>> Announcer: "theCUBE" presents HPE Discover 2022, brought to you by HPE. >> Greetings from Las Vegas, everyone. Lisa Martin, here with Dave Vellante. We are live at HPE Discover 2022 with about 8,000 folks here at The Sands Expo Convention Center. First HPE Discover in three years, everyone jammed in that keynote room, it was standing in only. Dave and I have a couple of exciting guests we're proud to introduce you to. Please, welcome back to "theCUBE," John Schultz, the EVP and general counsel of HPE. Great to have you back here. And Kay Firth-Butterfield, the head of AI and machine learning at the World Economic Forum. Kay, thank you so much for joining us. >> Thank you. It's an absolute pleasure. >> Isn't it great to be back in person? >> Fantastic. >> John, we were saying that. >> Fantastic. >> Last time you were on "theCUBE", it was Cube Virtual. Now, here we are back. A lot of news this morning, a lot's going on. The Edge to Cloud Conferences is the theme this year. In today's Edge to Cloud world, so much data being generated at the edge, it's just going to keep proliferating. AI plays a key role in helping to synthesize that, analyze large volumes of data. Can you start by talking about the differences of the two? The synergies, what you see? >> Yeah. Absolutely. And again, it is great to be back with the two of you, and great to be with Kay, who is a leading light in the world of AI, and particularly, AI responsibility. And so, we're going to talk a little bit about that. But really, this synergistic effect between data and AI, is as tight as they come. Really, data is just the raw materials by which we drive actionable insight. And at the end of the day, it's really about insights, and that speed to insight to make the difference. AI is really what is powering our ability to take vast amounts of data. Amounts of data that we'd never conceived of, being able to process before and bring it together into actionable insights. And it's simplest form, right? AI is simply making computers do what humans used to do, but the power of computing, what you heard about frontier on the main stage today, allows us to use technology to solve problems so complex that it would take humans millions of years to do it. So, this relationship between data and AI, it's incredibly tight. You need the right raw materials. You need the right engine, that is the AI, and then you will generate insights that could really change the world. >> So, Kay, there's a data point from the World Economic Forum which really caught my attention. It says the 15.7 billion of GDP growth is going to be a result of AI by 2030, 15.7 billion added. That includes the dilutive effects where we're replacing humans with machines. What is driving this in this incremental growth? >> Well, I think obviously, it's the access to the huge amounts of data that John pointed out. But one of the things that we have to remember about, AI is that actually, AI is pretty dumb unless you give it nice, clean, organized data. And so, it's not just all data, but it's data that has been through a process that enables the AI to gain insights from it. And so, what is it? It's the compute power, the ever increasing compute power. So, in the past, we would never have thought that we could use some of the new things that we're seeing in machine learning, so even deep learning. It's only been about for a small length of time, but it's really with the compute power, with the amount of data, being able to put AI on steroids, for luck of a better analogy. And I think it's also that we are now in business, and society, being able to see some of the benefits that can be generated from AI. Listening to Oakridge talk about the medical science advances that we can create for human beings, that's extraordinary. But we're also seeing that across business. >> That's why I was going to add. As impressive as those economic figures are in terms of what value it could add from a pure financial perspective? It's really the problems that could be solved. If you think about some of the things that happened in the pandemic, and what virtual experience allowed with a phone or with a tablet to check in with a doctor who was going to curate your COVID test, right? When they invented the iPhone, nobody thought that was going to be the use. AI has that same promise, but really on a macro global scale, some of the biggest problems we're trying to solve. So, huge opportunity, but as we're going to talk about a little later, huge risk for it to be misused if it's not guided and aimed in the right direction. >> Absolutely. >> That's okay. Maybe talk about that? >> Well, I was just going to come back about some of the benefits. California has been over the last 10 years trying to reduce emissions. One wildfire, absolutely wiped out all that good work over 10 years. But with AI, we've been developing an application that allows us to say, "Tomorrow, at this location, you will have a wildfire. So, please send your services to that location." That's the power of artificial intelligence to really help with things like climate change. >> Absolutely. >> Is that a probability model that's running somewhere? >> Yeah. Absolutely >> So, I wanted to ask you, but a lot of AI today, is modeling that's done, and the edge, you mentioned the iPhone, with all this power and new processors. AI inferencing at the edge in real time making real time decisions. So, one example is predicting, the other is there's actually something going on in this place. What do you see there? >> Yeah, so, I mean, yes we are using a predictive tool to ingest the data on weather, and all these other factors in order to say, "Please put your services here tomorrow at this time." But maybe you want to talk about the next edge. >> Yeah. Yeah. Well, and I think it's not just grabbing the data to do some predictive modeling. It's now creating that end-to-end value chain where the actions are being taken in real time based on the information that's being processed, especially out at the edge. So, you're ending up, not just with predictive modeling, but it's actually transferring into actual action on the ground that's happening... You know, we like to say automagically. So, to the point where you can be making real time changes based on information that continues to make you smarter and smarter. So, it's not just a group of people taking the inputs out of a model and figuring out, okay now what am I going to do with it? The system end-to-end, allows it to happen in a way that drives a time to value that is beyond anything we've seen in the pas- >> In every industry? >> In every industry. >> Absolutely, and that's something we learned during the pandemic, one of the many things. Access to real time data to actually glean those insights that can be acted on, is no longer a nice to have. >> No. >> For companies in any industry they've got to have that now, they've got to use it as their competitive advantage. Where do you see when you're talking with customers, John? Where are they in that capability and leveraging AI on steroids, as I said? >> Yeah. I think it varies. I mean, certainly I think as you look in the medical field, et cetera, I mean, I think they've been very comfortable, and that continues to up. The use cases are so numerous there, that in some ways we've only scratched the surface, I think. But there's a high degree of acceptance, and people see the promise. Manufacturing's another area where automation and relying on some form of what used to be kind of analog intelligence, people are very comfortable with. I would say candidly, I would say the public sector and government is the furthest behind. It may be used for intelligence purposes, and things like that, but in terms of advancing overall, the common good, I think we're trailing behind there. So, that's why