>> Narrator: Live, from Las Vegas, it's theCUBE, covering AWS re:Invent 2017, presented by AWS, intel, and our ecosystem of partners. Hello, everyone, welcome back to theCUBE. This is Silicon Angle's exclusive coverage with theCUBE, here at Amazon, re:Invent 2017. It's our 5th year covering Amazon's explosive growth. I'm John Furrier, the founder of Silicon Angle media. I'm here with Justin Warren, my cohost here, our next guest on set one is Tom Siebel, who is the founder and CEO of C3 IOT, industry legend, knows the software business, been around the block a few times, and now part of the new wave of innovation. Welcome to theCUBE. >> Thank you. >> I hear you just got in from San Francisco. What a world we're living in. You're at the front-end of your company that you founded and are running, an IOT big data play, doing extremely well. Even last year, the whisper in the hallway was C3 IOT is absolutely doing great, in the industrial side, certainly in the federal government side, and on commercial, congratulations! >> Thank you. >> What's the update, what's the secret formula? >> Well, we live at the convergence of elastic cloud computing, big data, AI, and IOT, and at the point where those converge, I think, is something called digital transformation, where you have these CEOs that, candidly, I think, they're concerned that companies are going through a mass-extinction event. I mean, companies are being, 52% of the Fortune 500 companies, as of 2000 are gone, right, they've disappeared and it's estimated as many 70% might disappear in the next 10 years, and we have this new species of companies with new DNA that look like Tesla and Uber, and Amazon, and, they have no drivers, no cars, and yet they own transportation, and I think that these CEOs are convinced that, unless they take advantage of this new class of technologies that they might be extinct. >> And it's certainly, we're seeing it, too, in a lot of the old guard, as Andy Jassy calls it, really talking about Oracle, IBM, and some of the other folks that are trying to do cloud, but they're winning. I gotta ask you, what's the main difference, from your perspective, that's different now that the culture of a company that's trying to transform, what's the big difference between the old way and new way now, that has to be implemented quickly, or extinction is a possibility? I mean, it's not just suppliers, it's the customers themselves. >> The customers have changed. >> What's the difference? >> So, this is my 4th decade in the information technology business and I've seen the business grow from a couple hundred billion to, say, two trillion worldwide, and I've seen it go from mainframe to mini-computers, to personal computers to the internet, all of that, and I was there when, in all of those generations of technology, when we brought those products to market, would come up in the organization, through the IT organization, to the CIO, and the CIO would say, "well, we're never gonna use a mini computer." or, "we're never gonna use relations database technology." or, "we're never gonna use a PC." And so, you'd wait for that CIO to be fired, then he'd come back two years later, right? Now, so meanwhile we build a two trillion dollar information technology business, globally. Now, what's happening in this space of big data, predictive analytics, IOT, is all of a sudden, it's the CEO at the table. CEO was never there before, and the CEO is mandating this thing called digital transformation, and he or she is appointing somebody in the person of a Chief Digital Officer, who has a mandate and basically a blank check to transform this company and get it done, and whereas it used to be the CIO would report to the CEO once a quarter at the quarterly off-site, the Chief Digital Officer reports to the CEO every week, so, and virtually everyone of our customers, CAT, John Deere, United Healthcare, you name, ENGIE, Enel, it's a CEO-driven initiative. >> You bring up a good point I wanna get your thoughts on, because the old way, and you mentioned, was IT reporting to the CIO. They ran things, they ran the business, they ran the plumbing, software was part of that, now software is the business. No one goes to the teller. The bank relationship's the software, or whatever vertical you're in there's now software, whether it's at the edge, whether it's data analytics, is the product to the consumer. So, the developer renaissance, we see software now changing, where the developer's now an influencer in this transformation. >> True. >> Not just, hey, go do it, and here's some tools, they're in part of that. Can you share your perspective on this because, if we're in a software renaissance, that means a whole new creativity's gonna unleash with software. With that role of the CDO, with the blank check, there's no dogma anymore. It's results. So, what's your perspective on this? >> Well, I think that there's enabling technologies that include the elastic cloud that include, computation and storage is basically free, right? Everything is a computer, so IOT, I used to think about IOT being devices, it's that IOT is a change in the form-factor of computers. In the future, everything's a computer, your eyeglasses, your watch, your heart monitor, your refrigerator, your pool pump, they're all computers, right, and then we have the network effect of Metcalfe's law, say we have 50 billion of theses devices fully connected and well, that's a pretty powerful network. Now, these technologies, in turn, enable AI, they enable machine learning and deep learning. Hey, that's a whole new ball game. Okay, we're able to solve classes of problems with predictive analytics and prescriptive analytics that were simply unsolvable before in history and this changes everything about the way we design products, the way we service customers, the way we manage companies. So, I think this AI thing is not to be underestimated. I think the cloud, IOT, big data, devices, those are just enablers, and I think AI is-- >> So, software and data's key, right? Data trains the AI, data is the fundamental new lifeblood. >> Big data, because now we're doing, what big data is about, people think that big data is the fact that an exabyte is more than a gigabyte, that's not it. Big data is about the fact that there is no sampling error. We have all the data. So, we used to, due to limitations to storage and processing we used to, you know, basically, take samples and infer results from those samples, and deal with it on the level of confidence error that was there. With big data, there's no sampling error. >> It's all there. >> It is a whole different game. >> We were talking before, and John, you mentioned before about the results that you need to show. Now, I know that you picked up a big new customer that I hope you can talk about publicly, which is a public-sector company, but that sounds like something where you're doing predictive maintenance for the Air Force, for the U.S. Air Force, so that's a big customer, good win there, but what is the result that they're actually getting from the use of big data and this machine learning analytics that you're doing? >> By aggregating all the telemetry and aggregating all their maintenance records, and aggregating all their pilot records, and then building machine learning class of ours, we can look at all the signals, and we can predict device failure or systems failure well in advance of failure, so the advantage is some pretty substantial percentages, say of F16s, will not deploy, of F18s will not deploy because, you know, they go to push the button and there's a system failure. Well, if we can predict system failure, I mean, the cost of maintenance goes down dramatically and, basically, it doubles the size of your fleet and, so the economic benefit is staggering. >> Tom, I gotta ask you a personal question. I mean, you've been through four decades, you're a legend in the industry, what was the itch that got you back with this company. Why did you found and run C3 IOT? What was the reason? Was it an itch you were scratching, like, damn, I want the action? I mean, what was the reason why you started the company? >> Well, I'm a computer scientist and out of graduate school, I went to work with a young entrepreneur by the name of Larry Ellison, turned out to be a pretty good idea, and then a decade later, we started Siebel Sytems, and I think, well, we did invent the CRM market and then it turned out to be a pretty good idea and I just see, at this intersection of these vectors we talked about, everything changes about computing. This has been a complete replacement market and I though, you know, there's opportunity to play a significant role in the game, and this what I do, you know. I collect talented people and try to build great companies and make customers satisfied. This is my idea of a good time. You're on the beach, you're on your board hangin' 10 on the big waves. What are the waves? We're seeing this inflection point, a lotta things comin' together, what are the waves that you're ridin' on right now? Obviously, the ones you mentioned, what's the set look like, if I can use a surfing analogy. What's coming in, what are the big waves? The two biggest ones are IOT and AI. I mean, since 2000 we've deployed 19 billion IOT sensors around the world. The next five years, we'll deploy 50 billion more. Everything will be a computer, and you connect all these things that they're all computing and apply AI, I mean we're gonna do things that were, you know, unthinkable, in terms of serving customers, building products, cost efficiencies, we're gonna revolutionize healthcare with precision health. Processes like energy extraction and power delivery will be much safer, much more reliable, much more environmentally-friendly, this is good stuff. So, what's your take on the security aspect of putting a computer in everything, because, I mean, the IT industry hasn't had a great track record of security, and now we're putting computers everywhere. As you say, they're gonna be in watches, they're gonna be in eyeglasses, what do you see as the trend in the way that security is gonna be addressed for this, computers everywhere? Well, I think that it is clearly not yet solved, okay, and it is a solvable problem. I believe that it's easier to secure data in cyber space than it is in your own data room. Maybe you could secure data in your data room when it took a forklift to move a storage device. It doesn't take a forklift anymore, right? It takes one of these little flash drives, you know, to move, to take all the data. So, I think the easiest place we can secure it is gonna be in cyber space. I think we'll use encryption, I think we'll be computing on encrypted data, and we haven't figured out algorithms to do that yet. I think blockchain will play an important role, but there's some invention that needs to happen and this is what we do. >> So, you like blockchain? >> I think blockchain plays a role in security. >> It does. So, I gotta ask you about the way, you're sinking your teeth into a new venture, exciting, it's on the cutting-edge, on the front lines of the innovation. There are a lotta other companies that are trying to retool. IBM, Microsoft, Oracle, if you were back them, probably not as exciting as what you're doing because you've got a new clean sheet of paper, but if you're Oracle, if you're Larry, and he went to be CTO, he's trying to transform, he's getting into the action, they got a lot to do there, IBM same thing, same with Microsoft, what's their strategy in your mind? If you were there, at the helm of those companies, what would you do? >> Well, number one, I would not bet against Larry. I know Larry pretty well and Larry is a formidable player in the information technology industry, and if you have to identify one of four companies that's surviving the long-run, it'll be Oracle that's in that consideration, in that set, so I think betting against Larry is a bad idea. >> He'll go to the mat big time, won't he? I mean, Jassy, there's barbs going back and forth, you gotta be careful there. >> Well, I mean, Andy Jassy is extraordinarily competent, I think, as it relates to this elastic cloud I think he's kinda got a lock on that, but, you know, IBM is hard to explain. I mean, IBM is a sad story. I think IBM is, there's some risk that IBM is the next Hewlett-Packard. I mean, they might be selling this thing off for piece parts this, you mean, if we look at the last 23 quarters, I mean, it's not good. >> And Microsoft's done a great job recently with Satya Nadella, and they're retooling fast. You can see them beavering away. >> But IBM, I mean, how do you bet against the cloud. I mean, are you kidding me? I mean, hello! IBM's a sad story. It's one of the world's great companies, it's an icon. If it fails, and companies like IBM's size do fail, I mean let's look at GE, that would be a sad state for America. >> Okay, on a more positive upbeat, what's next for you? Obviously, you're doing great, the numbers are good. Again, the rumors in the hallways we're hearing that you guys are doing great financially. Not sure if you can share any color on that, big wins, obviously, these are not little deals you're on, but what's next? What's the big innovation that you got comin' around the corner for C3 IOT. Well, so our business grew last year about 600%, this year it'll grow about 300%. We're a profitable, cash-positive business. Our average customer is, say, 20 to $200 billion business. We're engaged in very, very large transactions. In the last 18 months, we've done a lotta work in deep learning, okay. In the next 18 months, we'll do a lotta work in NLP. I think those technologies are hugely important. Technologically, this is where we'll be going. I think machine learning, traditional ML, we have that nailed, now we're exploiting deep learning in a big way using GPUs, and a lotta the work that Jensen Wang's doing at Nvidia, and now NLP, I think, is the next frontier for us. >> Final question for you, advice to other entrepreneurs. You're a serial entrepreneur. you've been very successful, inventive categories. You're looking at Amazon, how do you work with the Amazons of the world. What should entrepreneurs be thinking about in terms of how to enter the market, funding, just strategy in general. The rules have changed a little bit. What advice would you give the young entrepreneurs out there? >> Okay, become a domain expert at whatever domain you're proposing and whatever field you're gonna enter, and then surround yourself with people, whatever job they're doing, engineering, marketing, sales, F&A, who are better than you at what they do and, to the extent that I have succeeded, this is why I've succeeded. Now this might be easier for me than for others, but I try to surround myself with people who are better than me and, to the extent that I've been successful, that's why. >> We really appreciate you taking the time coming on. You're an inspiration, a serial entrepreneur, founder and CEO Tom Siebel of C3 IOT, hot company, big part of the Amazon Web Services ecosystem. Doing great stuff, again, serial entrepreneur. Great four-decade career. Thanks for coming on theCUBE, Tom Siebel. Here inside theCUBE, I'm John Furrier and Justin Warren, here in Las Vegas for AWS re:Invent. We'll be back with more live coverage after this short break. >> Thanks guys, good job.

