(upbeat music) >> In 2010 Wikibon predicted that the all flash data center was coming. The forecast at the time was that flash memory consumer volumes, would drive prices of enterprise flash down faster than those of high spin speed, hard disks. And by mid decade, buyers would opt for flash over 15K HDD for virtually all active data. That call was pretty much dead on and the percentage of flash in the data center continues to accelerate faster than that, of spinning disk. Now, the analyst that made this forecast was David FLoyer and he's with me today, along with Renen Hallak who is the founder and CEO of Vast Data. And they're going to discuss these trends and what it means for the future of data and the data center. Gentlemen, welcome to the program. Thanks for coming on. >> Great to be here. >> Thank you for having me. >> You're very welcome. Now David, let's start with you. You've been looking at this for over a decade and you know, frankly, your predictions have caused some friction, in the marketplace but where do you see things today? >> Well, what I was forecasting was based on the fact that the key driver in any technology is volume, volume reduces the cost over time and the volume comes from the consumers. So flash has been driven over the years by initially by the iPod in 2006 the Nano where Steve Jobs did a great job with Samsung and introducing large volumes of flash. And then the iPhone in 2008. And since then, all of mobile has been flash and mobile has been taking in a greater and greater percentage share. To begin with the PC dropped. But now the PCs are over 90% are using flash when there delivered. So flash has taken over the consumer market, very aggressively and that has driven down the cost of flash much much faster than the declining market of HDD. >> Okay and now, so Renen I wonder if we could come to you, we've got I want you to talk about the innovations that you're doing, but before we get there, talk about why you started Vast. >> Sure, so it was five years ago and it was basically the kill of the hard drive. I think what David is saying resonates very, very well. In fact, if you look at our original presentation for Vast Data. It showed flash and tape. There was no hard drive in the middle. And we said 10 years from now, and this was five years ago. So even the dates match up pretty well. We're not going to have hard drives anymore. Any piece of information that needs to be accessible at all will be on flash and anything that is dormant and never gets read will be on tape. >> So, okay. So we're entering this kind of new phase now, with which is being driven by QLC. David maybe you could give us a quick what is QLC? Just give us a bumper sticker there. >> There's 3D NAND, which is the thing that's growing, very very fast and it's growing on several dimensions. One dimension is the number of layers. Another dimension is the size of each of those pieces. And the third dimension is the number of bits which a QLC is five bits per cell. So those three dimensions have all been improving. And the result of that is that on a wafer of, that you create, more and more data can be stored on the whole wafer on the chip that comes from that wafer. And so QLC is the latest, set of 3D NAND flash NAND flash. That's coming off the lines at the moment. >> Okay, so my understanding is that there's new architectures that are entering the data center space, that could take advantage of QLC enter Vast. Someone said they've rented this, a nice set up for you and maybe before we get into the architecture, can you talk a little bit more about the company? I mean, maybe not everybody's familiar with with Vast, you share why you started it but what can you tell us about the business performance and any metrics you can share would be great? >> Sure, so the company as I said is five years old, about 170, 180 people today. We started selling product just around two years ago and have just hit $150 million in run rate. That's with eight sales people. And so, as you can imagine, there's a lot of demand for flash all the way down the stack in the way that David predicted. >> Wow, okay. So you got pretty comfortable. I think you've got product market fit, right? And now you're going to scale. I would imagine you're going to go after escape velocity and you're going to build your moat. Now part of that, I mean a lot of that is product, right? Product is sales. Those are the cool two golden pillars, but, and David when you think back to your early forecast last decade it was really about block storage. That was really what was under attack. You know, kind of fusion IO got it started with Facebook. They were trying to solve their SQL database performance problems. And then we saw pure storage. They hit escape velocity. They drove a truck through EMC sym metrics HDD based install base which precipitated the acquisition of XtremeIO by EMC. Something Renan knows a little bit about having led development, of the product but flash was late to the NAS party guys, Renan let me start with you. Why is that? And what is the relevance of QLC in that regard? >> The way storage has been always, it looks like a pyramid and you have your block devices up at the top and then your NAS underneath. And today you have object down at the bottom of that pyramid. And the pyramid basically represents capacity and the Y axis is price performance. And so if you could only serve a small subset of the capacity, you would go for block. And that is the subset that needed high performance. But as you go to QLC and PLC will soon follow the price of all flash systems goes down to a point where it can compete on the lower ends of that pyramid. And the capacity grows to a point where there's enough flash to support those workloads. And so now with QLC and a lot of innovation that goes with it it makes sense to build an all flash, NAS and object store. >> Yeah, okay. And David, you and I have talked about the volumes and Renan sort of just alluded to that, the higher volumes of NAS, not to mention the fact that NAS is hard, you know files difficult, but that's another piece of the equation here, isn't it? >> Absolutely, NAS is difficult. It's a large, very large scale. We're talking about petabytes of data. You're talking about very important data. And you're talking about data, which is at the moment very difficult to manage. It takes a lot of people to manage it, takes a lot of resources and it takes up a lot, a lot of space as well. So all of those issues with NAS and complexity is probably the biggest single problem. >> So maybe we could geek out a little bit here. You guys go at it, but Renan talk about the Vast architecture. I presume it was built from the ground up for flash since you were trying to kill HTD. What else do we need to know? >> It was built for flash. It was also built for Crosspoint which is a new technology that came out from Intel and micron about three years ago. Cross point is basically another level of persistent media above flash and below Ram. But what we really set out to do is, as I said to kill the hard drive, and for that what you need is to get the price parity. And of course, flash and hard drives are not at price parity today. As David said, they probably will be in a few years from now. And so we wanted to, jumpstart that, to accelerate that. And so we spent a lot of time in building a new type of architecture with a lot of new metadata structures and algorithms on top to bring that effective price down to a point where it's competitive today. And in fact, two years ago the way we did it was by going out to talk to these vendors Intel with 3D Crosspoint and QLC flash Mellanox with NVMe over fabrics, and very fast ethernet networks. And we took those building blocks and we thought how can we use this to build a completely different type of architecture, that doesn't just take flash one level down the stack but actually allows us to break that pyramid, to collapse it down and to build a single system that is as fast as your fastest all flash block device or faster but as affordable as your hard drive based archives. And once that happens you don't need to think about storage anymore. You have a single system that's big enough and cheap enough to throw everything at it. And it's fast enough such that everything is accessible as sub-millisecond latencies. The way the architecture is built is pretty much the opposite of the way scale-out storage has been done. It's not based on shared nothing. The way XtremIO was the way Isilon is the way Hadoop and the Google file system are. We're basing it on a concept called Dis-aggregated Shared Everything. And what that means is that we have the media on one set of devices, the logic running in containers, just software and you can scale each of those independently. So you can scale capacity independently from performance and you have this shared metadata space, that all of the containers can see. So the containers don't actually have to talk to each other in the synchronous path. That means that it's much more scalable. You can go up to hundreds of thousands of nodes rather than just a few dozen. It's much more resilient. You can have all of them fail and you still didn't lose any data. And it's much more easy to use to David's point about complexity. >> Thank you for that. And then you, you mentioned up front that you not only built for flash, but built for Crosspoint. So you're using Crosspoint today. It's interesting. There was always been this sort of debate about Crosspoint It's less expensive than Ram, or maybe I got that wrong but it's persistent, >> It is. >> Okay, but it's more expensive than flash. And it was sort of thought it was a fence sitter cause it didn't have the volume but you're using it today successfully. That's interesting. >> We're using it both to offset the deficiencies of the low cost flash. And the nice thing about QLC and PLC is that you get the same levels of read performance as you would from high-end flash. The only difference between high cost and low cost flash today is in right cycles and in right performance. And so Crosspoint helps us offset both of those. We use it as a large right buffer and we use it as a large metadata store. And that allows us not just to arrange the information in a very large persistent right buffer before we need to place it on the low cost flash. But it also allows us to develop new types of metadata structures and algorithms that allow us to make better use of the low cost flash and reduce the effective price down even lower than the rock capacity. >> Very cool. David, what are your thoughts on the architecture? give us kind of the independent perspective >> I think it's brilliant architecture. I'd like to just go one step down on the network side of things. The whole use of NBME over fabric allows the users all of the servers to get any data across this whole network directly to it. So you've got great performance right away across the stack. And then the other thing is that by using RDMA for NASS, you're able, if you need to, to get down in microseconds to the data. So overall that's a thousand times faster than any HDD system could manage. So this architecture really allows an any to any simple, single level of storage which is so much easier to think about, architect use or manage is just so much simpler. >> If you had I mean, I said I don't know if there's an answer to this question but if you had to pick one thing Renan that you really were dogmatic about and you bet on from an architectural standpoint, what would that be? >> I think what we bet on in the early days is the fact that the pyramid doesn't work anymore and that tiering doesn't work anymore. In fact, we stole Johnson and Johnson's tagline No More Tears. Only, It's not spelled the same way. The reason for that is not because of storage. It's because of the applications as we move to applications more and more that are machine-based and machines are now not just generating the data. They're also reading the data and analyzing it and providing insights for humans to consume. Then the workloads changed dramatically. And the one thing that we saw is that you can't choose which pieces of information need to be accessible anymore. These new algorithms, especially around AI and machine learning and deep learning they need fast access to the entirety of the dataset and they want to read it over and over and over again in order to generate those insights. And so that was the driving force behind us building this new type of architecture. And we're seeing every single day when we talk to customers how the old architecture is simply break down in the face of these new applications. >> Very cool speaking of customers. I wonder if you could talk about use cases, customers you know, and this NASS arena maybe you could add some color there. >> Sure, our customers are large in data. We started half a petabyte and we grow into the exabyte range. The system likes to be big as, as it grows it grows super linearly. If you have a 100 nodes or a 1000 nodes you get more than 10X in performance, in capacity efficiency and resilience, et cetera. And so that's where we thrive. And those workloads are today. Mainly analytics workloads, although not entirely. If you look at it geographically we have a lot of life science in Boston research institutes medical imaging, genomics universities pharmaceutical companies here in New York. We have a lot of financials, hedge funds, Analyzing everything from satellite imagery to trade data to Twitter feeds out in California. A lot of AI, autonomous driving vehicles as well as media and entertainment both generation of films like animation, as well as content distribution are being done on top of best. >> Great thank you and David, when you look at the forecast that you've made over the years and when I imagine that they match nicely with your assumptions. And so, okay, I get that, but that doesn't, not everybody agrees, David. I mean, certainly the HDD guys don't agree but they, they're obviously fighting to hang on to their awesome run for 50 years, but as well there's others to do in hybrids and the like, and they kind of challenge your assumptions and you don't have a dog in this fight. We just want the truth and try to do our best to report it. But let me start with this. One of the things I've seen is that you're comparing deduped and compressed flash with raw HDD. Is that true or false? >> It's in terms of the fundamentals of the forecast, et cetera, it's false. What I'm taking is the new egg price. And I did it this morning and I looked up a two terabyte disc drive, NAS disc drive. I think it was $54. And if you look at the cost of a a NAND for two terabytes, it's about $200. So it's a four to one ratio. >> So, >> So and that's coming down from what people saw last year, which was five or six and every year has been, that ratio has been coming down. >> The ratio between the cost Delta, between HDD is still cheaper. So Renan I wonder one of the other things that Floyer has said is that because of the advantages of flash, not only performance