>> Announcer: Live from San Francisco, it's theCUBE. Covering Google Cloud Next '19. Brought to you by Google Cloud and its ecosystem partners. >> Hey welcome back everyone. It's theCUBE's live coverage here in San Francisco in Moscone South. We're on the ground floor here at Google Next, Google's Cloud conference. I'm chatting with Stu Miniman; Dave Vellante's also hosting. He's out there getting stories. Our next two guests: Dana Berg, Chief Operating Officer of SADA and Chris Lehman, Head of Engineering for SADA. Guys, welcome to theCUBE. Thanks for joining us. We're here on the ground floor. >> Thank you. >> Thank you. >> This is exciting. I feel like a movie star right here. >> It's game day here. All the tech athletes are out, Dave. If you look at the show, look at the demographics, hardcore developers, lot of IT, leaders also here, cloud architects, a lot of people trying to figure it out. We heard the keynote. Google is bringing a lot to the table. So what's new with you guys? You guys recently sold your Microsoft business, going all-in on Google. Talk about that relationship. >> We are. This is a brand new day for SADA. The energy around this place, where we are in the market, and where we are with the expanded attendance here has actually reaffirmed our business strategy to go all-in with Google. I don't know if you are aware but SADA has been around for almost 20 years. Historically have always been leaders in bringing people to the cloud even before there was really much of a cloud. We were a you know a pilot partner within Microsoft and Google and had a great thriving Microsoft business but an even bigger Google business and you know, we looked at the tea leaves, we looked at where we wanted to be, and aligned with a company that shared our mission and values and it was a clear choice. We chose Google. We made a very specific and deliberate act to sell off our Microsoft business so that we could take the horsepower of all of our engineering staff and apply them to Google. >> It's interesting you know, we've been around for 10 years doing theCUBE, go to a lot of events, I mean Dave Vellante, Stu, and I have been around for 30 years covering the IT, you guys 20 years. You guys have seen many ways of innovation come and go. Now you're going all in on Google. What is it about this wave right now that made that decision? What do you guys see? You're seeing something early here. Expand on that. Give us some color commentary because there's a wave here, right? A lot of people try. It's a combination of things. I mean, we saw the client-server thing. We saw that movement. Also the internet, we saw the web, mobile, now it's cloud. What's the big wave? What are you guys riding? >> I think there's a couple of things and I think it's unique to, philosophically, how we think of our real special relationship with Google. There is a momentum, right, and not to quote like a Bernie Sanders, but, seems like there's a revolution going on here, right, and, you know, I think, you know, what we see when we look around and we hear conversations and even with our customers, the way that we're all winning together is because we're winning the hearts and minds of the people inside of our customer base that are actually the ones responsible for inventing and the ones responsible for building, so when we're in board rooms and we're selling and along with Google, we're talking with developers, we're talking with designers, we're talking about people that are actually driving the vision for these business applications. We're not always talking to the CIO down like some of our other competitors seems to have only been able to sell that way. We're talking about the people responsible for not only constructing it but maintaining it. So that revolution is there. These folks are bubbling that up and they're seeing the real value inside of Google and what is that value from our point of view, and why did we make such a bold statement just to stick with Google is, and we saw Thomas today echo this, I think there's very few cloud providers that are bold enough to actually lead with the fact that we want our customers to have full choice whether you're using GCP or not. We want to build, architect, and manufacture a product offering that allows you to keep your stuff in your data centers, move your stuff to AWS. That power of choice is really not like what we've never heard anywhere else. >> And then on top of that, too, you got an application renaissance, right? A whole new way of coding, infrastructure that's programmable and going away, I mean if you think about what that does to the existing infrastructures, they can now mix and match and rearchitect everything from scratch and accelerate the app movement. >> Well, that's absolutely true, and a lot of that has to do with the fact that there are managed services in the cloud which makes it dramatically easier to build applications of course, so there's no question about that. Some of the offerings on GCP are particularly attractive for our clients, particularly the managed Kubernetes service. That's where we're seeing perhaps most of the interest that we're seeing, like that's a very common theme. Also the ML stack is an area that our customers are very interested in. >> Chris, can you bring us in some of those customer environments, you know, one of the things you hear, you know, most customers, it's, "I've got my application portfolio." Modernizing that is pretty challenging. There are some things that are kind of easy, some things that take a lot more work, but, you know, migration is one of those things that makes most people that have been in IT a while cringe because there's always the devil in the details and something goes wrong once you've got 95 percent done. What are you seeing, what's working, what's not working, how's the role of data changing, and all of that? >> I think migrations are usually more complex than they at first appear and so even with best intentions thinking that customers can just move their workloads seamlessly to the cloud have actually in practice been more challenging. So some of the areas that we find challenges are around data migration, especially in the context of zero downtime. That's always more difficult than with applications. So that's definitely an area that were we're spending a lot of time working with our customers to deliver. >> Just to add to that, I have to keep reminding myself of the name, but obviously the Anthos announcement today sounds incredibly intriguing as a lower barrier of effort to actually migrate. Our customers have been trying to really absorb and take a hold of Kubernetes and can it containerize methods for a long time. Some are having a harder time doing it than others. I think Anthos promises to make that endeavor much, much easier, and I think about as we leave here this week and we go back and we reeducate our own engineering teams as well as our customers, I think we might see some highly accelerated project timelines go from here down to here. >> And the demo that Jennifer Lynn did was pretty impressive. I mean, running inside of containers, whether it's VMs, and then having service patches on the horizon coming to the table is going to change the implementation delivery piece too in a massive way. I mean, you've got-- >> Oh, absolutely. >> Code, build, run on the cloud side, but this this kind of changes the equation on your end. Can you guys share the insight into that equation, because Google's clearly posturing to be partner friendly. You guys are a big partner now. You're going all-in. This is an interesting dynamic because you can focus on solving customers' problems. All this heavy lifting kind of goes away. Talk about the impact to you as a partner when you look at Anthem, Anthem migrate in particular, some of these migration challenges with containers and Kubernetes seems like it's a perfect storm right now to kind of jump in and do more, faster. >> Yeah. >> Well, it's certainly very interesting. Well, we'll want to take a really hard look at it. I mean, a very, very cool announcement. Moving to containers in the source prior to the migration obviously solves a lot of challenges so for that reason, it's definitely a move forward. >> And I think... You know, we always talk about, in this industry, the acceleration for consumption, but really that's a poor way of saying... Probably what we should be saying is an acceleration of value. So we're constantly in this battle to try and deliver value to our customers faster. That's what our customers want, right, and in essence we see Anthos as being potentially a big game-changer there so that, you know, our CIOs that we're talking with can show to their various stakeholders that they are making very good proactive moves into the cloud at lower-caught barriers of entry, right? >> Yeah. So, you brought up the the ML piece of Google. Wondering if you could help share a little bit on that. When I think back two years ago, you know, data was really at the core of what a lot of what Google was talking about. I was actually surprised not to hear a lot of it on the main stage this morning, but you know, AI, ML, what are you doing, what are your customers doing, does Google have leadership in the space? >> Google certainly has leadership in the space. Our customers, I think, relatively universally, think that their ML stack is the strongest among the competitors, but I think in practice what we're finding is there's a lot more urgency as far as just literal data migrations off of their data centers into the cloud, and I foresee a lot more AI and ML work as more move in. >> John: Yeah. >> So you might, in our booth here, not to give a plug, but we've got a booth down at the end with a full-fledged racing car, just to talk about the art of the possible with AI and ML. Our engineering teams in the race teams that we sponsor, they're there, the driver's there, you should go down and talk to 'em. We've taken all the race telemetry data for the last six months and all of his races and practices, we've aggregated that data all into GCP, run AI and ML algorithms on it to provide his racing team some very predictive ways that he can get better and that team can get better, and so I'd invite just anybody that wants to go there and take a look at, even if you're in banking, or if you're in retail, or if you're in health care, take a look at some of how that was done, because it's a very, very powerful way, to answer your question, head and shoulders down why Google is actually accelerating and exceeding in AI. >> And one of the things that Thomas Kurian showed onstage was the recent Hack-a-Thon they had with the college students with the NCAA data of the game that just finished, and throughout that experience, this is a core theme of GCP, and now Anthos, which is getting data in and using it easily, and scaling at a scale level that seems unprecedented. So this team seems to be the application... The new differentiator. >> I think it is. I think that announcement, obviously the big three takeaways for us, certainly, scale, unmatched. Certainly speed and migration with Anthos. If I could highlight one other, I was incredibly pleased with, well I've been pleased since Thomas' arrival in general by bringing an enterprise class strategy within sight of Google that I think are going to respond well to our enterprise customers, and part of enterprise class is also making sure that their partner community has amazing enhancement programs that really incentivize those partners that are actually in the full managed services space from cradle to grave, lifetime customer value. So we're very excited about even further announcements this week that no doubt have been inspired by Thomas to try and really take advantage of their partner community that are in the business of cradle to grave support of customers. >> You feel comfortable with Thomas. He's taught a lot of customers, he knows the enterprise. >> We've had an opportunity to meet with him. We've had some shared customers that have had a great privilege of getting to know him and support us and collectively them. >> John: He knows the partner equation pretty well, and the enterprise. >> Without a doubt. >> It's about partnering, because there's a monetization, the shared go to markets together. Talk about the importance of that and what's it like to be a partner. >> Yeah, without a doubt, again, you know, his embrace of the open-source community that you saw today, really taking advantage of highlighting partner value is