>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is breaking analysis with Dave Vellante. >> Moore's Law is dead, right? Think again. Massive improvements in processing power combined with data and AI will completely change the way we think about designing hardware, writing software and applying technology to businesses. Every industry will be disrupted. You hear that all the time. Well, it's absolutely true and we're going to explain why and what it all means. Hello everyone, and welcome to this week's Wikibon Cube Insights powered by ETR. In this breaking analysis, we're going to unveil some new data that suggests we're entering a new era of innovation that will be powered by cheap processing capabilities that AI will exploit. We'll also tell you where the new bottlenecks will emerge and what this means for system architectures and industry transformations in the coming decade. Moore's Law is dead, you say? We must have heard that hundreds, if not, thousands of times in the past decade. EE Times has written about it, MIT Technology Review, CNET, and even industry associations that have lived by Moore's Law. But our friend Patrick Moorhead got it right when he said, "Moore's Law, by the strictest definition of doubling chip densities every two years, isn't happening anymore." And you know what, that's true. He's absolutely correct. And he couched that statement by saying by the strict definition. And he did that for a reason, because he's smart enough to know that the chip industry are masters at doing work arounds. Here's proof that the death of Moore's Law by its strictest definition is largely irrelevant. My colleague, David Foyer and I were hard at work this week and here's the result. The fact is that the historical outcome of Moore's Law is actually accelerating and in quite dramatically. This graphic digs into the progression of Apple's SoC, system on chip developments from the A9 and culminating with the A14, 15 nanometer bionic system on a chip. The vertical axis shows operations per second and the horizontal axis shows time for three processor types. The CPU which we measure here in terahertz, that's the blue line which you can't even hardly see, the GPU which is the orange that's measured in trillions of floating point operations per second and then the NPU, the neural processing unit and that's measured in trillions of operations per second which is that exploding gray area. Now, historically, we always rushed out to buy the latest and greatest PC, because the newer models had faster cycles or more gigahertz. Moore's Law would double that performance every 24 months. Now that equates to about 40% annually. CPU performance is now moderated. That growth is now down to roughly 30% annual improvements. So technically speaking, Moore's Law as we know it was dead. But combined, if you look at the improvements in Apple's SoC since 2015, they've been on a pace that's higher than 118% annually. And it's even higher than that, because the actual figure for these three processor types we're not even counting the impact of DSPs and accelerator components of Apple system on a chip. It would push this even higher. Apple's A14 which is shown in the right hand side here is quite amazing. It's got a 64 bit architecture, it's got many, many cores. It's got a number of alternative processor types. But the important thing is what you can do with all this processing power. In an iPhone, the types of AI that we show here that continue to evolve, facial recognition, speech, natural language processing, rendering videos, helping the hearing impaired and eventually bringing augmented reality to the palm of your hand. It's quite incredible. So what does this mean for other parts of the IT stack? Well, we recently reported Satya Nadella's epic quote that "We've now reached peak centralization." So this graphic paints a picture that was quite telling. We just shared the processing powers exploding. The costs consequently are dropping like a rock. Apple's A14 cost the company approximately 50 bucks per chip. Arm at its v9 announcement said that it will have chips that can go into refrigerators. These chips are going to optimize energy usage and save 10% annually on your power consumption. They said, this chip will cost a buck, a dollar to shave 10% of your refrigerator electricity bill. It's just astounding. But look at where the expensive bottlenecks are, it's networks and it's storage. So what does this mean? Well, it means the processing is going to get pushed to the edge, i.e., wherever the data is born. Storage and networking are going to become increasingly distributed and decentralized. Now with custom silicon and all that processing power placed throughout the system, an AI is going to be embedded into software, into hardware and it's going to optimize a workloads for latency, performance, bandwidth, and security. And remember, most of that data, 99% is going to stay at the edge. And we love to use Tesla as an example. The vast majority of data that a Tesla car creates is never going to go back to the cloud. Most of it doesn't even get persisted. I think Tesla saves like five minutes of data. But some data will connect occasionally back to the cloud to train AI models and we're going to come back to that. But this picture says if you're a hardware company, you'd better start thinking about how to take advantage of that blue line that's exploding, Cisco. Cisco is already designing its own chips. But Dell, HPE, who kind of does maybe used to do a lot of its own custom silicon, but Pure Storage, NetApp, I mean, the list goes on and on and on either you're going to get start designing custom silicon or you're going to get disrupted in our view. AWS, Google and Microsoft are all doing it for a reason as is IBM and to Sarbjeet Johal said recently this is not your grandfather's semiconductor business. And if you're a software engineer, you're going to be writing applications that take advantage of all the data being collected and bringing to bear this processing power that we're talking about to create new capabilities like we've never seen it before. So let's get into that a little bit and dig into AI. You can think of AI as the superset. Just as an aside, interestingly in his book, "Seeing Digital", author David Moschella says, there's nothing artificial about this. He uses the term machine intelligence, instead of artificial intelligence and says that there's nothing artificial about machine intelligence just like there's nothing artificial about the strength of a tractor. It's a nuance, but it's kind of interesting, nonetheless, words matter. We hear a lot about machine learning and deep learning and think of them as subsets of AI. Machine learning applies algorithms and code to data to get "smarter", make better models, for example, that can lead to augmented intelligence and help humans make better decisions. These models improve as they get more data and are iterated over time. Now deep learning is a more advanced type of machine learning. It uses more complex math. But the point that we want to make here is that today much of the activity in AI is around building and training models. And this is mostly happening in the cloud. But we think AI inference will bring the most exciting innovations in the coming years. Inference is the deployment of that model that we were just talking about, taking real time data from sensors, processing that data locally and then applying that training that has been developed in the cloud and making micro adjustments in real time. So let's take an example. Again, we love Tesla examples. Think about an algorithm that optimizes the performance and safety of a car on a turn, the model take data on friction, road condition, angles of the tires, the tire wear, the tire pressure, all this data, and it keeps testing and iterating, testing and iterating, testing iterating that model until it's ready to be deployed. And then the intelligence, all this intelligence goes into an inference engine which is a chip that goes into a car and gets data from sensors and makes these micro adjustments in real time on steering and braking and the like. Now, as you said before, Tesla persist the data for very short time, because there's so much of it. It just can't push it back to the cloud. But it can now ever selectively store certain data if it needs to, and then send back that data to the cloud to further train them all. Let's say for instance, an animal runs into the road during slick conditions, Tesla wants to grab that data, because they notice that there's a lot of accidents in New England in certain months. And maybe Tesla takes that snapshot and sends it back to the cloud and combines it with other data and maybe other parts of the country or other regions of New England and it perfects that model further to improve safety. This is just one example of thousands and thousands that are going to further develop in the coming decade. I want to talk about how we see this evolving over time. Inference is where we think the value is. That's where the rubber meets the road, so to speak, based on the previous example. Now this conceptual chart shows the percent of spend over time on modeling versus inference. And you can see some of the applications that get attention today and how these applications will mature over time as inference becomes more and more mainstream, the opportunities for AI inference at the edge and in IOT are enormous. And we think that over time, 95% of that spending is going to go to inference where it's probably only 5% today. Now today's modeling workloads are pretty prevalent and things like fraud, adtech, weather, pricing, recommendation engines, and those kinds of things, and now those will keep getting better and better and better over time. Now in the middle here, we show the industries which are all going to be transformed by these trends. Now, one of the point that Moschella had made in his book, he kind of explains why historically vertically industries are pretty stovepiped, they have their own stack, sales and marketing and engineering and supply chains, et cetera, and experts within those industries tend to stay within those industries and they're largely insulated from disruption from other industries, maybe unless they were part of a supply chain. But today, you see all kinds of cross industry activity. Amazon entering grocery, entering media. Apple in finance and potentially getting into EV. Tesla, eyeing insurance. There are many, many, many examples of tech giants who are crossing traditional industry boundaries. And the reason is because of data. They have the data. And they're applying machine intelligence to that data and improving. Auto manufacturers, for example, over time they're going to have better data than insurance companies. DeFi, decentralized finance platforms going to use the blockchain and they're continuing to improve. Blockchain today is not great performance, it's very overhead intensive all that encryption. But as they take advantage of this new processing power and better software and AI, it could very well disrupt traditional payment systems. And again, so many examples here. But what I want to do now is dig into enterprise AI a bit. And just a quick reminder, we showed this last week in our Armv9 post. This is data from ETR. The vertical axis is net score. That's a measure of spending momentum. The horizontal axis is market share or pervasiveness in the dataset. The red line at 40% is like a subjective anchor that we use. Anything above 40% we think is really good. Machine learning and AI is the number one area of spending velocity and has been for awhile. RPA is right there. Very frankly, it's an adjacency to AI and you could even argue. So it's cloud where all the ML action is taking place today. But that will change, we think, as we just described, because data's going to get pushed to the edge. And this chart will show you some of the vendors in that space. These are the companies that CIOs and IT buyers associate with their AI and machine learning spend. So it's the same XY graph, spending velocity by market share on the horizontal axis. Microsoft, AWS, Google, of course, the big cloud guys they dominate AI and machine learning. Facebook's not on here. Facebook's got great AI as well, but it's not enterprise tech spending. These cloud companies they have the tooling, they have the data, they have the scale and as we said, lots of modeling is going on today, but this is going to increasingly be pushed into remote AI inference engines that will have massive processing capabilities collectively. So we're moving away from that peak centralization as Satya Nadella described. You see Databricks on here. They're seen as an AI leader. SparkCognition, they're off the charts, literally, in the upper left. They have extremely high net score albeit with a small sample. They apply machine learning to massive data sets. DataRobot does automated AI. They're super high in the y-axis. Dataiku, they help create machine learning based apps. C3.ai, you're hearing a lot more about them. Tom Siebel's involved in that company. It's an enterprise AI firm, hear a lot of ads now doing AI and responsible way really kind of enterprise AI that's sort of always been IBM. IBM Watson's calling card. There's SAP with Leonardo. Salesforce with Einstein. Again, IBM Watson is right there just at the 40% line. You see Oracle is there as well. They're embedding automated and tele or machine intelligence with their self-driving database they call it that sort of machine intelligence in the database. You see Adobe there. So a lot of typical enterprise company names. And the point is that these software companies they're all embedding AI into their offerings. So if you're an incumbent company and you're trying not to get disrupted, the good news is you can buy AI from these software companies. You don't have to build it. You don't have to be an expert at AI. The hard part is going to be how and where to apply AI. And the simplest answer there is follow the data. There's so much more to the story, but we just have to leave it there for now and I want to summarize. We have been pounding the table that the post x86 era is here. It's a function of volume. Arm volumes are a way for volumes are 10X those of x86. Pat Gelsinger understands this. That's why he made that big announcement. He's trying to transform the company. The importance of volume in terms of lowering the cost of semiconductors it can't be understated. And today, we've quantified something that we haven't really seen much of and really haven't seen before. And that's that the actual performance improvements that we're seeing in processing today are far outstripping anything we've seen before, forget Moore's Law being dead that's irrelevant. The original finding is being blown away this decade and who knows with quantum computing what the future holds. This is a fundamental enabler of AI applications. And this is most often the case the innovation is coming from the consumer use cases first. Apple continues to lead the way. And Apple's integrated hardware and software model we think increasingly is going to move into the enterprise mindset. Clearly the cloud vendors are moving in this direction, building their own custom silicon and doing really that deep integration. You see this with Oracle who kind of really a good example of the iPhone for the enterprise, if you will. It just makes sense that optimizing hardware and software together is going to gain momentum, because there's so much opportunity for customization in chips as we discussed last week with Arm's announcement, especially with the diversity of edge use cases. And it's the direction that Pat Gelsinger is taking Intel trying to provide more flexibility. One aside, Pat Gelsinger he may face massive challenges that we laid out a couple of posts ago with our Intel breaking analysis, but he is right on in our view that semiconductor demand is increasing. There's no end in sight. We don't think we're going to see these ebbs and flows as we've seen in the past that these boom and bust cycles for semiconductor. We just think that prices are coming down. The market's elastic and the market is absolutely exploding with huge demand for fab capacity. Now, if you're an enterprise, you should not stress about and trying to invent AI, rather you should put your focus on understanding what data gives you competitive advantage and how to apply machine intelligence and AI to win. You're going to be buying, not building AI and you're going to be applying it. Now data as John Furrier has said in the past is becoming the new development kit. He said that 10 years ago and he seems right. Finally, if you're an enterprise hardware player, you're going to be designing your own chips and writing more software to exploit AI. You'll be embedding custom silicon in AI throughout your product portfolio and storage and networking and you'll be increasingly bringing compute to the data. And that data will mostly stay where it's created. Again, systems and storage and networking stacks they're all being completely re-imagined. If you're a software developer, you now have processing capabilities in the palm of your hand that are incredible. And you're going to rewriting new applications to take advantage of this and use AI to change the world, literally. You'll have to figure out how to get access to the most relevant data. You have to figure out how to secure your platforms and innovate. And if you're a services company, your opportunity is to help customers that are trying not to get disrupted are many. You have the deep industry expertise and horizontal technology chops to help customers survive and thrive. Privacy? AI for good? Yeah well, that's a whole another topic. I think for now, we have to get a better understanding of how far AI can go before we determine how far it should go. Look, protecting our personal data and privacy should definitely be something that we're concerned about and we should protect. But generally, I'd rather not stifle innovation at this point. I'd be interested in what you think about that. Okay. That's it for today. Thanks to David Foyer, who helped me with this segment again and did a lot of the charts and the data behind this. He's done some great work there. Remember these episodes are all available as podcasts wherever you listen, just search breaking it analysis podcast and please subscribe to the series. We'd appreciate that. Check out ETR's website at ETR.plus. We also publish a full report with more detail every week on Wikibon.com and siliconangle.com, so check that out. You can get in touch with me. I'm dave.vellante@siliconangle.com. You can DM me on Twitter @dvellante or comment on our LinkedIn posts. I always appreciate that. This is Dave Vellante for theCUBE Insights powered by ETR. Stay safe, be well. And we'll see you next time. (bright music)