but also data sharing, et cetera, which really drives other factors like TCO. That it doesn't have to be at parody in order for customers to consume that. I certainly saw that on my laptop, I could have got more storage and it could have been cheaper for per bit for my laptop. I took the flash. I mean, no problem. That that was an intelligence test but what are you seeing from customers? And by the way Floyer I think is forecasting by what, 2026 there will be actually a raw to raw crossover. So then it's game over. But what are you seeing in terms of what customers are telling you or any evidence you have that it doesn't have to be, even that customers actually get more value even if it's more expensive from flash, what are you seeing? >> Yeah in the enterprise space customers aren't buying raw flash they're buying storage systems. And so even if the raw numbers flash versus hard drive are still not there there is a lot of things that can be done at the system level to equalize those two. In fact, a lot of our IP is based on that we are taking flash today is, as David said more expensive than hard drives, but at the system level it doesn't remain more expensive. And the reason for that is storage systems waste space. They waste it on metadata, they waste it on redundancy. We built our new metadata structures, such that they everything lives in Crosspoint and is so much smaller because of the way Crosspoint is accessible at byte level granularity, we built our erasure codes in a way where you can sustain 10, 20, 30 drive failures but you only pay two or 1% in overhead. We built our data reduction mechanisms such that they can reduce down data even if the application has already compressed it and already de-duplicated it. And so there's a lot of innovation that can happen at the software level as part of this new direct dis-aggregated shared everything architecture that allows us to bridge that cost gap today without having customers do fancy TCO calculations. And of course, as prices of flash over the next few years continue declining, all of those advantages remain and it will just widen the gap between hard drives and flash. And there really is no advantage to hard drives once the price thing is solved. >> So thank you. So David, the other thing I've seen around these forecasts is that the comments that you can't really data reduce effectively hard disk. And I understand why the overhead and of course you can in flash you can use all kinds of data reduction techniques and not affect performance, or it's not even noticeable like put the cloud guys, do it upstream. Others do it upstream. What's your comment on that? >> Yes, if you take sequential data and you do a lot of work upfront you can write out in very lot big blocks and that's a perfect sequentially, good way of doing it. The challenge for the HDD people is if they go for that for that sort of sequential type of application that the cheapest way of doing that is to use tape which comes back to the discussion that the two things that are going to remain are tape and flash. So that part of the HDD market in my assertion will go towards tape and tape libraries. And those are serving very well at the moment. >> Yeah I mean, It's just the economics of tape are really attractive. I just feel like I've said this many times that the marketing of tape is lacking. Like I'd like to see, better thinking around how it could play. Cause I think customers have this perception tape, but there's actually a lot of value there. I want to carry on, >> Small point there. Yeah, I mean, there's an opportunity in the same way that Vast have created an architecture for flash. There's an opportunity out there for the tech people with flash to make an architecture that allows you to take that workload and really lower the price, enormously. >> You've called it Flape >> Flape yes. >> There's some interesting metadata opportunities there but we won't go into that. And then David, I want to ask you about NAND shortages. We saw this in 2016 and 2017. A lot of people saying there's an NAND shortage again. So that's a flaw in your forecast prices of you're assuming prices of flash continue to come down faster than those of HDD but the shortages of NAND could be problematic. What do you say to that? >> Well, I've looked at that in some detail and one of the big, important things is what's happening in the flash market and the Chinese, YMTC Chinese company has introduced a lot more volume into the market. They're making 100,000 wafers a month for this year. That's around six to 8% of market of NAND at this year, as a result, Samsung, micron, Intel, Hynix they're all increasing their volumes of NAND so that they're all investing. So I don't see that NAND itself is going to be a problem. There is certainly a shortage of processor chips which drive the intelligence in the NAND itself. But that's a problem for everybody. That's a problem for cars. It's a problem for disk drives. >> You could argue that's going to create an oversupply, potentially. Let's not go there, but you know what at the end of the day it comes back to the customer and all this stuff. It's interesting. I love talking about the architecture but it's really all about customer value. And so, so Renan, I want you to sort of close there. What should customers be paying attention to? And what should observers of Vast Data really watch as indicators for progress for you guys milestones and things in the market that we should be paying attention to but start with the customers. What's your advice to them? >> Sure, for any customer that I talked to I always ask the same thing. Imagine where you'll be five years from now because you're making an investment now that is at least five years long. In our case, we guaranteed the lifespan of the devices for a decade, such that you know that it's going to be there for you and imagine what is going to happen over those next five years. What we're seeing in most customers is that they have a lot of doormen data and with the advances in analytics and AI they want to make use of that data. They want to turn it from a cost center to a profit center and to gain insight from that data and to improve their business based on that information that they have the same way the hyperscalers are doing in order to do that, you need one thing you need fast access to all of that information. Once you have that, you have the foundation to step into this next generation type world where you can actually make money off of your information. And the best way to get very, very fast access to all of your information is to put it on Vast media like flash and Crosspoint. If I can give one example, Hedge Funds. Hedge funds do a lot of back-testing on Vast. And what makes sense for them is to test as much information back as they possibly can but because of storage limitations, they can't do that. And the other thing that's important to them is to have a real-time experience to be able to run those simulations in a few minutes and not as a batch process overnight, but because of storage limitations, they can't do that either. The third thing is if you have many different applications and many different users on the same system they usually step on each other's toes. And so the Vast architecture is solves those three problems. It allows you a lot of information very fast access and fast processing an amazing quality of service where different users of the system don't even notice that somebody else is accessing the same piece of information. And so Hedge Funds is one example. Any one of these verticals that make use of a lot of information will benefit from this architecture in this system. And if it doesn't cost any more, there's really no real reason delay this transition into all flash. >> Excellent very clear thinking. Thanks for laying that out. And what about, you know, things that we should how should we judge you? What are the things that we should watch? >> I think the most important way to judge us is to look at customer adoption and what we're seeing and what we're showing investors is a very high net dollar retention number. What that means is basically a customer buys a piece of kit today, how much more will they buy over the next year, over the next two years? And we're seeing them buy more than three times more, within a year of the initial purchase. And we see more than 90% of them buying more within that first year. And that to me indicates that we're solving a real problem and that they're making strategic decisions to stop buying any other type of storage system. And to just put everything on Vast over the next few years we're going to expand beyond just storage services and provide a full stack for these AI applications. We'll expand into other areas of infrastructure and develop the best possible vertically integrated system to allow those new applications to thrive. >> Nice, yeah. Think investors love that lifetime value story. If you can get above 3X of the customer acquisition cost is to IPO in the way. Guys hey, thanks so much for coming to the Cube. We had a great conversation and really appreciate your time. >> Thank you. >> Thank you. >> All right, Thanks for watching everybody. This is Dave Volante for the Cube. We'll see you next time. (gentle music)

>>From around the globe. It's the queue with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. >>Hi, this is Dave Volante. Welcome back to the cubes. Continuous coverage of AWS reinvent 2020, the virtual version of the cube and reinvent. I'm here with David foyer. Who's the CTO Wiki Bon, and we're going to break down today's infrastructure keynote, which was headlined by Peter DeSantis. David. Good to see you. Good to see you. So David, we have a very tight timeframe and I just want to cover a couple of things. Something that I've learned for many, many years, working with you is the statement. It's all about recovery. And that really was the first part of Peter's discussion today. It was, he laid out the operational practices of AWS and he talked a lot about, he actually had some really interesting things up there. You know, you use the there's no compression algorithm for experience, but he talked a lot about availability and he compared AWS's availability philosophy with some of its competitors. >>And he talked about generators being concurrent and maintainable. He got, he took it down to the batteries and the ups and the thing that impressed me, most of the other thing that you've taught me over the years is system thinking. You've got to look at the entire system. That one little component could have Peter does emphasis towards a huge blast radius. So what AWS tries to do is, is constrict that blast radius so he can sleep at night. So non-disruptive replacements of things like batteries. He talked a lot about synchronous versus asynchronous trade-offs and it was like, kind of async versus sync one-on-one synchronous. You got latency asynchronous, you got your data loss to exposure. So a lot of discussions around that, but what was most interesting is he CA he compared and contrasted AWS's philosophy on availability zones, uh, with the competition. And he didn't specifically call out Microsoft and Google, but he showed some screenshots of their websites and the competition uses terms like usually available and generally available this meaning that certain regions and availability zone may not be available. That's not the case with AWS, your thoughts on that. >>They have a very impressive track record, uh, despite the, a beta the other day. Um, but they've got a very impressive track record. I, I think there is a big difference, however, between a general purpose computing and, uh, mission critical computing. And when you've got to bring up, uh, databases and everything else like that, then I think there are other platforms, uh, which, uh, which in the longterm, uh, AWS in my view, should be embracing that do a better job in mission critical areas, uh, in terms of bringing things up and not using data and recovery. So that's, that's an area which I think AWS will need to partner with in the past. >>Yeah. So, um, the other area of the keynote that was critical was, um, he spent a lot of time on custom Silicon and you and I have talked about this a lot, of course, AWS and Intel are huge partners. Uh, but, but we know that Intel owns its own fabs, uh, it's competitors, you know, we'll outsource to the other, other manufacturers. So Intel is motivated to put as much function on the real estate as possible to create general purpose processors and, and get as much out of that real estate as they possibly can. So what AWS has been been doing, and they certainly didn't throw Intel under the bus. They were very complimentary and, and friendly, but they also lay it out that they're developing a number of components that are custom Silicon. They talked about the nitro controllers, uh, inferential, which is, you know, specialized chips around, around inference to do things like PI torch, uh, and TensorFlow. >>Uh, they talked about training them, you know, the new training ship for training AI models or ML models. They spent a lot of time on Gravatar, which is 64 bit, like you say, everything's 64 bit these days, but it's the arm processor. And so, you know, they, they didn't specifically mention Moore's law, but they certainly taught, they gave, uh, a microprocessor one Oh one overview, which I really enjoyed. They talked about, they didn't specifically talk about Moore's law, but they talked about the need to put, put on more, more cores, uh, and then running multithreaded apps and the whole new programming models that, that brings out. Um, and, and, and basically laid out the case that these specialized processors that they're developing are more efficient. They talked about all these cores and the overhead that, that those cores bring in the difficulty of keeping those processors, those cores busy. >>Uh, and so they talked about symmetric, uh, uh, a simultaneous multi-threading, uh, and sharing cores, which like, it was like going back to the old days of, of microprocessor development. But the point being that as you add more cores and you have that overhead, you get non-linear, uh, performance improvements. And so, so it defeats the notion of scale out, right? And so what I, what I want to get to is to get your take on this as you've been talking for a long, long time about arm in the data center, and remind me just like object storage. We talked for years about object storage. It never went anywhere until Amazon brought forth simple storage service. And then object storage obviously is, you know, a mainstream mainstream storage. Now I see the same thing happening, happening with, with arm and the data center specifically, of course, alternative processes are taking off, but, but what's your take on all this? You, you listened to the keynote, uh, give us your takeaways. >>Well, let's go back to first principles for a second. Why is this happening? It's happening because of volume, volume, volume, volume is incredibly important, obviously in