>>Well, welcome back to the cubes coverage of AWS reinvent 2021. We're here joined by Ian buck, general manager and vice president of accelerated computing at Nvidia I'm. John Ford, your host of the QB. And thanks for coming on. So in video, obviously, great brand congratulates on all your continued success. Everyone who has does anything in graphics knows the GPU's are hot and you guys get great brand great success in the company, but AI and machine learning was seeing the trend significantly being powered by the GPU's and other systems. So it's a key part of everything. So what's the trends that you're seeing, uh, in ML and AI, that's accelerating computing to the cloud. Yeah, >>I mean, AI is kind of drape bragging breakthroughs innovations across so many segments, so many different use cases. We see it showing up with things like credit card, fraud prevention and product and content recommendations. Really it's the new engine behind search engines is AI. Uh, people are applying AI to things like, um, meeting transcriptions, uh, virtual calls like this using AI to actually capture what was said. Um, and that gets applied in person to person interactions. We also see it in intelligence systems assistance for a contact center, automation or chat bots, uh, medical imaging, um, and intelligence stores and warehouses and everywhere. It's really, it's really amazing what AI has been demonstrated, what it can do. And, uh, it's new use cases are showing up all the time. >>Yeah. I'd love to get your thoughts on, on how the world's evolved just in the past few years, along with cloud, and certainly the pandemics proven it. You had this whole kind of full stack mindset initially, and now you're seeing more of a horizontal scale, but yet enabling this vertical specialization in applications. I mean, you mentioned some of those apps, the new enablers, this kind of the horizontal play with enablement for specialization, with data, this is a huge shift that's going on. It's been happening. What's your reaction to that? >>Yeah, it's the innovations on two fronts. There's a horizontal front, which is basically the different kinds of neural networks or AIS as well as machine learning techniques that are, um, just being invented by researchers for, uh, and the community at large, including Amazon. Um, you know, it started with these convolutional neural networks, which are great for image processing, but as it expanded more recently into, uh, recurrent neural networks, transformer models, which are great for language and language and understanding, and then the new hot topic graph neural networks, where the actual graph now is trained as a, as a neural network, you have this underpinning of great AI technologies that are being adventure around the world in videos role is try to productize that and provide a platform for people to do that innovation and then take the next step and innovate vertically. Um, take it, take it and apply it to two particular field, um, like medical, like healthcare and medical imaging applying AI, so that radiologists can have an AI assistant with them and highlight different parts of the scan. >>Then maybe troublesome worrying, or requires more investigation, um, using it for robotics, building virtual worlds, where robots can be trained in a virtual environment, their AI being constantly trained, reinforced, and learn how to do certain activities and techniques. So that the first time it's ever downloaded into a real robot, it works right out of the box, um, to do, to activate that we co we are creating different vertical solutions, vertical stacks for products that talk the languages of those businesses, of those users, uh, in medical imaging, it's processing medical data, which is obviously a very complicated large format data, often three-dimensional boxes in robotics. It's building combining both our graphics and simulation technologies, along with the, you know, the AI training capabilities and different capabilities in order to run in real time. Those are, >>Yeah. I mean, it's just so cutting edge. It's so relevant. I mean, I think one of the things you mentioned about the neural networks, specifically, the graph neural networks, I mean, we saw, I mean, just to go back to the late two thousands, you know, how unstructured data or object store created, a lot of people realize that the value out of that now you've got graph graph value, you got graph network effect, you've got all kinds of new patterns. You guys have this notion of graph neural networks. Um, that's, that's, that's out there. What is, what is a graph neural network and what does it actually mean for deep learning and an AI perspective? >>Yeah, we have a graph is exactly what it sounds like. You have points that are connected to each other, that established relationships and the example of amazon.com. You might have buyers, distributors, sellers, um, and all of them are buying or recommending or selling different products. And they're represented in a graph if I buy something from you and from you, I'm connected to those end points and likewise more deeply across a supply chain or warehouse or other buyers and sellers across the network. What's new right now is that those connections now can be treated and trained like a neural network, understanding the relationship. How strong is that connection between that buyer and seller or that distributor and supplier, and then build up a network