>> Announcer: Live from San Francisco, it's theCUBE, covering RSA Conference 2020 San Francisco, brought to you by SiliconANGLE Media. >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We are Thursday, day four of the RSA Show here in Moscone in San Francisco. It's a beautiful day outside, but the show is still going, 40,000-plus people. A couple of challenges with the coronavirus, and some other things going on, but everybody's here, everybody's staying the course, and I think it's really a good message going forward as to what's going to happen in the show season. We go to a lot of shows. Is 2020 the year we're going to know everything with the benefit of hindsight? It's not quite working out so far that way, but we're bringing in the experts to share the knowledge, and we're excited for our next guest, who's going to help us get to know what the answers are. He's Oliver Sherman, senior director, Enterprise Product Marketing for Juniper Networks. Oliver, great to see you. >> Thanks for having me. >> Absolutely, so first off, just general impressions of the show. I'm sure you've been coming here for a little while. >> We have, and I think the show's going very well, as you pointed out, there's a couple of challenges that are around, but I think everybody's staying strong, and pushing through, and really driving the agenda of security. >> So I've got some interesting quotes from you doing a little research for this segment. You said 2019 was the year of enforcement, but 2020 is the year of intelligence. What did you mean by that? >> Specifically, it's around Juniper. We have a Juniper connected security message and strategy that we proved last year by increasing the ability to enforce on all of your infrastructure without having to rip and replace technologies. For instance, on our widely rolled out MX routing platform, we offer second tell to block things like command and control traffic, or on our switching line for campus and data centers, we prevent lateral threat propagation with second tell, allowing you to block hosts as they're infected, and as we rounded that out, and it's a little bit in 2020 we were able to now deliver that on our Mist, or our wireless acquistion that we did last year around this time, so showing the integration of that product portfolio. >> Yeah, we met Bob Friday from Mist. >> Oliver: Excellent. >> He, doing the AI, some of the ethics around AI. >> Oliver: Sure. >> At your guys conference last year. It was pretty interesting conversation. Let's break down what you said a little bit deeper. So you're talking about inside your own product suite, and managing threats across once they get to that level to keep things clean across that first layer of defense. >> Right, well, I mean, whether you're a good packet or a bad packet, you have to traverse the network to be interesting. We've all put our phones in airplane mode at Black Hat or events like that because we don't want anybody on it, but they're really boring when they're offline, but they're also really boring to attackers when they're offline. As soon as you turn them on, you have a problem, or could have a problem, but as things traverse the network, what better place to see who and what's on your network than on the gears, and at the end of the day, we're able to provide that visibility, we're able to provide that enforcement, so as you mentioned, 2020 is now the year of an awareness for us, so the Threat Aware Network. We're able to do things like look at encrypted traffic, do heuristics and analysis to figure out should that even be on my network because as you bring it into a network, and you have to decrypt it, a, there's privacy concerns with that in these times, but also, it's computationally expensive to do that, so it becomes a challenge from both a financial perspective, as well as a compliance perspective, so we're helping solve that so you can offset that traffic, and be able to ensure your network's secure. >> So is that relatively new, and I apologize. I'm not deep into the weeds of feature functionality, but that sounds pretty interesting that you can actually start to do the analysis without encrypting the data, and get some meaningful, insightful information. >> Absolutely, we actually announced it on Monday at 4:45 a.m. Pacific, so it is new. >> Brand new. >> Yes. >> And what's the secret sauce to be able to do that because one would think just by rule encryption would eliminate the ability to really do the analysis, so what analysis can you still do while still keeping the data encrypted? >> You're absolutely right. We're seeing 70 to 80% of internet traffic is now encrypted. Furthermore, bad actors are using that to obfuscate themselves, right, obviously, and then, the magic to that, though, to look at it without having to crack open the package is using things like heuristics that look at connections per second, or connection patterns, or looking at significant exchanges, or even IP addresses to know this is not something you want to let in, and we're seeing a very high rate of success to block things like IoT botnets, for instance, so you'll be seeing more and more of that from us throughout the year, but this is the initial step that we're taking. >> Right, that's great because so much of it it sounds like, a, a lot of it's being generated by machines, but two, it sounds like the profile of the attacks keeps changing quite a bit from a concentrated attacks to more, it sounds like now, everyone's doing the slow creeper to try to get it under the covers. >> Right, and really, you're using your network to your full extent. I mean, a lot of things that we're doing including encrypted traffic analysis is an additional feature on our platform, so that comes with what you already have, so rather than walking in and saying, "Buy my suite of products, this will all" "solve all your problems," as we've done for the past, or as other vendors have done for the past 10, 20 years, and it's never worked. So you why not add things that you already have so you're allowed to amortize your assets, build your best of breed security, and do it within a multi-vendor environment, but also, do it with your infrastructure. >> Right, so I want to shift gears a little bit. Doing some research before you got on, you've always been technical lead. You've been doing technical lead roles. You had a whole bunch of them, and we don't have internet, unfortunately, here, so I can't read them off. >> Oliver: That's fine. >> But now, you've switched over. You've put the marketing hat on. I'm just curious the different, softer, squishy challenge of trying to take the talent that you have, the technical definitions that you have, the detailed compute and stuff you're doing around things like you just described, and now, putting the marketing hat, and trying to get that message out to the market, help people understand what you're trying to do, and break through, quite frankly, some crazy noise that we're sitting here surrounded by hundreds, if not thousands of vendors. >> I think that's really the key, and yes, I've been technical leads. I've run architecture teams. I've run development teams, and really, from a marketing perspective, it's to ensure that we're delivering a message that is, that the market will consume that is actually based in reality. I think a lot of times you see a lot of products that are put together with duct tape, baling twine, et cetera, but then, also have a great Powerpoint that makes it look good, but from a go to market perspective, from whether it's your sellers, meaning the sellers that work for Juniper, whether it's our partners, whether it's our customers, they have to believe in what's out there, and if it's tried and true, and we understand it from an engineering perspective, and we can say it's not a marketing texture, it's a strategy. >> Right. >> That really makes a difference, and we're really seeing that if you look at our year over year growth in security, if you look at what analysts are saying, if you look at what testing houses are saying about our product, that Juniper's back, and that's why I'm in this spot. >> And it really begs to have a deeper relationship with the customer, that you're not selling them a one-off market texture slide. You're not having a quick point solution that's suddenly put together, but really, have this trusted, ongoing relationship that's going to evolve over time. The products are going to evolve over time because the threats are evolving over time, right? >> Absolutely, and to help them get more out of what they already have, and from a go to market perspective, our partners have an addressful market that's naturally through the install base that we have, we're able to provide additional value and services to those customers that may want to lean on a partner to actually build some of these solutions for them. >> All right, well, Oliver, well thanks for stopping by. I'm glad I'm not too late on the encrypted analysis game, so just a couple of days. >> Absolutely. >> Thanks for stopping by. Best to you, and good luck with 2020, the year we'll know everything. >> Absolutely, thanks for having me. >> All right, he's Oliver, I'm Jeff, you're watching theCUBE. We're at RSA 2020 here in Moscone. Thanks for watching. We'll see you next time. (gentle electronic music)

>> Announcer: Live from San Francisco, it's theCUBE, covering RSA conference 2020 San Francisco, brought to you by SiliconANGLE Media. >> Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're at the RSA 2020 show, here in Moscone in San Francisco, it's Thursday, we've been going wall to wall, we're really excited for our next guest. We've been talking about some kind of interesting topics, getting a little bit into the weeds, not on the technology, but some of the philosophical things that are happening in this industry that you should be thinking about. And we're excited welcome, Laurence Pitt, he is the cyber security strategist at Juniper Networks. Laurence, great to meet you. >> Thank you very much, hi. >> Yeah, so before we turn the cameras off, we've been talking about all kinds of fancy things, so let's just jump into it. One of the topics that gets a lot of news is deepfakes, and there's a lot of cute funny things out there of people's voices and things that they're saying not necessarily being where you expect them to be, but there's a real threat here, and a real kind of scary situation that just barely beginning to scratch the surface, I want you to get share some of your thoughts on deepfakes. >> I'm going to think you made a good point at the start. There's a lot of cute and funny stuff out there, there's a lot of fake political stuff you see. So is it seen as being humorous some people are sharing it a lot. But there is a darker side that's going to happen to deepfakes, because a lot of the things that you see today that go out on video, the reason that it is what it is, is because you're very familiar with the person that you're seeing in that video. Is a famous politician, is a movie star, and they're saying something that's out of character or funny and that's it. But what if that was actually the Chief Financial Officer of a major company, where the company appears to have launched a video, very close to the bell ringing on the stock market, that makes some kind of announcement about product or delay or something to do with their quarterly figures or something like that? You know that one minute video, could do a huge amount of damage to that organization. It could that somebody's looking to take advantage of a dip at that point, video goes out, their stocks going to dip, buy it out, then they could profit, but it all could also be much darker. It could be somebody who's trying to do that to actually damage their business. >> So, would you define a very good text base phishing spear phishing as a deepfake, where they've got enough data, where they're, the relevance of the topic is so spot on, the names that are involved in the text are so spot on 'cause they've done their homework, and the transactions that they're suggesting, are really spot on and consistent with the behavior of the things that their target does each and every day. >> So I'm not sure I defined that as a deepfake yet, obviously you've got two types of a phish, you've got a spear phish, which is the the perfected version, the work has gone into target, you as a specific, high value individual for some reason in your organization, but what we are seeing is in the same way that deepfakes are leveraging technology to be able to manipulate somebody, things like the fact that we're all on Instagram, we're all on Facebook, we're all on Twitter, means that social manipulation is a lot easier for the bad guys to be able to create, phishing campaigns that appear to be very much more targeted, they can create emails because they know you've got a dog. They know roughly where you live, because you're this information is coming up in pictures and it's a metro on the internet. And so they can generate automated messaging and emails and things that are going to go out. That will appear to be from whomever you expect to receive it from, using words that you think that only they would know about to make that appear to be more realistic. >> Right. >> And that's actually something, we sort of seen the start of that, but still the thing to spot is that the grammar is very often not very good in these if they haven't perfected the language side of it. >> But that's coming right, but that's coming right. >> But they all getting much more accurate yeah. >> We is an automated transcription service to do all the transcription on these videos. And you know, It's funny you can you can pay for the machine or you can pay for the human, we do both. But it's amazing, even only in the last six months to see the Delta shrink between the machine generated and the person generated. And this is even in, you know, pretty technical stuff that we get in very specific kind of vocabulary around the tech conferences that we cover. And the machines are catching up very, very fast. >> They very much are. but then if you think about, this is not new. What's happened, it's been happening in the background for a while things like quite a lot of legal work is done. If you look at a state agency, for example, conveyancing it's not uncommon for the conveyancing to be done using machine learning and using computer generated documentation because it's within a framework. But of course, the more it does that, the more that it learns. And then that software can more easily be applied to other other areas to be able to do that accurately. >> Right. So another big topic that gets a lot of conversation is passwords. You know, it's been going on forever, and now we're starting to get The two factor authentication, you know, the new Apple phones, you can look at it and identify it, you say now you have kind of biometrics. But that can all be hacked, too, right? It's just a slightly different, a slightly different method. But, you know, even those, the biometric is not at all. >> Well. >> That's secure. >> I think the thing is, you see that when you're logging into something, there's two pieces of information you need. There's there's what you are you as a person and then there's the thing that you know, a lot of people confuse biometrics, thinking of biometric authentication is their password, we're actually the biometric is is the them. And so you still should back things with strong passwords, you still should have that behind it. Because if somebody does get through the biometric that shouldn't automatically just give them access to absolutely everything. It's you know, these are technologies that are provided to make things easier to make it so that you can have less strong passwords so that so that you do know where you're storing information. But People over people tend to rely on them too much, it is still very, very important to use strong passwords to think about the process for how you want to do that. Taking statements and then turning those statements into strange sentences that only you understand maybe having your own code to do that conversion. So that you have a very strong password that nobody's ever going to pick up, right? We know that common passwords, unfortunately, are still 1234567 password, its horrific. >> I know, i saw some article that you're quoted in and it had the worst 25 passwords for 2018 and 2019. And it's basically just pick and pick a string. >> They just don't change. >> But you know, but it's interesting cause, you know, having a hard Prat, you know, it's easy to make, take the time and go ahead and create that, that that strong password. But then, you know, three months later. Salesforce keeps making me do a new one or the bank keeps making me do a new one. What's your opinion in some of these kind of password managers? Because to me, it seems like okay, well, I might be doing a great job creating some crazy passwords for the specific accounts. But what if I could hacked on that thing right now they have everything in the same a single place. >> Yeah. So this is where things like two factor authentication become really, really important. So I use passwords manager. And I've been I'm very, very careful with the how my passwords are created and what goes in there so that i know where certain passwords are created for certain types of account and certain complexities. But I also turned on two factor. And if somebody does try to go into my online password account, I will get an alert to say that they've tried to do that a single failed authentication and I will get an alert to say that they've done it an authentication that happens where I'm not I you know, then I will get a note say I've done that. So this is where there's that second factor actually becomes very important. If you have something that gives you the option to use two factor authentication. Use it. >> Use it. >> You know, it may, you know, we it is a pain when you're trying to do something with your credit card and you have to do One time text. But it'd be more of a pain if you didn't and somebody else was to use it. And to fill it up nicely for you wouldn't right. >> Right. You know, it's funny part of the keynote from Rowan was talking about, you know, as a profession, spending way too much time thinking about the most kind of crazy bizarre, sophisticated attacks. At the at the fault of, you know, not necessarily paying attention to the basics and the basics is where still a lot of the damage was done right. >> You know what? This is the thing and then there's, you know, there's a, there's a few things in our industry. So exactly what you just said. Everybody seems to believe that they're going to be the target of the next really big complex, major attack. The reality is they aren't. And the reality is that they've been hit by the basic slight ransomware, phishing spearphishing credential stuffing all these attacks are hitting them all the time. And so they need to have those foundational elements in place against those understanding what those are and not worry about the big stuff because the reality is if your organization is going to be hit by a nation state level complex attack. Or you can do fight against that as well, it's going to happen. And that's the thing with a lot of the buzzwords that we see in in cyber today as Matt. >> And and with smaller companies SMB's, I mean is really their only solution to go with, you know, cloud providers and other types of organizations and have the resources to get the people and the systems and the processes to really protect them because you can't expect you to just flowers down down off fourth street to be have any type of sophistication needed. But as soon as you plug that server in with a website, you're instantly going to get, get attacked , right. >> So the thing is, you can expect that, that guy to be an expert. He's not going to be an expert in cybersecurity and the cost of hiring someone is going to outweigh the value who's getting back. My recommendation that case is to look for organizations that can actually help you to become more cyber resilience. So an organization that I work with, it's actually UK and US basis, the global cyber alliance. They actually produce a small business toolkit. So it's a set of tools which are not chargeable is put together. And some of it might be a white paper, a set of recommendations, it might actually be a vendor developed tool that they can use to download to check the vulnerabilities or something like that. But what it does is it provides a framework for them. So they go through and say, Okay, yeah, I get this. This is English, simple language. And it helps to protect me as a small business owner, not a massive enterprise where actually none of those solutions fits what i one's to. So that's my recommendation to small businesses, look for these types of organization, work with someone like that, listen to what they're doing and learn cyber from them. >> Yeah, that's good tip. I want to, kind of of double click on that. So that makes sense when it's easy to measure your ROI on a small business. I just can't afford the security pros. >> Yeah. >> For bigger companies when they're doing their budgeting for security. To me, it's always a really interesting as i can, it's insurance at some point, you know, wouldn't be great if i could ensure 100% coverage, but we can't. And there's other needs in the business beyond just investing in, in cyber security, how should people think about the budgets relative to, as you just said, the value that they're trying to protect? How do you help people think about their cyber security budgets and allocations. >> So then there needs to be and this is happening, a change in how the conversation works between the security team and the board who own those budgets. What tends to happen today is that there's a cyber team wants to provide the right information to the board that's going to make them see how good what they're doing is and how successful they are and justifies the spend that they've made and also justifies the future investments that they're going to need to make. But very often, that falls back on reporting on big numbers, statistics, we blocked billions of threats. We turned away millions of pieces of malware. Actually, that conversation needs to narrow down and the team should be saying, Okay, so in the last two months, we had Five attacks that came in, we actually dealt with them by doing this, this is the changes that we've made, this is what we've learned. However, if we had had this additional or this switched on, then we would have been more successful or we'd have been faster or we could have turned down the time on doing that. Having that risk and compliance type conversation is actually adding value to the security solutions they've got and the board understand that they get that conversation, you're going to be happy to engage. This is happening, this is something that is happening. And it will, it's going to get better and better. But that's that's where things need to go. >> Right. Cause the other hard thing is it's kind of like we've joked earlier, it's kind of like an offensive lineman, they do a great job for 69 plays. And on the seventh seventh play, they get a holding call. That's all anybody sees . And you know, there's, again, that was part of robots, keynote that we can't necessarily brag about all the DDoS taxes that we stopped cause we can't let the bad guys kind of know where we're, we're being successful. So it's a little bit of a challenge in tryna show the ROI. Show the value when you can't necessarily raise your hand and say, hey, we stopped the 87. Tax. >> Yeah, >> Cause it's only the 88. That really is the one that that showed up in the Wall Street Journal. >> I think the thing with that is when organizations are looking at security solutions, specifically, we're very aware of that. As you know, organizations struggle to get customer references, you'll see a lot of the references are major financial, large manufacturing organization, because companies don't want to step up and say, I implemented security, they did this because the reverse of that is, she didn't have it before then >> Right right, or we'll go in that door not that door. >> Yeah and so, but there are a lot of good testing organizations out there that actually do take the security solutions, and run them through very, very stringent tests and then report back on the success of those tests. So you know, we work closely with NSX labs, for example, we've had some very good reports that have come out from there, where they do a drill down into how fast how much, how many, and then that's the kind of You can then take to the board. That's the kind of thing that you can publicize to say, the reason that we're using Juniper X or x firewalls is because in this report, this is what it said, this is how good that product was. And then you're not admitting a weakness. You're actually saying we're strong because we did this work in this research background. >> Right, very different kind of different approach. >> Yeah, yeah. >> Yeah well, Lawrence really enjoyed the conversation. We'll have to leave it here. But I think you have no shortage of job security, even though we will know everything in 2020 with the benefit of hindsight. >> Really, yeah thank you very much for that. >> All right. Thanks a lot. Alright, he's Lawrence. I'm Jeff. You're watching the cube. We're at RSA 2020 in Moscone. Thanks for watching. We'll see you next time.

>> live from Anaheim, California. It's the queue covering nutanix dot next twenty nineteen Brought to you by nutanix. >> Hello, everyone. You are watching the Cube and we are live at nutanix dot Next here in Anaheim. I'm your host, Rebecca Night, along with my co host, John Farrier. We're joined by Michael Bushong. He is the vice president Enterprise marketing at Juniper Networks. Thank you so much for returning to the Cube, Your Cuba Lem. >> So thank you for this is this is awesome and you can't see it on the cameras. But this is a, like, just amazing. >> It's very We are in the clouds up here. It's a very high stage. Everything's coming full circle. >> Jim Cramer. Ask a little bit >> serious. Okay. >> Of course. I'm going to ask the tough questions >> going on. He's going to start slamming everything very soon, >> But we've known each other for a long time, Jennifer Going back ten years ago. So look, a tangle started. We're in our tenth year. You know, if you've seen the journey, I am a juniper. You left juniper startup brocade, then back to juniper. So you've seen that circle? You've seen the couple waves? I mean one of the things we were talking about before we came on camera was saw. Network fabrics to Dover had Juno's and then be anywhere. But you know, So this arrow, which became the ESPN Wave, are now suffer to find data center. So you've been in that journey is a product person. And now marking juniper, it's actually goes back about a decade. This whole esti n stuff networking. So what's What's the role now that you're doing? What's juniper doing? Why Nutanix? What's your story year? >> Sure. So I run enterprise marketing at Juniper, so my goal is effectively toe to make some of the hype makes sense, right? It goes back a decade. Actually, the early days of the only ESPN movement we didn't call it s tiene right. Juniper started with open flow and PC and alto and all these acronyms, and we actually, we're a great engineering company. Maybe not so great marketing company. And we actually call it network program ability. That didn't take off. But the technology's kind of endured. And I think what we saw was this lengthy incubation period to the point that now, as we sit here dot next in twenty nineteen. We're starting to see now some of the attraction of the last couple of years. That's a junipers general position. So we wantto dr Adoption. Certainly there's products and technology that underpins that, but But fundamentally, we're looking at a huge operational shift. And if that operational shift doesn't happen, then that's to the detriment of everyone in the industry. >> What's the relationship with NUTANIX? Can you talk about how you guys work together? What's the connection? >> Sure. So nutanix obviously does the whole hyper converge space. We provide the networking components to that. So whether that's the top Iraq connectivity, how do you get your traffic into the rest of the network? We've done some security stuff which we can talk more about. And then, if you look at the overall management piece, we've got integrations at the management policy layer as well. >> So your relationship you both got a very similar world view. How you see technology, you're both taken on VM. Where to? Can you talk a little bit about the relationships there and and why it works? >> Sure, fundamentally, if you look at what Nutanix is trying to do, it's this whole idea of one click. It ties ing everything right. They talk a lot in their keynote sessions. You hear the executives talk, You look at their collateral, the messages they take, the customers. It's about making things simple. Junipers Strategy is this idea of engineering simplicity. So just a top level? What's our purpose? What's our role in this industry at large? I think we have a very common worldview. Of course, driving simplicity is going to happen in the context of real architectural change on the change That's kind of everywhere is cloud and increasingly multi cloud. And so both Nutanix and Juniper about really driving simplicity in the context of Cloud multi cloud, giving customers the opportunity, toe run workloads wherever they need Teo without taking on additional operational burden. That's kind of cesarean unwanted in enterprises networking. >> So the Big Tran, this multi cloud you guys. That's a key part of the strategy. Dave along tonight and Stew Minutemen were arguing on the cute couple events ago. There are not one of our sessions about the hype around multi cloud. The reality of it. The reality is, is that everyone kind of has multiple clouds. It's not like that the clouds aren't talking to each other, and then we're just kind of riffing on the cloud is just big. One big distributed network, different computing, distributed networks. These air knew these aren't new paradigms. These are existing things that have computer science behind them. Engineering behind it. So juniper, you have been around for a long time. Connecting networks. The cloud is like some of the same concert on premise Hybrid Cloud and multiplied it basically a distributed network. It's all cloud operations. We get that, but the technology issue is not that hard, but I won't say that that hard, but it's similar to what you guys have done in the past. Just differently. How are you guys looking at that? Because multiple clouds, just like Internet working the switches routers, you move from packet that point A and point B get storage. His store stuff So concepts are all the same. How do you guys seeing the multi cloud opportunity within juniper? >> So I would make the distinction between multiple clouds and multi cloud? I agree with you. If you look at most enterprises, they have a workload in Amazon. They're using sales force, and so you know, they're multi cloud, right? They have multiple clouds, multi clouds, more of an operational condition. It's about taking disparate pools of resource is and managing. That is one thing. So think of it more about how you do stuff and less about where you host an application. If you look it even like describing Amazon, some people say, Well, Amazon is just, you know, Cloud is just using other people servers. It's not. You're not renting their servers. What you're leveraging is their operations. That's the transformation. That's this kind of underfoot. And so while some of the technology bits are common, the ability to do abstracted control moving to declare it over intent based management, right, these air right technology building blocks. What you're seeing now is the operational models are coming along, and that's really that's the change we have to drive on. I'll just kind of close with when you change technology. If it's just about deploying a piece of software, if it's just about deploying a piece of hardware like candidly, that challenge isn't that it's not that hard, right? We know how to deploy stuff when you start talking about changing how people fundamentally do their jobs. When you started talking about changing, you know how businesses operate. That's that's the piece that takes some time and I would venture. That's why you know, you look a decade ago why we're where we started. If you look at what's taking a decade, it's the operational change, not the technology piece >> and the cultural jobs movement. Certainly forcing function on that, which is awesome. And that's the tale when I think. And then again, Gene Came was on yesterday Who wrote The Devil's Handbook and also does that death. The Devil Enterprise. Someone said, We're three percent in. I would agree with him. I think it's so early, but But the challenge. I want to get your thoughts, Michael. And this is that Connecting multiple on disparity environments is great, but late in C kills now. So now late and see these air old school concepts, you know, get a time can't change the laws of physics. Right? So Leighton sees matters s l A's matter. So these air network challenges these air software challenges. What's your view on that piece of the puzzle? >> We leave when we say cloud, you know a lot of people probably think, um, you know, G C P Azure. They might think a WSB probably picture in your head, you know, some logically central cloud. First, we need to disavow people of the notion that cloud is this thing that somehow sits at the center of everything. It's not. There are centralized clouds. If you're optimizing for economics, that makes perfect sense. Tow To do that. There's distributed clouds. The whole rise of multi axis edge computing is about changing the paradigm from moving data to the application. Right. If your applications in Amazon and you're going to send your data there, that's one model Teo. Sometimes you might want to move the application to the data. If you have a lot of data like an i o t. Use case as an example, I was used oil platforms is a really good example. I don't know if you know, but you know how they get all their. They have all these mining and manufacturing bits. They've got lots of data. How did they get that data off the oil platforms? Snowball. So what they do is the helicopters come in, they take the drives off and they they they leave right. The reason they do that because if your reliance on satellite links just too much data, you can't statue >> is going to get a helicopter to ransom helicopter to come in, >> we'LL know when they're swapping the crew out every fourteen days, that's what happens. So here's the thing, right? If in that kind of model than the cloud, the data center exists on premises. And if that's the case, then when we think about you know kind of what the cloud is, cloud is, it's It's a lot. It's a lot more than what we most of us probably think about. Certainly, we see it with Outpost as a WS is starting to move on premises versions, and there's a lot of reasons you might wanna have a distributed cloud. Certainly it could be, you know, your comfort and security and control. There's real privacy implications, country of origin, so subpoenas can access your information depending on where it resides. >> What you're saying is, basically, it's all cloud. It's operational is the new definition. So you figured from an operational standpoint, Ops and Dev's That's it. The rest is just all connected somehow through the text, >> and then you need to have it. Yes. So we we understand the connectivity, bitch, you've gotta have the right, you know, elements. But if it's operational, it's about how do you do policy management? So part of the whole