things like the partnership with Oak Ridge National Laboratory, and some of the other things we're seeing. That's why organizations like the World Economic Forum are so important, because we've got to make sure that this isn't just a private sector piece, It's not just about commercialization, and finding that next cost savings. It really should be about, how do you solve the world's biggest problems and do in a way that's smarter than we've ever been able to do it before? >> It's interesting, you say public sectors is behind because in some respects, they're really advanced, but they're not sharing that because it's secretive. >> Yeah. >> Right? >> That's very fair. >> Yeah. So, Kay, the other interesting stat, was that by 2023 this is like next year, 6.8 trillion will be spent on digital transformation. So, there's this intersection of data. I mean, to me, digital is data. But a lot of it was sort of, we always talk about the acceleration 'cause of the pandemic. If you weren't a digital business you were out of business, and people sort of rushed, I call it the force-march to digital. And now, are people stepping back and saying, "Okay, what can we actually do?" And maybe being more planful? Maybe you could talk about the sort of that roadmap? >> Sure. I think that that's true. And whilst I agree with John, we also see a lot of small... A lot of companies that are really only at proof of value for AI at the moment. So, we need to ensure that everybody, we take everybody, not just the governments, but everybody with us. And one of the things I'm often asked, is if you're a small or medium-sized enterprise, how can you begin to use AI at scale? And I think that's one of the exciting things about building a platform. >> That's right. >> And enabling people to use that. I think that there is also, the fact that we need to take everybody with us on this adventure because AI is so important. And it's not just important in the way it's currently being used. But if we think about these new frontier technologies like Metaverse, for example. What's the Metaverse except an application of AI? But if we don't take everybody on the journey now, then when we are using applications in the Metaverse, or building applications in the Metaverse what happens at that point? >> Think about if only certain groups of people or certain companies had access to wifi, or had access to cellular, or had access to a phone, right? The advantage and the inequality would be manifest, right? We have to think of AI and super computing in the same way, because they are going to be these raw ingredients that are going to drive the future. And if they are not, if there isn't some level of AI equality, I think the potential negative consequences of that, are incredibly high, especially in the developing world. >> Talk about it from a responsibility perspective? Getting everybody on board is challenging from a cultural standpoint, but organizations have to do it as you both articulated. But then every time we talk about AI, we've got to talk about it's used responsibly. Kay, what are your thoughts there? What are you seeing out in the field? >> Yeah, absolutely. And I started working in this in about 2014 when there were maybe a handful of us. What's exciting for me, is that now you hear it on people's lips, much more. But we still got a long way to go. We still got that understanding to happen in companies that although you might, for example, be a drug discovery company, you are probably using AI not just in drug discovery but in a number of backroom operations such as human resources, for example. We know the use of AI and human resources is very problematic. And is about to be legislated against, or at least be set up as a high risk problem use of AI by the E.U. So, across the E.U, we know what happened with GDPR that it became something that lots and lots of countries used, and we expect the AI Act to also become used in that way. So, what you need, is you need not only for companies to understand that they are gradually becoming AI companies, but also that as part of that transformation, it's taking your workers with you. It's helping them understand that AI won't actually take their jobs, it will merely help them with reskilling or working better in what they do. And they think it's also in actually helping the board to understand. We know lots of boards that don't have any clue about AI. And then, the whole of the C-suite and the trickle all down, and understanding that at the end, you've got tools, you've got data, and you've got people, and they all need to be working together to create that functional, responsible AI layer. >> When we think about it, really, when we think about responsible AI, really think about at least three pillars, right? The first off, is that privacy aspect. It's really that data ingestion part, which is respecting the privacy of the individuals, and making sure that you're collecting only the data you should be collecting to feed into your AI mechanism, right? The second, is that inclusivity and equality aspect. We've got to make sure that the actions that are coming out, the insights were generate, driving, really are inclusive. And that goes back to the right data sets. It goes back to the integrity in the algorithm. And then, you need to make sure that your AI is both human and humane. We have to make sure we don't take that human factor out and lose that connection to what really creates our shared humanity. Some of that's transparency, et cetera. I think all of those sound great. We've had some really interesting discussions about in practice, how challenging that's going to be, given the sophistication of this technology. >> When you say transparency, you're talking about the machine made a decision. I have to see how, understand how the machine made a decision. >> Algorithmic transparency. Go ahead. >> Algorithmic transparency. And the United States is actually at the moment considering something which is called the Algorithmic Accountability Act. And so, there is a movement to particularly where somebody's livelihood is affected. Say, for example, whether you get a job, and it was the algorithm that did the pre-selection in the human resources area. So, did you get a job? No, you didn't get that job. Why didn't you get that job? Why did the algorithm- >> A mortgage would be another? >> A mortgage would be another thing. And John was talking about the data, and the way that the algorithms are created. And I think, one great example, is lots of algorithms are currently created by young men under 20. They are not necessarily representative of your target audience for that algorithm. And unless you create some diversity around that group of developers, you're going to create a product that's less than optimal. So, responsible AI, isn't just about being responsible and having a social conscience, and doing things, but in a human-centered way, it's also about your bottom line as well. >> It took us a long time to recognize the kind of the shared interest we have in climate change. And the fact that the things that are happening one part of the world, can't be divorced from the impact across the the globe. When you think about AI, and the ability to create algorithms, and engage in insights, that could happen in one part of the world, and then be transferred out, not withstanding the fact, that most other countries have said, "We wouldn't do it this way, or we would require accountability. You can see the risk." It's what we call the race to the bottom. If you think about some of the things that have happened over the time in the industrial world. Often, businesses flock to those places with the least amount of safeguards that allow them to go the fastest, regardless of the collateral damage. I think we feel that same risk exists today with AI. >> So, much more we could talk about, guys, unfortunately, we are out of time. But it's so amazing to hear where we are with AI, where companies need to be. And it's the tip of the iceberg. You're very exciting. >> Yes. >> Kay and John, thank you so much for joining Dave and me. >> Thank you. >> Thank you. >> Thank you. >> It's a pleasure. >> We want to thank you for watching this segment. Lisa Martin, with Dave Vellante for our guests. We are live at HPE Discover '22. We'll be back with our next guest in just a minute. (bright upbeat music)