from the silicon angle media office in Boston Massachusetts it's the queue now here's your host David on tape hi everybody Dave Volante here with Stu minumum we have some breaking analysis we're gonna break down the acquisition of IBM Red Hat by IBM was announced today that it closed Stu was originally announced in October a 34 billion dollar acquisition so not a surprise surprise that it closed a little bit earlier than people thought people would thinkin you know well into the second half closed in July they got through all the all the issues in Europe what does this mean in your view to the industry yeah so Dave we did a lot of analysis when the deal was announced absolutely the the cloud and the ripples of change that are happening because of cloud are the impetus for this and you know the the question we've been having for years Dave is you know how many companies can stay kind of independent in you know their swimlane to what they're doing or are we going to see more massive consolidations we're not that far off of the 67 billion dollar acquisition of Dell buying EMC to go heavily into the enterprise market and of course there are cloud implications what happened there and you know we're watching the growth of cloud what's happening in the developer world you know we've watched Red Hat for a long time and you know Red Hat has a nice position in the world and it carved themselves out a nice role into what has been emerging as hybrid and multi cloud and in my opinion that's you know the number one reason why arvind and the IBM team you know when to take that 20-year partnership and turn it into you know now just part of the IBM portfolio Arvind Krishna executive at IBM a longtime player there so the the the deal is so you talked about Dells acquisition we've talked a lot about the VMware model keeping the company separate and of course Red Hat is not going to be a separately traded public company it is going to be a distinct unit inside of IBM's cloud and cognitive software group as I understand it is that right so the question is will it be reported separately or is it going to be oh we're gonna throw everything into our cloud number yeah so Dave this is where all of us that have watched and known IBM you know for our entire careers because they've been around over a hundred years on ask what's going to happen so from a reporting structure Jim Whitehurst reports to Ginny from a Wall Street standpoint it sounds like it's gonna be just thrown into the cloud piece you know Dave isn't it that the the the standard practice today that you throw lots of stuff in there so we can't figure out what your cloud business really is I mean let's look at Oracle or even Microsoft and what they had you know Amazon's probably the only one that clearly differentiates you know this is revenue that we all understand is cloud and can you know touch and feel it so sure I IBM you know you've got all of the the piece that used to be soft layer it's now the IBM cloud piece there are lots of software pieces in that mix the cloud and cognitive is a big umbrella and you know Red Hat adds a few billion dollars worth of revenue into that stream so IBM's assumptions here juni talks a lot about chapter two chapter one was a lot of front-end systems that sort of the growth was everybody thought everything was going into the cloud that's really not the way it is 80% of the workloads are still on Prem and in Chapter two was all about you know connecting those to any cloud multi-cloud heard her words the IBM cloud or the Amazon Google or Microsoft cloud etc etc she made the statement that that we are the only hybrid multi-cloud open source company okay I guess that's true does it matter that they're the only hybrid multi-cloud open source company and are they yeah so I mean Dave anytime a vendor tries to paint themselves as the number one or you know leader in the space it's you know that's how they're defining it that's not how customers think of it customers you know don't think is much about whether it's multi cloud or hybrid cloud they're doing cloud and they're working with you know more than one supplier it is very rare that you find somebody I'm all-in and then you dig in oh yeah wait I'm using office 365 and Salesforce and oh wait there was that cool new thing that Google announced that somebody off on the sides doing so we understand that today it's a multi cloud world tomorrow to be a multi cloud we're absolutely open source is growing you know at great leaps and bounds Red Hat is you know the you know best example we've had of that that trend something I've been watching for the last 20 years and you know it is impressive to see it but you know even when you talk to customers of you know most customers are not you know flag-waving I must do everything open-source you know that they have a little bit more nuanced view of it sure lots of companies are participating in contributing to open source but you know I've yet to talk to too many companies that were like well when I'm making this decision you know this is absolutely what it is am i concerned about my overall costs and I'm concerned about transparency am i concerned about you know security and how fast I can get things resolved and by the way open-source can help with a lot of those things that's what they need to think about but look IBM you know had a longtime partnership with Red Hat Red Hat has a strong position in the marketplace but they're not the only ones there you know you mentioned VMware Dave VMware cross has a strong play across multi cloud environments you know we see Red Hat at all of the cloud shows you see yeah IBM at many of the cloud shows but you've got Cisco out there with their play it is still you know this this chapter - if you agree with Ginny's terminology we are relatively early in that but you know IBM I believe is strengthened in their positioning I don't think it radically changes the landscape just because you know Red Hat is still going to stay you know working with the Amazons and Microsoft and Google's and and and other players out there so it doesn't dramatically change the landscape it just consolidates two players that already worked closely let me ask a question so I mean was clearly positioning this as a cloud play you know generally and you know in a multi cloud specifically is this a cloud play okay um so I'll say yes but Dave so absolutely the future and where the growth for Red Hat and where IBM and for this thirty four billion dollars to be successful the tip of the spear is open shift and therefore you know how does that new cloud native multi cloud environment you know where do they play but at its core you know red heads still Linux Red Hat Enterprise Linux you know is it stills you know that is the primary driver of revenue and Linux isn't going away as a matter of fact Linux is growing Microsoft you know just revealed that there are more Linux workloads sitting in Azure than there are windows we already knew that there were you know strong Linux out there and Microsoft is embrace Linux we saw Satya Nadella at Red Hat summit and you know we've seen that proliferation of linux out there so linux is still you know growing in it where it's being used out there and in the cloud you know linux is what most people are using so the reason why I think this acquisition is interesting Jim Whitehurst today said publicly that it was a great deal that IBM was getting but then he couched he said of course it's a great deal for our shareholders too so and Ginni chimed in and said yes it was a fair deal okay fine 34 billion you know we'll see the reason why I think IBM likes this deal and IBM you know generally has been been good over in history with acquisitions you know clearly some mega acquisitions like PwC which was transformative me we have time to talk about that Cognos and some of the other software acquisitions done quite well not a hundred percent but the reason why I think IBM likes this deal is because it's a good cash flow deal so I think in many ways and they don't talk about this because it's not sexy marketing but iBM is a services company over 60% of the company's revenue comes from professional services IBM loves complexity because they can bring in services throw the big blue blanket around you and do a lot of integration work and the reason is that I think this is an interesting acquisition from from a financial standpoint and Ginny says this all the time this is not about cost synergies this is about revenue opportunities when you try to put everything in the cloud you always run into the back-end systems and her point is that those back-end systems need to be modernized how do you modernize those back-end systems openshift it's not trivial to do that you need services and so iBM has a large install base probably by my estimate you know certainly tens of billions of dollars of opportunity there to modernize back-end systems using Red Hat technology and that means that it's a front-loaded deal from a cash flow standpoint that they will find automatically revenue Cyn to plug in to IBM's captive install base what are your thoughts yeah Dave III think that your analysis is spot-on so RedHat has been one of these most consistent you know revenue companies out there you steadily when they went from a billion dollars to now they're right around three billion dollars they had the March to five billion dollars they had a couple of minor blips in their quarterly earnings but if you plug in that IBM services organization you really have the opportunity to supercharge this is not the opportunity is to have that that huge IBM services organization really helped you know grow those engagements do more openshift you know get more Linux help ansible you know really become the standard for you know automation in the modern workplace the challenge is that too many IBM people get involved because the the thing that everybody's a little worried about is IBM's done well with a lot of those acquisitions but they don't leave them stand alone even you know VMware for many years was a standalone company today VMware in Dell they're one company they're in lockstep from a management standpoint and they're working closely together what differentiates RedHat is you know iBM has groups that are much larger than RedHat that do some of the same things but RedHat with their open-source mission and and where they're driving things and the innovation they drive they move a little bit faster than IBM traditionally does so can will the Red Hat brand the Red Hat people and Red Hat still stay independent enough so that they can till you know hop on that next wave you know they they jumped early into kubernetes and that was the wave that really helped them drive for what they're doing the open shift you know even Dave you know Red Hat ahead bought core OS which was a smaller company moving even faster than Red Hat and while they've done a really good job of integrating those people absolutely from what I've heard it is slowed things down a little bit just because Red Hat compared to core OS was a much bigger company and of course IBM is a be a myth compared to Red Hat so will they throw these groups together and you know who will be making the decisions and can they you know maintain that that culture and that growth mindset well the point is structure we bring up VMware a lot as the model and of course when EMC bought VMware for paltry six hundred million six thirty five million dollars it folded it in and then spun it back out which was the right move certainly allowed the ecosystem to blossom I don't think IBM is gonna take that same approach blue wash is the term they'll probably blue wash that now cuz no Dave they said iBM has said they will not blue eyes there's no purple red stay separate absolutely there's concerns you know so to get those revenue synergies there's there's you're gonna have to plug into IBM systems and that requires some some work and IBM generally good at that so we'll see we'll keep our eyes on that it's but but I would predict that IBM is not going to do a VMware like well it's going to be some kind of hybrid Dave one of the other things is you talked about so Jim Whitehurst you know executive respective had him on the cube a lot he's reporting to Ginny you know the question is is this Ginny's last big move and who replaces her yeah let's talk about succession planning so a lot of a lot of rumors that Whitehurst is is next he's 52 years old I've said I don't I don't think they would do that but but let's talk about it first of all just you know Jim white her side sort of interviewed him the number of times but but you know I'm quite well you think even watch the job so you know I talked with Jim a little bit at red hat summit you know he kind of makes light of it he said you know knowing IBM the way we all know IBM IBM has always taken somebody from inside to do that he feels that he has a strong mission still to drive Red Hat he is super passionate about Red Hat he wrote a book book about the open source culture and is still driving that so I think from everything I see from him that's still the job that he loves and wants to do and you know it's a very different challenge to run IBM I'm not saying he would turn it down if that was the direction that it went if it went down to it but I did not see him angling and positioning like that would be where he wants to go well and of course you know Jim is from North Carolina he's got that kind of southern folksy demeanor you know comes across as the so the nicest guy in the room he's also the smartest guy in the room but oh we'll see we'll see what happens there I've said that I think Martin Schroder is going to be the next CEO of IBM Martin Schroder did three years of combat duty as the CFO in in what was a tough time for IBM to be a CFO they were going through those big transitions talking about you know they had to had to do the the SoftLayer acquisition they had to put together those strategic initiatives and so he's has he has CFO chops so he understands finance deeply he ran you know when IBM's big services business he's now responsible for IBM's revenue generation he's a spokesperson you know in many ways for the company he's like the prototypical choice he would not be surprising at all to see IBM plug him right in a little bit of history as you know still him a bit of a history historian of the industry have been around for a while John Akers back in the early 1990s when IBM's mainframe business was was tanking and the whole company was was tanking and it was at the risk of actually believe it or not running out of money they were gonna split up the company because the industry was breaking apart Intel and microprocessors Microsoft and software C gated disk drives you know Oracle and databases and to be more competitive from a product standpoint they were gonna split the company up into pieces Gerstner came in and said no way Gerson it was you know CEO of American Express said no that's not how customers want to buy he bought PwC for a song compared to what Carly Fiorina at HP a Carly Fiorina at HP wanted to pay I think 15 billion for it I want to say IBM paid five billion or maybe even less for PwC it completely transformed the company it transformed IBM into a services company and that's where what IBM is today they don't like when you say that but that's where the revenue was coming from what that did now and they also started to buy software companies IBM was restricted from getting into applications for years and years and years because of the DOJ because they owned the mainframe they had a monopoly while Microsoft and Intel changed all that IBM started to buy software companies and bought lots of them so they became a services company with a collection of software assets and the main mainframe and you know the power they have a storage business and you know Finance I'd be a global finance business etc etc so my my point is I'm not sure Jim Whitehurst would want to run that you know it's it's kind of messy now what you need run that is somebody who really understands finance knows how to turn the knobs and that's why I think you know Martin Schroeder is actually an excellent pick for that to keep the cash flow going to keep the dividend going to keep the stock buybacks going it's still in my view not a growth play I think there's certainly near-term growth that can be had by modernizing applications but I don't look at IBM as a growth company I look at IBM as a portfolio company that throws off a lot of cash and if and when the market stops rewarding growth and profit list growth a company like IBM will become more favorable to investors yeah and the question at the end of the day is after spending thirty four billion dollars for red hat does IBM help weather the storm of what is happening with the phenomenal growth of AWS the changes happening in Microsoft build more of a relationship than they've already had with Google and help position themselves for this next wave of IT there's IBM helped create a lot of the waves that you know happen in IT well the pure play cloud players are in it for the long game you know you know Amazon's philosophy is give tools to builders and allow them to disrupt the you know traditional old guard whether it's old guard technology companies or old guard industry players and you've seen the stat of how many Fortune 1000 companies or you know have gone out of business in the last 20 or 30 years or whatever it is that's going to continue and Amazon and and certainly Google and Microsoft want to support that disruption by providing cloud tooling and put the data in the hands of people that allows them to create new business models now that doesn't mean everybody's gonna throw up there mainframes it's it's not gonna happen it's certainly not gonna happen overnight and probably will never happen but I just don't see how IBM becomes a growth company in that scenario the growth is going to be continue to be with the cloud well but Dave we had seen IBM I'd say struggle a little bit when it comes to the the developers these days and the Red Hat acquisition is definitely going to be a boon to them in this space because Red Hat all about the developers that that's what you know that their customers are so you know that that's such a huge community that they've already tapped into so Ginny has said this hybrid multi-cloud is a chapter two with a trillion dollar opportunity so who else is going after that trillion dollar opportunity let's let's lay it out there who are the multi cloud players VMware obviously IBM Red Hat with open shift is in there Google with anthos Cisco is coming at it from a network perspective so they have coming at it from their position of strength even though you know you know they're relatively new entrants well ever everybody wants to be the new management layer in this multi cloud environment what VMware had done is had you know vCenter became you know the console for everyone as they were consolidating all of their silos and when I go to a multi cloud environment right where do I live you know Microsoft has a strong play there that's the other you know VMware IBM Red Hat anthos Google Mentos Cisco and Microsoft yeah and of course the one that while they won't say that they are multi cloud you can't talk about multi cloud without talking about Amazon because Amazon is a piece of everyone's cloud environment we were seeing what they're doing with outpost there so they are the kind of Spectre looming over this entire multi-cloud discuss yeah right on I think you got to put Amazon into that mix they will be an entrance into this multi cloud play and it's not gonna be a winner-take-all deal I could say cisco is coming at it from a position of networking strength Microsoft has its software estate and it's gonna do very well there IBM Red Hat coming at it from a standpoint of modernizing applications and there's a services could play and services component there and VMware of course coming at it from the the infrastructure operating system I don't see Oracle as interested in that market there may be some smaller players like turbo anomic you know who probably get gobbled up by one of these guys that we just mentioned but that really is the landscape and this is you know five six companies a trillion dollars there's plenty to go around all right Stu final thoughts on on the the Red Hat news the IBM news that they've finalized the Red Hat acquisition yes so you know what you want to look for is you know first of all you know what's happening organizationally you know if open shift is the primary you know the the tip of the sphere what we're talking about here for this you know cloud native multi-cloud world you know what does you know the IBM Cloud messaging looked like they're gonna have an analyst event here in a couple of weeks that you know that they've invited all the analysts to going into what does that cloud portfolio looks like how do they sort through all of the kubernetes options that they've had today do they try to elevate IBM cloud to be a stronger player or will they let Red Hat continue to play across all of the cloud environments that they have so you know organization and product positioning of the two things that I'm looking at the most Tom Siebel said publicly yesterday that IBM is a great company national international treasure but they miss cloud and they missed a I I wouldn't agree totally they didn't miss cloud they were late to cloud they had to buy software they're in cloud just like Oracle's in cloud not as competitive as the AWS cloud but they're they've got a cloud yeah HP doesn't have a cloud Dell doesn't have a cloud these these two companies that I just mentioned do AI yeah they're not sound of generalized AI like what Google and Amazon and Facebook and Microsoft are doing IBM's trying to solve you know big chewy problems iBM is a services company as they said so you know Watson you see a lot of negative stories about Watson but Watson requires a lot of services to make it work and it's as they say solving different problems so they're a player in AI multi cloud is new and this move the acquisition of red hat yes thirty four billion dollars expensive it's not gonna be pretty on the balance sheet but they get good cash flow so they'll deal with that over time it puts them right in the mix as a leader in multi cloud so thanks to for breaking down the the acquisition and thank you for watching this is Dave Volante what's do min and then we'll see you next time

