. >> Thank you. >> Good to see you, welcome back Steven, lets get into it. So, I was um paying close attention when Bob Picciano took over the cognitive systems group. I said, "Hmm, that's interesting". Recently a software guy, of course I know he's got some hardware expertise. But bringing in someone who's deep into software and machine learning, and deep learning, and AI, and cognitive systems into a systems organization. So you guys specifically set out to develop solutions to solve problems like Steven's trying to solve. Right, explain that. >> Yeah, so I think ugh there's a revolution going on in the market the computing market where we have all these new machine learning, and deep learning technologies that are having meaningful impact or promise of having meaningful impact. But these new technologies, are actually significantly I would say complex and they require very complex and high performance computing systems. You know I think Bob and I think in particular IBM saw the opportunity and realized that we really need to architect a new class of infrastructure. Both software and hardware to address what data scientist like Steve are trying to do in the space, right? The open source software that's out there: Denzoflo, Cafe, Torch - These things are truly game changing. But they also require GPU accelerators. They also require multiple systems like... In fact interestingly enough you know some of the super computers that we've been building for the scientific computing world, those same technologies are now coming into the AI world and the enterprise. >> So, the infrastructure for AI, if I can use that term? It's got to be flexible, Steven we were sort of talking about that elastic versus I'm even extending it to plastic. As Sumit you just said, it's got to have that tooling, got to have that modern tooling, you've got to accommodate alternative processor capabilities um, and so, that forms what you've used Steven to sort of create new capabilities new business capabilities within IBM. I wanted to, we didn't touch upon this before, but we touched upon your data strategy before but tie it back to the line of business. You essentially are a presume a liaison between the line of business and the chief data office >> Steven: Yeah. >> Officer office. How did that all work out, and shake out? Did you defining the business outcomes, the requirements, how did you go about that? >> Well, actually, surprisingly, we have very little new use cases that we're generating internally from my organization. Because there's so many to pick from already throughout the organization, right? There's all these business units coming to us and saying, "Hey, now the data is in the data lake and now we know there's more data, now we want to do this. How do we do it?" You know, so that's where we come in, that's where we start touching and massaging and enabling them. And that's the main efforts that we have. We do have some derivative works that have come out, that have been like new offerings that you'll see here. But mostly we already have so many use cases that from those businesses units that we're really trying to heighten and bring extra value to those domains first. >> So, a lot of organizations sounds like IBM was similar you created the data lake you know, things like "a doop" made a lower cost to just put stuff in the data lake. But then, it's like "okay, now what?" >> Steven: Yeah. >> So is that right? So you've got the data and this bog of data and you're trying to make more sense out of it but get more value out of it? >> Steven: Absolutely. >> That's what they were pushing you to do? >> Yeah, absolutely. And with that, with more data you need more computational power. And actually Sumit and I go pretty far back and I can tell you from my previous roles I heightened to him many years ago some of the deficiencies in the current architecture in X86 etc and I said, "If you hit these points, I will buy these products." And what they went back and they did is they, they addressed all of the issues that I had. Like there's certain issues... >> That's when you were, sorry to interrupt, that's when you were a customer, right? >> Steven: That's when I was... >> An external customer >> Outside. I'm still an internal customer, so I've always been a customer I guess in that role right? >> Yep, yep. >> But, I need to get data to the computational device as quickly as possible. And with certain older gen technologies, like PTI Gen3 and certain issues around um x86. I couldn't get that data there for like high fidelity imaging for autonomous vehicles for ya know, high fidelity image analysis. But, with certain technologies in power we have like envy link and directly to the CPU. And we also have PTI Gen4, right? So, so these are big enablers for me so that I can really keep the utilization of those very expensive compute devices higher. Because they're not starved for data. >> And you've also put a lot of emphasis on IO, right? I mean that's... >> Yeah, you know if I may break it down right there's actually I would say three different pieces to the puzzle here right? The highest level from Steve's perspective, from Steven's teams perspective or any data scientist perspective is they need to just do their data science and not worry about the infrastructure, right? They actually don't want to know that there's an infrastructure. They want to say, "launch job" - right? That's the level of grand clarity we want, right? In the background, they want our schedulers, our software, our hardware to just seamlessly use either one system or scale to 100 systems, right? To use one GPU or to use 1,000 GPUs, right? So that's where our offerings come in, right. We went and built this offering called Powder and Powder essentially is open source software like TensorFlow, like Efi, like Torch. But performace and capabilities add it to make it much easier to use. So for example, we have an extremely terrific scheduling software that manages jobs called Spectrum Conductor for Spark. So as the name suggests, it uses Apache Spark. But again the data scientist doesn't know that. They say, "launch job". And the software actually goes and scales that job across tens of servers or hundreds of servers. The IT team can determine how many servers their going to allocate for data scientist. They can have all kinds of user management, data management, model management software. We take the open source software, we package it. You know surprisingly ugh most people don't realize this, the open source software like TensorFlow has primarily been built on a (mumbles). And most of our enterprise clients, including Steven, are on Redhat. So we, we engineered Redhat to be able to manage TensorFlow. And you know I chose those words carefully, there was a little bit of engineering both on Redhat and on TensorFlow to make that whole thing work together. Sounds trivial, took several months and huge value proposition to the enterprise clients. And then the last piece I think that Steven was referencing too, is we also trying to go and make the eye more accessible for non data scientist or I would say even data engineers. So we for example, have a software called Powder Vision. This takes images and videos, and automatically creates a trained deep learning model for them, right. So we analyze the images, you of course have to tell us in these images, for these hundred images here are the most important things. For example, you've identified: here are people, here are cars, here are traffic signs. But if you give us some of that labeled data, we automatically do the work that a data scientist would have done, and create this pre trained AI model for you. This really enables many rapid prototyping for a lot of clients who either kind of fought to have data scientists or don't want to have data scientists. >> So just to summarize that, the three pieces: It's making it simpler for the data scientists, just run the job - Um, the backend piece which is the schedulers, the hardware, the software doing its thing - and then its making that