>> Live from San Francisco. It's theCube covering IBM Think 2019. Brought to you by IBM. >> Welcome back to Moscone Center everybody. The new, improved Moscone Center. We're at Moscone North, stop by and see us. I'm Dave Vellante, he's Stu Miniman and Lisa Martin is here as well, John Furrier will be up tomorrow. You're watching theCube, the leader in live tech coverage. This is day zero essentially, Stu, of IBM Think. Day one, the big keynotes, start tomorrow. Chairman's keynote in the afternoon. Jamie Thomas is here. She's the general manager of IBM's Systems Strategy and Development at IBM. Great to see you again Jamie, thanks for coming on. >> Great to see you guys as usual and thanks for coming back to Think this year. >> You're very welcome. So, I love your new role. You get to put on the binoculars sometimes the telescope. Look at the road map. You have your fingers in a lot of different areas and you get some advanced visibility on some of the things that are coming down the road. So we're really excited about that. But give us the update from a year ago. You guys have been busy. >> We have been busy, and it was a phenomenal year, Dave and Stu. Last year, I guess one of the pinnacles we reached is that we were named with our technology, our technology received the number one and two supercomputer ratings in the world and this was a significant accomplishment. Rolling out the number one supercomputer in Oakridge National Laboratory and the number two supercomputer in Lawrence Livermore Laboratory. And Summit as it's called in Oakridge is really a cool system. Over 9000 CPUs about 27,000 GPUs. It does 200 petaflops at peak capacity. It has about 250 petabytes of storage attached to it at scale and to cool this guy, Summit, I guess it's a guy. I'm not sure of the denomination actually it takes about 4,000 gallons of water per minute to cool the supercomputer. So we're really pleased with the engineering that we worked on for so many years and achieving these World records, if you will, for both Summit and Sierra. >> Well it's not just bragging rights either, right, Jamie? I mean, it underscores the technical competency and the challenge that you guys face I mean, you're number one and number two, that's not easy. Not easy to sustain of course, you got to do it again. >> Right, right, it's not easy. But the good thing is the design point of these systems is that we're able to take what we created here from a technology perspective around POWER9 and of course the patnership we did with Invidia in this case and the software storage. And we're able to downsize that significantly for commercial clients. So this is the world's largest artificial intlligence supercomputer and basically we are able to take that technology that we invented in this case 'cause they ended up being one of our first clients albeit a very large client, and use that across industries to serve the needs of artificial intelligence work loads. So I think that was one of the most significant elements of what we actually did here. >> And IBM has maintained, despite you guys selling off your microelectronics division years ago, you've maintained a lot of IP in the core processing and the design. You've also reached out certainly with open power, for example, to folks. You mentioned Invidia. But having that, sort of embracing that alternative processor mode as opposed to trying to jam everything in the die. Different philosophy that IBM is taking. >> Yeah we think that the workload specific processing is still very much in demand. Workloads are going to have different dimensions and that's what we really have focused on here. I don't think that this has really changed over the last decades of computing and so we're really focused on specialized computing purpose-built computing, if you will. Obviously using that on premise and also using that in our hybrid cloud strategies for clients that want to do that as well. >> What are some of the other cool things that you guys are working on that you can talk about. >> Well I would say last year was quite an interesting year in that from a mainframe perspective we delivered our first 19 inch form factor which allows us to fit nicely on a floor tile. Obviously allows clients to scale more effectively from a data center planning perspective. Allows us to have a cloud footprint, but with all the characteristics of security that you would normally expect in a mainframe system. But really tailored toward new workloads once again. So Linux form factor and going after the new workloads that a lot of these cloud data centers really need. One of our first and foremost focus areas continues to be security around that system and tomorrow there will be some announcements that will happen around Z security. I can't say what they are right now but you'll see that we are extending security in new ways to support more of these hybrid cloud scenarios. >> It's so funny. We were talking in one of our earlier segments talking about how the path of virtualization and trying to get lots of workloads into something and goes back to the device that could manage all workloads which was the Mainframe. So we've watched for many years system Z lots of Linux on there if you want to do some cool container, you know global Z that's an option, so it's interesting to watch while the pendulum swings in IT have happened the Z system has kept up with a lot of these innovations that have been going on in the industry. >> And you're right, one of our big focuses for the platform for Z and power of course is a container-based strategy. So we've created, you know last year we talked about secure container technology and we continue to evolve secure container technology but the idea is we want to eliminate any kind of friction from a developer's perspective. So if you want to design in a container-based environment then you're more easily able to port that technology or your applications, if you will to a Z mainframe environment if that's really what your target environment is. So that's been a huge focus. The other of course major invention that we announced at the Consumer Electronics show is our Quantum System One. And this represented an evolution of our Quantum system over the last year where we now have the world's really first self-contained universal quantum computer in a single form factor where we were able to combine the Quantum processor which is living in the dilution refrigerator. You guys remember the beautiful chandelier from last year. I think it's back this year. But this is all self-contained with it's electronics in a single form factor. And that really represents the evolution of the electronics in particular over the last year where we were able to miniaturize those electronics and get them into this differentiated form factor. >> What should people know about Quantum? When you see the demos, they explain it's not a binary one or zero, it could be either, a virtually infinite set of possibilities, but what should the lay person know about Quantum and try to understand? >> Well I think really the fundamental aspect of it is in today's world with traditional computers they're very powerful but they cannot solve certain problems. So when you look at areas like material science, areas like chemistry even some