>> from our studios in the heart of Silicon Valley, Palo Alto, California It is >> a cute conversation. Hey, welcome back. Get ready. Geoffrey here with the Cube. We're gonna rip out the studios for acute conversation. It's the middle of the summer, the conference season to slow down a little bit. So we get a chance to do more cute conversation, which is always great. Excited of our next guest. He's Ridge, IP, Ops Statik. He's a VP and GM from key. Cite, Reject. Great to see you. >> Thank you for hosting us. >> Yeah. So we've had Marie on a couple of times. We had Bethany on a long time ago before the for the acquisition. But for people that aren't familiar with key site, give us kind of a quick overview. >> Sure, sure. So I'm within the excess solutions group Exhale really started was founded back in 97. It I peered around 2000 really started as a test and measurement company quickly after the I poet became the number one vendor in the space, quickly grew around 2012 and 2013 and acquired two companies Net optics and an ooey and net optics and I knew we were in the visibility or monitoring space selling taps, bypass witches and network packet brokers. So that formed the Visibility Group with a nice Xia. And then around 2017 key cite acquired Xia and we became I S G or extra Solutions group. Now, key site is also a very large test and measurement company. It is the actual original HB startup that started in Palo Alto many years ago. An HB, of course, grew, um it also started as a test and measurement company. Then later on it, it became a get a gun to printers and servers. HB spun off as agile in't, agile in't became the test and measurement. And then around 2014 I would say, or 15 agile in't spun off the test and measurement portion that became key site agile in't continued as a life and life sciences organization. And so key sites really got the name around 2014 after spinning off and they acquired Xia in 2017. So more joy of the business is testing measurement. But we do have that visibility and monitoring organization to >> Okay, so you do the test of measurement really on devices and kind of pre production and master these things up to speed. And then you're actually did in doing the monitoring in life production? Yes, systems. >> Mostly. The only thing that I would add is that now we are getting into live network testing to we see that mostly in the service provider space. Before you turn on the service, you need to make sure that all the devices and all the service has come up correctly. But also we're seeing it in enterprises to, particularly with security assessments. So reach assessment attacks. Security is your eye to organization really protecting the network? So we're seeing that become more and more important than they're pulling in test, particularly for security in that area to so as you. As you say, it's mostly device testing. But then that's going to network infrastructure and security networks, >> Right? So you've been in the industry for a while, you're it. Until you've been through a couple acquisitions, you've seen a lot of trends, so there's a lot of big macro things happening right now in the industry. It's exciting times and one of the ones. Actually, you just talked about it at Cisco alive a couple weeks ago is EJ Computer. There's a lot of talk about edges. Ej the new cloud. You know how much compute can move to the edge? What do you do in a crazy oilfield? With hot temperatures and no powers? I wonder if you can share some of the observations about EJ. You're kind of point of view as to where we're heading. And what should people be thinking about when they're considering? Yeah, what does EJ mean to my business? >> Absolutely, absolutely. So when I say it's computing, I typically include Io TI agent. It works is along with remote and branch offices, and obviously we can see the impact of Io TI security cameras, thermal starts, smart homes, automation, factory automation, hospital animation. Even planes have sensors on their engines right now for monitoring purposes and diagnostics. So that's one group. But then we know in our everyday lives, enterprises are growing very quickly, and they have remote and branch offices. More people are working from remotely. More people were working from home, so that means that more data is being generated at the edge. What it's with coyote sensors, each computing we see with oil and gas companies, and so it doesn't really make sense to generate all that data. Then you know, just imagine a self driving car. You need to capture a lot of data and you need to process. It just got really just send it to the cloud. Expect a decision to mate and then come back and so that you turn left or right, you need to actually process all that data, right? We're at the edge where the source of the data is, and that means pushing more of that computer infrastructure closer to the source. That also means running business critical applications closer to the source. And that means, you know, um, it's it's more of, ah, madness, massively distributed computer architecture. Um, what happens is that you have to then reliably connect all these devices so connectivity becomes important. But as you distribute, compute as well as applications, your attack surface increases right. Because all of these devices are very vulnerable. We're probably adding about 5,000,000 I ot devices every day to our network, So that's a lot of I O T. Devices or age devices that we connect many of these devices. You know, we don't really properly test. You probably know from your own home when you can just buy something and could easily connect it to your wife. I Similarly, people buy something, go to their work and connect to their WiFi. Not that device is connected to your entire network. So vulnerabilities in any of these devices exposes the entire network to that same vulnerability. So our attack surfaces increasing, so connection reliability as well as security for all these devices is a challenge. So we enjoy each computing coyote branch on road officers. But it does pose those challenges. And that's what we're here to do with our tech partners. Toe sold these issues >> right? It's just instinct to me on the edge because you still have kind of the three big um, the three big, you know, computer things. You got the networking right, which is just gonna be addressed by five g and a lot better band with and connectivity. But you still have store and you still have compute. You got to get those things Power s o a cz. You're thinking about the distribution of that computer and store at the edge versus in the cloud and you've got the Leighton see issue. It seems like a pretty delicate balancing act that people are gonna have to tune these systems to figure out how much to allocate where, and you will have physical limitations at this. You know the G power plant with the sure by now the middle of nowhere. >> It's It's a great point, and you typically get agility at the edge. Obviously, don't have power because these devices are small. Even if you take a room order branch office with 52 2 100 employees, there's only so much compute that you have. But you mean you need to be able to make decisions quickly. They're so agility is there. But obviously the vast amounts of computer and storage is more in your centralized data center, whether it's in your private cloud or your public cloud. So how do you do the compromise? When do you run applications at the edge when you were in applications in the cloud or private or public? Is that in fact, a compromise and year You might have to balance it, and it might change all the time, just as you know, if you look at our traditional history off compute. He had the mainframes which were centralized, and then it became distributed, centralized, distributed. So this changes all the time and you have toe make decisions, which which brings up the issue off. I would say hybrid, I t. You know, they have the same issue. A lot of enterprises have more of a, um, hybrid I t strategy or multi cloud. Where do you run the applications? Even if you forget about the age even on, do you run an on Prem? Do you run in the public cloud? Do you move it between class service providers? Even that is a small optimization problem. It's now even Matt bigger with H computer. >> Right? So the other thing that we've seen time and time again a huge trend, right? It's software to find, um, we've seen it in the networking space to compete based. It's offered to find us such a big write such a big deal now and you've seen that. So when you look at it from a test a measurement and when people are building out these devices, you know, obviously aton of great functional capability is suddenly available to people, but in terms of challenges and in terms of what you're thinking about in software defined from from you guys, because you're testing and measuring all this stuff, what's the goodness with the badness house for people, you really think about the challenges of software defined to take advantage of the tremendous opportunity. >> That's a really good point. I would say that with so far defined it working What we're really seeing is this aggregation typically had these monolithic devices that you would purchase from one vendor. That wonder vendor would guarantee that everything just works perfectly. What software defined it working, allows or has created is this desegregated model. Now you have. You can take that monolithic application and whether it's a server or a hardware infrastructure, then maybe you have a hyper visor or so software layer hardware, abstraction, layers and many, many layers. Well, if you're trying to get that toe work reliably, this means that now, in a way, the responsibility is on you to make sure that you test every all of these. Make sure that everything just works together because now we have choice. Which software packages should I install from which Bender This is always a slight differences. Which net Nick Bender should I use? If PJ smart Nick Regular Nick, you go up to the layer of what kind of ax elation should I use? D. P. D K. There's so many options you are responsible so that with S T N, you do get the advantage of opportunity off choice, just like on our servers and our PCs. But this means that you do have to test everything, make sure that everything works. So this means more testing at the device level, more testing at the service being up. So that's the predeployment stage and wants to deploy the service. Now you have to continually monitor it to make sure that it's working as you expected. So you get more choice, more diversity. And, of course, with segregation, you can take advantage of improvements on the hardware layer of the software layer. So there's that the segregation advantage. But it means more work on test as well as monitoring. So you know there's there's always a compromise >> trade off. Yeah, so different topic is security. Um, weird Arcee. This year we're in the four scout booth at a great chat with Michael the Caesars Yo there. And he talked about, you know, you talk a little bit about increasing surface area for attack, and then, you know, we all know the statistics of how long it takes people to know that they've been reach its center center. But Mike is funny. He you know, they have very simple sales pitch. They basically put their sniffer on your network and tell you that you got eight times more devices on the network than you thought. Because people are connecting all right, all types of things. So when you look at, you know, kind of monitoring test, especially with these increased surface area of all these, Iet devices, especially with bring your own devices. And it's funny, the H v A c seemed to be a really great place for bad guys to get in. And I heard the other day a casino at a casino, uh, connected thermometer in a fish tank in the lobby was the access point. How is just kind of changing your guys world, you know, how do you think about security? Because it seems like in the end, everyone seems to be getting he breached at some point in time. So it's almost Maur. How fast can you catch it? How do you minimize the damage? How do you take care of it versus this assumption that you can stop the reaches? You >> know, that was a really good point that you mentioned at the end, which is it's just better to assume that you will be breached at some point. And how quickly can you detect that? Because, on average, I think, according to research, it takes enterprise about six months. Of course, they're enterprise that are takes about a couple of years before they realize. And, you know, we hear this on the news about millions of records exposed billions of dollars of market cap loss. Four. Scout. It's a very close take partner, and we typically use deploy solutions together with these technology partners, whether it's a PM in P. M. But very importantly, security, and if you think about it, there's terabytes of data in the network. Typically, many of these tools look at the packet data, but you can't really just take those terabytes of data and just through it at all the tools, it just becomes a financially impossible toe provide security and deploy such tools in a very large network. So where this is where we come in and we were the taps, we access the data where the package workers was essentially groom it, filtering down to maybe tens or hundreds of gigs that that's really, really important. And then we feed it, feed it to our take partners such as Four Scout and many of the others. That way they can. They can focus on providing security by looking at the packets that really matter. For example, you know some some solutions only. Look, I need to look at the package header. You don't really need to see the send the payload. So if somebody is streaming Netflix or YouTube, maybe you just need to send the first mega byte of data not the whole hundreds of gigs over that to our video, so that allows them to. It allows us or helps us increase the efficiency of that tool. So the end customer can actually get a good R Y on that on that investment, and it allows for Scott to really look at or any of the tech partners to look at what's really important let me do a better job of investigating. Hey, have I been hacked? And of course, it has to be state full, meaning that it's not just looking at flow on one data flow on one side, looking at the whole communication. So you can understand What is this? A malicious application that is now done downloading other malicious applications and infiltrating my system? Is that a DDOS attack? Is it a hack? It's, Ah, there's a hole, equal system off attacks. And that's where we have so many companies in this in this space, many startups. >> It's interesting We had Tom Siebel on a little while ago actually had a W s event and his his explanation of what big data means is that there's no sampling air. And we often hear that, you know, we used to kind of prior to big day, two days we would take a sample of data after the fact and then tried to to do someone understanding where now the more popular is now we have a real time streaming engines. So now we're getting all the data basically instantaneously in making decisions. But what you just bring out is you don't necessarily want all the data all the time because it could. It can overwhelm its stress to Syria. That needs to be a much better management approach to that. And as I look at some of the notes, you know, you guys were now deploying 400 gigabit. That's right, which is bananas, because it seems like only yesterday that 100 gigabyte Ethan, that was a big deal a little bit about, you know, kind of the just hard core technology changes that are impacting data centers and deployments. And as this band with goes through the ceiling, what people are physically having to do, do it. >> Sure, sure, it's amazing how it took some time to go from 1 to 10 gig and then turning into 40 gig, but that that time frame is getting shorter and shorter from 48 2 108 100 to 400. I don't even know how we're going to get to the next phase because the demand is there and the demand is coming from a number of Trans really wants five G or the preparation for five G. A lot of service providers are started to do trials and they're up to upgrading that infrastructure because five G is gonna make it easier to access state of age quickly invest amounts of data. Whenever you make something easy for the consumer, they will consume it more. So that's one aspect of it. The preparation for five GS increasing the need for band with an infrastructure overhaul. The other piece is that we're with the neutralization. We're generating more Eastern West traffic, but because we're distributed with its computing, that East West traffic can still traverse data centers and geography. So this means that it's not just contained within a server or within Iraq. It actually just go to different locations. That also means your data center into interconnect has to support 400 gig. So a lot of network of hitmen manufacturers were typically call them. Names are are releasing are about to release 400 devices. So on the test side, they use our solutions to test these devices, obviously, because they want to release it based the standards to make sure that it works on. So that's the pre deployment phase. But once these foreign jiggy devices are deployed and typically service providers, but we're start slowly starting to see large enterprises deploy it as a mention because because of visualization and computing, then the question is, how do you make sure that your 400 gig infrastructure is operating at the capacity that you want in P. M. A. P M. As well as you're providing security? So there's a pre deployment phase that we help on the test side and then post deployment monitoring face. But five G is a big one, even though we're not. Actually we haven't turned on five year service is there's tremendous investment going on. In fact, key site. The larger organization is helping with a lot of these device testing, too. So it's not just Xia but key site. It's consume a lot of all of our time just because we're having a lot of engagements on the cellphone side. Uh, you know, decide endpoint side. It's a very interesting time that we're living in because the changes are becoming more and more frequent and it's very hot, so adapt and make sure that you're leading that leading that wave. >> In preparing for this, I saw you in another video camera. Which one it was, but your quote was you know, they didn't create electricity by improving candles. Every line I'm gonna steal it. I'll give you credit. But as you look back, I mean, I don't think most people really grown to the step function. Five g, you know, and they talk about five senior fun. It's not about your phone. It says this is the first kind of network built four machines. That's right. Machine data, the speed machine data and the quantity of Mr Sheen data. As you sit back, What kind of reflectively Again? You've been in this business for a while and you look at five G. You're sitting around talking to your to your friends at a party. So maybe some family members aren't in the business. How do you How do you tell them what this means? I mean, what are people not really seeing when they're just thinking it's just gonna be a handset upgrade there, completely missing the boat? >> Yeah, I think for the for the regular consumer, they just think it's another handset. You know, I went from three G's to 40 year. I got I saw bump in speed, and, you know, uh, some handset manufacturers are actually advertising five G capable handsets. So I'm just going to be out by another cell phone behind the curtain under the hurt. There's this massive infrastructure overhaul that a lot of service providers are going through. And it's scary because I would say that a lot of them are not necessarily prepared. The investment that's pouring in is staggering. The help that they need is one area that we're trying to accommodate because the end cell towers are being replaced. The end devices are being replaced. The data centers are being upgraded. Small South sites, you know, Um, there's there's, uh how do you provide coverage? What is the killer use case? Most likely is probably gonna be manufacturing just because it's, as you said mission to make mission machine learning Well, that's your machine to mission communication. That's where the connected hospitals connected. Manufacturing will come into play, and it's just all this machine machine communication, um, generating vast amounts of data and that goes ties back to that each computing where the edge is generating the data. But you then send some of that data not all of it, but some of that data to a centralized cloud and you develop essentially machine learning algorithms, which you then push back to the edge. The edge becomes a more intelligent and we get better productivity. But it's all machine to machine communication that, you know, I would say that more of the most of the five communication is gonna be much information communication. Some small portion will be the consumers just face timing or messaging and streaming. But that's gonna be there exactly. Exactly. That's going to change. I'm of course, we'll see other changes in our day to day lives. You know, a couple of companies attempted live gaming on the cloud in the >> past. It didn't really work out just because the network latency was not there. But we'll see that, too, and was seeing some of the products coming out from the lecture of Google into the company's where they're trying to push gaming to be in the cloud. It's something that we were not really successful in the past, so those are things that I think consumers will see Maur in their day to day lives. But the bigger impact is gonna be for the for the enterprise >> or jet. Thanks for ah, for taking some time and sharing your insight. You know, you guys get to see a lot of stuff. You've been in the industry for a while. You get to test all the new equipment that they're building. So you guys have a really interesting captaincy toe watches developments. Really exciting times. >> Thank you for inviting us. Great to be here. >> All right, Easier. Jeff. Jeff, you're watching the Cube. Where? Cube studios and fellow out there. Thanks for watching. We'll see you next time.