wonderful, but I think Thomas, above anything else, knows that Google needs to scale. They need to scale, and then they have to have breadth and they have to have depth, and, you know, to get to where Google needs to be over the course of the next two, three years, it's wonderful, it's refreshing, it's 100% accurate that Google knows and Thomas knows that the path to do that is via partners; partners that share in Google's vision, that are 100% aligned to the same things that Google is aligned with, and I think that's why I'm so thankful to be at SADA, large in part, because all of the things that we care about in terms of our customer success as well as Google's success, we all share that, so it's a great trifecta. >> It's a ground-floor opportunity. Congratulations. Guys, talk about your business. What's going on? You've got some new offices I heard you opened up. What's going on in the state of the business? Obviously the Google focus you're excited about obviously. >> Yeah, yeah, yeah. >> There, at the beginning, I called Google the dark horse. I think with the tech that they have and the renewed focus on the enterprise, building on what Diane Greene had put foundationally, Thomas is meeting with hundreds of customers. He's so busy he doesn't have time to come on theCUBE, but he'll come on soon, but he's focused. This is now a great opportunity. Talk about your business. What's the state of the union there? Give an update. >> I can take that one if you don't mind. >> Go ahead. >> You can add poetic color if you want. (laughing) Yeah, so as I said, we're entering a new journey for SADA in light of renewed focus, renewed conviction to Google. We are investing more than we ever have into the common belief that Google is the one to beat in terms of momentum, drive, and ultimately winning the hearts and the minds of who we've talked about. So, over the last four months, we've opened five new offices in New York, Austin, Chicago, Denver. Our headquarters is in Los Angeles, and just recently, we just opened a brand new office in Toronto, so we can really help our Canadian customers really see the the same type of white-glove treatment we provide those customers in the States and so that's why, well, I wasn't earlier, but I'm walking around with a Canadian flag. We're very excited about the presence that we're going to have in Canada >> Its "Toronno." I always blow and I call it "Toron-to," being the American that I am. It's "Toronno." >> Dana: Glad you said it right. Good. >> Now, on the engineering side, so you guys are on the front lines as also a sales, development, there's also customer relationship, engineering side, so I'm sure you guys are hiring. There's some hard problems to solve out there. Can you guys share some color commentary on the type of solutions you guys are doing? What's the heavy? What solutions are you solving, problems that you're solving for customers, what are the key things that you got going on? >> Yeah. >> Well, a lot of cloud migrations, a lot of web and application development, custom development, and data pipelines. I'd say those are really the three key focus areas that we're working on at the moment. >> One other thing, too: so... we believe that we want 100% customer retention, always, and that goes above and beyond an implementation. So the other big area of investments that we're making is in a whole revamped technical account management team, so for those of our GCP customers that have had the privilege, we've had the privilege of working with and for, we are building out a team of individuals that will, well beyond the project, stay with that customer, work with them weekly, monthly, quarterly, and try to always find ways to expand and move workloads into the cloud. We think that provides stickiness. We think that provides ultimate value to try and help our customers identify where else they can take full advantage of the cloud, and it's a fairly new program, and large in part I just want to thank Thomas and the partner team for new programs that are coming out to help us so that we can actually reinvest in things that go you know throughout the lifecycle of the customer. So, very, very good stuff. >> Dana, Chris, thanks for coming on. Appreciate it. We'll check out your booth, the car's there, with the data. Bring that data exhaust to the table, pun intended. >> Yes. >> Analyzing with Google Cloud, Anthos. Good commentary. Thanks for sharing. >> Really appreciate being on board. Thanks for having us. >> Alright, great. CUBE coverage here live on the floor in San Francisco. Google Next 2019. This is Google's cloud conference. Customers are here. A lot of developers. More action, live on the day one of three days of coverage after this short break. Stay with us. (theCUBE Theme)

>>Hi lisa martin here with the cubes coverage of demon 2021. I've got to alumni joining me. Please welcome back to the cube Danny. Alan beam's ceo Danny. It's great to see you. >>I am delighted to be here lisa. >>Excellent brian Schwartz is here as well. Google director outbound product management brian welcome back to the program. Uh >>thanks for having me again. Excited to be >>here. Excited to be here. Yes, definitely. We're gonna be talking all about what Demon google are doing today. But let's go ahead and start Danny with you. Seems vision is to be the number one trusted provider of backup and recovery solutions for the, for for modern data protection. Unpack that for me, trust is absolutely critical. But when you're talking about modern data protection to your customers, what does that mean? >>Yeah. So I always, I always tell our customers there's three things in there that are really important. Trust is obviously number one and google knows this. You've been the most trusted search provider uh, forever. And, and so we have 400,000 customers. We need to make sure that our products work. We need to make sure they do data protection, but we need to do it in a modern way. And so it's not just back up and recovery, that's clearly important. It's also all of the automation and orchestration to move workloads across infrastructures, move it from on premises to the google cloud, for example, it also includes things like governance and compliance because we're faced with ransomware, malware and security threats. And so modern data protection is far more than just back up. It's the automation, it's the monitoring, it's a governance and compliance. It's the ability to move workloads. Um, but everything that we look at within our platform, we focus on all of those different characteristics and to make sure that it works for our customers. >>One of the things that we've seen in the last year, Danny big optic in ransom were obviously the one that everyone is the most familiar with right now. The colonial pipeline. Talk to me about some of the things that the team has seen, what your 400,000 customers have seen in the last 12 months of such a dynamic market, a massive shift to work from home and to supporting SAS for clothes and things like that. What have you seen? >>Well, certainly the employees working from home, there's a massive increase in the attack surface for organizations because now, instead of having three offices, they have, you know, hundreds of locations for their end users. And so it's all about protecting their data at the same time as well. There's been this explosion in malware and ransomware attacks. So we really see customers focusing on three different areas. The first is making sure that when they take a copy of their data, that it is actually secure and we can get into, you know, a mutability and keeping things offline. But really taking the data, making sure it's secure. The second thing that we see customers doing is monitoring their environment. So this is both inspection of the compute environment and of the data itself. Because when ransomware hits, for example, you'll see change rates on data explode. So secure your data monitor the environment. And then lastly make sure that you can recover intelligently is let us say because the last thing that you want to do if you're hit by ransomware is to bring the ransomware back online from a backup. So we call this security cover re secure, restore. We really see customers focusing on those three areas >>And that restoration is critical there because as we know these days, it's not if we get hit with ransomware, it's really a matter of when. Let's go ahead now and go into the google partnership, jenny talked to me about it from your perspective, the history of the strength of the partnership, all that good stuff. >>Yeah. So we have a very deep and long and lengthy relationship with google um, on a number of different areas. So for example, we have 400,000 customers. Where do they send their backups? Most customers don't want to continue to invest in storage solutions on their premises. And so they'll send their data from on premises and tear it into google cloud storage. So that's one integration point. The second is when the running workloads within the clouds. So this is now cloud native. If you're running on top of the google cloud platform, we are inside the google America place and we can protect those workloads. A third area is around the google vm ware engine, there's customers that have a hybrid model where they have some capacity on premises and some in google using the VM ware infrastructure and we support that as well. That's a third area and then 1/4 and perhaps the longest running um, google is synonymous with containers and especially kubernetes, they were very instrumental in the foundations of kubernetes and so r K 10 product which does data protection for kubernetes is also in the google America place. So a very long and deep relationship with them and it's to the benefit of our customers. >>Absolutely. And I think I just saw the other day that google celebrated the search engine. It's 15th birthday. I thought what, what did we do 16 years ago when we couldn't just find anything we wanted brian talked to me about it from Google's perspective of being partnership. >>Yeah, so as Danny mentioned, it's really multifaceted, um it really starts with a hybrid scenario, you know, there's still a lot of customers that are on their journey into the cloud and protecting those on premises workloads and in some senses, even using beams capabilities to move data to help migrate into the cloud is I'd say a great color of the relationship. Um but as Danny mentioned increasingly, more and more primary applications are running in the cloud and you know, the ability to protect those and have, you know, the great features and capabilities, uh you know, that being provides, whether it be for GCB er VM where you know, capability and google cloud or things like G k e R kubernetes offering, which has mentioned, you know, we've been deep and wide in kubernetes, we really birthed it many, many years ago um and have a huge successful business in, in the managing and hosting containers, that having the capabilities to add to those. It really adds to our ecosystem. So we're super excited about the partnership, we're happy to have this great foundation to build together with them into the future. >>And Danny Wien launched, just been in february a couple of months ago, being backup for google cloud platform. Talk to us about that technology and what you're announcing at them on this year. >>Yeah, sure. So back in february we released the first version of the VM backup for G C p product in the marketplace and that's really intended to protect of course, i as infrastructure as a service workloads running on top of G C p and it's been very, very successful. It has integration with the core platform and what I mean by that is if you do a backup in G C P, you can do you can copy that back up on premises and vice versa. So it has a light integration at the data level. What we're about to release later on this summer is version two of that product that has a deep integration with the VM platform via what we call the uh team service platform, a PS themselves. And that allows a rich bidirectional uh interaction between the two products that you can do not just day one operations, but also day to operations. So you can update the software, you can harmonize schedules between on premises and in the cloud. It really allows customers to be more successful in a hybrid model where they're moving from on premises to the cloud. >>And that seems to be really critically important. As we talk about hybrid club all the time, customers are in hybrid. They're living in