financial trading scenarios, the problems can either not be solved at all or they cannot be completed in the right amount of time. Particularly in the world of financial services. But in the area of chemistry for instance molecular modeling. Today we can model simple molecules but we cannot model something even as complex as caffeine. We simply don't have the traditional compute capacity to do that. A quantum computer will allow us once it comes to maturity allow us to solve these problems that are not solvable today and you can think about all the things that we could do if were able to have more sophisticated molecular modeling. All the kinds of problems we could solve probably in the world of pharmacology, material science which affects many, many industries right? People that are developing automobiles, people that are exploring for oil. All kinds of opportunities here in this space. The technology is a little bit spooky, I guess, that's what Einstein said when he first solved some of this, right? But it really represents the state of the universe, right? How the universe behaves today. It really is happening around us but that's what quantum mechanics helps us capture and when combined with IT technology the quantum computer can bring this to life over time. >> So one of the things that people point to is potentially a new security paradigm because Quantum can flip the way in which we do security on it's head so you got to be thinking around that as well. I know security is something that is very important to IBM's Systems division. >> Right, absolutely. So the first thing that happens when someone hears about quantum computing is they ask about quantum security. And as you can imagine there's a lot of clients here that are concerned about security. So in IBM research we're also working on quantum-safe encryption. So you got one team working on a quantum computer, you got another team ensuring that the data will be protected from the quantum computer. So we do believe we can construct quantum-safe encryption algorithms based on lattice-based technology that will allow us to encrypt data today and in the future when the quantum computer does reach that kind of capacity the data will be protected. So the idea is that we would start using these new algorithms far earlier than the computer could actually achieve this result but it would mean that data created today would be quantum safe in the future. >> You're kind of in your own arm's race internally. >> But it's very important. Both aspects are very important. To be able to solve these problems that we can't solve today, which is really amazing, right? And to also be able to protect our data should it be used in inappropriate ways, right? >> Now we had Ed Bausch on earlier today. Used to run the storage division. What's going on in that world? I know you've got your hands in that pie as well. What can you tell us about what's going on there? >> Well I believe that Ed and the team have made some phenomenal innovations in the past year around flash MVME technology and fusing that across product lines state-of-the-art. The other area that I think is particularly interesting of course is their data management strategy around things like Spectrum Discover. So, today we all know that many of our clients have just huge amounts of data. I visited a client last year that interesting enough had 1 million tapes, and of course we sell tapes so that's a good thing but then how do you deal and manage all the data that is on 1 million tapes. So one of the inventions that the team has worked on is a metadata tagging capability that they've now shipped in a product called spectrum discover. And that allows a client to have a better way to have a profile of their data, data governance and understand for different use cases like data governance or compliance how do they pull back the right data and what does this data really mean to them. So have a better lexicon of their data, if you will than what they can do in today's world. So I think that's very important technology. >> That's interesting. I would imagine that metadata could sit in Flash somewhere and then inform the serial technology to maybe find stuff faster. I mean, everybody thinks tape is slow because it's sequential. But actually if you do some interesting things with metadata you can-- >> There's all kinds of things you can do I mean it's one thing to have a data ocean if you will, but then how do you really get value out of that data over a long period of time and I think we're just the tip of the spear in understanding the use cases that we can use this technology for. >> Jamie, how does IBM manage that pipeline of innovation. I think we heard very specific examples of how the super computers drive HPC architectures which everybody is going to use for their AI infrastructure. Something like quantum computing is a little bit more out there. So how do you balance kind of the research through the product and what's going to be more useful to users today. >> Yeah, well, that's an interesting question. So IBM is one of the few organizations in the world really that have an applied research organization still. And Dario Gil is here this week he manages our research organization now under Arvind Krishna. An organization like IBM Systems has a great relationship with research. Research are the folks that had people working on Quantum for decades, right? And they're the reason that we are in a position now to be able to apply this in the way that we are. The great news is that along the way we're always working on a pipeline of this next generation set of technologies and innovations. Some of them succeed and some of them don't. But without doing that we would not have things like Quantum. We would not have advanced encryption capability that we pushed all the way down into our chips. We would not have quantum-safe encryption. Things like the metadata tagging that I talked about came out of IBM research. So it's working with them on problems that we see coming down the pipe, if you will that will affect our clients and then working with them to make sure we get those into the product lines at the right amount of time. I would say that Quantum is the ultimate partnership between IBM Systems and IBM research. We have one team in this case that are working jointly on this product. Bringing the skills to bear that each of us have on this case with them having the quantum physics experts and us having the electronics experts and of course the software stacks spanning both organizations is really a great partnership. >> Is there anything you could tell us about what's going on at the edge. The edge computing you hear a lot about that today. IBM's got some activities going on there? You haven't made huge splashes there but anything going on in research that you can share with us, or any directions. >> Well I believe the edge is going to be a practical endeavor for us and what I mean by that is there are certain use cases that I think we can serve very well. So if we look at the edge as perhaps a factory environment, we are seeing opportunities for our storaging compute solutions around the data management out in some of these areas. If you look at the self-driving automobile for instance, just design something like that can easily take over a hundred petabytes of data. So being able to manage the data at the edge, being able to then to provide insight appropriately using AI technologies is something we think we can do and we see that. I own factories based on what I do and I'm starting to use AI technology. I use Power AI technology in my factories for visual inspection. Think about a lot of the challenges around provenance of parts as well as making sure that they're finally put together in the right way. Using these kind of technologies in factories is