data science capability more accessible. >> Right, right, right. >> Those are the three layers. >> So you know, I'll resay it in my words maybe >> Yeah please. >> Ease of use right, hardware software optimized for performance and capability, and point and click AI, right. AI for non data scientists, right. It's like the three levels that I think of when I'm engaging with data scientists and clients. >> And essentially it's embedded AI right? I've been making the point today that a lot of the AI is going to be purchased from companies like IBM, and I'm just going to apply it. I'm not going to try to go build my own, own AI right? I mean, is that... >> No absolutely. >> Is that the right way to think about it as a practitioner >> I think, I think we talked about it a little bit about it on the panel earlier but if we can, if we can leverage these pre built models and just apply a little bit of training data it makes it so much easier for the organizations and so much cheaper. They don't have to invest in a crazy amount of infrastructure, all the labeling of data, they don't have to do that. So, I think it's definitely steering that way. It's going to take a little bit of time, we have some of them there. But as we as we iterate, we are going to get more and more of these types of you know, commodity type models that people could utilize. >> I'll give you an example, so we have a software called Intelligent Analytics at IBM. It's very good at taking any surveillance data and for example recognizing anomalies or you know if people aren't suppose to be in a zone. Ugh and we had a client who wanted to do worker safety compliance. So they want to make sure workers are wearing their safety jackets and their helmets when they're in a construction site. So we use surveillance data created a new AI model using Powder AI vision. We were then able to plug into this IVA - Intelligence Analytic Software. So they have the nice gooey base software for the dashboards and the alerts, yet we were able to do incremental training on their specific use case, which by the way, with their specific you know equipment and jackets and stuff like that. And create a new AI model, very quickly. For them to be able to apply and make sure their workers are actually complaint to all of the safety requirements they have on the construction site. >> Hmm interesting. So when I, Sometimes it's like a new form of capture says identify "all the pictures with bridges", right that's the kind of thing you're capable to do with these video analytics. >> That's exactly right. You, every, clients will have all kinds of uses I was at a, talking to a client, who's a major car manufacturer in the world and he was saying it would be great if I could identify the make and model of what cars people are driving into my dealership. Because I bet I can draw a ugh corelation between what they drive into and what they going to drive out of, right. Marketing insights, right. And, ugh, so there's a lot of things that people want to do with which would really be spoke in their use cases. And build on top of existing AI models that we have already. >> And you mentioned, X86 before. And not to start a food fight but um >> Steven: And we use both internally too, right. >> So lets talk about that a little bit, I mean where do you use X86 where do you use IBM Cognitive and Power Systems? >> I have a mix of both, >> Why, how do you decide? >> There's certain of work loads. I will delegate that over to Power, just because ya know they're data starved and we are noticing a complication is being impacted by it. Um, but because we deal with so many different organizations certain organizations optimize for X86 and some of them optimize for power and I can't pick, I have to have everything. Just like I mentioned earlier, I also have to support cloud on prim, I can't pick just to be on prim right, it so. >> I imagine the big cloud providers are in the same boat which I know some are your customers. You're betting on data, you're betting on digital and it's a good bet. >> Steven: Yeah, 100 percent. >> We're betting on data and AI, right. So I think data, you got to do something with the data, right? And analytics and AI is what people are doing with that data we have an advantage both at the hardware level and at the software level in these two I would say workloads or segments - which is data and AI, right. And we fundamentally have invested in the processor architecture to improve the performance and capabilities, right. You could offer a much larger AI models on a power system that you use than you can on an X86 system that you use. Right, that's one advantage. You can train and AI model four times faster on a power system than you can on an Intel Based System. So the clients who have a lot of data, who care about how fast their training runs, are the ones who are committing to power systems today. >> Mmm.Hmm. >> Latency requirements, things like that, really really big deal. >> So what that means for you as a practitioner is you can do more with less or is it I mean >> I can definitely do more with less, but the real value is that I'm able to get an outcome quicker. Everyone says, "Okay, you can just roll our more GPU's more GPU's, but run more experiments run more experiments". No no that's not actually it. I want to reduce the time for a an experiment Get it done as quickly as possible so I get that insight. 'Cause then what I can do I can get possibly cancel out a bunch of those jobs that are already running cause I already have the insight, knowing that that model is not doing anything. Alright, so it's very important to get the time down. Jeff Dean said it a few years ago, he uses the same slide often. But, you know, when things are taking months you know that's what happened basically from the 80's up until you know 2010. >> Right >> We didn't have the computation we didn't have the data. Once we were able to get that experimentation time down, we're able to iterate very very quickly on this. >> And throwing GPU's at the problem doesn't solve it because it's too much complexity or? >> It it helps the problem, there's no question. But when my GPU utilization goes from 95% down to 60% ya know I'm getting only a two-thirds return on investment there. It's a really really big deal, yeah. >> Sumit: I mean the key here I think Steven, and I'll draw it out again is this time to insight. Because time to insight actually is time to dollars, right. People are using AI either to make more money, right by providing better customer products, better products to the customers, giving better recommendations. Or they're saving on their operational costs right, they're improving their efficiencies. Maybe their routing their trucks in the right way, their routing their inventory in the right place, they're reducing the amount of inventory that they need. So in all cases you can actually coordinate AI to a revenue outcome or a dollar outcome. So the faster you can do that, you know, I tell most people that I engage with the hardware and software they get from us pays for itself very quickly. Because they make that much more money or they save that much more money, using power systems. >> We, we even see this internally I've heard stories and all that, Sumit kind of commented on this but - There's actually sales people that take this software & hardware out and they're able to get an outcome sometimes in certain situations where they just take the clients data and they're sales people they're not data scientists they train it it's so simple to use then they present the client with the outcomes the next day and the client is just like blown away. This isn't just a one time occurrence, like sales people are actually using this right. So it's getting to the area that it's so simple to use you're able to get those outcomes that we're even seeing it you know deals close quicker. >> Yeah, that's powerful. And Sumit to your point, the business case is actually really easy to make. You can say, "Okay, this initiative that you're driving what's your forecast