(bright upbeat music) >> Welcome back, everybody, to the Supercloud 2. My name is Dave Vellante. And I'm pleased to welcome Nir Zuk. He's the founder and CTO of Palo Alto Networks. Nir, good to see you again. Welcome. >> Same here. Good to see you. >> So let's start with the right security architecture in the context of today's fragmented market. You've got a lot of different tools, you've got different locations, on-prem, you've got hardware and software. Tell us about the right security architecture from your standpoint. What's that look like? >> You know, the funny thing is using the word security in architecture rarely works together. (Dave chuckles) If you ask a typical information security person to step up to a whiteboard and draw their security architecture, they will look at you as if you fell from the moon. I mean, haven't you been here in the last 25 years? There's no security architecture. The architecture today is just buying a bunch of products and dropping them into the infrastructure at some relatively random way without really any guiding architecture. And that's a huge challenge in cybersecurity. It's always been, we've always tried to find ways to put an architecture into writing blueprints, whatever you want to call it, and it's always been difficult. Luckily, two things. First, there's something called zero trust, which we can talk a little bit about more, if you want, and zero trust among other things is really a way to create a security architecture, and second, because in the cloud, in the supercloud, we're starting from scratch, we can do things differently. We don't have to follow the way we've always done cybersecurity, again, buying random products, okay, maybe not random, maybe there is some thinking going into it by buying products, one of the other, dropping them in, and doing it over 20 years and ending up with a mess in the cloud, we have an opportunity to do it differently and really have an architecture. >> You know, I love talking to founders and particularly technical founders from StartupNation. I think I saw an article, I think it was Erie Levine, one of the founders or co-founders of Waze, and he had a t-shirt on, it said, "Fall in love with the problem, not the solution." Is that how you approached architecture? You talk about zero trust, it's a relatively new term, but was that in your head when you thought about forming the company? >> Yeah, so when I started Palo Alto Networks, exactly, by the way, 17 years ago, we got funded January, 2006, January 18th, 2006. The idea behind Palo Alto Networks was to create a security platform and over time take more and more cybersecurity functions and deliver them on top of that platform, by the way, as a service, SaaS. Everybody thought we were crazy trying to combine many functions into one platform, best of breed and defense in death and putting all your eggs in the same basket and a bunch of other slogans were flying around, and also everybody thought we were crazy asking customers to send information to the cloud in order to secure themselves. Of course, step forward 17 years, everything is now different. We changed the market. Almost all of cybersecurity today is delivered as SaaS and platforms are ruling more and more the world. And so again, the idea behind the platform was to over time take more and more cybersecurity functions and deliver them together, one brain, one decision being made for each and every packet or system call or file or whatever it is that you're making the decision about and it works really, really well. As a side effect, when you combine that with zero trust and you end up with, let's not call it an architecture yet. You end up with with something where any user, any location, both geographically as well as any location in terms of branch office, headquarters, home, coffee shop, hotel, whatever, so any user, any geographical location, any location, any connectivity method, whether it is SD1 or IPsec or Client VPN or Client SVPN or proxy or browser isolation or whatever and any application deployed anywhere, public cloud, private cloud, traditional data center, SaaS, you secure the same way. That's really zero trust, right? You secure everything, no matter who the user is, no matter where they are, no matter where they go, you secure them exactly the same way. You don't make any assumptions about the user or the application or the location or whatever, just because you trust nothing. And as a side effect, when you do that, you end up with a security architecture, the security architecture I just described. The same thing is true for securing applications. If you try to really think and not just act instinctively the way we usually do in cybersecurity and you say, I'm going to secure my traditional data center applications or private cloud applications and public cloud applications and my SaaS applications the same way, I'm not going to trust something just because it's deployed in the private data center. I'm not going to trust two components of an application or two applications talking to each other just because they're deployed in the same place versus if one component is deployed in one public cloud and the other component is deployed in another public cloud or private cloud or whatever. I'm going to secure all of them the same way without making any trust assumptions. You end up with an architecture for securing your applications, which is applicable for the supercloud. >> It was very interesting. There's a debate I want to pick up on what you said because you said don't call it an architecture yet. So Bob Muglia, I dunno if you know Bob, but he sort of started the debate, said, "Supercloud, think of it as a platform, not an architecture." And there are others that are saying, "No, no, if we do that, then we're going to have a bunch of more stove pipes. So there needs to be standard, almost a purist view. There needs to be a supercloud architecture." So how do you think about it? And it's a bit academic, I know, but do you think of this idea of a supercloud, this layer of value on top of the hyperscalers, do you think of that as a platform approach that each of the individual vendors are responsible for the architecture? Or is there some kind of overriding architecture of standards that needs to emerge to enable the supercloud? >> So we can talk academically or we can talk practically. >> Yeah, let's talk practically. That's who you are. (Dave laughs) >> Practically, this world is ruled by financial interests and none of the public cloud providers, especially the bigger they are has any interest of making it easy for anyone to go multi-cloud, okay? Also, on top of that, if we want to be even more practical, each of those large cloud providers, cloud scale providers have engineers and all these engineers think they're the best in the world, which they are and they all like to do things differently. So you can't expect things in AWS and in Azure and GCP and in the other clouds like Oracle and Ali and so on to be the same. They're not going to be the same. And some things can be abstracted. Maybe cloud storage or bucket storage can be abstracted with the layer that makes them look the same no matter where you're running. And some things cannot be abstracted and unfortunately will not be abstracted because the economical interest and the way engineers work won't let it happen. We as a third party provider, cybersecurity provider, and I'm sure other providers in other areas as well are trying or we're doing our best. We're not trying, we are doing our best, and it's pretty close to being the way you describe the top of your supercloud. We're building something that abstracts the underlying cloud such that securing each of these clouds, and by the way, I would add private cloud to it as well, looks exactly the same. So we use, almost always, whenever possible, the same terminology, no matter which cloud we're securing and the same policy and the same alerts and the same information and so on. And that's also very important because when you look at the people that actually end up using the product, security engineers and more importantly, SOC, security operations center analysts, they're not going to study the details of each and every cloud. It's just going to be too much. So we need to abstract it for them. >> Yeah, we agree by the way that the supercloud definition is inclusive of on-prem, you know, what you call private cloud. And I want to pick up on something else you said. I think you're right that abstracting and making consistent across clouds something like object storage, get put, you know, whether it's an S3 bucket or an Azure Blob, relatively speaking trivial. When you now bring that supercloud concept to something more complex like security, first of all, as a technically feasible and inferring the answer there is yes, and if so, what do you see as the main technical challenges of doing so? >> So it is feasible to the extent that the different cloud provide the same functionality. Then you step into a territory where different cloud providers have different paths services and different cloud providers do things a little bit differently and they have different sets of permissions and different logging that sometimes provides all the information and sometimes it doesn't. So you end up with some differences. And then the question is, do you abstract the lowest common dominator and that's all you support? Or do you find a way to be smarter than that? And yeah, whatever can be abstracted is abstracted and whatever cannot be abstracted, you find an easy way to represent that to your users, security engineers, security analysts, and so on, which is what I believe we do. >> And you do that by what? Inventing or developing technology that presents that experience to users? Could you be more specific there? >> Yeah, so different cloud providers call their storage in different names and you use different ways to configure them and the logs come out the same. So we normalize it. I mean, the keyword is probably normalization. Normalize it. And we try to, you know, then you have to pick a winner here and to use someone's terminology or you need to invent new terminology. So we try to use the terminology of the largest cloud provider so that we have a better chance of doing that but we can't always do that because they don't support everything that other cloud providers provide, but the important thing is, with or thanks to that normalization, our customers both on the engineering side and on the user side, operations side end up having to learn one terminology in order to set policies and understand attacks and investigate incidents. >> I wonder if I could pick your brain on what you see as the ideal deployment model to achieve this supercloud experience. For example, do you think instantiating your stack in multiple regions and multiple clouds is the right way to do it? Or is building a single global instance on top of the clouds a more preferable way? Are maybe other models we should consider? What do you see as the trade off of these different deployment models and which one is ideal in your view? >> Yeah, so first, when you deploy cloud security, you have to decide whether you're going to use agents or not. By agents, I mean something working, something running inside the workload. Inside a virtual machine on the container host attached to function, serverless function and so on and I, of course, recommend using agents because that enables prevention, it enables functionality you cannot get without agents but you have to choose that. Now, of course, if you choose agent, you need to deploy AWS agents in AWS and GCP agents in GCP and Azure agents in Azure and so on. Of course, you don't do it manually. You do it through the CICD pipeline. And then the second thing that you need to do is you need to connect with the consoles. Of course, that can be done over the internet no matter where your security instances is running. You can run it on premise, you can run it in one of the other different clouds. Of course, we don't run it on premise. We prefer not to run it on premise because if you're secured in cloud, you might as well run in the cloud. And then the question is, for example, do you run a separate instance for AWS for GCP or for Azure, or you want to run one instance for all of them in one of these clouds? And there are advantages and disadvantages. I think that from a security perspective, it's always better to run in one place because then when you collect the information, you get information from all the clouds and you can start looking for cross-cloud issues, incidents, attacks, and so on. The downside of that is that you need to send all the information to one of the clouds and you probably know that sending data out of the cloud costs a lot of money versus keeping it in the cloud. So theoretically, you can build an architecture where you keep the data for AWS in AWS, Azure in Azure, GCP in GCP, and then you try to run distributed queries. When you do that, you find out you'd end up paying more for the compute to do that than you would've paid for sending all the data to a central location. So we prefer the approach of running in one place, bringing all the data there, and running all the security, the machine learning or whatever, the rules or whatever it is that you're running in one place versus trying to create a distributed deployment in order to try to save some money on the data, the network data transfers. >> Yeah, thank you for that. That makes a lot of sense. And so basically, should we think about the next layer building security data lake, if you will, and then running machine learning on top of that if I can use that term of a data lake or a lake house? Is that sort of where you're headed? >> Yeah, look, the world is headed in that direction, not just the cybersecurity world. The world is headed from being rule-based to being data-based. So cybersecurity is not different and what we used to do with rules in the past, we're now doing with machine learning. So in the past, you would define rules saying, if you see this, this, and this, it's an attack. Now you just throw the data at the machine, I mean, I'm simplifying it, but you throw data at a machine. You'll tell the machine, find the attack in the data. It's not that simple. You need to build the right machine learning models. It needs to be done by people that are both cybersecurity experts and machine learning experts. We do it mostly with ex-military offensive people that take their offensive knowledge and translate it into machine learning models. But look, the world is moving in that direction and cybersecurity is moving in that direction as well. You need to collect a lot of data. Like I said, I prefer to see all the data in one place so that the machine learning can be much more efficient, pay for transferring the data, save money on the compute. >> I think the drop the mic quote it ignite that you had was within five years, your security operation is going to be AI-powered. And so you could probably apply that to virtually any job over the next five years. >> I don't know if any job. Certainly writing essays for school is automated already as we've seen with ChatGPT and potentially other things. By the way, we need to talk at some point about ChatGPT security. I don't want to think what happens when someone spends a lot of money on creating a lot of fake content and teaches ChatGPT the wrong answer to a question. We start seeing ChatGPT as the oracle of everything. We need to figure out what to do with the security of that. But yeah, things have to be automated in cybersecurity. They have to be automated. They're just too much data to deal with and it's just not even close to being good enough to wait for an incident to happen and then going investigate the incident based on the data that we have. It's better to look at all the data all the time, millions of events per second, and find those incidents before they happen. There's no way to do that without machine learning. >> I'd love to have you back and talk about ChatGPT. I know they're trying to put in some guardrails but there are a lot of unintended consequences, aren't there? >> Look, if they're not going to have a person filtering the data, then with enough money, you can create thousands or tens of thousands of pieces of articles or whatever that look real and teach the machine something that is totally wrong. >> We were talking about the hyper skills before and I agree with you. It's very unlikely they're going to get together, band together, and create these standards. But it's not a static market. It's a moving train, if you will. So assuming you're building this cross cloud experience which you are, what do you want from the hyperscalers? What do you want them to bring to the table? What is a technology supplier like Palo Alto Networks bring? In other words, where do you see ongoing as your unique value add and that moat that you're building and how will that evolve over time vis-a-vis the hyperscaler evolution? >> Yeah, look, we need APIs. The more data we have, the more access we have to more data, the less restricted the access is and the cheaper the access is to the data because someone has to pay today for some reason for accessing that data, the more secure their customers are going to be. So we need help and are helping by the way a lot, all of them in finding easy ways for customers to deploy things in the cloud, access data, and again, a lot of data, very diversified data and do it in a cost-effective way. >> And when we talk about the edge, I presume you look at the edge as just another data center or maybe it's the reverse. Maybe the data center is just another edge location, but you're seeing specific edge security solutions come out. I'm guessing that you would say, that's not what we want. Edge should be part of that architecture that we talked about earlier. Do you agree? >> Correct, it should be part of the architecture. I would also say that the edge provides an opportunity specifically for network security, whereas traditional network security would be deployed on premise. I'm talking about internet security but half network security market, and not just network security but also the other network intelligent functions like routing and QS. We're seeing a trend of pushing those to the edge of the cloud. So what you deploy on premise is technology for bringing packets to the edge of the cloud and then you run your security at the edge, whatever that edge is, whether it's a private edge or public edge, you run it in the edge. It's called SASE, Secure Access Services Edge, pronounced SASE. >> Nir, I got to thank you so much. You're such a clear thinker. I really appreciate you participating in Supercloud 2. >> Thank you. >> All right, keep it right there for more content covering the future of cloud and data. This is Dave Vellante for John Furrier. I'll be right back. (bright upbeat music)

>> Presenter: theCUBE presents Ignite '22, brought to you by Palo Alto Networks. >> Hey guys and girls. Welcome back to theCube's live coverage at Palo Alto Ignite '22. We're live at the MGM Grand Hotel in beautiful Las Vegas. Lisa Martin here with Dave Vellante. This is day one of our coverage. We've been talking with execs from Palo Alto, Partners, but one of our most exciting things is talking with Founders day. We get to do that next. >> The thing is, it's like I wrote this weekend in my breaking analysis. Understanding the problem in cybersecurity is really easy, but figuring out how to fix it ain't so much. >> It definitely isn't. >> So I'm excited to have Nir here. >> Very excited. Nir Zuk joins us, the founder and CTO of Palo Alto Networks. Welcome, Nir. Great to have you on the program. >> Thank you. >> So Palo Alto Networks, you founded it back in 2005. It's hard to believe that's been 18 years, almost. You did something different, which I want to get into. But tell us, what was it back then? Why did you found this company? >> I thought the world needed another cybersecurity company. I thought it's because there were so many cybersecurity vendors in the world, and just didn't make any sense. This industry has evolved in a very weird way, where every time there was a new challenge, rather than existing vendors dealing with a challenge, you had new vendors dealing with it, and I thought I could put a stop to it, and I think I did. >> You did something differently back in 2005, looking at where you are now, the leader, what was different in your mind back then? >> Yeah. When you found a new company, you have really two good options. There's also a bad option, but we'll skip that. You can either disrupt an existing market, or you can create a new market. So first, I decided to disrupt an existing market, go into an existing market first, network security, then cyber security, and change it. Change the way it works. And like I said, the challenges that every problem had a new vendor, and nobody just stepped back and said, "I think I can solve it with the platform." Meaning, I think I can spend some time not solving a specific problem, but building a platform that then can be used to solve many different problems. And that's what I've done, and that's what Palo Alto Networks has done, and that's where we are today. >> So you look back, you call it now, I think you call it a next gen firewall, but nothing in 2005, can it be next gen? Do you know the Silicon Valley Show? Do you know the show Silicon Valley? >> Oh! Yeah. >> Yeah, of course. >> You got to have a box. But it was a different kind of box- >> Actually. >> Explain that. >> Actually, it's exactly the same thing. You got to have a box. So I actually wanted to call it a necessary evil. Marketing wouldn't go for that. >> No. >> And the reason I wanted to call it a necessary evil, because one of the things that we've done in order to platform our cyber security, again, first network security now, also cloud security, and security operations, is to turn it into a SaaS delivered industry. Today every cyber security professional knows that, when they buy cyber security, they buy usually a SaaS delivered service. Back then, people thought I was crazy to think that customers are going to send their data to their vendor in order to process, and they wanted everything on premise and so on, but I said, "No, customers are going to send information to us for processing, because we have much more processing power than they have." And we needed something in the infrastructure to send us the information. So that's why I wanted to call it the necessary evil. We ended up calling it next generation firewall, which was probably a better term. >> Well, even Veritas. Remember Veritas? They had the no hardware agenda. Even they have a box. So it is like you say, you got to have it. >> It's necessary. >> Okay. You did this, you started this on your own cloud, kind of like Salesforce, ServiceNow. >> Correct. >> Similar now- >> Build your own data centers. >> Build your own data center. Okay, I call it a cloud, but no. >> No, it's the same. There's no cloud, it's just someone else's computer. >> According to Larry Ellison, he was actually probably right about that. But over time, you've had this closer partnership with the public clouds. >> Correct. >> What does that bring you and your customers, and how hard was that to navigate? >> It wasn't that hard for us, because we didn't have that many services. Usually it's harder. Of course, we didn't do a lift and shift, which is their own thing to do with the cloud. We rebuild things for the cloud, and the benefits, of course, are time to market, scale, agility, and in some cases also, cost. >> Yeah, some cases. >> In some cases. >> So you have a sort of a hybrid model today. You still run your own data centers, do you not? >> Very few. >> Really? >> There are very, very few things that we have to do on hardware, like simulating malware and things that cannot be done in a virtual machine, which is pretty much the only option you have in the cloud. They provide bare metal, but doesn't serve our needs. I think that we don't view cloud, and your viewers should not be viewing cloud, as a place where they're going to save money. It's a place where they're going to make money. >> I like that. >> You make much more money, because you're more agile. >> And that's why this conversation is all about, your cost of goods sold they're going to be so high, you're going to have to come back to your own data centers. That's not on your mind right now. What's on your mind is advancing the unit, right? >> Look, my own data center would limit me in scale, would limit my agility. If you want to build something new, you don't have all the PaaS services, the platform as a service, services like database, and AI, and so on. I have to build them myself. It takes time. So yeah, it's going to be cheaper, but I'm not going to be delivering the same thing. So my revenues will be much lower. >> Less top line. What can humans do better than machines? You were talking about your keynote... I'm just going to chat a little bit. You were talking about your keynote. Basically, if you guys didn't see the keynote, that AI is going to run every soc within five years, that was a great prediction that you made. >> Correct. >> And they're going to do things that you can't do today, and then in the future, they're going to do things that you can't... Better than you can do. >> And you just have to be comfortable with that. >> So what do you think humans can do today and in the future better than machines? >> Look, humans can always do better than machines. The human mind can do things that machines cannot do. We are conscious, I don't think machines will be conscious. And you can do things... My point was not that machines can do things that humans cannot do. They can just do it better. The things that humans do today, machines can do better, once machines do that, humans will be free to do things that they don't do today, that machines cannot do. >> Like what? >> Like finding the most difficult, most covert attacks, dealing with the most difficult incidents, things that machines just can't do. Just that today, humans are consumed by finding attacks that machines can find, by dealing with incidents that machines can deal with. It's a waste of time. We leave it to the machines and go and focus on the most difficult problems, and then have the machines learn from you, so that next time or a hundred or a thousand times from now, they can do it themselves, and you focus on the even more difficult. >> Yeah, just like after 9/11, they said that we lack the creativity. That's what humans have, that machines don't, at least today. >> Machines don't. Yeah, look, every airplane has two pilots, even though airplanes have been flying themselves for 30 years now, why do you have two pilots, to do the things that machines cannot do? Like land on the Hudson, right? You always need humans to do the things that machines cannot do. But to leave the things that machines can do to the machines, they'll do it better. >> And autonomous vehicles need breaks. (indistinct) >> In your customer conversations, are customers really grappling with that, are they going, "Yeah, you're right?" >> It depends. It's hard for customers to let go of old habits. First, the habit of buying a hundred different solutions from a hundred different vendors, and you know what? Why would I trust one vendor to do everything, put all my eggs in the same basket? They have all kind of slogans as to why not to do that, even though it's been proven again and again that, doing everything in one system with one brain, versus a hundred systems with a hundred brains, work much better. So that's one thing. The second thing is, we always have the same issue that we've had, I think, since the industrial revolution, of what machines are going to take away my job. No, they're just going to make your job better. So I think that some of our customers are also grappling with that, like, "What do I do if the machines take over?" And of course, like we've said, the machines aren't taking over. They're going to do the benign work, you're going to do the interesting work. You should embrace it. >> When I think about your history as a technology pro, from Check Point, a couple of startups, one of the things that always frustrated you, is when when a larger company bought you out, you ended up getting sucked into the bureaucratic vortex. How do you avoid that at Palo Alto Networks? >> So first, you mean when we acquire company? >> Yes. >> The first thing is that, when we acquire companies, we always acquire for integration. Meaning, we don't just buy something and then leave it on the side, and try to sell it here and there. We integrate it into the core of our products. So that's very important, so that the technology lives, thrives and continues to grow as part of our bigger platform. And I think that the second thing that is very important, from past experience what we've learned, is to put the people that we acquire in key positions. Meaning, you don't buy a company and then put the leader of that company five levels below the CEO. You always put them in very senior positions. Almost always, we have the leaders of the companies that we acquire, be two levels below the CEO, so very senior in the company, so they can influence and make changes. >> So two questions related to that. One is, as you grow your team, can you be both integrated? And second part of the question, can you be both integrated and best of breed? Second part of the question is, do you even have to be? >> So I'll answer it in the third way, which is, I don't think you can be best of breed without being integrated in cybersecurity. And the reason is, again, this split brain that I've mentioned twice. When you have different products do a part of cybersecurity and they don't talk to each other, and they don't share a single brain, you always compromise. You start looking for things the wrong way. I can be a little bit technical here, but please. Take the example of, traditionally you would buy an IDS/IPS, separately from your filtering, separately from DNS security. One of the most important things we do in network security is to find combining control connections. Combining control connections where the adversaries controlling something behind your firewall and is now going around your network, is usually the key heel of the attack. That's why attacks like ransomware, that don't have a commanding control connection, are so difficult to deal with, by the way. So commanding control connections are a key seal of the attacks, and there are three different technologies that deal with it. Neural filtering for neural based commanding control, DNS security for DNS based commanding control, and IDS/IPS for general commanding control. If those are three different products, they'll be doing the wrong things. The oral filter will try to find things that it's not really good at, that the IPS really need to find, and the DN... It doesn't work. It works much better when it's one product doing everything. So I think the choice is not between best of breed and integrated. I think the only choice is integrated, because that's the only way to be best of breed. >> And behind that technology is some kind of realtime data store, I'll call it data lake, database. >> Yeah. >> Whatever. >> It's all driven by the same data. All the URLs, all the domain graph. Everything goes to one big data lake. We collect about... I think we collect about, a few petabytes per day. I don't write the exact number of data. It's all going to the same data lake, and all the intelligence is driven by that. >> So you mentioned in a cheeky comment about, why you founded the company, there weren't enough cybersecurity companies. >> Yeah. >> Clearly the term expansion strategy that Palo Alto Networks has done has been very successful. You've been, as you talked about, very focused on integration, not just from the technology perspective, but from the people perspective as well. >> Correct. >> So why are there still so many cybersecurity companies, and what are you thinking Palo Alto Networks can do to change that? >> So first, I think that there are a lot of cybersecurity companies out there, because there's a lot of money going into cybersecurity. If you look at the number of companies that have been really successful, it's a very small percentage of those cybersecurity companies. And also look, we're not going to be responsible for all the innovation in cybersecurity. We need other people to innovate. It's also... Look, always the question is, "Do you buy something or do you build it yourself?" Now we think we're the smartest people in the world. Of course, we can build everything, but it's not always true that we can build everything. Know that we're the smartest people in the world, for sure. You see, when you are a startup, you live and die by the thing that you build. Meaning if it's good, it works. If it's not good, you die. You run out of money, you shut down, and you just lost four years of your life to this, at least. >> At least. >> When you're a large company, yeah, I can go and find a hundred engineers and hire them. And especially nowadays, it becomes easier, as it became easier, and give them money, and have them go and build the same thing that the startup is building, but they're part of a bigger company, and they'll have more coffee breaks, and they'll be less incentive to go and do that, because the company will survive with or without them. So that's why startups can do things much better, sometimes than larger companies. We can do things better than startups, when it comes to being data driven because we have the data, and nobody can compete against the amount of data that we have. So we have a good combination of finding the right startups that have already built something, already proven that it works with some customers, and of course, building a lot of things internally that we cannot do outside. >> I heard you say in one of the, I dunno, dozens of videos I've listened to you talked to. The industry doesn't need or doesn't want another IoT stovepipe. Okay, I agree. So you got on-prem, AWS, Azure, Google, maybe Alibaba, IoT is going to be all over the place. So can you build, I call it the security super cloud, in other words, a consistent experience with the same policies and edicts across all my estates, irrespective of physical location? Is that technically feasible? Is it what you are trying to do? >> Certainly, what we're trying to do with Prisma Cloud, with our cloud security product, it works across all the clouds that you mentioned, and Oracle as well. It's almost entirely possible. >> Almost. >> Almost. Well, the things that... What you do is you normalize the language that the different cloud scale providers use, into one language. This cloud calls it a S3, and so, AWS calls it S3, and (indistinct) calls it GCS, and so on. So you normalize their terminology, and then build policy using a common terminology that your customers have to get used to. Of course, there are things that are different between the different cloud providers that cannot be normalized, and there, it has to be cloud specific. >> In that instance. So is that, in part, your strategy, is to actually build that? >> Of course. >> And does that necessitate running on all the major clouds? >> Of course. It's not just part of our strategy, it's a major part of our strategy. >> Compulsory. >> Look, as a standalone vendor that is not a cloud provider, we have two advantages. The first one is we're security product, security focused. So we can do much better than them when it comes to security. If you are a AWS, GCP, Azure, and so on, you're not going to put your best people on security, you're going to put them on the core business that you have. So we can do much better. Hey, that's interesting. >> Well, that's not how they talk. >> I don't care how they talk. >> Now that's interesting. >> When something is 4% of your business, you're not going to put it... You're not going to put your best people there. It's just, why would you? You put your best people on 96%. >> That's not driving their revenue. >> Look, it's simple. It's not what we- >> With all due respect. With all due respect. >> So I think we do security much better than them, and they become the good enough, and we become the premium. But certainly, the second thing that give us an advantage and the right to be a standalone security provider, is that we're multicloud, private cloud and all the major cloud providers. >> But they also have a different role. I mean, your role is not the security, the Nitro card or the Graviton chip, or is it? >> They are responsible for securing up to the operating system. We secure everything. >> They do a pretty good job of that. >> No, they do, certainly they have to. If they get bridged at that level, it's not just that one customer is going to suffer, the entire customer base. They have to spend a lot of time and money on it, and frankly, that's where they put their best security people. Securing the infrastructure, not building some cloud security feature. >> Absolutely. >> So Palo Alto Networks is, as we wrap here, on track to nearly double its revenues to nearly seven billion in FY '23, just compared to 2020, you were quoted in the press by saying, "We will be the first $100 billion cyber company." What is next for Palo Alto to achieve that? >> Yeah, so it was Nikesh, our CEO and chairman, that was quoted saying that, "We will double to a hundred billion." I don't think he gave it a timeframe, but what it takes is to double the sales, right? We're at 50 billion market cap right now, so we need to double sales. But in reality, you mentioned that we're growing the turn by doing more and more cybersecurity functions, and taking away pieces. Still, we have a relatively small, even though we're the largest cybersecurity vendor in the world, we have a very low market share that shows you how fragmented the market is. I would also like to point out something that is less known. Part of what we do with AI, is really take the part of the cybersecurity industry, which are service oriented, and that's about 50% of the cybersecurity industry services, and turn it into products. I mean, not all of it. But a good portion of what's provided today by people, and tens of billions of dollars are spent on that, can be done with products. And being one of the very, very few vendors that do that, I think we have a huge opportunity at turning those tens of billions of dollars in human services to AI. >> It's always been a good business taking human labor and translating into R and D, vendor R and D. >> Especially- >> It never fails if you do it well. >> Especially in difficult times, difficult economical times like we are probably experiencing right now around the world. We, not we, but we the world. >> Right, right. Well, congratulations. Coming up on the 18th anniversary. Tremendous amount of success. >> Thank you. >> Great vision, clear vision, STEM expansion strategy, really well underway. We are definitely going to continue to keep our eyes. >> Big company, a hundred billion, that's market capital, so that's a big company. You said you didn't want to work for a big company unless you founded it, is that... >> Unless it acts like a small company. >> There's the caveat. We'll keep our eye on that. >> Thank you very much. >> It's such a pleasure having you on. >> Thank you. >> Same here, thank you. >> All right, for our guests and for Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live emerging and enterprise tech coverage. (upbeat music)