just really an easy use case that we can see. And so what we anticipate is we will work with the other parts of IBM that are focused on edge as well and understand which areas we think our technology can best serve. >> That's interesting you mention visual inspection. That's an analog use case which now you're transforming into digital. >> Yeah well Power AI vision has been very successful in the last year . So we had this power AI package of open source software that we pulled together but we drastically simplified the use of this software, if you will the ability to use it deploy it and we've added vision capability to it in the last year. And there's many use cases for this vision capability. If you think about even the case where you have a patient that is in an MRI. If you're able to decrease the amount of time they stay in the MRI in some cases by less fidelity of the picture but then you've got to be able to interpret it. So this kind of AI and then extensions of AI to vision is really important. Another example for Power AI vision is we're actually seeing use cases in advertising so the use case of maybe you're at a sporting event or even a busy place like this where you're able to use visual inspection techniques to understand the use of certain products. In the case of a sporting event it's how many times did my logo show up in this sporting event, right? Particularly our favorite one is Formula One which we usually feature the Formula One folks here a little bit at the events. So you can see how that kind of technology can be used to help advertisers understand the benefits in these cases. >> Got it. Well Jamie we always love having you on because you have visibility into so many different areas. Really thank you for coming and sharing a little taste of what's to come. Appreciate it. >> Well thank you. It's always good to see you and I know it will be an exciting week here. >> Yeah, we're very excited. Day zero here, day one and we're kicking off four days of coverage with theCube. Jamie Thomas of IBM. I'm Dave Vellante, he's Stu Miniman. We'll be right back right after this short break from IBM Think in Moscone. (upbeat music)

>> Narrator: Live from Las Vegas. It's theCUBE. Covering VMWorld 2017. Brought to you by vmware and it's ecosystem partners. (bright pop music) >> Welcome back to theCUBE on day three of our continuing coverage of VMWorld 2017. I'm Lisa Martin. My co-host for this segment is John Troyer and we're excited to be joined by Rob Young, who is a CUBE alumni and the manager of product and strategy at Red Hat. Welcome back to theCUBE, Rob. >> Thanks, Lisa. It's great to be here. >> So, Red Hat and VMware. You've got a lot of customers in common. I imagine you've been to many many VMworlds. What are you hearing from some of the folks that you're talking to during the show this week? >> So, a lot of the interest that we're seeing is how Red Hat can help customers, VMware or otherwise, continue to maintain mode one applications, legacy applications, while planning for mode two, more cloud-based deployments. We're seeing a large interest in open-source technologies and how that model could work for them to lower cost, to innovate more quickly, deliver things in a more agile way, so there's a mixture of messages that we're getting, but we're receiving them loud and clear. >> Excellent. You guys have a big investment in OpenStack. >> Yes we do and even back in the early days when OpenStack was struggling as a technology, we recognized that it was an enabler for customers, partners, large enterprises that wanted to create and maintain their own private clouds or even to have a hybrid cloud environment to where they maintained and managed, controlled some aspect of it, while having some of the workloads on a public cloud environment as well so Red Hat has invested heavily in OpenStack to this point. We're now in our 11th version of Red Hat OpenStack platform and we continue to lead that market as far as OpenStack development, innovation, and contributions. >> Rob, we were with theCUBE at the last OpenStack summit in Boston. Big Red Hat presence there, obviously. I was very impressed at the maturity of the OpenStack market and community. I mean, we're past the hype cycle now, right? We're down to real people, real uses, real people using it. A lot of very, people with a strong business critical investment in OpenStack and many different use cases. Can you kind of give us a picture of the state of the OpenStack market and userbase now that we are past that hype cycle? >> So, I think what we're witnessing now in the market is that there's a thirst for OpenStack. One, because it's a very efficient architecture. It's very extensible. There's a tremendous ecosystem around the Red Hat distribution of OpenStack and what we're seeing from enterprises, specifically the TelCo industry, is that they see OpenStack as a way to lower their cost, raise their margins in a very competitive environment, so anywhere you see an industry or a vertical where there's very heavy competition for customers and eyeballs, that type of thing. OpenStack is going to play a role and if it's not already doing so, it's going to be there at some point because of the simplification of what was once complex but also in the cost savings, it could be realized by managing your own cloud within a hybrid cloud environment. >> You mention TelCo and specifically OpenStack kind of value for companies that need to compete for customers. Besides TelCo, what other industries are really kind of primed for embracing OpenStack technologies? >> So, we're seeing it across many industries, finance and banking, healthcare, public sector, anywhere where there's an emphasis on the move to open source and to open compute environment, open APIs. We're seeing a tremendous growth in traction and because Red Hat has been the leader in Linux, many of these same customers who trust us for Red Hat Enterprise Linux, are now looking to us for the very same reason on OpenStack platform, because much like we have done with Enterprise Linux, we have adopted an upstream community-driven project. We have made it safe to use within an environment in an enterprise way, in a supported way as well, the subscription. So, many industries, many verticals. We expect to see more, but primary-use cases, NFE and TelCo, healthcare, banking, public sector are among the top dogs out there. >> Is there a customer story that kind of stands out in your mind as really a hallmark that showcases the success of working with Red Hat and OpenStack? >> Well there are many customers, there are many partners that we have out there that we work with, but I would say that if you look at some of the, four of out of five of the large TelCos - Orange, Ericsson, Nokia, others that we've recently done business with would be really good examples of not only customer use cases but how they're using OpenStack to enable their customers to have better experience with their cell networks, with their billing, with their availability, that type of thing. And we had two press announcements that came out in May, one is an educational institution of a consortium, a very high profile Northeast learning institutions, public institutions, that are now standardized on OpenStack and that are contributing, and we've also got Oakridge, forgive me, it escapes me, but there's a case study out there on the Red Hat website that was posted on May the eighth that depicts how they're using our product and how others can do the same. >> Rob, switching over a little bit to talking a little bit more about the tech and how the levers get pulled, right, we're talking about cloud, right, another term, "past the hype cycle," right? It's a reality. And when you're talking about cloud, you're talking about scale. >> Rob: Yes. >> We mentioned Linux, OpenStack, and