the hybrid cloud for many reasons, whether it's acquisition or you know, just the nature of lines of business leveraging their cloud vendor of choice. So being able to support the hybrid cloud environment for customers and ensure that that data is recoverable is table stakes these days. Does that give them an advantage over your competition Danny? >>It does. Absolutely. So customers want the hybrid cloud experience. What we find over time is they do trend towards the cloud. There's no question. So if you have the hybrid experience, if they're sending their data there, for example, a step one, step two, of course, is just to move the workload into the cloud and then step three, they really start to be able to unleash their data. If you think about what google is known for, they have incredible capabilities around machine learning and artificial intelligence and they've been doing that for a very long time. So you can imagine customers after they start putting their data there, they start putting their workloads here, they want to unlock it into leverage the insights from the data that they're storing and that's really exciting about where we're going. It's, they were early days for most customers. They're still kind of moving and transitioning into the cloud. But if you think of the capabilities that are unlocked with that massive platform in google, it just opens up the ability to address big challenges of today, like climate change and sustainability and you know, all the health care challenges that we're faced with it. It really is an exciting time to be partnered with Google >>Ryan. Let's dig into the infrastructure in the architecture from your perspective, help us unpack that and what customers are coming to you for help with. >>Yeah. So Danny mentioned, you know the prowess that google has with data and analytics and, and a, I I think we're pretty well known for that. Uh, there's a tremendous opportunity for people in the future. Um, the thing that people get just right out of the box is the access to the technology that we built to build google cloud itself. Just the scale and, and technology, it's, you know, it's, it's a, you know, just incredible. You know, it's a fact that we have eight products here at google that have a billion users and when you have, you know, most people know the search and maps and gmail and all these things. When you have that kind of infrastructure, you build a platform like google cloud platform and you know, the network as a perfect example, the network endpoints, they're actually close to your house. There's a reason our technology is so fast because you get onto the google private network, someplace really close to where you actually live. We have thousands and thousands of points of presence spread around the world and from that point forward you're riding on our internal network, you get better quality of service. Uh the other thing I like to mention is, you know, the google cloud storage, that team is built on our object storage. It's uh it's the same technology that underpins Youtube and other things that most people are familiar with and you just think about that for a minute, you can find the most obscure Youtube video and it's gonna load really fast. You know, you're not going to sit there waiting for like two minutes waiting for something to load and that same under underlying technology underpins GCS So when you're going to go and you know, go back to an old restore, you know, to do a restore, it's gonna load fast even if you're on one of the more inexpensive storage classes. So it's a really nice experience for data protection. It has this global network properties you can restore to a different region if there was ever a disaster, there's just the scale of our foundation of infrastructure and also, you know, Danny mentioned if we're super proud about the investments that google has made for sustainability, You know, our cloud runs on 100% renewable energy at the cloud at our scale. That's a lot of, that's a lot of green energy. We're happy to be one of the largest consumers of green energy out there and make continued investments in sustainability. So, you know, we think we have some of the greenest data centers in the world and it's just one more benefit that people have when they come to run on Google Cloud. >>I don't know what any of us would do without google google cloud platform or google cloud storage. I mean you just mentioned all of the enterprise things as well as the at home. I've got to find this really crazy, obscure youtube video but as demanding customers as we are, we want things asAP not the same thing. If you know, an employee can't find a file or calendar has been deleted or whatnot. Let's go in to finish our time here with some joint customer use case examples. Let's talk about backing up on prem workloads to google cloud storage using existing VM licensing Danny. Tell us about that. >>Yeah. So one of the things that we've introduced at beam is this beam, universal licensing and it's completely portable license, you can be running your workloads on premises now and on a physical system and then you can, you know, make that portable to go to a virtual system and then if you want to go to the cloud, you can send that data up to the work load up to the cloud. One of the neat things about this transition for customers from a storage perspective, we don't charge for that. If you're backing up a physical system and sending your your back up on premises, you know, we don't charge for that. If you want to move to the cloud, we don't charge for that. And so as they go through this, there's a predictability and and customers want that predictability so much um that it's a big differentiating factor for us. They don't want to be surprised by a bill. And so we just make it simple and seamless. They have a single licensing model and its future proof as they move forward on the cloud journey. They don't have to change anything. >>Tell me what you mean by future proof as a marketer. I know that term very well, but it doesn't mean different things to different people. So for means customers in the context of the expansion of partnership with google the opportunities, the choices that you're giving customers to your customers, what does future proof actually delivered to them? >>It means that they're not locked into where they are today. If you think about a customer right now that's running a workload on premises maybe because they have to um they need to be close to the data that's being generated or feeding into that application system. Maybe they're locked into that on premises model. Now they have one of two choices when their hardware gets to the end of life. They can either buy more hardware which locks them into where they are today for the next three years in the next four years Or they can say, you know what, I don't want to lock into that. I want to model the license that is portable that maybe 12 months from now, 18 months from now, I can move to the cloud and so it future proof some, it doesn't give them another reason to stay on premises. It allows them the flexibility that licensing is taken off the table because it moves with you that there's zero thought or consideration and that locks you into where you are today. And that's exciting because it unlocks the capabilities of the cloud without being handicapped if you will by what you have on premises. >>Excellent. Let's go to the second uh use case lift and shift in that portability brian. Talk to us about it from your perspective. >>Yeah, so we obviously constantly in discussions with our customers about moving more applications to the cloud and there's really two different kind of approach is the lift and shift and modernization. You know, do you want to change and run on kubernetes when you come to the cloud as you move it in? In some cases people want to do that or they're gonna obviously build a new application in the cloud. But increasingly we see a lot of customers wanting to do lift and shift, they want to move into the cloud relatively quickly. As Danny said, there's like compelling events on like refreshes and in many cases we've had a number of customers come to us and say look we're going to exit our data centers. We did a big announcement Nokia, they're gonna exit 50 data centers in the coming years around the world and just move that into the cloud. In many cases you want to lift and shift that application to do the migration with his little change as possible. And that's one of the reasons we've really invested in a lot of enterprise, more classic enterprise support type technologies. And also we're super excited to have a really wide set of partners and ecosystem like the folks here at Wien. So the customers can really preserve those technologies, preserve that operational experience that they're already familiar with on prem and use that in the cloud. It just makes it easier for them to move to the cloud faster without having to rebuild as much stuff on the way in. >>And that's critical. Let's talk about one more use case and that is native protection of workloads that run on g c p Danny. What are you enabling customers to do there? >>Well? So we actually merged the capabilities of two different things. One is we leverage the native Api is of G C p to take a snapshot and we merge that with our ability to put it in a portable data format. Now. Why is that important? Because you want to use the native capabilities of G CPU want to leverage those native snapshots. The fastest way to recover a file or the fastest way to recover of'em is from the G C p snapshot. However, if you want to take a copy of that and move it into another locale or you want to pull it back on premises for compliance reasons or put it in a long term storage format, you probably want to put it in GCS or in our portable storage format. And so we merge those two capabilities, the snapshot and back up into a single product. And in addition to that, one of the things that we do, again, I talked about predictability. We tell customers what that policy is going to cost them because if for example a customer said, well I like the idea of doing my backups in the cloud, but I want to store it on premises. We'll tell them, well if you're copying that data continually, you know what the network charges look like, What the CPU and compute charges look like, What do the storage costs looks like. So we give them the forecast of what the cost model looks like even before they do a single backup. >>That forecasting has got to be key, as you said with so much unpredicted things that we can't predict going on in this world the last year has taught us that with a massive shift, the acceleration of digital business and digital transformation, it's really critical that customers have an idea of what their costs are going to be so that they can make adjustments and be agile as they need the technology to be. Last question Bryant is for you, give us a view uh, and all the V mon attendees, what can we expect from the partnership in the next 12 >>months? You know, we're excited about the foundation of the partnership across hybrid and in cloud for both VMS and containers. I think this is the real beginning of a long standing relationship. Um, and it's really about a marriage of technology. You think about all the great data protection and orchestration, all the things that Danny mentioned married with the cloud foundation that we have at scale this tremendous network. You know, we just signed a deal with SpaceX in the last couple of days to hook their satellite network up to the google cloud network, you know, chosen again because we just have this foundational capability to push large amounts of data around the world. And that's you know, for Youtube. We signed a deal with Univision, same type of thing, just massive media uh, you know, being pushed around the world. And if you think about it that that same foundation is used for data protection. Data protection. There's a lot of data and moving large sets of data is hard. You know, we have just this incredible prowess and we're excited about the future of how our technology and beans. Technology is going to evolve over time >>theme and google a marriage of technology Guys, thank you so much for joining me, sharing what's new? The opportunities that demand google are joined me delivering to your joint customers. Lots of great step. We appreciate your time. >>Thanks lisa >>For Danielle in and Brian Schwartz. I'm Lisa Martin. You're watching the cubes coverage of Lehman 2021.