terms of cost. Um, and if you, if you're, if you look at a volume, uh, arm is, is, was based on the volumes that came from that from the, uh, from the, um, uh, handhelds and all of their, all of the mobile stuff that's been generating. So there's billions of chips being made, uh, on that. >>I can interrupt you for a second, David. So we're showing a slide here, uh, and, and it's, it's, it, it, it relates to volume and somewhat, I mean, we, we talk a lot about the volume that flash for instance gained from the consumer. Uh, and, and, and now we're talking about these emerging workloads. You call them matrix workloads. These are things like AI influencing edge work, and this gray area shows these alternative workloads. And that's really what Amazon is going after. So you show in this chart, you know, basically very small today, 2020, but you show a very large and growing position, uh, by the end of this decade, really eating into traditional, the traditional space. >>That, that that's absolutely correct. And, and that's being led by what's happening in the mobile market. If you look at all of the work that's going on, on your, on your, uh, Apple, uh, Apple iPhone, there's a huge amount of, uh, modern, uh, matrix workloads are going there to help you with your photography and everything like that. And that's going to come into the, uh, into the data center within, within two years. Uh, and that's what, what, uh, AWS is focusing on is capabilities of doing this type of new workload in real time. And, and it's hundreds of times, hundreds of times more processing, uh, to do these workloads and it's gotta be done in real time. >>Yeah. So we have a, we have a chart on that this bar chart that you've, you've produced. Uh, I don't know if you can see the bars here. Um, I can't see them, but, but maybe we can, we can editorialize. So on the left-hand side, you basically have traditional workloads, uh, on blue and you have matrix workloads. What you calling these emerging workloads and red you, so you show performance 0.9, five versus 50, then price performance for traditional 3.6. And it's more than 150 times greater for ARM-based workload. >>Yeah. And that's a analysis of the previous generation of arm. And if you take the new ones, the M one, for example, which has come in to the, uh, to the PC area, um, that's going to be even higher. So the arm is producing hybrid computers, uh, multi, uh, uh, uh, heterogeneous computers with multiple different things inside the computer. And that is making life a lot more efficient. And especially in the inference world, they're using NPUs instead of GPU's, they conferred about four times more NPUs that you can GPU's. And, um, uh, it, it's just a, uh, it's a different world and, uh, arm is ahead because it's done all the work in the volume area, and that's now going to go into PCs and, and it's going to, going to go into the data center. >>Okay, great. Now, yeah, if we could, uh, uh, guys bring up the, uh, the, the other chart that's titled workloads moving to ARM-based servers, this one is just amazing to me, David, you'll see that I, for some reason, the slides aren't translating, so, uh, forget that, forget the slides. So, um, but, but basically you have the revenue coming from arm as to be substantially higher, uh, in the out years, uh, or certainly substantially growing more than the traditional, uh, workload revenue. Now that's going to take a decade, but maybe you could explain, you know, why you see that. >>Yeah, the, the, the, the, the reason is that these matrix workloads, uh, and also, uh, the offload of like nitro is doing it's the offload of the storage and the networking from the, the main CPU's, uh, the dis-aggregation of computing, uh, plus the traditional workloads, which can move, uh, over or are moving over and where AWS, uh, and, and Microsoft and the PC and Apple, and the PC where those leaders are leading us is that they are doing the hard work of making sure that their software, uh, and their API APIs can utilize the capabilities of arm. Uh, so, uh, it's, it's the it, and the advantage that AWS has of course, is that enormous economies of scale, across many, many users. Uh, that's going to take longer to go into the, the enterprise data center much longer, but the, the, uh, Microsoft, Google and AWS, they're going to be leading the charge of this movement, all of arm into the data center. Uh, it was amazing some of the people or what some of the arm customers or the AWS customers were seeing today with much faster performance and much lower price. It was, they were, they were affirming. Uh, and, and the fundamental reason is that arm are two generations of production. They are in at the moment at five nano meters, whereas, um, Intel is still at 10. Uh, so that's a big, big issue that, uh, Intel have to address. Yeah. And so >>You get, you've been getting this core creep, I'll call it, which brings a lot of overhead. And now you're seeing these very efficient, specialized processes in your premises. We're going to see these explode for these new workloads. And in particular, the edge is such an enormous opportunity. I think you've pointed out that you see a big, uh, uh, market for edge, these edge emergent edge workloads kind of start in the data center and then push out to the edge. Andy Jassy says that the edge, uh, or, or we're going to bring AWS to the edge of the data center is just another edge node. I liked that vision, your thoughts. >>Uh, I, I think that is a, a compelling vision. I think things at the edge, you have many different form factors. So, uh, you, you will need an edge and a car for example, which is cheap enough to fit into a car and it's, but it's gotta be a hundred times more processing than it is in the, in the computers, in the car at the moment, that's a big leap and, and for, to get to automated driving, uh, but that's going to happen. Um, and it's going to happen on ARM-based systems and the amount of work that's going to go out to the edge is enormous. And the amount of data that's generated at the edge is enormous. That's not going to come back to the center, that's going to be processed at the edge, and the edge is going to be the center. If you're like of where computing is done. Uh, it doesn't mean to say that you're not going to have a lot of inference work inside the data center, but a lot of, lot of work in terms of data and processing is move, is going to move into the edge over the next decade. >>Yeah, well, many of, uh, AWS is edge offerings today, you know, assume data is going to be sent back. Although of course you see outpost and then smaller versions of outposts. That's a, to me, that's a clue of what's coming. Uh, basically again, bringing AWS to, to, to the edge. I want to also touch on, uh, Amazon's, uh, comments on renewable. Peter has talked a lot about what they're doing to reduce carbon. Uh, one of the interesting things was they're actually reusing their cooling water that they clean and reuse. I think, I think you said three or multiple times, uh, and then they put it back out and they were able to purify it and reuse it. So, so that's a really great sustainable story. There was much more to it. Uh, but I think, you know, companies like Amazon, especially, you know, large companies really have a responsibility. So it's great to see Amazon stepping up. Uh, anyway, we're out of time, David, thanks so much for coming on and sharing your insights really, really appreciate it. Those, by the way, those slides of Wiki bond.com has a lot of David's work on there. Apologize for some of the data not showing through, but, uh, working in real time here. This is Dave Volante for David foyer. Are you watching the cubes that continuous coverage of AWS reinvent 2020, we'll be right back.

>> Announcer: From San Francisco, it's theCUBE. Covering VMware Radio 2019. Brought to you by VMware. >> Hi, welcome to theCUBE's exclusive coverage of VMware Radio 2019. Lisa Martin with John Furrier, in San Francisco. This is an internal R&D innovation off site that VMware does, lots of innovation going on here from engineers from all over the globe. We're pleased to welcome Joe Baguley, the CTO from EMEA, from VMware. Joe, welcome to theCUBE. >> Hi. >> So we've been having some great conversations this morning about this tremendous amount of innovation, I mean the potential is massive. Not just from Radio, but from all the other innovation programs that VMware has, really speaks very strongly to the culture of innovation that VMware has had. But of course all this innovation has to be able to be harnessed to deliver what customers need. Talk to us about that, you're in the field, field CTO. What is that connection with the innovation that happens within VMware? How do customers help influence that and vice versa? >> Yeah, I think we're very unique in the structure that we've put around that to drive that innovation over the years. So my job as field CTO is, I call it sort of 50, 50. So 50% is Chief Technology Officer, which is this kind of stuff for Radio and 50% is Chief Talking Officer, which is out with our customers and presenting at conferences, et cetera. But the general remit is connecting R&D in the field. And so for eight years now I've been connecting R&D in the field at VMware, I actually did at my previous company as well. And what we've done is, we've built a series of programs over the years to do that, and one of the biggest ones is the CTO Ambassadors. And so that was, you know, you get to a point, you get to a growth size, I've been here eight years, and suddenly you need someone else to help you because I can't be everywhere. And the original role was, back in the day I was hired to scale Steve Herrod, because Steve Herrod couldn't be in Europe all the time, I was like mini Steve Herrod that could be there when needed. But then eventually I can't be in every European country and our major regions as we get bigger and bigger, and we've grown dramatically. So the CTO Ambassadors is to support that. And that's really, we've got 140 of our top customer facing techies from around the globe in this program called the Ambassadors. And they have to be customer facing, and they have to be individual contributors, so like a pre-sales manager or something doesn't count. They're a massively active community, there's a whole bunch of them here at Radio as well. And their job is really that conduit, that source of information, and also a sounding board, a much shorter range sounding board for R&D. So if R&D want to get a feel of what's going on, they don't have to ask everyone they can bounce off the Ambassadors, which is part of what we do, and that makes it easier. >> So like a filter too, they're also also filtering input from the field, packaging it up for R&D. >> Totally. Yeah, and when you're at an organization of our scale, filtering is really important. Because obviously, you can't have every customer directly talking to every engineer, it's never going to work. (laughs) >> I mean another radio project stay right there, a machine learning based champion CTO to go through all the feedback. >> Yeah, so I started my career, with my previous company doing that, I was the filter. So I'd get a hundred questions a day from various people in the field, and 99 of those I'd bounce right back because I knew the answer. But there was the one that I was like, uh. Then I'd turn around to R&D and ask them. But the great thing was that R&D knew that if I was asking then it was a real question, it wasn't the 99. So the CTO Ambassadors, and what we do in Octo Global field is really a method of scaling that. >> I want to ask you about that because that's a great example of here reputation comes in. Because your reputation is on the line if you go back and pull the fire alarm, if you will, send too many lame requests back, you're going to be lame. So you've got to kind of check, balance there. So that begs the question, how do you do the filtering for the champions that work for you? Is there a high bar? Is there a certain line? Like being a kid, you've got to be this tall to ride the roller coaster. Is there criteria? Is there certification? Take us through the filtering there. >> The Ambassador program is a rotating nomination system. So essentially there's a two year tenure. So what happens is, if you're in the field and you want to be an ambassador, which is a really prestigious thing, then you nominate yourself or get nominated and then people vote on you and you put forward your case, et cetera. Essentially it's a democratic process based on your peers and other people in the company. And then after you're allowed a maximum of two years. Sorry, two tenures so you get four years, if that makes sense, I'm not confusing you. >> John: So term limits? >> Yeah there's term limits, right, we have term limits. And after two terms you have to go out for a year to give someone else a chance because otherwise it will just glub- >> It'll turn into the US government. (laughs) >> But no, it's important to maintain freshness, maintain diversity and all those kind of things. And so it comes back to that filter piece we were talking about before. The reputation is massive, of the CTO Ambassadors. I mean when we started this six years ago as a program, most of R&D were like, who are these Ambassador guys? What value are they going to add? Now, if you're in R&D, one of the best things you can say, if you want to get something done, is what the CTO Ambassador said. I mean, literally it is, you can go and we have- >> John: The routine approach to that. Talk about how you guys add in a new category. So, for instance kubernetes, we saw this years ago when KubeCon was started, theCUBE was there present at the creation of that trend we kind of got it right away. Now Gelsinger and the team sees this as a massive traction layer. So that would be an example, where we need an Ambassador. So do you like just create one or how does that work? >> They create themselves, that's the best thing. So we have an annual conference which is in February, held in Paolo Alto where we all get together along with all the chief technologists, which is the level below me. And the principles, which the most senior field people. So literally the best of the best get together. It's about 200 plus get together for a week. And we are an hour and a half on on one with Pat for example, so Pat's there with all of us in a room. But one of the sessions we do is the shark tank, and there's two of them. One of them is, come up with your really cool, crazy, wacky ideas, and the other one is the acquisition shark tank. So there we get the MNA team, include our E-staff sit in, and the Ambassadors, as teams, will come in and present. We think we should acquire, uh because that's making a big difference. The great thing is, not nine times out of 10 but probably seven times out of 10, the E-staff are going, yeah we know about that, when actually we can't really tell you what's going on but yeah we know about them. But there's the two or three times out of 10 that people are like, oh yeah, so tell me more