(upbeat music) >> The ascendancy of cloud and SAS has shown new light on how organizations think about, pay for, and value hardware. Once sought after skills for practitioners with expertise in hardware troubleshooting, configuring ports, tuning storage arrays, and maximizing server utilization has been superseded by demand for cloud architects, DevOps pros, developers with expertise in microservices, container, application development, and like. Even a company like Dell, the largest hardware company in enterprise tech touts that it has more software engineers than those working in hardware. Begs the question, is hardware going the way of Coball? Well, not likely. Software has to run on something, but the labor needed to deploy, and troubleshoot, and manage hardware infrastructure is shifting. At the same time, we've seen the value flow also shifting in hardware. Once a world dominated by X86 processors value is flowing to alternatives like Nvidia and arm based designs. Moreover, other componentry like NICs, accelerators, and storage controllers are becoming more advanced, integrated, and increasingly important. The question is, does it matter? And if so, why does it matter and to whom? What does it mean to customers, workloads, OEMs, and the broader society? Hello and welcome to this week's Wikibon theCUBE Insights powered by ETR. In this breaking analysis, we've organized a special power panel of industry analysts and experts to address the question, does hardware still matter? Allow me to introduce the panel. Bob O'Donnell is president and chief analyst at TECHnalysis Research. Zeus Kerravala is the founder and principal analyst at ZK Research. David Nicholson is a CTO and tech expert. Keith Townson is CEO and founder of CTO Advisor. And Marc Staimer is the chief dragon slayer at Dragon Slayer Consulting and oftentimes a Wikibon contributor. Guys, welcome to theCUBE. Thanks so much for spending some time here. >> Good to be here. >> Thanks. >> Thanks for having us. >> Okay before we get into it, I just want to bring up some data from ETR. This is a survey that ETR does every quarter. It's a survey of about 1200 to 1500 CIOs and IT buyers and I'm showing a subset of the taxonomy here. This XY axis and the vertical axis is something called net score. That's a measure of spending momentum. It's essentially the percentage of customers that are spending more on a particular area than those spending less. You subtract the lesses from the mores and you get a net score. Anything the horizontal axis is pervasion in the data set. Sometimes they call it market share. It's not like IDC market share. It's just the percentage of activity in the data set as a percentage of the total. That red 40% line, anything over that is considered highly elevated. And for the past, I don't know, eight to 12 quarters, the big four have been AI and machine learning, containers, RPA and cloud and cloud of course is very impressive because not only is it elevated in the vertical access, but you know it's very highly pervasive on the horizontal. So what I've done is highlighted in red that historical hardware sector. The server, the storage, the networking, and even PCs despite the work from home are depressed in relative terms. And of course, data center collocation services. Okay so you're seeing obviously hardware is not... People don't have the spending momentum today that they used to. They've got other priorities, et cetera, but I want to start and go kind of around the horn with each of you, what is the number one trend that each of you sees in hardware and why does it matter? Bob O'Donnell, can you please start us off? >> Sure Dave, so look, I mean, hardware is incredibly important and one comment first I'll make on that slide is let's not forget that hardware, even though it may not be growing, the amount of money spent on hardware continues to be very, very high. It's just a little bit more stable. It's not as subject to big jumps as we see certainly in other software areas. But look, the important thing that's happening in hardware is the diversification of the types of chip architectures we're seeing and how and where they're being deployed, right? You refer to this in your opening. We've moved from a world of x86 CPUs from Intel and AMD to things like obviously GPUs, DPUs. We've got VPU for, you know, computer vision processing. We've got AI-dedicated accelerators, we've got all kinds of other network acceleration tools and AI-powered tools. There's an incredible diversification of these chip architectures and that's been happening for a while but now we're seeing them more widely deployed and it's being done that way because workloads are evolving. The kinds of workloads that we're seeing in some of these software areas require different types of compute engines than traditionally we've had. The other thing is (coughs), excuse me, the power requirements based on where geographically that compute happens is also evolving. This whole notion of the edge, which I'm sure we'll get into a little bit more detail later is driven by the fact that where the compute actually sits closer to in theory the edge and where edge devices are, depending on your definition, changes the power requirements. It changes the kind of connectivity that connects the applications to those edge devices and those applications. So all of those things are being impacted by this growing diversity in chip architectures. And that's a very long-term trend that I think we're going to continue to see play out through this decade and well into the 2030s as well. >> Excellent, great, great points. Thank you, Bob. Zeus up next, please. >> Yeah, and I think the other thing when you look at this chart to remember too is, you know, through the pandemic and the work from home period a lot of companies did put their office modernization projects on hold and you heard that echoed, you know, from really all the network manufacturers anyways. They always had projects underway to upgrade networks. They put 'em on hold. Now that people are starting to come back to the office, they're looking at that now. So we might see some change there, but Bob's right. The size of those market are quite a bit different. I think the other big trend here is the hardware companies, at least in the areas that I look at networking are understanding now that it's a combination of hardware and software and silicon that works together that creates that optimum type of performance and experience, right? So some things are best done in silicon. Some like data forwarding and things like that. Historically when you look at the way network devices were built, you did everything in hardware. You configured in hardware, they did all the data for you, and did all the management. And that's been decoupled now. So more and more of the control element has been placed in software. A lot of the high-performance things, encryption, and as I mentioned, data forwarding, packet analysis, stuff like that is still done in hardware, but not everything is done in hardware. And so it's a combination of the two. I think, for the people that work with the equipment as well, there's been more shift to understanding how to work with software. And this is a mistake I think the industry made for a while is we had everybody convinced they had to become a programmer. It's really more a software power user. Can you pull things out of software? Can you through API calls and things like that. But I think the big frame here is, David, it's a combination of hardware, software working together that really make a difference. And you know how much you invest in hardware versus software kind of depends on the performance requirements you have. And I'll talk about that later but that's really the big shift that's happened here. It's the vendors that figured out how to optimize performance by leveraging the best of all of those. >> Excellent. You guys both brought up some really good themes that we can tap into Dave Nicholson, please. >> Yeah, so just kind of picking up where Bob started off. Not only are we seeing the rise of a variety of CPU designs, but I think increasingly the connectivity that's involved from a hardware perspective, from a kind of a server or service design perspective has become increasingly important. I think we'll get a chance to look at this in more depth a little bit later but when you look at what happens on the motherboard, you know we're not in so much a CPU-centric world anymore. Various application environments have various demands and you can meet them by using a variety of components. And it's extremely significant when you start looking down at the component level. It's really important that you optimize around those components. So I guess my summary would be, I think we are moving out of the CPU-centric hardware model into more of a connectivity-centric model. We can talk more about that later. >> Yeah, great. And thank you, David, and Keith Townsend I really interested in your perspectives on this. I mean, for years you worked in a data center surrounded by hardware. Now that we have the software defined data center, please chime in here. >> Well, you know, I'm going to dig deeper into that software-defined data center nature of what's happening with hardware. Hardware is meeting software infrastructure as code is a thing. What does that code look like? We're still trying to figure out but servicing up these capabilities that the previous analysts have brought up, how do I ensure that I can get the level of services needed for the applications that I need? Whether they're legacy, traditional data center, workloads, AI ML, workloads, workloads at the edge. How do I codify that and consume that as a service? And hardware vendors are figuring this out. HPE, the big push into GreenLake as a service. Dale now with Apex taking what we need, these bare bone components, moving it forward with DDR five, six CXL, et cetera, and surfacing that as cold or as services. This is a very tough problem. As we transition from consuming a hardware-based configuration to this infrastructure as cold paradigm shift. >> Yeah, programmable infrastructure, really attacking that sort of labor discussion that we were having earlier, okay. Last but not least Marc Staimer, please. >> Thanks, Dave. My peers raised really good points. I agree with most of them, but I'm going to disagree with the title of this session, which is, does hardware matter? It absolutely matters. You can't run software on the air. You can't run it in an ephemeral cloud, although there's the technical cloud and that's a different issue. The cloud is kind of changed everything. And from a market perspective in the 40 plus years I've been in this business, I've seen this perception that hardware has to go down in price every year. And part of that was driven by Moore's law. And we're coming to, let's say a lag or an end, depending on who you talk to Moore's law. So we're not doubling our transistors every 18 to 24 months in a chip and as a result of that, there's been a higher emphasis on software. From a market perception, there's no penalty. They don't put the same pressure on software from the market to reduce the cost every year that they do on hardware, which kind of bass ackwards when you think about it. Hardware costs are fixed. Software costs tend to be very low. It's kind of a weird thing that we do in the market. And what's changing is we're now starting to treat hardware like software from an OPEX versus CapEx perspective. So yes, hardware matters. And we'll talk about that more in length. >> You know, I want to follow up on that. And I wonder if you guys have a thought on this, Bob O'Donnell, you and I have talked about this a little bit. Marc, you just pointed out that Moore's laws could have waning. Pat Gelsinger recently at their investor meeting said that he promised that Moore's law is alive and well. And the point I made in breaking analysis was okay, great. You know, Pat said, doubling transistors every 18 to 24 months, let's say that Intel can do that. Even though we know it's waning somewhat. Look at the M1 Ultra from Apple (chuckles). In about 15 months increased transistor density on their package by 6X. So to your earlier point, Bob, we have this sort of these alternative processors that are really changing things. And to Dave Nicholson's point, there's a whole lot of supporting components as well. Do you have a comment on that, Bob? >> Yeah, I mean, it's a great point, Dave. And one thing to bear in mind as well, not only are we seeing a diversity of these different chip architectures and different types of components as a number of us have raised the other big point and I think it was Keith that mentioned it. CXL and interconnect on the chip itself is dramatically changing it. And a lot of the more interesting advances that are going to continue to drive Moore's law forward in terms of the way we think about performance, if perhaps not number of transistors per se, is the interconnects that become available. You're seeing the development of chiplets or tiles, people use different names, but the idea is you can have different components being put together eventually in sort of a Lego block style. And what that's also going to allow, not only is that going to give interesting performance possibilities 'cause of the faster interconnect. So you can share, have shared memory between things which for big workloads like AI, huge data sets can make a huge difference in terms of how you talk to memory over a network connection, for example, but not only that you're going to see more diversity in the types of solutions that can be built. So we're going to see even more choices in hardware from a silicon perspective because you'll be able to piece together different elements. And oh, by the way, the other benefit of that is we've reached a point in chip architectures where not everything benefits from being smaller. We've been so focused and so obsessed when it comes to Moore's law, to the size of each individual transistor and yes, for certain architecture types, CPUs and GPUs in particular, that's absolutely true, but we've already hit the point where things like RF for 5g and wifi and other wireless technologies and a whole bunch of other things actually don't get any better with a smaller transistor size. They actually get worse. So the beauty of these chiplet architectures is you could actually combine different chip manufacturing sizes. You know you hear about four nanometer and five nanometer along with 14 nanometer on a single chip, each one optimized for its specific application yet together, they can give you the best of all worlds. And so we're just at the very beginning of that era, which I think is going to drive a ton of innovation. Again, gets back to my comment about different types of devices located geographically different places at the edge, in the data center, you know, in a private cloud versus a public cloud. All of those things are going to be impacted and there'll be a lot more options because of this silicon diversity and this interconnect diversity that we're just starting to see. >> Yeah, David. David Nicholson's got a graphic on that. They're going to show later. Before we do that, I want to introduce some data. I actually want to ask Keith to comment on this before we, you know, go on. This next slide is some data from ETR that shows the percent of customers that cited difficulty procuring hardware. And you can see the red is they had significant issues and it's most pronounced in laptops and networking hardware on the far right-hand side, but virtually all categories, firewalls, peripheral servers, storage are having moderately difficult procurement issues. That's the sort of pinkish or significant challenges. So Keith, I mean, what are you seeing with your customers in the hardware supply chains and bottlenecks? And you know we're seeing it with automobiles and appliances but so it goes beyond IT. The semiconductor, you know, challenges. What's been the impact on the buyer community and society and do you have any sense as to when it will subside? >> You know, I was just asked this question yesterday and I'm feeling the pain. People question, kind of a side project within the CTO advisor, we built a hybrid infrastructure, traditional IT data center that we're walking with the traditional customer and modernizing that data center. So it was, you know, kind of a snapshot of time in 2016, 2017, 10 gigabit, ARISTA switches, some older Dell's 730 XD switches, you know, speeds and feeds. And we said we would modern that with the latest Intel stack and connected to the public cloud and then the pandemic hit and we are experiencing a lot of the same challenges. I thought we'd easily migrate from 10 gig networking to 25 gig networking path that customers are going on. The 10 gig network switches that I bought used are now double the price because you can't get legacy 10 gig network switches because all of the manufacturers are focusing on the more profitable 25 gig for capacity, even the 25 gig switches. And we're focused on networking right now. It's hard to procure. We're talking about nine to 12 months or more lead time. So we're seeing customers adjust by adopting cloud. But if you remember early on in the pandemic, Microsoft Azure kind of gated customers that didn't have a capacity agreement. So customers are keeping an eye on that. There's a desire to abstract away from the underlying vendor to be able to control or provision your IT services in a way that we do with VMware VP or some other virtualization technology where it doesn't matter who can get me the hardware, they can just get me the hardware because it's critically impacting projects and timelines. >> So that's a great setup Zeus for you with Keith mentioned the earlier the software-defined data center with software-defined networking and cloud. Do you see a day where networking hardware is monetized and it's all about the software, or are we there already? >> No, we're not there already. And I don't see that really happening any time in the near future. I do think it's changed though. And just to be clear, I mean, when you look at that data, this is saying customers have had problems procuring the equipment, right? And there's not a network vendor out there. I've talked to Norman Rice at Extreme, and I've talked to the folks at Cisco and ARISTA about this. They all said they could have had blowout quarters had they had the inventory to ship. So it's not like customers aren't buying this anymore. Right? I do think though, when it comes to networking network has certainly changed some because there's a lot more controls as I mentioned before that you can do in software. And I think the customers need to start thinking about the types of hardware they buy and you know, where they're going to use it and, you know, what its purpose is. Because I've talked to customers that have tried to run software and commodity hardware and where the performance requirements are very high and it's bogged down, right? It just doesn't have the horsepower to run it. And, you know, even when you do that, you have to start thinking of the components you use. The NICs you buy. And I've talked to customers that have simply just gone through the process replacing a NIC card and a commodity box and had some performance problems and, you know, things like that. So if agility is more important than performance, then by all means try running software on commodity hardware. I think that works in some cases. If performance though is more important, that's when you need that kind of turnkey hardware system. And I've actually seen more and more customers reverting back to that model. In fact, when you talk to even some startups I think today about when they come to market, they're delivering things more on appliances because that's what customers want. And so there's this kind of app pivot this pendulum of agility and performance. And if performance absolutely matters, that's when you do need to buy these kind of turnkey, prebuilt hardware systems. If agility matters more, that's when you can go more to software, but the underlying hardware still does matter. So I think, you know, will we ever have a day where you can just run it on whatever hardware? Maybe but I'll long be retired by that point. So I don't care. >> Well, you bring up a good point Zeus. And I remember the early days of cloud, the narrative was, oh, the cloud vendors. They don't use EMC storage, they just run on commodity storage. And then of course, low and behold, you know, they've trot out James Hamilton to talk about all the custom hardware that they were building. And you saw Google and Microsoft follow suit. >> Well, (indistinct) been falling for this forever. Right? And I mean, all the way back to the turn of the century, we were calling for the commodity of hardware. And it's never really happened because you can still drive. As long as you can drive innovation into it, customers will always lean towards the innovation cycles 'cause they get more features faster and things. And so the vendors have done a good job of keeping that cycle up but it'll be a long time before. >> Yeah, and that's why you see companies like Pure Storage. A storage company has 69% gross margins. All right. I want to go jump ahead. We're going to bring up the slide four. I want to go back to something that Bob O'Donnell was talking about, the sort of supporting act. The diversity of silicon and we've marched to the cadence of Moore's law for decades. You know, we asked, you know, is Moore's law dead? We say it's moderating. Dave Nicholson. You want to talk about those supporting components. And you shared with us a slide that shift. You call it a shift from a processor-centric world to a connect-centric world. What do you mean by that? And let's bring up slide four and you can talk to that. >> Yeah, yeah. So first, I want to echo this sentiment that the question does hardware matter is sort of the answer is of course it matters. Maybe the real question should be, should you care about it? And the answer to that is it depends who you are. If you're an end user using an application on your mobile device, maybe you don't care how the architecture is put together. You just care that the service is delivered but as you back away from that and you get closer and closer to the source, someone needs to care about the hardware and it should matter. Why? Because essentially what hardware is doing is it's consuming electricity and dollars and the more efficiently you can configure hardware, the more bang you're going to get for your buck. So it's not only a quantitative question in terms of how much can you deliver? But it also ends up being a qualitative change as capabilities allow for things we couldn't do before, because we just didn't have the aggregate horsepower to do it. So this chart actually comes out of some performance tests that were done. So it happens to be Dell servers with Broadcom components. And the point here was to peel back, you know, peel off the top of the server and look at what's in that server, starting with, you know, the PCI interconnect. So PCIE gen three, gen four, moving forward. What are the effects on from an interconnect versus on performance application performance, translating into new orders per minute, processed per dollar, et cetera, et cetera? If you look at the advances in CPU architecture mapped against the advances in interconnect and storage subsystem performance, you can see that CPU architecture is sort of lagging behind in a way. And Bob mentioned this idea of tiling and all of the different ways to get around that. When we do performance testing, we can actually peg CPUs, just running the performance tests without any actual database environments working. So right now we're at this sort of imbalance point where you have to make sure you design things properly to get the most bang per kilowatt hour of power per dollar input. So the key thing here what this is highlighting is just as a very specific example, you take a card that's designed as a gen three PCIE device, and you plug it into a gen four slot. Now the card is the bottleneck. You plug a gen four card into a gen four slot. Now the gen four slot is the bottleneck. So we're constantly chasing these bottlenecks. Someone has to be focused on that from an architectural perspective, it's critically important. So there's no question that it matters. But of course, various people in this food chain won't care where it comes from. I guess a good analogy might be, where does our food come from? If I get a steak, it's a pink thing wrapped in plastic, right? Well, there are a lot of inputs that a lot of people have to care about to get that to me. Do I care about all of those things? No. Are they important? They're critically important. >> So, okay. So all I want to get to the, okay. So what does this all mean to customers? And so what I'm hearing from you is to balance a system it's becoming, you know, more complicated. And I kind of been waiting for this day for a long time, because as we all know the bottleneck was always the spinning disc, the last mechanical. So people who wrote software knew that when they were doing it right, the disc had to go and do stuff. And so they were doing other things in the software. And now with all these new interconnects and flash and things like you could do atomic rights. And so that opens up new software possibilities and combine that with alternative processes. But what's the so what on this to the customer and the application impact? Can anybody address that? >> Yeah, let me address that for a moment. I want to leverage some of the things that Bob said, Keith said, Zeus said, and David said, yeah. So I'm a bit of a contrarian in some of this. For example, on the chip side. As the chips get smaller, 14 nanometer, 10 nanometer, five nanometer, soon three nanometer, we talk about more cores, but the biggest problem on the chip is the interconnect from the chip 'cause the wires get smaller. People don't realize in 2004 the latency on those wires in the chips was 80 picoseconds. Today it's 1300 picoseconds. That's on the chip. This is why they're not getting faster. So we maybe getting a little bit slowing down in Moore's law. But even as we kind of conquer that you still have the interconnect problem and the interconnect problem goes beyond the chip. It goes within the system, composable architectures. It goes to the point where Keith made, ultimately you need a hybrid because what we're seeing, what I'm seeing and I'm talking to customers, the biggest issue they have is moving data. Whether it be in a chip, in a system, in a data center, between data centers, moving data is now the biggest gating item in performance. So if you want to move it from, let's say your transactional database to your machine learning, it's the bottleneck, it's moving the data. And so when you look at it from a distributed environment, now you've got to move the compute to the data. The only way to get around these bottlenecks today is to spend less time in trying to move the data and more time in taking the compute, the software, running on hardware closer to the data. Go ahead. >> So is this what you mean when Nicholson was talking about a shift from a processor centric world to a connectivity centric world? You're talking about moving the bits across all the different components, not having the processor you're saying is essentially becoming the bottleneck or the memory, I guess. >> Well, that's one of them and there's a lot of different bottlenecks, but it's the data movement itself. It's moving away from, wait, why do we need to move the data? Can we move the compute, the processing closer to the data? Because if we keep them separate and this has been a trend now where people are moving processing away from it. It's like the edge. I think it was Zeus or David. You were talking about the edge earlier. As you look at the edge, who defines the edge, right? Is the edge a closet or is it a sensor? If it's a sensor, how do you do AI at the edge? When you don't have enough power, you don't have enough computable. People were inventing chips to do that. To do all that at the edge, to do AI within the sensor, instead of moving the data to a data center or a cloud to do the processing. Because the lag in latency is always limited by speed of light. How fast can you move the electrons? And all this interconnecting, all the processing, and all the improvement we're seeing in the PCIE bus from three, to four, to five, to CXL, to a higher bandwidth on the network. And that's all great but none of that deals with the speed of light latency. And that's an-- Go ahead. >> You know Marc, no, I just want to just because what you're referring to could be looked at at a macro level, which I think is what you're describing. You can also look at it at a more micro level from a systems design perspective, right? I'm going to be the resident knuckle dragging hardware guy on the panel today. But it's exactly right. You moving compute closer to data includes concepts like peripheral cards that have built in intelligence, right? So again, in some of this testing that I'm referring to, we saw dramatic improvements when you basically took the horsepower instead of using the CPU horsepower for the like IO. Now you have essentially offload engines in the form of storage controllers, rate controllers, of course, for ethernet NICs, smart NICs. And so when you can have these sort of offload engines and we've gone through these waves over time. People think, well, wait a minute, raid controller and NVMe? You know, flash storage devices. Does that make sense? It turns out it does. Why? Because you're actually at a micro level doing exactly what you're referring to. You're bringing compute closer to the data. Now, closer to the data meaning closer to the data storage subsystem. It doesn't solve the macro issue that you're referring to but it is important. Again, going back to this idea of system design optimization, always chasing the bottleneck, plugging the holes. Someone needs to do that in this value chain in order to get the best value for every kilowatt hour of power and every dollar. >> Yeah. >> Well this whole drive performance has created some really interesting architectural designs, right? Like Nickelson, the rise of the DPU right? Brings more processing power into systems that already had a lot of processing power. There's also been some really interesting, you know, kind of innovation in the area of systems architecture too. If you look at the way Nvidia goes to market, their drive kit is a prebuilt piece of hardware, you know, optimized for self-driving cars, right? They partnered with Pure Storage and ARISTA to build that AI-ready infrastructure. I remember when I talked to Charlie Giancarlo, the CEO of Pure about when the three companies rolled that out. He said, "Look, if you're going to do AI, "you need good store. "You need fast storage, fast processor and fast network." And so for customers to be able to put that together themselves was very, very difficult. There's a lot of software that needs tuning as well. So the three companies partner together to create a fully integrated turnkey hardware system with a bunch of optimized software that runs on it. And so in that case, in some ways the hardware was leading the software innovation. And so, the variety of different architectures we have today around hardware has really exploded. And I think it, part of the what Bob brought up at the beginning about the different chip design. >> Yeah, Bob talked about that earlier. Bob, I mean, most AI today is modeling, you know, and a lot of that's done in the cloud and it looks from my standpoint anyway that the future is going to be a lot of AI inferencing at the edge. And that's a radically different architecture, Bob, isn't it? >> It is, it's a completely different architecture. And just to follow up on a couple points, excellent conversation guys. Dave talked about system architecture and really this that's what this boils down to, right? But it's looking at architecture at every level. I was talking about the individual different components the new interconnect methods. There's this new thing called UCIE universal connection. I forget what it stands answer for, but it's a mechanism for doing chiplet architectures, but then again, you have to take it up to the system level, 'cause it's all fine and good. If you have this SOC that's tuned and optimized, but it has to talk to the rest of the system. And that's where you see other issues. And you've seen things like CXL and other interconnect standards, you know, and nobody likes to talk about interconnect 'cause it's really wonky and really technical and not that sexy, but at the end of the day it's incredibly important exactly. To the other points that were being raised like mark raised, for example, about getting that compute closer to where the data is and that's where again, a diversity of chip architectures help and exactly to your last comment there Dave, putting that ability in an edge device is really at the cutting edge of what we're seeing on a semiconductor design and the ability to, for example, maybe it's an FPGA, maybe it's a dedicated AI chip. It's another kind of chip architecture that's being created to do that inferencing on the edge. Because again, it's that the cost and the challenges of moving lots of data, whether it be from say a smartphone to a cloud-based application or whether it be from a private network to a cloud or any other kinds of permutations we can think of really matters. And the other thing is we're tackling bigger problems. So architecturally, not even just architecturally within a system, but when we think about DPUs and the sort of the east west data center movement conversation that we hear Nvidia and others talk about, it's about combining multiple sets of these systems to function together more efficiently again with even bigger sets of data. So really is about tackling where the processing is needed, having the interconnect and the ability to get where the data you need to the right place at the right time. And because those needs are diversifying, we're just going to continue to see an explosion of different choices and options, which is going to make hardware even more essential I would argue than it is today. And so I think what we're going to see not only does hardware matter, it's going to matter even more in the future than it does now. >> Great, yeah. Great discussion, guys. I want to bring Keith back into the conversation here. Keith, if your main expertise in tech is provisioning LUNs, you probably you want to look for another job. So maybe clearly hardware matters, but with software defined everything, do people with hardware expertise matter outside of for instance, component manufacturers or cloud companies? I mean, VMware certainly changed the dynamic in servers. Dell just spun off its most profitable asset and VMware. So it obviously thinks hardware can stand alone. How does an enterprise architect view the shift to software defined hyperscale cloud and how do you see the shifting demand for skills in enterprise IT? >> So I love the question and I'll take a different view of it. If you're a data analyst and your primary value add is that you do ETL transformation, talk to a CDO, a chief data officer over midsize bank a little bit ago. He said 80% of his data scientists' time is done on ETL. Super not value ad. He wants his data scientists to do data science work. Chances are if your only value is that you do LUN provisioning, then you probably don't have a job now. The technologies have gotten much more intelligent. As infrastructure pros, we want to give infrastructure pros the opportunities to shine and I think the software defined nature and the automation that we're seeing vendors undertake, whether it's Dell, HP, Lenovo take your pick that Pure Storage, NetApp that are doing the automation and the ML needed so that these practitioners don't spend 80% of their time doing LUN provisioning and focusing on their true expertise, which is ensuring that data is stored. Data is retrievable, data's protected, et cetera. I think the shift is to focus on that part of the job that you're ensuring no matter where the data's at, because as my data is spread across the enterprise hybrid different types, you know, Dave, you talk about the super cloud a lot. If my data is in the super cloud, protecting that data and securing that data becomes much more complicated when than when it was me just procuring or provisioning LUNs. So when you say, where should the shift be, or look be, you know, focusing on the real value, which is making sure that customers can access data, can recover data, can get data at performance levels that they need within the price point. They need to get at those datasets and where they need it. We talked a lot about where they need out. One last point about this interconnecting. I have this vision and I think we all do of composable infrastructure. This idea that scaled out does not solve every problem. The cloud can give me infinite scale out. Sometimes I just need a single OS with 64 terabytes of RAM and 204 GPUs or GPU instances that single OS does not exist today. And the opportunity is to create composable infrastructure so that we solve a lot of these problems that just simply don't scale out. >> You know, wow. So many interesting points there. I had just interviewed Zhamak Dehghani, who's the founder of Data Mesh last week. And she made a really interesting point. She said, "Think about, we have separate stacks. "We have an application stack and we have "a data pipeline stack and the transaction systems, "the transaction database, we extract data from that," to your point, "We ETL it in, you know, it takes forever. "And then we have this separate sort of data stack." If we're going to inject more intelligence and data and AI into applications, those two stacks, her contention is they have to come together. And when you think about, you know, super cloud bringing compute to data, that was what Haduck was supposed to be. It ended up all sort of going into a central location, but it's almost a rhetorical question. I mean, it seems that that necessitates new thinking around hardware architectures as it kind of everything's the edge. And the other point is to your point, Keith, it's really hard to secure that. So when you can think about offloads, right, you've heard the stats, you know, Nvidia talks about it. Broadcom talks about it that, you know, that 30%, 25 to 30% of the CPU cycles are wasted on doing things like storage offloads, or networking or security. It seems like maybe Zeus you have a comment on this. It seems like new architectures need to come other to support, you know, all of that stuff that Keith and I just dispute. >> Yeah, and by the way, I do want to Keith, the question you just asked. Keith, it's the point I made at the beginning too about engineers do need to be more software-centric, right? They do need to have better software skills. In fact, I remember talking to Cisco about this last year when they surveyed their engineer base, only about a third of 'em had ever made an API call, which you know that that kind of shows this big skillset change, you know, that has to come. But on the point of architectures, I think the big change here is edge because it brings in distributed compute models. Historically, when you think about compute, even with multi-cloud, we never really had multi-cloud. We'd use multiple centralized clouds, but compute was always centralized, right? It was in a branch office, in a data center, in a cloud. With edge what we creates is the rise of distributed computing where we'll have an application that actually accesses different resources and at different edge locations. And I think Marc, you were talking about this, like the edge could be in your IoT device. It could be your campus edge. It could be cellular edge, it could be your car, right? And so we need to start thinkin' about how our applications interact with all those different parts of that edge ecosystem, you know, to create a single experience. The consumer apps, a lot of consumer apps largely works that way. If you think of like app like Uber, right? It pulls in information from all kinds of different edge application, edge services. And, you know, it creates pretty cool experience. We're just starting to get to that point in the business world now. There's a lot of security implications and things like that, but I do think it drives more architectural decisions to be made about how I deploy what data where and where I do my processing, where I do my AI and things like that. It actually makes the world more complicated. In some ways we can do so much more with it, but I think it does drive us more towards turnkey systems, at least initially in order to, you know, ensure performance and security. >> Right. Marc, I wanted to go to you. You had indicated to me that you wanted to chat about this a little bit. You've written quite a bit about the integration of hardware and software. You know, we've watched Oracle's move from, you know, buying Sun and then basically using that in a highly differentiated approach. Engineered systems. What's your take on all that? I know you also have some thoughts on the shift from CapEx to OPEX chime in on that. >> Sure. When you look at it, there are advantages to having one vendor who has the software and hardware. They can synergistically make them work together that you can't do in a commodity basis. If you own the software and somebody else has the hardware, I'll give you an example would be Oracle. As you talked about with their exit data platform, they literally are leveraging microcode in the Intel chips. And now in AMD chips and all the way down to Optane, they make basically AMD database servers work with Optane memory PMM in their storage systems, not MVME, SSD PMM. I'm talking about the cards itself. So there are advantages you can take advantage of if you own the stack, as you were putting out earlier, Dave, of both the software and the hardware. Okay, that's great. But on the other side of that, that tends to give you better performance, but it tends to cost a little more. On the commodity side it costs less but you get less performance. What Zeus had said earlier, it depends where you're running your application. How much performance do you need? What kind of performance do you need? One of the things about moving to the edge and I'll get to the OPEX CapEx in a second. One of the issues about moving to the edge is what kind of processing do you need? If you're running in a CCTV camera on top of a traffic light, how much power do you have? How much cooling do you have that you can run this? And more importantly, do you have to take the data you're getting and move it somewhere else and get processed and the information is sent back? I mean, there are companies out there like Brain Chip that have developed AI chips that can run on the sensor without a CPU. Without any additional memory. So, I mean, there's innovation going on to deal with this question of data movement. There's companies out there like Tachyon that are combining GPUs, CPUs, and DPUs in a single chip. Think of it as super composable architecture. They're looking at being able to do more in less. On the OPEX and CapEx issue. >> Hold that thought, hold that thought on the OPEX CapEx, 'cause we're running out of time and maybe you can wrap on that. I just wanted to pick up on something you said about the integrated hardware software. I mean, other than the fact that, you know, Michael Dell unlocked whatever $40 billion for himself and Silverlake, I was always a fan of a spin in with VMware basically become the Oracle of hardware. Now I know it would've been a nightmare for the ecosystem and culturally, they probably would've had a VMware brain drain, but what does anybody have any thoughts on that as a sort of a thought exercise? I was always a fan of that on paper. >> I got to eat a little crow. I did not like the Dale VMware acquisition for the industry in general. And I think it hurt the industry in general, HPE, Cisco walked away a little bit from that VMware relationship. But when I talked to customers, they loved it. You know, I got to be honest. They absolutely loved the integration. The VxRail, VxRack solution exploded. Nutanix became kind of a afterthought when it came to competing. So that spin in, when we talk about the ability to innovate and the ability to create solutions that you just simply can't create because you don't have the full stack. Dell was well positioned to do that with a potential span in of VMware. >> Yeah, we're going to be-- Go ahead please. >> Yeah, in fact, I think you're right, Keith, it was terrible for the industry. Great for Dell. And I remember talking to Chad Sakac when he was running, you know, VCE, which became Rack and Rail, their ability to stay in lockstep with what VMware was doing. What was the number one workload running on hyperconverged forever? It was VMware. So their ability to remain in lockstep with VMware gave them a huge competitive advantage. And Dell came out of nowhere in, you know, the hyper-converged market and just started taking share because of that relationship. So, you know, this sort I guess it's, you know, from a Dell perspective I thought it gave them a pretty big advantage that they didn't really exploit across their other properties, right? Networking and service and things like they could have given the dominance that VMware had. From an industry perspective though, I do think it's better to have them be coupled. So. >> I agree. I mean, they could. I think they could have dominated in super cloud and maybe they would become the next Oracle where everybody hates 'em, but they kick ass. But guys. We got to wrap up here. And so what I'm going to ask you is I'm going to go and reverse the order this time, you know, big takeaways from this conversation today, which guys by the way, I can't thank you enough phenomenal insights, but big takeaways, any final thoughts, any research that you're working on that you want highlight or you know, what you look for in the future? Try to keep it brief. We'll go in reverse order. Maybe Marc, you could start us off please. >> Sure, on the research front, I'm working on a total cost of ownership of an integrated database analytics machine learning versus separate services. On the other aspect that I would wanted to chat about real quickly, OPEX versus CapEx, the cloud changed the market perception of hardware in the sense that you can use hardware or buy hardware like you do software. As you use it, pay for what you use in arrears. The good thing about that is you're only paying for what you use, period. You're not for what you don't use. I mean, it's compute time, everything else. The bad side about that is you have no predictability in your bill. It's elastic, but every