>>from the Cube Studios in >>Palo Alto and Boston connecting with thought >>leaders all around the world. This is a cube conversation. >>Hey, welcome back, everybody. Jeff Frick here with the Cube. We're in our Palo Alto Studios Cove. It is still going on. So, uh, all of our all of the interviews continue to be remote, but we're excited to have Ah, Cube alumni hasn't been on for a long time, but this guy has been in the weeds of the storage industry for a very, very long time, and we're happy to, uh, I have a mon and get an update because there continues to be a lot of exciting developments. He's Phill Bollinger. Ah, he is the SVP and general manager Data center business unit from Western Digital. Joining us, I think from Colorado. So, Phil, great to see you. How is the weather in Colorado today? >>Hi, Jeff. It's great to be here. Well, it's It's a hot, dry summer here. I'm sure like a lot of places. Yeah, enjoying enjoying this summer through these unusual times it >>is. It is unusual times, but fortunately, there's great things like the Internet and heavy duty. Ah, compute and store out there so we can we can get together this way. So let's jump into it. You've been in the business a long time. You've been a Western digital, your DMC you worked on I salon and you were at storage companies before that. And you've seen kind of this never ending up into the right slope that we see, you know, kind of ad nauseam. In terms of the amount of storage demands. It's not going anywhere but up in police. Increased complexity in terms of unstructured data, sources of data, speed of data, you know, the kind of classic big V's of big data. So I wonder before we jump into specifics if you can kind of share your perspective because you've been kind of sitting in the catbird seat. And Western Digital's a really unique company. You not only have solutions, but you also have media that feeds other people solutions. So you guys are really, you know, seeing. And ultimately all this computes gotta put this data somewhere, and a whole lot of it's in our western digital. >>Yeah, it's It's a great a great intro there. Yeah, it's been interesting, you know, through my career. I've seen a lot of advances in storage technology. Uh, you know, speeds and feeds like we often say, But you know, the advancement through mechanical innovation, electrical innovation, chemistry, physics, you know, just the relentless growth of data has been, has been driven in many ways by the relentless acceleration and innovation of our ability to store that data. And that's that's been a very virtuous cycle through you know what for me has been more than 30 years and in enterprise storage there are some really interesting changes going on that I think if you think about it in a relatively short amount of time, data has gone from, you know, just kind of this artifact of our digital lives, um, to the very engine that's driving the global economy, um, our jobs, our relationships, our health, our security. They all depend on data on for most companies, kind of irrespective of size. How you use data, how you how you store it, how you monetize it, how you use it to make better decisions to improve products and services. You know, it becomes not just a matter of whether your company's going to thrive and I bet in many industries it's it's almost an existential question. Is, is your company going to be around in the future? And it and it depends on how well you're using data. So this this drive toe capitalize on the value of data is is pretty significant. >>It's Ah, it's a really interesting topic. We've had a number of conversations around trying to get, like a book value of data, if you will. And I think there's a lot of conversations, whether it's accounting, kind of way or finance or kind of of good will of how do you value this data? But I think we see it intrinsically in a lot of the big companies that are really database, like the Facebooks and the Amazons and the Netflix and the Googles and those >>types >>of companies where it's really easy to see. And if you see you know the valuation that they have compared to their book value of assets, right, it's really baked into there. So it's it's it's fundamental to going forward. And then we have this thing called Covet Hit, which, you know, >>you've >>seen on the media on social media, right? What drove your digital transformation. The CEO CIO, the CMO, the board Rick over 19. And it became this light switch moment where your opportunities to think about it or no more, you've got to jump in with both feet. And it's really interesting to your point that it's the ability to store this and think about it differently as an asset driving business value versus a cost that I t has >>to >>accommodate to put this stuff somewhere. So it's a really different kind of a mind shift and really changes the investment equation for companies like Western Digital about how people should invest in higher performance and higher capacity and more unified it in kind of democratizing the accessibility that data to a much greater set of people with tools that can now start making much more business line and in line decisions than just the data scientists you know, kind of on mahogany row. >>Yeah, like as you mentioned Jeff Inherit Western Digital. We have such a unique kind of perch in the industry to see all the dynamics in the ODM space and the hyper scale space and the channel really across all the global economy's about this this growth of data. I have worked at several companies and have been familiar with what I would have called big data projects and and, ah, fleets in the past. But the Western digital you have to move the decimal point, you know, quite a few digits to the right to get to get the perspective that that we have on just the volume of data, that the world is just relentlessly, insatiably consuming. Just a couple examples for for our Dr Projects we're working on now, our capacity enterprise Dr. Projects. You know, we used to do business case analyses and look at their life cycle. Pass it ease and we measure them and exabytes and not anymore. Now we're talking about Zeta Bytes were actually measuring capacity Enterprise drive families in terms of how many's petabytes they're gonna ship in their life cycle. And if we look at just the consumption of this data the last 12 months of Industry tam for capacity enterprise, compared to the 12 months prior to that, that annual growth rate was north of 60%. So it's it's rare to see industries that are that are growing at that pace. And so the world is just consuming immense amounts of data. And as you mentioned, the dynamics have been both an accelerant in some areas as well as headwinds and others. But it's certainly accelerated digital transformation. I think a lot of companies were talking about digital transformation and and, um, hybrid models. And Covert has really accelerated that. And it's certainly driving continues to drive just this relentless need toe to store and access and take advantage of data. Yeah, >>well, filling In advance of this interview, I pulled up the old chart right with with the all the different bytes, right, kilobytes, megabytes, gigabytes, terabytes, petabytes, exabytes and petabytes. And just just for the Wikipedia page. What is is that a byte, a zoo? Much information as there are grains of sand in all the world's beaches. For one fight, you're talking about thinking in terms of those units. I mean, that is just mind boggling to think that that is the scale in which we're operating. >>It's really hard to get your head wrapped around a set amount of storage. And, you know, I think a lot of the industry thinks when we say that a byte scale era that It's just a buzzword. But I'm here to say it's a real thing where we're measuring projects and in terms of petabytes, that's >>amazing. Let's jump into some of the technology. So I've been fortunate enough here at the Cube toe to be there at a couple of major announcements along the way. We talked before we turned the cameras on the helium announcement and having the hard drive sit in the in the fish bowl, um, to get off types of interesting benefits from this less dense air that is helium versus oxygen. I was down at the mammary and hammer announcement, which was pretty interesting. Big, big, heavy technology moves there to again increase the capacity of the hard drive based systems. You guys are doing a lot of stuff on. This five I know is an open source projects. You guys have a lot of things happening, but now there's this new thing, this new thing called zoned storage. So first off before we get into, why do we need zone storage? And really, what does it now bring to the table in terms of ah, capability? >>Yeah, Great question, Jeff. So why now, right. I as I mentioned, you know, storage. I've been in storage for quite some time in the last. Let's just say, in the last decade we've seen the advent of the hyper scale model and certainly the, you know, a whole another explosion, level of, of data and just the veracity with which the hyper scaler is can create and consume and process and monetize data. And, of course, with that has also come a lot of innovation, frankly, in the compute space around had a process that data and moving from, you know, what was just a general purpose CPU model to GP use and DP use. And so we've seen a lot of innovation on that. But you know, frankly, in the storage side, we haven't seen much change at all in terms of how operating systems applications, final systems, how they actually use the storage or communicate with the storage. And sure we've seen, you know, advances in storage capacities. Hard drives have gone from 2 to 4 to 8 to 10 to 14 16 and now are leading 18 and 20 terabyte hard drives and similarly on the SSD side, you know, now we're dealing with the complexities of seven and 15 and 30 terabytes. So things have gotten larger, as you would expect, but and and some interfaces have improved, I think Envy Me, which we'll talk about, has been nice advance in the industry. It's really now brought a very modern, scalable, low latency, multi threaded interface to a NAND flash to take advantage of the inherent performance of transistor based, persistent storage. But really, when you think about it hasn't changed a lot and so but what has changed his workloads? One thing that definitely has evolved in the space of the last decade or so is this. The thing that's driving a lot of this