about them. And it might be a company that's just coming up, it might be 2013 and there's this company called Docker that no one's heard of, but the Ambassadors are shouting about Docker, and saying it's a big, you know. So there's that- >> So white space is too emerging you can see it's a telemetry, literally feedback from the field to direct management on business strategy. >> And our customers are pushing our field in directions faster than maybe R&D get pushed if you know what I mean. >> You guys deserve a lot of credit because Pat Gelsinger was just on this morning with Lisa and me, and we were talking about that. He just came back from the Sales President's club cruise, and one of the comments he said was the sales executive said, hey, who does strategy? Because everything's fitting together beautifully. Which kind of highlights how radiance this all progresses, not like magic, there's a process here, and this kind of points to your job is to fit that pieces in, is that correct? >> Yeah. People always say, as a CTO do you all sit down once a week and talk about strategy? And that's not what you do. There's a hive mind, there's a continual interaction, there's conference calls, there's phone calls, there's meetings, there's get togethers of various different types, groups, and levels. And what happens is there's themes that emerge over that. And so my role specifically, as the EMEA CTO is to represent Europe, Middle East, and Africa's voice in those conversations. And maybe the nuances that we might have around particular product requirements or whatever, to remind people that maybe sit in a bubble in Silicon Valley. >> John: I'm sure you raised your hand on privacy and GDPR? (laughs) >> Just a couple of times, yeah. Yeah, now and again. >> The canary in the coal mine is a really big point that helps companies, if they're not listening to the signals coming in. >> Well you do, and you see a lot. There's a lot of the tech companies that I see, it's often defined as the three bubbles, or your Massimo Re Ferrè, who's now at Amazon. When he was here, did this fantastic blog post talking about the first bubble is Silicon Valley, and the second bubble is North America, and the third bubble is everywhere else. And so you kind of watch these things emerge. And my job is to jump over that pop into the Silicon Valley bubble before something happens and say, no you should be thinking about X, you should think about Y. At an event like Radio I've got a force multiplier because I've got 40 plus Ambassadors with me all popping up at all these little booths you see behind you, and the shows, and the talks. >> And the goal here is not to be a bubble, but to be completely one hive mind. >> And the diversity at VMware just blows my mind, it really does. I think a lot of people comment on it quite often, and in fact I've been asked to be a non-exec director of other companies, to help them advise on their culture. Which is not in tech, in culture, which is quite interesting. And so the diversity that we have here is really infusing people to innovate in a way that they've not done before. It's that diverse set of opinions really helps. >> Well it does. And this, from what we've heard, Radio is a very, there's a lot of internal competition, it's like a badge of honor to be able to respond to the call for papers, let alone get selected. Touch on the synergies, the symbiosis that I feel like I'm hearing between the things that are presented here, the CTO Ambassadors and the customers. Like maybe a favorite example of a product or service that came from, maybe a CTO Ambassador, to Radio, to market. >> Yeah, I'm just trying to think of any one specific one. There are always bits and pieces, and things here and there. I think I should have thought of that before I came on really. I think what you're looking at here is, it's much more about an informed conversation and so it's those ideas around the fact. And also, quite often someone will have a cool idea, and they'll go to the Ambassadors, can you find me five customers that want to try this? Bang, we've got it. So if you're out there on a customer, and someone comes to you as an ambassador and says, I've got a really cool thing I'd like you to try. It might be before, we have a thing called Fling, so it might even be before it's made a fling. You probably heard from Morney how that process goes. Then engage fast, because you're probably getting that conduit direct into the core of R&D. So a lot of the features that people see and functions and products et cetera, that people see. A lot of the work you see, we're doing with the next version if you realized our management platform, a lot of that has been driven by work that's been done by Ambassadors in the field, and what we're doing there. All the stuff you'll see, I've got my jacket over there with NANO EDGE written on it. A lot of the EDGE stuff that you see, a lot of the stuff around ESXi on Arm, a lot of the stuff around that is driven specifically around a particular product range. So a really good example is, a few years ago, probably around four, myself and Ray sat down and had a meeting in VMware Barcelona, with a retail customer, and the retail customer was talking about could we get them an STDC, but small enough to fit in every store. They didn't say that at the time, but that's how we kind of got to it. So that started off a whole process in our minds, and then I went back and we, the easiest actual way for me to do it was to then get a bunch of the Ambassadors to present that as one of their innovation ideas, which became NANO EDGE. I originally called it VX Nook, because we were going to do it on intel Nooks. (laughs) Unfortunately the naming committee wouldn't allow VX Nook, so it became NANO EDGE. And that drove a whole change within the company, I think within R&D. So if you think up until that point, four years ago, most of what we were doing was, how do we run things bigger and faster? It was all like Monster VM, remember that? All those kinds of things, right? How do we get these SAP HANA 12 terabyte VMs running? And really NANO EDGE was not necessarily a product, per se but it was more of a movement driven by a particular individual, Simon Richardson, who had got promoted to Principle as a result, through the Ambassador program. That was driven through our R&D to get them to think small as well as big, you know. So next time you're building that thing, how small can you run your SX, how small can we get an SX? >> John: Small, at scale. Which is EDGE, right? >> And, you know, so get small, at scale, which was EDGE. And so suddenly everyone starts talking about EDGE, and I'm like, hang on I've been talking about this for a while now, but we just didn't really call it that. And then along comes technology like Kubernetes, which is how do you manage thousands of small things. And it's kind of, these things come together. But yes, totally, you can almost say our EDGE strategy, and a lot of the early EDGE work was done and driven out of stuff that was done from CTO Ambassadors. It's just one of the examples. >> What are some of the Kubernetes service mesh? Because one of the things we heard from Pat, and we've heard this before, but most recently at Dell Technologies World, in the last couple of weeks, was don't look down, look up. Which basically means we're automating the infrastructure. I get that, I've covered ad nauseam. But looking up the stack means you're talking about kubernetes app developers, you've got cloud native, you've got services meshes, microservices, new kinds of challenges around instrumentation. How are you guys inside Radio looking at that trend? Because there's some commercial impact, You've got Heptio, you've got Craig and the team, some of the original guys. >> Yeah, yeah. >> As well as you have a future state coming out, with state, pun intended, data, stateless. (laughs) These are new dynamics. >> Yeah, yeah. >> What's the R&D take on this? >> So there's two ways that I really talk to people about this. The first one is, I've got a concept that I talk about called application chromatography. Which sounds mental, but you remember from high school probably, chromatography was where you had that really special paper and you put the dot of liquid on and it spread it to all it's constituent parts. That's actually what's happening with our applications right now. So, we've gone through a history of re-platform. You know, mainframe, blah blah blah blah blah. So then when we got to x86, everything's on x86, along comes cloud, and as you know John, for the last 10 years it's been everything's going to cloud because we think that's the next platform. It's not, but then everything's not going to SAS, it's not all going to paths, it's not all going to Functions, it's not all going to containers. What you're seeing is those applications are coming off that one big server, and they're spreading themselves out to the right places. So I talk to customers now and they say, okay, well actually I need a management plan, and a strategy and an architecture for infrastructure as a service, containers as a service, functions as a service platform as a service and SAS, and I need a structure for that on premises and off premises. So that's truly driving R&D thinking is not how do we help our customers get from one of those to the other? They're going to all of them. >> It sounds like a green screen for media. >> It is, and then the other side of that is I've just had a conversation with some of the best, you know, what these events are like? Some of the best conversations in the water cooler, in the- >> In the hallway, yup exactly. >> I've just had a fascinating conversation with one of our guys has been talking about, oh it's really cool if we got kubernetes cause I could use it right down at the edge. I could use it to manage thousands as a tiny EDGE things. And as I'm talking to him and sort of saying, you know what he's doing, I suddenly went, hang on a second, how does a developer talk to that? He's like, well I've not really thought about that. I said, well that's your problem. We need to stop thinking about things from how can that framework help me? But how can I extend that framework? And so a lot of that- >> Moving beyond just standing up kubernetes, for what purpose? Or is that what you know, the why, what? >> So if the developers there, it shouldn't be all. I'm going to use this new framework to solve my problem or the EDGE if an R&D person would, but people like myself are there to drive them to think of the bigger picture. So ultimately at some point a developer in the future is going to want to sit there and through an API, push out software SQL server, a bit of Mongo over here, some stuff on AWS, go and use the service on our Azure at the same time pushing stuff into their own data center and maybe push a container to every store if they're a retailer and they want to do that through one place. That's what we're building. And you know, driving that, all these bits and pieces you see behind you pulling those all together into this sort of consistent operations model. As I'm sure you've heard many of- >> And it's dynamics not static, so it's not like provisioning the old way. You got to track what's being turned on and off because how do you log off? What goes turns on? What services get turned on? Turned off, turned on. >> If you don't get a theme of really, I suppose not only Radio, but our industry of the last few years, people have always said if that cliche change is constant, right? Oh, change is constant. Yet still architects build systems that are static, right? You guys that just, I'm designing an architect in this new system for the next three years. I'm like, that's stupid. What you need to do is design a system that you know is going to change before you've even finished starting it. More or less started going half way through it. So actually, as I see, I was in a fantastic session yesterday with the Architects around ESXi and VCenter, which might be boring to most, but where we architecting that for scale at a huge way. >> Well, I think that's the key thing I mean this is, first of all, we'd love this conversation because, if you can make it programmable with API and have data available, that's the architecture because it's programmable, it's not static. So you let it morph into however the application, because I think I mentioned green screen, you know chroma keys as we have those concepts here, but that's what you're saying. The apps are going to have this notion of, I need an app right now and then it goes away. Services are going to be provisioning and turning on and off. >> There is a transience, there's a transience to infrastructure, there's a transience to applications, there's a transience to components that traditional mechanisms aren't built to do. So if you look at actually, what are we building here? And what's that sort of hive mind message? It's how do we provide that platform going forward that supports transience? that allows customers to come, I mean people used to use the term agile, but it's been over years and it's not right. It's the fact that literally it's a situation of constant change. And what your deploying onto, it's constantly changing and what you're deploying is constantly changing. So we're trying to work out how do we put that piece in the middle, that is also changing but allows you some kind of constancy in what you're doing, right? So we can plug new things in the bottom, a new cloud here, a new piece of software there, a new piece of hardware there or whatever. And at the same time, there's new ways of doing architecture coming on top. That's the challenge of this, the software defined data centers, almost like an operating system for clouds or the future operating system for all apps on all clouds and all of- >> It's a systems thinking for sure, absolutely. >> Let's put your Chief Talking Officer hat on for a second as we look- >> I thought I've been doing that for the last fifteen minutes. (laughs) >> At VMWorld 2019, which is just around the corner. Any cool ANEA customers that are going to be on stage that we should be excited to hear about it? >> Actually, I was having a meeting yesterday morning about that, so I can't really say, but there's some exciting stuff we're lining up right now. We're obviously now is the time we start thinking about the keynotes, now at the time you start thinking about who's on stage. Myself and a few others are responsible for what those demos are, you know the cool demos you see on stage every year. So we literally had the meeting yesterday morning at Radio to discuss what's going to be the wow at VMWorld this year. So I'm not going to give anything away to you. I'll just say make sure you're there to watch it because it's going to be good. And we're also making sure there's a big difference between what we're doing in Moscone now and what we're going to be doing it in Barcelona when we- >> And when expand theCUBE outside of the United States certainly, we'd love to have you guys plug in and localize some of these unique challenges. Like you said, I agree bubble now the west of the world has different challenges content different. >> Definitely, I think to that end, multicloud is probably more of a thing in Europe than it was necessarily in, in North America for a longer time because those privacy laws you talked about before, people have always been looking at the fact that maybe they had to use a local cloud for some things. You know, a German cloud run by German people in a German data center and they could use another cloud like Amazon for other things. And you know, we have UK cloud who provide a specific government based cloud, et cetera. Whereas in America there was, you could use an American cloud and that was fine. So I think actually in Europe we've already been at the forefront of that multicloud thinking for a while. So it's worth watching. >> It is worth watching, I wish we had more time to, so you're just going to have to come back. >> Definitely, anytime tell me when. >> We look forward to seeing you at VMWorld. We thank you for sharing some insights with John and me on theCUBE today. >> Cool, thank you. >> For John Ferrier, I'm Lisa Martin. You're watching theCUBE's exclusive coverage of VMware Radio 2019, thanks for watching. (upbeat music)