>> Announcer: Live from Las Vegas, it's theCUBE. Covering AWS re:Invent 2017. Presented by AWS, Intel, and our ecosystem of partners. >> Hello and welcome to day three of exclusive CUBE coverage here at Las Vegas for live coverage of AWS re:Invent 2017. This is theCUBE's fifth year covering AWS re:Invent, and what a transformation it's been. Rocket ship growth. They got the tiger by the tail Full speed ahead. They're not looking in the rearview mirror. This is the mojo of Amazon Web Services. They're kicking ass and taking names, as we say here in theCUBE. But really, they're changing the game. A lot of game changing announcements, architectural rehab for engineering. Reimagining the future is really what they want, and they're trying to be everything to everyone. And, of course, that's always hard to do. I'm John Furrier with Stu Miniman on our kick off of day three. Breaking down Werner Vogel's keynote as well as kind of a review of what's been going on for the past few days. There is a lot of signal here. There's almost a noise around the signal meaning there is so much good content that it's really hard to get a hold of Stu. Great to kick it off day three. Rested. Didn't go out late last night. Went to bed by 10. I know you stayed out to three in the morning, but... >> Hoping my voice can hold out for another day in Vegas. John, good to see you, and I'm really excited. 3,951 announcements since the first re:Invent. We're going to go through every one of them. No, no, no. Werner Vogel. It was interesting because he's like, Oh, we've been told ahead of time, it's not going to be announcement heavy. Of course, there's some really awesome announcements. I hate we sound like fanboys sometimes, but you know, Alexa for business, the serverless marketplace. Some really good segments from Netflix, they were just talking about iRobot. Somebody who I had on theCUBE earlier this year. But Werner really kind of stepping back. Some people are like, what is this, a kinda computer science 101? But no, here's how you architect the future. Here's how Amazon's going to fit everything from how voice is going to be a major interface to a theme that I've really liked we've been covering for a number of years. The digital future is not robots taking over the world, but how do I take people and technology, put them together to really create that explosive future? 'Cause even the things like machine learning, the things that I've been talking to the people who are really in this environment is how are we going to train the people that are gonna put these things together? It's not just something that runs off by itself. >> And we had Sanjay Poonen who is the CEO, COO of VM Ware. Not CEO, that's Pat Gelsinger. But he kind of pointed out something that I wanna bring up here, which is Andy Jassy and the team at Amazon are highly competent, and they're executing. But, Stu, they're not just executing on the technical prowess, they're kicking ass on the technology. Certainly, I want to have a longer conversation with you about that. But they're really hitting some real high notes on societal change. So, if you look at what Amazon enables both at the startup level and the business transformation, even in the public sector with Teresa Carlson, who we'll have on later, they're enabling a new way to reimagine how to solve problems that never could be solved before. Two, they're kind of on the right vectors, and it's causing some competitive ripples. Just today in the news, you can see stories out there in the Wall Street Journal and other places where Apple is part of Stamford University to solve heart disease with the iWatch. Google's folding nest back into the hardware division as pressure because their playbook's not working because Amazon's kicking their ass on Alexa and you got Siri. So, Google's fumbling on that point. They're trying to figure it out. So, you're seeing the forces start to line up in this new era of competing on value, competing on software, competing on community and open source. Amazon has the right formula. If they keep this up, Microsoft and Google will not be able to catch them. And that is so obvious. So, until Amazon makes a misfire, which they have not yet, they experiment, but their solid track record, we're gonna call it as we see it. But calling balls and strikes right now on the cloud game, there is not even a close second place. >> Yes, so John, I've been searching for a word. We used to talk about a platform that you built or the marketplace or the ecosystem that we have around here. Amazon is enabling new things. The new AWS marketplace enabling anyone really to go in there, really could do for cloud and technology what Amazon.com helped do for retail and business. You know, I say, look, not every single one of the features that Amazon had is leaps and bounds ahead of what a Google or Microsoft has. I know you've done lots of reporting on the machine learning and everything happening, even Facebook and the like, going in there. But Amazon absolutely is in a class by itself and it's still, in our fifth year coming here, they impress and they continue to keep us-- >> Stu, let's dissect the competition. Let's lay it all out. To me, the top three are no doubt Amazon and then, way distance second place, Microsoft, and then, third on technology and then kind of, clustered like a bunch of Nascar clusters all trying to figure out what to do, is Oracle, IBM, and everybody else. >> Hold on, you didn't mention Google. You didn't mention Alibaba. >> I mean, sorry, Google would be third, Alibaba would be fourth. But their US presence, they're number four by sheer China volume, but Amazon's business in China's growing. They just cut a deal with China so we're gonna see that play out, we'll see. But Alibaba is a force to be reckoned with, as well as Tencent and Baidu and all those other platforms. But here's the deal, you can't be a pure play anymore. Look at Google, the search engine business, they're milking that cow dry, but the thing is that the business is shifting. So, I think Google, of all the competitors, probably has the best chance to accelerate because I think innovation has to be at the heart of that accelerated leadership position. Two, culture. The culture of solving not just tech problems, Stu. And this is where Amazon, no one's really unpacked this, is that if you look at Intel, for instance, they always have great tech, and they always do good things. Amazon is kind of doing the same thing. They're solving societal problems, but they're kicking ass on the business front. Google has that DNA. It's just not organized into the machinery. >> Yeah, I mean, John, we know Google has amazing technology, really good talent. We think Google spanner, oh my God, that's amazing. The thing we say is there's things that Google comes out with, and it's like, Wow, this is really cool. I really need to think about a while how can I do it. As opposed to most of the announcements you hear. In the sessions, people are like, Oh my God, I can't believe Amazon did this. I can immediately take this. I can change the way I'm doing something. I can increase my Codility. I can make my, how I just do my entire business different, better. >> Yeah, and so, Stu, I bring up the Alibaba comment. I wanna bring that back in because one of the things that Amazon's doing that Alibaba is kinda copying, I won't say copying, but emulating, is this notion of craftsmanship. If you look at the past 10 years the programmer culture, the Y Combinator, the Agile, lean, start-up kind of mindset, you look at a loss in craft in software development. Software development used to be a craft. You build software. We had to keep alumni benched from Apple, I talked about, you build a shrink-wrapped product, you ship it, you QA it, you ship it, but you don't know it's going to run. But in the Agile, you're shipping, you're shipping, and shipping, it kind of takes the craft and the artisan out of it. Yeah, US could be cool. But I think now you're going to start to see a swing-back, and whoever, whichever cloud can bring that artisan kind of craft, and blend the open source kind of community model, to me, will be the winning formula. Because that will change the game on these new use cases, the new user expectations, the new user experiences. >> And John, that's exactly what Werner was talking about in his keynote, is this is how we're architecting into the future, you know, everybody needs to be thinking about security. One of the critiques I saw is like, oh, well, you need to think about, you know, everything up and down the stack. It's like, you know, everybody needs to be the unicorn full-stack developer, you know, understand security, be on top of serverless, do all this, well, look, that's asking a lot as to, you know, not everybody's going to be able to do everything. Amazon might be everything is everything, but, you know, we need to be able to understand, you know, how do we take the vast majority of enterprises out there and move them along? I love, Keith Townsend and I did an interview with Chris Wolf from VMware, here at the show, and Keith said, you know, VMware used to move, you know, the speed of the CIO. Amazon's moving way faster than the CIO, you know, how do we help the enterprises move faster, and it's tough. I've talked, every customer I talk to is -- >> Well, we heard, we heard, we heard Intel saying they're moving faster than Intel. So, I mean, Intel has to get in these reference architectures, so, with FPGNAs and these new technologies, they have to accelerate and keep pace. But I think the Werner Vogels keynote here is kind of historic, and you brought this up before we came on, was that he was not going to do a lot of announcements. Although he did launch Alexa for business, and the Lambda Service is all in on that area, he kind of did a throwback to five years ago, or six years ago when he did his first keynote here, when he talked about the new architecture and reimagining it. But he took a modern version of what he was talking about then, and I think that highlights the Amazon greatness, but also their challenge. The one thing I'd be critical of Amazon is, well, two things, one is, I mentioned yesterday, Andy Jassy shouldn't be putting Gardener slides in a new guard presentation, because they're old guard. But that's one thing. What they're doing with the sales motion, it's hard. They have to convince customers and show them the new way. So what Werner painted the picture of is this is how we're thinking. This is how you should be thinking with customers. You have to reimagine what was traditional architecture, and think about it in a completely different way, which will change ultimately software methodologies, the life cycle of Agile, and hopefully bring in some, you know, value-oriented craftsmanship and artisan. >> Yeah, John, you know, this reminds me of many of the waves that we've seen throughout our careers. The customers, when they get in this ecosystem and they really start using it, they get religion. And, you know, number one advice I hear from a lot of the companies I talk to say, talking to your peers, what would you say? Say, get on it faster, and really just dive in. It's like, yeah, yeah, you start with one application. But get off the old stuff as fast as you can. Get on this, because there's, when you have access to all of these services, it just transforms your business. You can get, you know, these changes in these services, into more pieces of the organization, you know, John, we haven't brought up, you know, does IT matter? What's the role of IT in this versus the business lines and the developers? IT radically changing. Amazon looking to change that model. >> They are. I mean, there's no doubt. This show is kind of the final exclamation point on the fact that not only was it a collision course, it has absolutely happened. IT and Amazon have come together in a massive collision, and there's going to be carnage, too. There's going to be people, Lying on the side of the road. >> So, question for you. I've heard there's some people that like, this is the industry's biggest infrastructure show. And I'm an infrastructure guy by background, but I take, I don't think, this is not an infrastructure show. This is, you know, really about business. You know, absolutely, there's technology. Somebody I love, they said, you know, CES, this is now EES. This is the enterprise version of what's happening in technology. >> Well, I mean, we're going to have Teresa Carlson on. It's, you know, it's all digital, right, I mean, it's a digital culture, because their public sector business is booming. It's not just the enterprise. They nailed the start-up. They nailed the ElastiCLOUD, check. Tom Siebel pointed it out yesterday. And what they're nailing now with IT is they're becoming the lever, the catalyst for IT transformation at price points and functionality never seen before, and it's mind-boggling. Google's gotta re-organize, because they can't compete with Alexa. Alright, so things of that nature. So then you have the public sector, your government, and then global, regional, China, Europe, huge issues. So they're winning. And to me, this is a huge new thing. And why rant on the Gardener slide that Jessy puts up is, Amazon is the new guard, and they're putting up old guard metrics. So Stu, this is not an infrastructure as a service magic quadrant, so, the question we share, is what are the new guard metrics? My opinion, no one's developed it yet. So how would you define a modern metric for who's winning and who's losing? Because if you say number of customers, Oracle has a lot of customers, IBM's got a lot of customers. >> So John, Amazon's leading the vanguard in helping customers through digital transformation. I don't know how to measure that yet, but absolutely they're the ones that are doing this. It's not a product-centric. It's about the mindset and how we build things. I've really loved this week talking about, you know, how real is serverless? And like, well, really, Lambda's getting embedded everywhere. It's not about, you know, a product, and oh, hey, you're only going to pay for it by the microsecond, and it's 90% cheaper, no, no, no. It's about the triggers and the APIs and just integrating into the way I can build things faster, you know, yes, I can really get benefit out of microservices. That serverless application repository that Werner talked about, I mean, it's, we got really excited when we got for containers, like the Docker Hub, we had in virtualization, we had the same way, we could get kind of standard images out there. Serverless application repository's going to do the same thing for serverless. You know, is there a lock-in from AWS Lambda, how much is there going to be standards that come in? The CNCF next week is going to be digging into those. >> Is there a cost reduction? Or is it a cost increase? These are questions. >> Yeah. >> Alright, so final question for you. I know we've gotta move on to our full day here, but Stu, you, you know, you study it, you do the hallway conversations, you're at all the influencer events, how do you connect the dots between Andy Jassy's keynote and Werner's, where is the dots connecting? What is jumping out at you? Obviously Lambda, but what are the highlights, from your perspective, that you see just jumping out that Amazon's connecting and trying to present? >> Yeah, so, we always used to say it was like, you know, okay, is day one developer and day two enterprise? We're starting to see those lines blur. As the enterprise, we are still early in kind of the massive adoption there, but that's where it's coming together. There's, you know, lots of excitement, but, you know, as we talked about the continuum, now we had bare metal, we have instances, we have containers, we have serverless. And the enterprise is starting throughout that. I know there's a Sumo Logic report you've been quoting, and we've been-- >> And it came on yesterday. >> Absolutely. So good data there. New Relic had some good reports digging into this. So the wave, change is happening faster than ever. And, you know, Amazon is the lead horse driving this change throughout the industry. >> And don't forget Intel. Intel's just minding their business just watching all these compute requests come in. I mean, as more compute comes out, Intel just is a rising tide, and you know, they're a big boat in the harbor there. >> Absolutely. >> Alright, I'm John Furrier and Stu Miniman breaking down day three of theCUBE, day three here we've actually started on Sunday night at midnight. A lot of great action, a lot of great analysis, of course, check out our new Twitch channel, so, twitch.tv/siliconangle, twitch.tv/thecube, two new channels, or one rebooted channel, one new channel. And of course thecube.net. We're on Ustream, we're on YouTube. But check out our Twitch and join our community if you're a gamer. Back with more live coverage here, live in Las Vegas, for AWS re:Invent after the short break.