explosion of data and industry is around workloads that I would characterize as a sequential in nature there, see, really captured and written. They also have a very consistent lifecycle, so you would write them in a big chunk. You would read them, uh, maybe in smaller pieces, but the lifecycle of that data we can treat more as a chunk of data, but the problem is applications. Operating systems. File systems continue to interface with storage, using paradigms that are, you know, many decades old, they'll find 12 bite or even four K sectors. Size constructs were developed in, you know, in the hard drive industry, just as convenient paradigms to structure what is unstructured sea of magnetic grains into something structured that can be used to store and access data. But the reality is, you know, when we talk about SSD is structured really matters. And so these what has changed in the industry as the workloads are driving very, very fresh looks at how more intelligence could be applied to that application OS storage device interface to drive much greater officials. >>Right? So there's there's two things going on here that I want to drill down on one hand. You know, you talked about kind of the introduction of NAND flash Ah, and treating it like you did generically. You did a regular hard drive, but but you could get away and you could do some things because the interface wasn't taking full advantage of the speed that was capable in the nan. But envy me has changed that and forced kind of getting getting rid of some of those inefficient processes that you could live with. So it's just kind of classic. Next next level step up and capabilities. One is you got the better media. You just kind of plug it into the old way. Now, actually, you're starting to put in processes that take full advantage of the speed that that flash has. And I think you know, obviously, prices have come down dramatically since the first introduction. And for before, we always kind of clustered offer super high end, super low latency, super high value APS. You know, it just continues to Teoh to spread and proliferate throughout the data center. So, you know what did envy me force you to think about in terms of maximizing, you know, kind of the return on the NAND and flash? >>Yeah, yeah, in envy me, which, you know, we've been involved in the standardization after I think it's been a very successful effort, but we have to remember Envy me is is about a decade old, you know, or even more When the original work started around defining this this interface and but it's been very successful, you know, the envy, any standards, bodies, very productive, you know, across company effort, it's really driven a significant change. And what we see now is the rapid adoption of Envy Me in all data center architectures. Whether it's a very large hyper scale to, you know, classic on prim enterprise to even, you know, smaller applications. It's just a very efficient interface mechanism for connecting SSD, ease and Teoh into a server, you know, So the we continue to see evolution and envy me, which is great, and we'll talk about Z and s. Today is one of those evolutions. We're also very keenly interested in VM e protocol over fabrics. And so one of the things that Western Digital has been talking about a lot lately is incorporating Envy me over fabrics as a mechanism for now connecting shared storage into multiple post architectures. We think this is a very attractive way to build shared storage architectures in the future that are scalable, that air compose herbal that really are more have a lot more agility with respect two rack level infrastructure and applying that infrastructure to applications. Right >>now, one thing that might strike some people it's kind of counterintuitive is is within the zone, um, storage and zoning off parts of the media to think of the data also kind of in these big chunks, is it? It feels contrary to kind of optimization that we're seeing in the rest of the data center. Right? So smaller units of compute smaller units of store so that you can assemble and disassemble them in different quantities as needed. So what was the special attributes that you had to think about and and actually come back and provide a benefit in actually kind of re chunking, if you will in the zones versus trying to get as atomic as possible? >>Yeah, It's a great question, Jeff, and I think it's maybe not intuitive in terms of why zone storage actually creates a more efficient storage paradigm when you're storing stuff essentially in larger blocks of data. But if this is really where the intersection of structure and workload and sort of the nature of the data all come together, uh, if you turn back the clock, maybe 45 years when SMR hard drives host managers from our hard drives first emerged on the scene, this was really taking advantage of the fact that the right head on a hard describe is larger than the reader can't reach. It could be much smaller, and so then the notion of overlapping or singling the data on the drive giving the read had a smaller target to read. But the writer a larger right pad to write the data I could. Actually, what we found was it increases areal density significantly, Um, and so that was really the emergence of this notion of sequentially written larger blocks of data being actually much more efficiently stored. When you think about physically how it's being stored, what is very new now and really gaining a lot of traction is is the the SSD corollary to tomorrow in the hard drive. On the SSD side, we have the CNS specification, which is very similarly where you divide up a name space of an SSD and two fixed size zones, and those zones are written sequentially. But now those zones are are intimately tied to the underlying physical architecture of the NAND itself. The dies, the planes, the the three pages, the the race pages so that in treating data as a black, you're actually eliminating a lot of the complexity and the work that an SSD has to do to emulate a legacy hard drive. And in doing so, you're increasing performance and endurance and and the predictable performance of the device. >>I just love the way that that, you know, you kind of twist the lens on the problem and and on one hand, you know, by rule just looking at my notes of his own storage devices, the CS DS introduced a number of restrictions and limitations and and rules that are outside the full capabilities of what you might do. But in doing so in aggregate, the efficiency and the performance of the system in the hole is much, much better, even though when you first look at you think it's more of a limiter, but it's actually opens up. I wonder if there's any kind of performance stats you can share or any kind of empirical data, just to >>get people kind >>of a feel for what? That what that comes out as >>so if you think about the potential of zone storage in general, when again, When I talk about zone storage, there's two components. There's an HDD component of zone storage that we that we refer to as S. Some are, and there's an SSD version of that that we call Z and s So you think about SMR. The value proposition. There is additional capacity so effectively in the same Dr architecture with with, you know, roughly the same bill of material used to build the drive. We can overlap or single the data on the drive and generate for the customer additional capacity. Today with our 18 20 terabyte offerings, that's on the order of just over 10% but that Delta is going to increase significantly, going forward 20% or more. And when you think about ah, hyper scale customer that has not hundreds or thousands of racks but tens of thousands of racks, a 10 or 20% improvement and effective capacity is a tremendous TCO benefit, and the reason we do that is obvious. I mean, the the the the economic paradigm that drives large scale data centers is total cost of ownership, the acquisition costs and operating costs. And if you can put more storage in a square, you know, style of data center space, you're going to generally use less power. You're gonna run it more efficiently. You're actually from an acquisition cost. You're getting a more efficient purchase of that capacity. And in doing that, our innovation, you know, we benefit from it and our customers benefit from it so that the value proposition pours. Don't storage in in capacity. Enterprise HDD is very clear. It's it's additional capacity. The exciting thing is in the SSD side of things for Z and as it actually opens up even more value proposition for the customer. Um, because SSD is have had to emulate hard drives. There's been a lot of inefficiency in complexity inside an enterprise. SSD dealing with things like garbage collection and write amplification, reducing the endurance of the device. You have to over provision. You have to insert as much as 2025 28% additional NAND bits inside the device just too allow for that extra space, that working space to deal with with delete of the you know that that are smaller than the the a block of race that that device supports. And so you have to do a lot of reading and writing of data and cleaning up it creates for a very complex environment. Z and S by mapping the zone size with the physical structure of the SSD, essentially eliminates garbage collection. It reduces over provisioning by as much as 10% are 10 x And so if you were over provisioning by 20 or 25% in an enterprise SSD and Xeon SSD, that could be, you know, one or 2%. The other thing we have to keep in mind is enterprise. SSD is typically incorporate D RAM and that D RAM is used to help manage all those dynamics that I that I just mentioned, but with a very much simpler structure where the pointers to the data can be managed without all that d ram, we can actually reduce the amount of D ram in an enterprise SSD by as much as eight X. And if you think about the bill of material of an enterprise, SSD d ram is number two on the list in terms of the most expensive bomb components. So Z and S and SSD is actually have a significant customer. Total cost of ownership impact. Um, it's it's an exciting it's an exciting standard. And now that we have the standard ratified through the Envy me working group, um, you can really accelerate the development of the software ecosystem around >>right. So let's shift gears and talk a little bit about less about the tech and more about the customers and the implementation of this. So, you know, are there you