for how much revenue?" Now lets make an assumption for how much faster we're going to be able to deliver it. And if I can show them a one day turn around, on a corpus of data, okay lets say two months times whatever, my time to break. I can run the business case very easily and communicate to the CFO or whomever the line of business head so. >> That's right. I mean just, I was at a retailer, at a grocery store a local grocery store in the bay area recently and he was telling me how In California we've passed legislation that does not allow plastic bags anymore. You have to pay for it. So people are bringing their own bags. But that's actually increased theft for them. Because people bring their own bag, put stuff in it and walk out. And he didn't want to have an analytic system that can detect if someone puts something in a bag and then did not buy it at purchase. So it's, in many ways they want to use the existing camera systems they have but automatically be able to detect fraudulent behavior or you know anomalies. And it's actually quite easy to do with a lot of the software we have around Power AI Vision, around video analytics from IBM right. And that's what we were talking about right? Take existing trained AI models on vision and enhance them for your specific use case and the scenarios you're looking for. >> Excellent. Guys we got to go. Thanks Steven, thanks Sumit for coming back on and appreciate the insights. >> Thank you >> Glad to be here >> You're welcome. Alright, keep it right there buddy we'll be back with our next guest. You're watching "The Cube" at IBM's CDO Strategy Summit from San Francisco. We'll be right back. (music playing)

(energetic music) >> Announcer: Live from Las Vegas, it's the CUBE, covering IBM Think 2018. Brought to you by IBM. >> Hello everyone, welcome back to the cube's coverage here at IBM Think 2018. We are in Las Vegas, the Mandalay Bay, for IBM Think. Six shows are coming into one packed house. We have two great guests here, Brian Wallace, who's the CTO of Insurance for DXC technologies, and we have, Bala, The Bala, but goes by Bala, banking and capital markets CTO for DXC technologies. Guys, welcome to the cube. Thanks for joining us. >> Thank you. >> It's our pleasure, yeah, thanks. >> So, the innovation sandwich, I'm calling it IBM strategy. You've got in the middle, the meat, is data. And the bread is blockchain and AI. Two really fundamental technologies powered by cloud and a variety of other things. Obviously, AI is disrupted, we know what that looks like. Block Chain now emerging as a viable infrastructure enabler that's creating token economics, a lot of cool things, certainly on the banking side, seeing a lot of controversy. Block Chain really is driving it. You guys are out on the front lines. You're doing a lot crowd chats, been following your digital transformation story that you guys have been putting out there. Really you're on this. So, what's the conversations like that you guys are having with block chain and AI; Share? >> Bala: So, let me begin with a couple of quick points on block chain. DXC has done some fantastic work around the world leveraging both the trust capability that block chain brings to bear in financial banking industry use cases, like KYC for instance, institutional KYC in particular, but also, in simplification of entire value chains such as lending. And we're doing very interesting work in lending where not only are we looking at the up-front origination process of lending but also the downstream securitization. Which is where the tokenization of principle and interest payments and those type of things happen. >> John: Energy too? >> Oh yes, absolutely. So there are a number of these creating type use cases that follow into securitization. And with that, we're doing some very interesting work. >> John: Bala, talk about the globalization because one of the things we're seeing in the US a shrinking middle class, but outside the US in emerging markets, a growing middle class. Thanks to mobile technology, thanks to data, thanks to block chain, you're seeing, you know, countries that "hey, we have infrastructure but we don't have the core and modern infrastructure but you throw in a decentralized capability, You've got all these capabilities, and the killer app in all this is money. You're in, that's your vertical. >> Bala: Yes. >> That's your industry. The killer app is money and marketplaces. Your thoughts? >> Bala: I think, the beauty of what these technologies are doing, is for the first time creating financial inclusion to happen and the very first case of where financial inclusion is enabled, is in payments. So, when we open up the banking system predominantly from a payment perspective, which is what things like blockchain and others enable, if we succeed in doing that, then for the first time we've enabled, that's 2 billion people unbanked or underbanked-2 billion. >> John: Yeah. >> Bringing them into this financial system allows for. >> And some people are discriminated against too because they don't have a track record. Banks can't handle some of the things that others are now filling the void with crypto and blockchain. >> Bala: Right, or they can't service them profitably. But for the first time now, you're looking at the economics that cloud, and AI, and blockchain, these technologies bring, not just into banking and capital markets areas but into insurance and I'd love to have my colleague, Brian, talk with the insurance cases are enabled as well. >> John: Brian, insurance- go. >> Yeah, so it's a slightly different dynamic. There it's the, if you think about the fundamental pattern of blockchain it's around eliminating a central or a middle-man or a central, you know, gatekeeper, if you will. And the entire insurance industry is largely made up of middle-men, right? You've got people with risk at one end and you've got sources of capital at the other end and everybody's playing a role between a broker, and a carrier, and a re-insurer. In sort of facilitating that management and that transfer of risk. >> John: So you've got to extract some efficiencies out of that. Business model opportunity. >> So efficiencies, there's a lot of conversations around efficiencies, around automation, but interestingly, it's around the disruptive business model, right? The technology is mildly interesting but it's the new business models that blockchain will enable. >> John: Yeah, I see banking picking up. The early adopter on blockchain but I see, maybe it lagging a bit in insurance but I definitely see some opportunity there. But short term, data is driving insurance because, you know, I don't have a Tesla but my friend has a Tesla. The insurance company will know exactly who is rolling through those stop signs. They know everything that he's doing, All the data is there, so AI becomes really the low hanging fruit for insurance in that industry. Do you agree with that? Comment, reaction? >> Brian: Yeah, and we're just at the beginning, right? Because as you say, data is the asset that we manage. So we have a lot of data in terms of transactional data, the traditional operational data. What we're discovering, and what we're sort of licking our lips over almost is all of this new unstructured data, whether it's sensor data, behavioral data, and you're right, 'cause the challenge that we had around automation and cognitive computing, if you will. We're here at IBM with the Watson tech, was enough data, and the consistency and quality of that data. So we have that now, and we're making tremendous strides around in particular here, with the Watson brand, and the Watson cognitive. >> John: You know, one of the things I wish, was Dan Hutches was here, he's not, he's the CTO in charge. You've guys have been doing all these crowd chats our software that we wrote. That's pretty interesting. I've personally enjoyed all the conversations and give a shout out to Dan and you guys for really great conversation. You guys know what you're talking about. It's clear in the data you guys are taking an outside-in