>> Announcer: Live from Las Vegas, it's theCUBE, covering IBM Think 2018, brought to you by IBM. >> Welcome back to theCUBE. We are live on Day One of our three days of coverage of IBM Think, the inaugural single event from IBM. I'm Lisa Martin with Dave Vellante. We're at the Mandalay Bay in beautiful sunny Las Vegas, and we're excited to welcome to theCUBE for the first time, Steve Hill, the Global Head of Innovation at KPMG. Welcome. >> Thanks for having me here. >> So you are giving a talk Wednesday, you said, at the event. >> Yes. >> I want to get a little bit into your role at KPMG, as well as your session. So talk to us a little bit about what your role as the Global Head of Innovation. >> So Innovation is an overused word. I don't particular like the word innovation, but in the context of my role, it really is taking a look at our business and our clients, and saying what it is that our clients need for their futures. What's going to create relevance for our clients as we go forward, and how does our portfolio of services relate to that relevance? And if we have gaps where we see our services not serving them best, or not going to serve them best in the future, my job responsibility is to help for strategy purposes and for investment purposes, bring those points to bear, and to get either investment into those areas, right, or changes in the business as appropriate to make KPMG more relevant to our clients, and to their relevance to their clients, right, that's the whole idea. >> So, Lisa and I talk to a lot of people in theCUBE, and we talk lots about invention, startups inventing something or new technology that gets invented, but innovation to us, and I think KPMG is at the heart of this is taking an invention and actually applying it to effect change, getting it adopted, >> That's right. >> and changing a business, a societal change potentially, is that-- >> That's right, I mean, our short phrase for it is idea to cash for our clients, right. I mean at the end of the day, and I think this is profound in certainly corporate governance evolution, right. We've seen the advent of lots of escrow changes of how companies have been managed, enterprise has been managed, right. The Dutch started with the East Indian Trading Company, one of the first large global enterprises, and since that time we've seen the maturation, the new roles. The CIO role didn't exist much prior to 1950, right. Today we're starting to see innovation to be a very important skill and capability for all corporations, all enterprises, including government, right. And I think we're starting to see a maturation of corporate capability, I would say, in the innovation space, because the pace of change is so fast today, the political, economic, technological, social trends are so complex that you've got to get something in your muscle memory that helps you change your business as much as operate it effectively. >> I'd love to know who you're talking to within organizations. You mentioned CIO role, the CISO role, chief data officer. >> Steve: Right. >> Who are the minds that you're helping to bring together so that an enterprise that needs to digitalize to be competitive will survive, right, really survive these days? How do you help them really embrace a culture of innovation as really there's no other choice? How do you get these minds collectively agreeing, yes, this is the direction we need to go in? >> Yeah, I think, I mean first of all, this is a C-suite conversation and a board conversation in many cases, but the reality is when you start to look at the lack of innovation in an organization, right, and when the environment changes, competitors start to change, and the more complex it is, it's harder and harder for companies to pivot and to reinvent themselves. And we're seeing a lot of unbundling of businesses in today's environment, whether it's a company that moves packages, right, or a professional services firm, or a company that used to distribute videos, right. I mean things change and some of the irony is that sometimes the innovation in companies like Kodak, Steve Sasson invented digital camera, it took eight minutes to go from a snap to a picture, but they invented digital technology from cameras, and that the distribution of digital videos is that it actually would help to, further the demise of that organization. So that notion of how do you take change going on in the environment that you're working at, and more importantly your customers and clients, how does that convert into your business, that's a C-suite conversation, and I think innovation can be embodied in a person to help build process, meaning how do you take an idea, how do you look at the marketplace and get sensory input, convert that to ideas for strategy and for investment, and the investments have to be deployed to the field to the business, and that relationship, that whole lifecycle of innovation requires a lot of people from the enterprise to be involved in it. And I would argue the culture has to evolve because until recently most people, in fact, I would say, including current times, most people in organizations are rewarded for doing what they do well, not breaking what they do, not rethinking what they do. And the more you get into that operational mindset, that I want to wring all the efficiencies out of this process that I can. Right, the more you're wed to the status quo, the more somebody comes in from the side and takes you out. >> So I love this conversation 'cause Steve you're able to take the long view and then I want to sort of shorten it up, and then maybe put it into a longer term context. So over our, your guys 20-plus-year careers, mine a little longer, most of this industry has marched to the cadence of Moore's Law, that's where innovation came from. >> Yes. >> How do you take advantage of Moore's Law? How do you go to client server software, whatever it was, the innovation equation is changing now. It seems to be a function of, these guys have been hearing me say this all day but data that's not siloed, but data that you have access to, applying machine intelligence-- >> Yep. >> And then getting cloud, scale, economics and network effects, and then applying it to your business. >> Bingo. >> So talk about how you see the new wave of innovation in this world of digital or however you phrase it. >> Well, it's interesting, I mean, I don't hear a lot of people phrase it the way you do which I think is spot on which is, and my words are, ubiquitous access to technology which is cloud, data, and that's a huge question mark and a big C-suite conversation. Having a lot of data isn't the key, having the right lot of data is the key. Right so Dyson is moving into auto-making today, right. They have a lot of data and it's very different from what the incumbents have. Is it better or worse? We're going to see, right. And then of course smart computers which is the machine intelligence, right. Those three elements, I think they're fundamentally changing labor productivity. And what I would say is to your question is that innovation is really important here because if all you do is take those three elements and you just digitize a status quo process, you might get marginal benefits, you might get some labor productivity enhancement, you may get some marginal improvement, you may change an outsourcing agreement to an onshore RPA deal, but if that's all you do, you're setting yourself up for a disappointment because what's really going to happen with thinkers, i.e., those that have innovations, they're going to rethink the process. Most of our analog systems are created around people checking people, so you may have nine steps, I'm making it up, in a process, that in a digital world only requires one or two or zero when launching in some cases. And so if you can rethink that process to go from a nine-step to a zero-step process or a one-step that's a nano second long, that changes the dynamic of the process. In fact that's not even nirvana, right, the real nirvana is can you change your business model, right? And I would use IBM, since we're here, as an example of going from a big box with a lot of people running around it, called IBM of the past, Watson, to an API engine that David Kenny has helped to build that says, we're going to have a platform business model leveraging network effects, and I want to have a supply and a demand curve that are much faster growing than my sort of organic ways of growing a network could be, right, through people point clicking. That's innovation. >> IBM is an interesting company because it is a company with a lot of legacy, but I think gets, as you just described it, but you look at the top five companies by market value today, they're six, 700-billion dollar market companies, they are data companies not just with a lot of data, but they've put data at the core, so it's Amazon, it's Apple, it's Facebook, it's Google, et cetera. They've put data at the core whereas most organizations, I'm sure many that you deal with, they have human expertise built around other assets that aren't data. It might be factories, it might be the bottling plants, et cetera. So there's a gap, I don't know, machine, AI gap between sort of those that are innovating today, now granted the stock market can change and, >> Sure. >> Who knows, maybe the oil companies will be back involved, not to drop but how do you deal, how do you advice your clients on how to close that gap? That seems like a huge challenge. >> Well it is a huge challenge, and I think, going back to the three elements, it would be very easy for you to dive bomb into a transformation effort and say, I'm going to go and get some smart computers and hire a bunch of people that know machine intelligence and natural language process, and all that stuff, and put them in a room, and go create some applications, the bottom line is, that's not unimportant. You got to get your hand on the mountain and start climbing, but the data piece, I mean, if you don't understand how data is going to be relevant to your business and to your clients and their clients, right, in the future, you lose. And the reason why those five that you talked about earlier are so successful is they think a couple of steps ahead on the data strategy, right, and they're not thinking about, most organizations by the way, they'll say we want a data strategy and then they'll relegate the strategy thinking part to their businesses which are bifurcated, and they look at the world in silos. And they're doing exactly what they should do which is take care of those businesses, but when you step back into those five companies you've talked about, they step back from those silos and say, what is the enterprise implications, and how do I create new businesses with correlations of data that I didn't have before? I think that requires a whole different level of strategy. It's C-suite and board that has to guide those kinds of decisions. You don't see a lot of people really getting their hands dirty around intense forward-thinking data strategies at the enterprise level like we're talking about here. >> You believe we are entering or going to enter shortly a productivity renaissance. >> I agree, yes. >> That's sort of I'm talking about our off-camera conversation. Explain why you think that, compare it to sort of the Industrial Revolution. Take us through your scenario. >> Sure. So, I mean, when you think about labor, I mean, what are the things that I think those three elements will give us as a society, as a global community, is a pretty big S curve jump in labor productivity. In fact we have at KPMG some efforts to quantify what that might be, looking at what we call frontier firms, and applying those practices back to incumbents. 