nutanix thing and kind of what drove us together was this idea that if I want a one click everything. If you could do that within the hyper converge space, you still have to do that over the connected environment, which means managing policy from a single location, regardless of where it is. And of course, using that policy to Dr Security >> and their strategy is to take what that worked for. The CIA and the data center move that into this new operator operating model, which spans multiple quote, disparity, environments or clouds or edges. It's similar similar concept, but different environmental. Yeah, >> that's exactly right. And so then what Nutanix needs that is a strong networking partner because they have tto do the bits that they do. They need other people to do the bits that that you know that we can do. We pull those things together and then you can provide essentially a secure environment for hybrid workload. >> So you guys embed it into their product? You guys joined cell together. Is it more of a partnership? How deep is the partnership with you With Nutanix >> s all just They'LL say yes, we get along s o and it kind of the most surface level you know, you need to have top Iraq switches. You gotta connect to the network and so we do qualification there. So if you deploy nutanix, you can deploy juniper alongside and that looks more like a kind of a co selling meat in the channel type model. Beyond that, if you look at how we provide security over like a workload environment, the question is, then you know what's the security element? So we've taken our virtual firewall. We cut our V s are axe, which essentially runs in the V M. And we can run it on a V, and so that gives them a segmentation strategies. So if you look it workloads that air distributed across the cluster by having a firewall element that we can enforce policy. Of course, that firewall element is then integrated with prism. So if I want to deploy these things when I spin up a new V M. What I want to do is spin up the security with it, and so you see management integration. Then if we continue this too, it's kind of full conclusion. We have, ah, product suite We call contrail in the enterprise version Contra Enterprise Multi Cloud, which is all about policy management and underlay management. And so, as we extend the partnership, it gives us additional opportunity to take um to provide routed elements which provide policy enforcement points and then to give us a way of managing policy over a diverse environment. >> And you guys can bring in that platform element for nutanix. Is there now a platform? They have a full stack of software on Lee. So you guys, you cannot take their stuff, put it there and vice versa. >> That's exactly right. So whether the workload resides in a ws on two or whether it resides kind of on premises in a jiffy, weaken one, we're kind of co managed and then to it gives us the security elements toe play across that >> one of the things that we're talking a lot about at this rinse it and at a lot of other events like it, it's sort of or the dark side of technology. We're at a time where major presidential candidates are talking about breaking up. Big tech were becoming much more aware of the privacy concerns. The biases that are built into algorithms. Exactly. I want to hear your thoughts as a technology veteran. Do you? Are you still a technology optimist or do you did? Does this stuff keep you up at night? I mean, how where do you fit your personal views? I was >> somewhat of a technology optimists, but I'm a skeptic when it comes to the people. I think if the technology existed in a vacuum, I think some of the problems go away. I think privacy is a major concern. I think it's going to shape regulatory action, especially in Europe. Well, so I think we'LL see similar actions in the US I don't have quite a strong connection to what's happening in Asia. Um, I think that the regulatory, the challenge I have from a technology perspective is that if the regulations come in the absence of understanding how the technology works, then you end up with some really terrifying outcomes on DSO I'm Sam. I'm a fan of the technology. I'm nervous of the people on that in terms of like, our overall Ruelas is cos here, I think, you know, we need to do a candidate a better job of, of making sure things land before we move on to the next big thing on DH. You know, we're talking cloud. We're ten years into cloud and people were always talking about the next frontier. To some extent, I think the world doesn't move as fast as we like to think it does. I don't think that the even like the mark, I'm in a marketing role. I don't think that the marketing hype necessary. I don't think it serves us by moving too far ahead because I will tell you when the gap between the promise and the reality becomes insurmountable e wide. I think it's Ah, I think I think everyone loses Andi. You run the risk of stranding an entire generation of people who who gets stuck behind it, and I don't you know, I'm nervous about about what that means, and I think it's you asked the question that you're the dark side. I think it's Certainly it plays out in our industry. I think it plays out. You know, there's a digital divide that's growing in the U. S. Based on broadband access. By the way, that's gonna widen with five G. I think it plays out between different nation states. So I Yeah, I don't know. I'm an optimist. Maybe I'm a pragmatist. >> Realist. >> Yeah, I'm I'm I'm I'm a little scared. >> Little cloud definitely happened, and that's a good point. And we took a lot of heat at looking ankle. Keep on the cube. Was too many Men in the team put out the first private cloud report People like this is nonsense. Well, well. And our thesis was clouds grade if you want. If you're in the cloud as a cloud native or, you know, new startup, why wouldn't you go on Amazon? Everyone, we did that. But if once you taste cloud operations, you go Wow. This is so much awesome. Right? Then go into a modern and enterprise. It's not going to be overnight. Change over. I mean, we might say it's going to take about a decade. We fell from the beginning that cloud operations once you taste cloud you realize this is a new operating model. There's a lot of benefits to that, but to change it over in the enterprise, and that turned out to be what everyone's now do it. But that was three years ago. >> Well, there's implications. So if its operations then operations is inherently an end end proposition, you can't have operations in a silo. Things like you're monitoring tools. How do you do cloud monitoring it on premises monitoring. How do you do workflow Execution? How do you do? You know, automation, whether that's event driven or even just scripted. If you have wildly different environments that require you to buy for Kate, your investment, then there's a very real There's a complexity that comes with that your people have tto do more than one thing that's that's hard. There's a cost that comes with that because you have different teams for different things. There's a lack of coordination. I don't think you unlock the value of cloud in that in that environment. And I think that operational pieces really around converging on >> Michael your point about people in technology. It's so right on. We see that all the time where I'm a technology Optimus. I love technology, but I totally agree that people can really destroy it looked fake news. It's just, you know, it's infrastructure network effect with bad content policy because Facebook's immediate company not a platform >> well, technology's only is good on our end are >> gonna run. The government don't even have the Internet work. So you know when you when you go to the cloud, same >> knowledge just also want the government to come away with that we do it >> where the government just doesn't know how the Internet works. Some people that do but like the good hearings, it's ridiculous. But you know, there's a real D o. D project going on future military Jet I contract. We've been reporting on where modern data driven application workloads. I could use a soul, cloud or multi class so that the dogma of what multi vendor was in the old days is changing. >> I don't I actually don't know if you look at multi cloud. If it's an end end proposition, then by definition it's also going to be multi vendor like there's no future where it's like end in all one vendor. I think we have to come to grips with that is an industry. But I think if you're clinging to your you know, kind of I want my single procurement vehicle. I want my single certification. By the way, I think if you believe fundamentally that incumbency is going to be that your path forward, I think it's a dangerous place to be. That's not to say that. I think the incumbents all go away. I don't There's a there's a heavy rule to play but certainly were going to open things up. And >> you see procurement modernized. I mean, I mean, government goes back to nineteen ninety five procurement standards, but either the enterprise procurement moving So the text moves so fast. Procurement still has rules from >> so no, I don't think all >> of the second right. >> Then there's a whole A procurement in our industry is driven by our peace. Our peace tend to be derivative. I take my last r p. I had some new lines. If you want Esti n so you take the cup copy and paste five hundred seventy four lines at the five hundred seventy fifth line. S T n. You're gonna end up in the same solution because the first five seventy four of the same I do think we should learn a little bit from what the big public cloud cos they're doing, which is, you know, tightening refreshed cycles, retiring things with as much passion as they introduced new things tightening up. Ultimately, what gets deployed? Maintaining diversity of underlying components so you could maintain economic leverage when you're doing procurement. But then solidifying on operationally streamlined model, That's I think that's the future. That's certainly what we've been on as a company. I think that's what we're betting on with Nutanix From a partnership point of view, I think we'LL be on the right side of change on that, and I think it's going to, you know, it may take some time to play out. That's where I think things go >> well. Michael Bushong. Always a pleasure having you on the Cube. Thank you for coming on. >> Thank you very much. >> I'm Rebecca Knight for John Furrier. You are watching the Cube

>> Live from Seattle, Washington, it's the Cube, covering KubeCon andCloudNativeCon North America 2018. Brought to you buy Red Hat, the CloudNative computing foundation and it's ecosystem partners. (background crowd chatter) >> Okay welcome back everyone, live here in Seattle forKubeCon and CloudNativeCon. This is the Cube's coverage, I'm John Furrier with Stu Miniman. We've got two great guests, Chris Wright CTO of Red Hat, Scott Sneddon who's the senior director ofcloud at Juniper Networks, breaking down, windingdown day one of three days of coverage here. Rise of kubernetes, rise of cloudnatives, certainly impacting IT,open source communities, and developers. Guys, thanks for coming on the Cube. Appreciate it. It's good to see you. >> Yeah, good to see you. >> Welcome to the Cube. Okay, so, talk aboutthe relationship between Red Hat and Juniper. Why we're here, what are we talking about? >> Well, we're here to talkabout a combined solution. So, Red Hat's bringingkind of the software platform infrastructure piece and Juniper's bringinga networking component that ties it together.>> Yeah. >> So, we do have a fairly, well, in tech terms arelatively long history of working together. We've had a partnership for a little more than two years on sometelco Cloud initiatives around OpenStack, using the right OpenStackplatform with Contrail Juniper's contrail solutionas an SDN layer for these telco Cloud deployments. And have had a lot of successwith that partnership. A lot of large and smallto medium telco's around the world have deployed that. Earlier this year at theOpenStack summit in Vancouver, we announced an expandedpartnership to start to address some enterprise use cases. And, you know, naturallyopen shift is the lead technology that we wanted to tie in with around enterpriseadoption of cloud and some alternatives to someof the legacy platforms that are out there. >> And we were talkingearlier in the Cube here, we always get kind ofthe feel of the show, kubernetes maturing? But it kind of two worlds colliding and working together. A systems kind of view,almost like operating systems. The network systems, allkind of systems thinking. And then just apps. Okay, the old app thing. So these old legacy worldthat we all lived in kind of happening in really dynamic ways with the apps aren't thinkingabout what's below it. This is really kind of whereyou guys have a tailwind with Juniper.>> Yeah. Because you still gotto make things dynamic, you still got latency, onpremises not going away. You got IOT, so networkingplays a really big thing as software starts figuringthings out as kubernetes. Let's talk about that. Where is that value? How's it expanding? Cause clearly you stillneed to move packets from A to B.>> Yeah. Be more efficient with it. Apps going to have policy. >> The, well, I mean you've still got to, the network is always been the foundation of technology or at least for the last 20 plus years. And as cloud has been adopted, really we've seen network scale drive in different ways. The mega scalers thathave built infrastructure that we've been enabling for quite a while and have been working withthose customers as well. We've been developing a lot of simplified architecture just forthe physical plumbing to connect these things together. But what we've seen andis more and more important is, you know, it's all about the app, the app is the thing that'sgoing to consume these things. And the app developerdoesn't necessarily want to worry about IP addresses and port numbers and firewall rules and things like that, so how could we justmore simply extract that? And so, you know, we'vebeen developing automation and aimed at the networkfor quite a while, but I think more andmore it's becoming more important that theapplication can just consume that without having to directthe automation at the app. And so, you know, groupslike CloudNative foundation and a lot of the workwith kubernetes are on network policy, let's us use CloudNativeprivatives and then we can translate into the network primitives that we need to deploy to move packets, you know, IP addresses and subnets. >> And Chris, talk aboutthe multi cloud dynamic here because again, the dayof things are moving around the standardizationaround those core value propositions, youmentioned about networking and software networks, all kinds of software, you know, venations under the covers. I'm a customer, I havemultiple clouds now. This is going to be a core requirement. So you got to have a a clean integration between it. >> There's really two things. If you look at a modern application, you got your traditionalmonolithic application and as you tease itapart and into components and services, there's only one thingthat reconnects them and that's the network and so insuring that that's as easy to use as an applicationdevelopers focus is around the app and not aroundnetwork engineering is fundamental to a single cluster. And then if you have multiple clusters and you're trying to take advantage of different specialtiesin different clouds or geo replication or things like this that also require thenetwork to reconstitute those applications across thedifferent multiple clouds. If you expect your applicationengineers to become experts in networking, you're just sort ofsetting everybody up with misset expectations. >> It slows things down,requires all these other tasks you got to do. I mean it's like a rock fetch. You don't want to do it. Okay, stack a bunch of rocks, move them from there to there. I mean, this is whatthe holy grail of this infrastructure's code really is. >> Yeah.>> Yeah. I mean, that's the goal. >> Help connect the dots for us. When you look at multicloud networking obviously is a very critical component, what're your customers looking for? How does this solution goto market for your company? >> Absolute ease ofuse is top of the list. So, it can't be overly complicated. Because we're alreadybuilding complex systems, these are big distributive systems and you're adding multipleclusters and trying to connect them together. So ease of use is important. And then something that'sdynamic and reflects the current application requirements, I think is also really important. So that you don't over utilize resources in a cloud to maintainsort of a static connection that isn't actually needed at that moment. I'm sure you probably havea different perspective. >> Yeah, I mean, this isthe whole concept of SDN and network virtualization, a lot of the buzzwordsthat have been around for a few years now, is the ability to deliveron demand network services that are turned on whenthe application asks for it and are turned off when the application's done with it. We can create dynamic connectionsas applications scale. And then with a lot of thenewer things we've been doing around contrailand with Red Hat are the ability to extend thoseapplications environments with networking andsecurity into various cloud platforms. So, you know, if it's runningon top of an openstack environment or in a public cloud or, some other bare metal infrastructure, we're going to make surethat the network and security primitives are inplace when the application needs it and then get deepervisioned or pulled out when they go away. >> Being at a show like this, I don't think we need to talktoo much about open source, because that's reallycore and fundamental, but what we're doing here, but I guess, how doesthat play into customers? We've been watching the slow change in the networking world, you know, I'm a networking guy by background, used to measure changesin networks in decades and now it feels like we'removing a tiny bit faster, >> Little bit. >> What're we seeing is--? >> Well, I mean the historyof openness in networking was the ITF>> Standards. >> and IEEE and standards bodies, right? How do we interact? We're going to have ourlittle private playground and then we'll makesure to protocol layer, we can interact with each otherand we call that openness. But the new openness is open source and transparency into the platform and the ability tocontribute and participate. And so Juniper shifted a lot of our focus, I mean we still haveour own silicone and the operating system we built on our routers and switches, but we'vealso taken the contrail platform, open sourced it a few years ago, it's now called thetungsten fabric project under the Linux foundation. And we're activeparticipants in a community. And our customers really demand that. The telco's are drivingtowards an open source model, more and more enterpriseswant to be able to consume open source software with support, which is where we come in, but also be able to have an understanding of what's going on under the covers to participate if that's a possibility. But really drivinginteroperability through a different way then justa protocol interaction and a standards body. >> I can see how kubernetescan be a great fit for you guys at Juniper, clearly out of the boxyou have this kind of inter cloud, inter networking, paradigm that you're used to, right? How does the relationshipof Red Hat take it to the next level? What specifically areyou guys partnering on, where's that, what'sthat impact on customers? Can you just give a quick explanation, take a minute to explainthe Juniper Red Hat-- >> Well a lot of itcomes down to usability and ease of use, right? I mean what Red Hat's done with open shift is developed a platformleveraging kubernetes heavily, to make kubernetes easierto use with the great support model and a lot of tooling built on top of that to make thatmore easily deployable, more easily developersto develop on top of. What we're doing withcontrail is providing a supported version ofour open source project and then by tying thesethings together with some installation tools and packaging and most importantly a support model, that let's a customer have the proverbial single throat to choke. >> Have you ever hadcustomers that can run beautifully on your platform? >> Yeah yeah, and theinstallation process is seamless, it's a nob that installtime to consume contrail or some other networking stack and they can call Red Hat for support and they'll escalate toJuniper when appropriate and vice versa. And we've got all those things in place. >> I think one of the things that we have like shared vision on is, the ease of use andthen if you think about two separate systems with a plug in, there's going to be someintegration that needs to happen and we're lookingat how much automation can we do to keep thoseintegrations always functional so that ifwe need to do upgrades, we can do those together instead of abandoning one side or the other. And I think another areawhere we have shared vision is the multi cloud space where we really see the importance for our customer base toget applications deployed to the right locations. And that could be takingadvantage of different pricing structures in different clouds or it could be hardwarefeatures of functionality. Especially as we getinto edge computing and really creating a differentview of computing fabric, which isn't quite so, you know, client serveror cloud centralized, but much more distributed. >> I like how you said that Chris, earlier about how when you decomposethat monolithic app it connects with the network. That's also the other way around. Little pieces can cometogether and work with the network and then form in real time, whether it's an IOT datacoming into the data center, or pushing computdata to the edge, you got to have that network interaction. This is a real CloudNative evolution, this is the core. >> Yeah, and I think anotherpiece that we haven't touched on as much, Scott mentioned it, was the security component. >> Yeah, explain that. >> Again, with as youdecompose that application into components, you surface those components with APIs, those were internal APIswhen they're now exposed externally security really matters. And having simple policythat describes not just the connectivity topologybut who can speak to whom is pretty fundamentally important. So that you maintainsecurity posture and a risk profile that's acceptable. >> And then I think it'sreally important is, your traditionalenterprise starts to adopt these CloudNative models. You've got a securityteam there that might not necessarily be up to speed or on board. So you've got to havetooling and visualization and analytics to beable to present to them that policies are being enforced correctly and are compliant and all those things so. >> Yeah and they're tough customers too. They're not going to, they expectreally rock solid capability. >> They don't let youjust deploy a big flat network with no policy-- >> Hey what about the APIs? Service areas exposed in the IOT space. >> Yeah.>> Right. >> You got to nail it down. >> Yeah absolutely, sothat's a lot of what we're bringing to the table here, is a lot of Juniper'shistory around developing security products. >> Take a minute to explain,I want to give you some time to get a plug in for Juniper. I've been following youguys for a long time. Junos back on the old days, contrail. Juniper's has had a software, big time software view. >> Yeah. >> Explain the DNA of software at Juniper. >> You know the earlydays of Juniper were, we weren't the first networkvendor on the market. There was already somebodyon the market in the mid 90s that had a pretty solid stronghold on carrier and enterprise networking. We had to come in with a better model. Let's make the box easierto use and simpler. Let's make the interfacea little more structured and understandable. Let's make it programmable, right? I mean the first feature request for Junos was to have a CLI becausethe first interaction to it was just an API call. And that was out of the box from day one. We had to write a user interface to it just to fit in to theexisting network world in the mid 90s. And so we've alwaysbeen really proud of the Junos operating systemthat runs on our boxes. We've really been proudthat we've had this one Junos concept of a commonoperating system on every network device that we deliver. As we've started tovirtualize those network devices for NFE and things like that, it's again that same operatingsystem that we deliver. Contrail came to us through acquisition, so it's not Junos in and of itself, but still leveraging a lot of those same fundamentals around,model driven configuration management, understandableAPIs, and openness that we've always had. >> Cloud operating modelthat everyone's going to, the common operating modelfits in that unification vision that you guys have had. >> Yeah absolutely. >> And really early, by the way, was before SDN was SDN, I think that was SDN's kind of like-- >> I like to dry, I-- >> Should have called it SDN. >> Right, I described SDN as just a big distributed router andreally we've had big distributed routers for a long time. >> John, we are in Seattle, everything we're talkingabout in tech is hipster. >> Chris, great stuff. Great to have you on, Scott. Great smart commentary. CTO Red Hat, you guys are winning. Congratulations on the betsyou made at kubernetes early, >> Yeah. >> CoreOS great acquisition,great team there, and some news there aboutsome dealings out back into the C and CF, soI mean, you've got it-- >> A lot going on. >> A lot going on. And yeah, big news with that other things, I can't remember what it was, it was some big-->> Something in there. >> Something for a million dollars. >> Great news out there. Thanks for coming out, appreciate it. Good to see you.>> Good to see you. >> Alright, breakingdown day one coverage. I'm John Furrier, Stu Miniman. Day two starts tomorrow. Three days of wall towall coverage of KubeCon. And they're shutting down the hall. Be right back and see you tomorrow. Thanks for watching. (techy music)

>> Announcer: Live, from Vancouver, Canada it's the CUBE, covering OpenStack Summit North America 2018, brought to you by Red Hat, the Open Stack Foundation, and it's ecosystem partners. >> Welcome back, I'm Stu Miniman and my cohost John Troyer and you're watching the CUBE, the worldwide leader in tech coverage. Happy to welcome back to the program long time friend of the CUBE back from the earliest days, Randy Bias, Vice President with Juniper, Randy, great to see you. >> Absolutely, great to be back with you guys. >> All right, so Randy, we've been talking about, you know, community, and everything's going good and attendance might be down a little bit but how we fit in with containers and kubernetes, and everything, so we expect you to tear everything up for us and tell us the reality of what's happening in this community. >> I'll do my best (laughing). >> All right, so before we get to the kubernetic stuff, you're working on, we used to call it OpenContrail? Which you were involved in before Juniper acquired it, went through a rebranding recently, Tungsten, which I was looking up, came from the word heavy stone, give us the update from the networking side. >> Yeah, so the short history is that there was a company called Contrail, and they created a software defined networking controller, it was acquired by Juniper in 2012, 2013, and then that was open sourced, so Juniper for a long time was running with sort of two editions, Contrail which was the commercial offering, and OpenContrail which was the open source, and then shortly after I joined Juniper, identified that, you know, we really needed to go back to the drawing board on the way that we had organized the community, and transition it from being Juniper-led to community led, and so over the past year, I spearheaded that effort, and then that culminated in us announcing at the end of March at ONS that, you know, OpenContrail was now Tungsten Fabric. We renamed it, we moved it into the Linux foundation, under its governance, and now Juniper is one of many people of the community that have a seat at the table for the management, both from a business and technical perspective, and we're moving forward with a new reinvigorated community. >> Yeah, so networking sits at really the intersection of this multi-cloud world that we're living in. There's so many players trying to be there, you know Cisco, really moving to become more of a software company, when I interviewed their number two guy at their show, he's like, when you think of Cisco in the future, we're not even going to be a networking company, we'll be a software company. VMware, of course, pushed heavy through, then the Nicira acquisition, where does Tungsten fit, kind of compare and contrast for us, where it fits among some of these other offerings out there in the marketplace. >> Yeah, I mean, I think most enterprise vendors are in a similar transition from being a hardware to software companies. We're no different than any of the rest. I think we have a pretty significant advantage in that we have a lot of growth in the cloud sector, so a lot of the large public clouds are our customers and we're selling a tremendous amount of hardwaring to them, so I think we've got a lot longer runway. But, you know, we just recently hired CTO, Bikash Koley, out of Google, and we're starting to see some additional folks out of Google, like my new boss, Morgan, and what that's bringing with it is a very much a software first type perspective. So Bikash and Morgan really built everything for the Google network from the topper rack all the way out to the win and it's almost all software-based, disaggregated, hardware, software, opensource software running on top of white boxes, and so that kind of perspective is now really deep, start beginning to become embedded in Juniper. And at the head of that is Tungsten. So we see Tungsten Fabric as being sort of a tool that we use to create, you know, a global ubiquitous network fabric, that anybody can use anywhere, without talking to Juniper at all, without knowing that Juniper's part of Tungsten, and then as they grow up and they get to a point where they need multi-cloud, they need federation, or they need kind of day two enterprise operations, you know, we have a commercial version and a commercial distribution that they can use. >> Randy, we talked a little bit about OpenContrail and last year, at OpenStack Summit and moving it to a more of a community based governance model, and now that's happened with the Linux Foundation, can you talk a little bit about the role of opensource governance, and corporate governance, and then foundations, and just going forward, you know, what's an effective model for 2018 going forward, for a foundation-led project and maybe in the context of Tungsten Fabric, and how is that looking? >> Yeah, so again, OpenContrail's now Tungsten Fabrics, might be new for some of the viewers, lot of people still coming to terms with that. And so one of the things that we noticed is that, and when many people go and they say, hey, we want opensource first, the AT&T's of this world, part of what they're saying, one of the aspects of being opensource versus we want to be one of many around the table, we want to have a seat at the table, we want to have the option to contribute code back, and we want to feel like it's a group effort. And so that was a big factor, right? It was an opensource project, but it was largely the governance was carried by Juniper, all the testing infrastructure was Juniper, you know, all of the people who made architectural decisions were Juniper, all of the lead contributors were Juniper, and so, going to Linux Foundation was critical to us having a legal framework, for the trademarks, the code, the licenses, the contributor license agreements, are all owned and operated by the Linux Foundation and not by Juniper, so we basically have a trusted third party who can mediate all those things and create a structure, a governance small structure where Juniper has one seat at the table, and all the other community members do as well. So it was really key to getting, to moving to that model to increase people's interest in the project and to really go the next level. There just wasn't any way to do it without doing this. >> All right, so, Randy, let's talk about OpenStack. You were watching the keynote yesterday, you were, you know, in the Twitter stream, >> Randy: I don't usually watch keynotes, man. >> Stu: But you know this community, so-- >> I do know this community (laughing). >> Give us kind of the good, the bad, and the ugly from your standpoint as to, you know, where we've gone, you know, what's doing well, and what you're frustrated as heck that we still haven't fixed yet. >> Well, I mean, it's great that we have so much inroads amongst the carriers, it's great that, you know, that there's a segment that OpenStack has been able to land in. I mean, at some points when I was feeling particularly pessimistic on some days, I was like, oh man, this thing's never going to go anywhere, so that's great. On the other hand, you know, the promise that we had of sort of being the Linux operating center, operating system of the data center, and you know, really gaining inroads into private cloud and enterprise, that just hasn't materialized and I don't see a path to that. A lot of that has to do with history, I'm not sure how much of that I want to go into here, but I see those as being bright lights. I see the Ocata containers effort and sort of having this alternative structure that's more or less like the umbrella structure that I lobbied for while I was on the board. So for several years on the board, I said we need to really look more like the Apache Software Foundation, we need to look less like the Linux Operating System in terms of how we think about things. Not this big integrated monolithic release, you need more competition between projects and that just wasn't really embraced. And I think that that, in a way, that was one of several things that really kind of limited our ability to capture the market that we really wanted, which is the enterprise market. >> Yeah, well, I know, and one of those sticking points there that I've talked to you many times over the years about is how do I actually deploy this? You know, getting a base configuration and scaling this out, simplicity is tough, getting to those environments, you know, getting it up in two weeks, is good for some environments, but maybe not for others. >> Yeah, I mean I think there's sort of a spectrum, right? At one end of the spectrum, you say hey, I'm going to have a very opinionated approach like kubernetes does, and we're going to limit what we say we can do, you know, we're not all things to all people. And I think that opinionated approach, like the Linux operating system worked very, very well. And then other end of the spectrum is we've got no opinion like the Apache Software Foundation, and then it's up to vendors to go and cherry pick the pieces they want and turn that into some kind of commercial offering, whether it's Hortonworks, or Thi-dare or Du-per or whatever it is, the problem is that OpenStack wound up in the middle where it had the sort of integrated monolithic release cycle which it still does, which started to be all things to all people, and it was never as great as it could be, so it's like we got to support Hyper-V, we got to support VMware, and as the laundry list of all things we have to support grew longer, it became more and more difficult to have a compelling, easy to use, easy to scale offering that any enterprise could consume. >> Randy, a lot of talk this week about edge computing, with several different definitions, right? But it does strike me that, you know, there's a certain set of apps, that you write 'em and