>> Narrator: Live from Las Vegas it's theCube covering AWS reInvent 2017 presented by AWS, Intel and our ecosystem of partners. >> Hello and welcome to theCube's exclusive coverage here in Las Vegas for AWS, Amazon Web Services reinvent 2017, 45,000 people. It's theCube's fifth year in covering AWS, five years ago I think 7,000 people attended, this year close to 45,000, developers and industry participants. And of course this is theCube I'm John Furrier with my co-host Keith Townsend and we're excited to have Cube alumni Sanjay Poonen who's the chief operating officer for VMware. Sanjay great to see you, of course a good friend with Andy Jassy, you went to Harvard Business School together, both Mavericks, welcome to theCube. >> Thank you and you know what I loved about the keynote this morning? Andy and I both love music. And he had all these musical stuff man. He had Tom Petty, he had Eric Clapton. I an not sure I like all of his picks but at least those two, loved it man. >> The music thing really speaks to the artists, artists inside of this industry. >> Yes. >> And we were talking on theCube earlier that, we're in a time now where and I think Tom Siebel said it when he was on, that there's going to be a mass, just extinction of companies that don't make it on the digital transformation and he cited some. You're at VMware you guys are transforming and continue to do well, you've a relationship with Amazon Web Services, talk about the challenge that's in front of business executives right now around this transformation because possibly looking at extinction for some big brands potentially big companies in IT. >> It's interesting that Tom Siebel would say that in terms of where Siebel ended up and where salespersons now I respect him, he's obviously doing good things at C3. But listen that's I think what every company has got to ask itself, how do you build longevity? How do you make yourself sustainable? Next year will be our 20 year anniversary of VMware's founding. The story could have been written about VMware that you were the last good company and then you were a legacy company because you were relevant to yesterday's part of the world which was the data center. And I think the key thing that kept us awake the last two or three years was how do you make them relevant to the other side of history which is the public cloud? What we've really been able to do over the last two or three years is build a story of the company that's not just relevant to the data center and private cloud, which is not going away guys as you know but build a bridge into the public cloud and this partnership has been a key part of that and then of course the third part of that is our end user computing story. So I think cloud mobile security have become the pillars of the new VMware and we're very excited about that and this show, I mean if you combine the momentum of this show and VMworld, collectively at VMworld we have probably about 70, 80,000 people who come to VMworld and Vforums, there's 45,000 people here with all the other summits, there's probably have another 40,000 people, this is collectively about a 100, 150,000 people are coming to the largest infrastructure shows on the planet great momentum. >> And as an infrastructure show that's turning into a developer show line get your thoughts and I want to just clarify something 'cause we pointed this out at VMworld this year because it's pretty obvious what happened. The announcement that you guys did that Ragu and your team did with Ragu with AWS was instrumental. The proof was at VMworld where you saw clarity in the messaging. Everyone can see what's going on. I now know what's happening, my operations are gonna be secure, I can run VSphere on the cloud or on Prem, everything could be called what it is. But the reality was is that you guys have the operators, IT operations and Amazon has a robust cloud native developer community, not that they're conflicting in any way, they're coming together so it was a smart move so I got to ask you, as you guys continue your relationship with AWS, how are you guys tying the new ops role, ops teams with the dev teams because with IoT, this is where it's coming together you can see it right there? Your thoughts? >> I mean listen, the partnership is going great. I just saw Andy Jassy after his exec summit session, gave him a hug. We're very excited about it and I think of any of the technology vendors he mentioned on stage, we were on several slides there, mentioned a few times. I think we're probably one of the top tech partners of his and reality is, there's two aspects to the story. One is the developer and operations come together which you, you eloquently articulated. The other aspect is, we're the king of the private cloud and they're the king of the public cloud, when you can bring these together, you don't have to make it a choice between one or the other, we want to make sure that the private cloud is maximized to its full extent and then you build a bridge into the public cloud. I think those two factors, bringing developer and operations together and marrying the private and public cloud, what we call hybrid cloud computing, a term we coined and now of course many others-- >> I think-- >> On top of the term. Well whoever did. >> I think HP might have coined it. >> But nonetheless, we feel very good about the future about developer and operations and hybrid cloud computing being a good part of the world's future. >> Sanjay, I actually interviewed you 2016 VMworld and you said something very interesting that now I look back on it I'm like, "Oh of course." Which is that, you gave your developers the tools they needed to do their jobs which at the time included AWS before the announcement of VMware and AWS partnership. AWS doesn't change their data center for anyone so the value that obviously you guys are bringing to them and their customers speaks volumes. AWS has also said, Andy on stage says, he tries to go out and talk to customers every week. I joked that before the start of this that every LinkedIn request I get, you're already a connection of that LinkedIn request. How important is it for you to talk to your internal staff as well as your external customers to get the pulse of this operations and developer movement going and infused into the culture of VMware. >> Well Keith I appreciate the kind words. When we decided who to partner with and how to partner with them, when we had made the announcement last year, we went and talked to our customers. We're very customer and client focused as are they. And we began to hear a very proportional to the market share stats, AWS most prominently and every one of our customers were telling us the same thing that both Andy and us were asking which is "Why couldn't you get the best of both worlds? "You're making a choice." Now we had a little bit of an impediment in the sense that we had tried to build a public cloud with vCloud air but once we made the decision that we were getting out of that business, divested it, took care of those clients, the door really opened up and we started to test pulse with a couple of customers under NDA. What if you were to imagine a partnership between us and Amazon, what would you think? And man, I can tell you, a couple of these customers some of who are on stage at the time of the announcement, fell off their chair. This would be huge. This is going to be like a, one customer said it's gonna be like a Berlin Wall moment, the US and the Soviet Union getting together. I mean the momentum building up to it. So now what we've got to do, it's been a year later, we've shipped, released, the momentum still is pretty high there, we've gotta now start to really make this actionable, get customers excited. Most of my meetings here have been with customers. System integrators that came from one of the largest SIs in the world. They're seeing this as a big part of the momentum. Our booth here is pretty crowded. We've got to make sure now that the customers can start realizing the value of VMware and AWS as a build. The other thing that as you mentioned that both sides did very explicitly in the design of this was to ensure that each other's engineering teams were closely embedded. So it's almost like having an engineering team of VMware embedded inside Amazon and an engineering team of Amazon embedded inside VMware. That's how closely we work together. Never done before in the history of both companies. I don't think they've ever done it with anybody else, certainly the level of trying. That represents the trust we had with each other. >> Sanjay, I gotta ask you, we were talking with some folks last night, I was saying that you were coming on theCube and I said, "What should I ask Sanjay? "I want to get him a zinger, "I want to get him off as messaging." Hard to do but we'll try. They said, "Ask him about security." So I gotta ask you, because security has been Amazon's kryptonite for many years. They've done the work in the public sector, they've done the work in the cloud with security and it's paying off for them. Security still needs to get solved. It's a solvable problem. What is your stance on security now that you got the private and hybrid going on with the public? Anything change? I know you got the AirWatch, you're proud of that but what else is going on? >> I think quietly, VMware has become one of the prominent brands that have been talked about in security. We had a CIO survey that I saw recently in network security where increasingly, customers are talking about VMware because of NSX. When I go to the AirWatch conference I look at the business cards of people and they're all in the security domain of endpoint security. What we're finding is that, security requires a new view of it where, it can't be 6000 vendors. It feels like a strip mall where every little shop has got its boutique little thing that you ought to buy and when you buy a car you expect a lot of the things to be solved in the core aspects of the car as opposed to buying a lot of add-ons. So our point of view first off is that security needs to baked into the infrastructure, and we're gonna do that. With products like NSX that bake it into the data center, with products like AirWatch and Workspace ONE that bake it into the endpoint and with products like App Defence that even take it deeper into the core of the hypervisor. Given that we've begun to also really focus our education of customers on higher level terms, I was talking to a CIO yesterday who was educating his board on what are some of the key things in cyber security they need to worry about. And the CIO said this to me, the magic word that he is training all of his board members on, is segmentation. Micro segmentation segmentation is a very simple concept that NSX sort of pioneered. We'll finding that now to become very relevant. Same-- >> So that's paying off? >> Paying up big time. WannaCry and Petya taught us that, patching probably is a very important aspect of what people need to do. Encryption, you could argue a lot of what happened in the Equifax may have been mitigated if the data been encrypted. Identity, multi-factor authentication. We're seeing a couple of these key things being hygiene that we can educate people better on in security, it really is becoming a key part to our stories now. >> And you consider yourself top-tier security provider-- >> We are part of an ecosystem but our point of view in security now is very well informed in helping people on the data center to the endpoint to the cloud and helping them with some of these key areas. And because we're so customer focused, we don't come in at this from the way a traditional security players providing access to and we don't necessarily have a brand there but increasingly we're finding with the success of NSX, Workspace ONE and the introduction of new products like App Defense, we're building a point of security that's highly differentiated and unique. >> Sanjay big acquisition in SD-WAN space. Tell us how does that high stress security player and this acquisition in SD-WAN, the edge, the cloud plays into VMware which is traditionally a data center company, SD-wAN, help us understand that acquisition. >> Good question. >> As we saw the data center and the cloud starting to develop that people understand pretty well. We began to also hear and see another aspect of what people were starting to see happen which was the edge and increasingly IoT is one driver of that. And our customers started to say to us, "Listen if you're driving NSX and its success "in the data center, wouldn't it be good "to also have a software-defined wide area network strategy "that allows us to take that benefit of networking, "software-defined networking to the branch, to the edge?" So increasingly we had a choice. Do we build that ourselves on top of NSX and build out an SD-WAN capability which we could have done or do we go and look at our customers? For example we went and talked to telcos like AT&T and they said the best solution out there is a company that can develop cloud. We start to talk to customers who were using them and we analyzed the space and we felt it would be much faster for us to buy rather than build a story of a software-defined networking story that goes from the data center to the branch. And VeloCloud was well-regarded, I would view this, it's early and we haven't closed the acquisition as yet but once we close this, this has all the potential to have the type of transformative effect like in AirWatch or in nai-si-ra-hat in a different way at the edge. And we think the idea of edge core which is the data center and cloud become very key aspects of where infrastructure play. And it becomes a partnership opportunity. VeloCloud will become a partnership opportunity with the telcos, with the AWSs of the world and with the traditional enterprises. >> So bring it all together for us. Data center, NSX, Edge SD-WAN, AirWatch capability, IOT, how does all of that connect together? >> You should look at IoT and Edge being kind of related topics. Data center and the core being related topics, cloud being a third and then of course the end-user landscape and the endpoint being where it is, those would be the four areas. Data center being the core of where VMware started, that's always gonna be and our stick there so to speak is that we're gonna take what was done in hardware and do it in software significantly cheaper, less complex and make a lot of money there. But then we will help people bridge into the cloud and bridge into the edge, that's the core part of our strategy. Data center first, cloud, edge. And then the end user world sits on top of all of that because every device today is either a phone, a tablet or a laptop and there's no vendor that can manage the heterogeneous landscape today of Apple devices, Google devices, Apple being iOS and Mac, Android, Chrome in the case of Google, or Windows 10 in the case of Microsoft. That heterogeneous landscape, managing and securing that which is what AirWatch and Workspace ONE does is uniquely ours. So we think this proposition of data center, cloud, edge and end-user computing, huge opportunity for VMware. >> Can we expect to see NSX as the core of that? >> Absolutely. NSX becomes to us as important as ESX was, in fact that's kind of why we like the name. It becomes the backbone and platform for everything we do that connects the data center to the cloud, it's a key part of BMC for example. It connects the data center to the edge hence what we've done with SD-WAN and it's also a key part to what connects to the end user world. When you connect network security with what we're doing with AirWatch which we announced two years ago, you get magic. We think NSX becomes a fundamental and we're only in the first or second or third inning of software-defined networking. We have a few thousand customers okay of NSX, that's a fraction of the 500,000 customers of VMware. We think we can take that in and the networking market is an 80 billion dollar market ripe for a lot of innovation. >> Sanjay, I want to get your perspective on the industry landscape. Amazon announcing results, I laid it out on my Forbes story and in Silicon Angle all the coverage, go check it out but basically is, Amazon is going so fast the developers are voting with their workloads so their cloud thing is the elastic cloud, they check, they're winning and winning. You guys own the enterprised data center operating model which is private cloud I buy that but it's all still one cloud IoT, I like that. The question is how do you explain it to the people that don't know what's going on? Share your color on what's happening here because this is a historic moment. It's a renaissance-- >> I think listen, when I'm describing this to my wife or to my mother or somebody who's not and say "There's a world of tech companies "that applies to the consumer." In fact when I look at my ticker list, I divide them on consumer and enterprise. These are companies like Apple and Google and Facebook. They may have aspirations in enterprise but they're primarily consumer companies and those are actually what most people can relate to and those are now some of the biggest market cap companies in the world. When you look at the enterprise, typically you can divide them into applications companies, companies like Salesforce, SAP and parts of Oracle and others, Workday and then companies in infrastructure which is where companies like VMware and AWS and so on fit. I think what's happening is, there's a significant shift because of the cloud to a whole new avenue of spending where every company has to think about themselves as a technology company. And the same thing's happening with mobile devices. Cloud mobile security ties many of those conversations together. And there are companies that are innovators and there companies that you described earlier John at the start of this show that's going to become extinct. >> My thesis is this, I want to get your reaction to this. I believe a software renaissance is coming and it's gonna be operated differently and you guys are already kind of telegraphing your move so if that's the case, then a whole new guard is gonna be developing, he calls it the new garden. Old guard he refers to kind of the older guards. My criticism of him was is that he put a Gartner slide up there, that is as says old guard as you get. Andy's promoting this whole new guard thing yet he puts up the Gartner Magic Quadrant for infrastructure as a service, that's irrelevant to his entire presentation, hold on, the question is about you know I'm a Gardner-- >> Before I defend him. >> They're all guard, don't defend him too fast. I know the buyers see if they trust Gartner, maybe not. The point is, what are the new metrics? We need new metrics because the cloud is horizontally scalable. It's integrated. You got software driving decision making, it's not about a category, it's about a fabric. >> I'm not here to... I'm a friend of Andy, I love what he talked about and I'm not here to defend or criticize Gartner but what I liked about his presentation was, he showed the Gartner slide probably about 20 minutes into the presentation. He started off by his metrics of revenue and number of customers. >> I get that, show momentum, Gartner gives you like the number one-- >> But the number of customers is what counts the most. The most important metric is adoption and last year he said there was about a million customers this year he said several million. And if it's true that both startups and enterprises are adopting this, adopting, I don't mean just buying, there is momentum here. Irrespective, the analysts talking about this should be, hopefully-- >> Alright so I buy the customer and I've said that on theCube before, of course and Microsoft could say, "We listen to customers too and we have a zillion customers "running Office 365." Is that really cloud or fake cloud? >> At the end of the day, at the end of the day, it's not a winner take all market to one player. I think all of these companies will be successful. They have different strategies. Microsoft's strategy is driven from Office 365 and some of what they can do in Windows into Azure. These folks have come up from the bottom up. Oracle's trying to come at it from a different angle, Google's trying to come at a different angle and the good news is, all of these companies have deep pockets and will invest. Amazon does have a head start. They are number one in the market. >> Let me rephrase it. Modern applications could be, I'll by the customer workload argument if it's defined as a modern app. Because Oracle could say I got a zillion customers too and they win on that, those numbers are pretty strong so is Microsoft. But to me the cloud is showing a new model. >> Absolutely. >> So what is in your mind good metric to saying that's a modern app, that is not. >> I think when you can look at the modern companies like the Airbnb, the Pinterest, the Slacks and whoever. Some of them are going to make a decision to do their own infrastructure. Facebook does not put their IaaS on top of AWS or Azure or Google, they built their own data is because they can afford to do and want to do it. That's their competitive advantage. But for companies who can't, if they are building their apps on these platforms that's one element. And then the traditional enterprises, they think about their evolution. If they're starting to adopt these platforms not just to migrate old applications to new ones where VMware fits in, all building new cloud native applications on there, I think that momentum is clear. When was the last time you saw a company go from zero to 18 billion in 10 years, 10, 12 years that he's been around? Or VMware or Salesforce go from zero to eight billion in the last 18 years? This phenomenon of companies like Salesforce, VMware and AWS-- >> It's all the scale guys, you gotta get to scale, you gotta have value. >> This is unprecedented in the last five to 10 years, unprecedented. These companies I believe are going to be the companies of the tech future. I'm not saying that the old guard, but if they don't change, they won't be the companies that people talk about. The phenomenon of AWS just going from zero to 18 is, I personally think-- >> And growing 40% on that baseline. >> Andy's probably one of the greatest leaders of our modern time for his role in making that happen but I think these are the companies that we watch carefully. The companies that are growing rapidly, that our customers are adopting them in the hundreds of thousands if not millions, there's true momentum there. >> So Sanjay, data has gravity, data is also the new oil. We look at what Andy has in his arsenal, all of the date of that's in S3 that he can run, all his MI and AI services against, that's some great honey for this audience. When I look at VMware, there's not much of a data strategy, there's a security the data in transit but there's not a data strategy. What does VMware's data strategy to help customers take math without oil? >> We've talked about it in terms of our data analytics what we're doing machine learning and AI. We felt this year given so much of what we had to announce around security software-defined networking, the branch, the edge, putting more of that into VMworld which is usually our big event where we announce this stuff would have just crowded our people. But we began to lay the seeds of what you'll start to hear a lot more in 2018. Not trying to make a spoiler alert for but we acquired this company Wavefront that does, next-generation cloud native metrics and analytics. Think of it as like, you did that with AppDynamics in the old world, you're doing this with Wavefront in the new world of cloud native. We have really rethought through how, all the data we collect, whether it's on the data center or in the endpoint could be mined and become a telemetry that we actually use. We bought another company Apteligent, formerly called Criticism, that's allowing us to do that type of analytics on the endpoint. You're gonna see a couple of these moves that are the breadcrumbs of what we'll start announcing a lot more of a comprehensive analytics strategy in 2018, which I think we're very exciting. I think the other thing we've been cautious to do is not AI wash, there's a lot of cloud washing and machine learning washing that happened to companies-- >> They're stopping a wave on-- >> Now it's authentic, now I think it's out there when, when Andy talks about all they're doing in AI and machine learning, there's an authenticity to it. We want to be in the same way, have a measured, careful strategy and you will absolutely hear from us a lot more. Thank you for bringing it up because it's something that's on our radar. >> Sanjay we gotta go but thanks for coming and stopping by theCube. I know you're super busy and great to drop in and see you. >> Always a pleasure and thanks-- >> Congratulations-- >> And Keith good to talk to you again. >> Congratulations, all the success you're having with the show. >> We're doing our work, getting the reports out there, reporting here on theCube, we have two sets, 45,000 people, exclusive coverage on siliconangle.com, more data coming, every day, we have another whole day tomorrow, big night tonight, the Pub Crawl, meetings, VCs, I'll be out there, we'll be out there, grinding it out, ear to the ground, go get those stories and bring it to you. It's theCube live coverage from AWS reInvent 2017, we're back with more after this short break.