approach and collaborating. But your topics are on target. You're talking about digital transformation kind of holistically, but then you start to dive down into specific use cases. So, Bala, what is the favorite, or the most popular digital disruptive topic that's being discussed within DXC and your clients and in the marketplace? >> So, at the outset, within DXC, as digital transformation takes hold with our customers and we aim to be the premier provider of that enablement, what we've realized ourselves is that we provide a lot of services to our clients across many industries but there are commonalities across what we provide in terms of service delivery. And so it made sense for us to, number one: look at the commonalities and create a platform that was common across industries, across offerings that we bring to the marketplace. That commonality is what we call internally, and externally now, as bionics. And it's a platform that we are bringing forward that for the first time ties together what we are talking about both here at this event but also with our clients. Ties together intelligence, orchestration, and automation which are the fundamental, >> John: It's called bionics? >> Bionics. And internally we call it platform DXC upon which all of our offerings and services are brought to market. >> John: Well there's disruption going on in your business. So, I want to talk about, double-down on that for a second. I'm seeing a trend, certainly in the public sector market where the use cases are well enough defined. So you're seeing automatic code generation becoming a real part of the delivery process. Now, what that's going to do is essentially, think of provisioning and configuration management in cloud. If you could apply actual process code that you've done before in the commonalities, this is going to change the delivery timeframe. So you're looking at essentially auto-provisioning software. Not just like, configuration management resources. No, I'm saying here's a value chain, here's a block chain, here's some AI, just configure it like a LEGO block, push. That could take months to deliver the old way. >> Bala: Right. >> Your thoughts to that? Are you guys on that? Do you guys see that as something that's going to be an opportunity for you? Some companies, I've seen, Global system integrator, is being disrupted by this, cause they don't have this. New SI's, new system integrators, are thinking this way and that's a DevOps mindset. Are you prepared for that, do you see it coming? And what's your answer to that? >> So we saw that coming about 3 and a half plus years ago. And our shift away from being a pure SI began then. And so we are an SI, but we are a service integrator rather than a systems integrator. And we began that trend in our journey, 3 plus years ago. And the reason we began that trend was what you pointed out. Today, infrastructure is delivered as a code. So not even as a service but as a code, and so imagine provisioning infrastructure and all the capabilities that ride on it, just as code. And that's where this is headed. In that model, we become provider and provisioner of services, rather than just a system. >> John: And the cost structure is completely changed because the services, Amazon has proven, and now IBM is following suit with their power platform and other things, that you can actually have the kind of compute but it's a catalog of services. So this is going to change the price competitiveness. So you know, big bids, that used to be billions of dollars, you guys can compete. I mean, am I seeing it right? >> Brian: That transition's already, that ship's sailed, so to speak, in terms of the large outsourcing deals the large, where there's apps or infrastructure, it's all moving to digital transformation consumption based commercial models. And it's really bionics that Bala mentioned a minute ago, that is our answer to the threat you described a minute ago. It's really about automating and digitizing and building intelligence into the entire, if you will, build, deliver, operate value chain of our business. >> John: Talk about the multi-vendor, multi-choice, technology-choice, as your customers and people in general on this journey of digital transformation. They have to make, they used to make technology decisions. Now they're making business logic decisions around how to reconfigure their value chains to optimize for new efficiencies and extract away inefficiencies. Blockchain is a great example, AI is another, automation is in the middle, all the cloud. So you have now business logic as the risk, technology not so much because infrastructure as code has proven that you can have server-less, you can have all kinds of coolness that can be managed in an agile way. So the business model aspect is key. How are you guys dealing with that, cause I know you're here at the IBM Think Show, their partner. I see you at the Amazon shows. We see you guys everywhere. So you're horizontally scaling. By design, is that what customers want? What is the DXC view on this? >> So our value proposition has always had partners as the key element of what we do. And so if you look at what we do, you can look at it from two perspectives. One, proprietary ways of thinking, proprietary systems are long since gone. >> And waterfall methodologies, gone, dead. >> Yes, those are all long since gone. >> If you're still doing that, note to self: you're going to be out of business. >> Exactly, so we've actually hinged a lot of what we do on our offerings, our capabilities, and so on around openness, around open source, and so forth. So that's number one. Number Two: In this world, it's no longer about just DXC or just IBM or just somebody, one person bringing everything to our clients. It's about how do you engage proactively and build co-innovation and co-services with our partners and bring that to our clients. >> I mean, IBM just announced that a deal with Google. They've got tensorflow and their deal. So you have all kinds of melting pot. Okay, let's talk about blockchain again. Go back to my favorite topic. So, if you look up that stack, you've got blockchain, you've got cryptocurrency, protocols, and what-not, mentioned securitization, you've got security tokens, you've got utility tokens. You can almost see where this is going. And then you've got on top of that, what's coming, is a mass in-migration of decentralized application developers. Okay, kind of cloud plus. You know, they know cloud, they know DevOps, infrastructure as code, but they're looking at it from a decentralization standpoint, different makeup. And you see, ICOs, initial coin offerings, I think this is an application of you know, inefficiencies around capital markets but that's, you know, put that aside for a second. But blockchain, crypto currency, and decentralized applications, how do you guys see that trend? What are you guys doing? Are you integrating it in? You mentioned token economics, you're in the banking field. Your thoughts on that? >> Bala: Sure, on the blockchain front, as I mentioned to you, there are a number of platforms that are out there. There is the R3 Corda platform. There's a platform that JPMorgan initiated that we're leveraging as well. >> John: Yeah, so they pooh-poohed Bitcoin but then they're back in the game again. (laughter) >> Bala: Yes, that's right. And then there is the Hyperledger Fabric as well. So these platforms are going to take their course of evolution and we are working across all of those platforms. Now, the more interesting thing that you mentioned is people and skills. What we've find today in the marketplace is with our clients is a dramatic shortage of skills in these areas. And so internally, what we have done at DXC is actually open our own service delivery to a vast pool of developers that you talked about earlier as being freelance, independent folks. We open our entire service delivery to them as well. And we look at that global talent pool for our own service delivery. >> Using community as a way to scale. >> Bala: Using communities, yes. And