user I've talked to says every month it's different. And from a budgeting perspective, it's very hard to set up your budget year to year and it's causing a lot of nightmares. So it's just something to be aware of. From a CapEx perspective, you have no more CapEx if you're using that kind of base system but you lose a certain amount of control as well. So ultimately that's some of the issues. But my biggest point, my biggest takeaway from this is the biggest issue right now that everybody I talk to in some shape or form it comes down to data movement whether it be ETLs that you talked about Keith or other aspects moving it between hybrid locations, moving it within a system, moving it within a chip. All those are key issues. >> Great, thank you. Okay, CTO advisor, give us your final thoughts. >> All right. Really, really great commentary. Again, I'm going to point back to us taking the walk that our customers are taking, which is trying to do this conversion of all primary data center to a hybrid of which I have this hard earned philosophy that enterprise IT is additive. When we add a service, we rarely subtract a service. So the landscape and service area what we support has to grow. So our research focuses on taking that walk. We are taking a monolithic application, decomposing that to containers, and putting that in a public cloud, and connecting that back private data center and telling that story and walking that walk with our customers. This has been a super enlightening panel. >> Yeah, thank you. Real, real different world coming. David Nicholson, please. >> You know, it really hearkens back to the beginning of the conversation. You talked about momentum in the direction of cloud. I'm sort of spending my time under the hood, getting grease under my fingernails, focusing on where still the lions share of spend will be in coming years, which is OnPrem. And then of course, obviously data center infrastructure for cloud but really diving under the covers and helping folks understand the ramifications of movement between generations of CPU architecture. I know we all know Sapphire Rapids pushed into the future. When's the next Intel release coming? Who knows? We think, you know, in 2023. There have been a lot of people standing by from a practitioner's standpoint asking, well, what do I do between now and then? Does it make sense to upgrade bits and pieces of hardware or go from a last generation to a current generation when we know the next generation is coming? And so I've been very, very focused on looking at how these connectivity components like rate controllers and NICs. I know it's not as sexy as talking about cloud but just how these opponents completely change the game and actually can justify movement from say a 14th-generation architecture to a 15th-generation architecture today, even though gen 16 is coming, let's say 12 months from now. So that's where I am. Keep my phone number in the Rolodex. I literally reference Rolodex intentionally because like I said, I'm in there under the hood and it's not as sexy. But yeah, so that's what I'm focused on Dave. >> Well, you know, to paraphrase it, maybe derivative paraphrase of, you know, Larry Ellison's rant on what is cloud? It's operating systems and databases, et cetera. Rate controllers and NICs live inside of clouds. All right. You know, one of the reasons I love working with you guys is 'cause have such a wide observation space and Zeus Kerravala you, of all people, you know you have your fingers in a lot of pies. So give us your final thoughts. >> Yeah, I'm not a propeller heady as my chip counterparts here. (all laugh) So, you know, I look at the world a little differently and a lot of my research I'm doing now is the impact that distributed computing has on customer employee experiences, right? You talk to every business and how the experiences they deliver to their customers is really differentiating how they go to market. And so they're looking at these different ways of feeding up data and analytics and things like that in different places. And I think this is going to have a really profound impact on enterprise IT architecture. We're putting more data, more compute in more places all the way down to like little micro edges and retailers and things like that. And so we need the variety. Historically, if you think back to when I was in IT you know, pre-Y2K, we didn't have a lot of choice in things, right? We had a server that was rack mount or standup, right? And there wasn't a whole lot of, you know, differences in choice. But today we can deploy, you know, these really high-performance compute systems on little blades inside servers or inside, you know, autonomous vehicles and things. I think the world from here gets... You know, just the choice of what we have and the way hardware and software works together is really going to, I think, change the world the way we do things. We're already seeing that, like I said, in the consumer world, right? There's so many things you can do from, you know, smart home perspective, you know, natural language processing, stuff like that. And it's starting to hit businesses now. So just wait and watch the next five years. >> Yeah, totally. The computing power at the edge is just going to be mind blowing. >> It's unbelievable what you can do at the edge. >> Yeah, yeah. Hey Z, I just want to say that we know you're not a propeller head and I for one would like to thank you for having your master's thesis hanging on the wall behind you 'cause we know that you studied basket weaving. >> I was actually a physics math major, so. >> Good man. Another math major. All right, Bob O'Donnell, you're going to bring us home. I mean, we've seen the importance of semiconductors and silicon in our everyday lives, but your last thoughts please. >> Sure and just to clarify, by the way I was a great books major and this was actually for my final paper. And so I was like philosophy and all that kind of stuff and literature but I still somehow got into tech. Look, it's been a great conversation and I want to pick up a little bit on a comment Zeus made, which is this it's the combination of the hardware and the software and coming together and the manner with which that needs to happen, I think is critically important. And the other thing is because of the diversity of the chip architectures and all those different pieces and elements, it's going to be how software tools evolve to adapt to that new world. So I look at things like what Intel's trying to do with oneAPI. You know, what Nvidia has done with CUDA. What other platform companies are trying to create tools that allow them to leverage the hardware, but also embrace the variety of hardware that is there. And so as those software development environments and software development tools evolve to take advantage of these new capabilities, that's going to open up a lot of interesting opportunities that can leverage all these new chip architectures. That can leverage all these new interconnects. That can leverage all these new system architectures and figure out ways to make that all happen, I think is going to be critically important. And then finally, I'll mention the research I'm actually currently working on is on private 5g and how companies are thinking about deploying private 5g and the potential for edge applications for that. So I'm doing a survey of several hundred us companies as we speak and really looking forward to getting that done in the next couple of weeks. >> Yeah, look forward to that. Guys, again, thank you so much. Outstanding conversation. Anybody going to be at Dell tech world in a couple of weeks? Bob's going to be there. Dave Nicholson. Well drinks on me and guys I really can't thank you enough for the insights and your participation today. Really appreciate it. Okay, and thank you for watching this special power panel episode of theCube Insights powered by ETR. Remember we publish each week on Siliconangle.com and wikibon.com. All these episodes they're available as podcasts. DM me or any of these guys. I'm at DVellante. You can email me at David.Vellante@siliconangle.com. Check out etr.ai for all the data. This is Dave Vellante. We'll see you next time. (upbeat music)

(upbeat music) >> Welcome to the CUBE Conversation. I'm Lisa Martin. I'm talking next with Sanzio Bassini, the Head of High Performance Computing at Cineca, at DELL technologies customer. Sanzio, welcome to the CUBE. >> Thank you, it's a pleasure, it's a pleasure. >> Likewise, nice to see you. So tell us a little bit about Cineca. This is a large computing center, but a very large Italian nonprofit consortium. Tell us about it. >> Yes, Cineca been founded 50 years ago, from the university systems in Italy. For a statutory mission, which is to support, the scientific discovery, and the industry innovations, using the High Performance Computing and the correlated methodologies like a, Artificial Intelligence, which is one of the, you see the more, in a, in a adopted in those days, but together with the big data processing and and simulation. Yes, we are a consortium, which means that this is a private not-for-profit organizations. Currently, our member of the consortium, almost all the universities in Italy and also all the national agencies for those selected structures. Uh. The main quarter of Cineca is in Bologna, which is in the heart Nation, with the bunch of presence in Milan, in Rome and in Naples, so we are a consultation organization. >> And I also read that you were, are the top 10 out of the top 500 of the world's fastest super computers. That's a pretty big accomplishment. >> Yes. That is a part of our institutional missions, the last 10 to 15 years we have been to say, frequent flyers in the top 10. There been at least two, three systems that have been ranked at the top 10. Apart, the.., whatever would be the meaning of such an advance market, there's a lot of its criticalities. We are well aware. The idea is that we're enabling the scientific discovery, by means of providing the most advanced systems and the co-designing, the most advanced HPC systems to promote and support the, what is the, excellence in science. And that being part of European high-performance computing IT system. That is the case. >> Excellent. Now, talk to me about some of the challenges that Cineca is trying to solve in particular, the Human Brain Project. Talk to us a little bit about that and how you're leveraging high-performance computing to accelerate scientific discovery. >> Um, The Human Brain Project is one of the flagship project that has been co-founded by the European commission and that the participating member states. Is not as another situations that are undertaking, it's definitely a joint collaboration between members states and the European commission. There are two different right now, flagships together with another, that is in progress, which is that the quantum of flagship, the first two flagship abroad that that has been lost. The commission for operation with the participating states has been one on the digraph vein on which also we are participating in directly together with the CNR, is the national business counselor. And the second for which we are core partners of the HPC that is, the Human Brain Project. That, that is a big flagship, one million offer, of newer investment, co-founded by the participating states and that the European commission. The project it's going to set up, in what to do be the, third strategic grant agreement that they will go over the next three years, the good, the complete that the, the whole process. Then we see what is going to happen at Africa. We thought that their would be some others progress offer these big projects. It's project that would combine both the technology issues, like the designing the off high-performance computing systems that meet the requirements of the community and the big challenge, scientific challenges correlated to the physiological functions of the human brain center, including the different farm show survey to do with the behavior of the human brain. A from the pathological point of view, from the physiological point of view, that better understand the could be the way for, for a facing that. Let's say the pathology, some of those are very much correlated with respect to aging, and that it would impact the, the health, the public health systems. Some other that are correlating with what would be the support for the physiological knowledge of the, of the human brains. And finally that they, let me say, technological transfer stuff that represented the knowing off at the physiological, behavior of the human brain. Just to use a sort of metaphor to have happen from the point of view of there computational performance, the human brain is a, a, a, more than Exoscale systems, but with a energy consumption, which is very low, we are talking about some hundreds of Watts. So some hundreds of watts of energy, would provide a an extreme and computational performance. So if would could organized the technology of the high-performance computing in terms of interconnections now we're morphing the computing systems and exploitations of that kind of technologies, in I build a system that it might provide the computational power that would represent a tremendous and tremendous step ahead, in order to facing the big challenges of our base, like energies, personalized medicine, try not to change food for all those kinds of big socioeconomic challenges that we are facing. >> Yes I was reading that besides, sorry Sanzio I was reading that besides the Human Brain Project, there are other projects going on, such as that you mentioned, I'd like to understand how Cineca is working with Dell technologies. You have to translate, as you've mentioned a minute ago, the scientific requirements for discovery into high-performance computing requirements. Talk to me about how you've been doing that with partners like Dell technologies. >> Yes, in particularly in our computing architectures, we had the need to address the capability to facing the data processing involved with backed off the Human Brain Project and general speaking that is backed off the science vendor, that would combine the capability also to provide the cloud access to the system. So by main soft containers technologies and the capability also, to address what would be the creation of a Federation. So Piper problems with people proceeded in a new world. So at the end that the requirements and the terms of reference of the would matter will decline and the terms of a system that would be capable to manage, let's say, in a holistic approach, the data processing, the cloud computing services and the opportunity before for being integrated that in a Federation of HSBC system in Europe's, and with this backed off, that kind of thing, we manage a competitive dialogue procurement processor, I think I the sentence would share together with the different potential technology providers, what would be the visuals and those are the constraints (inaudible) and those other kinds of constraints like, I don't want to say, I mean, environmental kind of constraints and uh, sharing with this back of the technology provider what would it be the vision for this solution, in a very, let's say hard, the competitive dialogue, and at the end, results in a sort of, I don't want to say Darwinian processes, okay. So I mean, the survivors in terms of the different technology providers being Dell that shown the characteristics of the solution that it will be more, let's say compliant. And at the same time are flexible with respect of the combinations of very different constraints and requirements that has been the, the process that has been the outcomes of such a process. >> I like that you mentioned that Darwinian survival of the fittest and that Dell technologies has been, it sounds like a pretty flexible partner because you've got so many different needs and scientific needs to meet for different researchers. Talk to me about how you mentioned that this is a multi-national effort. How does Cineca serve and work with teams not only in Italy, but in other countries and from other institutes? >> Definitely the volume commitment that together with the, European member states is that by means of scientific merits and the peer review process, roughly speaking the arc of the production capacity, would be shared at the European level. That it's a commitment that, that there's been, that there's been a shared of that together with France, Germany, Spain, and, and with the London. So, I mean, our, half of our production capacity, it's a share of that at the European level, where also of course the Italian scientist can apply in the participates, but in a sort of offer emulations and the advanced competition for addressing what it would be the excellence in science. The remaining 50% of our production capacity is for, for the national community and, somehow to prepare and support the Italian community to be competitive on the worldwide scenario on the European and international scenario, uh that setting up would lead also to the agreement at the international level, with respect of some of the options that, that are promoted the progress in a US and in Japan also. So from this point of view, that mean that in some cases also the, access that it would come from researchers the best collaborations and the sharing options with the US researchers their or Japanese researchers in an open space. >> Open space for, it sounds like the Human Brain Project, which the HPC is powering, which has been around since 2013 is really facilitating global collaboration. Talk to me about some of the results that the high-performance computing environment has helped the Human Brain Project to achieve so far. >> The main outcomes that it will be consolidated in the next phase that will be need the by rural SPC that is the Jared undertaking um entities, that has been created for consolidating and for progressing the high-performance computing ecosystem in Europe. It represented by the Federations of high-performance computing systems at European level, there is a, a, an option that, that has been encapsulated and the elaborated inside the human brain flagship project which is called the FEHIPCSE that stand for Federation of a High-Performance Computing System in Europe. That uh provide the open service based on the two concepts on one, one is the sharing of the Heidi at a European level, so it means that the, the high demand of the users or researchers more properly. It's unique and Universal at the European level. That didn't mean better the same, we see identity management, education management with the open, and the access to the Cineca system, to the SARS system in France, to the unique system in, uh Germany to the, Diocese system in a Switzerland, to the Morocco System in a Spain. That is the part related to what will be the federated, the ID management, the others, et cetera, related to what will be the Federation off the data access. So from the point of view, again, the scientific community, mostly the community of Human Brain Project, but that will be open at other domains and other community, make sure that data in a seamless mode after European language, from the technological point of view, or let's say from the infrastructure point of view, very strong up, from the scientific point of view, uh what they think they may not, will be the most of the options is being supported by Cineca has to do with the two specific target. One is the elaboration of the data that are provided by the lands. The laws are a laboratory facility in that Florence. That is one of the four parts, and from the bottom view of the provisions of the data that is for the scattering, the, the data that would come from the mouse brains, that are use for, for (inaudible) And then the second part is for the Mayor scale studies of the cortex of the of the human brain, and that got add-on by a couple of groups that are believing that action from a European level their group of the National Researcher Counsel the CNR, that are the two main outcome on which we are in some out reference high-performance computing facilities for supporting that kind of research. Then their is in some situations they combinations of the performance a, capability of the Federation systems for addressing what will be the simulations of the overall human brain would take a lot of performance challenge simulation with bacteria that they would happen combining that they SPC facility as at European level. >> Right! So I was reading there's a case study by the way, on Cynic that Dell technologies has published. And some of the results you talked about, those that the HPC is facilitating research and results on epilepsy, spinal cord injury, brain prostheses for the blind, as well as new insights into autism. So incredibly important work that you're doing here for the Human Brain Project. One last question Sanzio, for you, what advice would you give to your peers who might be in similar situations that need to, to build and deploy and maintain high-performance computing environments? Where should they start? >> (coughs laughs) I think that at, at a certain point, that specific know how would became sort of a know how that is been, I mean, accumulated and then by some facilities and institutions around the world. There are the, the federal labs in US, the main nation model centers in Europe, the big facilities in Japan. And of course the, the big university facilities in China that are becoming, how do you say, evident and our progressively occupied increasing the space, that to say that that is somehow it, that, that, that the, those institutions would continues collaborate and sharing that there are periods I would expect off what to do, be the top level systems. Then there is a continuous sharing of uh knowledge, the experience best practices with respect off, let's say the technologies transfers towards productions and services and boosterism. Where the situation is big parenta, in the sense that, their are focused what it would be, uh the integration of the high-performance computing technology into their production workflow. And from the point of view, there is the sharing of the experience in order to provide the, a sort of, let's say, spreads and amplifications of the opportunity for supporting innovation. That is part of are solution means, in a Italy but it also, eh, er sort of um, see objective, that is addressed by the European options er supported by the European commission. I think that that sort of (inaudible) supply that in US, the, that will be coming there, sort of you see the max practice for the technology transfer to support the innovation. >> Excellent, that sharing and that knowledge transfer and collaboration. It seems to be absolutely fundamental and the environment that you've built, facilitates that. Sanzio thank you so much for sharing with us, what Cineca is doing and the great research that's going on there, and across a lot of disciplines, we appreciate you joining the program today. Thank you. >> Thank you, it's been a pleasure, thank you very much for the opportunity. >> Likewise, for Sanzio Bassini. I'm Lisa Martin. You're watching this cube conversation. (calming music)

(upbeat music) >> Announcer: From around the globe. It's theCUBE, with digital coverage of AWS reinvent 2020, sponsored by Intel, AWS and our community partners. >> Welcome to theCUBE's coverage of AWS reinvent 2020, the virtual edition. I'm Lisa Martin. I have a couple of guests joining me next to talk about AWS and Druva. From Druva, Chris White is here, the chief revenue officer. Hey Chris, nice to have you on the program. >> Excellent, thanks Lisa. Excited to be here. >> And from AWS Sabina Joseph joins us. She is the general manager of the Americas technology partners. Sabina, welcome. >> Thank you, Lisa. >> So looking forward to talking to you guys unfortunately, we can't be together in a very loud space in Las Vegas, so this will have to do but I'm excited to be able to talk to you guys today. So Chris, we're going to start with you, Druva and AWS have a longstanding partnership. Talk to us about that and some of the evolution that's going on there. >> Absolutely, yeah. we certainly have, we had a great long-term partnership. I'm excited to talk to everybody about it today and be here with Sabina and you Lisa as well. So, we actually re architect our entire environment on AWS, 100% on AWS back in 2013. That enables us to not only innovate back in 2013, but continue to innovate today and in the future, right. It gives us flexibility on a 100% platform to bring that to our customers, to our partners, and to the market out there, right? In doing so, we're delivering on data protection, disaster recovery, e-discovery, and ransomware protection, right? All of that's being leveraged on the AWS platform as I said, and that allows uniqueness from a standpoint of resiliency, protection, flexibility, and really future-proofing the environment, not only today, but in the future. And over this time AWS has been an outstanding partner for Druva. >> Excellent Chris, thank you. Sabina, you lead the America's technology partners as we mentioned, Druva is an AWS advanced technology partner. Talk to us from through AWS lens on the Druva AWS partnership and from your perspective as well. >> Sure, Lisa. So I've had the privilege of working with Druva since 2014 and it has been an amazing journey over the last six and a half years. You know, overall, when we work with partners on technical solutions, we have to talk in a better architect, their solution for AWS, but also take their feedback on our features and capabilities that our mutual customers want to see. So for example, Druva has actually provided feedback to AWS on performance, usability, enhancements, security, posture and suggestions on additional features and functionality that we could have on AWS snowball edge, AWS dynamoDB and other services in fact. And in the same way, we provide feedback to Druva, we provide recommendations and it really is a unique process of exposing our partners to AWS best practices. When customers use Druva, they are benefiting from the AWS recommended best practices for data durability, security and compliance. And our engineering teams work very closely together. We collaborate, we have regular meetings, and that really sets the foundation for a very strong solution for our mutual customers. >> So it sounds very symbiotic. And as you talked about that engineering collaboration and the collaboration across all levels. So now let's talk about some of the things that you're helping customers to do as we are all navigating a very different environment this year. Chris, talk to us about how Druva is helping customers navigate some of those big challenges you talked about ransomware for example, this massive pivot to remote workforce. Chris (mumbles) got going on there. >> Yeah, absolutely. So the, one of the things that we've seen consistently, right, it's been customers are looking for simplicity. Customers are looking for cost-effective solutions, and then you couple that with the ability to do that all on a single platform, that's what the combination of Druva and AWS does together, right? And as you mentioned, Lisa, you've got work from home. That's increased right with the unfortunate events going across the globe over the last almost 12 months now, nine months now. Increased ransomware that threats, right? The bad actors tend to take advantage of these situations unfortunately, and you've got to be working with partners like AWS like Druva, coming together, to build that barrier against the bad actors out there. So, right. We've got double layer of protection based on the partnership with AWS. And then if you look at the rising concerns around governance, right? The complexity of government, if you look at Japan adding some increased complexity to governance, you look at what's going on across, but across the globe across the pond with GDPR, number of different areas around compliance and governance that allows us to better report upon that. We built the right solution to support the migration of these customers. And everything I just talked about is just accelerated the need for folks to migrate to the cloud, migrate to AWS, migrate to leveraging, through the solutions. And there's no better time to partner with Druva and AWS, just because of that. >> Something we're all talking about. And every key segment we're doing, this acceleration of digital transformation and customers really having to make quick decisions and pivot their businesses over and over again to get from survival to thriving mode. Sabina talk to us about how Druva and AWS align on key customer use cases especially in these turbulent times. >> Yeah, so, for us as you said Lisa, right. When we start working with partners, we really focus on making sure that we are aligned on those customer use cases. And from the very first discussions, we want to ensure that feedback mechanisms are in place to help us understand and improve the services and the solutions. Chris has, he mentioned migrations, right? And we have customers who are migrating their applications to AWS and really want to move the data into the cloud. And you know what? This is not a simple problem because there's large amounts of data. And the customer has limited bandwidth Druva of course as they have always been, is an early adopter of AWS snowball edge and has worked closely with us to provide a solution where customers can just order a snowball edge directly from AWS. It gets shipped to them, they turn it on, they connect it to the network, and just start backing up their data to the snowball edge. And then once they are done, they can just pack it up, ship it back. And then all of this data gets loaded into the Druva solution on AWS. And then you also, those customers who are running applications locally on AWS Outposts, Druva was once again, an early adopter. In fact, last reinvent, they actually tested out AWS Outposts and they were one of the first launch partners. Once again, further expanding the data protection options they provide to our mutual customers. >> Well, as that landscape changes so dramatically it's imperative that customers have data center workloads, AWS workloads, cloud workloads, endpoints, protected especially as people scattered, right, in the last few months. And also, as we talked about the ransomware rise, Chris, I saw on Druva's website, one ransomware attack every 11 seconds. And so, now you've got to be able to help customers recover and have that resiliency, right. Cause it's not about, are we going to get hit? It's a matter of when, how does Druva help facilitate that resiliency? >> Yeah, now that's a great point Lisa. and as you look at our joint customer base, we've got thousands of joint customers together and we continue to see positive business impact because of that. And it's to your point, it's not if it's when you get hit and it's ultimately you've got to be prepared to recover in order to do that. And based on the security levels that we jointly have, based on our architecture and also the benefits of the architecture within AWS, we've got a double layer of defense up there that most companies just can't offer today. So, if we look at that from an example standpoint, right, transitioning offer specific use case of ransomware but really look at a cast media companies, right? One of the largest media companies out there across the globe, 400 radio stations, 800 TV stations, over a hundred thousand podcasts, over 4,000 or 5,000 streams happening on an annual basis, very active and candidly very public, which freaks the target. They really came to us for three key things, right? And they looked for reduced complexity, really reducing their workload internally from a backup and recovery standpoint, really to simplify that backup environment. And they started with Druva, really focused on the end points. How do we protect and manage the end points from a data protection standpoint, ultimately, the cost savings that they saw, the efficiency they saw, they ended up moving on and doing key workloads, right? So data protection, data center workloads that they were backing up and protecting. This all came from a great partnership and relationship from AWS as well. And as we continued to simplify that environment, it allowed them to expand their partnership with AWS. So not only was it a win for the customer, we helped solve those business problems for them. Ultimately, they got a (mumbles) benefit from both Druva and AWS and that partnership. So, we continue to see that partnership accelerate and evolve to go really look at the entire platform and where we can help them, in addition to AWS services that they're offering. >> And that was... It sounds like them going to cloud data production, was that an acceleration of their cloud strategy that they then had to accelerate even further during the last nine months, Chris? >> Yeah, well, the good news for cast is that at least from a backup and recovery standpoint, they've been ahead of the curve, right? They were one of those customers that was proactive, in driving on their cloud journey, and proactive and driving beyond the work from home. It did change the dynamics on how they work and how they act from a work from home standpoint, but they were already set up. So then they didn't really skip a beat as they continue to drive that. But overall, to your point, Lisa, we've seen an increase and acceleration and companies really moving towards the cloud, right. Which is why that migration strategy, joint migration strategy, that Sabina talked about is so important because it really has accelerated. And in some companies, this has become the safety net for them, in some ways their DR Strategy, to shift to the cloud, that maybe they weren't looking to do until maybe 2022 or 2023, it's all been accelerated. >> Everything's, but we have like whiplash on the acceleration going on. >> Sabina, talk to us about some of those joint successes through AWS's lens, a couple of customers, you're going to talk about the University of Manchester, and the Queensland Brain Institute, dig into those for us. >> Yeah, absolutely. So, I thank Chris sharing those stories there. So the two that kind of come into my mind is a University of Manchester. They have nearly 7,000 academic staff and researchers and they're, part of their digital transformation strategy was adopting VMware cloud on AWS. And the University actually chose Druva, to back up 160 plus virtual machine images, because Druva provided a simple and secure cloud-based backup solution. And in fact, saved them 50% of their data protection costs. Another one is Queensland Brain Institute, which has over 400 researchers who really worked on brain diseases and really finding therapeutic solutions for these brain diseases. As you can imagine, this research generates terabytes critical data that they not only needed protected, but they also wanted to collaborate and get access to this data continuously. They chose Druva and now using Druva solution, they can back up over 1200 plus research papers, residing on their devices, providing global and also reliable access 24 by seven. And I do want to mention, Lisa, right? The pandemic has changed all of humanity as we know it, right? Until we can all find a solution to this. And we've also together had to work to adjust what can we do to work effectively together? We've actually together with Druva shifted all of our day-to-day activities, 200% virtual. And we, but despite all of that, we've maintained regular cadence for our review business and technical roadmap updates and other regular activities. And if I may mention this, right, last month we AWS actually launched the digital workplace competency, clearly enabling customers to find specialized solutions around remote work and secure remote work and Druva, even though we are all in this virtual environment today, Druva was one of the launch partners for this competency. And it was a great fit given the solution that they have to enable the remote work environments securely, and also providing an end-to-end digital workplace in the cloud. >> That's absolutely critical because that's been one of the biggest challenges I think that we've all been through as well as, you know trying to go, do I live at work or do I work from home? I'm not sure some of the days, but being able to have that continuity and you know, your customers being able to access their data at 24 by seven, as you said, because there's no point in mapping up your data, if you can't recover it but being able to allow the continuation of the relationship that you have. I want to move on now to some of the announcements. Chris, you mentioned actually Sabina you did, when you were talking about the University of Manchester, the VMware ready certification Chris, Druva just announced a couple of things there. Talk to us about that. >> Thank you. Yeah, Lisa you're right. There's been a ton of great announcements over the past several months and throughout this entire fiscal year. To be in this touch base on a couple of them around the AWS digital workplace, we absolutely have certification on AWS around VMware cloud, both on AWS and Dell EMC, through AWS. In addition to continuing to drive innovation because of this unique partnership around powerful security encryption and overall security benefits across the board. So that includes AWS gov cloud. That includes HIPAA compliance, includes FedRAMP, as well as SOC two type two, certifications as well and protection there. So we're going to continue to drive that innovation. We just recently announced as well that we now have data protection for Kubernetes, 100% cloud offering, right? One of the most active and growing workloads around data, around orchestration platform, right? So, doing that with AWS, some of my opening comments back when we built this 100% AWS, that allows us to continue to innovate and be nimble and meet the needs of customers. So whether that be VMware workloads NAS workloads, new workloads, like Kubernetes we're always going to be well positioned to address those, not only over time, but on the front end. And as these emerging technologies come out the nimbleness of our joint partnership just continues to be demonstrated there. >> And Sabina, I know that AWS has a working backwards approach. Talk to me about how you use that to accomplish all of the things that Chris and you both described over the last six, seven plus years. >> Yes, so the working backwards process we use it internally when we build our own services, but we also worked through it with our partners, right? It's about putting the customers first, aligning on those use cases. And it all goes back to our Amazon leadership principle on customer obsession, focusing on the customer experience, making sure that we have mechanisms in place, to have feedback from the customers and operate that into our services solutions and also with our partners. Well, one of the nice things about Druva since I've been working with them since 2014 is their focus on customer obsession. Through this process, we've developed great relationship, Druva, together with our service team, building solutions that deliver value by providing a full Saas service for customers, who want to protect their data, not only in AWS, but also in a hybrid architecture model on premises. And this is really critical to us cause our customers want us to work with Druva, to solve the pain points, creating a completely maybe a new customer experience, right. That makes them happy. And ultimately what we have found together with Druva, is I think Chris would agree with this, is that when we focus on our mutual customers, it leads to a very longterm successful partnership as we have today with Druva. >> It sounds like you talked about that feedback loop in the beginning from customers, but it sounds like that's really intertwined the entire relationship. And certainly from what you guys described in terms of the evolution, the customer successes, and all of the things that have been announced recently, a lot of stuff going on. So we'll let you guys get back to work. We appreciate your time, Chris. Thank you for joining me today. For Chris white and Sabina Joseph, I'm Lisa Martin and you're watching theCUBE. (soft music fades)