talked to kind of generally, but are there certain certain types of workloads that you're seeing in the marketplace where this is, you know, a better fit? Or is it just really the big heavy lifts? Um, where they just need more and this is better. And then secondly, within you know, these both hyper scale companies, um, as well as just regular enterprises that are also seeing their data demands grow dramatically. Are you seeing you know, that this is a solution that they want to bring in for kind of the marginal kind of next data center extension data center or their next ah, cloud region? Or are they doing you know, lift and shift and ripping stuff out? Or do they have enough? Do they have enough data growth organically? >>Then >>there's plenty of new stuff that they can. They can put in these new systems. >>Yeah, well, the large customers don't don't rip and shift. They they write their assets for a long life cycle because with the relentless growth of data. You're primarily investing to handle what's what's coming in over the transom, but we're seeing we're seeing solid adoption in SMR. As you know, we've been working on that for a number of years. We've we've got, you know, significant interest in investment co investment, our engineering and our customers engineering, adapting the the application environments. Let's take advantage of SMR. The great thing is, now that we've got the envy me, the Xeon s standard ratified now, in the envy of the working group, um, we've got a very similar and all approved now situation where we've got SMR standards that have been approved for some time in the sand and scuzzy standards. Now we've got the same thing in the envy, any standard. And that's the great thing is once a company goes through the lifts, so it's B to adapt an application file system, operating system, ecosystem to zone storage. It pretty much works seamlessly between HDD and SSD. And so it's not. It's not an incremental investment when you're switching technologies and for obviously the early adopters of these technologies are going to be the large companies who designed their own infrastructure. You have you know, mega fleets of racks of infrastructure where these efficiencies really, really make a difference in terms of how they can monetize that data, how they compete against, you know, the landscape of competitors They have, um, for companies that are totally reliant on kind of off the shelf standard applications. That adoption curve is gonna be longer, of course, because there are there are some software changes that you need to adapt to to enable zone storage. One of the things Western Digital is has done, and taking the lead on is creating a landing page for the industry with zone storage. Not Iot. It's a Web page that's actually an area where, where many companies can contribute open source tools, code validation environments, technical documentation it's not. It's not a marketeering website. It's really a website bill toe land, actual open source content that companies can and use and leverage and contribute to. To accelerate the engineering work to adapt software stacks his own storage devices on to share those things. >>Let me just follow up on that, because again you've been around for a while and get your perspective on the power of open source and you know, it used to be, you know, the the best secrets, the best I p were closely guarded and held inside. And now really, we're in an age where it's not necessarily and you know, the the brilliant minds and use cases and people out there. You know, just by definition, it's a It's a more groups of engineers, more engineers outside your building than inside your building and how that's really changed. You know, kind of the strategy in terms of development when you can leverage open source. >>Yeah, Open source clearly has has accelerated innovation across the industry in so many ways. Um, and it's ah, you know, it's the paradigm around which, you know companies have built business models and innovated on top of it. I think it's always important as a company to understand what value add, you're bringing on what value add that customers want to pay for what unmet needs and your customers are you trying to solve for and what's the best mechanism to do that? And do you want to spend your R and D recreating things or leveraging what's available and and innovating on top of it? It's all about ecosystems in the days where the single company can vertically integrate. I talked about him a complete end solution. You know those air few and far between. I think it's It's about collaboration and building ecosystems and operating within those. >>Yeah, it's it's It's such an interesting change. And one more thing again, to get your perspective, you run the data center group. But there's this little thing happening out there that we see growing in I o T Internet of things and the industrial Internet of things and edge computing. As we, you know, try to move more, compute and store and power, you know, kind of outside the pristine world of the data center and out towards where this data is being collected and processed when you've got latency issues and and in all kinds of reasons to start to shift the balance of where the computers aware that store Ah, and the reliance on the network. So when you look back from a storage perspective in your history in this industry and you start to see that basically everything is now going to be connected, generating data and and and a lot of it is even open source. I talked to somebody the other day doing, you know, kind of open source, computer vision on surveillance, you know, video. So, you know, the amount of stuff coming off of these machines is growing like crazy ways at the same time, you know, it can't all be processed at the data center. It can all be kind of shift back and then have you have a decision and then ship that information back out to. So when you sit back and look at the edge from your kind of historical perspective, what goes through your mind? What gets you excited? You know, what are some of the opportunities that you see that maybe the Lehman is not paying close enough attention to? >>Yeah, it's It's really an exciting time in storage. I get asked that question from time to time, having been in storage for more than 30 years, you know what was the most interesting time, and there's been a lot of them, but I wouldn't trade today's environment for any other in terms of just the velocity with which data is is evolving and how it's being used and where it's being used. You know that the TCO equation made describe what a data center looks like. But data locality will determine where it's located and we're excited about the edge opportunity. We see that as a pretty significant, meaningful part of the TAM. As we look out 3 to 5 years, certainly five G is driving much of that. I think just anytime you speed up the speed of the connected fabric, you're going to increase storage and increase the processing of the data. So the edge opportunity is very interesting to us. We think a lot of it is driven by low latency workloads. So the concept of envy any, um is very appropriate for that. We think in general SSD is deployed in in edge data centers defined as anywhere from a meter to a few kilometres from the source of the data. We think that's going to be a very strong paradigm. Um, the workloads you mentioned especially I O. T just machine generated data in general now I believe, has eclipse human generated data in terms of just the amount of data stored, and so we think that curve is just going to keep going in terms of machine generated data, much of that data is so well suited for zone story because it's sequential, it's sequentially written, it's captured, it's it has a very consistent and homogeneous lifecycle associated with it. So we think what's going on with with Zone storage in general and and Z and S and SMR specifically are well suited for where a lot of the data growth is happening. And certainly we're going to see a lot of that at the edge. >>Well, Phil, it's always great to talk to somebody who's been in the same industry for 30 years and is excited about today and the future on as excited as they have been throughout the whole careers. That really bodes well for you both. Well, for for Western Digital. And we'll just keep hoping the smart people that you guys have over there keep working on the software and the physics, Um, and then in the mechanical engineering to keep moving this stuff along. It's really ah, it's just amazing and just relentless. >>Yeah, it is. It is relentless. What's what's exciting to me in particular, Jeff is we've we've we've driven storage advancements, you know, largely through. As I said, a you know a number of engineering disciplines, and those are still going to be important going forward the chemistry of the physics, the electrical, the hardware capabilities. But I think, as you know, is widely recognized in the industry that it's a diminishing curve. I mean, the amount of energy, the amount of engineering, effort, investment, the cost and complexity of these products to get to that next capacity step, um, is getting more difficult, not less. And so things like zone storage where we now bring intelligent data placement to this paradigm is what I think makes this current juncture that we're at a very exciting >>right, Right. Well, it is applied ai, right. Ultimately, you're gonna have, you know, more more compute, you know, compute power. You know, driving the storage process and how that stuff is managed. And, you know, as more cycles become available and they're cheaper and ultimately compute, um gets cheaper and cheaper. You know, as you said, you guys just keep finding new ways to ah, to move the curve. And we didn't even get into the totally new material science, which is also, you know, come down the pike at some point in time. Well, >>very exciting. >>It's been great to catch up with you. I really enjoy the Western Digital story. I've been fortunate to to sit in on a couple chapters. So again, congrats to you. And, uh, we'll continue to watch and look forward to our next update. Hopefully, it won't be another four years. >>Okay. Thanks, Jeff. I really appreciate the time. All >>right. Thanks a lot. Alright. He's Phill. I'm Jeff. You're watching the Cube. Thanks for watching. We'll see you next time. Yeah, Yeah, yeah, yeah.

>>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.