that they live fine in a big public cloud, and a big data center somewhere. But there's a lot of hardware that's going to be living out in the world, whether that's at the base of a radio tower, or in a wall, or in my shoe, that is going to be running hardware, and is going to be running something, and sometimes that something can be OpenStack, and we're seeing some examples of it, many examples of that already. Is that an area of growth for OpenStack? Is that an interesting part of how this fabric is going to expand? >> Well, I probably have a contrarian view here. So, I spent a bunch of time at Juniper, one of the things I worked on for a while was edge computing and we're still trying to decide what we want to do there and you know, kind of to the first point you made is everybody's edge is different, right? Is it on the mobile phone, is it back in the data center, the difference is that the real estate gets more expensive as you move out, right? And it's in terms of latency, and it's in terms of bandwidth and it's also in terms of cost of storage and compute. There's a move closer to the mobile device that becomes progressively more expensive, and so that's why a lot of people sort of look and say hey, wouldn't it be nice if we can get you out the closer lower latency and bandwidth and so on but as we looked at it, a lot of the different use cases it became really interesting in that, it wasn't clear if there was that much value between 5 milliseconds and 20 milliseconds, right? I mean, that's pretty, either one's pretty close, sure there's a lot of difference between 20 and a 100, but maybe not so much between 5 and 20. And so we kind of came to the conclusion that at least for right now, probably, the bulk of use cases are fine with 20 milliseconds, and what that means is that regional systems like AWS's Lambda at the Edge, they're in metro, those are probably good for most cases. I don't know that you need to be on the tower, I don't know that you need to be in the central office, so I think edge computing is still nascent, we don't know exactly what all those use cases are, but I think you might be able to service most of them from regional data centers, and then the question really becomes what does that stack need to be and if you have a regional data center that's got plenty of power, plenty of space, then it might be that OpenStack is a good solution, but if you're trying to scale down onto the tower, I got to have some doubts about whether OpenStack can really scale down that far. >> Randy, analytics is something we've been seeing, the networking people used for many years, at this show, starting to hear a lot of discussion about AI and ML, would love your view point as to what you're seeing in that space. >> You know I have some friends who started off in AI in very early days and he had a very pessimistic view. He said, you know this stuff comes and goes, but I'm actually very positive and optimistic about it because the way I look at this is there's a renaissance happening which is that, you know, now ML is really available to masses and you're seeing people do really interesting things like, we have a product called AppFormix, and what they do is they take ML and they apply it to operations and I love this because as an operations guy, you know, I used to have these problems in production where something would go out and the first thing I'd do, is I'm trying to do correlation and then root cause analysis, like, what was the actual failure? Like I can see the symptom on this end and now I have to get all the way back to what caused it, and the reality is that machine learning, AI techniques and protocols can do all the heavy lifting for operators very, very quickly and basically surface a problem for somebody to do the final analysis on. And so I do think that ML and AI apply to very specific vertical problems, it is just a place where we're going to see a tremendous amount of revolution in the next couple years. >> All right, and that hits right at really that intersection between kind of the developers and the operators there-- >> Absolutely. >> What are you seeing from an organizational standpoint, companies you're talking to these days, how are they doing adopting that change, dealing with that, you know, often schism or are they bringing those groups together? >> Well, I think you remember that like in the early days, I used bring my deck along and I would talk about assembly line IT versus the robotics spectrum all of IT and I would sort of make that sort of analogy to sort of the car manufacturing process, and I think what machine learning is really going to do is take us to that next level past that right? So we had the assembly line where we have all the specialists, we had the robotics factory where we had people who know how to build a robots and software, and it's really sort of like, just churning out with a lot of people on the line, and I think the next level after that is, you know, completely fully automated applications driving themselves, you know, self-driving applications, and I think that's when things get really interesting, and maybe we start to remove the traditional operator out of the equation and it really becomes about empowering developers with tools that are comfortable and that leverage all the cloud era and stuff that we built. >> All right, so Randy, you're credited with the pets versus cattle analogy, what's the latest, you were talking about some of the previous slide decks, what's Randy Bias looking on down the road? >> I mean, the stuff just comes to me, man. I can't like predict, but the thing I've been talking about a lot lately is services of platform, I think we might've talked about that last time, which is just this notion that if we look at where Amazon's invested and what's interesting, it's certainly not at the infrastructure layer and it's really not at the PAS layer, it's that thick layer in between with like database as a service and NoSQL as a service, and messaging service, and DNS and so on, where you can kind of cherry pick those things as you're assembling your own PAS for your application, and I still think that's the area that is under-discussed, and the reason is is the people back into basically doing that, building kind of the service as a platform system, but they're not like going into it, kind of like eyes wide open. >> Yeah, so just following up on that last piece, one of the criticisms I have this week is when you talk about multi-cloud, most of the people talk about, oh well people are clawing things back to their data centers. Juniper plays across the board, strong partnership with Amazon, yet you're here, what are you hearing from customers, you know, what do you see as kind of the balance there and, you know, the public cloud's role in the world? >> I mean, they're still winning, right? I don't think there's any doubt, I haven't seen a decline back here talking about, but we are starting to enter into the era of, okay, this stuff is out there, and it's running, but I need to find my governance model, I need to understand who's using what, I need to understand what it's costing me, and that's the sign of the maturation process. And so I think that, you know, we saw in the early days of cloud, people jumping the gun, creating compliance services, and you know, SAS products that would basically measure how much you're spending and think that it's time for that stuff to come back in vogue again, because the tool needs to be there for people to manage these extended supply chain of IT vendors which include the public cloud. And I think that the idea that would claw them back as opposed to like just see that as holistic part of what we're trying to accomplish doesn't make any sense. >> Well learned. Well, Randy Bias, always a pleasure to catch up with you. >> John. >> John Troyer, I'm Stu Miniman, getting towards the end of two days of three days of live coverage. Thanks for staying with the CUBE. (bubbly electronic music)

>> Announcer: Live from Las Vegas, it's theCUBE! Covering AWS re:Invent 2017. Presented by: AWS, Intel and our ecosystem of partners. (lively electronic music) >> Welcome back everyone, this is theCUBE special exclusive coverage of AWS re:Invent 2017. CUBE's our flagship program, we go out to the events and we extract the signal noise. I'm John Furrier your co-host. With me today is Justin Moore, an analyst. We have two sets here in Las Vegas. Our next guest is Sumeet Singh, Vice President of Cloud Analytics with Juniper Networks, formerly of AppFormix, which was bought about a year ago. CUBE alumni back. New team, Juniper, welcome back. Last time we chatted with you you were entrepreneurial. >> Yeah. >> Taking names, kicking ass, now you're-- >> Bought out Juniper Networks, yeah. >> You bought out Juniper Net, what's going on? >> So we've essentially been building, building more and more and it's actually been a totally awesome experience. So, Last year when we spoke, we were essentially looking at a whole lot of private cloud deployment. Looking at OpenStack, looking at (mumbles), looking at VMware, and since, what we've now started really expanding into is, of course, the multi-cloud and hybrid cloud scenario. And looking at how to secure these clouds on prem, in the cloud, multi-cloud, as well as bring rich analytics into real-time operations insight as to what's going on in all of these environments. And how to optimize them. >> Yeah, that whole multi-cloud hybrid cloud thing is really exploded in the last 12 months. I'm hearing from customers a lot more that they are pursuing a multi-cloud strategy, but it seems that there's just this proliferation of things that you've now got to monitor and secure. So, how are you helping customers to do that? >> So, I mean, you're going to start with the basics. Right? So, the first thing that we got to realize is there are, of course, those companies that are born in the cloud. But then, there's a whole bunch of others who have for long run their own data centers and run their application stacks on prem, who are now looking to migrate to the public cloud and build all that multi-cloud scenario. In that situation, I would say, you need a little bit of hand-holding. You need to understand how your application's running on prem, which ones can be moved to the cloud, how can they be moved to the cloud, you want to ensure that those policies that you were implementing on prem you'll be able to implement those same policies in the public cloud, as well. The monitoring really starts on prem. All of those policies that operation starts on prem, and then you take them and you build them and you >> I'll get your take on, we'll have to get your take too, Justin, on something that's going on that I see clear visibility on. Infrastructure operations, data center cloud, get your house in order, networks, migration, hybrid cloud, multi-cloud, and then all that stuff. Then you've got this developer tsunami going on, a renaissance of real new development, new kinds of development, multiple databases using in app, IoT, so, the software development methodologies are changing for developers. That's obvious. What's the impact to the infrastructure guys, because you're starting to see Lambda and Server List as a way for saying complete infrastructure is code. How does that change the notion of, what the hell the data center is? Because you could argue that's just an edge now. So, what's the software, what are some of the software practices you see that are notable? >> The ones truly amazing, like in all these things that you're saying, is that you no longer need to use one approach to build anything. Any product that we put out, or any service that you put out, now uses a combination of all of these things. It could be Lambda, it could be IoT, it could be a wholesale application that's office started using (mumbles), that's spanning that multi-cloud environment. So, it's the beauty of all of this is the power of choice. We have so much more choice available to us. Right? But then, when choices, with choice comes that explosion and that complexity. It's >> Complexity is key, but speed is also there. You see. So, the question is, at what point does the cream rise to the top, and the people that are slow get run over. >> We're just seeing another evolution in obstruction, really, it's like, we don't write an assembly code anymore. We're writing directly to the hardware. We added in high-level programming languages, and now, in terms of the infrastructure, developers don't care about infrastructure as much as people talk about dev ops, and the thing like dev ops is a thing, developers don't want to deal with the infrastructure. They want to deal with code, cuz that's where they live. And the infrastructure folks, well, a lot of them are actually becoming developers now. So, they're learning how to use tools like, using development tools to actually get their job done. Which is where we're seeing infrastructure is going. So, there's a lot more ob abstraction into pure software, so you don't have to worry about the underlying obstructions, at least, not very much. >> All right, Sumeet, question to you now on that is, that requires the network guys, Juniper, you're part of that, and all the analytics to think differently about what you're instrumenting. To do what he said, to make it free, you gotta enable a lot of policy, a lotta data analytics, take us through what's the current state of the art there. >> So, the current state of the art, is essentially, if you talk about Juniper products, we have our family of SRX products, where you can have on prem firewalls, as well as virtual firewalls in the cloud, and using these tools, you can have consistent policies on prem and in the cloud. You can free up transit VPCs. Then there are the obligations in the multi cloud world, and do all kinds of fancy things. But where we also going with our solutions is to make them much more simpler to consume. It's truly all about simplicity, right? Because now you have all this choice, and you can have Lambda, and you can have all these new ways to bring up your applications. What becomes key is that the policies that you wanna implement become automatic. Right? And the way to do that is, the way we are doing that is, essentially, doing this auto-learning of your environment. Right? Automatically understanding, Automation, right? But, not, automation in two parts, as in automatically detect what's going on, but then automatically apply the policies as well, no matter where the workload is and where it's scaling, we automatically apply the policies to it. >> So, it's a lot of investment in this mart of underly-- Making something simple is actually quite complex to do. So, you need to understand what are the right things to automate, and what are the few things where you actually wanna give humans that choice, without it becoming overwhelming, so that, okay, I have to choose between one of 800 different ways of doing this. That's just not something that humans cope well with, whereas machines are actually really good at that. >> And that's the value here. We want to hide all the complexity under the hood. You know, use those advanced logarithms, use, you know, where they be on prem or in the cloud, but running all the analysis, implementing all the right policies for you, right? And new, new workload comes up it should automatically get the policy, right? And we are now able to do that both in the private data center, as well as in the public cloud, and bridge those policies together for you, automatically. >> The common theme we're seeing in cloud, we had a guest on from Thorn, where they automate, essentially, police officers writing down notes in a notebook to fully spotting with machine learning and all this great stuff, to find missing and exploited children, manual sucks, basically. Manual's slow-- >> The workload's too dominant now for you to think about manual. >> I want real-time. So most organizations, what's going on there? How do you guys help there, what's the progress? >> Oh. So, this is actually a great question, by the way, so, and this is part of the reason why we like, as a company, as a start up, maybe, we're like, doing all this cool stuff, and, you know, not really thinking about all the, hey, this is slowing me down. The reason why we went to Juniper, if you look at the history of Juniper, and the product portfolio, and the stock at Juniper, when it comes to automation, when it comes to things like ABI, when it comes to things like policy, they've always kind of like led the pack in that networking space, and now this is the opportunity to take that that wealth of knowledge, and scale it out, and take it to a little bit broader multi-cloud, hybrid cloud space. But, that's truly where it is, and even if you, kind of like go down low level to the devices, all Juniper devices are able to stream real-time elements. We are able to do ML in real-time, even on the physical devices, right? Similar for all virtual devices, and now, with our Formix, we even bring in the performance and operations inside, from the running infrastructure, whether it's on prem and in the cloud, not just networking, but the compute, the databases, your applications, your clusters, all of that, to build for you this end-to-end view, right? Not just the networks, your servers, Vms, workloads, the underlying network, the connectivity, all of it. >> How does that, because the developers, they live in application land, and again, they don't really care about that infrastructure, but as it turns out, sometimes it's quite useful to know which particular network devices, or what the infrastructure is that underpins things, like where you sometimes need to be able to drop into assembly code to really optimize things, so are you making that information about the infrastructure visible to developers in a way that they like to, to know and consume? >> Absolutely, so, one key thing about, you know, our product portfolio, and how we are releasing our services, essentially, we've wrapped everything around, you know, these role-based access interfaces. Where both the operators are able to get their views, they're able to construct views that the developers are able to see, and then both can implement their own policies, right? If, let's say, there's some infrastructure that's down, or is unhealthy, then having that global topology view helps you in real-time totally, and in real-time informs you what the impact of that outage is. Like who are the developers who would be impacted, what are their obligations? And, you know, we can bring that insight, and consume it to run the automation. So, if, let's just say, some infrastructure's unhealthy, can you read off the graph? >> Sumeet, talk about what you guys are doing here. How's Amazon, big learning conference, but it's a massive show, 45,000 people here, across multiple hotels. A lot of sessions. What do you guys talk about? What's the big cloud piece for you guys? >> For us, really, first, it's just visibility, right? We have a product portfolio that gives you visibility. Like, both for your physical infrastructure, and your virtual structure. Then, the next thing is, of course, You know, yeah, you have the visibility. But then, at our scale, no human can consume all that information. It's too slow. It's too slow. So, you've gotta have the machine-learning built in. So, it's promoting that visibility into insights in real time, and then, it's about how do you secure your workloads? So, consuming all of that insight to implement all of the policies, implement all of the automation, to ensure that everything is running as you want it to. >> What's your Juniper message to the developers here? Is there a new face to Juniper, a new vibe? You mentioned Juniper's always had great products, like, you move packets around at lightning speeds, you know, wire speeds, all that great stuff. How do you, what's new? What's it mean for me as a developer, what is Juniper, how's it make my life easier? >> What's new is that now it's easier for developers to consume our products. Our products are now available in the Amazon marketplace, right? Our visibility products, our machine-learning products, our security products, right? You can just click, install, and start using them. That's new for Juniper, right? I mean, traditionally you would think of-- >> You probably get Juniper goodness just by treating it like a library. >> That's it. You can just download, not even download, right? You're -- >> It's server-less. It's router-less. It's device-less. >> There you go. You can just start consuming them. And then, if you do have that knowledge of how do you use those devices on prem, then you can apply that knowledge in the cloud, and then use them all. >> Must be computing back in, what, like 20 years ago. I mean, is it just like a grid now. >> Oh, yeah, pretty much, yeah. >> It's a fabric. >> It's the same, if you already know how to use it one place, you know how to use it everywhere. >> Yeah, but, I mean, it's, really, the value of the cloud is making it even simpler, right? Running all of that automation, like we talked about Lambda, like even within our products family, we can, we use Lambda to constantly see what's changing, and that's how we process lots of our internal transactions, as well. >> Sumeet, congratulations on your acquisition and your entrepreneurial journey, and now you're at Juniper. Looking forward to keeping in touch. Sumeet Singh, Vice President of Cloud Analytics, and now at Juniper Networks, formerly AppFormix, CUBE alumni, thanks for coming on and sharing your commentary. I'm John Furrier, and Justin Moore, here on theCUBE, main stage in Las Vegas at AMS re:Invent We'll be back with more after this short break. (lively electronic music)

(energetic instrumental music) >> Hey, everyone. Welcome to SiliconANGLE CUBE News here from Mobile World Congress. This is a Mobile World Congress news update. Dell in the news here partners with leading infrastructure companies, Dell Technologies, really setting up an ecosystem. Here, Dell, with leading telecom and infrastructure players accelerating the network adoption, announcing that it's launching the Dell's Open Telecom Ecosystem community. A community of multiple telecom partners and communication service providers aimed at becoming a unifying force in the telecom industry. This announcement comes just days after Dell introduced a host of new hardware, platforms designed to help the teleconference build cloud-native open radio network access, also called RAN architectures, using proprietary and sub-components for various suppliers. Dell's Open Telecom Ecosystem community has already partnered with Nokia, Qualcomm, Amdocs and Juniper Networks to create new offerings aimed at accelerating open RAN price performance for communication service providers. This includes creating a new virtual RAN offering using Open Telecom Ecosystem Labs, and as the center for testing and validation, building next-generation 5G virtualized distributed units and deploy and automated validated 5G-SA network with various partners across the ecosystem. Dell's promising that this is just the beginning of the collaboration with the telecom industry as it seeks to accelerate the adoption of 5G networking technologies and solve key industry challenges. More action's on the ground, go to thecube.net, theCUBE is broadcasting live for four days, Dave Vellante, Lisa Martin. I'm in the studios in Palo Alto bringing you the news. Lot of action happening, of course. Go to siliconangle.com to catch all the breaking news. We have a special report. We already got 10 plus stories already flowing. Probably have another 10 today. Day two tomorrow as MWC continues to power more news coverage for the edge and cloud-native technologies. (pensive ambient music)

>>from around the globe. It's the Cube presenting Cuban cloud brought to you by silicon angle. As I've said many times on the Cube for years, decades, even we've marched to the cadence of Moore's law, relying on the doubling of performance every 18 months or so. But no longer is this the mainspring of innovation for technology. Rather, it's the combination of data applying machine intelligence and the cloud supported by the relentless reduction of the cost of compute and storage and the build out of a massively distributed computer network. Very importantly, in the last several years, alternative processors have emerged to support offloading work and performing specific Test GP use of the most widely known example of this trend, with the ascendancy of in video for certain applications like gaming and crypto mining and, more recently, machine learning. But in the middle of last decade, we saw the early development focused on the DPU, the data processing unit, which is projected to make a huge impact on data centers in the coming years. As we move into the next era of Cloud. And with me is deep. Sindhu, who's this co founder and CEO of Fungible, a company specializing in the design and development of GPU deep Welcome to the Cube. Great to see you. >>Thank you, Dave. And thank you for having me. >>You're very welcome. So okay, my first question is, don't CPUs and GP use process data already? Why do we need a DPU? >>Um you know that that is a natural question to ask on. CPUs have been around in one form or another for almost, you know, 55 maybe 60 years. And, uh, you know, this is when general purpose computing was invented, and essentially all CPI use went to x 80 60 x 86 architecture. Uh, by and large arm, of course, is used very heavily in mobile computing, but x 86 primarily used in data center, which is our focus. Um, now, you can understand that that architectural off general purpose CPUs has been refined heavily by some of the smartest people on the planet. And for the longest time, uh, improvements you refer the Moore's Law, which is really the improvements off the price performance off silicon over time. Um, that, combined with architectural improvements, was the thing that was pushing us forward. Well, what has happened is that the architectural refinements are more or less done. Uh, you're not going to get very much. You're not going to squeeze more blood out of that storm from the general purpose computer architectures. What has also happened over the last decade is that Moore's law, which is essentially the doubling off the number of transistors, um, on a chip has slowed down considerably on and to the point where you're only getting maybe 10 20% improvements every generation in speed off the grandest er. If that. And what's happening also is that the spacing between successive generations of technology is actually increasing from 2, 2.5 years to now three, maybe even four years. And this is because we are reaching some physical limits in Seamus. Thes limits are well recognized, and we have to understand that these limits apply not just to general purpose if use, but they also apply to GP use now. General purpose, if used, do one kind of confrontation. They really general on bacon do lots and lots of different things. It is actually a very, very powerful engine, Um, and then the problem is it's not powerful enough to handle all computations. So this is why you ended up having a different kind of processor called the GPU, which specializes in executing vector floating point arithmetic operations much, much better than CPL. Maybe 2030 40 times better. Well, GPS have now been around for probably 15, 20 years, mostly addressing graphics computations. But recently, in the last decade or so, they have been used heavily for AI and analytics computations. So now the question is, why do you need another specialized engine called the DPU? Well, I started down this journey about almost eight years ago, and I recognize I was still at Juniper Networks, which is another company that I found it. I recognize that in the data center, um, as the workload changes due to addressing Mawr and Mawr, larger and larger corpus is of data number one. And as people use scale out as the standard technique for building applications, what happens is that the amount of East West traffic increases greatly. And what happens is that you now have a new type off workload which is coming, and today probably 30% off the workload in a data center is what we call data centric. I want to give you some examples of what is the data centric E? >>Well, I wonder if I could interrupt you for a second, because Because I want you to. I want those examples, and I want you to tie it into the cloud because that's kind of the topic that we're talking about today and how you see that evolving. It's a key question that we're trying to answer in this program. Of course, Early Cloud was about infrastructure, a little compute storage, networking. And now we have to get to your point all this data in the cloud and we're seeing, by the way, the definition of cloud expand into this distributed or I think the term you use is disaggregated network of computers. So you're a technology visionary, And I wonder, you know how you see that evolving and then please work in your examples of that critical workload that data centric workload >>absolutely happy to do that. So, you know, if you look at the architectural off cloud data centers, um, the single most important invention was scale out scale out off identical or near identical servers, all connected to a standard i p Internet network. That's that's the architectural. Now, the building blocks of this architecture er is, uh, Internet switches, which make up the network i p Internet switches. And then the servers all built using general purpose X 86 CPUs with D ram with SSD with hard drives all connected, uh, inside the CPU. Now, the fact that you scale these, uh, server nodes as they're called out, um, was very, very important in addressing the problem of how do you build very large scale infrastructure using general purpose computer? But this architectures, Dave, is it compute centric architectures and the reason it's a compute centric architectures. If you open this a server node, what you see is a connection to the network, typically with a simple network interface card. And then you have CP use, which are in the middle of the action. Not only are the CPUs processing the application workload, but they're processing all of the aisle workload, what we call data centric workload. And so when you connect SSD and hard drives and GPU that everything to the CPU, um, as well as to the network, you can now imagine that the CPUs is doing to functions it z running the applications, but it's also playing traffic cop for the I O. So every Io has to go to the CPU and you're executing instructions typically in the operating system, and you're interrupting the CPU many, many millions of times a second now. General Purpose CPUs and the architecture of the CPS was never designed to play traffic cop, because the traffic cop function is a function that requires you to be interrupted very, very frequently. So it's. It's critical that in this new architecture, where there's a lot of data, a lot of East West traffic, the percentage of work clothes, which is data centric, has gone from maybe 1 to 2% to 30 to 40%. I'll give you some numbers, which are absolutely stunning if you go back to, say, 1987 and which is, which is the year in which I bought my first personal computer. Um, the network was some 30 times slower. Then the CPI. The CPI was running at 50 megahertz. The network was running at three megabits per second. Well, today the network runs at 100 gigabits per second and the CPU clock speed off. A single core is about 3 to 2.3 gigahertz. So you've seen that there is a 600 x change in the ratio off I'll to compute just the raw clock speed. Now you can tell me that. Hey, um, typical CPUs have lots of lots, of course, but even when you factor that in, there's bean close toe two orders of magnitude change in the amount of ill to compute. There is no way toe address that without changing the architectures on this is where the DPU comes in on the DPU actually solves two fundamental problems in cloud data centers on these air. Fundamental. There's no escaping it, no amount off. Clever marketing is going to get around these problems. Problem number one is that in a compute centric cloud architectures the interactions between server notes are very inefficient. Okay, that's number one problem number one. Problem number two is that these data center computations and I'll give you those four examples the network stack, the storage stack, the virtualization stack and the security stack. Those four examples are executed very inefficiently by CBS. Needless to say that that if you try to execute these on GPS, you'll run into the same problem, probably even worse because GPS are not good at executing these data centric computations. So when U. S o What we were looking to do it fungible is to solve these two basic problems and you don't solve them by by just using taking older architectures off the shelf and applying them to these problems because this is what people have been doing for the for the last 40 years. So what we did was we created this new microprocessor that we call the DPU from ground doctor is a clean sheet design and it solve those two problems. Fundamental. >>So I want to get into that. But I just want to stop you for a second and just ask you a basic question, which is so if I understand it correctly, if I just took the traditional scale out, If I scale out compute and storage, you're saying I'm gonna hit a diminishing returns, It z Not only is it not going to scale linear linearly, I'm gonna get inefficiencies. And that's really the problem that you're solving. Is that correct? >>That is correct. And you know this problem uh, the workloads that we have today are very data heavy. You take a I, for example, you take analytics, for example. It's well known that for a I training, the larger the corpus of data relevant data that you're training on, the better the result. So you can imagine where this is going to go, especially when people have figured out a formula that, hey, the more data I collect, I can use those insights to make money. >>Yeah, this is why this is why I wanted to talk to you, because the last 10 years we've been collecting all this data. Now I want to bring in some other data that you actually shared with me beforehand. Some market trends that you guys cited in your research and the first thing people said is they want to improve their infrastructure on. They want to do that by moving to the cloud, and they also there was a security angle there as well. That's a whole nother topic. We could discuss the other staff that jumped out at me. There's 80% of the customers that you surveyed said they'll be augmenting their X 86 CPUs with alternative processing technology. So that's sort of, you know, I know it's self serving, but z right on the conversation we're having. So I >>want to >>understand the architecture. Er, aan den, how you've approached this, You've you've said you've clearly laid out the X 86 is not going to solve this problem. And even GP use are not going to solve this problem. So help us understand the architecture and how you do solve this problem. >>I'll be I'll be very happy to remember I use this term traffic cough. Andi, I use this term very specifically because, uh, first let me define what I mean by a data centric computation because that's the essence off the problem resolved. Remember, I said two problems. One is we execute data centric work clothes, at least in order of magnitude, more efficiently than CPUs or GPS, probably 30 times more efficiently on. The second thing is that we allow notes to interact with each other over the network much, much more efficiently. Okay, so let's keep those two things in mind. So first, let's look at the data centric piece, the data centric piece, um, for for workload to qualify as being data centric. Four things have to be true. First of all, it needs to come over the network