that figure out and understand patterns across them. For example, what products I may like. Cause I have this connection in my graph, what other products may meet those requirements, or also identifying things like fraud when, when patterns and buying patterns don't match, what a graph neural networks should say would be the typical kind of graph connectivity, the different kind of weights and connections between the two captured by the frequency half I buy things or how I rate them or give them stars as she used cases, uh, this application graph neural networks, which is basically capturing the connections of all things with all people, especially in the world of e-commerce, it's very exciting to a new application, but applying AI to optimizing business, to reducing fraud and letting us, you know, get access to the products that we want, the products that they have, our recommendations be things that, that excited us and want us to buy things >>Great setup for the real conversation that's going on here at re-invent, which is new kinds of workloads are changing. The game. People are refactoring their business with not just replatform, but actually using this to identify value and see cloud scale allows you to have the compute power to, you know, look at a note on an arc and actually code that. It's all, it's all science, all computer science, all at scale. So with that, that brings up the whole AWS relationship. Can you tell us how you're working with AWS before? >>Yeah. 80 of us has been a great partner and one of the first cloud providers to ever provide GPS the cloud, uh, we most more recently we've announced two new instances, uh, the instance, which is based on the RA 10 G GPU, which has it was supports the Nvidia RTX technology or rendering technology, uh, for real-time Ray tracing and graphics and game streaming is their highest performance graphics, enhanced replicate without allows for those high performance graphics applications to be directly hosted in the cloud. And of course runs everything else as well, including our AI has access to our AI technology runs all of our AI stacks. We also announced with AWS, the G 5g instance, this is exciting because it's the first, uh, graviton or ARM-based processor connected to a GPU and successful in the cloud. Um, this makes, uh, the focus here is Android gaming and machine learning and France. And we're excited to see the advancements that Amazon is making and AWS is making with arm and the cloud. And we're glad to be part of that journey. >>Well, congratulations. I remember I was just watching my interview with James Hamilton from AWS 2013 and 2014. He was getting, he was teasing this out, that they're going to build their own, get in there and build their own connections, take that latency down and do other things. This is kind of the harvest of all that. As you start looking at these new new interfaces and the new servers, new technology that you guys are doing, you're enabling applications. What does, what do you see this enabling as this, as this new capability comes out, new speed, more, more performance, but also now it's enabling more capabilities so that new workloads can be realized. What would you say to folks who want to ask that question? >>Well, so first off I think arm is here to stay and you can see the growth and explosion of my arm, uh, led of course, by grab a tiny to be. I spend many others, uh, and by bringing all of NVIDIA's rendering graphics, machine learning and AI technologies to arm, we can help bring that innovation. That arm allows that open innovation because there's an open architecture to the entire ecosystem. Uh, we can help bring it forward, uh, to the state of the art in AI machine learning, the graphics. Um, we all have our software that we released is both supportive, both on x86 and an army equally, um, and including all of our AI stacks. So most notably for inference the deployment of AI models. We have our, the Nvidia Triton inference server. Uh, this is the, our inference serving software where after he was trained to model, he wanted to play it at scale on any CPU or GPU instance, um, for that matter. So we support both CPS and GPS with Triton. Um, it's natively integrated with SageMaker and provides the benefit of all those performance optimizations all the time. Uh, things like, uh, features like dynamic batching. It supports all the different AI frameworks from PI torch to TensorFlow, even a generalized Python code. Um, we're activating how activating the arm ecosystem as well as bringing all those AI new AI use cases and all those different performance levels, uh, with our partnership with AWS and all the different clouds. >>And you got to making it really easy for people to use, use the technology that brings up the next kind of question I want to ask you. I mean, a lot of people are really going in jumping in the big time into this. They're adopting AI. Either they're moving in from prototype to production. There's always some gaps, whether it's knowledge, skills, gaps, or whatever, but people are accelerating into the AI and leaning into it hard. What advancements have is Nvidia made to make it more accessible, um, for people to move faster through the, through the system, through the process? >>Yeah, it's one of the biggest challenges. The other promise of AI, all the publications that are coming all the way research now, how can you make it more accessible or easier to