>>from around the globe. It's the Cube presenting accelerating automation with definite brought to you by Cisco. >>Hello and welcome to the Cube. I'm John for a year host. We've got a great conversation virtual event, accelerating automation with definite Cisco. Definite. And of course, we got the Cisco Brain Trust here. Cube alumni Suzy we Vice President, senior Vice President GM and also CTO of Cisco. Definite and ecosystem Success C X, All that great stuff. Many Wadley Who's the director? Senior director of definite certifications. Eric Field, director of developer advocacy. Susie Mandy. Eric, Great to see you. Thanks for coming on. >>Great to see you down. So >>we're not in >>person. We >>don't Can't be at the definite zone. We can't be on site doing definite created All the great stuff we've been doing in the past three years were virtual the cube Virtual. Thanks for coming on. Uh, Susie, I gotta ask you because you know, we've been talking years ago when you started this mission and just the succession had has been awesome. But definite create has brought on a whole nother connective tissue to the definite community. This is what this ties into the theme of accelerating automation with definite because you said to me, I think four years ago everything should be a service or X a s is it's called and automation plays a critical role. Um, could you please share your vision? Because this is really important. And still only 5 to 10% of the enterprises have containerized things. So there's a huge growth curve coming with developing and program ability. What's your What's your vision? >>Yeah, absolutely. I mean, what we know is that is, more and more businesses are coming online is I mean, they're all online, But is there growing into the cloud? Is their growing in new areas as we're dealing with security is everyone's dealing with the pandemic. There's so many things going on. But what happens is there's an infrastructure that all of this is built on and that infrastructure has networking. It has security. It has all of your compute and everything that's in there. And what matters is how can you take a business application and tie it to that infrastructure. How can you take, you know, customer data? How can you take business applications? How can you connect up the world securely and then be ableto really satisfy everything that businesses need. And in order to do that, you know, the whole new tool that we've always talked about is that the network is programmable, the infrastructure is programmable, and you don't need just acts writing on top. But now they get to use all of that power of the infrastructure to perform even better. And in order to get there, what you need to do is automate everything. You can't configure networks manually. You can't be manually figuring out policies, but you want to use that agile infrastructure in which you can really use automation. You can rise to a higher level business processes and tie all of that up and down the staff by leveraging automation. >>You remember a few years ago when definite create first started, I interviewed Todd Nightingale and we're talking about Muraki. You know, not to get in the weeds, but you know, switches and hubs and wireless. But if you look at what we were talking about, then this is kind of what's going on now. And we were just recently, I think our last physical event was Cisco um Europe in Barcelona before all the cove it hit and you had the massive cloud surgeon scale happening going on right when the pandemic hit. And even now, more than ever, the cloud scale the modern APS. The momentum hasn't stopped because there's more pressure now to continue addressing Mawr innovation at scale. Because the pressure to do that because >>the stay alive get >>your thoughts on, um, what's going on in your world? Because you were there in person. Now we're six months in scale is huge. >>We are, Yeah, absolutely. And what happened is as all of our customers as businesses around the world as we ourselves all dealt with, How do we run a business from home? You know, how do we keep people safe? How do we keep people at home and how do we work? And then it turns out, you know, business keeps rolling, but we've had to automate even more because >>you >>have to go home and then figure out how from home can I make sure that my I t infrastructure is automated out from home? Can I make sure that every employee is out there in working safely and securely? You know, things like call center workers, which had to go into physical locations and being kind of, you know, just, you know, blocked off rooms to really be secure with their company's information. They had to work from home. So we had to extend business applications to people's homes in countries like, you know, well around the world. But also in India, where it was actually not, you know, not they wouldn't let They didn't have rules toe let people work from home in these areas. So then what we had to do was automate everything and make sure that we could administer. You know, all of our customers could administer these systems from home, so that puts extra stress on automation. It puts extra stress on our customers digital transformation. And it just forced them toe, you know, automate digitally transform quicker. And they had to because you couldn't just go into a server room and tweak your servers. You have to figure out how to automate all of that. >>You know, one of them >>were still there, all in that environment today. >>You know, one of the hottest trends before the pandemic was observe ability, uh, kubernetes serve micro services. So those things again. All Dev ups. And you know, if you guys got some acquisitions, you thought about 1000 eyes. Um, you got a new one you just bought recently Port shift to raise the game in security, Cuban, All these micro services, So observe, ability, superhot. But then people go work at home, as you mentioned. How do you think? Observe, What do you observing? The network is under huge pressure. I mean, it's crashing on. People zooms and WebEx is and education, huge amount of network pressure. How are people adapting to this in the upside? How are you guys looking at the what's being programmed? What are some of the things that you're seeing with use cases around this program? Ability, challenge and observe ability, challenges? It's a huge deal. >>Yeah, absolutely. And, you know, going back to Todd Nightingale, right? You know, back when we talked to Todd before he had Muraki and he had designed this simplicity, this ease of use, this cloud managed, you know, doing everything from one central place. And now he has This goes entire enterprise and cloud business. So he is now applying that at that Bigger Attn. Bigger scale. Francisco and for our customers. And he is building in the observe ability and the dashboards and the automation of the A P. I s and all of it. But when we take a look at what our customers needed is again, they had to build it all in, um, they had to build in. And what happened was how your network was doing, how secure your infrastructure was, how well you could enable people toe work from home and how well you could reach customers. All of that used to be a nightie conversation. It became a CEO and a board level conversation. So all of a sudden CEOs were actually, you know, calling on the heads of I t and the CEO and saying, You know, how is our VPN connectivity? Is everybody working from home? How many people are, you know, connected and ableto work and watch their productivity? Eso All of a sudden, all these things that were really infrastructure I t stuff became a board level conversation and you know, once again, at first everybody was panicked and just figuring out how to get people working. But now what we've seen in all of our customers is that they're now building in automation, additional transformation and these architectures, and that gives them a chance to build in that observe ability. You know, looking for those events. The dashboards, you know? So it really has been fantastic to see what our customers are doing and what our partners air doing to really rise to that next level. >>Susan, I know you gotta go, but real quick, um, describe what? Accelerating automation with definite means. >>Well, you've been fault. You know, we've been working together on definite in the vision of the infrastructure program ability and everything for quite some time. And the thing that's really happened is yes, you need to automate, but yes, it takes people to do that. And you need the right skill sets in the program ability. So a networker can't be a networker. A networker has to be a network automation developer. And so it is about people. And it is about bringing infrastructure expertise together with software expertise and letting people run. Things are definite. Community has risen to this challenge. People have jumped in. They've gotten their certifications. We have thousands of people getting certified. You know, we have you know, Cisco getting certified. We have individuals. We have partners, you know, They're just really rising to the occasion. So accelerate accelerating automation while it is about going digital. It's also about people rising to the level of, you know, being able to put infrastructure and software expertise together to enable this next chapter of business applications of cloud directed businesses and cloud growth. So it actually is about people, Justus, much as it is about automation and technology. >>And we got definite create right around the corner virtual. Unfortunately, being personal will be virtual Susie. Thank you for your time. We're gonna dig into those people challenges with Mandy and Eric. Thank you for coming on. I know you got to go, but stay with us. We're gonna dig in with Mandy and Eric. Thanks. >>Thank you so much. Thank you. Thanks, John. Okay. >>Mandy, you heard Susie is about people, and one of the things that's close to your heart you've been driving is a senior director of definite certifications. Um is getting people leveled up? I mean, the demand for skills cybersecurity, network program, ability, automation, network design solution, architect cloud multi cloud design thes are new skills that are needed. Can you give us the update on what you're doing to help people get into the acceleration of automation game? >>Oh, yes, absolutely. The you know what we've been seeing is a lot of those business drivers that Susie was mentioning those air. What's accelerating? A lot of the technology changes, and that's creating new job roles or new needs on existing job roles where they need new skills. We are seeing, uh, customers, partners, people in our community really starting to look at, you know, things like Dev SEC ops engineer, network Automation engineer, network automation developer, which sues you mentioned and looking at how these fit into their organization, the problems that they solve in their organization. And then how do people build the skills to be able to take on these new job roles or add that job role to their current, um, scope and broaden out and take on new challenges? >>Eric, I want to go to you for a quick second on this, um uh, piece of getting the certifications. Um, first, before you get started, describe what your role is. Director of developer advocacy, because that's always changing and evolving what's the state of it now? Because with Cove and people are working at home, they have more time to contact, switch and get some certifications and that they can code more. What's your >>What's your role? Absolutely So it's interesting. It definitely is changing a lot. A lot of our historically a lot of focus for my team has been on those outward events. So going to the definite creates the Cisco lives and helping the community connect and help share technical information with them, doing hands on workshops and really getting people into. How do you really start solving these problems? Eso that's had to pivot quite a bit. Obviously, Sisco live us. We pivoted very quickly to a virtual event when when conditions changed and we're able to actually connect, as we found out with a much larger audience. So you know, as opposed to in person where you're bound by the parameters of you know how big the convention center is. We were actually able to reach a worldwide audience with are definite day that was kind of attached onto Sisco Live, and we got great feedback from the audience that now we're actually able to get that same enablement out to so many more people that otherwise might not have been able to make it. But to your broader question of you know what my team does. So that's one piece of it is is getting that information out to the community. So as part of that, there's a lot of other things we do as well. We were always helping out build new sandboxes, new learning labs, things like that that they can come and get whenever they're looking for it out on the definite site. And then my team also looks after communities such as the Cisco Learning Network, where there's there's a huge community that has historically been there to support people working on their Cisco certifications. We've seen a huge shift now in that group that all of the people that have been there for years are now looking at the definite certifications and helping other people that are trying to get on board with program ability. They're taking a lot of those same community enablement skills and propping up community with, you know, helping answer questions, helping provide content. They move now into the definite spaces well and are helping people with that sort of certifications. So it's great seeing the community come along and really see that >>I gotta ask you on the trends around automation. What skills and what developer patterns are you seeing with automation? Are Is there anything in particular? Obviously, network automation been around for a long time. Cisco's been leader in that. But as you move up, the staff has modern applications or building. Do you see any patterns or trends around what is accelerating automation? What people learning? >>Yeah, absolutely. So you mentioned observe ability was big before Cove it and we actually really saw that amplified during co vid. So a lot of people have come to us looking for insights. How can I get that better observe ability now that we needed? Well, we're virtual eso. That's actually been a huge uptick, and we've seen a lot of people that weren't necessarily out looking for things before that air. Now, figuring out how can I do this at scale? I think one good example that Susie was talking about the VPN example, and we actually had a number of SCS in the Cisco community that had customers dealing with that very thing where they very quickly had to ramp up and one in particular actually wrote a bunch of automation to go out and measure all of the different parameters that I T departments might care about about their firewalls, things that you didn't normally look at. The old days you would size your firewalls based on, you know, assuming a certain number of people working from home. And when that number went to 100% things like licenses started coming into play where they need to make sure they had the right capacity in their platforms that they weren't necessarily designed for. So one of the essays actually wrote a bunch of code to go out, use them open source, tooling to monitor and alert on these things, and then published it so the whole community code could go out and get a copy of it. Try it out in their own environment. And we saw a lot of interest around that and trying to figure out Okay, now I could take that. I can adapt into what I need to see for my observe ability. >>That's great, Mandy, I want to get your thoughts on this, too, because as automation continues to scale. Um, it's gonna be a focus. People are at home. And you guys had a lot of content online for you. Recorded every session that in the definite zone learning is going on sometimes literally and non linearly. You've got the certifications, which is great. That's key. Great success there. People are interested. But what other learnings are you seeing? What are people, um, doing? What's the top top trends? >>Yeah. So what we're seeing is like you said, people are at home, they've got time, they want toe advance, their skill set. And just like any kind of learning, people want choice. They wanna be able to choose which matches their time that's available and their learning style. So we're seeing some people who want to dive into full online study groups with mentors leading them through a study plan. On we have two new expert lead study groups like that. We're also seeing whole teams at different companies who want to do an immersive learning experience together with projects and office hours and things like that. And we have a new offer that we've been putting together for people who want those kind of team experiences called Automation Boot Camp. And then we're also seeing individual who want to be able to, you know, dive into a topic, do a hands on lab, gets, um, skills, go to the rest of the day of do their work and then come back the next day. And so we have really modular, self driven hands on learning through the Definite Fundamentals course, which is available through DEV. Net. And then there's also people who are saying, I just want to use the technology. I like Thio experiment and then go, you know, read the instructions, read the manual, do the deeper learning. And so they're They're spending a lot of time in our definite sandbox, trying out different technologies. Cisco Technologies with open source technologies, getting hands on and building things, and three areas where we're seeing a lot of interest in specific technologies. One is around SD wan. There's a huge interest in people Skilling up there because of all the reasons that we've been talking about. Security is a focus area where people are dealing with new scale, new kinds of threats, having to deal with them in new ways and then automating their data center using infrastructure as code type principles. So those were three areas where we're seeing a lot of interest and you'll be hearing more about that at definite create. >>Awesome Eric and man, if you guys can wrap up the accelerated automated with definite package and virtual event here, um, and also t up definite create because definite create has been a very kind of grassroots, organically building momentum over the years. Again, it's super important because it's now the app world coming together with networking, you know, end to end program ability. And with everything is a service that you guys were doing everything with a piece. Um Onley can imagine the enablement that's gonna enable create Can >>you hear the >>memory real quick on accelerating automation with definite and TF definite create. Mandy will start with you. >>Yes, I'll go first, and then Eric can close this out. Um, so just like we've been talking about with you at every definite event over the past years, you know, Devon, it's bringing a p I s across our whole portfolio and up and down the stack and accelerating automation with definite. Suzy mentioned the people aspect of that the people Skilling up and how that transformed team transforms teams. And I think that it's all connected in how businesses are being pushed on their transformation because of current events. That's also a great opportunity for people to advance their careers and take advantage of some of that quickly changing landscape. And so would I think about accelerating automation with definite. It's about the definite community. It's about people getting those new skills and all the creativity and problem solving that will be unleashed by that community with those new skills. >>Eric, take us home. He accelerate automation. Definite and definite create a lot of developer action going on cloud native right now, your thoughts? >>Absolutely. I I think it's exciting. I mentioned the transition to virtual for definite day this year for Cisco Live, and we're seeing we're able to leverage it even further with create this year. So whereas it used to be, you know, confined by the walls that we were within for the event. Now we're actually able to do things like we're adding a start now track for people that I want to be there. They want to be a developer. Network automation developer, for instance, We've now got a track just for them where they could get started and start learning some of the skills they'll need, even if some of the other technical sessions were a little bit deeper than what they were ready for. Eso. I love that we're able to bring that together with the experience community that we usually do from across the industry, bringing us all kinds of innovative talks, talking about ways that they're leveraging technology, leveraging the cloud to do new and interesting things to solve their business challenges. So I'm really excited to bring that whole mixed together as well as getting some of our business units together to and talk straight from their engineering departments. What are they doing? What are they seeing? What are they thinking about when they're building new AP eyes into their platforms? What are the what problems are they hoping that customers will be able to solve with them? So I think together, seeing all of that and then bringing the community together from all of our usual channels. So, like I said, Cisco Learning Network, we've got a ton of community coming together, sharing their ideas and helping each other grow those skills. I see nothing but acceleration ahead of us for automation. >>Awesome. Thanks so much. God, man, can >>I add one had >>one more thing. >>Yeah, I was just going to say the other really exciting thing about create this year with the virtual nature of it is that it's happening in three regions. And, you know, we're so excited to see the people joining from all the different regions. And, uh, content and speakers and the region stepping upto have things personalized to their area to their community. And so that's a whole new experience for definite create that's going to be fantastic this year. >>You know, that's what God is going to close out and just put the final bow on that by saying that you guys have always been successful with great content focused on the people in the community. I think now, during with this virtual definite virtual definite create virtual the cube virtual, I think we're learning new things. People working in teams and groups on sharing content. We're gonna learn new things. We're gonna try new things, and ultimately people will rise up and will be resilient. I think when you have this kind of opportunity, it's really fun. And whoa, we'll ride the wave with you guys. So thank you so much for taking the time to come on. The Cuban talk about your awesome accelerate automation and definitely looking forward to it. Thank you. >>Thank you so much. >>Happy to be here. >>Okay, I'm John for the Cube. Virtual here in Palo Alto studios doing the remote content amendment Virtual until we're face to face. Thank you so much for watching. And we'll see you at definite create. Thanks for watching.

>>from around the globe. It's the Cube presenting accelerating automation with definite brought to you by Cisco. >>Hello and welcome to the Cube. I'm John for a year host. We've got a great conversation virtual event, accelerating automation with definite Cisco. Definite. And of course, we got the Cisco Brain Trust here. Cube alumni Suzy we Vice President, senior Vice President GM and also CTO of Cisco. Definite and ecosystem Success C X, All that great stuff. Many Wadley Who's the director? Senior director of definite certifications. Eric Field, director of developer advocacy. Susie Mandy. Eric, Great to see you. Thanks for coming on. >>Great to see you >>down. So we're not in person. We >>don't Can't be at the definite zone. We can't be on site doing definite created All the great stuff we've been doing in the past three years were virtual the cube Virtual. Thanks for coming on. Uh, Susie, I gotta ask you because you know, we've been talking years ago when you started this mission and just the succession had has been awesome. But definite create has brought on a whole nother connective tissue to the definite community. This is what this ties into the theme of accelerating automation with definite because you said to me, I think four years ago everything should be a service or X a s is it's called and automation plays a critical role. Um, could you please share your vision? Because this is really important. And still only 5 to 10% of the enterprises have containerized things. So there's a huge growth curve coming with developing and program ability. What's your What's your vision? >>Yeah, absolutely. I mean, what we know is that is, more and more businesses are coming online is I mean, they're all online, But is there growing into the cloud? Is their growing in new areas as we're dealing with security is everyone's dealing with the pandemic. There's so many things going on. But what happens is there's an infrastructure that all of this is built on and that infrastructure has networking. It has security. It has all of your compute and everything that's in there. And what matters is how can you take a business application and tie it to that infrastructure. How can you take, you know, customer data? How can you take business applications? How can you connect up the world securely and then be ableto really satisfy everything that businesses need. And in order to do that, you know, the whole new tool that we've always talked about is that the network is programmable, the infrastructure is programmable, and you don't need just acts writing on top. But now they get to use all of that power of the infrastructure to perform even better. And in order to get there, what you need to do is automate everything. You can't configure networks manually. You can't be manually figuring out policies, but you want to use that agile infrastructure in which you can really use automation. You can rise to a higher level business processes and tie all of that up and down the staff by leveraging automation. >>You remember a few years ago when definite create first started, I interviewed Todd Nightingale and we're talking about Muraki. You know, not to get in the weeds, but you know, switches and hubs and wireless. But if you look at what we were talking about, then this is kind of what's going on now. And we were just recently, I think our last physical event was Cisco um Europe in Barcelona before all the cove it hit and you had the massive cloud surgeon scale happening going on right when the pandemic hit. And even now, more than ever, the cloud scale the modern APS. The momentum hasn't stopped because there's more pressure now to continue addressing Mawr innovation at scale. Because the pressure to do that because >>the stay alive get >>your thoughts on, um, what's going on in your world? Because you were there in person. Now we're six months in scale is huge. >>We are, Yeah, absolutely. And what happened is as all of our customers as businesses around the world as we ourselves all dealt with, How do we run a business from home? You know, how do we keep people safe? How do we keep people at home and how do we work? And then it turns out, you know, business keeps rolling, but we've had to automate even more because >>you >>have to go home and then figure out how from home can I make sure that my I t infrastructure is automated out from home? Can I make sure that every employee is out there in working safely and securely? You know, things like call center workers, which had to go into physical locations and being kind of, you know, just, you know, blocked off rooms to really be secure with their company's information. They had to work from home. So we had to extend business applications to people's homes in countries like, you know, well around the world. But also in India, where it was actually not, you know, not they wouldn't let They didn't have rules toe let people work from home in these areas. So then what we had to do was automate everything and make sure that we could administer. You know, all of our customers could administer these systems from home, so that puts extra stress on automation. It puts extra stress on our customers digital transformation. And it just forced them toe, you know, automate digitally transform quicker. And they had to because you couldn't just go into a server room and tweak your servers. You have to figure out how to automate all of that. >>You know, one of them >>were still there, all in that environment today. >>You know, one of the hottest trends before the pandemic was observe ability, uh, kubernetes serve micro services. So those things again. All Dev ups. And you know, if you guys got some acquisitions, you thought about 1000 eyes. Um, you got a new one you just bought recently Port shift to raise the game in security, Cuban, All these micro services, So observe, ability, superhot. But then people go work at home, as you mentioned. How do you think? Observe, What do you observing? The network is under huge pressure. I mean, it's crashing on. People zooms and WebEx is and education, huge amount of network pressure. How are people adapting to this in the upside? How are you guys looking at the what's being programmed? What are some of the things that you're seeing with use cases around this program? Ability, challenge and observe ability, challenges? It's a huge deal. >>Yeah, absolutely. And, you know, going back to Todd Nightingale, right? You know, back when we talked to Todd before he had Muraki and he had designed this simplicity, this ease of use, this cloud managed, you know, doing everything from one central place. And now he has This goes entire enterprise and cloud business. So he is now applying that at that Bigger Attn. Bigger scale. Francisco and for our customers. And he is building in the observe ability and the dashboards and the automation of the A P. I s and all of it. But when we take a look at what our customers needed is again, they had to build it all in, um, they had to build in. And what happened was how your network was doing, how secure your infrastructure was, how well you could enable people toe work from home and how well you could reach customers. All of that used to be a nightie conversation. It became a CEO and a board level conversation. So all of a sudden CEOs were actually, you know, calling on the heads of I t and the CEO and saying, You know, how is our VPN connectivity? Is everybody working from home? How many people are, you know, connected and ableto work and watch their productivity? Eso All of a sudden, all these things that were really infrastructure I t stuff became a board level conversation and you know, once again, at first everybody was panicked and just figuring out how to get people working. But now what we've seen in all of our customers is that they're now building in automation, additional transformation and these architectures, and that gives them a chance to build in that observe ability. You know, looking for those events. The dashboards, you know? So it really has been fantastic to see what our customers are doing and what our partners air doing to really rise to that next level. >>Susan, I know you gotta go, but real quick, um, describe what? Accelerating automation with definite means. >>Well, you've been fault. You know, we've been working together on definite in the vision of the infrastructure program ability and everything for quite some time. And the thing that's really happened is yes, you need to automate, but yes, it takes people to do that. And you need the right skill sets in the program ability. So a networker can't be a networker. A networker has to be a network automation developer. And so it is about people. And it is about bringing infrastructure expertise together with software expertise and letting people run. Things are definite. Community has risen to this challenge. People have jumped in. They've gotten their certifications. We have thousands of people getting certified. You know, we have you know, Cisco getting certified. We have individuals. We have partners, you know, They're just really rising to the occasion. So accelerate accelerating automation while it is about going digital. It's also about people rising to the level of, you know, being able to put infrastructure and software expertise together to enable this next chapter of business applications of cloud directed businesses and cloud growth. So it actually is about people, Justus, much as it is about automation and technology. >>And we got definite create right around the corner virtual. Unfortunately, being personal will be virtual Susie. Thank you for your time. We're gonna dig into those people challenges with Mandy and Eric. Thank you for coming on. I know you got to go, but stay with us. We're gonna dig in with Mandy and Eric. Thanks. >>Thank you so much. Thank you. Thanks, John. Okay. >>Mandy, you heard Susie is about people, and one of the things that's close to your heart you've been driving is a senior director of definite certifications. Um is getting people leveled up? I mean, the demand for skills cybersecurity, network program, ability, automation, network design solution, architect cloud multi cloud design thes are new skills that are needed. Can you give us the update on what you're doing to help people get into the acceleration of automation game? >>Oh, yes, absolutely. The you know what we've been seeing is a lot of those business drivers that Susie was mentioning those air. What's accelerating? A lot of the technology changes, and that's creating new job roles or new needs on existing job roles where they need new skills. We are seeing, uh, customers, partners, people in our community really starting to look at, you know, things like Dev SEC ops engineer, network Automation engineer, network automation developer, which sues you mentioned and looking at how these fit into their organization, the problems that they solve in their organization. And then how do people build the skills to be able to take on these new job roles or add that job role to their current, um, scope and broaden out and take on new challenges? >>Eric, I want to go to you for a quick second on this, um uh, piece of getting the certifications. Um, first, before you get started, describe what your role is. Director of developer advocacy, because that's always changing and evolving what's the state of it now? Because with Cove and people are working at home, they have more time to contact, switch and get some certifications and that they can code more. What's your >>What's your role? Absolutely So it's interesting. It definitely is changing a lot. A lot of our historically a lot of focus for my team has been on those outward events. So going to the definite creates the Cisco lives and helping the community connect and help share technical information with them, doing hands on workshops and really getting people into. How do you really start solving these problems? Eso that's had to pivot quite a bit. Obviously, Sisco live us. We pivoted very quickly to a virtual event when when conditions changed and we're able to actually connect, as we found out with a much larger audience. So you know, as opposed to in person where you're bound by the parameters of you know how big the convention center is. We were actually able to reach a worldwide audience with are definite day that was kind of attached onto Sisco Live, and we got great feedback from the audience that now we're actually able to get that same enablement out to so many more people that otherwise might not have been able to make it. But to your broader question of you know what my team does. So that's one piece of it is is getting that information out to the community. So as part of that, there's a lot of other things we do as well. We were always helping out build new sandboxes, new learning labs, things like that that they can come and get whenever they're looking for it out on the definite site. And then my team also looks after communities such as the Cisco Learning Network, where there's there's a huge community that has historically been there to support people working on their Cisco certifications. We've seen a huge shift now in that group that all of the people that have been there for years are now looking at the definite certifications and helping other people that are trying to get on board with program ability. They're taking a lot of those same community enablement skills and propping up community with, you know, helping answer questions, helping provide content. They move now into the definite spaces well and are helping people with that sort of certifications. So it's great seeing the community come along and really see that >>I gotta ask you on the trends around automation. What skills and what developer patterns are you seeing with automation? Are Is there anything in particular? Obviously, network automation been around for a long time. Cisco's been leader in that. But as you move up, the staff has modern applications or building. Do you see any patterns or trends around what is accelerating automation? What people learning? >>Yeah, absolutely. So you mentioned observe ability was big before Cove it and we actually really saw that amplified during co vid. So a lot of people have come to us looking for insights. How can I get that better observe ability now that we needed? Well, we're virtual eso. That's actually been a huge uptick, and we've seen a lot of people that weren't necessarily out looking for things before that air. Now, figuring out how can I do this at scale? I think one good example that Susie was talking about the VPN example, and we actually had a number of SCS in the Cisco community that had customers dealing with that very thing where they very quickly had to ramp up and one in particular actually wrote a bunch of automation to go out and measure all of the different parameters that I T departments might care about about their firewalls, things that you didn't normally look at. The old days you would size your firewalls based on, you know, assuming a certain number of people working from home. And when that number went to 100% things like licenses started coming into play where they need to make sure they had the right capacity in their platforms that they weren't necessarily designed for. So one of the essays actually wrote a bunch of code to go out, use them open source, tooling to monitor and alert on these things, and then published it so the whole community code could go out and get a copy of it. Try it out in their own environment. And we saw a lot of interest around that and >>trying >>to figure out Okay, now I could take that. I can adapt into what I need to see for my observe ability. >>That's great, Mandy, I want to get your thoughts on this, too, because as automation continues to scale. Um, it's gonna be a focus. People are at home. And you guys had a lot of content online for you. Recorded every session that in the definite zone learning is going on sometimes literally and non linearly. You've got the certifications, which is great. That's key. Great success there. People are interested. But what other learnings are you seeing? What are people, um, doing? What's the top top trends? >>Yeah. So what we're seeing is like you said, people are at home, they've got time, they want toe advance, their skill set. And just like any kind of learning, people want choice. They wanna be able to choose which matches their time that's available and their learning style. So we're seeing some people who want to dive into full online study groups with mentors leading them through a study plan. On we have two new expert lead study groups like that. We're also seeing whole teams at different companies who want to do an immersive learning experience together with projects and office hours and things like that. And we have a new offer that we've been putting together for people who want those kind of team experiences called Automation Boot Camp. And then we're also seeing individual who want to be able to, you know, dive into a topic, do a hands on lab, gets, um, skills, go to the rest of the day of do their work and then come back the next day. And so we have really modular, self driven hands on learning through the Definite Fundamentals course, which is available through DEV. Net. And then there's also people who are saying, I just want to use the technology. I like Thio experiment and then go, you know, read the instructions, read the manual, do the deeper learning. And so they're They're spending a lot of time in our definite sandbox, trying out different technologies. Cisco Technologies with open source technologies, getting hands on and building things, and three areas where we're seeing a lot of interest in specific technologies. One is around SD wan. There's a huge interest in people Skilling up there because of all the reasons that we've been talking about. Security is a focus area where people are dealing with new scale, new kinds of threats, having to deal with them in new ways and then automating their data center using infrastructure as code type principles. So those were three areas where we're seeing a lot of interest and you'll be hearing more about that at definite create. >>Awesome Eric and man, if you guys can wrap up the accelerated automated with definite package and virtual event here, um, and also t up definite create because definite create has been a very kind of grassroots, organically building momentum over the years. Again, it's super important because it's now the app world coming together with networking, you know, end to end program ability. And with everything is a service that you guys were doing everything with a piece. Um Onley can imagine the enablement that's gonna enable create Can >>you hear the >>memory real quick on accelerating automation with definite and TF definite create. Mandy will start with you. >>Yes, I'll go first, and then Eric can close this out. Um, so just like we've been talking about with you at every definite event over the past years, you know, Devon, it's bringing a p I s across our whole portfolio and up and down the stack and accelerating automation with definite. Suzy mentioned the people aspect of that the people Skilling up and how that transformed team transforms teams. And I think that it's all connected in how businesses are being pushed on their transformation because of current events. That's also a great opportunity for people to advance their careers and take advantage of some of that quickly changing landscape. And so would I think about accelerating automation with definite. It's about the definite community. It's about people getting those new skills and all the creativity and problem solving that will be unleashed by that community with those new skills. >>Eric, take us home. He accelerate automation. Definite and definite create a lot of developer action going on cloud native right now, your thoughts? >>Absolutely. I I think it's exciting. I mentioned the transition to virtual for definite day this year for Cisco Live, and we're seeing we're able to leverage it even further with create this year. So whereas it used to be, you know, confined by the walls that we were within for the event. Now we're actually able to do things like we're adding a start now track for people that I want to be there. They want to be a developer. Network automation developer, for instance, We've now got a track just for them where they could get started and start learning some of the skills they'll need, even if some of the other technical sessions were a little bit deeper than what they were ready for. Eso. I love that we're able to bring that together with the experience community that we usually do from across the industry, bringing us all kinds of innovative talks, talking about ways that they're leveraging technology, leveraging the cloud to do new and interesting things to solve their business challenges. So I'm really excited to bring that whole mixed together as well as getting some of our business units together to and talk straight from their engineering departments. What are they doing? What are they seeing? What are they thinking about when they're building new AP eyes into their platforms? What are the what problems are they hoping that customers will be able to solve with them? So I think together, seeing all of that and then bringing the community together from all of our usual channels. So, like I said, Cisco Learning Network, we've got a ton of community coming together, sharing their ideas and helping each other grow those skills. I see nothing but acceleration ahead of us for automation. >>Awesome. Thanks so much. God, man, can >>I add one had >>one more thing. >>Yeah, I was just going to say the other really exciting thing about create this year with the virtual nature of it is that it's happening in three regions. And, you know, we're so excited to see the people joining from all the different regions. And, uh, content and speakers and the region stepping upto have things personalized to their area to their community. And so that's a whole new experience for definite create that's going to be fantastic this year. >>You know, that's what God is going to close out and just put the final bow on that by saying that you guys have always been successful with great content focused on the people in the community. I think now, during with this virtual definite virtual definite create virtual the cube virtual, I think we're learning new things. People working in teams and groups on sharing content. We're gonna learn new things. We're gonna try new things, and ultimately people will rise up and will be resilient. I think when you have this kind of opportunity, it's really fun. And whoa, we'll ride the wave with you guys. So thank you so much for taking the time to come on. The Cuban talk about your awesome accelerate automation and definitely looking forward to it. Thank you. >>Thank you so much. >>Happy to be here. >>Okay, I'm John for the Cube. Virtual here in Palo Alto studios doing the remote content amendment Virtual until we're face to face. Thank you so much for watching. And we'll see you at definite create. Thanks for watching.