90% of most industry players is saying what are those differences that we can model. The fact of the matter is when you go back to the Mechanical Revolution, the Industrial Revolution, people did everything by hand prior, right. Equipment helped them do things whether it was, even the printing press saw changes in society and labor, but when you start to getting into heavy manufacture in the Industrial Revolution, productivity was enhanced dramatically, and instead of putting all of these people who were doing things by hand out of business and out of work, it actually created more jobs, a lot more jobs, and a lot more wealth for society. I think we're heading for a similar S-curve change with smart computers, cloud, and with data. And that the roboticism of people is going to be automated, and people are going to be allowed to practice and use what's between their ears a lot more. That's going to create value, insight, new questions to be asked. I mean, how many times have you ever heard this? Every time you answer a question on something that's very important, you want to understand there's two more questions to be asked. Medicine is that way for sure. But you're going to start to see massive advancement in areas where people have had to use a lot of cognitive skills, right. It's severely under-leveraged because they were doing so much roboticism and doing things that computers can start to do now. So I think you're going to start to see a renaissance, if you will, of people using their nogers in ways we haven't seen before, and that's going to change the dynamics of productivity and labor in a way that's going to create wealth for everyone. >> And it's going to change industry. So, okay, so I got a bunch of questions for you then. >> Steve: Yep. >> Here we go. And I asked this earlier but I didn't really get an answer. Will machines? >> Steve: From me or from somebody else? >> No, from somebody else. >> Steve: Okay. >> Will machines make better diagnoses than doctors and when? >> I mean, what's the regression line? I mean, the samples said, I think today you'll find machines giving better diagnoses than doctors in some cases. >> Dave: Okay. >> I don't know where the regression line sits today, but if you look at the productivity of doctors going a hundredfold, and the morals scattering around lung cancer, it's impressive. >> Dave: Yeah. >> And do you want a doctor involved? Yes, you do, because part of it is in an orthodoxy of trust which by the way ten years ago, you wouldn't put your credit card online to buy anything, right. It's the same kind of orthodoxy. But I do think that machines can read so much more data, interpolate so many more correlations than people that when you add that to an oncologist for example and cancer, you have a super oncologist capabilities which is really what you're looking for. We're not looking to replace the oncologist per se, what we're looking to do is get the productivity of the oncologist from two to 200. >> I was talking about diagnoses. So you would say yes, okay. >> Yep. >> Will large retail stores mostly disappear in your opinion? >> No, I think they'll change. I think that the customer experience is still, we're still people, we need physical space, and we need physical things to touch, smell, and feel. I think those things will change, but we'll still need experiences. >> I'm going to keep going 'cause Steve's playing along. Will driving and owning your own car become an exception? >> Yes. >> Okay. >> I can elaborate if you want. >> Please, yeah, go ahead. >> So, I mean, the first, I mean, we actually did at KPMG a study called islands of autonomy which modeled LA and San Diego, Atlanta and Chicago, and we modeled how do people move. And we did this for a reason because autonomous vehicles are often times amalgamated as one thing. Oh well autonomous vehicle is coming so you better sell your sports cars and your SUVs, not so fast. The reality is mobility is very different based on where you are. If you're in the middle of Kansas or something, you're going to need a truck to run around in your farm, but if you're in LA or Atlanta or Chicago, you're going to move with autonomy, with autonomous vehicles, and then you're going to really enable mobility as a service very clearly, but differently. The way people move in these cities is different, and if the US auto industry understands those differences, and extrapolates those to a global marketplace, they're going to be very advantaged as mobility as a service becomes real, but the first car that goes, I hate all of the viewers that love this category, but sedan is the first cars to go. I would say sports cars, I race cars, so I love sports cars. People still ride horses today but they don't need them for transportation. And SUVs, right, specialty vehicles that you may, it may not, the economies may not be there, but as we know transportation and car ownership, it's going to change fundamentally, and that's going to have a massive effect on FS, right, insurance companies, banks that are doing loans today. It's going to have a big effect on healthcare. Mobility as a service is going to transcend to healthcare, mobile healthcare in ways that we can't see. >> You got great perspective. I got one more for you, maybe a couple more. Do you think traditional banks will lose control over payment systems? >> Well, a lot of them are already nervous about that, wouldn't you think? >> Yeah, but it hasn't happened yet though. >> I understand, the bottom line is no 'cause I think the traditional banks are getting smarter and they're leveraging their own innovation horsepower to understand things like Blockchain, and how to incorporate those things into their business models. So the answer is I think the way they do, look, banks exist because of one reason, trust. They have trusted brands, right. As long as they can stay current enough to be relevant to your banking needs, you're going to stay with that trusted brand. I think the trick for banks is how do they move fast enough, leverage the technologies that make your life easier, and not waiting three or four days for bank clearing of a check, for example. >> That's they say if you're-- >> And get to that trust in a new way. >> Unless you're a Bitcoin millionaire or a billionaire. >> You still need a bank. >> Maybe somewhere down the line. >> Yeah. >> Okay, last one, I promise. Will robots and maybe even RPA reverse offshore manufacturing advantages? >> Yes. >> Can you elaborate and give us a sense of-- >> I think, first of all, if you really look at what RPA is doing in many ways, is disintermediating the value of geographic location in many ways, right. So where I may have had, again this is important that you understand, so I can still go offshore today and get labor arbitrage and get margin, but I'm not rethinking the business. What I really want to do is own, I want to have more control and I want to have more flexibility and growth in that back office function. So it would behoove when you think about our RPA, and bring in our RPA technology so I have it one onshore, two, leverage the data more securely potentially, and then leverage that data as part of my lake to say how do I use that data to correlate to get to what I really need which is customer relevance at the front office, right. So, look, I think that this whole notion of you're in a different country, and therefore the labor pools are different, and therefore their arbitrage will get benefits from that, those days are over. I mean, it's just a question of when does it die. >> Dave: The data value offsets that arbitrage advantage. >> Well, forget that. The arbitrage is dead itself because the machines, >> Yeah, yeah, right. >> You're talking about orders that have made it to a cheaper per unit cost for an RPA, for a bot to do something than it is for a person that has to eat, sleep, take vacation, and get sick, and all that stuff. And so no matter where they are in the world. So what I would say is that notion is dead. It's just not buried. And overtime we're going to migrate again to machines doing all that robotic stuff. But, again, those people, they're going to do different things. It's not like we're going to see hordes, hundreds of thousands and millions of people not be able to work, I think they're going to be doing different things using their heads in different ways. >> Lisa: I like that answer. >> That's a plan. >> Dave: It's good. >> There's a price somewhere? >> I'm absolutely wrong, I just don't know how wrong, right. >> Well, it's fun to think about, and you provided some context. It was very useful. So, thank you. >> And I imagine folks that are attending your session at IBM Think on Wednesday are going to hear a little bit more into that. So thanks for sharing. >> We going to see some specifics, yeah. >> Thanks for sharing your insights, Steve, and for joining us on theCUBE. You guys, the innovation equation is changing, and I thank you for letting me sit between a very innovative and informative conversation. >> Thank you both. It was fun. >> Thanks Steve. >> For Dave Vellante, I am Lisa Martin. You're watching theCUBE live on Day One of IBM Think 2018. Head over to thecube.net to watch all of our videos with our guests, and siliconanglemedia.com for all the written articles about that. Also check out Wikibon, find out what our analysts are saying about all things digital transformation, Blockchain, AI, ML, et cetera. Dave and I are going to be right back after a short break with our next guest. We'll see you then. (upbeat music)

(lively synth music) >> Announcer: Live, from Santa Clara, California, it's The Cube, covering Open Networking Summit 2017. Brought to you by The Linux Foundation. >> Hey welcome back everybody, Jeff Frick here with The Cube. We are at Open Networking Summit, joined here in this segment by Scott Raynovich, my guest host for the next couple days, great to see you again Scott. >> Good to see you. >> And real excited to have a long-time Cube alumni, a many-time Cube alumni always up to some interesting and innovative thing. (Scott laughs) Sam Greenblat, he's now amongst other things the CTO of Nano Global, nano like very very small. Sam, great to see ya. >> Great to see you too Jim. >> So you said before we went offline, you thought you would retire, but there's just too many exciting things going on, and it drug you back into this crazy tech world. >> Just when you think you're out, they pull you back in. (all laugh) >> All right, so what is Nano Global, for people that aren't familiar with the company? >> Nano Global is a Amosil-Q, which is the compound, which is a nano compound that basically kills viruses, pathogens, funguses, and it does it by attaching itself at the nano level to these microbiol, microlife, and it implodes it, and technically that term is called lysis. >> (Jeff) That sounds very scary. >> It's very scary, because we try to sell it as a hand processing. >> You just told me it kills everything, I don't know if I want to put that on my hands, Sam. (all laugh) >> No it's good, that it kills some of the good bacteria, but it basically protects you for 24 hours. You don't have to reapply it, you can wash your hands. >> (Scott) It's like you become Superman or something. >> Absolutely, I literally use it to wash off the trays on the planes, and the armrests, while the guy next to me is sneezing like crazy, to try to kill any airborne pathogens. >> So what about the nanotechnology's got you traveling up to Santa Clara today for? >> Well, what I'm doing is, one of the things we're working on, besides that, is we're working on genomics, and I worked with some other companies on genomics besides Nano, and genomics has me totally fascinated. When I was at Dell, I went to ASU, and for the first time, I saw, pediatric genomics being processed quickly, and that was in a day. Today, a day is unheard of, it's terrible, you want to do it in less than an hour, and I was fascinated by how many people can be affected by the use of genomic medicine, and genomic pharmacology. And you see some of the ads on TV like Teva, that's genomic medicine, added tax, a genomic irregularity in your DNA, so it's amazing. And the other thing I'm very interested in is eradicating in my lifetime, which I don't know if it's going to happen, cancer, and how you do that is very simple. They found that chemotherapy is interesting, but not fascinating, it doesn't always work, but what they're finding is if they can find enough biometric information from genomes, from your proteomics, from your RNA, they can literally customize, it's called precision medicine, a specific medicine track for you, to actually fight the cancer successfully. >> I can't wait for the day, and hopefully it will be in your lifetime, when they look back at today's cancer treatments, and said "now what did you do again? (Sam laughs) You gave them as much poison as they could take, right up to the time they almost die, and hopefully the cancer dies first?" >> I'll take the - >> It's like bloodletting, it will not be that long from now that we look back at this time and say that was just archaic, which is good. >> It's called reactive medicine. It's funny, there's a story, that the guy who actually did the sequencing of the DNA, the original DNA strand tells, that when he was younger, he basically were able to see his chromosomes, and then he was able to get down to the DNA and to the proteins, and he could see that he had an irregularity that was known for basically cancer. And he went to the doctor, and he said "I think I have cancer of the pancreas." And the guy said "your blood tests don't show it." and by the way you don't get that blood test until you're over 40 years old, PS-1, the PS scan. And what happened was they actually found out that he had cancer of the pancreas, so... >> Yeah, it's predictive isn't it? So basically what you're doing is you're data mining the human and the human genome, and trying to do some sort of - >> We're not doing the 23andme, which tells you you have a propensity to be fat. >> Right, right, but walk us through what you're doing. You're obviously, you're here at an IT cloud conference so you're obviously using cloud technology to help accelerate the discovery of medicine, so walk us through how you're doing that. >> What happens is, when you get the swab, or the blood, and your DNA is then processed, it comes in and it gets cut to how many literal samples that they need. 23andme uses the 30x, that's 30 pieces. That's 80, by the way, gigabytes of data. If you were to take a 50x, is what you need for cancer, which is probably low, but it's, that takes you up to 150 gigabytes per person. Now think about the fact, you got to capture that, then you got to capture the RNA of the person, you got to capture his biometrics, and you got to capture his electronic medical record, and all the radiology that's done. And you got to bring it together, look at it, and determine what they should do. And the problem is the oncologic doctors today are scared to death of this, because they know how, if you have this, I'm going to take you in and basically do some radiation. I'm going to do chemotherapy on you and run the course. What's happening is, when you do all of this, you got to correlate all this data, it's probably the world's largest big data outside of Youtube. It's number two in number of bytes, and we haven't sequenced everybody on the planet. Everybody should get sequenced, it should be stored, and then when you get, that's called a germline you're healthy, then you take the cancer and you look at the germline and compare it, and then you're able to see what the difference is. Now open source has great technology to deal with this flood of data. LinkedIn, as you know open source, cacafa and one of the things that's great about that is it's a pull model, it's a producer, broker, subscriber model, and you can open up multiple channels, and by opening up multiple channels, since the subscribers are doing the pull instead of trying to send it all and overflow it, and we all know what it's like to overflow a pipe. It goes everywhere. But doing it through a cacafa model or a NiFi model, which is, by the way, donated by the NSA. We're not going to unmask who donated it but, (laughs) no, I'm only kidding, but the NSA donated it, and data flows now become absolutely critical, because as you get these segments of DNA, you got to send it all down, then what you got to do is do, and you're going to love this, a hidden Markovian chain, and put it all back together, so you can match the pattern, and then once you match the pattern, then you got to do quality control to see whether or not you screwed it up. And then, beyond that, you then have to do something called Smith-Waterman, which is a QC time, and then you can give it to somebody to figure out where the variant is. The whole key is all three of us share 99.9% of the same DNA. That one percent, tenth of a percent, is what is a variant. The variant is what causes all the diseases. We're all born with cancer. You have cancer in you, I have it, Jeff has it, and the only difference between a healthy person and a sick