use by more people rather than just being an AI researcher, which is, uh, uh, obviously a very challenging and interesting field, but not one that's directly in the business. Nvidia is trying to write a full stack approach to AI. So as we make, uh, discover or see these AI technologies come available, we produce SDKs to help activate them or connect them with developers around the world. Uh, we have over 150 different STKs at this point, certain industries from gaming to design, to life sciences, to earth scientist. We even have stuff to help simulate quantum computing. Um, and of course all the, all the work we're doing with AI, 5g and robotics. So, uh, we actually just introduced about 65 new updates just this past month on all those SDKs. Uh, some of the newer stuff that's really exciting is the large language models. Uh, people are building some amazing AI. That's capable of understanding the Corpus of like human understanding, these language models that are trained on literally the continent of the internet to provide general purpose or open domain chatbots. So the customer is going to have a new kind of experience with a computer or the cloud. Uh, we're offering large language, uh, those large language models, as well as AI frameworks to help companies take advantage of this new kind of technology. >>You know, each and every time I do an interview with Nvidia or talk about Nvidia my kids and their friends, they first thing they said, you get me a good graphics card. Hey, I want the best thing in their rig. Obviously the gaming market's hot and known for that, but I mean, but there's a huge software team behind Nvidia. This is a well-known your CEO is always talking about on his keynotes, you're in the software business. And then you had, do have hardware. You were integrating with graviton and other things. So, but it's a software practices, software. This is all about software. Could you share kind of more about how Nvidia culture and their cloud culture and specifically around the scale? I mean, you, you hit every, every use case. So what's the software culture there at Nvidia, >>And it is actually a bigger, we have more software people than hardware people, people don't often realize this. Uh, and in fact that it's because of we create, uh, the, the, it just starts with the chip, obviously building great Silicon is necessary to provide that level of innovation, but as it expanded dramatically from then, from there, uh, not just the Silicon and the GPU, but the server designs themselves, we actually do entire server designs ourselves to help build out this infrastructure. We consume it and use it ourselves and build our own supercomputers to use AI, to improve our products. And then all that software that we build on top, we make it available. As I mentioned before, uh, as containers on our, uh, NGC container store container registry, which is accessible for me to bus, um, to connect to those vertical markets, instead of just opening up the hardware and none of the ecosystem in develop on it, they can with a low-level and programmatic stacks that we provide with Kuda. We believe that those vertical stacks are the ways we can help accelerate and advance AI. And that's why we make as well, >>Ram a little software is so much easier. I want to get that plug for, I think it's worth noting that you guys are, are heavy hardcore, especially on the AI side. And it's worth calling out, uh, getting back to the customers who are bridging that gap and getting out there, what are the metrics they should consider as they're deploying AI? What are success metrics? What does success look like? Can you share any insight into what they should be thinking about and looking at how they're doing? >>Yeah. Um, for training, it's all about time to solution. Um, it's not the hardware that that's the cost, it's the opportunity that AI can provide your business and many, and the productivity of those data scientists, which are developing, which are not easy to come by. So, uh, what we hear from customers is they need a fast time to solution to allow people to prototype very quickly, to train a model to convergence, to get into production quickly, and of course, move on to the next or continue to refine it often. So in training is time to solution for inference. It's about our, your ability to deploy at scale. Often people need to have real time requirements. They want to run in a certain amount of latency, a certain amount of time. And typically most companies don't have a single AI model. They have a collection of them. They want, they want to run for a single service or across multiple services. That's where you can aggregate some of your infrastructure leveraging the trading infant server. I mentioned before can actually run multiple models on a single GPU saving costs, optimizing for efficiency yet still meeting the requirements for latency and the real time experience so that your customers have a good, a good interaction with the AI. >>Awesome. Great. Let's get into, uh, the customer examples. You guys have obviously great customers. Can you share some of the use cases, examples with customers, notable customers? >>Yeah. I want one great part about working in videos as a technology company. You see, you get to engage with such amazing customers across many verticals. Uh, some of the ones that are pretty exciting right now, Netflix is using the G4 instances to CLA um, to do a video