Red Hat kind of built on a foundation of Linux, it's super solid, super huge community, super rich, super long history, but can you talk about scale up, scale out, data center, public cloud, private, how are you seeing enterprises of various sizes address the scale problem and using technologies like the Red Hat and CloudStack to address that? >> So there's a couple things, there's many aspects to that question but what we have seen from OpenStack is when we first got involved with the project, it was very much bounded by the number of servers that you needed to deploy an OpenStack infrastructure on. What Red Hat has done, or what we've done as a company is we've looked at the components and we have unshackled them from each other, so that you can scale individual storage, individual network, individual high availability, on the number of servers that best fit your needs. So if you want to have a very large footprint with you know, many nodes of storage, you can do that. If you want to scale that just when peak season hits, you can do that as well. But we have led the community efforts to de-shackle the dependencies between components so from that aspect we have scaled the technology, now scaling operational capabilities and skillsets as well. We've also led the effort to create open APIs for management tools. We've created communities around the different components of OpenStack and other outsourced technologies - >> Automation a big part of that as well, right? >> Automation as well, so if you look at Ansible, as an example, Red Hat has a major stake in Ansible, and it is predominantly the management scripting language of choice, or the management platform of choice, so we have baked that into our products, we have made it very simple for customers to not only deploy things like OpenStack but OpenShift, CloudForms, other management capabilities that we have, but we've also added APIs to these products so that even if you choose not to use a Red Hat solution, you can easily plug in a third-party solution or a home-grown solution into our framework or our stack so that you can use our toolset, single pane of glass, to manage it all. >> So with that, can you tell us a little bit about the partner ecosystem that Red Hat has, and what you've done sounds like to expand that to make your customers successful in OpenStack deployments. >> Absolutely, so as you're aware, Red Hat Enterprise Linux, we certified most of the hardware, or all of the hardware, OEMs on Red Hat Enterprise Linux. We have a tremendous ecosystem around Enterprise Linux. For OpenStack, this is probably one of the most exciting aspects of Red Hat right now. If you look at the ecosystem and the partners that are just around OpenStack on its own, we've got an entire catalog of hundreds of partners, some at a deeper level than others, integration-wise, business-wise, whatever, but the ecosystem is growing and it's not because of Red Hat's efforts. We have customers and partners that are coming to us saying, we need a storage solution, we're using, you know, NetAMP as an example. You need to figure out a way to integrate with these guys, and certify it, make sure that it's something that we've already invested in, it's going to work with your product as well as it works with our legacy stuff. So the ecosystem around OpenStack is growing, we're also looking at growing the ecosystem around OpenShift, around Red Hat virtualization as well, so I think you'll see a tremendous amount of overlap in those ecosystem as well, which is a great thing for us. The synergies are there, and I just think it's only going to help us multiply our efforts in the market. >> Go ahead John. >> Oh Rob, talking again, partnerships, I've always been intrigued at the role of open source upstream, the open source community, and the role of the people that take that open source and then package it for customers and do the training, enablement. So can you talk maybe a little bit about some of the open source partners and maybe how the role of Red Hat in translating all that upstream code into a product that is integrated and has training, and is available for consumption from the IT side. >> Sure. So at Red Hat, we partner not only with open source community members and providers but also with proprietaries. So I just want to make sure that everybody understands we're not exclusive to who we partner with. Upstream, we look for partners that have the open source spirit and mind, so everything that they're doing that they're asking us to either consider as a component within our solution or to integrate with, we're going to make sure that they're to the letter of the law, contributing their code back, and there's no hooks or strings attached. Really the value comes in, are they providing value to their customers with the contribution and also to our combined customers, and what we're seeing in our partnerships is that many of our partners, even proprietary partners like Microsoft as an example, are looking at open source in a different way. They're providing open source options for their customers and subscription-based, consumption-based models as well, so we hope that we're having a positive impact in that way, because if you look at our industry it's really headed toward the open source, open API, open model and the proprietary model still has the place and time I believe but I think it's going to diminish over time and open source is going to be just the way people do business together. >> One of the things that you were talking about kind of reminded me of one of the things Michael Dell said yesterday during the keynote with Pat Gelsinger and that was about innovation and that you really got to, companies to be successful need to be innovating with their customers and it sounds like that's definitely one of the core elements of what you're doing with customers. You said customers and partners are bringing us together to really drive that innovation. >> Yeah, I couldn't agree more. It's an honor to be mentioned in the same breath as Michael Dell, by the way. But what we see is because of the open source model, you can release early and often, and you can fail early, and what that does is encourage innovation. So it's not only corporations like Red Hat that are contributing to upstream projects, OpenStack as an example or Linux as an example, or KVM as an example. There's also college students, there's people out there who work for Bank of America. Across the fruited plains all over the world. And the one thing that unites us is this ability to recognize the value of our contributions to an open source community, and we think that that really helps with agile development, agile delivery, and if you look at our project deliveries for OpenStack as an example, OpenStack releases a major version of its product every six months. And because of contributions that we get from our community, we're able to release our - and testing, it's not just, contributions come in many forms. Testing is a huge part of that. Because of the testing we get from a worldwide community, we're able to release shortly after a major version of upstream OpenStack because that innovation. In a pure waterfall model, it's not even possible. In an open source model, it's just the way of life . >> So as we're kind of wrapping up VMworld day three, what are some of the key takeaways for you personally from the event and that Red Hat has observed in the last couple of days here in Las Vegas. >> So there's a couple of observations that have kind of been burned into my brain. One is we believe at Red Hat, our opinion is that virtualization as a model will remain core, not only to legacy applications, mode one, but also to mode two, and the trend that we see in the model, that we see is that for mode two, virtualization is going to be a