person is your killer cell went to sleep and doesn't attack the cancer. The only way to attack cancer is not chemotherapy, and I know every oncologic person who sees this is going to have a heart attack, it's basically let your immune system fight it. So what this tech does is it moves all that massive data into the variant. Once you get the variant, then you got to look at the RNA and see if there's variance there. Then you got to look at the radiology, the germline, and the biometric data, and once you get that, you can make a decision. I'll give you the guy who's my hero in this is the guy named Dr. Soon. He's the guy who came up with Abroxane. Abroxane is for pancreatic -- >> Jeff: Who is he with now? >> NantHealth. (both laugh) And why I, he discovered, he knew all about medicine, but he didn't know anything about technology. So then this becomes probably the best machine learning issue that you can have, because you have all this data, you're going to learn what it works on patients. And you're going to get all the records back, so what I'm going to talk about, because they wanted to talk about using SDM, using NFA, opening up hundreds of channels from source to, from provider to the subscriber, or consumer, as they call it, with the broker in the middle. And moving that data, then getting it over there, and doing the processing fast enough that it can be done while the patient still hasn't had any other problems. So I have great charts of what the genome looks like. I sent it to you. >> So it's clear these two fields are going to continue to merge, and the bioinformatics, and IT cloud. >> Sam: They're merging, as fast as possible. >> And we just plug our brain and our bodies into the health cloud, and it tells us what's up. >> Exactly, if Ginni was here, Ginni Rometty from IBM, she would tell you that quantum, she'd just announce it first commercially, an available quantum computer. Her first use for it is genomics, because genomics is a very repetitive process that is done in parallel. Remember you just cut this thing into 50 pieces, you put it back together, and now you're looking to see what's hidden, and it doesn't look like it's normal. If you looked at my genetics, one of the things you'll notice, that I will not consume a lot of caffeine. And how they know that is because there's a set of chromosomes, and my 23 chromosomes, that basically says I won't consume it. Turns out to be totally wrong, because of my behavior over the day. (all laugh) But what the Linux Foundation was interesting is everybody here wants to talk about, are we going to use this technology or that technology. What they want is an application, using the technology, and NantHealth that I talked about, can transport a terabyte of data virtually. In other words, it's not really doing it, but it's doing it through multiple sources and multiple consumers, and that's what people are fascinated by. >> All right, well like I said, Sammy gets into the wild and wooly ways and exciting new things. (Sam laughs) So sounds great, and a very bright future on the health care side. Thanks for stopping by. >> Thank you very much. I hope I didn't bore you with... (Jeff and Sam laugh) >> No, no, no, we don't want more chemotherapy, so that's definitely better to have less chemotherapy and more genetic fixing of sickness. So Sam, nice to see you again, thanks for stopping by. >> Thank you very much. >> Scott Raynovich, Jeff Frick, you're watching The Cube, from Open Networking Summit in Santa Clara, we'll be back after this short break. Thanks for watching. (synth music) >> Announcer: Robert Hershevech.

(uplifting music) >> Narrator: Partners. Now, here are your hosts, John Furrier and Stu Miniman. >> US Amazon Web Services re:Invent 2016 their annual conference. 32,000 people, record setting number. I'm John Furrier, Stu Miniman co-host in theCUBE for three days of wall-to-wall coverage. Day two, day one of the conference our next guest is Kiran Bhageshpur, who's the CEO and co-founder of Igneous Systems. He was a hot startup in the, I don't want to say storage area, kind of disrupting storage in a new way. Kiran great to see you, thanks for coming on theCUBE. >> Thanks a lot, glad to be here, John. >> So, you're living the dream the cloud dream, it's not a nightmare for you because you're one of the progressive new ways. I want to get your thoughts on Andy Jassy's Keynote because he really lays out the new mindset of the cloud. Your startup that you founded with your team is doing something kind of, I won't say contrarian, some might say contrarian, but contrarians usually become the big winners, like Amazon was a contrarian now they're obviously the winning. So, take a minute to explain what you guys are doing. You're funded by Madrona Ventures and NEA, New Enterprise Associates, great backers, smart. Your track record at Isilon, you know the business. Take a minute to describe what you guys are doing. >> Great, yes I will. So, Igneous Systems was founded to really deliver cloud services to the enterprise data center for data-centric workloads. So what to we mean by that? With cloud services, just like with Amazon, customers don't buy hardware, license software. They do not monitor or manage your infrastructure. They consume it across API and they pay for it by the drip rather than the drink. Similarly, the same case with us but we make that all available within a customer's data center itself. And we focus on sort of data-centric, data heavy workloads. I don't know whether you saw James Hamilton's-- >> Yeah. >> Speech yesterday, but he also talked about the same thing that Mary Meeker talked about earlier this year which is an overwhelming amount of data generated today is machine generated and machine consumed and that's growing really rapidly. And our view is the same techniques that have made Amazon so powerful and so valuable are needed out at the edge or on-premise, close to where users and machines are generating and using the data. So that's kind of what we do. Very much the cloud model taken out to the enterprise data center. So, think of it as a hybrid. >> Kiran, let's talk about storage and where it lives because I think something that many people miss is that cloud typically starts with very compute heavy types of applications and we know that data is tough to move. I mean, Amazon rolled out a truck to show how they move 100 petabyes. And not just to show it, this is a new product they had 'cause customers do want to be able to migrate data and that's really tough and takes a lot of time. You mentioned IoT at the edge, they announced kind of query services on your data up in S3, so what are you hearing from customers? You know, kind of large data from your previous jobs. Where's the data living, where's data being created, where does data need to be worked on and how does that play into what you're doing? >> That's a great question Stu. What we find with customers, especially the one's with large and growing data sets is there is still a challenge of not just how to go store it but how to go process that on the fly. On a camera today or a next generation microscope could produce tens of terabytes of data per hour and that is not stuff that you can move across the internet to the cloud. And so the ask and the call from customers is to be able to go ingest that, curate that, process that locally and the cloud still has a very compelling role to play as a distribution mechanism and for a sharing mechanism of that data. I found it pretty wild that a big part of Andy Jassy's Keynote was for the first time they talked about hybrid and acknowledged the fact that it is the cloud and cloud-like techniques out in the enterprise data center. So, I look at that as hugely validating what we have been talking about which is bringing cloud native paradigms into the enterprise data center. >> Let's talk about that operational model because what you're highlighting and what Jassy pointed out is an operational model now for IT. >> Kiran: Yep. >> How are you guys creating value for customers? And be specific, is it, 'cause the on-prem is not going away, we've talked about this before and certainly VMware sees the cloud but also on-prem too. What is the value for customers? Because now this operational model of on the cloud is there, one way-- >> Yes. >> But how do I get cloud inside my data center? >> The way we do that is, very similar to the cloud operating model, right? So, we sell customers essentially an annual subscription service and that service is delivered using appliances that are purpose-built. Think of it as, like snowball, if you will, that goes into the customers data centers fully managed by our software running in our cloud. So, for a customer point of view, it happens to live within their data center, but they are consuming it pretty much the same way that they would consume a cloud service. That's the value, it's the same tool chains, the same programming paradigms that they are used to with, say, a native OS. But within their data centers at lower latencies addressing the same things that Andy Jassy brought up, which is you need a truck to go move large amounts of data. >> Well, I want to also bring up James Hamilton's presentation. You mentioned that yesterday one of the key points he made was that scaling up for these peak loads like they have on the Friday's, their Prime Friday spikes, they do instantly and elastic is a big deal we know that. His point though was they would have to provision on bare metal or in the data center months in advance to even rationalize what that peak could be which still is an unknown number. So, the scale point and provisioning is a huge headache for customers, so that's why that's relevant. How do you guys answer that claim when you say, "Hey, I need stuff to be done fast, "I don't have time to provision"? How do you guys, do you address that at all? How do you talk to that specific point? >> We take care of the provisioning and the additional expansion and shrinking of capacity within the customer's data center, because just like Amazon monitors their infrastructure users in the data center, we do that for our infrastructure within the customer's data center, and therefore we can react to go scale up or scale down. But then there's another point to the whole thing, which is the interesting thing is the elasticity is much more important for compute as opposed to data. Data just linearly grows, you never throw that stuff away. The things that you captured, the processing is highly elastic and you might want to do some additional processing and burst out and so on. So, that's another aspect of hybrid we see with our customers which is, I want my work flow here, I want to be able to burst out to the public cloud for that peak capacity that I don't want to have infrastructure locally for. >> So Kiran, sorry. So James Hamilton's presentation talks a lot about, just that hyper scale. They claim they've got the most scale and therefore nobody else should do anything because oversimplifying a little bit, but we've got the best price, we've got the whole stack, give you all the solutions. You talk to enterprises. Scale means different things for different applications for what I need to get done, what I have. What does that really mean to you? How does that hybrid piece fit in to the whole scale discussion? >> So, a lot of what we do is really ride on the coattails of the Amazon and the Google and the Microsoft because everyone has access to the same raw components, hard drives and CPUs and so on and so forth. And then the question is how do you go assemble those in a form factor that is appropriate for that particular use case? If you're going to go build a data center that's one level of scale, but if you look at a vast majority of applications and enterprises, their scales are much smaller. So, we literally look at taking a rack of infrastructure which might have, say, 40 servers and a couple of switches in sheet metal and shrinking that to a 4U form factor which has got 60 of our nano servers which has got switches and has got sheet metal. So, it's shrinking the whole thing down. The economy's of scale are still quite compelling because we use the exact same raw materials from the same suppliers to the cloud guys, right? And the real difference in cost is how things are put together and how they are operationalized. In which case, we are much more like Amazon than not. >> The other thing that's really interesting to watch, if you look at Amazon's storage move, is storage is in a silo, they've now got all these services that I can start doing this. How does the enterprise look at that? How does the solution like yours enable us to be able to use our data more? >> I absolutely think there is a palpable need for and desire for those sorts of new paradigms in the enterprise data center too because what you can do with not just storage but with lambda and with a bunch of other advanced services on top of that, what that really does is allows enterprises and customers to just focus on what is differentiated to them. This is the whole low-code, no-code moment, if you will, right, movement, and that's a compelling trend. It is something that we've actively embraced. We've got our architecture enables that on day one and that's kind of the way you're going to go build applications now onwards. >> So will we see lambda functions calling things on your end? >> Stay tuned. I think my, yeah, stay tuned. >> That's a smile, that's a yes. (laughs) Talk about the drivers in your business, 'cause you guys are new, you're a startup. For the folks watching you're making some bets, big bets obviously funded by some pretty big venture capitalists out there. What is your big bet? Is it true private cloud is going to emerge on-premise? Is the bet that cloud adoption with scalable compute and storage is going to be unmanaged or manageless or serverless, what's the big bet? >> So our bet is the cloud is going to win and I mean the cloud paradigm, which means consuming infrastructure by the drip rather than the drink across APIs. Flexibility, agility is going to win. One answer which is very compelling is the public cloud today. We believe that similar patterns will exist on the on-premise world and we believe we are very well positioned to supply that thing. And the infrastructure which shrinks would be very traditional infrastructure and software technology stacks which has really existed in the enterprise data center for the last 20 years. That will shrink and everything will look similar as in highly flexible, highly scalable, very easy to go put things together and you're going to have very similar patterns in both the public cloud and within your data center. >> Our Wikibon research team is looking at the practitioner side of the market. One of the things they're observing is, among a lot of things, is that you're seeing AWS teams come together. We're seeing Accenture was on earlier talking about the same dynamic. That's the pattern that we're seeing is these teams are coming together, some handful of people, the pizza box