effects and animation content. And, you know, from anywhere in the world, in the cloud, uh, as a cloud creation content platform, uh, we work in the energy field that Siemens energy is actually using AI combined with, um, uh, simulation to do predictive maintenance on their energy plants, um, and, and, uh, doing preventing or optimizing onsite inspection activities and eliminating downtime, which is saving a lot of money for the engine industry. Uh, we have worked with Oxford university, uh, which is Oxford university actually has over two, over 20 million artifacts and specimens and collections across its gardens and museums and libraries. They're actually using convenient GPS and Amazon to do enhance image recognition, to classify all these things, which would take literally years with, um, uh, going through manually each of these artifacts using AI, we can click and quickly catalog all of them and connect them with their users. Um, great stories across graphics, about cross industries across research that, uh, it's just so exciting to see what people are doing with our technology together with, >>And thank you so much for coming on the cube. I really appreciate Greg, a lot of great content there. We probably going to go another hour, all the great stuff going on in the video, any closing remarks you want to share as we wrap this last minute up >>Now, the, um, really what Nvidia is about as accelerating cloud computing, whether it be AI, machine learning, graphics, or headphones, community simulation, and AWS was one of the first with this in the beginning, and they continue to bring out great instances to help connect, uh, the cloud and accelerated computing with all the different opportunities integrations with with SageMaker really Ks and ECS. Uh, the new instances with G five and G 5g, very excited to see all the work that we're doing together. >>Ian buck, general manager, and vice president of accelerated computing. I mean, how can you not love that title? We want more, more power, more faster, come on. More computing. No, one's going to complain with more computing know, thanks for coming on. Thank you. Appreciate it. I'm John Farrell hosted the cube. You're watching Amazon coverage reinvent 2021. Thanks for watching.

(bright music) >> Well, welcome back to theCUBE's coverage of AWS re:Invent 2021. We're here joined by Ian Buck, general manager and vice president of Accelerated Computing at NVIDIA. I'm John Furrrier, host of theCUBE. Ian, thanks for coming on. >> Oh, thanks for having me. >> So NVIDIA, obviously, great brand. Congratulations on all your continued success. Everyone who does anything in graphics knows that GPU's are hot, and you guys have a great brand, great success in the company. But AI and machine learning, we're seeing the trend significantly being powered by the GPU's and other systems. So it's a key part of everything. So what's the trends that you're seeing in ML and AI that's accelerating computing to the cloud? >> Yeah. I mean, AI is kind of driving breakthroughs and innovations across so many segments, so many different use cases. We see it showing up with things like credit card fraud prevention, and product and content recommendations. Really, it's the new engine behind search engines, is AI. People are applying AI to things like meeting transcriptions, virtual calls like this, using AI to actually capture what was said. And that gets applied in person-to-person interactions. We also see it in intelligence assistance for contact center automation, or chat bots, medical imaging, and intelligence stores, and warehouses, and everywhere. It's really amazing what AI has been demonstrating, what it can do, and its new use cases are showing up all the time. >> You know, Ian, I'd love to get your thoughts on how the world's evolved, just in the past few years alone, with cloud. And certainly, the pandemic's proven it. You had this whole kind of fullstack mindset, initially, and now you're seeing more of a horizontal scale, but yet, enabling this vertical specialization in applications. I mean, you mentioned some of those apps. The new enablers, this kind of, the horizontal play with enablement for, you know, specialization with data, this is a huge shift that's going on. It's been happening. What's your reaction to that? >> Yeah. The innovation's on two fronts. There's a horizontal front, which is basically the different kinds of neural networks or AIs, as well as machine learning techniques, that are just being invented by researchers and the community at large, including Amazon. You know, it started with these convolutional neural networks, which are great for image processing, but has expanded more recently into recurrent neural networks, transformer models, which are great for language and language and understanding, and then the new hot topic, graph neural networks, where the actual graph now is trained as a neural network. You have this underpinning of great AI technologies that are being invented around the world. NVIDIA's role is to try to productize that and provide a platform for people to do that innovation. And then, take the next step and innovate vertically. Take it and apply it to a particular field, like medical, like healthcare and medical imaging, applying AI so that radiologists can have an AI assistant with them and highlight different parts of the scan that may be troublesome or worrying, or require some more investigation. Using