commodity feature. People are going to expect it to be baked into the operating system or into the infrastructure that they're running the operating system or their applications on. So we see that trend and we've suspected it, but coming to VMworld this week helped confirm that. And I say that because of the folks I've talked to, after sessions, at dinner, in the partner pavilion. I really see that as a trend. The other thing I see is that there's a tremendous thirst within the VMware customer base to learn more about open source and learn more about how they can, you know, leverage some of this not only to lower their total cost of ownership and not to replace VMware, but how they can complement what they've already invested in with faster, more agile-based mode two development. And that's where we see the market from a Red Hat standpoint. >> Excellent. Well there's a great TEI study that you guys did recently, Total Economic Impact, on virtualization that folks can find on the website. And Rob, we thank you for sticking around and sharing some of your insights and innovations that Red Hat is pioneering and we look forward to having you back on the show. >> Great to be here. Thanks. >> Absolutely, and for my co-host John Troyer, I'm Lisa Martin and you're watching theCUBE's continuing coverage, day three, of VMworld 2017. Stick around, we'll be right back. (bright pop music)

>> Narrator: Live from Las Vegas. It's The Cube covering VMworld 2017 brought to you by VMware and its ecosystem partners. >> Welcome back to The Cube on day three of our continuing coverage of Vmworld 2017. I'm Lisa Martin, our cohost for this segment is John Troyer and we're excited to be joined by Rob Young, who is a Cube alumni, and the manager of product and strategy at RedHat. Welcome back to the Cube, Rob. >> Thanks, Lisa, it's great to be here. >> So RedHat and VM where you, you get a lot of customers in common. I imagine you've been to many, many Vmworlds. What are you hearing from some of the folks you were talking to on during the show this week? >> So a lot of the interest that we're seeing is how RedHat can help customers, VMware or otherwise, continue to maintain mode one applications, like Z applications while planning for mode two, more cloud based deployments. And we're seeing a large interest in open source technologies and how that model could work for them to lower cost, to innovate more quickly, deliver things in a more agile way. So there's a mixture of messages that we're getting, but we're receiving them loud and clear. >> Excellent. You guys have a big investment in OpenStack. >> Yes we do, and even back in the early days when OpenStack was struggling as a technology, we recognized that it was an enabler for customers, partners, large enterprises that wanted to create, maintain their own private clouds or even to maintain a hybrid cloud environment where they maintained and managed controlled some aspect of it while having some of it, some of the work loads on a public cloud environment as well, so RedHat has invested heavily in OpenStack to this point. We're now in our 11th version of RedHat/OpenStack platform and we continue to lead that market as far as OpenStack development, animation, and contributions. >> Rob, we were with the Cube at the last Openstack summit in Boston, big Redhat presence there obviously, I was very impressed with the maturity of the Openstack market and community, I mean we're past the hype cycle now, we're down to real people, real uses, real people using it, a lot of varied people with strong business critical investment in Openstack in many different use cases. Can you kind of give us a picture of the state of the Openstack market and the userbase now that we are past that hype cycle. >> So I think what we're witnessing now in the market is a thirst for Openstack, one because it's a very efficient architecture, it's very extensible, there's a tremendous ecosystem around the Redhat distribution of Openstack, and what we're seeing from enterprises, specifically in the telecom industry is that they see Openstack as away to lower their costs, raise their margins in a very competitive environment, so anywhere you see an industry where there's very heavy competition for customers, that type of thing, Openstack is going to play a role, if it's not already doing so, it's going to be there at some point because of the simplification of what was once complex, but also In the cost savings can be realized by managing your own cloud within a hybrid cloud environment. >> You mentioned Telco, and specifically Openstack and the value for companies that need to compete for customers, besides Telco, what other industries are really primed for embracing Openstack technologies? >> So we're seeing across many industries, finance and banking, healthcare, public sector, anywhere where there is a emphasis on the move to opensource and to open compute environments, open APIs we're seeing a tremendous growth in traction, and because Redhat has been later than Linux, many of these same customers, who trust for Redhat Enterprise Linux and now looking to us for the very same reason on Openstack platform, because we much like we have done with Enterprise Linux, we have adopted an upstream community driven project we have made it safe to use within an environment, in an enterprise way, in a supported way as well, via subscription, so many industries, many versicles, we expect to see more, but primary use cases in FE, in Telco, healthcare, banking, public sector are among the top dogs out there. >> IS there a customer story that sort of stands out in you mind as a hallmark that showcases the success of working with Redhat and Openstack? >> Well there are many customers, many partners out there that we work with, if you look at four out of the five large Telcos, Orange, Ericsson, Nokia, others that we've recently done business with, would be really good examples, of not only customer use cases, but how they're using Openstack to allow their customers to have better experience with their cell networks with their billing with their availability, that type of thing, and we had two press announcements that came out in May, one of them is an educational institution of a consortium of very high profile Northeast learning institutions, public institutions that are now standardized on Openstack and are contributing, and we've also got Oakridge, forgive me, it escapes me, but there's a case study out there on the Redhat website that was posted on May 8th that depicts how they're using our product and how others can do the same. >> Rob, switching over a little bit to talking a little bit more about the tech and how the levers get pulled, we're talking about cloud, another term past the hype cycle, it's a reality, but when you're talking about cloud you're talking about scale, we mentioned Linux and Openstack and Redhat, built on a foundation of Linux, super solid super huge community, super rich, super long history, but can you talk about scale up, scale out, data center, public cloud, private, how are you seeing enterprises of various seizes address the scale problem and using technologies like the Redhat cloud stack to address that? >> So there's a couple of things, there's many aspects to that question, but what we have seen from Openstack, is when we first got involved with the project, it was very much bounded by the number of servers that you needed to deploy an Openstack infrastructure on, what we're done as a company is we've looked at the components and we have unshackled them from each other, so that you can scale individual storage, individual network, individual high availability on the number of servers that best for your needs, so if you want to have a very large footprint with many nodes of storage, you can do that, if you want to scale that