in the form of packets. Well, this is all workloads, so I'm not saying anything new. Secondly, uh, this workload is heavily multiplex in that there are many, many, many computations that are happening concurrently. Thousands of them. Yeah, that's number two. So a lot of multiplexing number three is that this workload is state fel. In other words, you have to you can't process back. It's out of order. You have to do them in order because you're terminating network sessions on the last one Is that when you look at the actual computation, the ratio off I Oto arithmetic is medium to high. When you put all four of them together, you actually have a data centric workout, right? And this workload is terrible for general purpose, C p s not only the general purpose, C p is not executed properly. The application that is running on the CPU also suffers because data center workloads are interfering workloads. So unless you designed specifically to them, you're going to be in trouble. So what did we do? Well, what we did was our architecture consists off very, very heavily multi threaded, general purpose CPUs combined with very heavily threaded specific accelerators. I'll give you examples of some some of those accelerators, um, de Emma accelerators, then radio coding accelerators, compression accelerators, crypto accelerators, um, compression accelerators, thes air, just something. And then look up accelerators. These air functions that if you do not specialized, you're not going to execute them efficiently. But you cannot just put accelerators in there. These accelerators have to be multi threaded to handle. You know, we have something like 1000 different threads inside our DPU toe address. These many, many, many computations that are happening concurrently but handle them efficiently. Now, the thing that that is very important to understand is that given the paucity off transistors, I know that we have hundreds of billions of transistors on a chip. But the problem is that those transistors are used very inefficiently today. If the architecture, the architecture of the CPU or GPU, what we have done is we've improved the efficiency of those transistors by 30 times. Yeah, so you can use >>the real estate. You can use their real estate more effectively, >>much more effectively because we were not trying to solve a general purpose computing problem. Because if you do that, you know, we're gonna end up in the same bucket where General Focus CPS are today. We were trying to solve the specific problem off data centric computations on off improving the note to note efficiency. So let me go to Point number two, because that's equally important, because in a scale out architecture, the whole idea is that I have many, many notes and they're connected over a high performance network. It might be shocking for your listeners to hear that these networks today run at a utilization of no more than 20 to 25%. Question is why? Well, the reason is that if I tried to run them faster than that, you start to get back. It drops because there are some fundamental problems caused by congestion on the network, which are unsolved as we speak today. There only one solution, which is to use DCP well. DCP is a well known is part of the D. C. P I. P. Suite. DCP was never designed to handle the agencies and speeds inside data center. It's a wonderful protocol, but it was invented 42 year 43 years ago, now >>very reliable and tested and proven. It's got a good track record, but you're a >>very good track record, unfortunately, eats a lot off CPU cycles. So if you take the idea behind TCP and you say, Okay, what's the essence of TCP? How would you apply to the data center? That's what we've done with what we call F C P, which is a fabric control protocol which we intend toe open way. Intend to publish standards on make it open. And when you do that and you you embed F c p in hardware on top of his standard I P Internet network, you end up with the ability to run at very large scale networks where the utilization of the network is 90 to 95% not 20 to 25% on you end up with solving problems of congestion at the same time. Now, why is this important today that zall geek speak so far? But the reason this stuff is important is that it such a network allows you to disaggregate pool and then virtualized, the most important and expensive resource is in the data center. What are those? It's computer on one side, storage on the other side. And increasingly even things like the Ram wants to be disaggregated in food. Well, if I put everything inside a general purpose server, the problem is that those resource is get stranded because they're they're stuck behind the CPI. Well, once you disaggregate those resources and we're saying hyper disaggregate, the meaning, the hyper and the hyper disaggregate simply means that you can disaggregate almost all the resources >>and then you're gonna re aggregate them, right? I mean, that's >>obviously exactly and the network is the key helping. So the reason the company is called fungible is because we are able to disaggregate virtualized and then pull those resources and you can get, you know, four uh, eso scale out cos you know the large aws Google, etcetera. They have been doing this aggregation and pulling for some time, but because they've been using a compute centric architecture, er that this aggregation is not nearly as efficient as we could make on their off by about a factor of three. When you look at enterprise companies, they're off by any other factor of four. Because the utilization of enterprises typically around 8% off overall infrastructure, the utilization the cloud for A W S and G, C, P and Microsoft is closer to 35 to 40%. So there is a factor off almost, uh, 4 to 8, which you can gain by disaggregated and pulling. >>Okay, so I wanna interrupt again. So thes hyper scaler zehr smart. A lot of engineers and we've seen them. Yeah, you're right. They're using ah, lot of general purpose. But we've seen them, uh, move Make moves toward GP use and and embrace things like arm eso I know, I know you can't name names but you would think that this is with all the data that's in the cloud again Our topic today you would think the hyper scaler zehr all over this >>all the hyper scale is recognized it that the problems that we have articulated are important ones on they're trying to solve them. Uh, with the resource is that they have on all the clever people that they have. So these air recognized problems. However, please note that each of these hyper scale er's has their own legacy now they've been around for 10, 15 years, and so they're not in a position to all of a sudden turn on a dime. This is what happens to all companies at some >>point. Have technical debt. You mean they >>have? I'm not going to say they have technical debt, but they have a certain way of doing things on. They are in love with the compute centric way of doing things. And eventually it will be understood that you need a third element called the DPU to address these problems. Now, of course, you heard the term smart neck, and all your listeners must have heard that term. Well, a smart thing is not a deep you what a smart Nick is. It's simply taking general purpose arm cores put in the network interface on a PC interface and integrating them all in the same chip and separating them from the CPI. So this does solve the problem. It solves the problem off the data centric workload, interfering with the application work, work. Good job. But it does not address the architectural problem. How to execute data centric workloads efficiently. >>Yeah, it reminds me. It reminds me of you I I understand what you're saying. I was gonna ask you about smart. Next. It does. It's almost like a bridge or a Band Aid. It's always reminds me of >>funny >>of throwing, you know, a flash storage on Ah, a disc system that was designed for spinning disk gave you something, but it doesn't solve the fundamental problem. I don't know if it's a valid analogy, but we've seen this in computing for a long time. >>Yeah, this analogy is close because, you know. Okay, so let's let's take hyper scaler X. Okay, one name names. Um, you find that, you know, half my CPUs are twiddling their thumbs because they're executing this data centric workload. Well, what are you going to do? All your code is written in, uh, C c plus plus, um, on x 86. Well, the easiest thing to do is to separate the cores that run this workload. Put it on a different Let's say we use arm simply because you know x 86 licenses are not available to people to build their own CPUs. So arm was available, so they put a bunch of encores. Let's stick a PC. I express and network interface on you. Port that quote from X 86 Tow arm. Not difficult to do, but it does yield you results on, By the way, if, for example, um, this hyper scaler X shall we call them if they're able to remove 20% of the workload from general purpose CPUs? That's worth billions of dollars. So of course you're going to do that. It requires relatively little innovation other than toe for quote from one place to another place. >>That's what that's what. But that's what I'm saying. I mean, I would think again. The hyper scale is why Why can't they just, you know, do some work and do some engineering and and then give you a call and say, Okay, we're gonna We're gonna attack these workloads together. You know, that's similar to how they brought in GP use. And you're right. It's it's worth billions of dollars. You could see when when the hyper scale is Microsoft and and Azure, uh, and and AWS both announced, I think they depreciated servers now instead of four years. It's five years, and it dropped, like a billion dollars to their bottom line. But why not just work directly with you guys. I mean, Z the logical play. >>Some of them are working with us. So it's not to say that they're not working with us. So you know, all of the hyper scale is they recognize that the technology that we're building is a fundamental that we have something really special, and moreover, it's fully programmable. So you know, the whole trick is you can actually build a lump of hardware that is fixed function. But the difficulty is that in the place where the DPU would sit, which is on the boundary off a server, and the network is literally on that boundary, that place the functionality needs to be programmable. And so the whole trick is how do you come up with an architectural where the functionality is programmable? But it is also very high speed for this particular set of applications. So the analogy with GPS is nearly perfect because GP use, and particularly in video that's implemented or they invented coulda, which is a programming language for GPS on it made them easy to use mirror fully programmable without compromising performance. Well, this is what we're doing with DP use. We've invented a new architectures. We've made them very easy to program. And they're these workloads or not, Workload. The computation that I talked about, which is security virtualization storage and then network. Those four are quintessential examples off data centric, foreclosed on. They're not going away. In fact, they're becoming more and more and more important over time. >>I'm very excited for you guys, I think, and really appreciate deep we're gonna have you back because I really want to get into some of the secret sauce you talked about these accelerators, Erasure coding, crypto accelerators. I want to understand that. I know there's envy me in here. There's a lot of hardware and software and intellectual property, but we're seeing this notion of programmable infrastructure extending now, uh, into this domain, this build out of this I like this term dis aggregated, massive disaggregated network s so hyper disaggregated. Even better. And I would say this on way. I gotta go. But what got us here the last decade is not the same is what's gonna take us through the next decade. Pretty Thanks. Thanks so much for coming on the cube. It's a great company. >>You have it It's really a pleasure to speak with you and get the message of fungible out there. >>E promise. Well, I promise we'll have you back and keep it right there. Everybody, we got more great content coming your way on the Cube on Cloud, This is David. Won't stay right there.

>> As I've said many times on theCUBE for years, decades even we've marched to the cadence of Moore's law relying on the doubling of performance every 18 months or so, but no longer is this the main spring of innovation for technology rather it's the combination of data applying machine intelligence and the cloud supported by the relentless reduction of the cost of compute and storage and the build-out of a massively distributed computer network. Very importantly, the last several years alternative processors have emerged to support offloading work and performing specific tests. GPUs are the most widely known example of this trend with the ascendancy of Nvidia for certain applications like gaming and crypto mining and more recently machine learning. But in the middle of last decade we saw the early development focused on the DPU, the data processing unit, which is projected to make a huge impact on data centers in the coming years as we move into the next era of cloud. And with me is Pradeep Sindhu who's the co-founder and CEO of Fungible, a company specializing in the design and development of DPUs. Pradeep, welcome to theCUBE. Great to see you. >> Thank-you, Dave and thank-you for having me. >> You're very welcome. So okay, my first question is don't CPUs and GPUs process data already. Why do we need a DPU? >> That is a natural question to ask. And CPUs have been around in one form or another for almost 55, maybe 60 years. And this is when general purpose computing was invented and essentially all CPUs went to x86 architecture by and large and of course is used very heavily in mobile computing, but x86 is primarily used in data center which is our focus. Now, you can understand that that architecture of a general purpose CPUs has been refined heavily by some of the smartest people on the planet. And for the longest time improvements you refer to Moore's law, which is really the improvements of the price, performance of silicon over time that combined with architectural improvements was the thing that was pushing us forward. Well, what has happened is that the architectural refinements are more or less done. You're not going to get very much, you're not going to squeeze more blood out of that storm from the general purpose computer architecture. what has also happened over the last decade is that Moore's law which is essentially the doubling of the number of transistors on a chip has slowed down considerably and to the point where you're only getting maybe 10, 20% improvements every generation in speed of the transistor if that. And what's happening also is that the spacing between successive generations of technology is actually increasing from two, two and a half years to now three, maybe even four years. And this is because we are reaching some physical limits in CMOS. These limits are well-recognized. And we have to understand that these limits apply not just to general purposive use but they also apply to GPUs. Now, general purpose CPUs do one kind of competition they're really general and they can do lots and lots of different things. It is actually a very, very powerful engine. And then the problem is it's not powerful enough to handle all computations. So this is why you ended up having a different kind of a processor called the GPU which specializes in executing vector floating-point arithmetic operations much, much better than CPU maybe 20, 30, 40 times better. Well, GPUs have now been around for probably 15, 20 years mostly addressing graphics computations, but recently in the last decade or so they have been used heavily for AI and analytics computations. So now the question is, well, why do you need another specialized engine called the DPU? Well, I started down this journey about almost eight years ago and I recognize I was still at Juniper Networks which is another company that I founded. I recognize that in the data center as the workload changes to addressing more and more, larger and larger corpuses of data, number one and as people use scale-out as these standard technique for building applications, what happens is that the amount of east-west traffic increases greatly. And what happens is that you now have a new type of workload which is coming. And today probably 30% of the workload in a data center is what we call data-centric. I want to give you some examples of what is a data-centric workload. >> Well, I wonder if I could interrupt you for a second. >> Of course. >> Because I want those examples and I want you to tie it into the cloud 'cause that's kind of the topic that we're talking about today and how you see that evolving. I mean, it's a key question that we're trying to answer in this program. Of course, early cloud was about infrastructure, little compute, little storage, little networking and now we have to get to your point all this data in the cloud. And we're seeing, by the way the definition of cloud expand into this distributed or I think a term you use is disaggregated network of computers. So you're a technology visionary and I wonder how you see that evolving and then please work in your examples of that critical workload, that data-centric workload. >> Absolutely happy to do that. So if you look at the architecture of our cloud data centers the single most important invention was scale-out of identical or near identical servers all connected to a standard IP ethernet network. That's the architecture. Now, the building blocks of this architecture is ethernet switches which make up the network, IP ethernet switches. And then the server is all built using general purpose x86 CPUs with DRAM, with SSD, with hard drives all connected to inside the CPU. Now, the fact that you scale these server nodes as they're called out was very, very important in addressing the problem of how do you build very large scale infrastructure using general purpose compute. But this architecture did is it compute centric architecture and the reason it's a compute centric architecture is if you open this server node what you see is a connection to the network typically with a simple network interface card. And then you have CPUs which are in the middle of the action. Not only are the CPUs processing the application workload but they're processing all of the IO workload, what we call data-centric workload. And so when you connect SSDs, and hard drives, and GPUs, and everything to the CPU, as well as to the network you can now imagine the CPUs is doing two functions. It's running the applications but it's also playing traffic cop for the IO. So every IO has to go through the CPU and you're executing instructions typically in the operating system and you're interrupting the CPU many, many millions of times a second. Now, general purpose CPUs and the architecture CPUs was never designed to play traffic cop because the traffic cop function is a function that requires you to be interrupted very, very frequently. So it's critical that in this new architecture where there's a lot of data, a lot of these stress traffic the percentage of workload, which is data-centric has gone from maybe one to 2% to 30 to 40%. I'll give you some numbers which are absolutely stunning. If you go back to say 1987 and which is the year in which I bought my first personal computer the network was some 30 times slower than the CPU. The CPU is running at 15 megahertz, the network was running at three megabits per second. Or today the network runs at a 100 gigabits per second and the CPU clock speed of a single core is about three to 2.3 gigahertz. So you've seen that there's a 600X change in the ratio of IO to compute just the raw clock speed. Now, you can tell me that, hey, typical CPUs have lots, lots of cores, but even when you factor that in there's been close to two orders of magnitude change in the amount of IO to compute. There is no way to address that without changing the architecture and this is where the DPU comes in. And the DPU actually solves two fundamental problems in cloud data centers. And these are fundamental there's no escaping it. No amount of clever marketing is going to get around these problems. Problem number one is that in a compute centric cloud architecture the interactions between server nodes are very inefficient. That's number one, problem number one. Problem number two is that these data-centric computations and I'll give you those four examples. The network stack, the storage stack, the virtualization stack, and the security stack. Those four examples are executed very inefficiently by CPUs. Needless to say that if you try to execute these on GPUs you will run into the same problem probably even worse because GPUs are not good at executing these data-centric computations. So what we were looking to do at Fungible is to solve these two basic problems. And you don't solve them by just taking older architectures off the shelf and applying them to these problems because this is what people have been doing for the last 40 years. So what we did was we created this new microprocessor that we call DPU from ground up. It's a clean sheet design and it solves those two problems fundamentally. >> So I want to get into that. And I just want to stop you for a second and just ask you a basic question which is if I understand it correctly, if I just took the traditional scale out, if I scale out compute and storage you're saying I'm going to hit a diminishing returns. It's not only is it not going to scale linearly I'm going to get inefficiencies. And that's really the problem that you're solving. Is that correct? >> That is correct. And the workloads that we have today are very data-heavy. You take AI for example, you take analytics for example it's well known that for AI training the larger the corpus of relevant data that you're training on the better the result. So you can imagine where this is going to go. >> Right. >> Especially when people have figured out a formula that, hey the more data I collect I can use those insights to make money- >> Yeah, this is why I wanted to talk to you because the last 10 years we've been collecting all this data. Now, I want to bring in some other data that you actually shared with me beforehand. Some market trends that you guys cited in your research. And the first thing people said is they want to improve their infrastructure and they want to do that by moving to the cloud. And they also, there was a security angle there as well. That's a whole another topic we could discuss. The other stat that jumped out at me, there's 80% of the customers that you surveyed said there'll be augmenting their x86 CPU with alternative processing technology. So that's sort of, I know it's self-serving, but it's right on the conversation we're having. So I want to understand the architecture. >> Sure. >> And how you've approached this. You've clearly laid out this x86 is not going to solve this problem. And even GPUs are not going to solve the problem. >> They re not going to solve the problem. >> So help us understand the architecture and how you do solve this problem. >> I'll be very happy to. Remember I use this term traffic cop. I use this term very specifically because, first let me define what I mean by a data-centric computation because that's the essence of the problem we're solving. Remember I said two problems. One is we execute data-centric workloads at least an order of magnitude more efficiently than CPUs or GPUs, probably 30 times more efficient. And the second thing is that we allow nodes to interact with each other over the network much, much more efficiently. Okay, so let's keep those two things in mind. So first let's look at the data-centric piece. The data-centric piece for workload to qualify as being data-centric four things have to be true. First of all, it needs to come over the network in the form of packets. Well, this is all workloads so I'm not saying anything. Secondly, this workload is heavily multiplex in that there are many, many, many computations that are happening concurrently, thousands of them, okay? That's the number two. So a lot of multiplexing. Number three is that this workload is stateful. In other words you can't process back it's out of order. You have to do them in order because you're terminating network sessions. And the last one is that when you look at the actual computation the ratio of IO to arithmetic is medium to high. When you put all four of them together you actually have a data-centric workload, right? And this workload is terrible for general purpose CPUs. Not only the general purpose CPU is not executed properly the application that is running on the CPU also suffers because data center workloads are interfering workloads. So unless you designed specifically to them you're going to be in trouble. So what did we do? Well, what we did was our architecture consists of very, very heavily multi-threaded general purpose CPUs combined with very heavily threaded specific accelerators. I'll give you examples of some of those accelerators, DMA accelerators, then ratio coding accelerators, compression accelerators, crypto accelerators, compression accelerators. These are just some, and then look up accelerators. These are functions that if you do not specialize you're not going to execute them efficiently. But you cannot just put accelerators in there, these accelerators have to be multi-threaded to handle. We have something like a 1,000 different treads inside our DPU to address these many, many, many computations that are happening concurrently but handle them efficiently. Now, the thing that is very important to understand is that given the velocity of transistors I know that we have hundreds of billions of transistors on a chip, but the problem is that those transistors are used very inefficiently today if the architecture of a CPU or a GPU. What we have done is we've improved the efficiency of those transistors by 30 times, okay? >> So you can use a real estate much more effectively? >> Much more effectively because we were not trying to solve a general purpose computing problem. Because if you do that we're going to end up in the same bucket where general purpose CPUs are today. We were trying to solve a specific problem of data-centric computations and of improving the note to note efficiency. So let me go to point number two because that's equally important. Because in a scalar or architecture the whole idea is that I have many, many notes and they're connected over a high performance network. It might be shocking for your listeners to hear that these networks today run at a utilization of no more than 20 to 25%. Question is why? Well, the reason is that if I tried to run them faster than that you start to get back at drops because there are some fundamental problems caused by congestion on the network which are unsolved as we speak today. There are only one solution which is to use TCP. Well, TCP is a well-known, is part of the TCP IP suite. TCP was never designed to handle the latencies and speeds inside data center. It's a wonderful protocol but it was invented 43 years ago now. >> Yeah, very reliable and tested and proven. It's got a good track record but you're right. >> Very good track record, unfortunately eats a lot of CPU cycles. So if you take the idea behind TCP and you say, okay, what's the essence of TCP? How would you apply it to the data center? That's what we've done with what we call FCP which is a fabric control protocol, which we intend to open. We intend to publish the standards and make it open. And when you do that and you embed FCP in hardware on top of this standard IP ethernet network you end up with the ability to run at very large-scale networks where the utilization of the network is 90 to 95%, not 20 to 25%. >> Wow, okay. >> And you end up with solving problems of congestion at the same time. Now, why is this important today? That's all geek speak so far. The reason this stuff is important is that it such a network allows you to disaggregate, pull and then virtualize the most important and expensive resources in the data center. What are those? It's computer on one side, storage on the other side. And increasingly even things like DRAM wants to be disaggregated. And well, if I put everything inside a general purpose server the problem is that those resources get stranded because they're stuck behind a CPU. Well, once you disaggregate those resources and we're saying hyper disaggregate meaning the hyper and the hyper disaggregate simply means that you can disaggregate almost all the resources. >> And then you going to reaggregate them, right? I mean, that's obviously- >> Exactly and the network is the key in helping. >> Okay. >> So the reason the company is called Fungible is because we are able to disaggregate, virtualize and then pull those resources. And you can get for so scale-out companies the large AWS, Google, et cetera they have been doing this aggregation tooling for some time but because they've been using a compute centric architecture their disaggregation is not nearly as efficient as we can make. And they're off by about a factor of three. When you look at enterprise companies they are off by another factor of four because the utilization of enterprise is typically around 8% of overall infrastructure. The utilization in the cloud for AWS, and GCP, and Microsoft is closer to 35 to 40%. So there is a factor of almost four to eight which you can gain by dis-aggregating and pulling. >> Okay, so I want to interrupt you again. So these hyperscalers are smart. They have a lot of engineers and we've seen them. Yeah, you're right they're using a lot of general purpose but we've seen them make moves toward GPUs and embrace things like Arm. So I know you can't name names, but you would think that this is with all the data that's in the cloud, again, our topic today. You would think the hyperscalers are all over this. >> Well, the hyperscalers recognized here that the problems that we have articulated are important ones and they're trying to solve them with the resources that they have and all the clever people that they have. So these are recognized problems. However, please note that each of these hyperscalers has their own legacy now. They've been around for 10, 15 years. And so they're not in a position to all of a sudden turn on a dime. This is what happens to all companies at some point. >> They have technical debt, you mean? (laughs) >> I'm not going to say they have technical debt, but they have a certain way of doing things and they are in love with the compute centric way of doing things. And eventually it will be understood that you need a third element called the DPU to address these problems. Now, of course, you've heard the term SmartNIC. >> Yeah, right. >> Or your listeners must've heard that term. Well, a SmartNIC is not a DPU. What a SmartNIC is, is simply taking general purpose ARM cores, putting the network interface and a PCI interface and integrating them all on the same chip and separating them from the CPU. So this does solve a problem. It solves the problem of the data center workload interfering with the application workload, good job, but it does not address the architectural problem of how to execute data center workloads efficiently. >> Yeah, so it reminds me of, I understand what you're saying I was going to ask you about SmartNICs. It's almost like a bridge or a band-aid. >> Band-aid? >> It almost reminds me of throwing a high flash storage on a disc system that was designed for spinning disc. Gave you something but it doesn't solve the fundamental problem. I don't know if it's a valid analogy but we've seen this in computing for a longtime. >> Yeah, this analogy is close because okay, so let's take a hyperscaler X, okay? We won't name names. You find that half my CPUs are crippling their thumbs because they're executing this data-centric workload. Well, what are you going to do? All your code is written in C++ on x86. Well, the easiest thing to do is to separate the cores that run this workload. Put it on a different let's say we use Arm simply because x86 licenses are not available to people to build their own CPUs so Arm was available. So they put a bunch of Arm cores, they stick a PCI express and a network interface and you bought that code from x86 to Arm. Not difficult to do but and it does you results. And by the way if for example this hyperscaler X, shall we called them, if they're able to remove 20% of the workload from general purpose CPUs that's worth billions of dollars. So of course, you're going to do that. It requires relatively little innovation other than to port code from one place to another place. >> Pradeep, that's what I'm saying. I mean, I would think again, the hyperscalers why can't they just do some work and do some engineering and then give you a call and say, okay, we're going to attack these workloads together. That's similar to how they brought in GPUs. And you're right it's worth billions of dollars. You could see when the hyperscalers Microsoft, and Azure, and AWS bolt announced, I think they depreciated servers now instead of four years it's five years. And it dropped like a billion dollars to their bottom line. But why not just work directly with you guys? I mean, let's see the logical play. >> Some of them are working with us. So that's not to say that they're not working with us. So all of the hyperscalers they recognize that the technology that we're building is a fundamental that we have something really special and moreover it's fully programmable. So the whole trick is you can actually build a lump of hardware that is fixed function. But the difficulty is that in the place where the DPU would sit which is on the boundary of a server and the network, is literally on that boundary, that place the functionality needs to be programmable. And so the whole trick is how do you come up with an architecture where the functionality is programmable but it is also very high speed for this particular set of applications. So the analogy with GPUs is nearly perfect because GPUs and particularly Nvidia implemented or they invented CUDA which is the programming language for GPUs. And it made them easy to use, made it fully programmable without compromising performance. Well, this is what we're doing with DPUs. We've invented a new architecture, we've made them very easy to program. And they're these workloads, not workloads, computation that I talked about which is security, virtualization, storage and then network. Those four are quintessential examples of data center workloads and they're not going away. In fact, they're becoming more, and more, and more important over time. >> I'm very excited for you guys, I think, and really appreciate Pradeep, we have your back because I really want to get into some of the secret sauce. You talked about these accelerators, eraser code and crypto accelerators. But I want to understand that. I know there's NBMe in here, there's a lot of hardware and software and intellectual property, but we're seeing this notion of programmable infrastructure extending now into this domain, this build-out of this, I like this term disaggregated, massive disaggregated network. >> Hyper disaggregated. >> It's so hyper disaggregated even better. And I would say this and then I got to go. But what got us here the last decade is not the same as what's going to take us through the next decade. >> That's correct. >> Pradeep, thanks so much for coming on theCUBE. It's a great conversation. >> Thank-you for having me it's really a pleasure to speak with you and get the message of Fungible out there. >> Yeah, I promise we'll have you back. And keep it right there everybody we've got more great content coming your way on theCUBE on cloud. This is Dave Vellante. Stay right there. >> Thank-you, Dave.