it for robotics, building virtual worlds where robots can be trained in a virtual environment, their AI being constantly trained and reinforced, and learn how to do certain activities and techniques. So that the first time it's ever downloaded into a real robot, it works right out of the box. To activate that, we are creating different vertical solutions, vertical stacks, vertical products, that talk the languages of those businesses, of those users. In medical imaging, it's processing medical data, which is obviously a very complicated, large format data, often three-dimensional voxels. In robotics, it's building, combining both our graphics and simulation technologies, along with the AI training capabilities and difference capabilities, in order to run in real time. Those are just two simple- >> Yeah, no. I mean, it's just so cutting-edge, it's so relevant. I mean, I think one of the things you mentioned about the neural networks, specifically, the graph neural networks, I mean, we saw, I mean, just go back to the late 2000s, how unstructured data, or object storage created, a lot of people realized a lot of value out of that. Now you got graph value, you got network effect, you got all kinds of new patterns. You guys have this notion of graph neural networks that's out there. What is a graph neural network, and what does it actually mean from a deep learning and an AI perspective? >> Yeah. I mean, a graph is exactly what it sounds like. You have points that are connected to each other, that establish relationships. In the example of Amazon.com, you might have buyers, distributors, sellers, and all of them are buying, or recommending, or selling different products. And they're represented in a graph. If I buy something from you and from you, I'm connected to those endpoints, and likewise, more deeply across a supply chain, or warehouse, or other buyers and sellers across the network. What's new right now is, that those connections now can be treated and trained like a neural network, understanding the relationship, how strong is that connection between that buyer and seller, or the distributor and supplier, and then build up a network to figure out and understand patterns across them. For example, what products I may like, 'cause I have this connection in my graph, what other products may meet those requirements? Or, also, identifying things like fraud, When patterns and buying patterns don't match what a graph neural networks should say would be the typical kind of graph connectivity, the different kind of weights and connections between the two, captured by the frequency of how often I buy things, or how I rate them or give them stars, or other such use cases. This application, graph neural networks, which is basically capturing the connections of all things with all people, especially in the world of e-commerce, is very exciting to a new application of applying AI to optimizing business, to reducing fraud, and letting us, you know, get access to the products that we want. They have our recommendations be things that excite us and want us to buy things, and buy more. >> That's a great setup for the real conversation that's going on here at re:Invent, which is new kinds of workloads are changing the game, people are refactoring their business with, not just re-platforming, but actually using this to identify value. And also, your cloud scale allows you to have the compute power to, you know, look at a note in an arc and actually code that. It's all science, it's all computer science, all at scale. So with that, that brings up the whole AWS relationship. Can you tell us how you're working with AWS, specifically? >> Yeah, AWS have been a great partner, and one of the first cloud providers to ever provide GPUs to the cloud. More recently, we've announced two new instances, the G5 instance, which is based on our A10G GPU, which supports the NVIDIA RTX technology, our rendering technology, for real-time ray tracing in graphics and game streaming. This is our highest performance graphics enhanced application, allows for those high-performance graphics applications to be directly hosted in the cloud. And, of course, runs everything else as well. It has access to our AI technology and runs all of our AI stacks. We also announced, with AWS, the G5 G instance. This is exciting because it's the first Graviton or Arm-based processor connected to a GPU and successful in the cloud. The focus here is Android gaming and machine learning inference. And we're excited to see the advancements that Amazon is making and AWS is making, with Arm in the cloud. And we're glad to be part of that journey. >> Well, congratulations. I remember, I was just watching my interview with James Hamilton from AWS 2013 and 2014. He was teasing this out, that they're going to build their own, get in there, and build their own connections to take that latency down and do other things. This is kind of the harvest of all that. As you start looking at these new interfaces, and the new servers, new technology that you guys are doing, you're enabling applications. What do you see this enabling? As this new capability comes out, new speed, more performance, but also, now it's enabling more capabilities so that new workloads can be realized. What would you say to folks who want to ask that question? >> Well, so first off, I think Arm is here to stay. We can see the growth and explosion of Arm, led of