that's exactly what we're doing in our talent process. It's not just about our people, our employees, but our partners as well as what exists in the open marketplace. >> Brian, talk about the insurance area as a way to tease out other trends. Specifically, the question is What is the biggest things that people know they're walking into? What's the tail-wind that they see, that's going to give them hope? And then, What's the head-winds? What are the blockers? And what should they be aware of? What are some of the marketplace dynamics that translate into other industries? >> Brian: Well, let's start with the obvious blocker is legacy debt, right? So you talked about the risk of all that business knowledge, that domain expertise, that's all today encapsulated in existing, what you may call legacy systems, right? So that's the head-wind by far. The tail-wind is that unlike, say 15 years ago, and we were in the last sort of, dot-com boom, when it was all about the front office and customer experience, the customer is way ahead of us. So culturally, the customer is challenging industry to catch up. So that's the tail-wind in my mind. And the real opportunity is to think about it in terms of a dual agenda. So think about it in terms as progressively, simultaneously building new digital capability, whilst ultimately beginning to unbundle and tackle that legacy debt. And I think customers now are starting to see a path forward. We're in the market in both banking and insurance with digital platforms, with industry resource models, API fabrics that can go back in, modernize legacy systems. So there's a real fast time to market. >> And it changes your engagement with clients. It's not a one and done, you're sticking through the service layer. >> Brian: Oh it's a journey, but the difference, I think, between DXC and a lot of other people is that we are in the market, in production, with real assets. And you can show that journey. So it just becomes a conversation around what's your pain point? Where are you starting from? Where do you want to go? >> And you're bringing the community in to help on the delivery side, everyone wins. >> Brian: And that community is a combination of three things. That's our own employees, obviously within the industry, and within our offerings that know banking, that know insurance. It's all of the DXC people in the horizontals. Because we're bringing everything now. These platforms encapsulate infrastructure, security, service management, analytics, mobility, all of that is built into these platforms. And then, it's going out into our partner community. And then, it's going out into the open community. And we're tapping into all of those. >> John: Brian and Bala, thanks so much. 2 power CTOs here on the Cube, having a CTO conversation around how scale, cloud, AI, blockchain, new technologies are enabling new business models at a faster pace of change, with a lower cost structure, and more time to value. Again, it's all about the value creation. The killer app is money and marketplaces and community. Guys, thanks so much for sharing. I'm John Furrier here at IBM Think 2018 Cube Studios. More after this short break. (electronic music)

>> Narrator: Live, from Las Vegas, it's theCUBE. Covering IBM Think, 2018. Brought to you by IBM >> Hello everyone, welcome back to theCUBE, we are here on the floor at IBM Think 2018 in theCUBE studios, live coverage from IBM Think. I'm John Furrier, the host of theCUBE, and we're here with Stefanie Chiras, who is the Vice President of Offering Management IBM Cognitive Systems, that's Power Systems, a variety of other great stuff, real technology performance happening with Power, it's been a good strategic bet for IBM. Stefanie, great to see you again, thanks for coming back on theCUBE. >> Absolutely, I love to be on, John, thank you for inviting me. >> When we we had a brief (mumbles) Bob Picciano, who's heading up Power and that group, one of the things we learned is there's a lot of stuff going on that's really going to be impacting the performance of things. Just take a minute to explain what you guys are offering in this area. Where does it fit into the IBM portfolio? What's the customer use cases? Where does that offering fit in? >> Yeah, absolutely. So I think here at Think it's been a great chance for us to see how we have really transformed. You know, we have been known in the market for AIX and IBMI. We continue to drive value in that space. We just GA'd on, yesterday, our new systems, based Power9 Processor chip for AIX and IBMI in Linux. So that remains a strong strategic push. Enterprise Linux. We transformed in 2014 to embrace Linux wholeheartedly, so we really are going after now the Linux base. SAP HANA has been an incredible workload where over a thousand customers run in SAP HANA. And boy we are going after this cognitive and AI space with our performance and our acceleration capabilities, particularly around GPUs, so things like unique differentiation in our NVLink is driving our capabilities with some great announcements here that we've had in the last couple of days. >> Jamie Thomas was on earlier, and she and I were talking about some of the things around really the software stack and the hardware kind of coming together. Can you just break that out? Because I know Power, we've been covering it, Doug Balog's been on many times. A lot of great growth right out of the gate. Ecosystem formed right around it. What else has happened? And separate out where the hardware innovation is and technology and what's software and how the ecosystem and people are adopting it. Can you just take us through that? >> Yeah, absolutely. And actually I think it's an interesting question because the ecosystem actually has happened on both sides of the fence, with both the hardware side and the software side, so OpenPOWER has grown dramatically on the hardware side. We just released our Power9 processor chip, so here is our new baby. This is the Power9. >> Hold it up. >> So this is our Power9 here, 8 billion transistors, 14 miles of wiring and 17 layers of metal, I mean it's a technology wonder. >> The props are getting so small we can't even show on the camera. (laughing) >> This is the Moore's Law piece that Jenny was talking about in her keynote. >> That's exactly it. But what we have really done strategically is changed what gets delivered from the CPU to more what gets delivered at a system level, and so our IO capabilities. First chip to market, delivering the first systems to market with PCIe Gen 4. So able to connect to other things much faster. We have NVLink 2.0, which provides nearly 10x the bandwidth to transport data between this chip and a GPU. So Jensen was onstage yesterday from NVIDIA. He held up his chip proudly as well. The capabilities that are coming out from being able to transport data between the power CPU and the GPU is unbelievable. >> Talk about the relationship with NVIDIA for a second, 'cause that's also, NVIDIA stocks up a lot of (mumbles) the bitcoin mining graphics card, but this is, again, one use case, NVIDIA's been doing very well, they're doing really well in IOT, self-driving cars, where data performance is critical. How do you guys play in that? What's the relationship with NVIDIA? >> Yeah, so it has been a great partnership with NVIDIA. When we launched in 2013, right at the end of 2013 we launched OpenPOWER, NVIDIA was one of the five founding members with us, Google, Mellanox, and Tyan. So they clearly wanted to change the game at the systems value level. We launched into that with we went and jointly bid with NVIDIA and Mellanox, we jointly bid for the Department of Energy when we co-named it Coral. But that came to culmination at the end of last year when we delivered the Summit and Sierra supercomputers to Oak Ridge and Lawrence Livermore. We did that with innovation from both us and NVIDIA, and that's what's driving things like this capability. And now we bring in software that exploits it. So that NVLink connection between the CPU and the GPU, we deliver software