>>from Las Vegas. It's the Q covering. Next work. 2019 America's Do You buy Juniper Networks? Come back already. Jeffrey here with the Cube were in Las Vegas at Caesar's at the Juniper. Next work event. About 1000 people kind of going over a lot of new cool things. 400 gigs. Who knew that was coming out of new information for me? But that's not what we're here today. We're here for the fourth installment of around the Cube unpacking. I were happy to have all the winners of the three previous rounds here at the same place. We don't have to do it over the phone s so we're happy to have him. Let's jump into it. So winner of Round one was Bob Friday. He is the VP and CTO at Missed the Juniper Company. Bob, Great to see you. Good to be back. Absolutely. All the way from Seattle. Sharna Parky. She's a VP applied scientist at Tech CEO could see Sharna and, uh, from Google. We know a lot of a I happen to Google. Rajan's chef. He is the V p ay ay >>product management on Google. Welcome. Thank you, Christy. Here >>All right, so let's jump into it. So just warm everybody up and we'll start with you. Bob, What are some When you're talking to someone at a cocktail party Friday night talking to your mom And they say, What is a I What >>do you >>give him? A Zen examples of where a eyes of packing our lives today? >>Well, I think we all know the examples of the south driving car, you know? Aye, aye. Starting to help our health care industry being diagnosed cancer for me. Personally, I had kind of a weird experience last week at a retail technology event where basically had these new digital mirrors doing facial recognition. Right? And basically, you start to have little mirrors were gonna be a skeevy start guessing. Hey, you have a beard, you have some glasses, and they start calling >>me old. So this is kind >>of very personal. I have a something for >>you, Camille, but eh? I go walking >>down a mall with a bunch of mirrors, calling me old. >>That's a little Illinois. Did it bring you out like a cane or a walker? You know, you start getting some advertising's >>that were like Okay, you guys, this is a little bit over the top. >>Alright, Charlotte, what about you? What's your favorite example? Share with people? >>Yeah, E think one of my favorite examples of a I is, um, kind of accessible in on your phone where the photos you take on an iPhone. The photos you put in Google photos, they're automatically detecting the faces and their labeling them for you. They're like, Here's selfies. Here's your family. Here's your Children. And you know, that's the most successful one of the ones that I think people don't really think about a lot or things like getting loan applications right. We actually have a I deciding whether or not we get loans. And that one is is probably the most interesting one to be right now. >>Roger. So I think the father's example is probably my favorite as well. And what's interesting to me is that really a I is actually not about the Yeah, it's about the user experience that you can create as a result of a I. What's cool about Google photos is that and my entire family uses Google photos and they don't even know actually that the underlying in some of the most powerful a I in the world. But what they know is they confined every picture of our kids on the beach whenever they whenever they want to. Or, you know, we had a great example where we were with our kids. Every time they like something in the store, we take a picture of it, Um, and we can look up toy and actually find everything that they've taken picture. >>It's interesting because I think most people don't even know the power that they have. Because if you search for beach in your Google photos or you search for, uh, I was looking for an old bug picture from my high school there it came right up until you kind of explore. You know, it's pretty tricky, Raja, you know, I think a lot of conversation about A They always focus the general purpose general purpose, general purpose machines and robots and computers. But people don't really talk about the applied A that's happening all around. Why do you think that? >>So it's a good question. There's there's a lot more talk about kind of general purpose, but the reality of where this has an impact right now is, though, are those specific use cases. And so, for example, things like personalizing customer interaction or, ah, spotting trends that did that you wouldn't have spotted for turning unstructured data like documents into structure data. That's where a eyes actually having an impact right now. And I think it really boils down to getting to the right use cases where a I right? >>Sharon, I want ask you. You know, there's a lot of conversation. Always has A I replace people or is it an augmentation for people? And we had Gary Kasparov on a couple years ago, and he talked about, you know, it was the combination if he plus the computer made the best chess player, but that quickly went away. Now the computer is actually better than Garry Kasparov. Plus the computer. How should people think about a I as an augmentation tool versus a replacement tool? And is it just gonna be specific to the application? And how do you kind of think about those? >>Yeah, I would say >>that any application where you're making life and death decisions where you're making financial decisions that disadvantage people anything where you know you've got u A. V s and you're deciding whether or not to actually dropped the bomb like you need a human in the loop. If you're trying to change the words that you are using to get a different group of people to apply for jobs, you need a human in the loop because it turns out that for the example of beach, you type sheep into your phone and you might get just a field, a green field and a I doesn't know that, uh, you know, if it's always seen sheep in a field that when the sheep aren't there, that that isn't a sheep like it doesn't have that kind of recognition to it. So anything were we making decisions about parole or financial? Anything like that needs to have human in the loop because those types of decisions are changing fundamentally the way we live. >>Great. So shift gears. The team are Jeff Saunders. Okay, team, your mind may have been the liquid on my bell, so I'll be more active on the bell. Sorry about that. Everyone's even. We're starting a zero again, so I want to shift gears and talk about data sets. Um Bob, you're up on stage. Demo ing some some of your technology, the Miss Technology and really, you know, it's interesting combination of data sets A I and its current form needs a lot of data again. Kind of the classic Chihuahua on blue buried and photos. You got to run a lot of them through. How do you think about data sets? In terms of having the right data in a complete data set to drive an algorithm >>E. I think we all know data sets with one The tipping points for a I to become more real right along with cloud computing storage. But data is really one of the key points of making a I really write my example on stage was wine, right? Great wine starts a great grape street. Aye, aye. Starts a great data for us personally. L s t M is an example in our networking space where we have data for the last three months from our customers and rule using the last 30 days really trained these l s t m algorithms to really get that tsunami detection the point where we don't have false positives. >>How much of the training is done. Once you once you've gone through the data a couple times in a just versus when you first started, you're not really sure how it's gonna shake out in the algorithm. >>Yeah. So in our case right now, right, training happens every night. So every night, we're basically retraining those models, basically, to be able to predict if there's gonna be an anomaly or network, you know? And this is really an example. Where you looking all these other cat image thinks this is where these neural networks there really were one of the transformational things that really moved a I into the reality calling. And it's starting to impact all our different energy. Whether it's text imaging in the networking world is an example where even a I and deep learnings ruling starting to impact our networking customers. >>Sure, I want to go to you. What do you do if you don't have a big data set? You don't have a lot of pictures of chihuahuas and blackberries, and I want to apply some machine intelligence to the problem. >>I mean, so you need to have the right data set. You know, Big is a relative term on, and it depends on what you're using it for, right? So you can have a massive amount of data that represents solar flares, and then you're trying to detect some anomaly, right? If you train and I what normal is based upon a massive amount of data and you don't have enough examples of that anomaly you're trying to detect, then it's never going to say there's an anomaly there, so you actually need to over sample. You have to create a population of data that allows you to detect images you can't say, Um oh, >>I'm going to reflect in my data set the percentage of black women >>in Seattle, which is something below 6% and say it's fair. It's not right. You have to be able thio over sample things that you need, and in some ways you can get this through surveys. You can get it through, um, actually going to different sources. But you have to boot, strap it in some way, and then you have to refresh it, because if you leave that data set static like Bob mentioned like you, people are changing the way they do attacks and networks all the time, and so you may have been able to find the one yesterday. But today it's a completely different ball game >>project to you, which comes first, the chicken or the egg. You start with the data, and I say this is a ripe opportunity to apply some. Aye, aye. Or do you have some May I objectives that you want to achieve? And I got to go out and find the >>data. So I actually think what starts where it starts is the business problem you're trying to solve. And then from there, you need to have the right data. What's interesting about this is that you can actually have starting points. And so, for example, there's techniques around transfer, learning where you're able to take an an algorithm that's already been trained on a bunch of data and training a little bit further with with your data on DSO, we've seen that such that people that may have, for example, only 100 images of something, but they could use a model that's trained on millions of images and only use those 100 thio create something that's actually quite accurate. >>So that's a great segue. Wait, give me a ring on now. And it's a great Segway into talking about applying on one algorithm that was built around one data set and then applying it to a different data set. Is that appropriate? Is that correct? Is air you risking all kinds of interesting problems by taking that and applying it here, especially in light of when people are gonna go to outweigh the marketplace, is because I've got a date. A scientist. I couldn't go get one in the marketplace and apply to my data. How should people be careful not to make >>a bad decision based on that? So I think it really depends. And it depends on the type of machine learning that you're doing and what type of data you're talking about. So, for example, with images, they're they're they're well known techniques to be able to do this, but with other things, there aren't really and so it really depends. But then the other inter, the other really important thing is that no matter what at the end, you need to test and generate based on your based on your data sets and on based on sample data to see if it's accurate or not, and then that's gonna guide everything. Ultimately, >>Sharon has got to go to you. You brought up something in the preliminary rounds and about open A I and kind of this. We can't have this black box where stuff goes into the algorithm. That stuff comes out and we're not sure what the result was. Sounds really important. Is that Is that even plausible? Is it feasible? This is crazy statistics, Crazy math. You talked about the business objective that someone's trying to achieve. I go to the data scientist. Here's my data. You're telling this is the output. How kind of where's the line between the Lehman and the business person and the hard core data science to bring together the knowledge of Here's what's making the algorithm say this. >>Yeah, there's a lot of names for this, whether it's explainable. Aye, aye. Or interpret a belay. I are opening the black box. Things like that. Um, the algorithms that you use determine whether or not they're inspect herbal. Um, and the deeper your neural network gets, the harder it is to inspect, actually. Right. So, to your point, every time you take an aye aye and you use it in a different scenario than what it was built for. For example, um, there is a police precinct in New York that had a facial recognition software, and, uh, victim said, Oh, it looked like this actor. This person looked like Bill Cosby or something like that, and you were never supposed to take an image of an actor and put it in there to find people that look like them. But that's how people were using it. So the Russians point yes, like it. You can transfer learning to other a eyes, but it's actually the humans that are using it in ways that are unintended that we have to be more careful about, right? Um, even if you're a, I is explainable, and somebody tries to use it in a way that it was never intended to be used. The risk is much higher >>now. I think maybe I had, You know, if you look at Marvis kind of what we're building for the networking community Ah, good examples. When Marvis tries to do estimate your throughput right, your Internet throughput. That's what we usually call decision tree algorithm. And that's a very interpretive algorithm. and we predict low throughput. We know how we got to that answer, right? We know what features God, is there? No. But when we're doing something like a NAMI detection, that's a neural network. That black box it tells us yes, there's a problem. There's some anomaly, but that doesn't know what caused the anomaly. But that's a case where we actually used neural networks, actually find the anomie, and then we're using something else to find the root cause, eh? So it really depends on the use case and where the night you're going to use an interpreter of model or a neural network which is more of a black box model. T tell her you've got a cat or you've got a problem >>somewhere. So, Bob, that's really interested. So can you not unpacking? Neural network is just the nature of the way that the communication and the data flows and the inferences are made that you can't go in and unpack it, that you have to have the >>separate kind of process too. Get to the root cause. >>Yeah, assigned is always hard to say. Never. But inherently s neural networks are very complicated. Saito set of weights, right? It's basically usually a supervised training model, and we're feeding a bunch of data and trying to train it to detect a certain features, sir, an output. But that is where they're powerful, right? And that's why they basically doing such good, Because they are mimicking the brain, right? That neural network is a very complex thing. Can't like your brain, right? We really don't understand how your brain works right now when you have a problem, it's really trialling there. We try to figure out >>right going right. So I want to stay with you, bought for a minute. So what about when you change what you're optimizing? Four? So you just said you're optimizing for throughput of the network. You're looking for problems. Now, let's just say it's, uh, into the end of the quarter. Some other reason we're not. You're changing your changing what you're optimizing for, Can you? You have to write separate algorithm. Can you have dynamic movement inside that algorithm? How do you approach a problem? Because you're not always optimizing for the same things, depending on the market conditions. >>Yeah, I mean, I think a good example, you know, again, with Marvis is really with what we call reinforcement. Learning right in reinforcement. Learning is a model we use for, like, radio resource management. And there were really trying to optimize for the user experience in trying to balance the reward, the models trying to reward whether or not we have a good balance between the network and the user. Right, that reward could be changed. So that algorithm is basically reinforcement. You can finally change hell that Algren works by changing the reward you give the algorithm >>great. Um, Rajan back to you. A couple of huge things that have come into into play in the marketplace and get your take one is open source, you know, kind of. What's the impact of open source generally on the availability, desire and more applications and then to cloud and soon to be edge? You know, the current next stop. How do you guys incorporate that opportunity? How does it change what you can do? How does it open up the lens of >>a I Yeah, I think open source is really important because I think one thing that's interesting about a I is that it's a very nascent field and the more that there's open source, the more that people could build on top of each other and be able to utilize what what others others have done. And it's similar to how we've seen open source impact operating systems, the Internet, things like things like that with Cloud. I think one of the big things with cloud is now you have the processing power and the ability to access lots of data to be able to t create these thes networks. And so the capacity for data and the capacity for compute is much higher. Edge is gonna be a very important thing, especially going into next few years. You're seeing Maur things incorporated on the edge and one exciting development is around Federated learning where you can train on the edge and then combine some of those aspects into a cloud side model. And so that I think will actually make EJ even more powerful. >>But it's got to be so dynamic, right? Because the fundamental problem used to always be the move, the computer, the data or the date of the computer. Well, now you've got on these edge devices. You've got Tanya data right sensor data all kinds of machining data. You've got potentially nasty hostile conditions. You're not in a nice, pristine data center where the environmental conditions are in the connective ity issues. So when you think about that problem yet, there's still great information. There you got latent issues. Some I might have to be processed close to home. How do you incorporate that age old thing of the speed of light to still break the break up? The problem to give you a step up? Well, we see a lot >>of customers do is they do a lot of training on the cloud, but then inference on the on the edge. And so that way they're able to create the model that they want. But then they get fast response time by moving the model to the edge. The other thing is that, like you said, lots of data is coming into the edge. So one way to do it is to efficiently move that to the cloud. But the other way to do is filter. And to try to figure out what data you want to send to the clouds that you can create the next days. >>Shawna, back to you let's shift gears into ethics. This pesky, pesky issue that's not not a technological issue at all, but right. We see it often, especially in tech. Just cause you should just cause you can doesn't mean that you should. Um so and this is not a stem issue, right? There's a lot of different things that happened. So how should people be thinking about ethics? How should they incorporate ethics? Um, how should they make sure that they've got kind of a, you know, a standard kind of overlooking kind of what they're doing? The decisions are being made. >>Yeah, One of the more approachable ways that I have found to explain this is with behavioral science methodologies. So ethics is a massive field of study, and not everyone shares the same ethics. However, if you try and bring it closer to behavior change because every product that we're building is seeking to change of behavior. We need to ask questions like, What is the gap between the person's intention and the goal we have for them? Would they choose that goal for themselves or not? If they wouldn't, then you have an ethical problem, right? And this this can be true of the intention, goal gap or the intention action up. We can see when we regulated for cigarettes. What? We can't just make it look cool without telling them what the cigarettes are doing to them, right so we can apply the same principles moving forward. And they're pretty accessible without having to know. Oh, this philosopher and that philosopher in this ethicist said these things, it can be pretty human. The challenge with this is that most people building these algorithms are not. They're not trained in this way of thinking, and especially when you're working at a start up right, you don't have access to massive teams of people to guide you down this journey, so you need to build it in from the beginning, and you need to be open and based upon principles. Um, and it's going to touch every component. It should touch your data, your algorithm, the people that you're using to build the product. If you only have white men building the product, you have a problem you need to pull in other people. Otherwise, there are just blind spots that you are not going to think of in order to still that product for a wider audience, but it seems like >>they were on such a razor sharp edge. Right with Coca Cola wants you to buy Coca Cola and they show ads for Coca Cola, and they appeal to your let's all sing together on the hillside and be one right. But it feels like with a I that that is now you can cheat. Right now you can use behavioral biases that are hardwired into my brain is a biological creature against me. And so where is where is the fine line between just trying to get you to buy Coke? Which somewhat argues Probably Justus Bad is Jule cause you get diabetes and all these other issues, but that's acceptable. But cigarettes are not. And now we're seeing this stuff on Facebook with, you know, they're coming out. So >>we know that this is that and Coke isn't just selling Coke anymore. They're also selling vitamin water so they're they're play isn't to have a single product that you can purchase, but it is to have a suite of products that if you weren't that coke, you can buy it. But if you want that vitamin water you can have that >>shouldn't get vitamin water and a smile that only comes with the coat. Five. You want to jump in? >>I think we're going to see ethics really break into two different discussions, right? I mean, ethics is already, like human behavior that you're already doing right, doing bad behavior, like discriminatory hiring, training, that behavior. And today I is gonna be wrong. It's wrong in the human world is gonna be wrong in the eye world. I think the other component to this ethics discussion is really round privacy and data. It's like that mirror example, right? No. Who gave that mirror the right to basically tell me I'm old and actually do something with that data right now. Is that my data? Or is that the mirrors data that basically recognized me and basically did something with it? Right. You know, that's the Facebook. For example. When I get the email, tell me, look at that picture and someone's take me in the pictures Like, where was that? Where did that come from? Right? >>What? I'm curious about to fall upon that as social norms change. We talked about it a little bit for we turn the cameras on, right? It used to be okay. Toe have no black people drinking out of a fountain or coming in the side door of a restaurant. Not that long ago, right in the 60. So if someone had built an algorithm, then that would have incorporated probably that social norm. But social norms change. So how should we, you know, kind of try to stay ahead of that or at least go back reflectively after the fact and say kind of back to the black box, That's no longer acceptable. We need to tweak this. I >>would have said in that example, that was wrong. 50 years ago. >>Okay, it was wrong. But if you ask somebody in Alabama, you know, at the University of Alabama, Matt Department who have been born Red born, bred in that culture as well, they probably would have not necessarily agreed. But so generally, though, again, assuming things change, how should we make sure to go back and make sure that we're not again carrying four things that are no longer the right thing to do? >>Well, I think I mean, as I said, I think you know what? What we know is wrong, you know is gonna be wrong in the eye world. I think the more subtle thing is when we start relying on these Aye. Aye. To make decisions like no shit in my car, hit the pedestrian or save my life. You know, those are tough decisions to let a machine take off or your balls decision. Right when we start letting the machines Or is it okay for Marvis to give this D I ps preference over other people, right? You know, those type of decisions are kind of the ethical decision, you know, whether right or wrong, the human world, I think the same thing will apply in the eye world. I do think it will start to see more regulation. Just like we see regulation happen in our hiring. No, that regulation is going to be applied into our A I >>right solutions. We're gonna come back to regulation a minute. But, Roger, I want to follow up with you in your earlier session. You you made an interesting comment. You said, you know, 10% is clearly, you know, good. 10% is clearly bad, but it's a soft, squishy middle at 80% that aren't necessarily super clear, good or bad. So how should people, you know, kind of make judgments in this this big gray area in the middle? >>Yeah, and I think that is the toughest part. And so the approach that we've taken is to set us set out a set of AI ai principles on DDE. What we did is actually wrote down seven things that we will that we think I should do and four things that we should not do that we will not do. And we now have to actually look at everything that we're doing against those Aye aye principles. And so part of that is coming up with that governance process because ultimately it boils down to doing this over and over, seeing lots of cases and figuring out what what you should do and so that governments process is something we're doing. But I think it's something that every company is going to need to do. >>Sharon, I want to come back to you, so we'll shift gears to talk a little bit about about law. We've all seen Zuckerberg, unfortunately for him has been, you know, stuck in these congressional hearings over and over and over again. A little bit of a deer in a headlight. You made an interesting comment on your prior show that he's almost like he's asking for regulation. You know, he stumbled into some really big Harry nasty areas that were never necessarily intended when they launched Facebook out of his dorm room many, many moons ago. So what is the role of the law? Because the other thing that we've seen, unfortunately, a lot of those hearings is a lot of our elected officials are way, way, way behind there, still printing their e mails, right? So what is the role of the law? How should we think about it? What shall we What should we invite from fromthe law to help sort some of this stuff out? >>I think as an individual, right, I would like for each company not to make up their own set of principles. I would like to have a shared set of principles that were following the challenge. Right, is that with between governments, that's impossible. China is never gonna come up with same regulations that we will. They have a different privacy standards than we D'oh. Um, but we are seeing locally like the state of Washington has created a future of work task force. And they're coming into the private sector and asking companies like text you and like Google and Microsoft to actually advise them on what should we be regulating? We don't know. We're not the technologists, but they know how to regulate. And they know how to move policies through the government. What will find us if we don't advise regulators on what we should be regulating? They're going to regulate it in some way, just like they regulated the tobacco industry. Just like they regulated. Sort of, um, monopolies that tech is big enough. Now there is enough money in it now that it will be regularly. So we need to start advising them on what we should regulate because just like Mark, he said. While everyone else was doing it, my competitors were doing it. So if you >>don't want me to do it, make us all stop. What >>can I do? A negative bell and that would not for you, but for Mark's responsibly. That's crazy. So So bob old man at the mall. It's actually a little bit more codified right, There's GDP are which came through May of last year and now the newness to California Extra Gatorade, California Consumer Protection Act, which goes into effect January 1. And you know it's interesting is that the hardest part of the implementation of that I think I haven't implemented it is the right to be for gotten because, as we all know, computers, air, really good recording information and cloud. It's recorded everywhere. There's no there there. So when these types of regulations, how does that impact? Aye, aye, because if I've got an algorithm built on a data set in in person, you know, item number 472 decides they want to be forgotten How that too I deal with that. >>Well, I mean, I think with Facebook, I can see that as I think. I suspect Mark knows what's right and wrong. He's just kicking ball down tires like >>I want you guys. >>It's your problem, you know. Please tell me what to do. I see a ice kind of like any other new technology, you know, it could be abused and used in the wrong waste. I think legally we have a constitution that protects our rights. And I think we're going to see the lawyers treat a I just like any other constitutional things and people who are building products using a I just like me build medical products or other products and actually harmful people. You're gonna have to make sure that you're a I product does not harm people. You're a product does not include no promote discriminatory results. So I >>think we're going >>to see our constitutional thing is going applied A I just like we've seen other technologies work. >>And it's gonna create jobs because of that, right? Because >>it will be a whole new set of lawyers >>the holdings of lawyers and testers, even because otherwise of an individual company is saying. But we tested. It >>works. Trust us. Like, how are you gonna get the independent third party verification of that? So we're gonna start to see a whole terrorist proliferation of that type of fields that never had to exist before. >>Yeah, one of my favorite doctor room. A child. Grief from a center. If you don't follow her on Twitter Follower. She's fantastic and a great lady. So I want to stick with you for a minute, Bob, because the next topic is autonomous. And Rahman up on the keynote this morning, talked about missed and and really, this kind of shifting workload of fixing things into an autonomous set up where the system now is, is finding problems, diagnosing problems, fixing problems up to, I think, he said, even generating return authorizations for broken gear, which is amazing. But autonomy opens up all kinds of crazy, scary things. Robert Gates, we interviewed said, You know, the only guns that are that are autonomous in the entire U. S. Military are the ones on the border of North Korea. Every single other one has to run through a person when you think about autonomy and when you can actually grant this this a I the autonomy of the agency toe act. What are some of the things to think about in the word of the things to keep from just doing something bad, really, really fast and efficiently? >>Yeah. I mean, I think that what we discussed, right? I mean, I think Pakal purposes we're far, you know, there is a tipping point. I think eventually we will get to the CP 30 Terminator day where we actually build something is on par with the human. But for the purposes right now, we're really looking at tools that we're going to help businesses, doctors, self driving cars and those tools are gonna be used by our customers to basically allow them to do more productive things with their time. You know, whether it's doctor that's using a tool to actually use a I to predict help bank better predictions. They're still gonna be a human involved, you know, And what Romney talked about this morning and networking is really allowing our I T customers focus more on their business problems where they don't have to spend their time finding bad hard were bad software and making better experiences for the people. They're actually trying to serve >>right, trying to get your take on on autonomy because because it's a different level of trust that we're giving to the machine when we actually let it do things based on its own. But >>there's there's a lot that goes into this decision of whether or not to allow autonomy. There's an example I read. There's a book that just came out. Oh, what's the title? You look like a thing. And I love you. It was a book named by an A I, um if you want to learn a lot about a I, um and you don't know much about it, Get it? It's really funny. Um, so in there there is in China. Ah, factory where the Aye Aye. Is optimizing um, output of cockroaches now they just They want more cockroaches now. Why do they want that? They want to grind them up and put them in a lotion. It's one of their secret ingredients now. It depends on what parameters you allow that I to change, right? If you decide Thio let the way I flood the container, and then the cockroaches get out through the vents and then they get to the kitchen to get food, and then they reproduce the parameters in which you let them be autonomous. Over is the challenge. So when we're working with very narrow Ai ai, when use hell the Aye. Aye. You can change these three things and you can't just change anything. Then it's a lot easier to make that autonomous decision. Um and then the last part of it is that you want to know what is the results of a negative outcome, right? There was the result of a positive outcome. And are those results something that we can take actually? >>Right, Right. Roger, don't give you the last word on the time. Because kind of the next order of step is where that machines actually write their own algorithms, right? They start to write their own code, so they kind of take this next order of thought and agency, if you will. How do you guys think about that? You guys are way out ahead in the space, you have huge data set. You got great technology. Got tensorflow. When will the machines start writing their own A their own out rhythms? Well, and actually >>it's already starting there that, you know, for example, we have we have a product called Google Cloud. Ottawa. Mel Village basically takes in a data set, and then we find the best model to be able to match that data set. And so things like that that that are there already, but it's still very nascent. There's a lot more than that that can happen. And I think ultimately with with how it's used I think part of it is you have to start. Always look at the downside of automation. And what is what is the downside of a bad decision, whether it's the wrong algorithm that you create or a bad decision in that model? And so if the downside is really big, that's where you need to start to apply Human in the loop. And so, for example, in medicine. Hey, I could do amazing things to detect diseases, but you would want a doctor in the loop to be able to actually diagnose. And so you need tohave have that place in many situations to make sure that it's being applied well. >>But is that just today? Or is that tomorrow? Because, you know, with with exponential growth and and as fast as these things are growing, will there be a day where you don't necessarily need maybe need the doctor to communicate the news? Maybe there's some second order impacts in terms of how you deal with the family and, you know, kind of pros and cons of treatment options that are more emotional than necessarily mechanical, because it seems like eventually that the doctor has a role. But it isn't necessarily in accurately diagnosing a problem. >>I think >>I think for some things, absolutely over time the algorithms will get better and better, and you can rely on them and trust them more and more. But again, I think you have to look at the downside consequence that if there's a bad decision, what happens and how is that compared to what happens today? And so that's really where, where that is. So, for example, self driving cars, we will get to the point where cars are driving by themselves. There will be accidents, but the accident rate is gonna be much lower than what's there with humans today, and so that will get there. But it will take time. >>And there was a day when will be illegal for you to drive. You have manslaughter, right? >>I I believe absolutely there will be in and and I don't think it's that far off. Actually, >>wait for the day when I have my car take me up to Northern California with me. Sleepy. I've only lived that long. >>That's right. And work while you're while you're sleeping, right? Well, I want to thank everybody Aton for being on this panel. This has been super fun and these air really big issues. So I want to give you the final word will just give everyone kind of a final say and I just want to throw out their Mars law. People talk about Moore's law all the time. But tomorrow's law, which Gardner stolen made into the hype cycle, you know, is that we tend to overestimate in the short term, which is why you get the hype cycle and we turn. Tend to underestimate, in the long term the impacts of technology. So I just want it is you look forward in the future won't put a year number on it, you know, kind of. How do you see this rolling out? What do you excited about? What are you scared about? What should we be thinking about? We'll start with you, Bob. >>Yeah, you know, for me and, you know, the day of the terminus Heathrow. I don't know if it's 100 years or 1000 years. That day is coming. We will eventually build something that's in part of the human. I think the mission about the book, you know, you look like a thing and I love >>you. >>Type of thing that was written by someone who tried to train a I to basically pick up lines. Right? Cheesy pickup lines. Yeah, I'm not for sure. I'm gonna trust a I to help me in my pickup lines yet. You know I love you. Look at your thing. I love you. I don't know if they work. >>Yeah, but who would? Who would have guessed online dating is is what it is if you had asked, you know, 15 years ago. But I >>think yes, I think overall, yes, we will see the Terminator Cp through It was probably not in our lifetime, but it is in the future somewhere. A. I is definitely gonna be on par with the Internet cell phone, radio. It's gonna be a technology that's gonna be accelerating if you look where technology's been over last. Is this amazing to watch how fast things have changed in our lifetime alone, right? Yeah, we're just on this curve of technology accelerations. This in the >>exponential curves China. >>Yeah, I think the thing I'm most excited about for a I right now is the addition of creativity to a lot of our jobs. So ah, lot of we build an augmented writing product. And what we do is we look at the words that have happened in the world and their outcomes. And we tell you what words have impacted people in the past. Now, with that information, when you augment humans in that way, they get to be more creative. They get to use language that have never been used before. To communicate an idea. You can do this with any field you can do with composition of music. You can if you can have access as an individual, thio the data of a bunch of cultures the way that we evolved can change. So I'm most excited about that. I think I'm most concerned currently about the products that we're building Thio Give a I to people that don't understand how to use it or how to make sure they're making an ethical decision. So it is extremely easy right now to go on the Internet to build a model on a data set. And I'm not a specialist in data, right? And so I have no idea if I'm adding bias in or not, um and so it's It's an interesting time because we're in that middle area. Um, and >>it's getting loud, all right, Roger will throw with you before we have to cut out, or we're not gonna be able to hear anything. So I actually start every presentation out with a picture of the Mosaic browser, because what's interesting is I think that's where >>a eyes today compared to kind of weather when the Internet was around 1994 >>were just starting to see how a I can actually impact the average person. As a result, there's a lot of hype, but what I'm actually finding is that 70% of the company's I talked to the first question is, Why should I be using this? And what benefit does it give me? Why 70% ask you why? Yeah, and and what's interesting with that is that I think people are still trying to figure out what is this stuff good for? But to your point about the long >>run, and we underestimate the longer I think that every company out there and every product will be fundamentally transformed by eye over the course of the next decade, and it's actually gonna have a bigger impact on the Internet itself. And so that's really what we have to look forward to. >>All right again. Thank you everybody for participating. There was a ton of fun. Hope you had fun. And I look at the score sheet here. We've got Bob coming in and the bronze at 15 points. Rajan, it's 17 in our gold medal winner for the silver Bell. Is Sharna at 20 points. Again. Thank you. Uh, thank you so much and look forward to our next conversation. Thank Jeffrey Ake signing out from Caesar's Juniper. Next word unpacking. I Thanks for watching.