course, by Graviton and AWS, but many others. And by bringing all of NVIDIA's rendering graphics, machine learning and AI technologies to Arm, we can help bring that innovation that Arm allows, that open innovation, because there's an open architecture, to the entire ecosystem. We can help bring it forward to the state of the art in AI machine learning and graphics. All of our software that we release is both supportive, both on x86 and on Arm equally, and including all of our AI stacks. So most notably, for inference, the deployment of AI models, we have the NVIDIA Triton inference server. This is our inference serving software, where after you've trained a model, you want to deploy it at scale on any CPU, or GPU instance, for that matter. So we support both CPUs and GPUs with Triton. It's natively integrated with SageMaker and provides the benefit of all those performance optimizations. Features like dynamic batching, it supports all the different AI frameworks, from PyTorch to TensorFlow, even a generalized Python code. We're activating, and help activating, the Arm ecosystem, as well as bringing all those new AI use cases, and all those different performance levels with our partnership with AWS and all the different cloud instances. >> And you guys are making it really easy for people to use use the technology. That brings up the next, kind of, question I wanted to ask you. I mean, a lot of people are really going in, jumping in big-time into this. They're adopting AI, either they're moving it from prototype to production. There's always some gaps, whether it's, you know, knowledge, skills gaps, or whatever. But people are accelerating into the AI and leaning into it hard. What advancements has NVIDIA made to make it more accessible for people to move faster through the system, through the process? >> Yeah. It's one of the biggest challenges. You know, the promise of AI, all the publications that are coming out, all the great research, you know, how can you make it more accessible or easier to use by more people? Rather than just being an AI researcher, which is obviously a very challenging and interesting field, but not one that's directly connected to the business. NVIDIA is trying to provide a fullstack approach to AI. So as we discover or see these AI technologies become available, we produce SDKs to help activate them or connect them with developers around the world. We have over 150 different SDKs at this point, serving industries from gaming, to design, to life sciences, to earth sciences. We even have stuff to help simulate quantum computing. And of course, all the work we're doing with AI, 5G, and robotics. So we actually just introduced about 65 new updates, just this past month, on all those SDKs. Some of the newer stuff that's really exciting is the large language models. People are building some amazing AI that's capable of understanding the corpus of, like, human understanding. These language models that are trained on literally the content of the internet to provide general purpose or open-domain chatbots, so the customer is going to have a new kind of experience with the computer or the cloud. We're offering those large language models, as well as AI frameworks, to help companies take advantage of this new kind of technology. >> You know, Ian, every time I do an interview with NVIDIA or talk about NVIDIA, my kids and friends, first thing they say is, "Can you get me a good graphics card?" They all want the best thing in their rig. Obviously the gaming market's hot and known for that. But there's a huge software team behind NVIDIA. This is well-known. Your CEO is always talking about it on his keynotes. You're in the software business. And you do have hardware, you are integrating with Graviton and other things. But it's a software practice. This is software. This is all about software. >> Right. >> Can you share, kind of, more about how NVIDIA culture and their cloud culture, and specifically around the scale, I mean, you hit every use case. So what's the software culture there at NVIDIA? >> Yeah, NVIDIA's actually a bigger, we have more software people than hardware people. But people don't often realize this. And in fact, that it's because of, it just starts with the chip, and obviously, building great silicon is necessary to provide that level of innovation. But it's expanded dramatically from there. Not just the silicon and the GPU, but the server designs themselves. We actually do entire server designs ourselves, to help build out this infrastructure. We consume it and use it ourselves, and build our own supercomputers to use AI to improve our products. And then, all that software that we build on top, we make it available, as I mentioned before, as containers on our NGC container store, container registry, which is accessible from AWS, to connect to those vertical markets. Instead of just opening up the hardware and letting the ecosystem develop on it, they can, with the low-level and programmatic stacks that we provide with CUDA. We believe that those vertical stacks are the ways we can help accelerate and advance AI. And that's why we make them so available. >> And programmable software is so much easier. I want to get that plug in for, I think it's worth noting that you guys are heavy hardcore, especially on the AI side, and it's worth calling out. Getting back to the customers who are bridging that gap and getting out there, what are the metrics