called PowerAI, we've optimized the frameworks to take advantage of that data transport between that CPU and GPU so it makes it consumable. With all of these things it's not just about the technology, it's about is it easy to consume at the software level? So great announcement yesterday with the capabilities to do logistic regression. Unbelievable, taking the ability to do advertising analytics, taking it from 70 minutes to 1 and 1/2. >> I mean we're going to geek out here. But let's go under the hood for a second. This is a really kind of a high end systems product, at the kind of performance levels. Where does that connect to the go to market? Who's the buyer of it? Is it OEMs? Is it integrators? Is it new hardware devices? How do I get involved and who's the target customer? And what kind of developers are you reaching? Can you just take us through that who's buying this product? >> So this is no longer relegated to the elite set. What we did, and I think this is amazing, when we delivered the Summit and Sierra, right? Huge cluster of these nodes. We took that same node, we pulled it into our product line as the AC922, and we delivered a 4 GPU air-cooled version to market. On December 22nd we GA'd, of last year. And we sold to over 40 independent clients by the end of 2017, so that's a short runway. And most of it, honestly, is all driven around AI. The AI adoption, and it's a cross enterprise. Our goal is really to make sure that the enterprises who are looking at AI now with their developer are ready to take it into production. We offer support for the frameworks on the system so they know that when they do development on this infrastructure, they can take it to production later. So it's very much driven toward taking AI to the enterprise, and it's all over. It's insurance, it's financial services sector. It's those kinds of enterprise that are using AI. >> So IO sensitive, right? So IOT not a target or maybe? >> So you know when we talk out to edge it's a little bit different, right? So the IOT today for us is driving a lot of data, that's coming in, and then you know at different levels-- >> There's not a lot of (mumbles) power needed at the edge. >> There is not, there is not. And it kind of scales in. We are seeing, I would say, kind of progression of that compute moving out closer. Whether or not it's on, it doesn't all come home necessarily anymore. >> Compute is being pushed to where the data is. >> Stefanie: Absolutely right. >> That's head room for you guys. Not a priority now because there's not an intense (mumbles) compute can solve that. >> Stefanie: That's right. >> All right, so where does the Cloud fit into it? You guys powering IBMs Cloud? >> So IBM Cloud has been a great announcement this year as well. So you've seen the focus here around AI and Cloud. So we announced that HANA will come on Power into the Cloud, specializing in large memory sets, so 24 terabyte memory sets. For clients that's huge to be able to exploit that-- >> Is IBM Cloud using Power or not? >> That will be in IBM Cloud. So go to IBM Cloud, be able to deploy an SAP certified HANA on Power deployment for large memory installs, which is great. We also announced PowerAI access, on Power9 technology in IBM Cloud. So we definitely are partnering both with IMB Cloud as well as with the analytics pieces. Data Science Experience available on Power. And I think it's very important, what you said earlier, John, about you want to bring the capabilities to where the data is. So things like a lot of clients are doing AI on prem where we can offer a solution. You can augment that with capabilities like Watson, right? Off prem. You can also do dev ops now with AI in the IBM Cloud. So it really becomes both a deployment model, but the client needs to be able to choose how they want to do it. >> And the data can come from multiple sources. There's always going to be latencies. So what about blockchain? I want to get to blockchain. Are you guys doing anything in the blockchain ecosystem? Obviously one complaint we've been hearing, obviously, is some of these cryptocurrency chains like Ethereum, has performance issues, they got projects coming out. A lot of open source in there. Is Power even puttin' their toe in the water with blockchain? >> We have put our toe in the water. Blockchain runs on Power. From an IBM portfolio perspective-- >> IBM blockchain runs on Power or blockchain, or other blockchains? >> Like Hyperledger. Like Hyperledger will run. So open source, blockchain will run on Power, but if you look at the IBM portfolio, the security capabilities in Z14 that that brings and pulling that into IBM Cloud, our focus is really to be able to deliver that level of security. So we lead with system Z in that space, and Z has been incredible with blockchain. >> Z is pretty expensive to purchase, though. >> But now you can purchase it in the Cloud through IBM Cloud, which is great. >> Awesome, this is the benefit of the Cloud. Sounds like soft layer is moving towards more of a Z mainframe, Power, backend? >> I think the IBM Cloud is broadening the capabilities that it has, because the workloads demand different things. Blockchain demands security. Now you can get that in the Cloud through Z. AI demands incredible compute strength with GPU acceleration, Power is great for that. And now a client doesn't have to choose. They can use the Cloud and get the best infrastructure for the workload they want, and IBM Cloud runs it. >> You guys have been busy. >> We've been busy. (laughing) >> Bob Picciano's been bunkered in. You guys have been crankin' out... love to do a deeper dive on this, Stefanie, and so we'd love to follow up with you guys, and we told Bob we would dig into that, too. Question I have for you now is, how do you talk about this group that you're building together? You know, the names are all internal IBM names, Power... Is it like a group? Do you guys call yourself like the modern infrastructure group? Is it like, what is it called, if you had to explain it to outside IBM, AIs easy, I know what AI team does. You're kind of doing AI. You're enabling AI. Are you a modern infrastructure? What is the pillar are you under? >> Yeah, so we sit under IBM systems, and we are definitely systems proud, right? Everything runs on infrastructure somewhere. And then within that three spaces you certainly have Z storage, and we empower, since we've set our sites on AI and cognitive workloads, internally we're called IBM Cognitive Systems. And I think that's really two things, both a focus on the workloads and differentiation we want to bring to clients, but also the fact that it's not just about the hardware, we're now doing software with things like PowerAI software, optimized for our hardware. There's magic that happens when the software and the hardware are co-optimized. >> Well if you look, I mean systems proud, I love that conversation because you look at the systems revolution that I grew up in, the computer science generation of the 80s, that was the open movement, BSD, pre-Linux, and then now everything about the Cloud and what's going on with AI and what I call the innovation sandwich with data in the middle and blockchain and AI as bread. >> Stefanie: Yep. >> You have all the perfect elements of automation, you know, Cloud. That's all going to be powered by a system. >> Absolutely. >> Especially operating systems skills are super imprtant. >> Super important. Super important. >> This is the foundational elements. >> Absolutely, and I think your point on open, that has really come in and changed how quickly this innovation is happening, but completely agree, right? And we'll see more fit for purpose types of things, as you mentioned. More fit for purpose. Where the infrastructure and the OS are driving huge value at a workload level, and that's what the client needs. >> You know, what dev ops proved with the Cloud movement was you can have programmable infrastructure. And what we're seeing with blockchain and decentralized web and AI, is that the real value, intellectual property, is going to be the business logic. That is going to be dealing with now a whole 'nother layer of programmability. It used to be the other way around. The technology determined >> That's right. >> the core decision, so the risk was technology purchase. Now that this risk is business model decision, how do you code your business? >> And it's very challenging for any business because the efficiency happens when those decisions get made jointly together. That's when real business efficiency. If you make one decision on one side of the line or the other side of the line only, you're losing efficiency that can be driven. >> And open is big because you have consensus algorithms, you got regulatory issues, the more data you're exposed to, and more horsepower that you have, this is the future, perfect storm. >> Perfect storm. >> Stefanie, thanks for coming on theCUBE, >> It's exciting. >> Great to see you. >> Oh my pleasure John, great to see you. >> You're awesome. Systems proud here in theCUBE, we're sharing all the systems data here at IBM Think. I'm John Furrier, more live coverage after this short break. All right.