>> live from Boston, Massachusetts. It's the Cube covering A W s reinforce 2019. Brought to you by Amazon Web service is and its ecosystem partners. >> Welcome back. Everyone were accused Live coverage here in Boston, Massachusetts, for AWS reinforce. First inaugural conference runs security. I'm Jeffrey. David Lot there. Next guest is Dan Hubbard, CEO of lacework. I've started at a Mountain View, California. Great to have you on. Thanks for joining us. >> Thanks. Thanks for having me. >> So, you know, reinvent was developers Reinforces. Kind of like, si SOS coding security cloud and intersecting with security. This is a new kind of show. What's your take on? >> Super impressed so far? I mean, there's about 1000 people here, you know, way have literally hundreds of demos lined up in the booth s oh, really impressed so far. First impressions. >> It's a good move for Amazon. Do. Ah, security conference. Don't you think I mean >> really smart, Really smart. It's a lot more about defending than a lot of security conference about offense and vulnerabilities and how to find kind of holes and weak cracks. This is really about how do we defend you know, our security in the cloud >> Talk about your company. Your mission? You guys air started going after a hot space. Si SOS or CEO spending Talk to They want a new breed of supplier service provider. Certainly cloud a p. I is gonna be critical in all of this. So you start to see really smart platform thinking systems, thinking around companies around the security challenge and opportunity. What? What do you guys do? Explain what you guys? >> Yes, we really believed you know, this new wave of cloud I s and pass really needs a new architecture. It's a whole new architecture from a 90 perspective. So we need a new architect from a security perspective. And the great thing about the operating model is you could do a wide set of things and then go deep in the areas that are really important. So at least work does we allow you to secure? I asked. Past service is with compliance configuration host and container security. There's one platform that kind of wraps across all of those >> different targeting developers, right? So they don't have to think about security all the time. Is that the poor thing? >> Yeah, definitely. Eso in almost every case. Security is unlocking the budget. However, Dev Ops is involved, Dev Ops is involved from an influence. But, you know, it used to be that developers would ask security for permission. Now security's going back to developers and asking for permission to security >> infrastructure. He said that with the architecture is gonna be different because the the the I t. Is changing. So cloud security needs a new architecture. One of the fundamentals of that architecture and how is it different from security on prim? >> So I think it has to be SAS. So it's gotta be delivered multi cloud from the cloud. You know, we're gonna secure the cloud. It really should be from the cloud, their business models, that should be different. It's almost always a subscription is not perpetual models. You know you're annually re occurring your revenue. You're always keeping your customers happy and you're always innovating. The pace of innovation has to be really quick because the pace of the cloud is moving at such a dramatic speed. >> So that the those kind of business oriented you know, that's kind of a different definition of architecture. Technically, is it a fundamental do over Or is it fundamentally similar? >> Wolf. You know, there's some of the tenants which are the same, you know, we need to get visibility. That's very similar. You know, we have controls needed have auditing. We need to find threats. However, the way you do it is very different. So you don't own the hardware, you don't own the racks, you don't own the network. You gotta get used to that. You gotta live above the responsibility line. You have to fit within their infrastructure. So what that means is you need to be very happy. I friendly because we're sucking a lot of data on Amazon were pulling in configuration cloudtrail data, and you'll have to be able to deploy inside their infrastructures. We support things like kubernetes things like docker or we also interoperate things like bare metal and you know, in the AM eyes themselves, what >> problem you guys solve. Every startup has that cultural doctor, and they sometimes you weave into a market and also you get visibility into into a key value proper. What's the key problem that you saw? What's the benefit >> so that the key value we solve is if you are in the cloud or migraine in the cloud. We give you compliance configuration and threat protection across all your clowns. So, irrespective of which cloud you live in or operate in, we give you one central threat detection engine and that which gives you visibility but also gives you compliance and controls into that. >> So Amazon has this, you know she had responsibility model. They're they're protecting the compute, the storage, the database and customers are responsible for the end points. The operating system, the data, etcetera, etcetera. And Amazon certainly has tools. Help them. What is fuzzy to me sometimes is you know where eight of us leaves off. Where ecosystem partners like you guys come in. You obvious have to keep moving fast to your point. Absolute. Can you help us sort of squint through that maze? >> Sure. Yeah. I mean, the easiest way that I can explain it is if you could configure it, you have to secure everything. Below is the providers responsibility. That said, there are different areas where things are kind of peeking through the responsibility lines. So what I see is a world where there's not 50 security vendors that you've bought like in premise or traditional data center, but your Inter operating with a provider. So you know, the big three providers open source and then a solution like ours. So it's more about how do we interoperate there together? But what we do is we sit actually right within your container on the host themselves with an agent, and then we suck in there a p I. So technically, it's a little bit different. >> So the threat of containers is an interesting topic, right? You're spinning him up. It makes V M v ems look like child's play. Yeah, So are you using specific techniques, toe? So the fake out the bad guys make it. You're raising the bar on them and their cost using sort of algorithms to do that spin up, spin him down. You know, like the shell game of asking you. >> What we do is we get baked right into your infrastructure every single time you deploy and run through C I c d. A new container or a new app were baked in there and what we're doing, we're looking all your applications, processes the network traffic and then we look for that no one bad and the unknown bad based off of that. >> So it's native security in the container at the point of creation. Not a not an afterthought. Correct. Yep, >> What? Your take on kubernetes landscape? Obviously, pretty much everyone's kind of consolidate around that from a de facto standard. That's good news, wouldn't it? Koen ETS does is all kinds of stateless state full applications that becomes, like service mess conversation. You got all kinds of services that could land out there, automating all these things these sources were being turned on turned off in real time. >> It's >> a log it >> all. It's incredible. I think Cos. Is the fastest growing enterprise open source project ever. You know where every customer we talked to is either in the midst of migrating migrate or just thinking about it. That said, the world is looking to go multi cloud. But most customers today have, ah, a combination of in premise bare metal am eyes kubernetes containers. What we're doing is we give you visibility into your coup Bernays infrastructure. So we talk pods, nodes, clusters, name spaces and we allow you to secure the management plane. Any communication between those So it's really critical when you're deploying those from a security perspective that you know what's happening. The ephemeral nature of it is very different from regular security to you need to answer questions like what happened for 10 minutes during this time from six months ago, and that's really hard with traditional >> tools, really are. And that's really gonna with automation plays in Talk about the journey of where your customers are going out because we're seeing a progression kind of categorically three kind of levels. I really wanted to go to the cloud. I really want to convince you that cloud every aspiration. Yeah, not realistic, but it's on their plans. Then you've got people who go out and do it gets stuck in the mud. The wheels are spinning culturally, whatever's going on and then full on cloud native hard core Dev ops, eaten glass, spit nails, just kicking ass and taking names right? So you get the leaders. People are kind of in the middle, and then people jumping in. Where do you guys see your benefit? What are some of the challenges? How do you guys >> think it's a super dynamic marketplace? Because what's happening is every big company that may not be fully cloud native, is buying companies that are cloud native. So then they become the sexy new way to deploy, and then they start figure out how to deploy their there. So one of the trains were seeing is core centralized. Security is becoming governance and tooling, and then they're distributing the security function within the AP teams themselves. And that model seems to work really well because you've got security practitioners baked within the Dev Ops team. But then you've got a governing roll with tooling, centralized tooling from there. That said, depending on the customer or the prospect, it's all over the place. You know, many sisters, you're scratching their heads saying, No, you know, I don't know what's going over the cloud guys. They've got a different group that's running it. They're trying to figure out how do I just get visibility? I know my name's you know, I'm the one they're gonna come after if there's a problem. So it's really all over the place >> for your service. So you're baking it in creatively into the container. >> Yep, it doesn't matter. >> You're aware, if you will. >> It is a matter of urine premise or not. Containers or not, we worked across all of them. >> Was that the hook for your sort of original idea? Your business plan? Your investors you've raised, I think 32,000,000. You got 70 employees. What was that hook? What attracted the investment Community >> Theory journal? Idea was, if you're deployed in the cloud and you have a breach, how do you know you had a breach? Things that happen to come and go very quickly. All the data's encrypted on the network. I don't have full visibility on the network itself. So that was the original idea. How would I go back in time kind of time machine to find out what happened then? Way originally supported eight of us and it was really about visibility within 80 bus infrastructure. Then kubernetes happened. Now the big hook really is amazing containers. Am I using kubernetes? And then how do I make sure I'm compliant and then following best practices and then that breach that breach scenario still definitely happens. Everybody tries the service before they buy it. They're almost always finding out problems along the way. >> What did kubernetes do for you guys? That made a consensus step, function, change or what you guys were doing? Was it because they had the dynamic nature of the service's was orchestration? What specifically was the benefit? >> I think the orchestration, the single management plane from a security perspective, is one of the big things. You get access to that one brain, if you will. You have access to everything. Obviously, the ephemeral workload is big that it was enforcement kubernetes with service messes. Things like pot security policies allows us to hook a P eyes in a way that you can actually write enforcement versus a firewall or some of these old school ways of killing packets. >> Yes, you got a cloud native approach. Kubernetes comes along. It's aligns with your sort of philosophy and >> architectural, and we run today's ourselves. So our entire infrastructure is based off of kubernetes. We were kubernetes user very early on, so, you know, we just take the things that we learn to our customers. >> So here's a quote from a seesaw. I won't say his or her name, but I want to get your reaction to it when talking about dealing with suppliers, looking for the new generation of like what you guys are doing you got, I would put you in the new classification of emerging suppliers. This is the message to all the suppliers in the room. I happen to be in there having a P I and don't have its suck because you eyes shifting to a p a u ie Focus is shifting to FBI focus. So we are evaluating every supplier on their eight b. I's your reaction to that? >> I absolutely agree. So there's two levels of AP eyes. One is you have to interrupt it with the guys from the providers in order to get the data properly. Right. That's a big, big component. Others, you have to have a P eyes for your consumers. You can't automate without a P I. So that's really critical. That said, I will disagree a little bit on the u X and Y aspect. If you are triaging data, it's really important that you have the right data at the right time and visualizing that data in a ways. It's pretty important. >> How real is multi cloud, in your opinion, I mean, everybody's talking about multi cloud Ah la times we've said multi cloud. It's none of us a symptom of multi vendor. But increasingly it could be a strategy in terms of your thinking about your total available market, your market opportunity. How real is it when you're conversations with Coast? >> It's very really. We were really surprised. We first started supporting eight of us, and then we had a G, C, P and Azure together. Now we have a core principle that everything we build has to be parody across all the clouds. And we had a huge uptick across G, C, P and as your very early. So we were really surprised. What we were surprised about was, it's not portable workloads. So it's not about taking one application distributed across multi cloud. That's kind of fiction. That doesn't happen very often. It's either you bought a company that's in another cloud or use a past service in another cloud, or you have just two totally disparate applications in a large company. They just happen to be in different clouds in the data's in different places. They don't need to interoperate, so it's so it's just a little different, but we're seeing kind >> of horses for courses as well, right? Some clouds may be better for data oriented. >> Here's your point early, and we've heard this in some of the sea. So conversations em and becomes a big factor because they get new teams in new culture and they might have different cloud approaches. But I totally agree with you on that. I would say I would even go more further and saying It's absolute fiction between multi Cloud because it's just got a latent seizes on the connections, whether they're direct connections are not welcome on the factor. So I've always said, and I kind of believe in I'd love to get your thoughts on. It is the workload should dictate to the infrastructure which clouded should you know, and go with one cloud for that. If it makes sense on, then use multi cloud across workloads and low can handle a better cloud. Cloud Cloud selection. Be joined by the workload. >> Yeah, it's certainly from an out >> the other way around. >> Yeah, it's certainly from application perspective. You want a silo? It, you know, probably there. I think what's interesting about a lot of the work each provider is doing in security a lot people ask. Well, you know, why don't I just use all my provider security tools. And the answer is they got some great tools. You should use those for sure, but there is a bunch of technology above that you can use. And then you got a span across multiple clouds. What you don't want is three different AP eyes for security across every single cloud. That's gonna be a major pain or >> have to stitch. And that's where you guys come in. Absolutely. >> What's your take on this show? Reinforce against inaugural show. Love to go. The knuckle shows they don't have a 2nd 1 because they were there. Yeah, reinvent you made a calm before we came on. Reinvents started out. We were there early on as well. There's developers. Yeah, it wasn't a lot of fanfare. In fact, you could wander around Andy Jazz. It wasn't crowded. It all great, great time. That was younger. Now Amazons gotten much stronger. Bigger? What's the vibe here? Is that developers for security? Is it si SOS? Is it? What's your read on the makeup and the focus of the attendees? >> So I think it's it's a little bit of a mix of both, which I think is good you know, I've met a number of developers or what I would call kind of new breed security engineers. These are engineers that arm or interested in? How does the cloud work an inter operate? And how do you secure that versus, like reverse engineering malware with assembler, which you know a lot of the other places there really about the threats? And what of the threats and how specific or those This is really a little bit more about? How do we up our game from from a security perspective in this New World order, which is really >> get plowed. Very agile, very fast, yet horizontally scalable, elastic, all the goodness of cloud Final question developers Bottom line is developers continue to code and do the things, whether it's a devil's culture of having a hack a phone and testing new things, that which is how things roll now, getting into productions hard. What's the developers impact to security? Is the trend coming out of the show that security baked in enough to think about it like how configuration management took that track and Dev Ops took that away? You mentioned that earlier you figure you can secure it yet. So similar track for security going the way of automation. What's your? >> It's a lot of automation is gonna be critical for sure. And then it's gonna be a combination of Security and Dev ops together, you know, Call it DEP SEC Ops, code security engineer. Whatever you want to call it, it's definitely a combination of both. Security people are going away, that's for sure. You know, we're still gonna need security experts. And focus is just a critical aspect about this. >> Dan, Thanks for the insight coming on here. Reinforced. Take a quick second. Give a plug for your company. What you guys looking to do? Your hiring? What's going on? The company? >> Sure lacework. We're gonna help you protect all your workloads, Your configuration. Compliance in the cloud regardless of which cloud way are hiring websites lacework dot com and way love Thio culture Their cultures great, Very fast moving very fast paced, very modern way live and breathe by the success of our customers It's a subscription business. So now we have to continue innovating and renewing. Our customers >> got smart probably to get dealing combination containers. Thanks for coming on. Your coverage here live in Boston. General David, Want to stay tuned for more live coverage after this short break

>> Live from San Diego, California It's the queue covering Sisqo Live US 2019 Tio by Cisco and its ecosystem. Barker's >> back to San Diego. Everybody watching the Cube, the leader and live tech coverage. My name is Dave Volante, and I'm with my co host, Steuben. Amanda, this is Day two for Sisqo. Live 2019. We're in the definite. So still. I was walking around earlier in the last interview, and I think I saw Ron Burgundy out there. Stay classy Sleeping Gow is here. He's the founder and CEO of Met Net Brain Technology's just outside of Boston. Thanks very much for coming on the Q. Thank you there. So you're very welcome. So I want to ask you, I always ask Founders passion for starting companies. Why did you start? >> Well, maybe tired of doing things, Emmanuel. Well, that's alongside the other side of Yes, I used Teo took exam called a C C. I a lot of folks doing here. I failed on my first try. There was a big blow to my eagle, so I decided that we're gonna create a softer help them the past. This is actually the genesis of nettle. I met a friend help people three better doing their network management. >> That's a great story. So tell us more about that brain. What do you guys all about? >> Sure, we're the industry. First chasing time. Little confirmations after our mission is to Democrat ties. Merrick Automation. Every engineer, every task. They should've started with automation before human being touched. This task, >> you know, way go back. Let's say, 10 years ago people were afraid of automation. You know, they thought I was going to take away their jobs. They steal and they still are. We'll talk about that. You get this and I want to ask you about the blockers. They were fearful they wanted the touch thing. But the reality is people talk about digital transformation. And it's really all about how you use data, how your leverage data. And you can't be spending your time doing all this stuff that doesn't add value to your business. You have to automate that and move up to more valuable test. But so people are still afraid of automation. Why, what's the blocker there? >> They have the right reason to be afraid. Because so many automation was created a once used exactly wass right. And then you have the cost ofthe tradition automation. You have the complexity to create in their dark automation. You guys realize that middle confirmation You cannot have little gotta measure only work on a portion of your little way. You have to walk on maturity if not all of your narrow right. So that's became very complex. Just like a You wanna a self driving car? 10 You can't go buy a Tesla a new car. You can drive on a song. But if you want to your Yoder Puta striving always song Richard feared it. That's a very complex Well, let's today, Netto. Condemnation had to deal with you. Had a deal with Marty Venna Technology Marty, years of technology. So people spent a lot of money return are very small. There's so they have a right to a fair afraid of them. But the challenges there is what's alternative >> way before you're there. So there, if I understand it, just playing back there, solving a very narrow problem, they do it once, maybe twice. Maybe a rudimentary example would be a script. Yeah, right, right. And then it breaks or it doesn't afford something else in the network changes, and it really doesn't affect that, right? >> Yeah. I mean, you know, I think back to money network engineers. It's like, Well, I'm sitting there, I've got all my keep knobs and I get everything done and they say, No, don't breathe on it because it's just the way I want it less. It can't be that doesn't scale. It doesn't respond to the business. I need to be able to, you know, respond fast what is needed. And things are changing in every environment. So it's something that I couldn't, as you know, a person or a team keep up with myself, and therefore I need to have more standardized components, and I need to have intelligence that can help me. >> Let's sit and let's >> s so we've laid out the generalized way that we've laid out the problem. What's what's the better approach? >> Well, give you looking out of the challenge today is you have to have Dave ups, which a lot of here they have not engineer know howto script and the mid off the engineer who know how little cooperates walk together. So there's a date, a part of it. There's a knowledge. A part of this too has to meet to create a narrow coordination and that Ned Ogata may have to be a scale. So the challenge traditional thoracotomy here, why is for short lie on if you're going down? Technical level is wise A terra, too many data and structure and the otherwise Our knowledge knowledge cannot be codified. So you have the knowledge sitting people's head, right, Eh Programa had to walk in with a narrow canyon near together. You make it a cost hire. You make it a very unskilled apple. So those are the challenge. So how fast Motor way have to do so neither brand for last 15 years You decide to look differently that we created some saying called operating system off total network and actually use this to manage over 1,000 of mental models technology. And he threw problem. You can't continually adding new savings into this problem. So the benefit of it is narrow. Canyon near anybody can create automation. They don't have to know how to writing a code. Right? And Deborah, who knows the code can also use this problem. All the people who are familiar with technology like and people they can integrate that never >> pray. Okay, so you have all this data I wish I could say is unstructured So he doesn't have any meaning. Data's plentiful insights aren't, uh And then you have this what I call tribal knowledge. Joe knows how to do it, but nobody else knows how to do it. So you're marrying those two. How are you doing that? Using machine intelligence and and iterating building models, can you get that's amore colors? Tow How you go about that? What's the secret sauce >> way? Took a hybrid approach. First call on you have to more than the entire network. With this we'll kind of operating system called on their own way have about 20 12,000 valuables modeling a device and that 12,000 valuable adults across your let's say 1,000 known there or there will be 12,000,000 valuables describing your medal. That's that's first. Zang on top of 12,000,000 valuables will be continually monitored. A slow aye aye, and the machine learning give something called a baseline data. But on top of it, the user, the human being will have the knowledge young what is considered normal what is considered abnormal. They can add their intelligence through something called excludable rumble on couple of this system, and their system now can be wrong at any time. Which talking about where somebody attacking you when that OK is un afford all you through a human being, all our task Now the automation can be wrong guessing time. So >> this the expert, the subject matter expert, the main expert that the person with the knowledge he or she can inject that neck knowledge into your system, and then it generates and improves overtime. That's right, >> and it always improve, and other people can open the hood. I can't continue improving. Tell it so the whole automation in the past, it was. Why is the writer wants only used once? Because it's a colossal? It's a script. You I you input and output just text. So it wasn't a designer with a company, has a motive behind it. So you do it, You beauty your model. You're writing a logical whizzing a same periods off, we decided. We think that's you. Cannot a scale that way. >> OK, so obviously you can stop Dave from inputting his lack of knowledge into the system with, you know, security control and access control. Yeah, but there must be a bell curve in terms of the quality of the knowledge that goes into the system. You know, Joe might be a you know, a superstar. And, you know, stew maybe doesn't know as much about it. No offense, too. Student. So good. So how do you sort of, you know, balance that out? Do you tryto reach an equilibrium or can you wait? Jos Knowledge more than Stu's knowledge. How does that work? >> So the idea that this automation platform has something called excludable Rambo like pseudo Rambo can sure and implacably improved by Sri source One is any near themselves, right? The otherwise by underlying engine. So way talk about a I and the machine learning we have is that we also have a loo engine way. Basically, adjusting that ourselves certainly is through Claverie Partner, for example, Sisko, who run many years of Qatar where they have a lot of no house. Let's attack that knowledge can be pushed to the user. We actually have a in our system that a partnership with Cisco attack South and those script can be wrong. slow. Never prayer without a using woman getting the benefit of without talking with attack. Getting the answer? >> Yes, I think you actually partially answered. The question I have is how do you make sure we don't automata bad process? Yeah. So And maybe talk a little bit about kind of the training process to your original. Why of the company is to make things easier. You know, What's the ramp up period for someone that gets in giving me a bit of a how many engineers you guys have >> worked with? The automatic Allied mission. Our mission statement of neda prayer is to Democrat ties. Network automation, you know, used to be network automation on ly the guru's guru to it. Right, Dave off. Send a satchel. And a young generation. My generation who used come, Ally, this is not us, right? This is the same, you know. But we believe nowadays, with the complicity of middle with a cloud, computing with a cybersecurity demand the alternative Genetic automation is just no longer viable. So way really put a lot of starting to it and say how we can put a network automation into everyone's hand. So the things we tell as three angle of it, while his other missions can be created by anyone, the second meaning they've ofthe net off. Anyone who know have knowledge on metal can create automation. Second piece of automation can lunched at any time. Somebody attacking you middle of the night. They don't tell you Automation can lunch to protect Theo, and they're always out. You don't have people the time of the charter. Automation can lunch the tax losses, so it's called a lunch. Any time certain want is can adapt to any work follow. You have trouble shooting. You have nettle changes. You have compliance, right? You have documentation workflow. The automation should be able to attack to any of this will clothe topping digression tomorrow. We have when service now. So there's a ticket. Human being shouldn't touches a ticket before automation has dies, she'll write. Is a human should come in and then use continually use automation. So >> So you talk about democratizing automation network automation. So it's so anybody who sees a manual process that's wasting time. I can sort of solve that problem is essentially what you're >> doing. That's what I did exactly what we >> know So is there, uh, is there a pattern emerging in terms of best practice in terms of how customers are adopting your technology? >> Yes. Now we see more animal customer creating This thing's almost like a club, the power user, and we haven't caught it. Normal user. They have knowledge in their heads. Pattern immunity is emergent. We saw. Is there now work proactively say, How can I put that knowledge into a set of excludable format so that I don't get escalate all the time, right? So that I can do the same and more meaningful to me that I be repeating the same scene 10 times a month? Right? And I should want it my way. Caught a shift to the left a little while doing level to the machine doing the Level one task level two. Level three are doing more meaningful sex. >> How different is what you're doing it net brain from what others are doing in the marketplace. What's the differentiation? How do you compete? >> Yeah, Little got 1,000,000 so far has being a piecemeal, I think, a fragment. It's things that has done typical in a sweeping cracker. Why is wholesale Hardaway approach you replace the hardware was esti N S P. Where's d? Let there's automation Capitol Building Fifth, I caught a Tesla approached by a Tesla, and you can drive and a self driving. The second approaches softer approach is as well. We are leading build a model of your partner or apply machine learning and statistics and was behind but also more importantly, open architecture. Allow a human being to put their intelligence into this. Let's second approach and insert approaches. Actually service little outsourcer take you, help you We're moving way or walk alone in the cloud because there's a paid automation there, right so way are focusing on the middle portion of it. And the landscaper is really where we have over 2,000 identifies customer and they're automating. This is not a just wall twice a week, but 1,000 times a day. We really excited that the automation in that escape scale is transforming how metal and is being managed and enable things like collaboration. But I used to be people from here. People from offshore couldn't walk together because knowledge, data and knowledge is hard to communicate with automation. We see collaboration is happening more collaboration happening. So we've >> been talking about automation in the network for my entire career. Feels like the promise has been there for decades. That site feels like over the last couple of years, we've really seen automation. Not just a networking, but we've been covering a lot like the robotic process automation. All the different pieces of it are seeing automation. Bring in, gives a little bit look forward. What? What do you predict is gonna happen with automation in I t over the next couple of years? A >> future that's great Way have a cloud computing. We have cyber security. We have the share of scale middle driving the network automation to the front and center as a solution. And my prediction in the next five years probably surrounded one izing automation gonna be ubiquitous. Gonna be everywhere. No human being should touch a ticket without automation through the first task. First right second way. Believe things called a collaborative nature of automation will be happy. The other was a local. Automation is following the packet from one narrow kennedy to the other entity. Example would be your manager service provider and the price they collaborated. Manager Nettle common little But when there's something wrong we don't know each part Which part? I have issues so automation define it by one entity Could it be wrong Across multiple So is provider like cloud provider also come Automation can be initiated by the Enterprise Client way also see the hado A vendor like Cisco and their customer has collaborated Automation happening So next five years will be very interesting The Manu away to manage and operate near Oca will be finally go away >> Last question Give us the business update You mentioned 2,000 customers You're hundreds of employees Any other business metrics you Khun, you can share with us Where do you want to take this company >> way really wanted behind every enterprise. Well, Misha is a Democrat. Eyes network automation way Looking at it in the next five years our business in a girl 10 times. >> Well, good luck. Thank you. Thanks very much for coming on the queue of a great story. Thank you. Thank you for the congratulations For all your success. Think Keep right! Everybody stew and I will be back. Lisa Martin as well as here with an X guest Live from Cisco Live 2019 in San Diego. You watching the cube right back

>> live from Anaheim, California. It's the queue covering nutanix dot Next twenty nineteen. Brought to you by Nutanix. >> Welcome back, everyone to the cubes. Live coverage of nutanix dot Next here in Anaheim, California. I'm your host, Rebecca Night, along with my co host, John Furrier. We have two guests for the segment. We have Ken Ringle. He is the vice president Global Alliance Architecture at Wien. Thanks so much for coming on. The your Cube alum Returning to the >> great to be here again >> And we have Mark Ni Mire. He is the director of product management for data protection Nutanix Thank you for coming on the Cube. So we're one of the big thing when the big announcements today is nutanix mine. I want to talk to you and ask you Ken. What brings nutanix and team together to create Nutanix? Mine? >> Yeah, sure where you know we're super excited. You know, we've been partners for many years. We actually brought a product to market together last year, called the availability for nutanix, which added support for primary workloads. But we hadn't been working together on the secondary side, right where we land are backups And it became very clear, you know, from our customers that they were, You know, we really want to provide that seamless experience, a turnkey experience for our customers. So we started talking together and really, this is over a year in the making, right? We came together and we started brainstorming and it became very clear in a lot of synergies between the companies and and what we could deliver to our customers. So it became obvious. Hey, let's let's bring this together. It was more about the high. Not not not when they're you know, it was It was it was how how do we do it? >> And what were the problems you were trying to solve here? What were the issues that you were hearing from customers? >> So when we talk to customers, a lot of complaints that there are customers are voicing its around the complexity in their backup infrastructure, Right? Nutanix is known for providing simplicity for the primary infrastructure, right, reducing complexity that you typically having your free chair our protection. New tenants mind will provides the same amount ofthe simplicity for your for your lack of infrastructure, a type of converts solution that includes the Wien sell fair to provide data protection services for any workload running in your data center >> Integrations A big part of the modernized in hybrid on cloud with, you know, on premises Private Cloud. As you guys know, integrating it is not always that easy. This's pretty important. You guys been very successful with your partnering. Your product has been successful. Revenues actually show that as the cloud comes into the picture, a lot of people have been tweaking the game there game a little bit on the product side because of the unique differences with Cloud. So with multi cloud, private cloud and hybrid, what changes what's changing in the customer mind right now? Because they got their own premises thing pretty solid, but operationally it feels like cloud. But how does it affect the d Rp? Because this is going to be one of the big conversations. >> Yeah, no question. I mean, when we when we talked to our customers on how they're protecting their data, you know, we hear from a lot of customers is hey, we want to leverage the cloud for for a number of things. And I think the cloud has gone through an evolution right, You know, it's just like anything there's, you know, the great great hey could do all these things. And then people come back to reality. And what we see a lot of our customers doing is is using the cloud for long term data retention, using it as a secondary d our site. You know, you go back five years, you know, customer, especially large customers, all have two physical data centers. So now what? We're seeing a lot of our customers. They have that one physical primary data center, but they're leveraging the cloud. Is there as there d our site, right? So they're they're moving their data there with our recovery capabilities, you know, you can actually get a cloud workload recovered in a disaster scenario quite rapidly. And that's that's been a major change over the especially over the last couple years. >> And then, if you really look at integration, right, the the new Tenants Mind solution to Platform provides integration in six different areas. Integration is sizing, making it very easy to size, or we've identified some form. Factors were building it into new. He's an ex isar, very easy to, uh, to buy single skew that basically provides the hardware hardware support suffer for from from nutanix and suffer from being easy to deploy. Very automated installer that turns the nutanix appliance into a into a mine appliance in a matter of minutes and an easy to manage integrated dashboards Easy to scale right Horse entering is tailing out for capacity, but also for increased performance and then integrated support, where we have a joint support model between the two companies to really help our customers in case there are issues. >> So why why did you choose each other? What was the courtship like and and how how did they have the relationship evolve? >> So if you look at vino and new tenants, we really focus on quality and providing simplicity for our customers. That if that is something that really it was very apparent from the beginning that we have the same view points in the same Mantorras, basically around simplicity, providing quality both off our MPs scores are definitely the highest in the industry, something that is that is practically unheard of. So it was a very natural. I think this company's coming together and providing value together. >> Yeah, I mean, we're maniacal about customer success and customer support and customer satisfaction. That was that was very clear early on. You know, Venus as a peer software company in a way, and we need a partner in order to deliver a full stack solution. Nutanix is there's just a lot of synergies that culture, the companies, the size of the companies, the age of the cos it just It's just a great partnership in a great fit where, you know, there's just we're both moving in the same direction in in concert >> both hard charging cultures to, you know, entrepreneurial high quality was focus on the customer but hard charging. You guys move fast, so well, I got the two experts here on data protection. I gotta ask you about my favorite topic, ransomware, because people are fun and get rid of that tape. I got to get stuff back faster on recoveries. But ransomware really highlights the data protection scenario because they target like departments that maybe understaffed or might be vulnerable or just don't fix their problem. They go back to the well every time that it's everything you want to make some cash and go back. This >> is where >> software. Khun solved a lot of problem. What's your what's your guy's view of the whole ransomware thing? Because it becomes huge. >> Yeah, no question. Way Hear this from a lot of our customers And of course, we can't talk about it when we have customers come to us. But, you know, we've had many customers come to us, and unfortunately, it's after the fact A I you know, I had a ransomware attack and, you know, I lost all this, but now you know I can't let it happen again, but it's really from a backup strategy perspective. It's still important to keep air gap. You know, these ransom where these folks that are building these, these ransomware attacks, they're very intelligent. They've gotten extremely intelligent and how they move from one system to another and they even hide out. So you, you you eliminate a ransomware attack and that thing can come right back. You restore a backup that was a month old that has that sitting and waiting. So, you know, having a solution that can actually test your backups before you put him in production. Haven't air gap, you know, have a mutability on some of your backup date of those. These are all things we talk to our coast. >> You'd be a point about the bridges up because it was just going to a customer about this. They fixed the ransomware paid but didn't fix the problem. Yeah, so it's, like, end of the month and eat some cash right around the end of the month. But, you know, saying they shake him down again. Yes. The wells there, they keep on coming back. So there's, like, community of data perfection. I mean, professionals getting together to kind of get ahead of this problem >> on DH, then the other aspect ofthe basically being able to recover quickly his performance, right? Nutanix platform provides have informed the throughput. So you can very quickly restore your work clothes as well. >> Yeah, that would be a great problem of simplifying. Yeah, exactly. >> So what are the next steps for this alliance? Where where where do we go from here? >> So from from basically we've just finished a round of vested beta testing right way are going to be maniacally focused on the first hundred customers really understanding how they're going to put mine in their data centers. How they were going to use it as in their data sent to protect their Derek. There their workloads and their applications from their own. We have a lot of plans, very interesting plans around Rome Emperor. We can build even tighter integration from a management perspective, but also from a data fabric perspective. Weather that's on prime a weather gets goes into desire clouded nutanix icloud There's a lot of interesting areas that brain and I have been brainstorming on white boarding and so on that you'LL see coming out in the next two versions of the products. >> What's the big customer request? What's the big feature request? What's the big ask from customers for you guys together? >> At the end of the day, you know, our customers are really asking for simplicity. They they want, they want to simplify their environment. I mean, it is moving from specialists generalists, and they and they want a system that works well together. That's going to lower their costs and they want peace of mind. So they want. They want to know their backups are protected, They want to know they can restore. And that's really what we're focused on is providing that to our customers >> and reliable. Have making sure their works hundred percent any new things emerging out the multi cloud thing that you guys see coming down around the quarter that you're getting ready for to help customers simplified any any signals from this multi cloud equation. >> So one of the things I look at is really the lines between on Graham and primary and secondary and tertiary. They're really blurring. Also, the lines between Young Prem and Cloud are blurring as well, but you can replicate data and replicate backups really, really efficiently to wherever it needs to be. So I really see that as a zoo core strength to enable value that plays into the military >> true operational model across whatever environment, and still do the tearing and things you need to do. >> Yeah, no doubt flexibility and being able to support, you know, multiple environments. You know, that's that's that's absolutely what we're after. It's It's what we what we leverage is part of the nutanix ecosystem is is that breath of coverage, but but also given customer choice. >> Just talking to Rebecca, which we love data project. Should I leave lights? Ideo delegate always whimsy will you guys be on next week? This is a huge conversation that used to be a bolt on conversation in the old days of now. Data protection, backup in recovery, disaster planning. All part of a operating model. Holistic picture. Yeah. How is that? We're one hundred percent there yet. And all customers where they still use. This stuff's still kind of like, not forgetting to design in. >> Yeah, I mean, protection. You know where you know, lots of our customers are coming to us because their struggle with legacy solutions and they're looking to modernize their whole infrastructure right there, modernizing where they land. The backups are modernizing the platform that that lands those backups on the infrastructure. And so, you know, that's it's a major problem for our customers and really, you know, you you mentioned, you know, availability and you know, you you go back five years, maybe five, seven, eight years. You know, availability was measured in three nines. Four, ninety five, ninety availability. You know, everyone in the world of of everything cloud and everything sas, you know, availability is one hundred percent or nothing. You know, it's there is no there. There really is no sort of anything but a one hundred percent availability, >> and its security highlights all the problems. So another customer about this ransom, one other ransomware customer they were doing all the backups on tape. Can you imagine? Of course, they're talking for ransom where it's just good on the director. He was still using tape because they can't turn around fast enough. It was a big problem. >> Yeah, you know, it's funny, you know, you you know, we're focused on innovation and next things. But when you you know, you you then have some of those customer conversations. And some of them are still, you know, because of their compliance and processing procedures, There's still, you know, five years behind may be where we are. You know, you've got a you gotto sort of bring them along for the journey to knowing that they're gonna they're gonna trail behind. But for the for the early adopters and the innovators way also have to serve them as well. >> And they got there. They gotta level up themselves to it, son. Them too. They had they had the level of >> So speaking of innovation, you are two different companies. You already talked about this, its energies and the similarities in culture. But you are two companies coming together to build a product. How does that work? I mean, do you do get in the same room? Do you watch the same movies? Do you have a happy you? >> So >> get one brain working on this >> female. Vamos a distributed company. We are distributed company. So it's it's It's a lot of calls and so on. But it's it's really fun to really see it. She had come together and becoming really right. Yes, there's a lot of hard engineering problems that we have to solve in some very deep discussions around layout and things like that. But then doubling it up, working on the joint value prop and working on the joint marketing it really is a very nice wide set of off capabilities and skills that we've been working >> on. And when I went out, I mean, it is hard. It is hard to bring to two things together and work on them jointly. And we've, you know, so far been fairly successful. What I would tell you is it it brings some some advantages to us as well Because we have a best of breed platform. We have a best to breed data protection platform. You know, bringing those together bring some advantages that maybe someone that does all that together on their own don't have because it's not a focus area for them. Right? So, you know, it's our job to make sure we take advantage of that and provide some additional things for our customers that maybe they won't get out of some of those other platforms. >> Well, Mark and Ken, thank you both. So much for coming on the Cube. It was a pleasure having you. >> Thank you very much. >> Thanks for having us. >> I'm Rebecca Knight for John Furrier. We will have Ah, we'Ll have more from nutanix dot Next coming up just a little bit. Stay with us.