they should consider as they're deploying AI? What are success metrics? What does success look like? Can you share any insight into what they should be thinking about, and looking at how they're doing? >> Yeah. For training, it's all about time-to-solution. It's not the hardware that's the cost, it's the opportunity that AI can provide to your business, and the productivity of those data scientists which are developing them, which are not easy to come by. So what we hear from customers is they need a fast time-to-solution to allow people to prototype very quickly, to train a model to convergence, to get into production quickly, and of course, move on to the next or continue to refine it. >> John Furrier: Often. >> So in training, it's time-to-solution. For inference, it's about your ability to deploy at scale. Often people need to have real-time requirements. They want to run in a certain amount of latency, in a certain amount of time. And typically, most companies don't have a single AI model. They have a collection of them they want to run for a single service or across multiple services. That's where you can aggregate some of your infrastructure. Leveraging the Triton inference server, I mentioned before, can actually run multiple models on a single GPU saving costs, optimizing for efficiency, yet still meeting the requirements for latency and the real-time experience, so that our customers have a good interaction with the AI. >> Awesome. Great. Let's get into the customer examples. You guys have, obviously, great customers. Can you share some of the use cases examples with customers, notable customers? >> Yeah. One great part about working at NVIDIA is, as technology company, you get to engage with such amazing customers across many verticals. Some of the ones that are pretty exciting right now, Netflix is using the G4 instances to do a video effects and animation content from anywhere in the world, in the cloud, as a cloud creation content platform. We work in the energy field. Siemens energy is actually using AI combined with simulation to do predictive maintenance on their energy plants, preventing, or optimizing, onsite inspection activities and eliminating downtime, which is saving a lot of money for the energy industry. We have worked with Oxford University. Oxford University actually has over 20 million artifacts and specimens and collections, across its gardens and museums and libraries. They're actually using NVIDIA GPU's and Amazon to do enhanced image recognition to classify all these things, which would take literally years going through manually, each of these artifacts. Using AI, we can quickly catalog all of them and connect them with their users. Great stories across graphics, across industries, across research, that it's just so exciting to see what people are doing with our technology, together with Amazon. >> Ian, thank you so much for coming on theCUBE. I really appreciate it. A lot of great content there. We probably could go another hour. All the great stuff going on at NVIDIA. Any closing remarks you want to share, as we wrap this last minute up? >> You know, really what NVIDIA's about, is accelerating cloud computing. Whether it be AI, machine learning, graphics, or high-performance computing and simulation. And AWS was one of the first with this, in the beginning, and they continue to bring out great instances to help connect the cloud and accelerated computing with all the different opportunities. The integrations with EC2, with SageMaker, with EKS, and ECS. The new instances with G5 and G5 G. Very excited to see all the work that we're doing together. >> Ian Buck, general manager and vice president of Accelerated Computing. I mean, how can you not love that title? We want more power, more faster, come on. More computing. No one's going to complain with more computing. Ian, thanks for coming on. >> Thank you. >> Appreciate it. I'm John Furrier, host of theCUBE. You're watching Amazon coverage re:Invent 2021. Thanks for watching. (bright music)

>>The cube presents, Coon and cloud native con Europe, 2022, brought to you by red hat, the cloud native computing foundation and its ecosystem partners. >>Welcome to Valencia Spain in Coon cloud native con Europe, 2022. I'm your host Keith Townsend. And we're continuing the conversation with community, with startups, with people building cloud native, a cube alum joint by a CTO. And not as the CTO advisor. I really appreciate talking to CTOs Capel. Th Lou don't forgive me if I murder the name, that's a tough one. I'm I'm, I'm getting warmed up to the cubey, but don't worry. When we get to the technical parts, it's gonna be fun. And then a cube alum, Umer K director of marketing Capel. You're the CTO. So we we'll start out with you. What's the problem statement? What, what, what are you guys doing? >>So, uh, we're building on top of an open source project podcast, custodian, uh, that is in CNCF. And that I built when I was at capital one and just as they were going, they're taking those first few steps. It's a large regulated enterprise into the cloud. And the challenge that I saw was, you know, how do we enable developers to pick whatever tools and technologies they want, if they wanna use Terraform or cloud formation or Ansible? I mean, the cloud gives us APIs and we wanna be able to enable people to use those APIs through innovative ways. Uh, but at the same time, we wanna make sure that