(upbeat music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CUBE Conversation. >> Welcome everyone to this special CUBE Conversation Around the CUBE segment, Unpacking AI, number two, sponsored by Juniper Networks. We've got a great lineup here to go around the CUBE and unpack AI. We have Ken Jennings, all-time Jeopardy champion with us. Celebrity, great story there, we'll dig into that. John Hinson, director of AI at Evotek and Charna Parkey, who's the applied scientist at Textio. Thanks for joining us here for Around the CUBE Unpacking AI, appreciate it. First question I want to get to, Ken, you're notable for being beaten by a machine on Jeopardy. Everyone knows that story, but it really brings out the question of AI and the role AI is playing in society around obsolescence. We've been hearing gloom and doom around AI replacing people's jobs, and it's not really that way. What's your take on AI and replacing people's jobs? >> You know, I'm not an economist, so I can't speak to how easy it's going to be to retrain and re-skill tens of millions of people once these clerical and food prep and driving and whatever jobs go away, but I can definitely speak to the personal feeling of being in that situation, kind of watching the machine take your job on the assembly line and realizing that the thing you thought made you special no longer exists. If IBM throws enough money at it, your skill essentially is now obsolete. And it was kind of a disconcerting feeling. I think that what people need is to feel like they matter, and that went away for me very quickly when I realized that a black rectangle can now beat me at a game show. >> Okay John, what's your take on AI replacing jobs? What's your view on this? >> I think, look, we're all going to have to adapt. There's a lot of changes coming. There's changes coming socially, economically, politically. I think it's a disservice to us all to get to too indulgent around the idea that these things are going to change. We have to absorb these things, we have to be really smart about how we approach them. We have to be very open-minded about how these things are going to actually change us all. But ultimately, I think it's going to be positive at the end of the day. It's definitely going to be a little rough for a couple of years as we make all these adjustments, but I think what AI brings to the table is heads above kind of where we are today. >> Charna, your take around this, because the role of humans versus machines are pretty significant, they help each other. But is AI going to dominate over humans? >> Yeah, absolutely. I think there's a thing that we see over and over again in every bubble and collapse where, you know, in the automotive industry we certainly saw a bunch of jobs were lost, but a bunch of jobs were gained. And so we're just now actually getting into the phase where people are realizing that AI isn't just replacement, it has to be augmentation, right? We can't simply use images to replace recognition of people, we can't just use black box to give our FICO credit scores, it has to be inspectable. So there's a new field coming up now called explainable AI that actually is where we're moving towards and it's actually going to help society and create jobs. >> All right so let's stay on that next point for the next round, explainable AI. This points to a golden age. There's a debate around are we in a bubble or a golden age. A lot of people are negative right now on tech. You can see all the tech backlash. Amazon, the big tech companies like Apple and Facebook, there's a huge backlash around this so-called tech for society. Is this an indicator of a golden age coming? >> I think so, absolutely. We can take two examples of this. One would be where, you remember when Amazon built a hiring algorithm based upon their own resume data and they found that it was discriminating against women because they had only had men apply for it. Now with Textio we're building augmented writing across the audience and not from a single company and so companies like Johnson and Johnson are increasing the pipeline by more than nine percent which converts to 90,000 more women applying for their jobs. And so part of the difference there is one is explainable, one isn't, and one is using the right data set representing the audience that is consuming it and not a single company's hiring. So I think we're absolutely headed into more of a golden age, and I think these are some of the signs that people are starting to use it in the right way. >> John, what's your take? Obviously golden age doesn't look that to us right now. You see Facebook approving lies as ads, Twitter banning political ads. AI was supposed to solve all these problems. Is there light at the end of this dark tunnel we're on? >> Yeah, golden age for sure. I'm definitely a big believer in that. I think there's a new era amongst us on how we handle data in general. I think the most important thing we have here though is education around what this stuff is, how it works, how it's affecting our lives individually and at the corporate level. This is a new era of informing and augmenting literally everything we do. I see nothing but positives coming out of this. We have to be obviously very careful with our approaching all the biases that already exist today that are only going to be magnified with these types of algorithms at mass scale. But ultimately if we can get over that hurdle, which I believe collectively we all need to do together, I think we'd live in much better, less wasteful world just by approaching the data that's already at hand. >> Ken, what's your take on this? It's like a daily double question. Is it going to be a golden age? >> Laughs >> It's going to come sooner or later. We have to have catastrophe before, we have to have reality hit us in the face before we realize that tech is good, and shaping it? It's pretty ugly right now in some of the situations out there, especially in the political scene with the election in the US. You're seeing some negative things happening. What's your take on this? >> I'm much more skeptical than John and Charna. I feel like that kind of just blinkered, it's going to be great, is something you have to actually be in the tech industry and hearing all day to actually believe. I remember seeing kind of lay-person's exposure to Watson when Watson was on Jeopardy and hearing the questions reporters would ask and seeing the memes that would appear, and everyone's immediate reaction just to something as innocuous as a AI algorithm playing on a game show was to ask, is this Skynet from Terminator 2? Is this the computer from The Matrix? Is this HAL pushing us out of the airlock? Everybody immediately first goes to the tech is going to kill us. That's like everybody's first reaction, and it's weird. I don't know, you might say it's just because Hollywood has trained us to expect that plot development, but I almost think it's the other way around. Like that's a story we tell because we're deeply worried about our own meaning and obsolescence when we see how little these skills might be valued in 10, 20, 30 years. >> I can't tell you how much, by the way, Star Trek, Star Wars and Terminators probably affected the nomenclature of the technology. Everyone references Skynet. Oh my God, we're going to be taken over and killed by aliens and machines. This is a real fear. I thinks it's an initial reaction. You felt that Ken, so I've got to ask you, where do you think the crossover point is for people to internalize the benefits of say, AI for instance? Because people will say hey, look back at life before the iPhone, look at life before these tools were out there. Some will say society's gotten better, but yet there's this surveillance culture, things... And on and on. So what do you guys think the crossover point is for the reaction to change from oh my God, it's Skynet, gloom and doom to this actually could be good? >> It's incredibly tricky because as we've seen, the perception of AI both in and out of the industry changes as AI advances. As soon as machine learning can actually do a task, there's a tendency to say there's this no true Scotsman problem where we say well, that clearly can't be AI because I see how the trick worked. And yeah, humans lose at chess now. So when these small advances happen, the reaction is often oh, that's not really AI. And by the same token, it's not a game-changer when your email client can start to auto-complete your emails. That's a minor convenience to you. But you don't think oh, maybe Skynet is good. I really do think it's going to have to be, maybe the inflection point is when it starts to become so disruptive that actually public policy has to change. So we get serious about >> And public policy has started changing. >> whatever their reactions are. >> Charna, your thoughts. >> The public policy has started changing though. We just saw, I think it was in September, where California banned the use of AI in the body cameras, both real-time and after the fact. So I think that's part of the pivot point that we're actually seeing is that public policy is changing.` The state of Washington currently has a task force for AI who's making a set of recommendations for policy starting in December. But I think part of what we're missing is that we don't have enough digital natives in office to even attempt to, to your point Ken, predict what we're even going to be able to do with it, right? There is this fear because of misunderstanding, but we also don't have a respect of our political climate right now by a lot of our digital natives, and they need to be there to be making this policy. >> John, weigh in on this because you're director of AI, you're seeing positive, you have to deal with the uncertainty as well, the growth of machine learning. And just this week Google announced more TensorFlow for everybody. You're seeing Open Source. So there's a tech push, almost a democratization, going on with AI. So I think this crossover point might be sooner in front of us than people think. What's your thoughts? >> Yeah it's here right now. All these things can be essentially put into an environment. You can see these into products, or making business decisions or political decisions. These are all available right now. They're available today and its within 10 to 15 lines of code. It's all about the data sets, so you have to be really good stewards of the data that you're using to train your models. But I think the most important thing, back to the Skynet and all this science-fiction side, we have to collectively start telling the right stories. We need better stories than just this robots are going to take us over and destroy all of our jobs. I think more interesting stories really revolve around, what about public defenders who can have this informant augmentation algorithm that's going to help them get their job done? What about tailor-made medicine that's going to tell me exactly what the conditions are based off of a particular treatment plan instead of guessing? What about tailored education that's going to look at all of my strengths and weaknesses and present a plan for me? These are things that AI can do. Charna's exactly right, where if we don't get this into the right political atmosphere that's helping balance the capitalist side with the social side, we're going to be in trouble. So that's got to be embedded in every layer of enterprise as well as society in general. It's here, it's now, and it's real. >> Ken, before we move on to the ethics question, I want to get your thoughts on this because we have an Alexa at home. We had an Alexa at home; my wife made me get rid of it. We had an Apple device, what they're called... the Home pods, that's gone. I bought a Portal from Facebook because I always buy the earliest stuff, that's gone. We don't want listening devices in our house because in order to get that AI, you have to give up listening, and this has been an issue. What do you have to give to get? This has been a big question. What's your thoughts on all this? >> I was at an Amazon event where they were trumpeting how no technology had ever caught on faster than these personal digital assistants, and yet every time I'm in a use case, a household that's trying to use them, something goes terribly wrong. My friend had to rename his because the neighbor kids kept telling Alexa to do awful things. He renamed it computer, and now every time we use the word computer, the wall tells us something we don't want to know. >> (laughs) >> This is just anecdata, but maybe it speaks to something deeper, the fact that we don't necessarily like the feeling of being surveilled. IBM was always trying to push Watson as the star Trek computer that helpfully tells you exactly what you need to know in the right moment, but that's got downsides too. I feel like we're going to, if nothing else, we may start to value individual learning and knowledge less when we feel like a voice from the ceiling can deliver unto us the fact that we need. I think decision-making might suffer in that kind of a world. >> All right, this brings up ethics because I bring up the Amazon and the voice stuff because this is the new interface people want to have with machines. I didn't mention phones, Androids and Apple, they need to listen in order to make decisions. This brings up the ethics question around who sets the laws, what society should do about this, because we want the benefits of AI. John, you point out some of them. You got to give to get. Where are we on ethics? What's the opinion, what's the current view on this? John, we'll start with you on your ethics view on what needs to change now to move the ball faster. >> Data is gold. Data is gold at an exponential rate when you're talking about AI. There should be no situation where these companies get to collect data at no cost or no benefit to the end consumer. So ultimately we should have the option to opt out of any of these products and any of this type of surveillance wherever we can. Public safety is a little bit different situation, but on the commercial side, there is a lot of more expensive and even more difficult ways to train these models with a data set that isn't just basically grabbing everything our of your personal lives. I think that should be an option for consumers and that's one of those ethical check-marks. Again, ethics in general, the way that data's trained, the way that data's handled, the way models actually work, it has to be a primary reason for and approach of how you actually go about developing and delivering AI. That said, we cannot get over-indulgent in the fact that we can't do it because we're so fearful of the ethical outcomes. We have to find some middle ground and we have to find it quickly and collectively. >> Charna, what's your take on this? Ethics is super important to set the agenda for society to take advantage of all this. >> Yeah. I think we've got three ethical components here. We certainly have, as John mentioned, the data sets. However, it's also what behavior we're trying to change. So I believe the industry could benefit from a lot more behavioral science, so that we can understand whether or not the algorithms that we're building are changing behaviors that we actually want to change, right? And if we aren't, that's unethical. There is an entire field of ethics that needs to start getting put into our companies. We need an ethics board internally. A few companies are doing this already actually. I know a lot of the military companies do. I used to be in the defense industry, and so they've got a board of ethics before you can do things. The challenge is also though that as we're democratizing the algorithms themselves, people don't understand that you can't just get a set of data that represents the population. So this is true of image processing, where if we only used 100 images of a black woman, and we used 1,000 images of a white man because that was the distribution in our population, and then the algorithm could not detect the difference between skin tones for people of color, then we end up with situations where we end up in a police state where you put in an image of one black woman and it looks like ten of them and you can't distinguish between them. And yet, the confidence rate for the humans are actually higher, because they now have a machine backing their decision. And so they stop questioning, to your point, Ken, about what is the decision I'm making, they're like I'm so confident, this data told me so. And so there's a little bit of you need some expert in the loop and you also can't just have experts, because then you end up with Cambridge Analytica and all of the political things that happened there, not just in the US, but across 200 different elections and 30 different countries. And we are upset because it happened in the US, but this has been happening for years. So its just this ethical challenge of behavior change. It's not even AI and we do it all the time. Its why the cigarette industry is regulated (laughs). >> So Ken, what's your take on this? Obviously because society needs to have ethics. Who runs that? Companies? The law-makers? Someone's got to be responsible. >> I'm honestly a little pessimistic the general public will even demand this the way we're maybe hoping that they will. When I think about an example like Facebook, people just being able to, being willing to give away insane amounts of data through social media companies for the smallest of benefits: keeping in touch with people from high school they don't like. I mean, it really shows how little we value not being a product in this kind of situation. But I would like to see this kind of ethical decisions being made at the company-level. I feel like Google kind of surreptitiously moved away from it's little don't be evil mantra with the subtext that eh, maybe we'll be a little evil now. It just reminds me of Manhattan Project era thinking, where you could've gone to any of these nuclear scientists and said you're working on a real interesting puzzle here, it might advance the field, but like 200,000 civilians might die this summer. And I feel like they would've just looked at you and thought that's not really my bailiwick. I'm just trying to solve the fission problem. I would like to see these 10 companies actually having that kind of thinking internally. Not being so busy thinking if they can do something that they don't wonder if they should. >> That's a great point. This brings up the point of who is responsible. Almost as if who is less evil than the other person? Google, they don't do evil, but they're less evil than Amazon and Facebook and others. Who is responsible? The companies or the law-makers? Because if you look up some of the hearings in Washington, D.C., some of the law-makers we see up there, they don't know how the internet works, and it's pretty obvious that this is a problem. >> Yeah, well that's why Jack Dorsey of Twitter posted yesterday that he banned not just political ads, but also issue ads. This isn't something that they're making him do, but he understands that when you're using AI to target people, that it's not okay. At some point, while Mark is sitting on (laughs) this committee and giving his testimony, he's essentially asking to be regulated because he can't regulate himself. He's like well, everyone's doing it, so I'm going to do it too. That's not an okay excuse. We see this in the labor market though actually, where there's existing laws that prevent discrimination. It's actually the company's responsibility to make sure that the products that they purchase from any vendor isn't introducing discrimination into that process. So its not even the vendor that's held responsible, it's the company and their use of it. We saw in the NYPD actually that one of those image recognition systems came up and someone said well, he looked like, I forget the name of what the actor was, but some actor's name is what the perpetrator looked like and so they used an image of the actor to try and find the person who actually assaulted someone else. And that's, it's also the user problem that I'm super concerned about. >> So John, what's your take on this? Because these are companies are in business to make money, for profit, they're not the government. And who's the role, what should the government do? AI has to move forward. >> Yeah, we're all responsible. The companies are responsible. The companies that we work with, I have yet to interact with customers, or with our customers here, that have some insidious goal, that they're trying to outsmart their customers. They're not. Everyone's looking to do the best and deliver the most relevant products in the marketplace. The government, they absolutely... The political structure we have, it has to be really intelligent and it's got to get up-skilled in this space and it needs to do it quickly, both at the economy level, as well as for our defense. But the individuals, all of us as individuals, we are already subjected to this type of artificial intelligence in our everyday lives. Look at streaming, streaming media. Right now every single one of us goes out through a streaming source, and we're getting recommendations on what we should watch next. And we're already adapting to these things, I am. I'm like stop showing me all the stuff you know I want to watch, that's not interesting to me. I want to find something I don't know I want to watch, right? So we all have to get educated, we're all responsible for these things. And again, I see a much more positive side of this. I'm not trying to get into the fear-mongering side of all the things that could go wrong, I want to focus on the good stories, the positive stories. If I'm in a courtroom and I lose a court case because I couldn't afford the best attorney and I have the bias of a judge, I would certainly like artificial intelligence to make a determination that allows me to drive an appeal, as one example. Things like that are really creative in the world that we need to do. Tampering down this wild speculation we have on the markets. I mean, we are all victims of really bad data decisions right now, almost the worst data decisions. For me, I see this as a way to actually improve all those things. Fraud fees will be reduced. That helps everybody, right? Less speculation and these wild swings, these are all helpful things. >> Well Ken, John and Charna, thank- (audio feedback) >> Go ahead, finish. Get that word in. >> Sorry. I think that point you were making though John, is we are still a capitalist society, but we're no longer a shareholder capitalist society, we are a stakeholder capitalist society and the stakeholder is the society itself. It is us, it what we want to see. And so yes, I still want money. Obviously there are things that I want to buy, but I also care about well-being. I think it's that little shift that we're seeing that is actually you and I holding our own teams accountable for what they do. >> Yeah, culture first is a whole new shift going on in these companies that's a for-profit, mission-based. Ken, John, Charna, thanks for coming on Around the CUBE, Unpacking AI. Let's go around the CUBE Ken, John and Charna in that order, and just real quickly, unpacking AI, what's your final word? >> (laughs) I really... I'm interested in John's take that there's a democratization coming provided these tools will be available to everyone. I would certainly love to believe that. It seems like in the past, we've seen no, that access to these kind of powerful, paradigm-changing tools tend to be concentrated among a very small group of people and the benefits accrue to a very small group of people. But I hope that doesn't happen here. You know, I'm optimistic as well. I like the utopian side where we all have this amazing access to information and so many new problems can get solved with amazing amounts of data that we never could've touched before. Though you know, I think about that. I try to let that help me sleep at night, and not the fact that, you know... every public figure I see on TV is kind of out of touch about technology and only one candidate suggests the universal basic income, and it's kind of a crackpot idea. Those are the kind of things that keep me up at night. >> All right, John, final word. >> I think it's beautiful, AI's beautiful. We're on the cusp of a whole new world, it's nothing but positivity I see. We have to be careful. We're all nervous about it. None of us know how to approach these things, but as human beings, we've been here before. We're here all the time. And I believe that we can all collectively get a better lives for ourselves, for the environment, for everything that's out there. It's here, it's now, it's definitely real. I encourage everyone to hurry up on their own education. Every company, every layer of government to start really embracing these things and start paying attention. It's catching us all a little bit by surprise, but once you see it in production, you see it real, you'll be impressed. >> Okay, Charna, final word. >> I think one thing I want to leave people with is what we incentivize is what we end up optimizing for. This is the same for human behavior. You're training a new employee, you put incentives on the way that they sell, and that's, they game the system. AI's specifically find the optimum route, that is their job. So if we don't understand more complex cost functions, more complex representative ways of training, we're going to end up in a space, before we know it, that we can't get out of. And especially if we're using uninspectable AI. We really need to move towards augmentation. There are some companies that are implementing this now that you may not even know. Zillow, for example, is using AI to give you a cost for your home just by the photos and the words that you describe it, but they're also purchasing houses without a human in the loop in certain markets, based upon an inspection later by a human. And so there are these big bets that we're making within these massive corporations, but if you're going to do it as an individual, take a Coursera class on AI and take a Coursera class on ethics so that you can understand what the pitfalls are going to be, because that cost function is incredibly important. >> Okay, that's a wrap. Looks like we have a winner here. Charna, you got 18, John 16. Ken came in with 12, beaten again! (both laugh) Okay, Ken, seriously, great to have you guys on, a pleasure to meet everyone. Thanks for sharing on Around the CUBE Unpacking AI, panel number two. Thank you. >> Thanks a lot. >> Thank you. >> Thanks. I've been defeated by artificial intelligence again! (all laugh) (upbeat music)

>> From the Silicon Angle Media office in Boston, Massachusetts, It's theCUBE. (upbeat music) Now, here's your host, Stu Miniman. >> Hi. I'm Stu Miniman, and this is theCUBE's Boston area studio; our actually brand-new studio, and I'm really excited to have I believe is a first-time guest, a long-time caller, you know, a long time listener >> Yeah, yep. first time caller, good buddy of mine Dave Graham, who is the director, is a director of emerging technologies: messaging at Dell Technologies. Disclaimer, Dave and I worked together at a company some of you might have heard on the past, it was EMC Corporation, which was a local company. Dave and I both left EMC, and Dave went back, after Dell had bought EMC. So Dave, thanks so much for joining, it is your first time on theCUBE, yes? >> It is the first time on theCUBE. >> Yeah, so. >> Lets do some, Some of the first times that I actually interacted with, with this team here, you and I were bloggers and doing lots of stuff back in the industry, so it's great to be able to talk to you on-camera. >> Yeah, same here. >> All right, so Dave, I mentioned you were a returning former EMC-er, now Dell tech person, and you spent some time at Juniper, at some startups, but give our audience a little bit about your background and your passions. >> Oh, so background-wise, yep, so started my career in technology, if you will, at EMC, worked, started in inside sales of all places. Worked my way into a consulting/engineer type position within ECS, which was, obviously a pretty hard-core product inside of EMC now, or Dell Technologies now. Left, went to a startup, everybody's got to do a start up at some point in their life, right? Take the risk, make the leap, that was awesome, was actually one of those Cloud brokers that's out there, like Nasuni, company called Sertis. Had a little bit of trouble about eight months in, so it kind of fell apart. >> Yeah, the company did, not you. >> The company did! (men laughing) I was fine, you know, but the, yeah, the company had some problems, but ended up leaving there, going to Symantec of all places, so I worked on the Veritas side, kind of the enterprise side, which just recently got bought out by Avago, evidently just. >> Broadcom >> Broadcom, Broadcom, art of the grand whole Avago. >> Dave, Dave, you know we're getting up there in years and our tech, when we keep talking about something 'cause I was just reading about, right, Broadcom, which was of course Avago bought Broadcom in the second largest tech acquisition in history, but when they acquired Broadcom, they took on the name because most people know Broadcom, not as many people know Avago, even those of us with backgrounds in the chip semiconductor and all those pieces. I mean you got Brocade in there, you've got some of the software companies that they've bought over the time, so some of those go together. But yeah, Veritas and Symantec, those of us especially with some storage and networking background know those brands well. >> Absolutely, PLX's being the PCI switched as well, it's actually Broadcom, those things. So yeah, went from Symantec after a short period of time there, went to Juniper Networks, ran part of their Center of Excellence, kind of a data center overlay team, the only non-networking guy in a networking company, it felt like. Can't say that I learned a ton about the networking side, but definitely saw a huge expansion in the data center space with Juniper, which was awesome to see. And then the opportunity came to come back to Dell Technologies. Kind of a everything old becoming new again, right? Going and revisiting a whole bunch of folks that I had worked with 13, you know, 10 years ago. >> Dave, it's interesting, you know, I think about, talk about somebody like Broadcom, and Avago, and things like that. I remember reading blog posts of yours, that you'd get down to some of that nitty-level, you and I would be ones that would be the talk about the product, all right now pull the board out, let me look at all the components, let me understand, you know, the spacing, and the cooling, and all the things there, but you know here it's 2019, Dave. Don't you know software is eating the world? So, tell us a little bit about what you're working on these days, because the high-level things definitely don't bring to mind the low-level board pieces that we used to talk about many years ago. >> Exactly, yeah, it's no longer, you know, thermals and processing power as much, right? Still aspects of that, but a lot of what we're focused on now, or what I'm focused on now is within what we call the emerging technology space. Or horizon 2, horizon 3, I guess. >> Sounds like something some analyst firm came up with, Dave. (Dave laughing) >> Yeah, like Industry 4.0, 5.0 type stuff. It's all exciting stuff, but you know when you look at technologies like five, 5G, fifth generation wireless, you know both millimeter waves, sub six gigahertz, AI, you know, everything old becoming new again, right? Stuff from the fifties, and sixties that's now starting to permeate everything that we do, you're not opening your mouth and breathing unless you're talking about AI at some point, >> Yeah, and you bring up a great point. So, we've spent some time with the Dell team understanding AI, but help connect for our audience that when you talk high AI we're talking about, we're talking about data at the center of everything, and it's those applications, are you working on some of those solutions, or is it the infrastructure that's going to enable that, and what needs to be done at that level for things to work right? >> I think it's all of the above. The beauty of kind of Dell Technologies that you sit across, both infrastructure and software. You look at the efforts and the energies, stuff like VMware buying, BitFusion, right, as a mechanism trying to assuage some of that low-level hardware stuff. Start to tap into what the infrastructure guys have always been doing. When you bring that kind of capability up the stack, now you can start to develop within the software mindset, how, how you're going to access this. Infrastructure still plays a huge part of it, you got to run it on something, right? You can't really do serverless AI at this point, am I allowed to say that? (man laughing) >> Well, you could say that, I might disagree with you, because absolutely >> Eh, that's fine. there's AI that's running on it. Don't you know, Dave, I actually did my serverless 101 article that I had, I actually had Ashley Gorakhpurwalla, who is the General Manager of Dell servers, holding the t-shirt that "there is no serverless, it's just, you know, a function that you only pay the piece that you need when you need and everything there." But the point of the humor that I was having there is even the largest server manufacturer in the world knows that underneath that serverless discussion, absolutely, there is still infrastructure that plays there, just today it tends to primarily be in AWS with all of their services, but that proliferation, serverless, we're just letting the developers be developers and not have to think about that stuff, and I mean, Dave, the stuff we've had background, you know, we want to get rid of silos and make things simpler, I mean, it's the things we've been talking about for decades, it's just, for me it was interesting to look at, it is very much a developer application driven piece, top-down as opposed to so many of the virtualization and infrastructure as a service is more of a bottom-up, let me try to change this construct so that we can then provide what you need above it, it's just a slightly different way of looking at things. >> Yeah, and I think we're really trying to push for that stuff, so you know you can bundle together hardware that makes it, makes the development platform easy to do, right? But the efforts and energy of our partnerships, Dell has engaged in a lot of partnerships within the