>> Narrator: Live from Las Vegas, it's theCUBE. Covering AWS re:Invent 2017 presented by AWS, Intel and our ecosystem of partners. >> Hello and welcome back. Live here is theCUBE's exclusive coverage here in Las Vegas. 45,000 people attending Amazon Web Services' AWS re:Invent 2017. I'm John Furrier with Lisa Martin. Our next guest is Miles Kingston, he is the General Manager of the Smart Home Group at Intel Corporation. Miles, it's great to have you. >> Thank you so much for having me here, I'm really happy to be here. >> Welcome to theCUBE Alumni Club. First time on. All the benefits you get as being an Alumni is to come back again. >> Can't wait, I'll be here next year, for sure. >> Certainly, you are running a new business for Intel, I'd like to get some details on that, because smart homes. We were at the Samsung Developer Conference, we saw smart fridge, smart living room. So we're starting to see this become a reality, for the CES, every 10 years, that's smart living room. So finally, with cloud and all of the computing power, it's arrived or has it? >> I believe we're almost there. I think the technology has finally advanced enough and there is so much data available now that you have this combination of this technology that can analyze all of this data and truly start doing some of the artificial intelligence that will help you make your home smarter. >> And we've certainly seen the growth of Siri with Apple, Alexa for the home with Amazon, just really go crazy. In fact, during the Industry Day, yesterday, you saw the repeat session most attended by developers, was Alexa. So Alexa's got the minds and has captured the imagination of the developers. Where does it go from here and what is the difference between a smart home and a connected home? Can you just take a minute to explain and set the table on that? >> Yeah and I agree, the voice capability in the home, it's absolutely foundational. I think I saw a recent statistic that by 2022, 55% of US households are expected to have a smart speaker type device in their home. So that's a massive percentage. So I think, if you look in the industry, connected home and smart home, they're often use synonymously. We personally look at it as an evolution. And so what I mean by that is, today, we think the home is extremely connected. If I talk about my house, and I'm a total geek about this stuff, I've got 60 devices connected to an access point, I've got another 60 devices connected to an IOT hub. My home does not feel very smart. It's crazy connected, I can turn on lights on and off, sprinklers on and off, it's not yet smart. What we're really focused on at Intel, is accelerating that transition for your home to truly become a smart home and not just a connected home. >> And software is a key part of it, and I've seen developers attack this area very nicely. At the same time, the surface area with these Smart Homes for security issues, hackers. Cause WiFi is, you can run a process on, these are computers. So how does security fit into all of this? >> Yeah, security is huge and so at Intel we're focused on four technology pillars, which we'll get through during this discussion. One of the first ones is connectivity, and we actually have technology that goes into a WiFi access point, the actual silicon. It's optimized for many clients to be in the home, and also, we've partnered with companies, like McAfee, on security software that will sit on top of that. That will actually manage all of the connected devices in your home, as that extra layer of security. So we fundamentally agree that the security is paramount. >> One of the things that I saw on the website that says, Intel is taking a radically different approach based on proactive research into ways to increase smart home adoption. What makes Intel's approach radically different? >> Yeah, so I'm glad that you asked that. We've spent years going into thousands of consumers' homes in North America, Western Europe, China, etc. To truly understand some of the pain points they were experiencing. From that, we basically, gave all this information to our architects and we really synthesized it into what areas we need to advance technology to enable some of these richer use cases. So we're really working on those foundational building blocks and so those four ones I mentioned earlier, connectivity, that one is paramount. You know, if you want to add 35 to 100 devices in your home, you better make sure they're all connected, all the time and that you've got good bandwidth between them. The second technology was voice, and it's not just voice in one place in your home, it's voice throughout your home. You don't want to have to run to the kitchen to turn your bedroom lights on. And then, vision. You know, making sure your home has the ability to see more. It could be cameras, could be motion sensors, it could be vision sensors. And then this last one is this local intelligence. This artificial intelligence. So the unique approach that Intel is taking is across all of our assets. In the data center, in our artificial intelligence organization, in our new technology organization, our IOT organization, in our client computing group. We're taking all of these assets and investing them in those four pillars and kind of really delivering unique solutions, and there's actually a couple of them that have been on display this week so far. >> How about DeepLens? That certainly was an awesome keynote point, and the device that Andy introduced is essentially a wireless device, that is basically that machine learning an AI in it. And that is awesome, because it's also an IOT device, it's got so much versatility to it. What's behind that? Can you give some color to DeepLens? What does it mean for people? >> So, we're really excited about that one. We partnered with Amazon at AWS on that for quite some time. So, just a reminder to everybody, that is the first Deep Learning enabled wireless camera. And what we're helped do in that, is it's got an Intel Atom processor inside that actually runs the vision processing workload. We also contributed a Deep Learning toolkit, kind of a software middleware layer, and we've also got the Intel Compute Library for deep neural networks. So basically, a lot of preconfigured algorithms that developers can use. The bigger thing, though, is when I talked about those four technology pillars; the vision pillar, as well as the artificial intelligence pillar, this is a proof point of exactly that. Running an instance of the AWS service on a local device in the home to do this computer vision. >> When will that device be available? And what's the price point? Can we get our hands on one? And how are people going to be getting this? >> Yeah, so what was announced during the keynote today is that there are actually some Deep Learning workshops today, here at re:Invent where they're actually being given away, and then actually as soon as the announcement was made during the keynote today, they're actually available for pre-order on Amazon.com right now. I'm not actually sure on the shipping date on Amazon, but anybody can go and check. >> Jeff Frick, go get one of those quickly. Order it, put my credit card down. >> Miles: Yes, please do. >> Well, that's super exciting and now, where's the impact in that? Because it seems like it could be a great IOT device. It seems like it would be a fun consumer device. Where do you guys see the use cases for these developing? >> So the reason I'm excited about this one, is I fundamentally believe that vision is going to enable some richer use cases. The only way we're going to get those though, is if you get these brilliant developers getting their hands on the hardware, with someone like Amazon, who's made all of the machine learning, and the cloud and all of the pieces easier. It's now going to make it very easy for thousands, ideally, hundreds of thousands of developers to start working on this, so they can enable these new use cases. >> The pace of innovation that AWS has set, it's palpable here, we hear it, we feel it. This is a relatively new business unit for Intel. You announced this, about a year ago at re:Invent 2016? Are you trying to match the accelerated pace of innovation that AWS has? And what do you see going on in the next 12 months? Where do you think we'll be 12 months from now? >> Yeah, so I think we're definitely trying to be a fantastic technology partner for Amazon. One of the things we have since last re:Invent is we announced we were going to do some reference designs and developer kits to help get Alexa everywhere. So during this trade show, actually, we are holding, I can't remember the exact number, but many workshops, where we are providing the participants with a Speech Enabling Developer toolkit. And basically, what this is, is it's got an Intel platform, with Intel's dual DSP on it, a microarray, and it's paired with Raspberry Pi. So basically, this will allow anybody who already makes a product, it will allow them to easily integrate Alexa into that product with Intel inside. Which is perfect for us. >> So obviously, we're super excited, we love the cloud. I'm kind of a fanboy of the cloud, being a developer in my old days, but the resources that you get out of the cloud are amazing. But now when you start looking at these devices like DeepLens, the possibilities are limitless. So it's really interesting. The question I have for you is, you know, we had Tom Siebel on earlier, pioneer, invented the CRM category. He's now the CEO of C3 IOT, and I asked him, why are you doing a startup, you're a billionaire. You're rich, you don't need to do it. He goes, "I'm a computer guy, I love doing this." He's an entrepreneur at heart. But he said something interesting, he said that the two waves that he surfs, they call him a big time surfer, he's hanging 10 on the waves, is IOT and AI. This is an opportunity for you guys to reimagine the smart home. How important is the IOT trend and the AI trend for really doing it right with smart home, and whatever we're calling it. There's an opportunity there. How are you guys viewing that vision? What progress points have you identified at Intel, to kind of, check? >> Completely agree. For me, AI really is the key turning point here. 'Cause even just talking about connected versus smart, the thing that makes it smart is the ability to learn and think for itself. And the reason we have focused on those technology pillars, is we believe that by adding voice everywhere in the home, and the listening capability, as well as adding the vision capability, you're going to enable all of this rich new data, which you have to have some of these AI tools to make any sense of, and when you get to video, you absolutely have to have some amount of it locally. So, that either for bandwidth reasons, for latency reasons, for privacy reasons, like some of the examples that were given in the keynote today, you just want to keep that stuff locally. >> And having policy and running on it, you know, access points are interesting, it gives you connectivity, but these are computers, so if someone gets malware on the home, they can run a full threaded process on these machines. Sometimes you might not want that. You want to be able to control that. >> Yes, absolutely. We would really believe that the wireless access point in the home is one of the greatest areas where you can add additional security in the home and protect all of the devices. >> So you mentioned, I think 120 different devices in your home that are connected. How far away do you think your home is from being, from going from connected to smart? What's that timeline like? >> You know what I think, honestly, I think a lot of the hardware is already there. And the examples I will give is, and I'm not just saying this because I'm here, but I actually do have 15 Echos in my house because I do want to be able to control all of the infrastructure everywhere in the home. I do believe in the future, those devices will be listening for anomalies, like glass breaking, a dog barking, a baby crying, and I believe the hardware we have today is very capable of doing that. Similarly, I think that a lot of the cameras today are trained to, whenever they see motion, to do certain things and to start recording. I think that use case is going to evolve over time as well, so I truly believe that we are probably two years away from really seeing, with some of the existing infrastructure, truly being able to enable some smarter home use cases. >> The renaissance going on, the creativity is going to be amazing. I'm looking at a tweet that Bert Latimar, from our team made, on our last interview with the Washington County Sheriff, customer of Amazon, pays $6 a month for getting all the mugshots. He goes, "I'm gonna use DeepLens for things like "recognizing scars and tattoos." Because now they have to take pictures when someone comes in as a criminal, but now with DeepLens, they can program it to look for tattoos. >> Yeah, absolutely. And if you see things like the Ring Doorbell today, they have that neighborhood application of it so you can actually share within your local neighborhood if somebody had a package stolen, they can post a picture of that person. And even just security cameras, my house, it feels like Fort Knox sometimes, I've got so many security cameras. It used to be, every time there was a windstorm, I got 25 alerts on my phone, because a branch was blowing. Now I have security cameras that actually can do facial recognition and say, your son is home, your daughter is home, your wife is home. >> So are all the houses going to have a little sign that says,"Protected by Alexa and Intel and DeepLens" >> Don't you dare, exactly. (laughs) >> Lisa: And no sneaking out for the kids. >> Yes, exactly. >> Alright, so real quick to end the segment, quickly summarize and share, what is the Intel relationship with Amazon Web Services? Talk about the partnership. >> It's a great relationship. We've been partnering with Amazon for over a decade, starting with AWS. Over the last couple of years, we've started working closely with them on their first party products. So, many of you have seen the Echo Show and the Echo Look, that has Intel inside. It also has a RealSense Camera in the Look. We've now enabled the Speech Enabling Developer Kit, which is meant to help get Alexa everywhere, running on Intel. We've now done DeepLens, which is a great example of local artificial intelligence. Partnered with all the work we've done with them in the cloud, so it really is, I would say the partnership expands all the way from the very edge device in the home, all the way to the cloud. >> Miles, thanks for coming, Miles Kingston with Intel, General Manager of the Smart Home Group, new business unit at Intel, really reimagining the future for people's lives. I think in this great case where technology can actually help people, rather than making it any more complicated. Which we all know if we have access points and kids gaming, it can be a problem. It's theCUBE, live here in Las Vegas. 45,000 people here at Amazon re:Invent. Five years ago, our first show, only 7,000. Now what amazing growth. Thanks so much for coming out, Lisa Martin and John Furrier here, reporting from theCUBE. More coverage after this short break. (light music)