>> Announcer: Live from Nassau in the Bahamas, it's the Cube. Covering Polycon '18. Brought to you by Polymath. >> Welcome back, everyone. This is the Cube's exclusive coverage. We're live in the Bahamas, here for day two of our wall to wall coverage of Polycon '18. It's a security token conference, securitizing, you know, token economics, cryptography, cryptocurrency. All this is in play. Token economics powering the world. New investors are here. I'm John Furrier, Dave Vellante. Our next guest is Nithin Eapen Who's the Chief Investment Officer for Arcadia Crypto Ventures. Welcome to the Cube. >> Thank you very much gentlemen. >> Thanks for joining us. >> Thanks for coming out. >> Excited to have you on for a couple reasons. One, we've been talking since day one, lot of hallway conversations. Small, intimate conference, so we've had a chance to talk. Folks haven't heard that yet, so let's kind of get some of the key things we discussed. You are very bullish and long on cryptocurrency and Blockchain. You guys are doing a variety of deals. You're also advising companies and you guys are rolling your sleeves up. So kind of interesting dynamics. So take a minute to explain what you guys are doing, your model. >> Okay. >> And we're going to try to get some of your partners on later. You have a great team. >> Yep. >> Experienced pros in investing. And you got wales, you got pros. So you got a nice balance. >> Yes we do. >> So take a minute to explain Arcadia, your approach and philosophy. >> Okay. Okay. So Arcadia Crypto Ventures primarily we are a private fund. We invest other money. We believe in the whole crypto space. We believe this market is expanding and it is growing and it's going to be the biggest thing that ever happened. It's going to be this fusion of internet and PC and mobile. And everything is going to go batshit, okay. We believe in the whole tokenization world. Everything is going to be tokenized. So as a whole, we believe this space is going to go very big. Okay, so that's one piece and because of that, we invest in the space, the whole space. Not one bitcoin or Ethereum, but everything in the space that makes sense. People who have a use case. Now the second piece of it is we advised great founders. We want to get founders to come out and build these new things because this is the new internet of the new era and people have to come out and build these things. And so many of them are traditional businesses and we have to explain to them why this matters, why you should come to this space and be decentralized and reach the whole world. Because initially, the internet came. The idea of the internet was everybody gets information. Now information did get everywhere. You don't have to worry that the mailman is there to deliver your email anymore. Even if it's a Sunday, your mail will get delivered. So that part was good. But now you have these few companies that's holding all your data. It's okay for most people, but they do censor a lot of people. So that is one point. That censorship. We want a censorship-resistant world where everybody's ideas get out. So that way, we believe that's how this whole internet space itself is going to change because of that. See this is if I explained in one word, this is the greatest sociopolitical economic experimental revolution ever that has happened in humankind. >> In the history of the world. I mean this is important. I'd said that on my opening today. >> Uh-huh. >> Dave and I were riffing and Dave and I have always been studying. We've been entre-- We are entrepreneurs. We live in Silken Valleys in Boston and so you seeing structural change going on. So it's not just make money. >> Nope. >> There's mission-based, younger demographics. So you starting to see really great stuff. So I want to ask you specifically, 'cause you guys are unique in the sense that you're investing in a lot of things. But startups, pure-playing startups? >> Which had only one path before, or two paths. >> Right, yeah. >> Cashflow financing and venture capital. >> Okay. >> So that's a startup model. The growing companies that are transform their growth business with token economics, those would have long odds. Those are the best deals. >> Okay. Then there's like the third deal. Well we're out of business, throw the Hail Mary, repivot. (laughs) Right, so categorically, you're starting to see the shape of the kinds of swim lanes of deals. >> Okay. >> Okay, pivoting, that Hail Mary. Okay, you can evaluate that pretty much straight up on that. Startups need nurturing, right? >> Yeah. >> So the VC1 al-oc-chew works really well for startups because of the product market fits going to be developed. You got cloud computing so you can go faster. So you guys are nurturing startups. At the same time, you're also doing growth deals. >> We do. >> Explain the dynamic between those kinds of deals, how you guys approach them. What's the dynamic? What are the key things that you're bringing? Is it just packaging? Is it tech? So on, so forth. >> So with a lot of people, when they are on the advisory side. Primarily we look at the founder and the tech. What are they trying to solve? That is key. If it's a turd, you can't package it. No matter how you package it, that's not going to work. >> You can't package dog you-know-what. >> Yeah, exactly, okay. >> So that's one thing that we look at. The founders and their idea. Now their idea, can it be decentralized? Some models are meant to be centralized maybe so it doesn't work, okay. Like, see it all boils down to-- Let me break it down. We look at it. Okay, do you have an asset? Behind the scenes, is there an asset? Is that asset being transferred among parties? If you have an asset and it's being transferred, is there some central mechanism in between? Because if there is a central mechanism in between, that means you're going to be paying rent to that. Okay, all right. You have these things. Okay, great. Now you have your asset. Do you have that in between party? But in some of them, let's say you have money in your pocket. You walk, it falls down. Somebody else pick ups the money. It's his. It's a bearer asset, okay? So that's where bitcoin solved a very big problem. It was bearer asset. >> Unless they hack your wallet, then they take your money. >> Right. That happens in real life too, right? Somebody can take money from your wallet. So it can happen in bitcoin. They can hack your wallet. All right. So bitcoin was solving that problem. Now the second piece is a registered asset. And I mean by registered asset is take your car. You buy your car, you go to the DMV, stand in line, register. There's a record of data at the DMV in their central database. If somebody steals your car, the car is still not his. It's only if they can change the record over there in DMV. Then it becomes his. Now there maybe you do want the DMV to be there. Or maybe we can-- But the DMV being there, now you have a problem. They're going to charge you rent and they can decide, oh you know what? John, I'm not going to give him a license or a car in the state of California. They can decide, right? So that is where now you decide do you want to go the centralized route or the decentralized route? So we break it down to the asset. >> So there could be a fit for decentralized. I get that. >> Yeah. >> Let me ask you a tactical question, because I know a lot of entrepreneurs out there. They're watching and they'll hear this. A big strategic decision up front is, obviously, token selection. >> So it's pretty clear that security token works really well for funding and whatnot. Then there's a role for security tokens. I mean utility tokens. >> Yes. >> So do people, should they start from a risk management standpoint, a new company. So let's just say we had an existing business. Entrepreneur says, "Hey, you know what? We're doing well. We're doing 10 million dollars in revenue and I want to do tokenize 'cause we're a decentralized business. That's a perfect fit." Do they start a new company or do they just use the security token with their existing stable company? >> I would suggest, usually at that time, that's more of a legal question at that time. I don't know if I'm a lawyer to answer that. I tell them, you have a business. The business model is going well. If you're happy with it, let that be there. Make a new company. If your business model was not doing good, you might as well start from there because you figure out it's not working. But again, at that time, we tried to come up with this question. Are you trying to put the old wine in a new bottle kind of thing? If the wine is old, it ain't going to work. You have to get to that realization. So, here. >> People are being sued. So mainly the legal question is do I want to risk being. >> All right, let me hop in here. I wanted to ask, go back to something you said about censorship. I had this conversation with my kid the other day. I was explaining Google essentially censors your search results based on what they think you're going to click on. >> They do that. >> He's like no and then he thought about it and he's like okay, yeah they kind of do that. Okay, so that's an underpinning of we're going to take back the internet, right? >> Yeah. >> Okay, I just wanted to sort of clarify that. From an investment philosophy standpoint, you're technical, yet you don't exclusively vet or invest in infrastructure protocols and dig deep into what-- You read the white papers, but there are some folks out there hedge funds, et cetera. All they do is just invest in utility tokens. They're trying to invest in stuff that's going to be infrastructure for the next internet. Your philosophy is different. You're saying, we talked about this, we don't really know what's going to win, but we make prudent investments in areas that we think will win. We like to spread it around a little bit. Why that philosophy? May reduce your return, but it also reduces your risk. Maybe you could describe that a little bit. >> Sure. See, in general, picking winners in the long run has been-- It's a proved fact that nobody could pick winners. Like if you take active hedge fund managers. Active hedge fund managers, in the long run, if you take 10 to 20 years, they lag the S and P. So if you had money, if you give it to an active hedge fund manager, and so that you just had to buy the S and P, you will have beaten 93%. >> That's Buffet's advice. Buy an S and P 500. >> Buffet made a bet for a billion dollars or something where, you know. So take Warren Buffet for that matter, his fund is lagging too. In reality, all his stock investments are down. He put it in IBM at $200 after eight years, it's at the 143 or something, right? So realistically,-- There's a lot of luck element, okay. You can do all of the analysis and you could still end up buying Enron, Lehman, and Bear Stearns, right? >> Right, yeah. >> And at that time, see they were using some models that they knew 'til then. Most people, investment comes from, you have this background that you know, okay this is what I look at. Cash flow, discounted cash flow. Great. If that is there, price to earnings, I'm going to buy. But then an Amazon came, most of the traditional investors never invested in Amazon. They were like, it's a loss- making company. They never going to survive. But they forgot the fact that companies like that there's this network effect and once the people are there, at any point, Jeff Bezos can just turn off the switch and take off the discount. You're not going to change your shopping from Amazon at that point because this month I lost my 15%. We're so used to it so people missed that. Nowadays they see that, but when it came to Blockchain they're like, oh, no, no, this is a fad. That's what most people said. >> So we talked about discounted cashflow as a classic valuation method. I see guys trying to do DCF on these investments. I mean, we were joking about that. (laughs) How do you-- What's your reaction to that? >> If anybody's saying