teams-- >> Yep. >> As Jeff Bezos calls it, growing into fully functional bigger teams. So, depending upon that progression, what's your advice to practitioners? And how do you add value into this momentum of as they scratch their head go, "Okay, we're going to go to the cloud"? So they know that's the mandate. How do you help them and why should they look at your solution and where do you fit into that? >> So one of the things customers and partners tell us is we are a great on-ramp to the cloud if you will. Everybody wants to embrace the new programming patterns, new programming paradigms and many people have taken that big leap and done the full shift in one step. You've heard Finra, you've heard Capital One all of these guys talk, but not everyone is that far out there. So what we sort of become for these folks is a stepping stone. We are on-premise. It allows them to get used to it. They start using the same patterns that can scale there. There can decide what workflows remain local and why and what go there, and that's our view. We very much live in they hybrid world to burst out to the world, bring it back as appropriate. >> Kiran thanks so much for coming on theCUBE, we really appreciate it, we're getting the break but I do want to ask one personal question. You're back in the entrepreneurial zeal again, you've got the startup, you have some capital but you're not loaded with cash, a good amount to achieve what you need to do. What's it like for you right now? I mean, what do you believe in? What's your guiding principles and what's it like to get back on the entrepreneurial treadmill again? >> You know, it's actually quite exhilarating and liberating to be back in a startup environment because it forces you to focus on what is important what is urgent and important at all points in time, and a guiding principle for us is less is more. Let's be driven by customers and do what is required there and then slowly extend that out. And actually, being a startup and not having infinite money to throw like, large legacy players would frees you from trying to do too many things and focus on only what is important and that's really key to success. >> And how are you making the decisions as an executive like, product-wise? Is it more agile, are you guys doubling down? >> Very, very agile, we can move very quickly. Since we are delivering a service, we are continuously updating infrastructure just like Amazon does within their data center so we can turn around very, very quickly. So I'm very impressed the fact that the Amazon rolls out 1,000 new features this year, but I can see how that is possible at scale and that's what we're doing. >> At Isilon you were very successful scaling up that generation of web scale, we saw that with Facebook and the Apples of the world. What's different now than then? Just in the short years between the web scalers dominating to now full Multi-Cloud, Hybrid Cloud cloud. In your mind, what's different about the landscape out there? Share your thoughts. >> I think there's a couple of things. One of them is Isilon was incredible, was a very useful infrastructure, was something that was easy to deploy, but it was still that something you built, you managed, you owned, if you will. The big transition is away from that, from build to consume and not worry about that infrastructure at all. And that is not something that you can retrofit into an existing architecture, you have to start from scratch to go do that. So, that's the biggest number one. Two, second one is just the scale is bigger. You heard Andy Jassy talk about the exobyte moving problem and he commented on the fact that exobytes are not all that rare and he's true because you go back 10 years ago, maybe four companies had an exobyte problem. It's now a lot more than that. And so the scale is two or three orders of magnitude larger than when Isilon was growing up. >> Scales at table stakes and consumption of infrastructure, that's a dev-ops ethos gone mainstream. >> Yes. >> Thanks so much for sharing. We're live here in Las Vegas for Amazon re:Invent. I'm John Furrier, Stu Miniman, we're back with more live coverage, three days of wall-to-wall coverage. theCUBE will be right back. (upbeat electronic music) (relaxing guitar music)

okay we're back live here at the OpenStack summit in Portland Oregon I'm John furry the founder SiliconANGLE comment rose mykos Dave a latte from Wikibon org this is silicon angles the cube our flagship program we go out to the events and extract the signal from the noise and certainly here OpenStack there's not a lot of noise but a lot of signal a lot of developers a lot of use cases really really the Alpha geeks the practitioner is really putting new technology into place to power this modern era of computing cloud mobile and social David Floria we're here with Demetri stilly at us from nudge networks and mountain view welcome to the cube thank you David I want to get your take on this before we set up this interview because honestly we've heard from right scale there in the management side just previous we've had Rackspace on earlier there on the Omni on the provider side we had big switch-on software-defined networking and now Dimitri's company the software is eating the world what's your take on the SDN market right now relative to OpenStack relative to open saying well what you're clearly wanting to do in every part of it is separate out all of the different layers and you ought to be able to separate out the physical and the the logical and the the software is the way that that's going to be done so instead of having to have a switch which is a piece of hardware and the software you want to separate the two out so that you have the logical function and the physical function from from the two pieces so that's very important to be able to contribute to every layer take new technologies along with you and then define the software element of that as the piece that you keep constant as technologies themselves adjust so durable code we walk manageable and build on and we clean can take advantage of new technologies as they come along and obviously I coming back to you what are you contributing what I think needs to be contributing was the white space in that area that you're going after right so see when people started thinking about the cloud and OpenStack and to always kind of think they they quickly realize that the network is a fundamental piece right you have to start with the network you have to interconnect your components and so on the angle that we are taking is yes it's good with in your data center within your cloud you have to create this network services interconnect applications and so on but much more importantly you need to be able to dynamically connect these applications with your existing network services right so you have a large amount of enterprise VPN services you have hybrid clouds coming out so you need to be able the moment you activate a network service in the data center to be able to seamlessly interconnect this now with your enterprise side with other network services in other data centers in other clouds and so on right so the network is always a network of networks and we have to bring everything together we cannot just restrict ourselves with is the confinements of a single administrative model so that's that's a fundamental part of what we are trying to to bring here together okay and so how are you fitting in with the the network layer right so our view is say that first of all we need to talk both both languages if you don't think of it as a as a translation thing right so we need to understand the language of the cloud we need to understand the language of the application developers in the cloud they want to use some abstract mechanism to define their network services and install them if you want in the hypervisors and OpenStack quantum seems to be the prevalent way to do that so that's language number one but then we have all these thousands of networks out there where their language is bgp so what we are doing is we are marrying the two we allow you to codon define services in OpenStack and we allow you to define the mekinese between interconnect the service is automatically with all the other networks that are out there right so I call it sometimes we are just translating between languages all right a language translator live from an application point of view they want to consume resources and previously networks and the computers were the main things they consumed but it seems now that sorry computing and storage with the main things they consumed but it now it seems that networks themselves have to pay a much bigger role in providing a quality of service to those places Rick you've got a quality of service down in the nano seconds when you get to the server level and used to have milliseconds for the for the storage side it's now coming down to micro second what are you doing to make sure that that quality of service no it is not just the bandwidth but it's also the latency are you planning to marry that see the weight datacenter networks of all these people are quickly realizing that the same if you want principles that we used in order to build the Internet itself can be used inside the data center so if you think about the internet right in the internet there is voice services that is video services there is all these other services running and they are actually running by assuming you have a well-engineered IP network and then you run the service is at the edges if you want all that you push all the intelligence at the edges it's the same thing where the network on the data center is going the data center network becomes a very scalable IP fabric it it is very well managed if you want very well traffic engineer and you push the edges at the hypervisors you push essentially the services at the hypervisors where traffic is differentiated so if you see for example a tenant misbehaving you are going to block him at the hypervisor layer if you're going to provide us or map different tenants to different classes of traffic it's happening at the hypervisor so the center of the network behaves like a scalable IP fabric and all the intelligence it's pushed around the edges and the reason you want to do that is because this allows you the ultimate scalability right the network or doesn't need to know about every flow that goes into the through through the corner of the network there right you don't need to know the IP addresses of virtual machines you don't need to know what individual virtual machines no need to know I want to do there you just need to worry about aggregates so you can engineer and scale the core make it very cheap and because you make it very tip you can increase the capacity at the core and you can say distribute all the intelligence at the edges of the network right but so you said that you can do the hypervisor and that's obviously on the compute side that side of it but what about the data network isn't that a don't you need to regulate the priorities and flex all the data through and isn't that today that's that's a very big part of it yes but it is still happening at the hypervisor right the the first touch of it enough an application with a network it is not anymore the top of rack sheets let's say on the data center but it does it is actually the hypervisor virtual sheets right that's the first time that you see a packet when a packet comes out of a virtual machine the first time you see it is at the hypervisor itself and at this layer when the first time you see the bucket of the hypervisor itself is where you apply all your policies right in other words the edge of the network is not the hardware is not the switch on the top of rack the edge of the network is inside the server now ok yeah ok excellent so I want to ask you we have a couple minutes left here I wanted we have two minutes less I want to get your perspective on the state of the business around OpenStack what is your view ok because your chief architects you're looking at the tech yes and you but you have to intersect the business objectives what are you seeing as the core business drivers that are that are causing you to make your technology in a certain way right so it's clear that what people want to do is they they want to provide this ability to their end users to consume services rapidly right that is what is driving this call OpenStack development and more important the community came together in order to unify view on the core engine and the core AP is in order to make this consumption of services very easy and in order to allow the application developers to move from one cloud to the other and so on right what we do is what we try to do is in addition is expanding view on this model amazing the network as consumable as the storage and compute facilities right and I'm not talking just about the network in the data center I'm talking about also the network in the way that the service in the data center of a cloud provider will interconnect with the enterprise read if you see then the next if you want Holy Grail that everybody is talking about is the hybrid cloud the hybrid cloud is only possible if you can connect the network and the services in the service provider cloud with a network and services in the in the in the enterprise itself right so they what links the two together is the network so we have to make this network to be consumable final question for you is actually DevOps is a mindset we heard from right scale that that adoption is in mainstream enterprises and service providers but the word infrastructure as code is becoming more popular outside of the the geeks and the album the architects the coders what in your mind how would you describe infrastructure as code to the folks out there give it a try it's okay no right answer it's a moving target that's what it is realities it's that the applications and code is a living organization it's constantly changing and you cannot assume at any point it's static right it's not there it's not the good old days if you want and that's what it really means right it's a living organism it it will constantly adapt to the new to the new requirements out there like switches in the old days you knew exactly ports and you you knew i was going now it's all kinds of weird stuff happening right it's all stuff you you have to be you you have to accept change if you want right so it's the actually there is a there is an okay Isaac Asimov code right there another the author of the science fiction yes that's the only constant is change yeah we should be no project just on the network genome here Software Defined Networking Dmitry stylianos thanks for jumping inside the cube again you're here like with a lot of the chief architects making things happen congratulations thanks for joining us thank you we'll be right back with more analysis from David's lawyer after the at break on a breakdown day 1 and day chu here in more depth from the analysts here at opens Dec 2 SiliconANGLE Gibbons exclusive coverage of OpenStack summit be right back