>> Hi, this is George Gilbert. We have an extra-special guest today on our CUBEcast, Aman Naimat, Senior Vice President and CTO of Demandbase started with a five-person startup, Spiderbook. Almost like a reverse IPO, Demandbase bought Spiderbook, but it sounds like Spiderbook took over Demandbase. So Aman, welcome. >> Thank you, excited to be here. Always good to see you. >> So, um, Demandbase is a Next Gen CRM program. Let's talk about, just to set some context. >> Yes. >> For those who aren't intimately familiar with traditional CRM, what problems do they solve? And how did they start, and how did they evolve? >> Right, that's a really good question. So, for the audience, CRM really started as a contact manager, right? And it was replicating what a salesperson did in their own private notebook, writing contact phone numbers in an electronic version of it, right? So you had products that were really built for salespeople on an individual basis. But it slowly evolved, particularly with Siebel, into more of a different twist. It evolved into more of a management tool or reporting tool because Tom Siebel was himself a sales manager, ran a sales team at Oracle. And so, it actually turned from an individual-focused product to an organization management reporting product. And I've been building this stuff since I was 19. And so, it's interesting that, you know, the products today, we're going, actually pivoting back into products that help salespeople or help individual marketers and add value and not just focus on management reporting. >> That's an interesting perspective. So it's more now empowering as opposed to, sort of, reporting. >> Right, and I think some of it is cultural influence. You know, over the last decade, we have seen consumer apps actually take a much more, sort of predominant position rather than in the traditional, earlier in the 80s and 90s, the advanced applications were corporate applications, your large computers and companies. But over the last year, as consumer technology has taken off, and actually, I would argue has advanced more than even enterprise technology, so in essence, that's influencing the business. >> So, even ERP was a system of record, which is the state of the enterprise. And this is much more an organizational productivity tool. >> Right. >> So, tell us now, the mental leap, the conceptual leap that Demandbase made in terms of trying to solve a different problem. >> Right, so, you know, Demandbase started on the premise or around marketing automation and marketing application which was around identifying who you are. As we move towards more digital transaction and Web was becoming the predominant way of doing business, as people say that's 70 to 80 percent of all businesses start using online digital research, there was no way to know it, right? The majority of the Internet is this dark, unknown place. You don't know who's on your website, right? >> You're referring to the anonymity. >> Exactly. >> And not knowing who is interacting with you until very late. >> Exactly, and you can't do anything intelligent if you don't know somebody, right? So if you didn't know me, you couldn't really ask. What will you do? You'll ask me stupid questions around the weather. And really, as humans, I can only communicate if you know somebody. So the sort of innovation behind Demandbase was, and it still continues to be to actually bring around and identify who you're talking to, be it online on your website and now even off your website. And that allows you to have a much more sort of personalized conversation. Because ultimately in marketing and perhaps even in sales, it comes down to having a personal conversation. So that's really what, which if you could have a billion people who could talk to every person coming to your website in a personalized manner, that would be fantastic. But that's just not possible. >> So, how do you identify a person before they even get to a vendor's website so that you can start on a personalized level? >> Right, so Demandbase has been building this for a long time, but really, it's a hard problem. And it's harder now than ever before because of security and privacy, lots of hackers out there. People are actually trying to hide, or at least prevent this from leaking out. So, eight, nine years ago, we could buy registries or reverse DNS. But now with ISBs, and we are behind probably Comcast or Level 3. So how do you even know who this IP address is even registered to? So about eight years ago, we started mapping IP addresses, 'cause that's how you browse the Internet, to companies that they work at, right? But it turned out that was no longer effective. So we have built over the last eight years proprietary methods that know how companies relate to the IP addresses that they have. But we have gone to doing partnerships. So when you log into certain websites, we partner with them to identify you if you self-identify at Forbes.com, for example. So when you log in, we do a deal. And we have hundreds of partners and data providers. But now, the state of the art where we are is we are now looking at behavioral signals to identify who you are. >> In other words, not just touch points with partners where they collect an identity. >> Right. >> You have a signature of behavior. >> That's right. >> It's really interesting that humans are very unique. And based on what they're reading online and what they're reading about, you can actually identify a person and certainly identify enough things about them to know that this is an executive at Tesla who's interested in IOT manufacturing. >> Ah, so you don't need to resolve down to the name level. >> No. >> You need to know sort of the profile. >> Persona, exactly. >> The persona. >> The persona, and that's enough for marketing. So if I knew that this is a C-level supply chain executive from Tesla who lives in Palo Alto and has interests in these areas or problems, that's enough for Siemens to then have an intelligent conversation to this person, even if they're anonymous on their website or if they call on the phone or anything else. >> So, okay, tell us the next step. Once you have a persona, is it Demandbase that helps them put together a personalized? >> Profile. >> Profile, and lead it through the conversation? >> Yeah, so earlier, well, not earlier, but very recently, rebuilding this technology was just a very hard problem. To identify now hundreds of millions of people, I think around 700 are businesspeople globally which is majority of the business world. But we realize that in AI, making recommendations or giving you data in advanced analytics is just not good enough because you need a way to actually take action and have a personalized conversation because there are 100 thousand people on your website. Making recommendations, it's just overwhelming for humans to get that much data. So the better sort of idea now that we're working on is just take the action. So if somebody from Tesla visits your website, and they are an executive who will buy your product, take them to the right application. If they go back and leave your website, then display them the right message in a personalized ad. So it's all about taking actions. And then obviously, whenever possible, guiding humans towards a personalized conversation that will maximize your relationship. >> So, it sounds like sometimes it's anticipating and recommending a next best action. >> Yeah. >> And sometimes, it's your program taking the next best action. >> That's right, because it's just not possible to scale people to take actions. I mean, we have 30, 40 sales reps in Demandbase. We can't handle the volume. And it's difficult to create that personalized letter, right? So we make recommendations, but we've found that it's just too overwhelming. >> Ah, so in other words, when you're talking about recommendations, you're talking about recommendations for Demandbase for? >> Or our clients, employees, or salespeople, right? >> Okay. >> But whenever possible, we are looking to now build systems that in essence are in autopilot mode, and they take the action. They drive themselves. >> Give us some examples of the actions. >> That's right, so some actions could be if you know that a qualified person came to your website, notify the salesperson and open a chat window saying, "This is an executive. "This is similar to a person who will buy "a product from you. "They're looking for this thing. "Do you want to connect with a salesperson?" And obviously, only the people that will buy from you. Or, the action could be, send them an email automatically based on something they will be interested in, and in essence, have a conversation. Right? So it's all about conversation. An ad or an email or a person are just ways of having a conversation, different channels. >> So, it sounds like there was an intermediate marketing automation generation. >> Right. >> After traditional CRM which was reporting. >> Right, that's true. >> Where it was basically, it didn't work until you registered on the website. >> That's right. >> And then, they could email you. They could call you. The inside sales reps. >> That's right. >> You know, if you took a demo, >> That's right. >> you had to put an idea in there. >> And that's still, you know, so when Demandbase came around, that was the predominant between the CRM we were talking about. >> George: Right. >> There was a gap. There was a generation which started to be marketing. It was all about form fills. >> George: Yeah. >> And it was all about nurturing, but I think that's just spam. And today, their effectiveness is close to nothing. >> Because it's basically email or outbound calls. >> Yeah, it's email spam. Do you know we all have email boxes filled with this stuff? And why doesn't it work? Because, not only because it's becoming ineffective and that's one reason. Because they don't know me, right? And it boils down to if the email was really good and it related to what you're looking for or who you are, then it will be effective. But spam, or generic email is just not effective. So it's to some extent, we lost the intimacy. And with the new generation of what we call account-based marketing, we are trying to build intimacy at scale. >> Okay, so tell us more. Tell us first the philosophy behind account-based marketing and then the mechanics of how you do it. >> Sure, really, account-based marketing is nothing new. So if you walk into a corporation, they have these really sophisticated salespeople who understand their clients, and they focus on one-on-one, and it's very effective. So if you had Google as a client or Tesla as a client, and you are Siemens, you have two people working and keeping that relationship working 'cause you make millions of dollars. But that's not a scalable model. It's certainly not scalable for startups here to work with or to scale your organization, be more effective. So really, the idea behind account-based marketing is to scale that same efficacy, that same personalized conversation but at higher volume, right? And maximize, and the only way to really do that is using artificial intelligence. Because in essence, we are trying to replicate human behavior, human knowledge at scale. Right? And to be able to harvest and know what somebody who knows about pharma would know. >> So give me an example of, let's stay in pharma for a sec. >> Sure. >> And what are the decision points where based on what a customer does or responds to, you determine the next step or Demandbase determines what next step to take? >> Right. >> What are some of those options? Like a decision tree maybe? >> You can think of it, it's quite faddish in our industry now. It's reinforcement learning which is what Google used in the Go system. >> George: Yeah, AlphaGo. >> AlphaGo, right, and we were inspired by that. And in essence, what we are trying to do is predict not only what will keep you going but where you will win. So we give rewards at each point. And the ultimate goal is to convert you to a customer. So it looks at all your possible futures, and then it figures out in what possible futures you will be a customer. And then it works backwards to figure out where it should take you next. >> Wow, okay, so this is very different from >> They play six months ahead. So it's a planning system. >> Okay. >> Cause your sales cycles are six months ahead. >> So help us understand the difference between the traditional statistical machine learning that is a little more mainstream now. >> Sure. >> Then the deep learning, the neural nets, and then reinforcement learning. >> Right. >> Where are the sweet spots? What are the sweet spots for the problems they solve? >> Yeah, I mean, you know, there's a lot of fad and things out there. In my opinion, you can achieve a lot and solve real-world problems with simpler machine learning algorithms. In fact, for the data science team that I run, I always say, "Start with like the most simplest algorithm." Because if the data is there and you have the intuition, you can get to a 60% F-score or quality with the most naive implementation. >> George: 60% meaning? >> Like accuracy of the model. >> Confidence. >> Confidence. Sure, how good the model is, how precise it is. >> Okay. >> And sure, then you can make it better by using more advanced algorithms. The reinforcement learning, the interesting thing is that its ability to plan ahead. Most machine learning can only make a decision. They are classifiers of sorts, right? They say, is this good or bad? Or, is this blue? Or, is this a cat or not? They're mostly Boolean in nature or you can simulate that in multi-class classifiers. But reinforcement learning allows you to sort of plan ahead. And in CRM or as humans, we're always planning ahead. You know, a really good salesperson knows that for this stage opportunity or this person in pharma, I need to invite them to the dinner 'cause their friends are coming and they know that last year when they did that, then in the future, that person converted. Right, if they go to the next stage and they, so it plans ahead the possible futures and figures out what to do next. >> So, for those who are familiar with the term AB testing. >> Sure. >> And who are familiar with the notion that most machine learning models have to be trained on data where the answer exists, and they test it out, train it on one set of data >> Sure. >> Where they know the answers, then they hold some back and test it and see if it works. So, how does reinforcement learning change that? >> I mean, it's still testing on supervised models to know. It can be used to derive. You still need data to understand what the reward function would be. Right? And you still need to have historical data to understand what you should give it. And sure, have humans influence it as well, right? At some point, we always need data. Right? If you don't have the data, you're nowhere. And if you don't have, but it also turns out that most of the times, there is a way to either derive the data from some unsupervised method or have a proxy for the data that you really need. >> So pick a key feature in Demandbase and then where you can derive the data you need to make a decision, just as an example. >> Yeah, that's a really good question. We derive datas all the time, right? So, let me use something quite, quite interesting that I wish more companies and people used is the Internet data, right? The Internet today is the largest source of human knowledge, and it actually know more than you could imagine. And even simple queries, so we use the Bing API a lot. And to know, so one of the simple problems we ran into many years ago, and that's when we realized how we should be using Internet data which in academia has been used but not as used as it should be. So you know, you can buy APIs from Bing. And I wish Google would give their API, but they don't. So, that's our next best choice. We wanted to understand who people are. So there's their common names, right? So, George Gilbert is a common name or Alan Fletcher who's my co-founder. And, you know, is that a common name? And if you search that, just that name, you get that name in various contexts. Or co-occurring with other words, you can see that there are many Alan Fletchers, right? Or if you get, versus if you type in my name, Aman Naimat, you will always find the same kind of context. So you will know it's one person or it's a unique name. >> So, it sounds to me that reinforcement learning is online learning where you're using context. It's not perfectly labeled data. >> Right. I think there is no perfectly labeled data. So there's a misunderstanding of data scientists coming out of perfectly labeled data courses from Stanford, or whatever machine learning program. And we realized very quickly that the world doesn't have any perfect labeled data. We think we are going to crowdsource that data. And it turns out, we've tried it multiple times, and after a year, we realized that it's just a waste of time. You can't get, you know, 20 cents or 25 cents per item worker somewhere in wherever to hat and label data of any quality to you. So, it's much more effective to, and we were a startup, so we didn't have money like Google to pay. And even if you had the money, it generally never works out. We find it more effective to bootstrap or reuse unsupervised models to actually create data. >> Help us. Elaborate on that, the unsupervised and the bootstrapping where maybe it's sort of like a lawnmower where you give it that first. >> That's right. >> You know, tug. >> I mean, we've used it extensively. So let me give you an example. Let's say you wanted to create a list of cities, right? Or a list of the classic example actually was a paper written by Sergey Brin. I think he was trying to figure out the names of all authors in the world, and this is 1988. And basically if you search on Google, the term "has written the book," just the term "has written the book," these are called patterns, or hearse patterns, I think. Then you can imagine that it's also always preceded by a name of a person who's an author. So, "George Gilbert has written the book," and then the name of the book, right? Or "William Shakespeare has written the book X." And you seed it with William Shakespeare, and you get some books. Or you put Shakespeare and you get some authors, right? And then, you use it to learn other patterns that also co-occurred between William Shakespeare and the book. >> George: Ah. >> And then you learn more patterns and you use it to extract more authors. >> And in the case of Demandbase, that's how you go from learning, starting bootstrapping within, say, pharma terminology. >> Yes. >> And learning the rest of pharma terminology. >> And then, using generic terminology to enter an industry, and then learning terminology that we ourselves don't understand yet it means. For example, I always used this example where if we read a sentence like "Takeda has in-licensed "a molecule from Roche," it may mean nothing to us, but it means that they're partnered and bought a product, in pharma lingo. So we use it to learn new language. And it's a common technique. We use it extensively, both. So it goes down to, while we do use highly sophisticated algorithms for some problems, I think most problems can be solved with simple models and thinking through how to apply domain expertise and data intuition and having the data to do it. >> Okay, let's pause on that point and come back to it. >> Sure. >> Because that sounds like a rich vein to explore. So this is George Gilbert on the ground at Demandbase. We'll be right back in a few minutes.