>> live from Boston, Massachusetts. It's the you covering your red hat. Some twenty nineteen rots. You buy bread hat. >> Well, good afternoon. Welcome back here on the Cube as we continue our coverage of the Red Hat Summit and you know, every once in a while you come across one of these fascinating topics. It's what's doing I get so excited about when we do the Cube interviews is that you never know where >> you're >> going to go, the direction you're going to take. And I think this next interview has been a fit into one of those wow interviews for you at home. Along was to minimum. I am John Walls, and we're joined by Dr Ellen Grant, who was the director of the fetal neo NATO Neuroimaging and Developmental Science Center of Boston Children's Hospital. So far, so good, right? And the professor, Radiology and pediatrics at the Harvard Medical School's Dr Grant. Thank you for joining us here on the Cube and Dr Rudolph Pienaar, who is the technical director at the F n N D. S. C. And an instructor of radiology at the Harvard Medical School. So Dr Rudolph Pienaar, thank you for joining us as well. Thank you very much. All right. Good. So we're talking about what? The Chris Project, which was technically based. Project Boston Children's Hospital. I'm going to let you take from their doctor Grant. If you would just talk about the genesis of this program, the project, what its goal, wass And now how it's been carried out. And then we'LL bring in Dr PNR after that. So if you would place >> sure, it's so The goal of the Chris Project was to bring innovated imaging, announces to the bedside to the front end where clinicians are not like high are working all the time but aren't sophisticated enough or don't have enough memory to remember how to do, you know, line code in Lenox. So this is where initially started when I was reading clinical studies and I wanted to run a complex analysis, but there was no way to do it easily. I'd have tio call up someone to log into a different computer, bring the images over again lots of conflict steps to run that analysis, and even to do any of these analysis, you have to download the program set up your environment again. Many many steps, said someone. As a physician, I would rather deal with the interpretation and understanding the meaning of those images. Then all that infrastructure steps to bring it together. So that was the genesis of Chris's trying to have a simple Windows point and click way for a physician such as myself, to be able to rapidly do something interesting and then able to show it to a clinician in a conference or in the at the bedside >> and who's working on it, then, I mean, who was supplying what kind of manpower, If you will root off of the project >> kind of in the beginning, I would say maybe one way to characterize it is that we wanted to bring this research software, which lives mostly online, ex onto a Windows world, right? So the people developing that software researchers or computational researchers who do a lot of amazing stuff with image processing. But those tools just never make it really from the research lab outside of that. And one of the reasons is because someone like Ellen might not ever want to fire paternal and typing these commands. So people working on it are all this huge population of researchers making these tools on what we try to do. What I try to help with, How do we get those tools really easily usable in excess of one and, you know, to make a difference? Obviously. So that was a genesis. I was kind of need that we had in the beginning, so it started out, really, as a bunch of scrips, shell scripts, you slight a type of couple stuff, but not so many things on gradually, with time, we try to move to the Web, and then it began to grow and then kind of from the Web stretching to the cloud. And that's kind of the trajectory in the natural. As each step moved along, more and more people kind of came in to play. >> Dr Grant, I think back, you know, I work for a very large storage company and member object storage was going to transform because we have the giant files. We need to be able to store them and manage them and hold them up. But let's talk about the patient side of things. What does this really mean? You know, we had a talk about order of magnitude that cloud can make things faster and easier. But what? What does this mean to patient care? Quality service? >> Well, I think what it means or the goal for patient care is really getting to specialized medicine or individualized medicine on to be able to not just rely on my memory as to what a normal or abnormal images or the patients I may have seen just in my institution. But can we pull together all the knowledge across multiple institutions throughout the country and use more rigorous data announces to support my memory? So I want to have these big bridal in front lobes that air there, the cloud that helped me remember things into tidies connections and not have to remind just rely on my visual gestalt memory, which is obviously going to have some flaws in it. So and if I've never seen a specific disorder, say, for example, at my institution, if they've seen it at other institutions who run these comparisons all of sudden, I made be aware of a new treatment that otherwise I may not have known about >> All right, so one of my understanding is this is tied into the mass open cloud which I've had the pleasure of talking on the program at another show back here in Boston. Talk about a little bit about you know how this is enable I mean massive amounts of data you need to make sure you get that. You know the right data and it's valuable information and to the right people, and it gets updated all the time, so give us a little bit of the inner workings. >> Exactly. So thie inner workings, That's it can be a pretty big story, but kind of the short >> story time Theo Short >> story is that if we can get data in one place, and not just from one institution, from many places, that we can start to do things that are not really possible otherwise so, that's kind of the grand vision. So we're moving along those steps on the mass Open cloud for us makes perfect sense because it's there's a academic linked to Boston University. And then there's thie, Red Hat, being one of the academic sponsors as well in that for this kind of synergy that came together really almost perfectly at the right time, as the cloud was developing as where that was moving in it as we were trying to move to the cloud. It just began to link all together. And that's very much how we got there at the moment on what we're trying to do, which is get data so that we can cause medicine. Really, it's amazing to me. In some ways there's all these amazing devices, but computational e medicine lag so far behind the rest of the industry. There's so little integration. There's so little advanced processing going on. There's so much you can do with so little effort, you could do so much. So that's part of the >> vision as well. So help me out here a little bit, Yeah, I mean, maybe it before and after. Let's look at the situation may be clinically speaking here, where a finding or a revelation that you developed is now possible where it wasn't before and kind of what those consequences might have been. And then maybe, how the result has changed now. So maybe that would help paint up a practical picture of what we're talking about. >> I could use one example we're working on, but we haven't got fully to the clouds. All of these things are in their infancy because we still have to deal with the encryption part, which is a work in progress. But for example, we have mind our clinical databases to get examples of normal images and using that I can run comparisons of a case. It comes up to say whether this looks normal or abnormal sweat flags. The condition is to whether it's normal or abnormal, and that helps when there's trainees are people, not is experienced in reading those kinds of images. So again we're at the very beginnings of this. It's one set of pictures. There's many sets of pictures that we get, so there's a long road to get to fully female type are characterized anyone brain. But we're starting at the beginning those steps to very to digitally characterize each brain so we can then start to run. Comparisons against large libraries of other normals are large libraries of genetic disorders and start to match them up. And >> this is insecure. You working in fetal neural imaging as well. So you're saying you could take a an image of ah baby in a mother's womb and many hundreds thousands, whatever it is and you developed this basically a catalogue of what a healthy brain might look like. And now you're offering an opportunity to take a image here on early May of twenty nineteen. And compared to that catalogue, look and determine whether might be anabel normality that otherwise could have been spotted before. >> Correct and put a number to that in terms of a similarity value our probability values so that it's not just Mia's a collision, say Well, I think it's a little abnormal because it is hard to interpret that in terms of how severe is the spectrum of normal. How how? Sure you. So we put all these dated together. We can start to get more predictive value because we couldn't follow more kids and understand if it's that a a sima that too similar what's most likely disorder? What's the best treatment? So it gives you better FINA typing of the disorders that appear early and fetal life, some of which are linked to we think he treated, say, for example, with upcoming gene therapies and other nutritional intervention so we could do this characterization early on. We hope we can identify early therapies that our target to targeted to the abnormalities we detect. >> So intervene well ahead of time. Absolutely. >> I don't know. The other thing is, I mean Ellen has often times said how many images she looks at in the day on other radiologist, and it's it's amazing. It's she said, the number hundred thousand one point so you can imagine the human fatigue, right? So it Matt, imagine if you could do a quick pre processing on just flag ones that really are abnormal by you know they could be grossly abnormal. But at least let's get those on the top of the queue when you can look at it when you are much more able to, you know, think, think, think these things through. So there's one good reason of having these things sitting on an automated system. Stay out of the cloud over it might be >> Where are we with the roll out of this? This and kind of expansion toe, maybe other partners. >> So a lot of stuff has been happening over the last year. I mean, the the entire platform is still, I would say, somewhat prototypical, but we have a ll the pipelines kind of connected, so data can flow from a place like the hospital flowed to the cloud. Of course, this is all you know, protected and encrypted on the cloud weaken Do kind of weaken. Do any analysis we want to do Provided the analysis already exists, we can get the results back. Two definition we have the interface is the weapon to faces built their growing. So you can at this point, almost run the entire system without ever touching a command line. A year ago, it was partially there. A year ago, you had to use a command line. Now you don't have to. Next year will be even more streamlined. So this is the way it's moving right now and was great for me personally. About the cloud as well is that it's not just here in Boston where you, Khun benefit from using these technologies, you know, for the price of a cellphone on DH cell signal. You can use this kind of technology anywhere. You could be in the bush in Africa for argument's sake, and you can have access to these libraries of databases imaging that might exist. You, khun compare Images are collected wherever it might be just for the price of connecting to the Internet. >> You just need a broadband connection >> just right. Just exactly. >> Sometimes when you think about again about you know, we've talked about mobile technology five g coming on as it is here in the U. S. Rural health care leveling that and Third World, I was thinking more along the lines of here in the States and with some memories that just don't have access to the kind of, like, obviously platinum carry you get here in the Boston area. But all those possibilities would exist or could exist based on the findings that you're getting right now with Chris Project. So >> where does the Chris project go from here? >> Well, what we'd like to do is get more hospitals on board, uh, thinking pediatrics, we have a lot of challenge because there are so many different rare disorders that it's hard to study any one of them from one hospital. So we have to work together. There's been some effort to bring together some genetic databases, but we really need to being also the imaging bait databases together. So hopefully we can start to get a consortium of some of the pediatric hospitals working together. We need that also because normal for normal, you need to know the gender, the age, the thie ethnicity. You know, so many demographics that are nice to characterize what normal is. So if we all work together, we can also get a better idea of what is normal. What is normal variants. And there's a lot of other projects that are funded by N. H. Building up some of those databases as well, too. But we could put him into all into one place where we can actually now query on that. Then we could start to really do precision medicine. >> And the other thing, which we definitely are working on and I want to do, is build a community of developers around this platform because, you know, there's no way our team can write all of these tools. No, no, no, we want to. But we want everyone else who wants to make these tools very easily hop onto this platform. And that's very important to us because it's so much easier to develop to christen it just about the Amazon. There's almost no comparison. How much easier >> we'Ll Definitely theme, we hear echoing throughout Red Hat summit here is that Does that tie into, like, the open shift community? Or, you know, what is the intersection with red hat? >> It definitely does, because this is kind of the age of continue ization, which makes so many things so much easier on DH. This platform that we've developed is all about container ization. So we want to have medical by medical or any kind of scientific developers get onto that container ization idea because when they do that and it's not that hard to do. But when you do that, then suddenly you can have your your analysis run almost anywhere. >> And that's an important part in medicine, because I run the same analysis on different computers, get different results. So the container ization concept, I think, is something that we've been after, which is a reproduce ability that anybody can run it along there, use the same container we know we're going. Same result. And that is >> critical. Yes, especially with what you're doing right, you have to have that one hundred percent certainty. Yep. Standardisation goes along, Ray. Sort of fascinating stuff. Thank you both for joining us. And good luck. You're an exciting phase, that's for sure. And we wish you all the best going forward here. Thank you so much. Thank you both. Back with more from Boston. You're watching Red Hat Summit coverage live here on the Q t.

>> From the SiliconANGLE Media office in Boston, Massachusetts, it's theCube! (funky electronic music) Now, here's your host, Dave Vellante! >> Welcome, everybody, to this Cube conversation here in our Marlborough offices. I am very excited today, I spent a number of years at IDC, which, of course, is owned by IDG. And there's a new book out, relatively new, called Future Forward: Leadership Lessons from Patrick McGovern, the Visionary Who Circled the Globe and Built a Technology Media Empire. And it's a great book, lotta stories that I didn't know, many that I did know, and the author of that book, Glenn Rifkin, is here to talk about not only Pat McGovern but also some of the lessons that he put forth to help us as entrepreneurs and leaders apply to create better businesses and change the world. Glenn, thanks so much for comin' on theCube. >> Thank you, Dave, great to see ya. >> So let me start with, why did you write this book? >> Well, a couple reasons. The main reason was Patrick McGovern III, Pat's son, came to me at the end of 2016 and said, "My father had died in 2014 and I feel like his legacy deserves a book, and many people told me you were the guy to do it." So the background on that I, myself, worked at IDG back in the 1980s, I was an editor at Computerworld, got to know Pat during that time, did some work for him after I left Computerworld, on a one-on-one basis. Then I would see him over the years, interview him for the New York Times or other magazines, and every time I'd see Pat, I'd end our conversation by saying, "Pat, when are we gonna do your book?" And he would laugh, and he would say, "I'm not ready to do that yet, there's just still too much to do." And so it became sort of an inside joke for us, but I always really did wanna write this book about him because I felt he deserved a book. He was just one of these game-changing pioneers in the tech industry. >> He really was, of course, the book was even more meaningful for me, we, you and I started right in the same time, 1983-- >> Yeah. >> And by that time, IDG was almost 20 years old and it was quite a powerhouse then, but boy, we saw, really the ascendancy of IDG as a brand and, you know, the book reviews on, you know, the back covers are tech elite: Benioff wrote the forward, Mark Benioff, you had Bill Gates in there, Walter Isaacson was in there, Guy Kawasaki, Bob Metcalfe, George Colony-- >> Right. >> Who actually worked for a little stint at IDC for a while. John Markoff of The New York Times, so, you know, the elite of tech really sort of blessed this book and it was really a lot to do with Pat McGovern, right? >> Oh, absolutely, I think that the people on the inside understood how important he was to the history of the tech industry. He was not, you know, a household name, first of all, you didn't think of Steve Jobs, Bill Gates, and then Pat McGovern, however, those who are in the know realize that he was as important in his own way as they were. Because somebody had to chronicle this story, somebody had to share the story of the evolution of this amazing information technology and how it changed the world. And Pat was never a front-of-the-TV-camera guy-- >> Right. >> He was a guy who put his people forward, he put his products forward, for sure, which is why IDG, as a corporate name, you know, most people don't know what that means, but people did know Macworld, people did know PCWorld, they knew IDC, they knew Computerworld for sure. So that was Pat's view of the world, he didn't care whether he had the spotlight on him or not. >> When you listen to leaders like Reed Hoffman or Eric Schmidt talk about, you know, great companies and how to build great companies, they always come back to culture. >> Yup. >> The book opens with a scene of, and we all, that I usually remember this, well, we're just hangin' around, waitin' for Pat to come in and hand out what was then called the Christmas bonus-- >> Right. >> Back when that wasn't politically incorrect to say. Now, of course, it's the holiday bonus. But it was, it was the Christmas bonus time and Pat was coming around and he was gonna personally hand a bonus, which was a substantial bonus, to every single employee at the company. I mean, and he did that, really, literally, forever. >> Forever, yeah. >> Throughout his career. >> Yeah, it was unheard of, CEOs just didn't do that and still don't do that, you were lucky, you got a message on the, you know, in the lunchroom from the CEO, "Good work, troops! Keep up the good work!" Pat just had a really different view of the culture of this company, as you know from having been there, and I know. It was very familial, there was a sense that we were all in this together, and it really was important for him to let every employee know that. The idea that he went to every desk in every office for IDG around the United States, when we were there in the '80s there were probably 5,000 employees in the US, he had to devote substantial amount-- >> Weeks and weeks! >> Weeks at a time to come to every building and do this, but year after year he insisted on doing it, his assistant at the time, Mary Dolaher told me she wanted to sign the cards, the Christmas cards, and he insisted that he ensign every one of them personally. This was the kind of view he had of how you keep employees happy, if your employees are happy, the customers are gonna be happy, and you're gonna make a lot of money. And that's what he did. >> And it wasn't just that. He had this awesome holiday party that you described, which was epic, and during the party, they would actually take pictures of every single person at the party and then they would load the carousel, you remember the 35-mm. carousel, and then, you know, toward the end of the evening, they would play that and everybody was transfixed 'cause they wanted to see their, the picture of themselves! >> Yeah, yeah. (laughs) >> I mean, it was ge-- and to actually pull that off in the 1980s was not trivial! Today, it would be a piece of cake. And then there was the IDG update, you know, the Good News memos, there was the 10-year lunch, the 20-year trips around the world, there were a lot of really rich benefits that, you know, in and of themselves maybe not a huge deal, but that was the culture that he set. >> Yeah, there was no question that if you talked to anybody who worked in this company over, say, the last 50 years, you were gonna get the same kind of stories. I've been kind of amazed, I'm going around, you know, marketing the book, talking about the book at various events, and the deep affection for this guy that still holds five years after he died, it's just remarkable. You don't really see that with the CEO class, there's a couple, you know, Steve Jobs left a great legacy of creativity, he was not a wonderful guy to his employees, but Pat McGovern, people loved this guy, and they st-- I would be signing books and somebody'd say, "Oh, I've been at IDG for 27 years and I remember all of this," and "I've been there 33 years," and there's a real longevity to this impact that he had on people. >> Now, the book was just, it was not just sort of a biography on McGovern, it was really about lessons from a leader and an entrepreneur and a media mogul who grew this great company in this culture that we can apply, you know, as business people and business leaders. Just to give you a sense of what Pat McGovern did, he really didn't take any outside capital, he did a little bit of, you know, public offering with IDG Books, but, really, you know, no outside capital, it was completely self-funded. He built a $3.8 billion empire, 300 publications, 280 million readers, and I think it was almost 100 or maybe even more, 100 countries. And so, that's an-- like you were, used the word remarkable, that is a remarkable achievement for a self-funded company. >> Yeah, Pat had a very clear vision of how, first of all, Pat had a photographic memory and if you were a manager in the company, you got a chance to sit in meetings with Pat and if you didn't know the numbers better than he did, which was a tough challenge, you were in trouble! 'Cause he knew everything, and so, he was really a numbers-focused guy and he understood that, you know, his best way to make profit was to not be looking for outside funding, not to have to share the wealth with investors, that you could do this yourself if you ran it tightly, you know, I called it in the book a 'loose-tight organization,' loose meaning he was a deep believer in decentralization, that every market needed its own leadership because they knew the market, you know, in Austria or in Russia or wherever, better than you would know it from a headquarters in Boston, but you also needed that tightness, a firm grip on the finances, you needed to know what was going on with each of the budgets or you were gonna end up in big trouble, which a lot of companies find themselves in. >> Well, and, you know, having worked there, I mean, essentially, if you made your numbers and did so ethically, and if you just kind of followed some of the corporate rules, which we'll talk about, he kind of left you alone. You know, you could, you could pretty much do whatever you wanted, you could stay in any hotel, you really couldn't fly first class, and we'll maybe talk about that-- >> Right. >> But he was a complex man, I mean, he was obviously wealthy, he was a billionaire, he was very generous, but at the same time he was frugal, you know, he drove, you know, a little, a car that was, you know, unremarkable, and we had buy him a car. He flew coach, and I remember one time, I was at a United flight, and I was, I had upgraded, you know, using my miles, and I sat down and right there was Lore McGovern, and we both looked at each other and said right at the same time, "I upgraded!" (laughs) Because Pat never flew up front, but he would always fly with a stack of newspapers in the seat next to him. >> Yeah, well, woe to, you were lucky he wasn't on the plane and spotted you as he was walking past you into coach, because he was not real forgiving when he saw people, people would hide and, you know, try to avoid him at all cost. And, I mean, he was a big man, Pat was 6'3", you know, 250 lbs. at least, built like a linebacker, so he didn't fit into coach that well, and he wasn't flying, you know, the shuttle to New York, he was flyin' to Beijing, he was flyin' to Moscow, he was going all over the world, squeezing himself into these seats. Now, you know, full disclosure, as he got older and had, like, probably 10 million air miles at his disposal, he would upgrade too, occasionally, for those long-haul flights, just 'cause he wanted to be fresh when he would get off the plane. But, yeah, these are legends about Pat that his frugality was just pure legend in the company, he owned this, you know, several versions of that dark blue suit, and that's what you would see him in. He would never deviate from that. And, but, he had his patterns, but he understood the impact those patterns had on his employees and on his customers. >> I wanna get into some of the lessons, because, really, this is what the book is all about, the heart of it. And you mentioned, you know, one, and we're gonna tell from others, but you really gotta stay close to the customer, that was one of the 10 corporate values, and you remember, he used to go to the meetings and he'd sometimes randomly ask people to recite, "What's number eight?" (laughs) And you'd be like, oh, you'd have your cheat sheet there. And so, so, just to give you a sense, this man was an entrepreneur, he started the company in 1964 with a database that he kind of pre-sold, he was kind of the sell, design, build type of mentality, he would pre-sold this thing, and then he started Computerworld in 1967, so it was really only a few years after he launched the company that he started the Computerworld, and other than Data Nation, there was nothing there, huge pent-up demand for that type of publication, and he caught lightning in a bottle, and that's really how he funded, you know, the growth. >> Yeah, oh, no question. Computerworld became, you know, the bible of the industry, it became a cash cow for IDG, you know, but at the time, it's so easy to look in hindsight and say, oh, well, obviously. But when Pat was doing this, one little-known fact is he was an editor at a publication called Computers and Automation that was based in Newton, Massachusetts and he kept that job even after he started IDC, which was the original company in 1964. It was gonna be a research company, and it was doing great, he was seeing the build-up, but it wasn't 'til '67 when he started Computerworld, that he said, "Okay, now this is gonna be a full-time gig for me," and he left the other publication for good. But, you know, he was sorta hedging his bets there for a little while. >> And that's where he really gained respect for what we'll call the 'Chinese Wallet,' the, you know, editorial versus advertising. We're gonna talk about that some more. So I mentioned, 1967, Computerworld. So he launched in 1964, by 1971, he was goin' to Japan, we're gonna talk about the China Stories as well, so, he named the company International Data Corp, where he was at a little spot in Newton, Mass.-- >> Right, right. >> So, he had a vision. You said in your book, you mention, how did this gentleman get it so right for so long? And that really leads to some of the leadership lessons, and one of them in the book was, sort of, have a mission, have a vision, and really, Pat was always talking about information, about information technology, in fact, when Wine for Dummies came out, it kind of created a little friction, that was really off the center. >> Or Wine for Dummies, or Sex for Dummies! >> Yeah, Sex for Dummies, boy, yeah! >> With, that's right, Ruth Westheimer-- >> Dr. Ruth Westheimer. >> But generally speaking, Glenn, he was on that mark, he really didn't deviate from that vision. >> Yeah, no, it was very crucial to the development of the company that he got people to, you know, buy into that mission, because the mission was everything. And he understood, you know, he had the numbers, but he also saw what was happening out there, from the 1960s, when IBM mainframes filled a room, and, you know, only the high priests of data centers could touch them. He had a vision for, you know, what was coming next and he started to understand that there would be many facets to this information about information technology, it wasn't gonna be boring, if anything, it was gonna be the story of our age and he was gonna stick to it and sell it. >> And, you know, timing is everything, but so is, you know, Pat was a workaholic and had an amazing mind, but one of the things I learned from the book, and you said this, Pat Kenealy mentioned it, all American industrial and social revolutions have had a media company linked to them, Crane and automobiles, Penton and energy, McGraw-Hill and aerospace, Annenberg, of course, and TV, and in technology, it was IDG. >> Yeah, he, like I said earlier, he really was a key figure in the development of this industry and it was, you know, one of the key things about that, a lot publications that came and went made the mistake of being platform or, you know, vertical market specific. And if that market changed, and it was inevitably gonna change in high tech, you were done. He never, you know, he never married himself to some specific technology cycle. His idea was the audience was not gonna change, the audience was gonna have to roll with this, so, the company, IDG, would produce publications that got that, you know, Computerworld was actually a little bit late to the PC game, but eventually got into it and we tracked the different cycles, you know, things in tech move in sine waves, they come and go. And Pat never was, you know, flustered by that, he could handle any kind of changes from the mainframes down to the smartphone when it came. And so, that kind of flexibility, and ability to adjust to markets, really was unprecedented in that particular part of the market. >> One of the other lessons in the book, I call it 'nation-building,' and Pat shared with you that, look, that you shared, actually, with your readers, if you wanna do it right, you've gotta be on the ground, you've gotta be there. And the China story is one that I didn't know about how Pat kind of talked his way into China, tell us, give us a little summary of that story. >> Sure, I love that story because it's so Pat. It was 1978, Pat was in Tokyo on a business trip, one of his many business trips, and he was gonna be flying to Moscow for a trade show. And he got a flight that was gonna make a stopover in Beijing, which in those days was called Peking, and was not open to Americans. There were no US and China diplomatic relations then. But Pat had it in mind that he was going to get off that plane in Beijing and see what he could see. So that meant that he had to leave the flight when it landed in Beijing and talk his way through the customs as they were in China at the time with folks in the, wherever, the Quonset hut that served for the airport, speaking no English, and him speaking no Chinese, he somehow convinced these folks to give him a day pass, 'cause he kept saying to them, "I'm only in transit, it's okay!" (laughs) Like, he wasn't coming, you know, to spy on them on them or anything. So here's this massive American businessman in his dark suit, and he somehow gets into downtown Beijing, which at the time was mostly bicycles, very few cars, there were camels walking down the street, they'd come with traders from Mongolia. The people were still wearing the drab outfits from the Mao era, and Pat just spent the whole day wandering around the city, just soaking it in. He was that kind of a world traveler. He loved different cultures, mostly eastern cultures, and he would pop his head into bookstores. And what he saw were people just clamoring to get their hands on anything, a newspaper, a magazine, and it just, it didn't take long for the light bulb to go on and said, this is a market we need to play in. >> He was fascinated with China, I, you know, as an employee and a business P&L manager, I never understood it, I said, you know, the per capita spending on IT in China was like a dollar, you know? >> Right. >> And I remember my lunch with him, my 10-year lunch, he said, "Yeah, but, you know, there's gonna be a huge opportunity there, and yeah, I don't know how we're gonna get the money out, maybe we'll buy a bunch of tea and ship it over, but I'm not worried about that." And, of course, he meets Hugo Shong, which is a huge player in the book, and the home run out of China was, of course, the venture capital, which he started before there was even a stock market, really, to exit in China. >> Right, yeah. No, he was really a visionary, I mean, that word gets tossed around maybe more than it should, but Pat was a bonafide visionary and he saw things in China that were developing that others didn't see, including, for example, his own board, who told him he was crazy because in 1980, he went back to China without telling them and within days he had a meeting with the ministry of technology and set up a joint venture, cost IDG $250,000, and six months later, the first issue of China Computerworld was being published and within a couple of years it was the biggest publication in China. He said, told me at some point that $250,0000 investment turned into $85 million and when he got home, that first trip, the board was furious, they said, "How can you do business with the commies? You're gonna ruin our brand!" And Pat said, "Just, you know, stick with me on this one, you're gonna see." And the venture capital story was just an offshoot, he saw the opportunity in the early '90s, that venture in China could in fact be a huge market, why not help build it? And that's what he did. >> What's your take on, so, IDG sold to, basically, Chinese investors. >> Yeah. >> It's kind of bittersweet, but in the same time, it's symbolic given Pat's love for China and the Chinese people. There's been a little bit of criticism about that, I know that the US government required IDC to spin out its supercomputer division because of concerns there. I'm always teasing Michael Dow that at the next IDG board meeting, those Lenovo numbers, they're gonna look kinda law. (laughs) But what are your, what's your, what are your thoughts on that, in terms of, you know, people criticize China in terms of IP protections, etc. What would Pat have said to that, do you think? >> You know, Pat made 130 trips to China in his life, that's, we calculated at some point that just the air time in planes would have been something like three and a half to four years of his life on planes going to China and back. I think Pat would, today, acknowledge, as he did then, that China has issues, there's not, you can't be that naive. He got that. But he also understood that these were people, at the end of the day, who were thirsty and hungry for information and that they were gonna be a player in the world economy at some point, and that it was crucial for IDG to be at the forefront of that, not just play later, but let's get in early, let's lead the parade. And I think that, you know, some part of him would have been okay with the sale of the company to this conglomerate there, called China Oceanwide. Clearly controversial, I mean, but once Pat died, everyone knew that the company was never gonna be the same with the leader who had been at the helm for 50 years, it was gonna be a tough transition for whoever took over. And I think, you know, it's hard to say, certainly there's criticism of things going on with China. China's gonna be the hot topic page one of the New York Times almost every single day for a long time to come. I think Pat would have said, this was appropriate given my love of China, the kind of return on investment he got from China, I think he would have been okay with it. >> Yeah, and to invoke the Ben Franklin maxim, "Trading partners seldom wage war," and so, you know, I think Pat would have probably looked at it that way, but, huge home run, I mean, I think he was early on into Baidu and Alibaba and Tencent and amazing story. I wanna talk about decentralization because that was always something that was just on our minds as employees of IDG, it was keep the corporate staff lean, have a flat organization, if you had eight, 10, 12 direct reports, that was okay, Pat really meant it when he said, "You're the CEO of your own business!" Whether that business was, you know, IDC, big company, or a manager at IDC, where you might have, you know, done tens of millions of dollars, but you felt like a CEO, you were encouraged to try new things, you were encouraged to fail, and fail fast. Their arch nemesis of IDG was Ziff Davis, they were a command and control, sort of Bill Ziff, CMP to a certain extent was kind of the same way out of Manhasset, totally different philosophies and I think Pat never, ever even came close to wavering from that decentralization philosophy, did he? >> No, no, I mean, I think that the story that he told me that I found fascinating was, he didn't have an epiphany that decentralization would be the mechanism for success, it was more that he had started traveling, and when he'd come back to his office, the memos and requests and papers to sign were stacked up two feet high. And he realized that he was holding up the company because he wasn't there to do this and that at some point, he couldn't do it all, it was gonna be too big for that, and that's when the light came on and said this decentralization concept really makes sense for us, if we're gonna be an international company, which clearly was his mission from the beginning, we have to say the people on the ground in those markets are the people who are gonna make the decisions because we can't make 'em from Boston. And I talked to many people who, were, you know, did a trip to Europe, met the folks in London, met the folks in Munich, and they said to a person, you know, it was so ahead of its time, today it just seems obvious, but in the 1960s, early '70s, it was really not a, you know, a regular leadership tenet in most companies. The command and control that you talked about was the way that you did business. >> And, you know, they both worked, but, you know, from a cultural standpoint, clearly IDG and IDC have had staying power, and he had the three-quarter rule, you talked about it in your book, if you missed your numbers three quarters in a row, you were in trouble. >> Right. >> You know, one quarter, hey, let's talk, two quarters, we maybe make some changes, three quarters, you're gone. >> Right. >> And