industry, NVIDIA, Intel, AMD, Graphcore, you name it, right? We're out in that space working along with those folks, but a lot of that is driven by software. It's, you write to a library, like Kudu, or, you know pyEight, you know, PyTorch, you're using these type of elements and you're moving towards that, but then it has to run on something, right? So we want to be in that both-end space, right? We want to enable that kind of flexibility capability, and obviously not prevent it, but we want to also expose that platform to as many people within the industry as possible so they can kind of start to develop on it. You're becoming a platform company, really, when it comes down to it. >> I don't want to get down the semantical arguments of AI, if you will, but what are you hearing from customers, and what's some kind of driving some of the discussions lately that's the reality of AI as opposed to some of just the buzzy hype that everybody talks about? >> Well I still think there's some ambiguity in market around AI versus automation even, so what people that come and ask us are well, "you know, I believe in this thing called artificial intelligence, and I want to do X, Y, and Z." And these particular workloads could be better handled by a simple, not to distill it down to the barest minimum, but like cron jobs, something that's, go back in the history, look at the things that matter, that you could do very very simply that don't require a large amount of library, or sort of an understanding of more advanced-type algorithms or developments that way. In the reverse, you still have that capability now, where everything that we're doing within industry, you use chat-bots. Some of the intelligence that goes into those, people are starting to recognize, this is a better way that I could serve my customers. Really, it's that business out kind of viewpoint. How do I access these customers, where they may not have the knowledge set here, but they're coming to us and saying, "it's more than just, you know, a call, an IVR system," you know, like an electronic IVR system, right? Like I come in and it's just quick response stuff. I need some context, I need to be able to do this, and transform my data into something that's useful for my customers. >> Yeah, no, this is such a great point, Dave. The thing I've asked many times, is, my entire career we've talked about intelligence and we've talked about automation, what's different about it today? And the reality is, is it used to be all right. I was scripting things, or I would have some Bash processes, or I would put these things together. The order of magnitude and scale of what we're talking about today, I couldn't do it manually if I wanted to. And that automation is really, can be really cool these days, and it's not as, to set all of those up, there is more intelligence built into it, so whether it's AI or just machine learning kind of underneath it, that spectrum that we talk about it, there's some real-use cases, a real lot of things that are happening there, and it definitely is, order of magnitudes more improved than what we were talking about say, back when we were both at EMC and the latest generation of Symmetrix was much more intelligent than the last generation, but if you look at that 10 years later, boy, it's, it is night and day, and how could we ever have used those terms before, compared to where we are today. >> Yeah it's, it's, somebody probably at some point coined the term, "exponential". Like, things become exponential as you start to look at it. Yeah, the development in the last 10 years, both in computing horsepower, and GPU/GPGPU horsepower, you know, the innovation around, you know FPGAs are back in a big way now, right? All that brainpower that used to be in these systems now, you now can benefit even more from the flexibility of the systems in order to get specific workloads done. It's not for everybody, we all know that, but it's there. >> I'm glad you brought up FPGAs because those of us that are hardware geeks, I mean, some reason I studied mechanical engineering, not realizing that software would be a software world that we live in. I did a video with Amy Lewis and she's like, "what was your software-defined moments?" I'm like, "gosh, I'm the frog sitting in the pot, and, would love to, if I can't network-diagram it, or put these things together, networking guy, it's my background! So, the software world, but it is a real renaissance in hardware these days. Everything from the FPGAs you mentioned, you look at NVIDIA and all of their partners, and the competitors there. Anything you geeking out on the hardware side? >> I, yeah, a lot of the stuff, I mean, the era of GPU showed up in a big way, all right? We have NVIDIA to thank for that whole, I mean, the kudos to them for developing a software ecosystem alongside a hardware. I think that's really what sold that and made that work. >> Well, you know, you have to be able to solve that Bitcoin mining problem, so. >> Well, you know, depending on which cryptocurrency you did, EMD kind of snuck in there with their stuff and they did some of that stuff better. But you have that kind of competing architecture stuff, which is always good, competition you want. I think now that what we're seeing is that specific workloads now benefit from different styles of compute. And so you have the companies like Graphcore, or the chip that was just launched out of China this past week that's configurable to any type of network, enteral network underneath the covers. You see that kind of evolution in capability now, where general purpose is good, but now you start to go into reconfigurable elements so, I'll, FPGAs are some of these more advanced chips. The neuromorphic hardware, which is always, given my background in psychology, is always interesting to me, so anything that is biomorphic or neuromorphic to me is pinging around up here like, "oh, you're going to emulate the brain?" And Intel's done stuff, BraincChip's done stuff, Netspace, it's amazing. I just, the workloads that are coming along the way, I think are starting to demand different types or more effectiveness within that hardware now, so you're starting to see a lot of interesting developments, IPUs, TPUs, Teslas getting into the inferencing bit now, with their own hardware, so you see a lot of effort and energy being poured in there. Again, there's not going to be one ring to rule them all, to cop Tolkien there for a moment, but there's going to be, I think you're going to start to see the disparation of workloads into those specific hardware platforms. Again, software, it's going to start to drive the applications for how you see these things going, and it's going to be the people that can service the most amount of platforms, or the most amount of capability from a single platform even, I think are the people who are going to come out ahead. And whether it'll be us or any of our August competitors, it remains to be seen, but we want to be in that space we want to be playing hard in that space as well. >> All right Dave, last thing I want to ask you about is just career. So, it's interesting, at Vmworld, I kind of look at it in like, "wow, I'm actually, I'm sitting at a panel for Opening Acts, which is done by the VMunderground people the Sunday, day before VMworld really starts, talking about jobs and there's actually three panels, you know, careers, and financial, and some of those things, >> I'm going to be there, so come on by, >> Maybe I should join startin' at 1 o'clock Monday evening, I'm actually participating in a career cafe, talking about people and everything like that, so all that stuff's online if you want to check it out, but you know, right, you said psychology is what you studied but you worked in engineering, you were a systems engineer, and now you do messaging. The hardcore techies, there's always that boundary between the techies and the marketings, but I think it's obvious to our audience when they hear you geeking out on the TPUs and all the things there that you are not just, you're quite knowledgeable when it comes about the technology, and the good technical marketers I find tend to come from that kind of background, but give us a little bit, looking back at where you've been and where you're going, and some of those dynamics. >> Yeah, I was blessed from a really young age with a father who really loved technology. We were building PCs, like back in the eighties, right, when that was a thing, you know, "I built my AMD 386 DX box" >> Have you watched the AMC show, "Halt and Catch Fire," when that was on? >> Yeah, yeah, yeah, so there was that kind of, always interesting to me, and I, with the way my mind works, I can't code to save my life, that's my brother's gift, not mine. But being able to kind of assemble things in my head was kind of always something that stuck in the back. So going through college, I worked as a lab resident as well, working in computer labs and doing that stuff. It's just been, it's been a passion, right? I had the education, was very, you know, that was my family, was very hard on the education stuff. You're going to do this. But being able to follow that passion, a lot of things fell into place with that, it's been a huge blessing. But even in grad school when I was getting my Masters in clinical counseling, I ran my own consulting business as well, just buying and selling hardware. And a lot of what I've done is just I read and ask a ton of questions. I'm out on Twitter, I'm not the brightest bulb in the, of the bunch, but I've learned to ask a lot of questions and the amount of community support in that has gotten me a lot of where I am as well. But yeah, being able to come out on this side, marketing is, like you're saying, it's kind of an anathema to the technical guys, "oh those are the guys that kind of shine the, shine the turd, so to speak," right? But being able to come in and being able to kind of influence the way and make sure that we're technically sound in what we're saying, but you have to translate some of the harder stuff, the more hardcore engineering terms into layman's terms, because not everybody's going to approach that. A CIO with a double E, or an MS in electrical engineering are going on down that road are very few and far between. A lot of these folks have grown up or developed their careers in understanding things, but being able to kind of go in and translate through that, it's been a huge blessing, it's nice. But always following the areas where, networking for me was never a strong point, but jumping in, going, "hey, I'm here to learn," and being willing to learn has been one of the biggest, biggest things I think that's kind of reinforced that career process. >> Yeah, definitely Dave, that intellectual curiosity is something that serves anyone in the tech industry quite well, 'cause, you know, nobody is going to be an expert on everything, and I've spoken to some of the brightest people in the industry, and even they realize nobody can keep up with all of it, so that being able to ask questions, participate, and Dave, thank you so much for helping me, come have this conversation, great as always to have a chat. >> Ah, great to be here Stu, thanks. >> Alright, so be sure to check out the theCUBE.net, which is where all of our content always is, what shows we will be at, all the history of where we've been. This studio is actually in Marlborough, Massachusetts, so not too far outside of Boston, right on the 495 loop, we're going to be doing lot more videos here, myself and Dave Vellante are located here, we have a good team here, so look for more content out of here, and of course our big studio out of Palo Alto, California. So if we can be of help, please feel free to reach out, I'm Stu Miniman, and as always, thanks for watching theCUBE. (upbeat electronic music)

>> Live from Las Vegas, it's theCUBE covering AWS re:Invent 2018, brought to you by Amazon Web Services, Intel, and their ecosystem partners. (techy music) >> Hey, welcome back, everybody. Jeff Frick here with theCUBE, we're at AWS re:Invent 2018 in Las Vegas, day two of four days of coverage. I think we'll do 120 interviews. I mean, this is the most poppin' show in tech right now. We're really excited to be here, and joined by my cohost, Lauren Cooney. Lauren, great to see you. >> Thank you. Great to see you, too. >> And we've got... (chuckling) We've got our next guest, it's Disha Chopra, she's a senior manager, product line manager for Juniper Networks, welcome. >> Thank you, feels great to be here. >> Good. >> So, what do you think of this show, have you been to re:Invent before? >> Oh, my God, no, this is my first one, and I am so excited. The energy is so great, it's vibrant, I'm learning a lot, I'm very happy to be here. >> So, Juniper's been around for a long time, way predating this cloud, this whole cloud thing, so what are you guys up to, what's the latest, and really, why are you here at re:Invent? What's your story with AWS? >> Yeah, absolutely. So, I think the latest thing with us is as early as today there was... We were posted on the AWS partner solution website. Vodafone is partnering with Juniper for their SD-WAN offering with, you know, the SD-WAN controller that's sitting in AWS, managing all their branch offices, so that's what's the newest with us, and you know, we've been making waves with a lot of partnerships recently. Couple of months ago, or maybe just a month ago, we announced with Nutanix, so that announcement was focused more for our enterprise customers. Integration with Nutanix is a hyperconverged infrastructure where Juniper will be, you know, integral part of their networking, providing for their converged infrastructure, and then before that, I think a few months ago we had Red Hat. We announced a partnership with Red Hat, and you know, that's focused on our telco cloud. So, as you were mentioning, Juniper's been around for a long time-- >> Right. >> And you know, telco clouds are our strong suite. Telcos, now telco cloud, right, and similarly for enterprise. If you think about it, you know, large enterprises and telcos, they're not that different, right? So, that's where we were at, and that's more kind of... We're following the evolution like our customers are, right? They used to be telco, now they're telco cloud. Juniper, I think the newest thing with Juniper, to be honest, in technology I spoke about partnerships, but it's our cloud-first strategy. That's what we have in mind. We are evolving with our customers, helping them in their journey for cloud adoption, cloud migration, right? It's a couple of sentences to say that, "Oh, we're helping our customers with cloud migration," but we're, you know, there's so many steps in between. They are very complex, you need a lot of handholding, and we're right there for our customers. >> So, what does that actually mean when you are, you know, saying that you're helping your customers? Are you working with them to bring them multicloud solutions from AWS and Microsoft and Google, or you know-- >> Correct, exactly. >> Can you give me a scenario or a use case? >> Yeah, absolutely, so like I was saying, traditionally, Juniper was, you know, a hardware-focused company, so our existing customer base, they bought a lot of big, heavy boxes from us, and of course, on top of it came a world class routing and switching software component, right, and it was all bundled up and sold together. Now, you know, they're moving towards the cloud, towards AWS, towards GCP, towards Azure. We want to be able to provide to them, and who better to provide this service to them. We understand their on-prem network. We understand cloud networking. We understand the transport in between. So, what we're doing is for our customers we manage their existing on-prem network, which you know, a lot of our customers, you know, they're huge and they have a significant amount of footprint, global footprint, right, so we understand that, we're able to connect them to the AWS, to the GCP, to the Azure, right, and the value proposition for them is that if they wanted to do it themselves they have to understand, you know, three different or five different clouds, right. You have IBM, you have DigitalOcean. There's a lot out there, right, and getting the opecs or getting the talent to be able to understand all these things and do the migration, it's hard, right? This is a complex problem to solve, so what Juniper brings to the table is we abstract it out. So, for example, I wanted to move-- >> Yeah, well I just want to say, you know, one of the things that you're talking about here, and this is a total switch, if I'm right, is are you becoming a managed service provider? >> We do have a managed service-- >> Because it sounds like you're going to be putting a lot more money into that side of the business-- >> Correct. >> Versus the straight-up product side of the business. >> Yeah, yeah, that's where we are pivoting from, you know, we want... Our perception used to be that we're a hardware company, now we're a cloud-first company. We're a software company, so we're definitely pivoting towards the, you know software-based solutions, software-based, you know, offerings. It's like your iPhone, right, or your phone. You buy the hardware but you're really buying it for the iOS or for the applications that run on it. Networking is following a similar paradigm now, right? The hardware boxes, they're definitely our bread and butter still, but it's the software now that's enabling and giving it all the cool factor and the innovation that's happening, it's all in the software. Contrail, that's our story for multicloud. That's one of our product offerings. So, what Contrail does is, and I think that's what I was kind of referring to earlier, it gives you that higher level of abstraction where you don't have to worry about: "Is my workload running in AWS? "Is my workload running in GCP?" It doesn't matter, right, you as a enterprise, or as a telco, we want you to focus on, you know, powering your applications, powering your services. We don't want you to worry about your infrastructure, that's our job, right? We want to completely hide all the complexity away from you, and just, you know, let you do what generates revenue. >> So, as an application developer, right, so I'm an application developer and I use Azure, for example, right-- >> Yeah. >> And that's kind of my platform, and I'm, you know, doing some interesting stuff with like, you know, some scripting, or I'm building, you know, just a general, like, new website or something like that with, you know, a couple different things. So, as a developer at that level, I don't even know about Contrail. >> Exactly, exactly. >> Exactly, but I don't think Contrail yet extends up to that layer where it can manage everything across multiple clouds. >> So, it provides you as a developer, like you said, you're writing an application, you don't care about the infrastructure. It's just there, right? >> Mm-hm. >> And we want to keep it that way. Contrail is there, Contrail is at that level. Contrail is going to provide the plumbing, so you as a developer, today everything, all developers are moving towards containers, right? So, for example, the Red Hat partnership that I brought up earlier, that's focused on the Red Hat OpenShift platform, their path service, which is a container-based service. Contrail integrates with Kubernetes, we integrate with Mesos, we integrate with Docker. So, as a developer, when you employ these tools to write your code, you know, using a CICD platform, Contrail is sitting right under it, giving you that connectivity. So, for example, when you're developing your application and (clearing throat) you know, you deploy it, you deploy part of it in Azure, you deploy part of it in AWS, right, and you don't care where it goes, you just-- >> Or you use one for, like, bursting or something like that. >> Exactly, yeah, yeah. >> You know, the rest of it on-prem. >> Correct, so-- >> That sort of thing. >> You know, it's distributed, right? So, who's going to plumb it and make sure that it's giving you the results that you need? That's where Contrail comes in. Gives you that plumbing between on-prem, between AWS. >> So, how is that different from Kubernetes as a whole? Like, I know that it's, you know, it does like container management, orchestration, deployment-- >> Correct. >> Delivery, how does-- >> Right. >> Contrail kind of come in and work with Kubernetes? >> Right. So, great question, by the way, you know your stuff, so (laughing) Kubernetes is... Kubernetes is orchestration for your workloads, right? It's services, Kubernetes provides a service, like it gives you a service web. You deploy a bunch of Kubernetes minions, they all work together to give you that application that you need. Now, what Contrail does is it provides the networking between those Kubernetes pods. So, let's say you want to scale up your application. Okay, you had 10 pods, now you want to go to 20. Kubernetes makes that decision for you that you need the 20 pods, and then Contrail is sitting under it giving you the networking for those 20 pods. So, when those 20 pods spin up, Kubernetes pokes Contrail and says, "Hey, 20 more, and these need to talk to "those 10 pods that were already there," right? >> So, Contrail is opensource, right? >> Correct. >> Why haven't you donated it yet to the CNCF? >> (chuckling) We are part of CNCF, we recently-- >> I know that. >> Yeah. >> But fundamentally, if you want that to be pulled as much as you do... >> Yeah. >> It's already opensource. >> Yeah, you're right. >> You might as well kind of get on that thread with the Kubernetes folks-- >> Right, yeah. >> And start talking to them about how you make it part of, you know, the core distribution that then goes into, you know, six different distro. >> Correct, correct, yeah. >> You know, something along those lines versus don't start your own distro. (chuckling) >> Sorry. >> Right, don't start your own distro, but look at how you can become integrated into that Kubernetes stream, the main stream. >> Correct, yeah, yeah, yeah, exactly. Yeah, no, that is definitely something that, like you're saying, it's something that we, you know, we want to do, that's the direction that we want to go at, but I think the actual decision is maybe above my pay grade, so I don't (chuckling) want to make a commitment here. >> Fair enough. >> So, you know... (chuckling) >> Disha, I want to followup on a slightly different track. When you talk about cloud-first, and you answered the question, which is when you say cloud-first, is that, you know, kind of the way you're going to market with your customers, or is that the way you guys are looking at Juniper in terms of transforming the company? >> Mm-hm. >> And it sounds like you said it's more of the latter, really starting to reformulate Juniper-- >> Correct. >> As a cloud first service company. >> Exactly. >> So, how is that transformation going inside the company, that's a pretty significant-- >> It is, it is, yeah. >> Shift from selling boxes and maintenance agreements and-- >> Yeah. >> Shipping metal. >> Yeah, we are definitely modernizing from within, right, but a lot of it is driven by our customers. Like I was saying, you know, they are evolving, they want to connect to the cloud, and you know, we obviously want to help them do that. As part of that, we want to be microservices-based, right, because we want to be able to support containers. These are just things that, you know, we need to do. Juniper is a leader as far as, you know, innovation and networking is concerned. >> Right, right. >> So, it was never a question of if we want to do this, or if we want to go down this path or not, right, it's when, right? >> Right, right. >> And we are definitely working day in and day out to make that happen, so you know, a lot of our offerings, like recently we came out with our containerized SRX solution. SRX is our full-feature, full-service, next generation firewall, and we have containerized it, right. I believe it's the first offering of its kind, containerized, host-based firewall, so you know, innovative stuff happening all the time. Like you said, you know, it's definitely a Herculean task-- >> Right, right. >> But we're up for it-- >> Right. >> And we're doing it. >> And I'm just curious to when the customer conversations-- >> Yeah. >> You know, the hybrid cloud, multicloud, public cloud conversation, right, it's a lot of conversation. How do you take your customers down the path? Where do you see them, you know, trying to navigate in what's got to be a pretty complex world for-- >> It is, definitely. >> A CIO trying to figure out what they're supposed to buy and not buy, how to pay attention, can I hit all the booths-- >> Right, right, right, right. >> Here at AWS in three days, I don't think so. >> (laughing) I know, yeah, these conversations, to be honest, have been going for the past couple of years, right. A lot of our customers, the intent is there to move to the cloud, and you know, we are trying to help them with it, so you know, we design with them. We design their network, we design their topologies, we handhold them telling them how to do this, right, their existing networks that they have. The complexity comes in because everything, right, think of a company, right, a large company. It then goes ahead and acquires 10 more, and they all have their own networks, they all have their own environments, VMware, Red Hat, you know, Tabix, so different kinds of environments now all need to connect to the cloud. You don't want them to be siloed. You also don't want to deal with, you know, all those different kinds of, like I was saying, you know, skillset to be able to connect them all individually. So, when we talk to our customers, that's what we tell them, that you know, with a Juniper-based solution we have so many of them that work together in a cohesive way to give you that end-to-end connectivity. Secure, automated multicloud, that's our mantra, right, and it's as far as, you know, engineering is concerned, engineering simplicity. If you come down to Juniper it's plastered all over the walls, right, engineering simplicity. We were really driving that message internally so that... And a lot of the CICD stuff, right? The way we want our customers to use it is how we're using it, so that, you know, that improves our quality, that improves reliability, and all those things. So, in terms of handling our customers, we talk, you know, we're there on the table day one. We talk to them about their design. I see that a lot of our customers, currently where they're at is they are trying to connect to the cloud. They all want to move towards the container, you know, the containerized services. They know that's the right thing to do. They're not quite there yet, right? The intent is definitely there, they're playing with it, but in terms of being in production, we're still, you know, a little bit off. Not too much, but we'll get there soon, right. So, we talk to them, we talk about, you know, how they can make their applications cloud ready. There's a couple of ways to do it. You lift and shift, or you know, directly move, go cloud native. >> Right, right. >> So, we have all these discussions with them. You know, what fits their bill, right? What is good for them, what is it that's going to work for them? And then, you know, of course the connectivity piece, right, but with it security, reliability, and scale. Right, a company like Juniper obviously, you know, innovator in networking, we solve problems at a different level, right? >> Right, right. >> For our much larger customers. So, we talk to them about scale, we talk to them about, you know, reliable security is huge, right. You have a workload that you spun up on-prem, and then, now, you know, you have... Your requirements have changed, you're going to have to replicate it, say, in AWS. When you replicate it, you still want the same security that you had on-prem to apply to this workload, which is now going to be in AWS, how do you do that? It's easy with Contrail, right, because it's intent-driven. You specify the intent, in fact, you specified the intent when you brought up the first workload, and it captured it, "Okay, I'm supposed to talk to..." You know, say I'm workload red and I can only talk to other red workloads and I cannot talk to the blue workloads, something like that, right? >> Right, right. >> So, you specify the intent, and then when that red workload now comes up in AWS, it already knows that I wasn't supposed to talk to the green workload, so that policy and all the intent moves with that workload. >> Right, right. >> And this is all done through Contrail, right, and the other thing, that single pane of glass. I'm sure you've heard about it a lot today, right. The single pane of glass, you specify it one time. Again, the abstraction away from all those, you know, five clouds that you're working with, you specify the red workload, the policy for the red workload one time, and then it doesn't matter where you bring it up, Contrail will automatically apply it everywhere, and you know, it's good to go. >> That's great. >> Well, Disha, thanks for coming on, you certainly got the energy to attack this big problem, so... (laughing) Juniper's fortunate to have you. >> Great, thank you for having me. >> Thanks for coming on and sharing the story. >> It's been wonderful talking to you guys. >> All right, Disha, she's Lauren, I'm Jeff. You're watching theCUBE, we're at AWS re:Invent 2018. Come on down, we're in the main expo hall right by the center, thanks for watching. (techy music)