that if they come to me and I'm like you-- I don't know what Kool-Aid do you drink at that point because what cashflow are they discounting? There's no cashflow. It's not like you're going to get dividends from these tokens. There's no dividends. It's like can you find out how many people are going to use it. What is the network effect? And again, for that, a lot of people are coming with a lot of these matrices or matrix right now. But I think even that, they're trying to retrofit into it. They're like, oh I can use this matrix. But, really we don't know. >> So people tend to want metrics. Dave and I talk about this all the time. When people part with their money, they need to know what they're betting on. So the question is when you look at investments, when you spend cash, when you write checks, what is your valuation technique? Do you look for the l-- How do you play that long game? What's the criteria? Besides like the normal stuff like founders, disruptive, like you got to write the check, let's say. Okay, buying a token. It's got to be worth something in the future, obviously. >> So we look at that space, where invariably they are trying to disrupt. Is there a big market? And even if it's a niche market, okay? So we're doing an error chain token. It's a very niche market. It's just the pilot, the maintenance folks, and the charter people, or the plain charter guys. It's a very small market, but that's good enough. It's very niche. They can have an ecosystem between themselves rather than being incentivized to long game miles and stuff like that, right? It doesn't have to be a very big market. We just look at it, okay. Founder is good, he has an idea, it is a space that can be decentralized and people can come in and they feel that they're part of the ecosystem. See the whole thing with the token economy and a traditional economy like let's say I'm spending money to buy a stock. So I buy stock. As an investor, what do I want? I want maximum returns. The employee, he wants to get maximum pay. And the consumer who's buying the product, he wants to get it at the cheapest price. So there's a-- It start aligned, okay? The moment you give 'em the cheapest price, my profits go down. If I increase the employees' salary, my profits go down. So we are all three of us are totally misaligned. >> If I for an important point, do you favor certain asset classes, you know, token, security tokens, or utility tokens, or you looking for equity? I mean, maybe just ... >> Right now, we've moved away from the whole equity bonds, or any of those things. We are totally concentrated on the utility or security tokens. We don't mind if it's a security token or utility token. >> And if it's a security token, are you looking for dividends, are you looking for >> At that point it's some kind of dividend. >> So you're not expecting equity as part of that security token? >> No, I like to expect equity, but if they are saying okay my token, if people buy and if they pay me $10, and out of that you're going to get $1 back, okay that's fine. We don't mind that as long as it's legal and all those things we're fine because it just makes the process easier. Earlier you invest and you didn't know when you could get out of your investment. At this point, it's become so liquid, at any point of time within two or three months, the token is less to people are either buying and selling. We know, otherwise, earlier when we used to do Ren Chain investments, we would get into our product, have it it's time seven to 10 years to get out. And in the meanwhile, they say great stories. Oh we're doing great. Who do I check with that we are doing great? I'm not getting any dividends. Nobody's buying this from me. How do I know? Where am I? I really don't know. I can make these values up and on my Excel sheet and say okay we valuing this company at a billion. >> So your technique is to say okay look at the equity plays the long game. You need an exit on liquidity, either M and A or IPO. >> Yes. >> Now you have a new liquidity market, so you play the game differently. I won't say spray and pray, but you have multiple bets going on so you can monitor liquidity opportunity. So that's a new calculation. >> And it's a great calculation, also. Because see we're in the market and now we know at any point of time, we don't have things on our books that are like we don't know what the value is. We know what that price is because the market is there, the exchange is there. What other people are willing to pay for us doesn't surprise. It's like saying my house is worth a million dollars. Actually it might be worth to me. It depends on what people are willing to pay me. >> Right exactly. >> If I have to synthesize this, you're taking high frequency trading techniques with classic venture investing, handling token from those two perspectives. >> Yes. >> High frequency trading meaning I'm looking at volatility and then option to abandon and get rid of whatever or whatever. >> The only thing is, we're not exiting our positions. We are in the long game. We believe the score market is supposed to at least reach eight trillion. When we started this whole investing, at that time, the whole market was at six billion and we said okay this market, based on our thesis, is supposed to reach eight trillion. Until then, we keep buying, okay? >> But to your HFT, you're not really arbitraging. >> No, no, we're not doing any of those. Because see >> They're applying real time techniques to token evaluations so they're game is try to get into a winner. >> Yes. >> With some tokens. >> A lot of the funds, they're doing this arbitrage more. They're trying to do arbitrage. But the problem is they're missing the big picture that way. So, arbitrage works in a very tight market. So S and P, let's say, somebody's doing 5% return on S and P. The guy with a arbitrage is coming and saying I made five point three, 5.5% or 6%. That's great in the equity world. Now, I want returns last year are 10 x or 30 x or 50 x. And somebody comes and tells me I made an extra 0.2%, doesn't really matter to me. I'm like instead of wasting that time doing arbitrage and paying taxes, I might just hold it. >> You believe in the fundamentals. >> You guys are in New York. Obviously, Arcadia Crypto Ventures, that's how they get ahold of you guys. Final question for you to end the segment. As new real pros come in, and let's take New York as a since you're in New York. The New York crowd comes in or the Silken Valley comes crowd existing market players other markets come in here. How important is optics packaging and compatibility with the sector, meaning I just can't throw my weight around on the hedge fund scene. We do it this way, I got money. Because people here have money. So what's the dynamic of pros coming in, we're seeing institutional folks come in, we're seeing real pros come in. They've never been to Burning Man. So, you know, they get that Burning Man culture exists, but this is not a Burning Man industry. >> Right, right. >> Business doesn't run like Burning Man. Maybe it should, that's a debate we'll have. Your take. >> So the new funds that are coming in, so they have a fear that they have missed out. They are missing the picture that this is just the beginning. So they've seen that this industry has gone from six billion to 500 billion in a year or year and a half. They're like, oh my god, I missed it. >> It's got to be over. >> So I have to write these big checks to get this. We don't write big checks. We write much smaller checks because we believe that if a founder is raising money, he has to raise it through small checks from everybody. That means all those people are really interested in this. And they're all of them really want the token to go up. Whether it's the investor, the user, and the employee who is working there because all of them they're interests are aligned. The moment you give a big check, so let's say you could raise 10 million from 10,000 people or you could raise it from one person. So when the big check is there, let's say I go to raise my money. There's this fund who's missed it and he says here's 10 million dollars. Okay, now I've got me and the fund and my tokens. Nobody else knows about my tokens. My tokens are as good as valueless. Now the funders looking okay, I need to exit. Nobody knows about my tokens. The fund is the only guy who has my tokens, he's trying to exit. Obviously the market is going to crash. There's no market. And he's like why did I get into this. So he missed that point that you need people around you. It's not just you alone. See, earlier days when ... >> This is your point about understanding how token economics works. >> Yes. >> So having more people in actually creates a game mechanic for trading. >> Because then you know that you're not the only guy interested in this. And earlier venture capital space there were these bunch of few venture capitals who wanted to capture that whole thing and tried to sell it to the next guy. Here, I'm what I'm saying is, we all have to come in together. We all can be together at the same price, which is good because the small person has, the common man has a chance to be a VC right now. Earlier you could never be a VC. I could only see Google, after IPO. I could never get it at what KPCB or Sequoia got it at. I had to wait 'til they got through CDA, CDB, which they bought at five cents. I would get at about $40 maybe. In this case, the big fund has a lot more money than me, but I can have my small 5,000 or 10,000. I can invest in the ICO. >> If you picked the right spot and you were there at the right place, the right time. 'Cause you are seeing guys come in and try to buy up all the tokens early on. >> They're trying to do that. They don't get it, but they will understand. So it is a learning (mumbles). Even they will evolve. They're like okay this is not how it works. And you have to make mistakes. >> Sorry, got to ask you one final, final since you brought it up. More people the better. So we're hearing rumors inside the hallways here that big wales are buying full allocations and then sharing them with all their friends. >> Possible, it is possible. >> We see some of that behavior. Dave calls it steel on steel, you know. Groups, you know. I'm going to take this whole deal down. We see that in venture capital. Used to be syndicates. Now you seeing Andreessen Horowitz doing the whole deals. That kind of creates some alienation, my opinion, but what's your take on that? I'm a big wale. I'm taking down the whole allocation. >> It's okay. Some of those things are going to happen, okay. It is fine. The only problem is usually when that happens the big wale who takes it he will realize very quickly. >> He's got to get more people. >> He needs more people otherwise he might be able to exit to his five buddies who were always taking it from him. Now those guys, they also have to exit at some point. Nobody knows about the product. Might as well just take a small piece, even the founders in this case typically in a token model. Founders who've taken 20% or 10% have done better than founders who took 60% of the whole tokens. >> Right. Nithin, great to have you on. Love your business model. Arcadia Crypto Ventures. They got real pros, they got a wale, they got people who know what they're doing, and they're active. They understand the ethos. I think you guys are well-aligned and you're not trying to come in and saying this is how we did it in New York before. You get the culture. You're aligned and you're making investments. Great perspective. Thanks for sharing. >> Thank you so much. >> This is the Cube, bringing the investor perspective live here in the Bahamas. More exclusive Cube coverage. Token economics, huge opportunity for entrepreneurs and investors to create value and capture it. That's Blockchain, that's crypto, that's token economics. I'm John with Dave Vallante. We'll be back with more coverage after this short break. (futuristic digital music)