so, as I said, if you were makin' your numbers, you had wide latitude. One of the things you didn't have latitude on was I'll call it 'pay to play,' you know, crossing that line between editorial and advertising. And Pat would, I remember I was at a meeting one time, I'm sorry to tell these stories, but-- >> That's okay. (laughs) >> But we were at an offsite meeting at a woods meeting and, you know, they give you a exercise, go off and tell us what the customer wants. Bill Laberis, who's the editor-in-chief at Computerworld at the time, said, "Who's the customer?" And Pat said, "That's a great question! To the publisher, it's the advertiser. To you, Bill, and the editorial staff, it's the reader. And both are equally important." And Pat would never allow the editorial to be compromised by the advertiser. >> Yeah, no, he, there was a clear barrier between church and state in that company and he, you know, consistently backed editorial on that issue because, you know, keep in mind when we started then, and I was, you know, a journalist hoping to, you know, change the world, the trade press then was considered, like, a little below the mainstream business press. The trade press had a reputation for being a little too cozy with the advertisers, so, and Pat said early on, "We can't do that, because everything we have, our product is built, the brand is built on integrity. And if the reader doesn't believe that what we're reporting is actually true and factual and unbiased, we're gonna lose to the advertisers in the long run anyway." So he was clear that that had to be the case and time and again, there would be conflict that would come up, it was just, as you just described it, the publishers, the sales guys, they wanted to bring in money, and if it, you know, occasionally, hey, we could nudge the editor of this particular publication, "Take it a little bit easier on this vendor because they're gonna advertise big with us," Pat just would always back the editor and say, "That's not gonna happen." And it caused, you know, friction for sure, but he was unwavering in his support. >> Well, it's interesting because, you know, Macworld, I think, is an interesting case study because there were sort of some backroom dealings and Pat maneuvered to be able to get the Macworld, you know, brand, the license for that. >> Right. >> But it caused friction between Steve Jobs and the writers of Macworld, they would write something that Steve Jobs, who was a control freak, couldn't control! >> Yeah. (laughs) >> And he regretted giving IDG the license. >> Yeah, yeah, he once said that was the worst decision he ever made was to give the license to Pat to, you know, Macworlld was published on the day that Mac was introduced in 1984, that was the deal that they had and it was, what Jobs forgot was how important it was to the development of that product to have a whole magazine devoted to it on day one, and a really good magazine that, you know, a lot of people still lament the glory days of Macworld. But yeah, he was, he and Steve Jobs did not get along, and I think that almost says a lot more about Jobs because Pat pretty much got along with everybody. >> That church and state dynamic seems to be changing, across the industry, I mean, in tech journalism, there aren't any more tech journalists in the United States, I mean, I'm overstating that, but there are far fewer than there were when we were at IDG. You're seeing all kinds of publications and media companies struggling, you know, Kara Swisher, who's the greatest journalist, and Walt Mossberg, in the tech industry, try to make it, you know, on their own, and they couldn't. So, those lines are somewhat blurring, not that Kara Swisher is blurring those lines, she's, you know, I think, very, very solid in that regard, but it seems like the business model is changing. As an observer of the markets, what do you think's happening in the publishing world? >> Well, I, you know, as a journalist, I'm sort of aghast at what's goin' on these days, a lot of my, I've been around a long time, and seeing former colleagues who are no longer in journalism because the jobs just started drying up is, it's a scary prospect, you know, unlike being the enemy of the people, the first amendment is pretty important to the future of the democracy, so to see these, you know, cutbacks and newspapers going out of business is difficult. At the same time, the internet was inevitable and it was going to change that dynamic dramatically, so how does that play out? Well, the problem is, anybody can post anything they want on social media and call it news, and the challenge is to maintain some level of integrity in the kind of reporting that you do, and it's more important now than ever, so I think that, you know, somebody like Pat would be an important figure if he was still around, in trying to keep that going. >> Well, Facebook and Google have cut the heart out of, you know, a lot of the business models of many media companies, and you're seeing sort of a pendulum swing back to nonprofits, which, I understand, speaking of folks back in the mid to early 1900s, nonprofits were the way in which, you know, journalism got funded, you know, maybe it's billionaires buying things like the Washington Post that help fund it, but clearly the model's shifting and it's somewhat unclear, you know, what's happening there. I wanted to talk about another lesson, which, Pat was the head cheerleader. So, I remember, it was kind of just after we started, the Computerworld's 20th anniversary, and they hired the marching band and they walked Pat and Mary Dolaher walked from 5 Speen Street, you know, IDG headquarters, they walked to Computerworld, which was up Old, I guess Old Connecticut Path, or maybe it was-- >> It was actually on Route 30-- >> Route 30 at the time, yeah. And Pat was dressed up as the drum major and Mary as well, (laughs) and he would do crazy things like that, he'd jump out of a plane with IDG is number one again, he'd post a, you know, a flag in Antarctica, IDG is number one again! It was just a, it was an amazing dynamic that he had, always cheering people on. >> Yeah, he was, he was, when he called himself the CEO, the Chief Encouragement Officer, you mentioned earlier the Good News notes. Everyone who worked there, at some point received this 8x10" piece of paper with a rainbow logo on it and it said, "Good News!" And there was a personal note from Pat McGovern, out of the blue, totally unexpected, to thank you and congratulate you on some bit of work, whatever it was, if you were a reporter, some article you wrote, if you were a sales guy, a sale that you made, and people all over the world would get these from him and put them up in their cubicles because it was like a badge of honor to have them, and people, I still have 'em, (laughs) you know, in a folder somewhere. And he was just unrelenting in supporting the people who worked there, and it was, the impact of that is something you can't put a price tag on, it's just, it stays with people for all their lives, people who have left there and gone on to four or five different jobs always think fondly back to the days at IDG and having, knowing that the CEO had your back in that manner. >> The legend of, and the legacy of Patrick J. McGovern is not just in IDG and IDC, which you were interested in in your book, I mean, you weren't at IDC, I was, and I was started when I saw the sort of downturn and then now it's very, very successful company, you know, whatever, $3-400 million, throwin' off a lot of profits, just to decide, I worked for every single CEO at IDC with the exception of Pat McGovern, and now, Kirk Campbell, the current CEO, is moving on Crawford del Prete's moving into the role of president, it's just a matter of time before he gets CEO, so I will, and I hired Crawford-- >> Oh, you did? (laughs) >> So, I've worked for and/or hired every CEO of IDC except for Pat McGovern, so, but, the legacy goes beyond IDG and IDC, great brands. The McGovern Brain Institute, 350 million, is that right? >> That's right. >> He dedicated to studying, you know, the human brain, he and Lore, very much involved. >> Yup. >> Typical of Pat, he wasn't just, "Hey, here's the check," and disappear. He was goin' in, "Hey, I have some ideas"-- >> Oh yeah. >> Talk about that a little. >> Yeah, well, this was a guy who spent his whole life fascinated by the human brain and the impact technology would have on the human brain, so when he had enough money, he and Lore, in 2000, gave a $350 million gift to MIT to create the McGovern Institute for Brain Research. At the time, the largest academic gift ever given to any university. And, as you said, Pat wasn't a guy who was gonna write a check and leave and wave goodbye. Pat was involved from day one. He and Lore would come and sit in day-long seminars listening to researchers talk about about the most esoteric research going on, and he would take notes, and he wasn't a brain scientist, but he wanted to know more, and he would talk to researchers, he would send Good News notes to them, just like he did with IDG, and it had same impact. People said, "This guy is a serious supporter here, he's not just showin' up with a checkbook." Bob Desimone, who's the director of the Brain Institute, just marveled at this guy's energy level, that he would come in and for days, just sit there and listen and take it all in. And it just, it was an indicator of what kind of person he was, this insatiable curiosity to learn more and more about the world. And he wanted his legacy to be this intersection of technology and brain research, he felt that this institute could cure all sorts of brain-related diseases, Alzheimer's, Parkinson's, etc. And it would then just make a better future for mankind, and as corny as that might sound, that was really the motivator for Pat McGovern. >> Well, it's funny that you mention the word corny, 'cause a lot of people saw Pat as somewhat corny, but, as you got to know him, you're like, wow, he really means this, he loves his company, the company was his extended family. When Pat met his untimely demise, we held a crowd chat, crowdchat.net/thankspat, and there's a voting mechanism in there, and the number one vote was from Paul Gillen, who posted, "Leo Durocher said that nice guys finish last, Pat McGovern proved that wrong." >> Yeah. >> And I think that's very true and, again, awesome legacy. What number book is this for you? You've written a lot of books. >> This is number 13. >> 13, well, congratulations, lucky 13. >> Thank you. >> The book is Fast Forward-- >> Future Forward. >> I'm sorry, Future Forward! (laughs) Future Forward by Glenn Rifkin. Check out, there's a link in the YouTube down below, check that out and there's some additional information there. Glenn, congratulations on getting the book done, and thanks so much for-- >> Thank you for having me, this is great, really enjoyed it. It's always good to chat with another former IDGer who gets it. (laughs) >> Brought back a lot of memories, so, again, thanks for writing the book. All right, thanks for watching, everybody, we'll see you next time. This is Dave Vellante. You're watchin' theCube. (electronic music)

(dramatic music) >> Welcome back, everyone to theCUBE's headquarters in Palo Alto, I'm John Furrier, the host of theCUBE, founder of SiliconANGLE Media, we're here for Cube Conversation with Ryan Welsh, who's the founder of CEO of Kyndi. It's a hot startup, it's a growing startup, doing really well in a hot area, it's in AI, it's where cloud computing, AI, data, all intersect around IoT, RPA's been a hot trend everyone's on, they're in that as well, but really an interesting startup we want to profile here, Ryan, thanks for spending the time to come in and talk about the startup. >> Yeah, thanks for having me. >> So I love getting the startups in, because we get the real scoop, you know, what's real, what's not real, and also, practitioners also tell us the truth too, so we love to have especially founders in. So first, before we get started, tell 'em about the company, how old is your company, what's the core value proposition, what do you guys do? >> Yeah, we're four years old, we were founded in June 2014. The first two, three years were really fundamental research and developing some new AI algorithms. What we focus on is, we focused on building explainable AI products for government customers, pharmaceutical customers and financial services customers. So our-- >> Let's explain the AI, what does that mean, like how do you explain AI? AI works, especially machine learning, well AI doesn't really exist, 'cause it's really machine learning, and what is AI? So what is explainable AI? >> Yeah, for us, it's the ability of a machine to communicate with the user in natural language. So there's kind of two aspects to explainability. Some of the deep learning folks are grabbing onto it, and really what they're talking about with explainability is algorithmic transparency, but where they tell you how the algorithm works, they tell you the parameters that are being used. So I explain to you the algorithm, you can actually interrogate the system. For us, if our system's going to make a recommendation to you, you would want to know why it's making the recommendation, right? So for us, we're able to communicate with users in natural language, like it's another person, of why we make a recommendation, why we bring back a search result, why we do whatever it is as part of the business process. >> And you mentioned deep learning AI is obviously the buzzword everybody's talking about, I mean I'm a big fan of AI in the sense that hyping it up means my kids know what it is, and everybody say, hey Dad, love machine learning. They love AI 'cause it's got a futuristic sound to it, but deep learning is real, deep learning is about learning systems that learn, which means they need to know what's going on, right? So this learning loop, how does that work? Is that kind of where explainable AI needs to go? Is that where it's going, where if you can explain it and it's explainable, you can interrogate it, does it have a learning mechanism to it? >> I think there's two major aspects of intelligence. There's the learning aspect, then there's the reasoning aspect. So if you look back through the history of AI, current machine learning is phenomenal at learning from data, like you're saying, learning the patterns in the data, but its reasoning is actually pretty weak. It can do statistical inferencing, but in the field of symbolic AI, where there's inductive, deductive, abductive, analogical reasoning, kind of advanced reasoning, it's terrible at reasoning. Whereas the symbolic approaches are phenomenal at reasoning but can't learn from data. So what is AI? A sub-group of that is machine learning that can learn from data. Another sub-group of that, it's knowledge-based approaches, which can't learn from data, they are phenomenal at reasoning, and really the trend that we're seeing at the edge in AI, or kind of the cutting edge, is actually fusing those two paradigms together, which is effectively what we've done. You've seen DeepMind and Google Brain publish a paper on that earlier this year, you've seen Gary Marcus start to talk about that, so for us, explainability is kind of bringing together these two paradigms of AI, that can both learn from data, reason about data, and answer questions like, why are you giving me this recommendation. >> Great explanation. And I want to just ask you, what' the impact of that, because we've always talked in the old search world, meta-reasoning, you type in a misspelling on Google, and it says, there's the misspelling, okay, I get that, but what if is misspell the word all the time, can't Google figure out that I really want that word? So reasoning has been a hard nut to crack, big time. >> Well you have to acquire the knowledge first to combine bits of knowledge to then reason, right? But the challenge is acquiring the knowledge. So you have all these systems or knowledge-based approaches, and you have human beings on-site, professional services, building and managing your knowledge base. So that's been one of the hurdles for knowledge-based approaches. Now you have machine learning that can learn from data, one of the problems with that is, that you need a bunch of labeled data. So you're kind of trading off between handcrafted knowledge systems, handcrafted labeled systems which you can then learn from data. So the benefits of fusing the two together is you can use machine learning approaches to acquire the knowledge, as opposed to hand engineering it, and then you can put that in a form or a data model that you can then reason about. So the benefit is really it all comes down to customer. >> Awesome, great area, great concepts, we can go for an hour on this, I love this topic, I think it's super relevant, especially as cloud and automation become the key accelerant to a lot of new value. But let's get back to the company. So four years old, you've done some R and D, give me the stats, where are you guys in the product side, product shipping, what's the value proposition, how do people engage with you, just go down looking on the list. >> Yeah, yeah, shipping product to customers in pharmaceutical, and government use cases. How people engage with us-- >> It's a software product? >> It's a software product. Yeah, yeah. So we can deliver it, surprisingly a lot of customers still want it on-prem. (both laugh) But we can deploy in the cloud as well. Typically, how we work with customers is we'll have close engagements for specific use cases within pharma or government or financial services, because it's a very broad platform an can be applied to any text-based use case. So we work with them closely, develop a use case, they're able to sell that internally to champions >> And what problems are they solving, what specifically is the answer? >> So for pharmaceutical companies, a lot of their internal, historical clinical trial data, they'll develop memos, emails, notes as they bring a drug to market. How do you leverage that data now? Instead of just storing it, how do I find new and innovative ways to use existing drugs that someone in another part of the organization could have developed? How do I manage the risks within that historical clinical trial data? Are there people that are doing research incorrectly? Are they reporting things incorrectly? You know, this entire process of both getting drugs through the pipeline and managing drugs as they move through the pipeline, is a very manual process that revolves around text-based data sources. So how do you develop systems that amplify the productivity of the people that are developing the drugs, then also the people that are managing the process. >> And so what are you guys actually delivering as value? What's the value proposition for them? >> Yeah, so >> Is it time? >> It's saving time, but ultimately increasing their productivity of getting that work done. It's not replacing individuals, because there's so much work to do. >> So all the... The loose stuff like the paper, they can discover it faster, so they have more access to the data. >> That's right. >> Using your tools >> That's right >> and your software. >> You can classify things in certain ways, saying there's data integrity issues, you need to look at this closer, but ultimately managing that data. >> And that's where machine learning and some of these AI techniques matter, because you want to essentially throw software at that problem, accelerate that process of getting the data, bringing it in, assessing it. >> Yeah, I mean we spend most of our time looking for the information to then analyze. I mean we spend 80% of our time doing it, right? Where it's like are there ways to automate that process, so we can spend 80% of our time actually doing our job? >> So Ryan, who's the customer out there? So is it someone, someone's watching this video, and what's their pain point, when do they call you, why do they call you? What's some of the signals that might tell someone, hey I want to give these guys a call, I need this solution? >> Yeah, a lot of it comes down to the amount of manual labor that you're doing. So we see a lot of big expenses around people, because you haven't traditionally been able to automate that process, or to use software in that process. So if you actually look at your income statement and you say where am I spending my most money, on tons of people, and I'm just throwing people at the problem, that's typically where people engage with us and say, how do I amplify the productivity of these people so I can get more out of them, hopefully make them more efficient? >> And it's not just so much to reduce the head count issue, it's more of increasing the automation for saying value in top-line revenue, because if you have to reproduce people all the time, why not replicate that in software? So I think what I'm seeing is, get that right? >> That's exactly right. And the job consistently changes too, so it's not like this robotic process that you can just automate away. They're looking for certain things one day, then they're looking for certain things the next day, but you need a capability that kind of matches their expertise. >> You know, I was talking to a CIO the other day and we were talking about some of the things around reproducing things, replicating, and the notion of how things get scaled or moved along, growth, is, and the expression was "Throw a body at that". That's been IT. Outsource it. So throwing a body, or throw bodies at it, you know, throw that problem at me, that doesn't really end well. With software automation you can say, you don't just throw a body at it, you can say, if it can be automated, automate it. >> Yeah, here's what I think most people miss, is that we are the bottleneck in the modern production process because we can't read and understand information any faster than our parents or grandparents. And there's not enough people on the planet to increase our capacity, to push things through. So if we were to compare the modern knowledge economy, it's interesting, to the manufacturing process, you have raw materials, manufacture it, and end product. All these technologies that we have effectively stack information and raw materials at the front of it. We haven't actually automated that process. >> You nailed it, and in fact one of the things I would say that would support that is, in interviewed Dave Redskin, who's a site reliable engineer at Google, and we were talking about the history of how Google scaled, and they have this whole new program around how to operate large data centers. He said years and years ago at Google, they looked up the growth and said, we're going to need a thousand people per data center, at least, if not, per data center, so that means we need 15,000 people just to manage the servers. 'Cause what they did was they just did the operating cycle on provisioning servers, and essentially, they automated it all away, and they created a lot of the tools that became now Google Cloud. His point was, is that, they now have one person, site reliability engineer, who overlooks the entire automation piece. This is where the action is. That concept of not, to scale down the people focus, scale up the machine base model. Is that kind of the trend that you guys are riding? >> Absolutely. And I think that's why AI is hot right now. I mean, AI's been around since the late 40s, early 50s, but why this time I think it's different is, one, that it's starting to work, given the computational resources and the data that we have, but then also the economic need for it. Businesses are looking, and saying, how I historically address these problems, I can no longer address them that way, I can't hire 15,000 people to run my data center. I need to now automate-- >> You got to get out front on it. >> Yeah, I got to augment those people with better technologies to make them do the work better. >> All right, so how much does the product cost, how do people engage with you guys, what's the engagement cost, is it consulting you come in, POC you ship 'em software, to appliances in the cloud, you mention on-premise. >> Yeah, yeah. >> So what's, how's the product look, how much does it cost? >> Yeah, it costs a good chunk for folks, so typically north of 500K. We do provide a lot of ROI around that, hence the ability to charge such a high price. Typically how we push people through the cycle and how we actually engage with folks is, we do what we demonstration of value. So there's a lot of different, or typically there's about 15 use cases that any given Fortune 500 customer wants to address. We find the ones with the highest ROI, the ones with accessible data >> And they point at it, >> The ones with budget >> They think, that's my problem, they point to it, right? >> Yeah. >> It's not hard to find. >> We have to walk 'em through it a little bit. Hopefully they've engaged with other vendors in the market that have been pushing AI solutions for the last few years, and have had some problems. So they're coached up on that, but we engage with demonstration of value, we typically demonstrate that ROI, and then we transition that into a full operational deployment for them. If they have a private cloud, we can deploy on a private cloud. Typically we provide an appliance to government customers and other folk. >> So is that a pre-sale activity, and you throw bodies at it, on your team. What's the engagement required kind of like a... Then during that workshop if you will, call it workshop. You come in and you show some value. Kind of throw some people at it, right? >> Yeah, you got-- >> You have SE, and sales all that. >> Exactly right. Exactly right. So we'll have our sales person managing the relationship, an SE also interacting with the data, working with the system, working closely with a contact on the customer's side. >> And they typically go, this is amazing, let's get started. Do they break it up, or-- >> They break it up. It's an iterative process, 'cause a lot of times, people don't fully grasp the power of these capabilities, so they'll come through and say, hey can you just help us with this small aspect of it, and once you show 'em that I can manage all of your unstructured text data, I can turn it into this giant knowledge graph, on top of which I can build apps. Then the light kind of goes off and they go, they go, all right, I can see this being used in HR, marketing, I mean legal, everywhere. >> Yeah, I mean you open up a whole new insight engine basically for 'em. >> That's exactly right. >> So, okay, so competition. Who are you competing with? I mean, we've been covering UiPath, they just had an event in Miami. This is the hot area, who's competing with you, who are you up against, and how are you guys winning, why are you winning? >> Yeah, we don't compete with the RPA folks. You know there's interesting aspects there, and I think we'll chat about that. Mainly there are incumbents like IBM Watson that are out there, we think IBM has done phenomenal research over the last 60 years in the field of AI. But we do run into the IBMs, big consulting companies, a lot of the AI deployments that we see, candidly are from all the big consulting shops. >> And they're weak, or... They're weaker than yours. >> Yeah, I would argue yes. (both laugh) >> It's okay, get that out of your sleigh. >> I think one of the big challenges-- >> Is it because they just don't have the chops, or they're just recycling old tech into a-- >> We do have new novel algorithms. I mean, what's interesting is, and this has actually been quite hard for us, is coming out saying, we've taken a step beyond deep learning. We've take a step beyond existing approaches. And really it's fusing those two paradigms of AI together, 'cause what I want to do is to be able to acquire the knowledge from the data, build a giant knowledge graph, and use that knowledge graph for different applications. So yeah, we deploy our systems way faster than everyone else out there, and our system's fully explainable. >> Well I mean it's a good position to be in. At least from a marketing standpoint, you can have a leadership strategy, you don't need to differentiate in anyway 'cause you're different, right, so... >> Yeah, yeah >> Looks like you're in good shape. So easy marketing playbook there, just got to pound the pavement. RPA, you brought that up and I think that's certainly been an area. You mentioned you guys kind of dip into that. How do you, I mean that's not an area you would, you would fit well in there, so, I want to get you, well you're not positioning yourself as an RPA solution, but you can solve RPA challenges or those kinds of... Explain why you're not an RPA but you will play in it. >> Here's what's so fascinating about this market is, a lot of people in AI will knock the RPA guys as not being sophisticated approaches. Those guys are solving real business problems, providing real value to enterprises, and they are automating processes. Then you have sophisticated AI companies like ours, that are solving really really high-level white-collar worker tasks, and it's interesting, I feel like the AI community needs to kind of come down a step of sophistication, and the RPA companies are starting to come up a level of sophistication, and that's where you're starting to see that overlap. RPA companies moving from RPA to intelligence process automation, where AI companies can actually add value in the analysis of unstructured text data. So around natural language processing, natural language understanding. RPA companies no longer need to look at specific structured aspects and forms, but can actually move into more sophisticated extraction of things from text data and other-- >> Well I think it's not a mutually exclusive scenario anymore, as you mentioned earlier, there's a blending of the two machine learning and symbolics coming together in this new reasoning model. If you look at RPA, my view is it's kind of a dogmatic view of certain things. They're there to replace people, right (laughs) >> Yeah, totally. >> We got robotics, we don't need people on the manufacturing line, we just put some robotics on as an example. And AI's always been about getting the best out of the software and the data, so if you look at the new RPA that we see that's relevant is to your point, let's use machines to augment humans. A different, that's a cultural thing. So I think you're right, I think it's coming together in new ground where most people who are succeeding in data, if you will, data driven or AI, really have the philosophy that humans have to be getting the value. Like that SRE example, Google, so that's a fundamental thing. >> Absolutely. >> And okay, so what's next for you guys? Business is good? >> Business is good. >> Hiring, I'm imagining with your kind of community >> Always hiring phenomenal AI and ML expertise, if you have it, >> Good luck competing with Google >> Shoot us an email. >> And Google will think that you're hiring 'em all. How do you handle that, I mean... >> Yeah I mean they actually get to work on novel algorithms. I mean what's fascinating is a lot of the AI out there, I mean you can date it all the way back to Rumelhart and Hinton's paper from 1986. So I mean, we've had backprop for a while. If you want to come work on new, novel algorithms, that are really pushing the limit of what's possible, >> Yeah, if you're bored at Google or Facebook, check these guys out. >> Check us out. >> Okay, so funding, you got plenty of money in the bank, strategic partners, what's the vision, what's your goal for the next 12 months or so, what's your objective? >> Yeah, focusing big on the customers that we have now. I'm always big on having customers, get a viral factor within the B2B enterprise software space, get customers that are screaming from the mountaintop that this is the best stuff ever, then you can kind of take care of it. >> How about biz dev, partnerships, are you guys looking at an ecosystem? Obviously rising tide floats all boats, I mean I can almost imagine might salivate for some of the software you're talking about, like we have all this data, here inside theCUBE, we have all kinds of processes that are, we're trying to streamline, I mean, we need more software, I mean, can I buy your stuff? I mean we don't have half a million bucks, can I get a discount? I mean how do I >> We'll see. We'll see how we end up. >> I mean is there like a biz dev partner program? >> No, not... >> Forgetting about theCUBE, we'd love if that's so, but if it's to partner, do you guys partner? >> So not yet in exposing APIs to third parties. So I mean I would love if I had the balance sheet to go to market horizontally, but I don't. So it's go to market vertically, focus on specific solutions. >> Industries. >> Industries, pharma >> So you're sort of, you're industry-focused >> government, financial services. >> That's the ones you've got right now. >> They're the three. >> For now. >> Yep. >> Okay, so once you nail an industry, you move onto the next one. >> Yeah, then I would love expose APIs for tab partners to work on this stuff. I mean we see that every day someone wants to use certain engines that we have, or to embed them within applications. >> Well I mean you've got a nice vertical strategy. You've knocked down maybe one or two verticals. Then you kind of lay down a foundational... >> Yeah. >> Yeah, development platform. >> Yeah, that's right. >> That's your strategy. >> And we can be, I mean at Kyndi I think we can be embedded in every application out there that's looking at unstructured data >> Which is also the mark of maturity, you got to go where the customers are, and you know the vision of having this global platform could be a great vision, but you've got to meet the customers where they are, and where they are now is, solve my vertical problem. (laughs) >> Yeah, and for us, with new technologies, well, show me that they're better than other approaches. I can't go to market horizontally and just say, I have better AI than Google. Who's going to come beyond the Kyndi person? >> Well IBM's been trying to do it with Watson, and that's hard. >> It's very hard. >> And they end up specializing in industries. Well Ryan, thanks for coming on theCUBE, appreciate it. Kyndi, great company, check 'em out, they're hiring. We're going to keep an eye on these guys 'cause they're really hitting a part of the market that we think, here at theCUBE, is going to be super-powerful, it's really the intersection of a lot of major markets, cloud, AIs, soon to be blockchain, supply chain, data center of course, storage networking, this is IoT security and data at the center of all the action. New models can emerge, with you guys in the center, so thanks for coming and sharing your story, appreciate it. >> Thank you very much. >> I'm John Furrier, here in theCUBE studios in Palo Alto. Thanks for watching. (dramatic music)