>> From Sand Hill Road, in the heart of Silicon Valley, it's theCUBE! Presenting the People First Network, insights from entrepreneurs and tech leaders. >> Hello everyone, I'm John Furrier with theCUBE. We are here on Sand Hill Road at Mayfield's Venture Capital Headquarters for the People First Network. I'm here with Pradeep Sindhu, who's the co-founder of Juniper Networks and now the co-founder and CEO of Fungible. Thanks for joining me on this special conversation for the People First Program. >> Thank you, John. >> So I want to talk to you about entrepreneurship. You're doing a new startup, you've been so successful as an entrepreneur over the years, uh you keep building a great company at Juniper Networks, everyone kind of knows the success there, great success. We've interviewed you before on that, but now you got a new startup! >> I do. >> You're building a company I thought startups were for young people. (Pradeep laughs) Come on! We're nine years into our startup, we're still a startup. >> Well, I'm not quite over the hill yet. (John Laughs) One of the reasons I jumped back in to the startup world was I saw an opportunity to solve a very important industry problem and to do it rapidly and so, I took the step. >> Well, we're super excited that you shared your vision with us and folks can check that video out on theCUBE and deep dive on the future of that startup. So, it's exciting, check it out. Entrepreneurship has changed and one of the things that we're talking about here is how things have changed just since the last time you've done a round. I mean, you're now a couple years in, you've been stealth for a while building out this amazing chip, the the Data Processing Unit, the DPU. What's different about building companies now? I mean, are you a unicorn? You have a billion-dollar evaluation yet? I mean, that's the new bar, it's different. What are some of the differences now in building a company? >> You know, one thing, John, that I saw is a clear difference between when I started Juniper and started Fungible, is that the amount of bureaucracy and paperwork that one has to go through is tremendously larger. And this was disappointing because one of the things that the US does very well is to keep it light and keep it fast so that it's easy for people to create new companies. That was one difference. The other difference that I saw was actually reluctance on the part of Venture to take big bets. Because people had gotten used to the idea of a quick turn around with maybe a social media company or something. Now, you know, my tendency to work on problems is I tend to work on fundamental problems that take time to do, but the outcome is potentially large. So, I'm attracted to that kind of problem. And so, the number of VCs that were willing to look at those kinds of problems were far fewer this time around than last time. >> So you got some no's then? >> Of course, I got no's. Even from people that-- >> You're the Founder of Juniper Networks, you've done amazing things, like you created billions of dollars of value, you should be gold-plated. >> What you did 20 years ago only goes so far. I think what what people were reluctant, and remember, I started Fungible in 2015. At that time, silicon was still a dirty word. I think now there are several people who said, no, we're regretting because they see that it's kind of the second coming of silicon and it's for reasons that we have talked about in the other discussion that, you know, Moore's Law is coming to a close. And that the largest that it was distributing over the last 30, 40 years is going away so what we have to do is we have to innovate on silicon. You know, as we discussed, the world has only seen a few architectures for computing engines on silicon. One of the things that makes me very happy is that now people are going to apply their creativity to painting on this canvas. >> So, silicon's got some new life blood. What's your angle with your silicon strategy? >> So, our silicon strategy is really to focus on one aspect of computations in the data center and this aspect we call Data Centric Computing. Data Centric Computing is really computing where there's a lot more movement of data and lot less arithmetic on data. And today, giving scaled out architectures, data has to move and be stored and retrieved and so on as much as it has to be computed on. So, existing engines are not very good at doing these Data Centric Computations, so we are building a programmable DPU to actually do those computations much, much better than any engine can today. >> And that's great. And just a reminder, we got a deep dive on that topic, so check out the video on that. So, I got to ask you the question, why are people resistant at the silicon trend? Was it trendy? Was it the lack of information? You almost see people almost less informed on computer architecture these days as people Blitzscale for SASPA businesses. Cloud certainly is great for that , but there's now this renaissance. Why was it, what was the problem? >> I think the problem is very easy to identify. Building silicon is expensive. It takes very specialized set of skills. It takes a lot of money, and it takes time. Well, anything that takes a long time is risky. And Venture, while it likes risk, it tries to minimize it. So, it's completely understandable to me that, you know, people don't want to take, they don't want to put money in ventures that might take two, three years. Actually, you know, going back to the Juniper era, there are Venture folks, I won't name them, but who said, well, if you could do this thing in six months, we're in, but otherwise no. >> How long did it take? >> 2 1/2 years. >> And then the rest is history. >> Yeah. >> So, there's a lot of naysayers, it's just categorical kind of like, you know, courses for horses for courses, as they say, that expression. All right, so now with with your experience, okay, you got some no's, how did that, how did that make you feel? You're like, damn, I got to get out and do the rounds? >> Actually-- >> You just kind of moved on or? >> I just moved on because, you know, the fact that I did Juniper should not give me any special treatment. It should be the quality of the idea that I've come up with. And so, what I tried to do, my response was to make the idea more compelling, sharpen it further, and and try to convince people that, hey there was value here. I think that I've not been often wrong about predicting things maybe two, three years out, so on the basis of that people were willing to give me that credibility, and so, there were enough people who were interested in investing. >> What did you learn in the process? What was the one thing that you sharpened pretty quickly? Was it the story, was it the architecture message? What was the main thing that you just had to sharpen really fast? >> The thing I had to sharpen really fast was while the technology we were developing is disruptive, customers really, really care, they don't want to be disrupted. They actually want the insertion to be smooth. And so, this is the piece that we had to sharpen. Anytime you have a new technology, you have to think about, well, how can I make it easy for people to use? This is very, very important. >> So the impact to the architecture itself, if it was deployed in the use case, and then look at the impact of ripple effect. >> For example, you cannot require people to change their applications. That's a no-no. Nobody's going to rewrite their software. You also probably don't want to ask people to change their network architecture. You don't want to ask people to change their deployment model. So, there are certain things that need to be held constant. So, that was a very quick learning. >> So, one of the other things that we've been talking about with other entrepreneurs is okay, the durability of the company. You're going down, playing the long game, but also innovation and and attracting people and so you've done, built companies before, as with Juniper, and you've worked with a great team of people in your network. How did you attract people for this? Obviously, they probably were attracted on the merit of the idea, but how do you pick people? What's the algorithm? What's the method that you use to choose team members or partners? Because that's also super important. If you got a gestation period where you're building out. You got to have high quality DNA. How do you make that choice? What's the thought process? >> So John, the the only algorithm that I know works is to look for people that are either known to you directly or known to somebody that you trust because in an interview, it's a hit or miss. At least, I'm not so good at interviewing that I can have a 70, 80% success rate. Because people can fake it in an interview, but you cannot fake it once you've worked with somebody, so that's one very important test. The other one was, it was very important for me to have people who were collaborative. It is possible to find lots of people who are very smart but they are not collaborative. And in an endeavor like the one we're doing, collaboration is very important, and of course the base skill set is very important so, you know, almost half of our team is software because we are-- >> It's a programmable chip. >> It's a programmable chip. We're writing our own operating system, very lightweight. So, you need that combination of hardware and software skills which is getting more and more scarce regrettably. >> I had a chat with Andy Bechtolsheim at VMworld and he and I had a great conversation similar to this, he said, you know, hardware is hard, software is easier, (laughs) and that was his point, and he also was saying that with merchant silicon, it's the software that's key. >> It is absolutely the key. Software, you know, software is always important. But software doesn't run on air. We should also remember that. And there are certain problems, for example, switching packets inside a data center where the problem is reasonably well-solved by merchant silicon. But there are other problems for which there is no merchant silicon solution, like the DPU that we're talking about. Eventually, there might be. But today there isn't. So, I think Apple is a great example for me of a company that understands the value of software hardware integration. Everybody thinks of Apple as a software only company. They have thousands of silicon engineers, thousands. If you look at your Apple Watch, there are probably some 20 chips inside it. You look at the iPhone. It won't do the magic that it does without the silicon team that they have. They don't talk about it a lot on purpose because-- >> 'Cause they don't want a China chip in there. >> Well, they don't want a China chip, but not only that, they don't know to advertise. It's part of their core value. >> Yeah. >> And so, as long as people keep believing that everything can be done in software, that's good for Apple. >> So, this is the trend, and this is why, Larry also brought this up years ago when he was talking about Oracle. He tried to make the play that Oracle would be the iPhone of the data center. >> Mm-hmm. >> Which people poo-pooed and they're still struggling with that idea, but he was pointing out the benefit of the iPhone, how they are integrating into the hardware and managing what Steve Jobs always wanted which was security number one >> Absolutely. >> for the customer. >> And seamlessness of use. And the reason the iPhone actually works as well as it does is because the hardware and the software are co-designed. And the reason it delivers the value that it does to the company is because of those things. >> So you see, this as a big trend, now you see that hardware and software will work together. You see cloud native heterogeneous almost server-less environments abstracted away with software and other components, fabric and specialized processors? >> Yes. >> And just application developers just programming at will? >> Correct, and edge data centers, so computing, I would say that maybe in a decade we will see roughly half of the computing and storage being done closer to the edge and the remaining half being done in these massively skilled data centers. >> I want to get geeky with you for a second, I want to ask you a question, I want to get your take on something. I've been thinking about and haven't really talked publicly about, kind of said on theCUBE a few times in a couple interviews, but I want to get your thoughts. There's been a big discussion about hybrid cloud, private cloud, multi-cloud, all that stuff going on, and I was talking with Andy Jassy, the CEO of Amazon, and Diane Greene at Google and I'm like okay, I can buy all these definitions, I don't believe any of 'em, but, you know, what the hell does that mean, what I know. I said to Diane Greene, I said, well, if everyone's going cloud operations, if cloud operations and edge is the new paradigm, isn't the data center just a big fat edge? And she looked at me and said, hmm, interesting. So, is the data center ultimately just a device on this network? If the operating model is horizontally scalable, isn't it just a a big fat edge? >> So you can, so here's the thing, right, if we talk about, you know, what is cloud? It's essentially a particular architecture, which is scaled out architecture uh to build a data center and then having this data center be connected by a very fast network. To consumers anytime, anywhere. So, let's take that as the definition of cloud. Well, if that's the definition of cloud, now you're talking about what kind of data centers will be present over time, and I think what we observed was it's really important for many applications to come, and with the advent of 5G, with the advent of things like augmented reality, now, with the advent of self-driving cars, a lot of computing needs to be done close to the edge because it cannot be done, because of laws of physics reasons, it cannot be done far away. So, once you have this idea that you also have small scale out data centers close to the edge, all these arguments about whether it's a hybrid cloud or this cloud or that cloud, they kind of vanish because-- >> So, you agree then, it's kind of like an edge? >> It is. >> Because it's an operational philosophy if you're running it that way, then it's just what it is, it's a scale out entity. >> Correct. >> It could be a small sensor network or it could be a data center. >> Correct. So, the key is actually the operational model and the idea of using scaled out design principles, which is don't try to build 50,000 different types of widgets which are then hard to manage. Try to build a small set of things, tinker toys that you can connect together in different ways. Make it easy to manage, manage it using software, which is then centralized by itself. >> That's a great point. You you jumped the gun on me on this one. I was going to ask you that next question. As an entrepreneur who's looking at this new architecture you just mentioned, what advice would you give them? How should they attack this market? 'Cause the old way was you get a PowerPoint, you show a presentations of the VCs, they give you some money, you provision some hardware, you go on next generation, get a prototype, it's up and running, you got some users. Built it then you get some cash, you scale it (laughs). Now with this new architecture, what's the strategy of the eager entrepreneur who wants to create a valuable opportunity with this new architecture. What would you advise them? >> So I, you know, I think it really depends on what is the underlying technology that you have for your startup. There's going to be lots and lots of opportunities. >> Oh don't fight the trend, which is, the headwind would be, don't compete against the scale out. Ride that wave, right? >> Yeah, people who are competing against scale out by building large scale monolithic machines, I think they're going to have difficulty, there's fundamental difficulties there. So, don't fight the trend. There's plenty of opportunities for software. Plenty of opportunities for software. But it's not the vertical software stack that you have to go through five or six different levels before you get to doing the real work. It's more a horizontal stack, it's a more agile stack. So, if it's a software company, you can actually build prototypes very quickly today. Maybe on AWS, maybe on Google Cloud, maybe on Microsoft. >> So, maybe the marketing campaign for your company, or maybe the trend might that's emerging is data first companies. We heard cloud mobile first, cloud first, data first. >> Correct. We think that the world really, the world of infrastructure is going from compute centric to data centric. This is absolutely the case. So, data first companies, yes. >> All right, so final question for you, as someone who's had a lot of experience in building public company, multi-billions of dollars of value, embarking on a big idea that that we like, I love the idea. A lot of people struggle with the entrepreneurial equation of how to leverage their board, how to leverage their investors and advisors and service providers. What would you share to the folks watching that are out there that have struggled? Some think, oh the VCs, they don't add value. Some do, some don't. There's always missed reactions. There's different, different types out there. Some do, some don't. But in general, it's about leveraging the resources and the people involved. How should entrepreneurs leverage their advisors, their board, their investors? >> I think it's very important for an entrepreneur to look for complementarity. It's very easy to want to find people that think like you do. If you just find people that think like you do, you're not, they're not going to find weaknesses in your arguments. It's more difficult, but if you look to entrepreneurs to provide complementarity, you look to advisors to provide the complementarity, look to customers to give you feedback, that's how you build value. >> Pradeep, thanks so much for sharing the insight, a lot of opportunities. Thanks for sharing here on-- >> Thank you, John. >> The People Network. I'm John Furrier at Mayfield on Sand Hill Road for theCUBE's coverage of the People First Network series, part of Mayfield's 50th Anniversary. Thanks for watching. (upbeat music)

[Music] hi I'm Stu Mittleman and welcome to a cube conversation really excited to have to the program a first-time guest and a user Vinny Chopra is an IT engineer with Medallia Vinny thank you so much for joining us thank you and - Vinny's left we have Krishnan bad Rena Ryan in who's a director of product marketing with Nutanix Chris thanks so much for you here okay so we always love to be able to dig in with the customers understand the challenges they're facing Chris let's set the table first I'm very familiar with Nutanix we go to all the new tannic shows and the like but for customers what is Nutanix to them why do they turn to Nutanix okay absolutely so I think it's a great time to be in IT you see new businesses that are sprouting at all the last 10 years or so starting with uber Airbnb specifically the ones we've really heard of that have disrupted some really really big industries right so technology is making it happen while IT teams are the ones that help make that happen and helps those CEOs disrupt they're not in the best of positions to utilize infrastructure they have today the way it's set up to be able to get more done be more agile and truly serve the needs of the business and help create those competitive differentiation which is why neutronics is here to help our partners within companies such as yourself to be able to be those people to lean in and help CEOs really achieve what they're trying to get that yeah that's great yeah we definitely see it used to be okay IT was a cost center IT you know business would actually ask for something in IT would often be the no or be really slow and do they work with that so Vinnie before we dig into the IDE piece of it tell us a little bit about Medallia the business what's happening what's Sherma Delia's been around for about 15 years now we're located in it we're headquartered in San Mateo we used to be in Palo Alto moved last year we have a brand new building right off 101 a 92 we our analytics company and we and there's a lot of lots of fields in analytics we specialize in an area called CX which stands for customer experience and our goal is to make our customers customers happy which therefore makes our customers happy and we specialize in doing surveys and then especially in designing surveys for different types of companies and then and then we analyze that data you know surveys well Vinny I I find there's very few companies that I talked to whose industries are stagnant or not changing much the analytic space space that we cover heavily you know here here on the cube and with our research it's boy has that changed a lot I mean five years ago we were talking very much about Big Data today you know all the AI ml and and things like that what what give us a little bit about what's it like being in that business you know fast driving your silicon valley-based I have to imagine that the business is going through a lot of changes that put stresses and strains on IT oh definitely so I better the IT industry for many years and IT area different big companies Sun Microsystems Juniper Networks NetApp in the past excite calm which was a search engine way back when before Google days I remember excite you know because Microsoft didn't they buy that or things well there was an early cerulean at home there's a partnership with that on but yeah excited people would confuse as to wait excite calm what kind of site was that it's like no no it's a search engine back before by the way audience for those of you that haven't been around a while it wasn't all just being in Google there were a lot of predecessors that there was four or five big search engines at that time so most of my company had been out we've always been packaging stuff in a box and selling it in this is my first time at an analytics company and it's it's like you said it's a fast-moving field things are being the things there's no development staging production type of stuff things are just continuously being put into production changes are made you know customized you know customer's applications and their interface so it's it's a very fast-moving alright and Vinny you say IT engineers your job what does that encompass what your role how many people in the group what is your sure so we have basically two IT groups we have one that manages our production data centers which are which our customers interface with and we have one that supports our engineers so I'm part of that group and it's kind of a week up art of the IT system and engineering team and that involves traditional IT tasks like backups monitoring application install new server installs managing storage networking basically keeping infrastructure and applications running as efficiently as possible and therefore keeping our engineers happy because they can get their work done and their development done okay sounds like a you know pretty typical from from what I hear from companies is it what do you hear from customers structure-wise challenges they're facing absolutely so it's very much in line with what you were just talking about where there's these multiple needs from the business and customer expectations so how do you really help IT organizations be able to keep up with those needs infrastructure needs to be the big quittez data needs to be Vic witness application services need to be Vic Willis and you need to be able to scale out as your business needs needs to do so to be able to serve all those multiple requirements so whether it's standardizing internal applications that are delivered through virtual desktops or deploying databases are starting up customer websites you need to be able to do that and respond as quickly as possible and if you're spending cycles on acquiring infrastructure deploying it making sure it's well integrated and then once it's up and running figuring out what went wrong and enjoying those multiple nights of pizza right to figure out how to get this thing going back to the way it was it's it just distracts you from what's important so it's only when you make infrastructure invisible and truly scalable very much cloud-like and and make it your own as a process of doing so can you truly be that business partner and you and I hope we've done that with you definitely all right so Bennie let's go inside was there a specific project rollout that you would that led towards Nutanix was there a pain point you were having would give us kind of the before and what was the mature so traditionally an IT you would you want to set up a new application at you in your infrastructure environment you would buy servers and you would buy storage you would buy HBA cards which helps you connect the servers to the storage you've got things like worldwide numbers to worry about getting the right cables getting the right cards and then you put it all together you get all the stuff delivered and then two weeks later you might have things working and but you having some permission issues security issues so it was always a big challenge to get things up and running so it was the fun of ideas let's roll up our sleeves let's turn those geek knobs and you know optimize everything and yeah within six months I'm sure everything's rocking in right everything's rocking rolling but you're still not quite confident that things are running you're worried that a card might go bad you're worried that a world-wide number might change somewhere or somebody might you know mess up your security so you would spend a lot of time just getting things up and running versus spending time on development and you know working with your people you're supporting and trying to try to enhance things versus just keeping things getting things up and running so Nutanix you know with the hyper-converged infrastructure you know what kind of we're not worried about those things anymore it has our storage needs it has our compute needs it has our memory needs so what was it a refresh cycle what was the impetus that led to looking at a new arc sugar as we were growing and entering base was growing an IT was growing and our requests and you know what we need to satisfy was increasing tremendously we before we were working with just individual desks like desktops or blade servers but each one was kind of working individually with its own storage its own applications not the notion things weren't being shared or anything and we were just growing fast so we needed some we need a new infrastructure where we could actually have everything working of most efficiently and be secure and fast and and easy to manage and so we did look at we did some analysis on a few products and Nutanix you know after some a few pocs Nutanix was our product of choice yeah I mean you described something we heard a lot is it used to be every application you would kind of build your own temple for it yeah let me build it let me get the performance I need let me optimize certain things let me forecast how it's gonna grow but I get islands out there as opposed to I want to be able to scale I don't want to worry about you know here's one of the challenges out there most people and across the board forecasting is really hard or impossible I either overestimated a bunch and then I bought stuff I didn't eat her right under missed it estimate it and then oh my gosh I need to look to a new architecture yeah and then things ended up burning like at 10% of you know you utilizing temperature of the resources that you're purchasing yeah I remain poor virtualization it was like you know six seven percent is usually what we were running awesome so challenges before and we had you know silos out there I couldn't share I couldn't do talk about that that role how did you get from that old environment to the new one there's something I said when you you look at this wave of really a distributed architecture in the old world migrations were really really tough yeah and you had to do it with every cycle hopefully moving to an architecture like this this is your last migration it was like you know my wife always said the last time that's the last time I never want to have to move well I T I'm sure those migrations were always painful what was the experience my heading to migrations was is one thing that we went through but also just now it's just setting up new VMs or new applications new servers it's you know within a few minutes versus hours as far as migration we were we were running a hypervisor before but like I said it was on individual servers so the migration was basically picking your VMs or your servers one at a time and just migrating over to Tenex once it was there and you know with the hypervisor tools that are available it's very easy to use it's like things like vmotion or different types of migration tools that Nutanix offers with their hv hypervisor so it was just it was pretty seamless it was just you just pick and choose and identify your destination host ons Nutanix node or Nutanix cluster and all your stories that you want to move it to and just go okay so so Vinnie you went through a bit of a bake-off to figure out the solution tell us when you finish the deployment how are you measuring what does success mean to in deployment of your stand point and give us the after what show does this change for your process your organization sure qualitatively success is when our engineers are smiling and not calling us too much and asking us go to lunch versus telling us about issues they're having so that's qualitatively quantitatively looking at performance CPU memory I ops performance on a storage how our applications responding that that's what we measured it quantitatively yeah did you know like what kind of utilization you're getting on your current infrastructure then with the Nutanix um also currently you meet as far as uh what you said you were lucky to get 10% in the old world do you measure that yeah we met her that week we kind of um you know we have our kind of have our choices of how much storage you want to use how much CPU remember you want to allocate to each VM and we we just monitor it and through the prism interface that Nutanix offers the image you can actually see performance of each VM and you can decide when to throttle things so but as far as you know how much we're utilizing we're you know we have it we have a structured where we have room to grow so yeah absolutely and if we do need to grow later we can easily add nodes or you know chassis wood notes yeah I think back to the early years of you know what we call hyper converge environments and it was like oh well they are monolithic blocks even if they're small and but you don't have flexibility there when I look at you know many of the solutions especially what Nutanix ups there's a lot of flexibility into how I can grow in scale and get the the utilization that I need but get the performance the ops and everything what I think from your customers how is that story play out today yeah I mean ultimately it's all about empowering people right it's about making IT people truly successful broadening their skillset giving them greater control over the full stack if you will right so it's no longer siloed across functions you're no longer found helpless relying on a different team to deliver upon something that was promised based on a certain SLA so how do we do that how do we make evolved functional specialists into IT journalists would then become cloud engineers true cloud engineers right the world is changing technology is adapting businesses are a craving for more and the only way we can keep up is to adapt ourselves and utilize the best of breed technologies that gives us that power so as a result we hear that a lot where we find a lot of a customer's progressing from being either storage admins network specialists but most likely virtualization admins who then become these cloud engineers if you will they reorganize that way they tend to be in a position where they are a lot more infrastructure we're talking about 100x of what they used to do prior in the in the earlier days so the the number of the ratios just grow immensely as well as the quality of service provided the SAS are far reduced as they used to be so all of that goodness that our customers are able to deliver to their state goes in the organization makes us feel good about what we do if any would love we talked about you know this the engineers now they're smiling and going out to further then you know fighting bugs anything complaining about is yeah anything kind of when you look at skill set if they're you know I've talked to some entertainment customer he's like oh you know I had that security project that was sitting on my desk for years I can finally tackle that or there's I can be more responsive to the business so that they don't you know I can engage with them rather than just going off running it and do in stealth IT any anything along those lines that you can share I mean one thing like IT admins we typically want to know everything right so we all know what's happening behind the scenes with Nutanix we don't have to as much but we still like to and so we we take the opportunity to you know do trainings learn what's happening in an interface you support when needed so as far as yeah as far as skills go I think it's you know the skills you keep up with it's just different like Chris mentioned it's different different type of administration like we're managing virtualization or managing cloud you know you're not just managing loans and cables you know I love you sounds like you've got a team that's got that intellectual curiosity wants to understand what's going on how was the how was the on-ramp how was the kind of the cycle to understand the Nutanix piece how did you yeah so we learned a lot of the POC of course that's when you kind of you know you can play around with stuff and break stuff and try to break stuff if you want we use professional we used some freshly served since to help us get set up originally and after that it was just kind of learning day to day and just improving improving our knowledge in different areas like not if we're not used to having everything in one like in you know in one kind of a couple jassi's storage and you know compute so that was a networking as well so that was a little bit not challenged technically but just just you just need to reset the mindset these are the way I used to do things versus the the way now I can't do three and in troubleshooting um you know the great thing is when we have troubleshooting we're not calling three different vendors like a networking company a storage company in a compute company and having them point fingers oh it's networking now we if I ever have an issue or a question I call Nutanix supporting it so if any how long has it been since you the solution was deployed about two and a half years now awesome so it but you first of all I love your viewpoint as to how Nutanix has changed in those two those two years and along those lines too now that you look at things through the lens of 2018 if you could go back to peers of yours what would you tell them now that you wish you had known back when you rolled this out a couple of years ago I would you know how to tell them there's a much easier way to minute you know the deploy and manager infrastructure and you know this is this is one of the new techniques is definitely something you should look at alright Chris what what advice do you give to the IP people of the world that you know I'm sure most of them heard about this but you know what misconceptions might they have what what things do we want to make sure we open the door for sure so as a former developer myself you know several years ago I think it's very easy for us to forget the role we play in our organizations we're not all about the applications we're not all over the speeds and feeds we had a critical core part of how businesses go to market and achieve success right so let us recognize that and use the best approaches that are available out there to be able to deliver that value right if it means going where the good hyper-converged infrastructure solution if it means leaning in and building new disruptive technologies and such that can help your businesses do better the other thing that I want to highlight is just as you are in the the customer service business I believe we are as well we pride ourselves on our support so if you have if ask questions about how hyper-converged infrastructure can add value call us give support a call you would be put in touch with anyone who can speak about all the values we deliver to our customers and begin to get some of those ideas all right Vinnie uh want to ask you you you've got some experience works for some of the you know really well-known companies you not only here in the valley but in tech in general what's exciting you these days what do you look at either in the analytic space or an IT that that's getting you excited for me it's I like to get up without stress and so ease of management ease of deployment in the IT area is very that that's one of things I look forward to like you know being able to do other stuff than just focusing on data you know routine stuff yeah and one of those lines if I could give you you know the one wish to help make that goal even more either from Nutanix or you know the broad ecosystem out there what would what would make your job even easier you know it's it's I don't know I'm trying to think of a good answer but it's typically you know when issues once them all we have application issues it would just be some kind of self-healing type things you know maybe or maybe some automatic adjustments that could be done that maybe something in the future yeah like I just means as far as resources allocated to different types of yeah all right Chris sure I'll let you have the final word there cuz absolutely once we simplify modernize the platform modernizing the application some it's definitely something I've heard from many of your customers as to you know that role of infrastructure really is to serve up and support those applications and that seems to be where it's going that's right that's right the the business partners right partners the business CFO whoever on the other side of the fence they care about applications and services not so much about all the blood sweat and tears we put into the infrastructure so I think it's an opportunity for us to help us elevate beyond the infrastructure and focus on apps and services along with making sure we have some of those self-healing capabilities such that take care of us and not require us to pay heat to all those infrastructure speeds and feeds so it's a great opportunity to do and you know be truly strategic in the company right alright well Chris really appreciate you sharing the updates Vinny really appreciate you sharing your customer story it's our purpose here at the cube to always help bring out the information so make sure to check out the cube net if you actually go to the top there's a search we've got over five or six thousand interviews we've done including many customers including many of Nutanix go in search Nutanix you'll find a plethora of content out there if you ever have any question for us please reach out to us see us at any of the shows or in between so I'm Stu minimun and thanks again for watching the cube thank you