just when peak season hits you can do that as well, but we have led the community efforts to deshackle the dependencies between components, so from that aspect we have scaled the technology, now scaling operational capabilities and skillsets as well, we've also led the effort to create open APIS for management tools, we've created communities around Openstack and other Opensource technologies. >> Automation a big part of that. >> Automation as well. So if you look at Anserable, Redhat has a major stake in Anserable, and it is predominately the management scripting language of choice, or the management platform of choice, so we have baked that in our products, we have made it very simple for customers to not only deploy things like openstack but Openshift Cloudforms, other management capabilities that we have, but we've also added APIs to these products, so that if you choose not to use a Redhat solution, you can easily plugin a third party solution, or a homegrown solution, into our framework for our stack so that you can use our toolset, single pane of glass to manage it all. >> So with that, can you tell us a little bit about the partner ecosystem that Redhat has, and what you've done to expand that to make your customers successful in Openstack environments? >> Absolutely, as you're aware, Redhat Enterprise Linux, we certified most of the hardware, all of of the hardware OEMs on Redhat Enterprise Linux, we have a tremendous ecosystem around Enterprise Linux for Openstack, this is probably one of the most exciting aspects of Redhat right now, if you look at the ecosystem and the partners that are around Openstack on its own, we've got an entire catalog of hundreds of partners, some at a deeper level than others, integration wise, business wise whatever, but the ecosystem is growing and it's not because of Redhat's efforts, we have customers and partners that are coming to us, we need a storage solution, we're using Netapp as an example, you need to figure out a way to integrate with these guys, and certify, and make sure that it's something that we've already invested in is going to work with your product as well as it works with our legacy stuff, so the ecosystem around openstack is growing, we're also looking at growing the ecosystem around Openshift, around Rethat virtualization as well, so I think you'll see a tremendous amount of overlap in those ecosystems as well, which his a great thing for us, the synergies are there, and I think it's only going to help us multiply our efforts in the market. >> Go on John. >> So Rob, taking again partnerships, I've always been intrigued at the role of Opensource Upstream, the Opensource community, and the people who then take that Opensource and then package for customers and do the training enablement, so can you maybe talk a little bit about some of the Opensource training partners, and how the role of Redhat in translating all that upstream code into a product that is integrated and has training and is available for consumption for the IT side. >> Sure, so at Redhat we partner not only with opensource community member and providers, but also with proprietary, so I just wanted to make sure everybody understands, we're not exclusive to who we partner with. Upstream, we look for partners that have the opensource spirit in mind, so everything that they're asking us to either consider as a component within our solution or to integrate with we want to make sure that they are to the letter of the law, contributing their code back, and there's no strings attached, really the value comes in, are they providing value to their customers, with the contribution, and also to our combined customers, and what we're seeing in our partnerships, is that many of our partners even proprietary partners such as Microsoft for example, are looking at opensource in a different way, and they're providing opensource options for their customers and consumption based models as well, so we hope that we're having a positive impact in that way, because if you look at our industry, it's really headed towards the opensource openAPI open model and the proprietary model still has a time and place I believe, but I think it's going to diminish over time, and opensource is going to be the way people do business together. >> One of the things that you were talking about reminded me of one of the things that Michael Delft said yesterday, during the keynote with Pat Gelsinger, and that was about innovation, and that you really got companies to be successfully innovating with their customers, and that sounds like that definitely one of the core elements of what you're doing with customers, he said customers and partners are bringing us together to really drive that innovation. >> Yeah, I couldn't agree more, and it's an honor to be mentioned in the same breath as Michael Delft by the way, but what we see is because of the opensource model, you can release early and often, and you can fail early, and what that does is it encourages innovation, so its not only corporations like Redhat that are contributing to upstream projects, Openstack as an example, or Linux as an example, or KBM as an example, there's also college students, there's people out there who work for Bank of America, across the plains all over the world, and the one thing that unites us is to recognize the value of our contributions to an opensource community, and we think that really helps with agile development, agile delivery, and if you look a tour project deliveries for Openstack as an example, Openstack releases a major version of its product every six months, and because of contributions that we get from our community, we're able to release our, in testing, it's not just, contributions come in many forms, testing is a huge part of that, because of the testing we get from a world wide community, we're able to release shorty after a major version of upstream Openstack because that innovation in a pure waterfall model, its not even possible, in an opensource model, it's just a way of life. >> So as we're kind of wrapping up VM World day three, what are some of the key takeaways for you personally from the event and that Redhat has observed in the last couple of days here in Las Vegas? >> So there's a couple of observations that have been burned into my brain, one is we believe at Redhat, that virtualization as a model will remain core, not only to legacy application, Mode one, but also to Mode two, and the trend that we see in the model, for mode two virtualization is going to be a commodity feature, people are going to expect it to be baked into the operating system, or into the infrastructure where they're running the operating system where their application's on, so we see that trend, and we suspected, but coming to VMware this week helped confirm that, and I say that because the folks I've talked to after sessions, at dinner, in the partner pavilion, so I really se that as a trend, the other thing I see is that there's a tremendous thirst within the VMware customer base to learn more about opensource and learn more about how they can leverage this, not only to lower their total cost of ownership, and to to replace VMware, but how they can compliment what they've already invested in with faster more agile based Mode two development, and that's where we see the market from a Redhat standpoint. >> Thanks Dan, well there's a great TEI study that you guys did recently, Total Economic Impact on virtualization that you can find on the website, and Rob we thank you for sticking around and sharing some of your insights and innovations that Redhat is pioneering, and we look forward to having you back on the show. >> It's great to be here, thanks. >> Absolutely, and for my co-host John, I am Lisa Martin, you're watching the Cube continuing coverage, day three of VMware 2017