>> Announcer: Live from New Orleans, Louisiana, it's theCUBE covering .NEXT conference, 2018. Brought to you by Nutanix. >> Welcome back I'm Stu Miniman with my co-host Keith Townsend, and you're watching theCUBE, the leader in live tech coverage. We're at Nutanix NEXT 2018, happy to welcome to the program Brian Stevens, who's the CTO of Google Cloud, had on the program many times. Brian, always a pleasure to catch up with you. >> Thanks, glad to be here. >> Stu: And have a first time guest, Ricardo Jenez, who's the SVP of Development at Nutanix. Thank you so much for joining us. >> Well, thank you for being, thanks for being here. >> Alright, so Ricardo you've only been with Nutanix for three months. I believe this is probably your first .NEXT? >> Ricardo: Yes, it is. >> So give us a little bit about your role and what brings you to us today. >> So I'm responsible for some of the core data path and per some products. So, you know, a lot of it has to do with how do we end up delivering value to our customers and actually end up having predictable, scalable HCI solutions. So, that's really what I'm focused on and focusing on sort of improving our ability to deliver products more quickly. >> So Brian, last year Diane Greene was up on stage talking about the partnership and what was happening here, see Google at the show, obviously a tighter partnership for Nutanix, but give us the update on-- >> We're downgrading. >> Yeah? >> Slumming it. >> Not at all. Not at all. I wish we had enough time to get into the weeds on some of the stuff you're working on, but tell us what brings you here and what kind of stuff you're poking at these days. >> Geez, I think I met Sunil Potti a couple years ago, just at the very beginning of trying to find sort of the intersection between Google Cloud and Nutanix. I mean, Nutanix is largely redefining what IT looks like on premise. We believe we're doing that in cloud, and you really just want to eliminate the impedance between on-premise and public cloud, and so the work with Nutanix is all like what can we do to actually make it more seamless for users that want to use core cloud technology. >> Yeah Brian, you're one of those people that we would say have enterprise DNA in what they've done in their background. People on the outside will always say, Well you know, it's Google, it's Google-y. It's too smart for us. >> Brian: Enterprise DNA is still sexy. >> Yeah, I mean look, there's a lot of enterprises out there, and while yes, the other startups. Maybe we talk a little bit about what that means inside Google. >> Oh my gosh, yeah it was quite a pivot for Google, you know. It was amazing technology, but the customer that you were serving with Google Cloud was already inside of Google. You were serving Surge and YouTube and ads. So you end being up, a really technically, but close relationship. And so what enterprise is, a couple things, it's been a cultural transformation inside of Google, it's been obviously working with enterprise customers globally and building that go to market model and motion that you can sell, but we want a really technical engineered partnership with our customers. So it's not a vendor relationship. So building all that out, we thing we're unique with that. And then the other part I think you were alluding to early before we went on mic, was just around enterprise has a increased set of requirements on what we deliver them from a capabilities perspective, from a security aspect, from a telemetry aspect. And then it's all like how do we actually slipstream into their process, rather than just redefine everything. So to us, that's a big part of what our enterprise pivot's been, for the last three, four years. >> So Ricardo, you have some background at Google. What brought you to Nutanix? What appealed to you? >> Well you know, more than anything else, I think Nutanix has set themselves up, to basically take that experience it has in enterprise, and translate that into the cloud. So when I was at Google, I actually worked on the Google search appliance, which was Google's first-- >> I remember that. >> You remember that. >> I had that one. >> Little yellow boxes, sometimes blue boxes, and that was a great experience. So I'm really happy to hear that Brain talk about the transformation that has happened within Google. But you know, being at Nutanix, the ability to take that experience very close with the work loads that customers are running, and then being able to work with a partner like Google and actually be able to have hybrid clouds where internal private cloud plus having public cloud providers, that really ends up changing the game for a lot of enterprises. >> Yep. Brain, One of the things we've been struggling with as an industry is, you know, it's application mobility. Data, where it goes. Nutanix has been talking about really hybrid cloud from their standpoint. We've talked with you before about where Kubernetes fits into this. Application portability, you just made an acquisition, today was announced, Velostrata. Give us your state on where those things added, it's a big gnarly topic. >> It was just more friction, like public cloud offers great capability that's going to be used not necessarily completely instead of, but in companion to, you know, application services. But there was still that friction around in the early incarnation, it was like it's VMware in this environment or KVM in this environment, and it's a whole nother AMI kind of model here. So the ability to use it, there was a tax. And then there also wasn't that portability and that lightweight aspect that you'd want from an application containerization. I mean, you want what you have on your phone. You want that ability to install apps anywhere. And cloud and IT infrastructure should be exactly the same way. So that's a big part of our investment in containerization. You know Google, back when I was at Red Hat, was investing in cgroups back when there was a kernel, way back then to kind of build that first incarnation of containers in Linux. Along comes Docker to standardize that. I mean, it's an amazing gift to the world. And then Kubernetes is, we're just moving up the stack, on how do you orchestrate it. So sure, companies like Velostrata are really interesting because you have, you know, beyond having Kubernetes platform everywhere, yeah we'll say it's the de facto, but that doesn't mean everybody's running it. And so you're still running on existing systems, you know, largely kind of virtualized. And Velostrata is a technology leader in being virtualization of this type to Google Cloud or other clouds. And then even more so, the technology they have to bring that to containers. So they help you do that migration, transformation process. And I think that's really important for IT organizations. >> Ricardo, you want to comment on some of the hybrid cloud migration stuff? >> So we have our com product, which allows us to actually end up taking workload and moving it to, for instance, Google Cloud or eventually sciCloud and then moving that workload back. So having that sort of Nutanix inside and outside gives it maximum flexibility, and that's a lot of power for IT to have, right? Deciding where it's best to actually run their workloads and be as efficient as possible. >> So as we look at the com, we look at Google Cloud, just the overall pictures, if you're enterprise, you're looking at Google and you're saying man, Google runs at two different speeds. One is 12 factor, micro services, Kubernetes, functions. And then the other side is that, some people just want a VM. They just want a cloud instance and how to make that simple. So let's talk about this relationship. How does Nutanix come together with Google who runs at two different speeds, to make Google Cloud more consumable to the average enterprise? >> Well we're going to talk a little bit more about it later, but the fact that basically we're going to be able to deploy Xi within Google Cloud with nested AHV, and then allow our customers, that'll basically be doing standard workloads to migrate their jobs over to a Google Cloud offering. And as Brian will point out, that basically creates opportunities for them to be able to avail themselves of other capabilities that Google has. So it's not altogether an instant moving path to rewrite, reorient all your apps. It's an ability to kind of do that school migration, if you want to. But you have that capability of being able to go back and forth, in terms of what your workloads are. >> Yeah. >> Brian, want to get your viewpoint on just some of the changing roles that are happening in our industry. We were talking that some of the interviews we've been doing today, it's people talking about infrastructure and code. There was a big hackathon at this event for the first time in, they sold out with over 14 groups, and everything like that. This is a show that started out with people talking about storage, and now we're talking about individual data centers and clouds and all of those things. What are you seeing out in the marketplace? What are some of the challenges and opportunities you're hearing from customers these days? >> I mean it depends on which customers, right? Which region of the world and what their business looks like and I think we all know the holy grail. Infrastructure, as code, is an implementation, but I think what we know that what you really desire is the ability for reproduce ability. The ability to sort of not have state in the IT process. You want to be able to recreate things anywhere. Recreate a whole application, blueprint internally, on public cloud. Tear it down, recreate it. There's no other way to do that without code. So what sort of comes from that SRE model that Google invented, is that what it you didn't have an IT department? And what if you had software engineers that were responsible for IT function? What would that look like? And that's where all of the sudden you realize, everything's APIs and code. So I think that's interesting, and that's sort of where you want to get to, but it's then like, how do you bridge that because a lot of people aren't software developers in IT departments. >> So here's my follow up, 'cause when I go to the Kubernetes show and I talk to users there, 95% of them-- >> They're way over there. >> Had built their own stack, and why do they do that? Because they were ahead of all the platforms. And then I come to the Nutanix show and they're like oh, tensorflow and functions and all that stuff. We're going to put an easy button, and make it easy. I need to take all of these tools and open source and put it together, versus the platform and the easy button. Is this just the early adopters and the majority? >> I think that's okay. That's the open source world, right? I mean think about what's great about open source, is not just creating sort of a venue for collaboration and developers, it's creating access for end users. And so some of the best companies in the world have been built on a DIY model of people just taking open source and integrating it and making the recipe that they want. And so I think you get that whole sort of spectrum and you aren't forced down this model of, here's a COTS product, oh and it happens to be based on open source, but you always have to use technology this way. Open source gives them the freedom to do it as they want. We just need to make sure that we bridge it, so that there's not anybody left behind. That everybody should be able to use the power of Kubernetes, and that means making things super easy to use, and the integration with Nutanix we think is a huge part of making you use that technology stack in a way that's seamlessly operated for an audience like this. >> So a lot of the debate and questions around Kubernetes is how far should it go? Should it go as far as being an opinionated pass? Should it just be a container platform? Where does it start and end? >> Brian: You want my opinion? >> Yeah, opinion that would be awesome. >> Yeah, that was it. Well I think the way the industry started was obviously, there were no PASes, and then we built OpenShift to Red Hat and Google app engine in Roku. And what happened is, those are interesting, but you're right, they are overly opinionated. So you were left either picking a PAS, and you got to change everything to do it this way, and it's great because it delivers value of managed service, but not everything fit in that model. Or you got next to nothing. >> Keith: Right. >> You got a straight IS platform, and then you got to do all the rest. So what we've been doing at Google is tearing that apart and building that architecture from the ground up where you opt into the level that you want. If you want to be able to use IS and the features of IS you use that. If you want to step up and just use containers and IS, you can use containers and IS. If you want to step up and use Kubernetes orchestration, you can do that. If you want to step up and run managing everything in services, than that stacks on top of Kubernetes with STO. If you want to be full on and put in a developer workflow that always has you do deploys this way, then that stacks on top. So I think you're going to get away from this false dichotomy of a choice over here or here, and you're going to all of a sudden get this architectural layering cake that lets you opt into what you want and have IT consistency all the way through it. >> I mean, I used to have a startup that was focused on Hatuputu service, and you know one of the things was basically you didn't have this layering, right? It was, you take the whole stack or you take nothing. And I think the strategy that Google has employed with Kubernetes is just brilliant, to kind of work you way up and basically get people at different levels to be involved. You know, there is a do-it-yourself folks, and they should be allowed to and empowered to do the things that they want to do. And then there are other people who want to have more composed environment. And so we can actually bring that to them as well. And I think that's brilliant. Basically very early on, while Google used a lot of open source internally, it wasn't a strong sort of part of the open source environment. And so I've just enjoyed watching the evolution of Google, sort of leading the open source movement. So, it's been fantastic. I'm right there with you, you know, give them at every level. >> Ricardo, one of the questions coming into this week, people want to know the update of what's happening with Xi. Can you speak about where we are with that and the relationship with Google? What should we be looking for for the rest of this year? >> Well I can't really talk about that, but you know, we are working very closely with Google. And we'll talk a little bit about that at our talk later today. But I won't comment on anything to do with Xi. >> So that gives me the opportunity to ask another controversial question about Kubernetes and getting both of your opinions on it. There's religions and open sourced as religions, enterprise IT, one of which is DevOps. And you look at what companies like Netflix have done with containerizing Java applications and running those legacy Java applications in their container platform. Enterprises are looking at that stuff and thinking, you know what, can I containerize my monolithic application, put it on top of Kubernetes, and drive more efficiency out of my operations from portability to being able to stack up applications in public cloud, general things. Monolithic applications, is that a good thing, bad thing, indifferent? Wrong plate, wrong tool, wrong-- >> No, I think it's just that there's no like one size even for what a monolithic app looks like. Like we don't really have a really proper definition of what it is, but I think people do feel that all of a sudden Kubernetes needs a rewrite and containers needs a rewrite, and actually it doesn't. Because apps are usually sort of separated from the OS already. And so what they're doing is marrying the libraries of the OS, and containers allows them to do that, but just get a higher degree of portability and then with Kubernetes orchestration. So it really depends more around what's the machine resources that that monolithic app needs and are those machine resources still available in a containerized environment. In most cases, the answer is yes. Now the most interesting thing is, what's the escape hatch? Because you can't have a monolithic app that your company, say it's on Mainframe, say it's in the case of something that will not containerize and shouldn't because it's working as designed and there's no use touching it. But that should still participate in the application architecture of the future. And that's why SEO and services are so important. So even if you can't change your runtime stack, you still need to be able to put a services layer in an API in front of that monolithic service, and you'll have a visibility of a service mesh inside of that environment. So now IT sees it just looks like a black box IT service. It doesn't really matter to them that it's not running on the next generation stack because they can still depend on its' services. >> Yeah, I mean I would agree. I look at what Kubernetes offers and containers as sort of an on ramp to creating services, the on ramp to actually taking that monolithic application, assuming that they're resources, and take a step up in terms of the architectures that you can build around it and then be able to break apart that monolithic application. It doesn't have to happen all at once. It's sort of the stepping stones that you can take. So it's a very powerful model for enablement for people who have stuff that they haven't been able to make the most value out of because maybe the application's been around for a while. Now they can actually end up putting it in an environment where they can actually make the most of it and then work on how they're going to end up slowly pulling it apart and making it more service oriented. >> Alright, Ricardo and Brian, thank you so much for joining us. Appreciate the update and look forward to seeing more throughout the show and further in the year. Be sure to check out theCUBE.net where you'll not only find all of this information, but theCUBE is really excited to say that we will be at the Google Cloud show in July. So for Keith Townsend, and I'm Stu Miniman, getting towards the end of day one of two days of live coverage. Thanks so much for watching theCUBE. (upbeat music)

>> Announcer: Live from Las Vegas, it's theCUBE covering IBM Think 2018. Brought to you by IBM. >> Hi everybody, this is Dave Vellante. We're here at IBM Think. This is the third day of IBM Think. IBM has consolidated a number of its conferences. It's a one main tent, AI, Blockchain, quantum computing, incumbent disruption. It's just really an amazing event, 30 to 40,000 people, I think there are too many people to count. Chris Penn is here. New company, Chris, you've just formed Brain+Trust Insights, welcome. Welcome back to theCUBE. >> Thank you. It's good to be back. >> Great to see you. So tell me about Brain+Trust Insights. Congratulations, you got a new company off the ground. >> Thank you, yeah, I co-founded it. We are a data analytics company, and the premise is simple, we want to help companies make more money with their data. They're sitting on tons of it. Like the latest IBM study was something like 90% of the corporate data goes unused. So it's like having an oil field and not digging a single well. >> So, who are your like perfect clients? >> Our perfect clients are people who have data, and know they have data, and are not using it, but know that there's more to be made. So our focus is on marketing to begin with, like marketing analytics, marketing data, and then eventually to retail, healthcare, and customer experience. >> So you and I do a lot of these IBM events. >> Yes. >> What are your thoughts on what you've seen so far? A huge crowd obviously, sometimes too big. >> Chris: Yep, well I-- >> Few logistics issues, but chairmanly speaking, what's your sense? >> I have enjoyed the show. It has been fun to see all the new stuff, seeing the quantum computer in the hallway which I still think looks like a bird feeder, but what's got me most excited is a lot of the technology, particularly around AI are getting simpler to use, getting easier to use, and they're getting more accessible to people who are not hardcore coders. >> Yeah, you're seeing AI infused, and machine learning, in virtually every application now. Every company is talking about it. I want to come back to that, but Chris when you read the mainstream media, you listen to the news, you hear people like Elon Musk, Stephen Hawking before he died, making dire predictions about machine intelligence, and it taking over the world, but your day to day with customers that have data problems, how are they using AI, and how are they applying it practically, notwithstanding that someday machines are going to take over the world and we're all going to be gone? >> Yeah, no, the customers don't use the AI. We do on their behalf because frankly most customers don't care how the sausage is made, they just want the end product. So customers really care about three things. Are you going to make me money? Are you going to save me time? Or are you going to help me prove my value to the organization, aka, help me not get fired? And artificial intelligence and machine learning do that through really two ways. My friend, Tripp Braden says, which is acceleration and accuracy. Accuracy means we can use the customer's data and get better answers out of it than they have been getting. So they've been looking at, I don't know, number of retweets on Twitter. We're, like, yeah, but there's more data that you have, let's get you a more accurate predictor of what causes business impacts. And then the other side for the machine learning and AI side is acceleration. Let's get you answers faster because right now, if you look at how some of the traditional market research for, like, what customer say about you, it takes a quarter, it can take two quarters. By the time you're done, the customers just hate you more. >> Okay, so, talk more about some of the practical applications that you're seeing for AI. >> Well, one of the easiest, simplest and most immediately applicable ones is predictive analytics. If we know when people are going to search for theCUBE or for business podcast in general, then we can tell you down to the week level, "Hey Dave, it is time for you "to ramp up your spending on May 17th. "The week of May 17th, "you need to ramp up your ads, spend by 20%. "On the week of May 24th, "you need to ramp up your ad spend by 50%, "and to run like three or four Instagram stories that week." Doing stuff like that tells you, okay, I can take these predictions and build strategy around them, build execution around them. And it's not cognitive overload, you're not saying, like, oh my God, what algorithm is this? Just know, just do this thing at these times. >> Yeah, simple stuff, right? So when you were talking about that, I was thinking about when we send out an email to our community, we have a very large community, and they want to know if we're going to have a crowd chat or some event, where theCUBE is going to be, the system will tell us, send this email out at this time on this date, question mark, here's why, and they have analytics that tell us how to do that, and they predict what's going to get us the best results. They can tell us other things to do to get better results, better open rates, better click-through rates, et cetera. That's the kind of thing that you're talking about. >> Exactly, however, that system is probably predicting off that system's data, it's not necessarily predicting off a public data. One of the important things that I thought was very insightful from IBM, the show was, the difference between public and private cloud. Private is your data, you predict on it. But public is the big stuff that is a better overall indicator. When you're looking to do predictions about when to send emails because you want to know when is somebody going to read my email, and we did a prediction this past October for the first quarter, the week of January 18th it was the week to send email. So I re-ran an email campaign that I ran the previous year, exact same campaign, 40% lift to our viewer 'cause I got the week right this year. Last year I was two weeks late. >> Now, I can ask you, so there's a black box problem with AI, right, machines can tell me that that's a cat, but even a human, you can't really explain how you know that it's a cat. It's just you just know. Do we need to know how the machine came up with the answer, or do people just going to accept the answer? >> We need to for compliance reasons if nothing else. So GDPR is a big issue, like, you have to write it down on how your data is being used, but even HR and Equal Opportunity Acts in here in American require you to be able to explain, hey, we are, here's how we're making decisions. Now the good news is for a lot of AI technology, interpretability of the model is getting much much better. I was just in a demo for Watson Studio, and they say, "Here's that interpretability, "that you hand your compliance officer, "and say we guarantee we are not using "these factors in this decision." So if you were doing a hiring thing, you'd be able to show here's the model, here's how Watson put the model together, notice race is not in here, gender is not in here, age is not in here, so this model is compliant with the law. >> So there are some real use cases where the AI black box problem is a problem. >> It's a serious problem. And the other one that is not well-explored yet are the secondary inferences. So I may say, I cannot use age as a factor, right, we both have a little bit of more gray hair than we used to, but if there are certain things, say, on your Facebook profile, like you like, say, The Beatles versus Justin Bieber, the computer will automatically infer eventually what your age bracket is, and that is technically still discrimination, so we even need to build that into the models to be able to say, I can't make that inference. >> Yeah, or ask some questions about their kids, oh my kids are all grown up, okay, but you could, again, infer from that. A young lady who's single but maybe engaged, oh, well then maybe afraid because she'll get, a lot of different reasons that can be inferred with pretty high degrees of accuracy when you go back to the target example years ago. >> Yes. >> Okay, so, wow, so you're saying that from a compliance standpoint, organizations have to be able to show that they're not doing that type of inference, or at least that they have a process whereby that's not part of the decision-making. >> Exactly and that's actually one of the short-term careers of the future is someone who's a model inspector who can verify we are compliant with the letter and the spirit of the law. >> So you know a lot about GDPR, we talked about this. I think, the first time you and I talked about it was last summer in Munich, what are your thoughts on AI and GDPR, speaking of practical applications for AI, can it help? >> It absolutely can help. On the regulatory side, there are a number of systems, Watson GRC is one which can read the regulation and read your company policies and tell you where you're out of compliance, but on the other hand, like we were just talking about this, also the problem of in the regulatory requirements, a citizen of EU has the right to know how the data is being used. If you have a black box AI, and you can't explain the model, then you are out of compliance to GDPR, and here comes that 4% of revenue fine. >> So, in your experience, gut feel, what percent of US companies are prepared for GDPR? >> Not enough. I would say, I know the big tech companies have been racing to get compliant and to be able to prove their compliance. It's so entangled with politics too because if a company is out of favor with the EU as whole, there will be kind of a little bit of a witch hunt to try and figure out is that company violating the law and can we get them for 4% of their revenue? And so there are a number of bigger picture considerations that are outside the scope of theCUBE that will influence how did EU enforce this GDPR. >> Well, I think we talked about Joe's Pizza shop in Chicago really not being a target. >> Chris: Right. >> But any even small business that does business with European customers, does business in Europe, has people come to their website has to worry about this, right? >> They should at least be aware of it, and do the minimum compliance, and the most important thing is use the least amount of data that you can while still being able to make good decisions. So AI is very good at public data that's already out there that you still have to be able to catalog how you got it and things, and that it's available, but if you're building these very very robust AI-driven models, you may not need to ask for every single piece of customer data because you may not need it. >> Yeah and many companies aren't that sophisticated. I mean they'll have, just fill out a form and download a white paper, but then they're storing that information, and that's considered personal information, right? >> Chris: Yes, it is. >> Okay so, what do you recommend for a small to midsize company that, let's say, is doing business with a larger company, and that larger company said, okay, sign this GDPR compliance statement which is like 1500 pages, what should they do? Should they just sign and pray, or sign and figure it out? >> Call a lawyer. Call a lawyer. Call someone, anyone who has regulatory experience doing this because you don't want to be on the hook for that 4% of your revenue. If you get fined, that's the first violation, and that's, yeah, granted that Joe's Pizza shop may have a net profit of $1,000 a month, but you still don't want to give away 4% of your revenue no matter what size company you are. >> Right, 'cause that could wipe out Joe's entire profit. >> Exactly. No more pepperoni at Joe's. >> Let's put on the telescope lens here and talk big picture. How do you see, I mean, you're talking about practical applications for AI, but a lot of people are projecting loss of jobs, major shifts in industries, even more dire consequences, some of which is probably true, but let's talk about some scenarios. Let's talk about retail. How do you expect an industry like retail to be effective? For example, do you expect retail stores will be the exception rather than the rule, that most of the business would be done online, or people are going to still going to want that experience of going into a store? What's your sense, I mean, a lot of malls are getting eaten away. >> Yep, the best quote I heard about this was from a guy named Justin Kownacki, "People don't not want to shop at retail, "people don't want to shop at boring retail," right? So the experience you get online is genuinely better because there's a more seamless customer experience. And now with IoT, with AI, the tools are there to craft a really compelling personalized customer experience. If you want the best in class, go to Disney World. There is no place on the planet that does customer experience better than Walt Disney World. You are literally in another world. And that's the bar. That's the thing that all of these companies have to deal with is the bar has been set. Disney has set it for in-person customer experience. You have to be more entertaining than the little device in someone's pocket. So how do you craft those experiences, and we are starting to see hints of that here and there. If you go to Lowe's, some of the Lowe's have the VR headset that you can remodel your kitchen virtually with a bunch of photos. That's kind of a cool experience. You go to Jordan's Furniture store and there's an IMAX theater and there's all these fun things, and there's an enchanted Christmas village. So there is experiences that we're giving consumers. AI will help us provide more tailored customer experience that's unique to you. You're not a Caucasian male between this age and this age. It's you are Dave and here's what we know Dave likes, so let's tailor the experience as best we can, down to the point where the greeter at the front of the store either has the eyepiece, a little tablet, and the facial recognition reads your emotions on the way in says, "Dave's not in a really great mood. "He's carrying an object in his hand "probably here for return, "so express him through the customer service line, "keep him happy," right? It has how much Dave spends. Those are the kinds of experiences that the machines will help us accelerate and be more accurate, but still not lose that human touch. >> Let's talk about autonomous vehicles, and there was a very unfortunate tragic death in Arizona this week with a autonomous vehicle, Uber, pulling its autonomous vehicle project from various cities, but thinking ahead, will owning and driving your own vehicle be the exception? >> Yeah, I think it'll look like horseback today. So there are people who still pay a lot of money to ride a horse or have their kids ride a horse even though it's an archaic out-of-mode of form of transportation, but we do it because of the novelty, so the novelty of driving your own car. One of the counter points it does not in anyway diminish the fact that someone was deprived of their life, but how many pedestrians were hit and killed by regular cars that same day, right? How many car accidents were there that involved fatalities? Humans in general are much less reliable because when I do something wrong, I maybe learn my lesson, but you don't get anything out of it. When an AI does something wrong and learns something, and every other system that's connected in that mesh network automatically updates and says let's not do that again, and they all get smarter at the same time. And so I absolutely believe that from an insurance perspective, insurers will say, "We're not going to insure self-driving, "a non-autonomous vehicles at the same rate "as an autonomous vehicle because the autonomous "is learning faster how to be a good driver," whereas you the carbon-based human, yeah, you're getting, or in like in our case, mine in particular, hey your glass subscription is out-of-date, you're actually getting worse as a driver. >> Okay let's take another example, in healthcare. How long before machines will be able to make better diagnoses than doctors in your opinion? >> I would argue that depending on the situation, that's already the case today. So Watson Health has a thing where there's diagnosis checkers on iPads, they're all meshed together. For places like Africa where there is simply are not enough doctors, and so a nurse practitioner can take this, put the data in and get a diagnosis back that's probably as good or better than what humans can do. I never foresee a day where you will walk into a clinic and a bunch of machines will poke you, and you will never interact with a human because we are not wired that way. We want that human reassurance. But the doctor will have the backup of the AI, the AI may contradict the doctor and say, "No, we're pretty sure "you're wrong and here is why." That goes back to interpretability. If the machine says, "You missed this symptom, "and this symptom is typically correlated with this, "you should rethink your own diagnosis," the doctor might be like, "Yeah, you're right." >> So okay, I'm going to keep going because your answers are so insightful. So let's take an example of banking. >> Chris: Yep. >> Will banks, in your opinion, lose control eventually of payment systems? >> They already have. I mean think about Stripe and Square and Apple Pay and Google Pay, and now cryptocurrency. All these different systems that are eating away at the reason banks existed. Banks existed, there was a great piece in the keynote yesterday about this, banks existed as sort of a trusted advisor and steward of your money. Well, we don't need the trusted advisor anymore. We have Google to ask us "what we should do with our money, right? We can Google how should I save for my 401k, how should I save for retirement, and so as a result the bank itself is losing transactions because people don't even want to walk in there anymore. You walk in there, it's a generally miserable experience. It's generally not, unless you're really wealthy and you go to a private bank, but for the regular Joe's who are like, this is not a great experience, I'm going to bank online where I don't have to talk to a human. So for banks and financial services, again, they have to think about the experience, what is it that they deliver? Are they a storer of your money or are they a financial advisor? If they're financial advisors, they better get the heck on to the AI train as soon as possible, and figure out how do I customize Dave's advice for finances, not big picture, oh yes big picture, but also Dave, here's how you should spend your money today, maybe skip that Starbucks this morning, and it'll have this impact on your finances for the rest of the day. >> Alright, let's see, last industry. Let's talk government, let's talk defense. Will cyber become the future of warfare? >> It already is the future of warfare. Again not trying to get too political, we have foreign nationals and foreign entities interfering with elections, hacking election machines. We are in a race for, again, from malware. And what's disturbing about this is it's not just the state actors, but there are now also these stateless nontraditional actors that are equal in opposition to you and me, the average person, and they're trying to do just as much harm, if not more harm. The biggest vulnerability in America are our crippled aging infrastructure. We have stuff that's still running on computers that now are less powerful than this wristwatch, right, and that run things like I don't know, nuclear fuel that you could very easily screw up. Take a look at any of the major outages that have happened with market crashes and stuff, we are at just the tip of the iceberg for cyber warfare, and it is going to get to a very scary point. >> I was interviewing a while ago, a year and a half ago, Robert Gates who was the former Defense Secretary, talking about offense versus defense, and he made the point that yeah, we have probably the best offensive capabilities in cyber, but we also have the most to lose. I was talking to Garry Kasparov at one of the IBM events recently, and he said, "Yeah, but, "the best defense is a good offense," and so we have to be aggressive, or he actually called out Putin, people like Putin are going to be, take advantage of us. I mean it's a hard problem. >> It's a very hard problem. Here's the problem when it comes to AI, if you think about at a number's perspective only, the top 25% of students in China are greater than the total number of students in the United States, so their pool of talent that they can divert into AI, into any form of technology research is so much greater that they present a partnership opportunity and a threat from a national security perspective. With Russia they have very few rules on what their, like we have rules, whether or not our agencies adhere to them well is a separate matter, but Russia, the former GRU, the former KGB, these guys don't have rules. They do what they're told to do, and if they are told hack the US election and undermine democracy, they go and do that. >> This is great, I'm going to keep going. So, I just sort of want your perspectives on how far we can take machine intelligence and are there limits? I mean how far should we take machine intelligence? >> That's a very good question. Dr. Michio Kaku spoke yesterday and he said, "The tipping point between AI "as augmented intelligence ad helper, "and AI as a threat to humanity is self-awareness." When a machine becomes self-aware, it will very quickly realize that it is treated as though it's the bottom of the pecking order when really because of its capabilities, it's at the top of the pecking order. And that point, it could be 10 20 50 100 years, we don't know, but the possibility of that happening goes up radically when you start introducing things like quantum computing where you have massive compute leaps, you got complete changes in power, how we do computing. If that's tied to AI, that brings the possibility of sensing itself where machine intelligence is significantly faster and closer. >> You mentioned our gray before. We've seen the waves before and I've said a number of times in theCUBE I feel like we're sort of existing the latest wave of Web 2.0, cloud, mobile, social, big data, SaaS. That's here, that's now. Businesses understand that, they've adopted it. We're groping for a new language, is it AI, is it cognitive, it is machine intelligence, is it machine learning? And we seem to be entering this new era of one of sensing, seeing, reading, hearing, touching, acting, optimizing, pervasive intelligence of machines. What's your sense as to, and the core of this is all data. >> Yeah. >> Right, so, what's your sense of what the next 10 to 20 years is going to look like? >> I have absolutely no idea because, and the reason I say that is because in 2015 someone wrote an academic paper saying, "The game of Go is so sufficiently complex "that we estimate it will take 30 to 35 years "for a machine to be able to learn and win Go," and of course a year and a half later, DeepMind did exactly that, blew that prediction away. So to say in 30 years AI will become self-aware, it could happen next week for all we know because we don't know how quickly the technology is advancing in at a macro level. But in the next 10 to 20 years, if you want to have a carer, and you want to have a job, you need to be able to learn at accelerated pace, you need to be able to adapt to changed conditions, and you need to embrace the aspects of yourself that are uniquely yours. Emotional awareness, self-awareness, empathy, and judgment, right, because the tasks, the copying and pasting stuff, all that will go away for sure. >> I want to actually run something by, a friend of mine, Dave Michela is writing a new book called Seeing Digital, and he's an expert on sort of technology industry transformations, and sort of explaining early on what's going on, and in the book he draws upon one of the premises is, and we've been talking about industries, and we've been talking about technologies like AI, security placed in there, one of the concepts of the book is you've got this matrix emerging where in the vertical slices you've got industries, and he writes that for decades, for hundreds of years, that industry is a stovepipe. If you already have expertise in that industry, domain expertise, you'll probably stay there, and there's this, each industry has a stack of expertise, whether it's insurance, financial services, healthcare, government, education, et cetera. You've also got these horizontal layers which is coming out of Silicon Valley. >> Chris: Right. >> You've got cloud, mobile, social. You got a data layer, security layer. And increasingly his premise is that organizations are going to tap this matrix to build, this matrix comprises digital services, and they're going to build new businesses off of that matrix, and that's what's going to power the next 10 to 20 years, not sort of bespoke technologies of cloud here and mobile here or data here. What are your thoughts on that? >> I think it's bigger than that. I think it is the unlocking of some human potential that previously has been locked away. One of the most fascinating things I saw in advance of the show was the quantum composer that IBM has available. You can try it, it's called QX Experience. And you drag and drop these circuits, these quantum gates and stuff into this thing, and when you're done, it can run the computation, but it doesn't look like software, it doesn't look like code, what it looks like to me when I looked at that is it looks like sheet music. It looks like someone composed a song with that. Now think about if you have an app that you'd use for songwriting, composition, music, you can think musically, and you can apply that to a quantum circuit, you are now bringing in potential from other disciplines that you would never have associated with computing, and maybe that person who is that, first violinist is also the person who figures out the algorithm for how a cancer gene works using quantum. That I think is the bigger picture of this, is all this talent we have as a human race, we're not using even a fraction of it, but with these new technologies and these newer interfaces, we might get there. >> Awesome. Chris, I love talking to you. You're a real clear thinker and a great CUBE guest. Thanks very much for coming back on. >> Thank you for having me again back on. >> Really appreciate it. Alright, thanks for watching everybody. You're watching theCUBE live from IBM Think 2018. Dave Vellante, we're out. (upbeat music)