>> Narrator: Live from Las Vegas, it's the Cube, covering IBM Think 2018, brought to you by IBM. >> We're back at IBM Think 2018. You're watching the Cube, the leader in live tech coverage. My name is Dave Vellante and I'm here with my co-host, Peter Burris. Ken King is here; he's the general manager of OpenPOWER from IBM, and Sumit Gupta, PhD, who is the VP, HPC, AI, ML for IBM Cognitive. Gentleman, welcome to the Cube >> Sumit: Thank you. >> Thank you for having us. >> So, really, guys, a pleasure. We had dinner last night, talked about Picciano who runs the OpenPOWER business, appreciate you guys comin' on, but, I got to ask you, Sumit, I'll start with you. OpenPOWER, Cognitive systems, a lot of people say, "Well, that's just the power system. "This is the old AIX business, it's just renaming it. "It's a branding thing.", what do you say? >> I think we had a fundamental strategy shift where we realized that AI was going to be the dominant workload moving into the future, and the systems that have been designed today or in the past are not the right systems for the AI future. So, we also believe that it's not just about silicon and even a single server. It's about the software, it's about thinking at the react level and the data center level. So, fundamentally, Cognitive Systems is about co-designing hardware and software with an open ecosystem of partners who are innovating to maximize the data and AI support at a react level. >> Somebody was talkin' to Steve Mills, probably about 10 years ago, and he said, "Listen, if you're going to compete with Intel, "you can copy them, that's not what we're going to do." You know, he didn't like the spark strategy. "We have a better strategy.", is what he said, and "Oh, strategies, we're going to open it up, "we're going to try to get 10% of the market. "You know, we'll see if we can get there.", but, Ken, I wonder if you could sort of talk about, just from a high level, the strategy and maybe go into the segments. >> Yeah, absolutely, so, yeah, you're absolutely right on the strategy. You know, we have completely opened up the architecture. Our focus on growth is around having an ecosystem and an open architecture so everybody can innovate on top of it effectively and everybody in the ecosystem can profit from it and gains good margins. So, that's the strategy, that's how we design the OpenPOWER ecosystem, but, you know, our segments, our core segments, AIX in Unix is still a core, very big core segment of ours. Unix itself is flat to declining, but AIX is continuing to take share in that segment through all the new innovations we're delivering. The other segments are all growth segments, high growth segments, whether it's SAP HANA, our cognitive infrastructure in modern day to platform, or even what we're doing in the HyperScale data centers. Those are all significant growth opportunities for us, and those are all Linux based, and, so, that is really where a lot of the OpenPOWER initiatives are driving growth for us and leveraging the fact that, through that ecosystem, we're getting a lot of incremental innovation that's occurring and it's delivering competitive differentiation for our platform. I say for our platform, but that doesn't mean just for IBM, but for all the ecosystem partners as well, and a lot of that was on display on Monday when we had our OpenPOWER summit. >> So, to talk about more about the OpenPOWER summit, what was that all about, who was there? Give us some stats on OpenPOWER and ecosystem. >> Yeah, absolutely. So, it was a good day, we're up to well over 300 members. We have over 50 different systems that are coming out in the market from IBM or our partners. Over 20 different manufacturers out there actually developing OpenPOWER systems. A lot of announcements or a lot of statements that were made at the summit that we thought were extremely valuable, first of all, we got the number one server vendor in Europe, Atos, designing and developing P9, the number on in Japan, Hitachi, the number one in China, Inspur. We got top ODMs like Super Micro, Wistron, and others that are also developing their power nine. We have a lot of different component providers on the new PCIe gen four, on the open cabinet capabilities, a lot of announcements made by a number of component partners and accelerator partners at the summit as well. The other thing I'm excited about is we have over 70 ISVs now on the platform, and a number of statements were made and announcements on Monday from people like MapD, Anaconda, H2O, Conetica and others who are leveraging those innovations bought on the platform like NVLink and the coherency between GPU and CPU to do accelerated analytics and accelerated GPU database kind of capabilities, but the thing that had me the most excited on Monday were the end users. I've always said, and the analysts always ask me the questions of when are you going to start penetration in the market? When are you going to show that you've got a lot of end users deploying this? And there were a lot of statements by a lot of big players on Monday. Google was on stage and publicly said the IO was amazing, the memory bandwidth is amazing. We are deploying Zaius, which is the power nine server, in our data centers and we're ready for scale, and it's now Google strong which is basically saying that this thing is hardened and ready for production, but we also (laughs) had a number of other significant ones, Tencent talkin' about deploying OpenPOWER, 30% better efficiency, 30% less server resources required, the cloud armor of Alibaba talkin' about how they're putting on their on their X-Dragon, they have it in a piler program, they're asking everybody to use it now so they can figure out how do they go into production. PayPal made statements about how they're using it, but the machine learning and deep learning to do fraud detection, and we even had Limelight, who is not as big a name, but >> CDN, yeah. >> They're a CDN tool provider to people like Netflix and others. We're talkin' about the great capability with the IO and the ability to reduce the buffering and improve the streaming for all these CDN providers out there. So, we were really excited about all those end users and all the things they're saying. That demonstrates the power of this ecosystem. >> Alright, so just to comment on the architecture and then, I want to get into the Cognitive piece. I mean, you guys did, years ago, little Indians, recognizing you got to get software based to be compatible. You mentioned, Ken, bandwidth, IO bandwidth, CAPI stuff that you've done. So, there's a lot of incentives, especially for the big hyperscale guys, to be able to do more with less, but, to me, let's get into the AI, the Cognitive piece. Bob Picciano comes over from running a $15 billion analytics business, so, obviously, he's got some knowledge. He's bringin' in people like you with all these cool buzzwords in your title. So, talk a little bit about infrastructure for AI and why power is the right platform. >> Sure, so, I think we all recognize that the performance advantages and even power advantages that we were getting from Dennard scaling, also known as Moore's law, is over, right. So, people talk about the end of Moore's Law, and that's really the end of gaining processor performance with Dennard scaling and the Moore's Law. What we believe is that to continue to meet the performance needs of all of these new AI and data workloads, you need accelerators, and not just computer accelerators, you actually need accelerated networking. You need accelerated storage, you need high-density memory sitting very close to the compute power, and, if you really think about it, what's happened is, again, system view, right, we're not silicon view, we're looking at the system. The minute you start looking at the silicon you realize you want to get the data to where the computer is, or the computer where the data is. So, it all becomes about creating bigger pipelines, factor of pipelines, to move data around to get to the right compute piece. For example, we put much more emphasis on a much faster memory system to make sure we are getting data from the system memory to the CPU. >> Coherently. >> Coherently, that's the main memory. We put interfaces on power nine including NVLink, OpenCAPI, and PCIe gen four, and that enabled us to get that data either from the network to the system memory, or out back to the network, or to storage, or to accelerators like GPUs. We built and embedded these high-speed interconnects into power nine, into the processor. Nvidia put NVLink into their GPU, and we've been working with marketers like Xilinx and Mellanox on getting OpenCAPI onto their components. >> And we're seeing up to 10x for both memory bandwidth and IO over x86 which is significant. You should talk about how we're seeing up to 4x improvement in training of MLDL algorithms over x86 which is dramatic in how quickly you can get from data to insight, right? You could take training and turn it from weeks to days, or days to hours, or even hours to minutes, and that makes a huge difference in what you can do in any industry as far as getting insight out of your data which is the competitive differentiator in today's environment. >> Let's talk about this notion of architecture, or systems especially. The basic platform for how we've been building systems has been relatively consistent for a long time. The basic approach to how we think about building systems has been relatively consistent. You start with the database manager, you run it on an Intel processor, you build your application, you scale it up based on SMP needs. There's been some variations; we're going into clustering, because we do some other things, but you guys are talking about something fundamentally different, and flash memory, the ability to do flash storage, which dramatically changes the relationship between the processor and the data, means that we're not going to see all of the organization of the workloads around the server, see how much we can do in it. It's really going to be much more of a balanced approach. How is power going to provide that more balanced systems approach across as we distribute data, as we distribute processing, as we create a cloud experience that isn't in one place, but is in more places. >> Well, this ties exactly to the point I made around it's not just accelerated compute, which we've all talked about a lot over the years, it's also about accelerated storage, accelerated networking, and accelerated memories, right. This is really, the point being, that the compute, if you don't have a fast pipeline into the processor from all of this wonderful storage and flash technology, there's going to be a choke point in the network, or they'll be a choke point once the data gets to the server, you're choked then. So, a lot of our focus has been, first of all, partnering with a company like Mellanox which builds extremely high bandwidth, high-speed >> And EOF. >> Right, right, and I'm using one as an example right. >> Sure. >> I'm using one as an example and that's where the large partnerships, we have like 300 partnerships, as Ken talked about in the OpenPOWER foundation. Those partnerships is because we brought together all of these technology providers. We believe that no one company can own the agenda of technology. No one company can invest enough to continue to give us the performance we need to meet the needs of the AI workloads, and that's why we want to partner with all these technology vendors who've all invested billions of dollars to provide the best systems and software for AI and data. >> But fundamentally, >> It's the whole construct of data centric systems, right? >> Right. >> I mean, sometimes you got to process the data in the network, right? Sometimes you got to process the data in the storage. It's not just at the CPU, the GPUs a huge place for processing that data. >> Sure. >> How do you do that all coherently and how do things work together in a system environment is crucial versus a vertically integrated capability where the CPU provider continues to put more and more into the processor and disenfranchise the rest of the ecosystem. >> Well, that was the counter building strategies that we want to talk about. You have Intel who wants to put as much on the die as possible. It's worked quite well for Intel over the years. You had to take a different strategy. If you tried to take Intel on with that strategy, you would have failed. So, talk about the different philosophies, but really I'm interested in what it means for things like alternative processing and your relationship in your ecosystem. >> This is not about company strategies, right. I mean, Intel is a semiconductor company and they think like a semiconductor company. We're a systems and software company, we think like that, but this is not about company strategy. This is about what the market needs, what client workloads need, and if you start there, you start with a data centric strategy. You start with data centric systems. You think about moving data around and making sure there is heritage in this computer, there is accelerated computer, you have very fast networks. So, we just built the US's fastest supercomputer. We're currently building the US's fastest supercomputer which is the project name is Coral, but there are two supercomputers, one at Oak Ridge National Labs and one at Lawrence Livermore. These are the ultimate HPC and AI machines, right. Its computer's a very important part of them, but networking and storage is just as important. The file system is just as important. The cluster management software is just as important, right, because if you are serving data scientists and a biologist, they don't want to deal with, "How many servers do I need to launch this job on? "How do I manage the jobs, how do I manage the server?" You want them to just scale, right. So, we do a lot of work on our scalability. We do a lot of work in using Apache Spark to enable cluster virtualization and user virtualization. >> Well, if we think about, I don't like the term data gravity, it's wrong a lot of different perspectives, but if we think about it, you guys are trying to build systems in a world that's centered on data, as opposed to a world that's centered on the server. >> That's exactly right. >> That's right. >> You got that, right? >> That's exactly right. >> Yeah, absolutely. >> Alright, you guys got to go, we got to wrap, but I just want to close with, I mean, always says infrastructure matters. You got Z growing, you got power growing, you got storage growing, it's given a good tailwind to IBM, so, guys, great work. Congratulations, got a lot more to do, I know, but thanks for >> It's going to be a fun year. comin' on the Cube, appreciate it. >> Thank you very much. >> Thank you. >> Appreciate you having us. >> Alright, keep it right there, everybody. We'll be back with our next guest. You're watching the Cube live from IBM Think 2018. We'll be right back. (techno beat)