the, regardless of what choices those developers make, that the organization is being is being well managed, that all those resources, all that infrastructure is complying to the organizational's policies. And what we saw at the time was that what we were getting impediments around our velocity into the cloud, because we had to cover off on all of the compliance and regulation aspects. >>And we were doing that them as one offs. And so, uh, taking a step back, I realized that what we really needed was a way to go faster on the compliance side and clock custodian was born out of that effort side of desk that we took through enterprise wide. And it was really about, um, accelerating the velocity around compliance, but doing it in the same way that we do application and infrastructure is code. So doing policy as code in a very simple readable YAML DSL, um, because, you know, PO you have, we, anytime we write code, we're gonna more people are gonna read that code than, than are going to need to be able to write it. And so being able to make it really easy to understand from both the developers that are in the environment from the compliance folks or auditors or security folks that might wanna review it, um, it was super important. And then instead of being at the time, we saw lots of very under products and they were all just big walls of red in somebody's corner office and getting that to actually back the information back in the hands of developers so that they can fix things, um, was problematic. So being able to do time remediation and real time collaboration and communication back to developers, Hey, you put a database on the internet. It's okay. We fixed it for you. And here's the corporate policy on how to do it better in the future. >>So this is a area of focus of mind that people, I think don't get right. A lot, the technology hard enough by itself. The transformation cloud is not just about adopting new technologies, but adopting new processes, the data, and information's there automatically. But when I go to an auditor or, or, uh, compliance and say, Hey, we've changed the process for how do we do change control for our software stack? I get a blank stare. It's what do you mean we've been doing it this way for the past 15, 20 years, that's resistance, it's a pain point and projects fail due to this issue. So talk to me about that initial customer engagement. What's what's that conversation like? >>So we start off by deploying our, our platform on top of buck custodian. Um, and as far as our customers, and we give them a view of all the things that are in their cloud, what is their baseline, so to speak. Um, but I think it's really important. Like I think you bring up a good point, like communication, the challenge, larger challenge for enterprises in the cloud, and especially with grocery compliance is understanding that it is not a steady state. It's always, there's always something new in the backlog. And so being able, and the, one of the challenges for larger orgs is just being able to communicate out what that is. I remember changing a tag policy and spending the next two years, explaining it to people what the actual tag policy was. Um, and so being able to actually inform them, you know, via email, via slack, via, you know, any communication mechanism, uh, as they're doing things is, is so powerful to be able to, to help the organization grow together and move and get an alignment about what, what the, what the new things are. >>And then additionally, you know, from a perspective of, uh, tooling that is built for the real world, like being able to, as those new policies come into play, being able to say, okay, we're going to segment into stopping the bleeding on the net new and being able to then take action on what's already deployed that now needs to become into compliance is, is really important. But coming back to your question on customer engagements, so we'll go in and we'll deploy, uh, a SAC platform for them. We'll basically show them all of the things that are there already and extent. Um, we provide a real time SQL interface that customers can use, um, that is an asset inventory of all their cloud assets. Uh, and then we provide, uh, policy packs that sort of cover off on compliance, security, cost, optimizations, and opportunities for them. Uh, and then we help them through, uh, get ops around those policies, help deploy remediation activities and capabilities for their environment. >>So walk me through some of the detail of, of, of the process and where the software helps and where people need to step in. I'm making I'm, I'm talking to my security auditor, and he's saying, you know what, Keith, I understand that the Aw, that the, uh, VM talking to the application, VM talking to the Oracle database, there is a firewall rule that says that that can happen. Show me that rule in cloud custodian. And you're trying to explain, well, well, there's no longer a firewall. There's a service. And the service is talking to that. And it, it is here and clouds, custodian and St is whether Stant help come to either help with the conversation, or where do I inject more of my experience and my ability to negotiate with the auditor. >>So stalet from the perspective, uh, and if we take a step back, we, we talk about governances code and, and the four pillars around compliance, security, cost, optimization operations, uh, that we help organizations do. But if we take a step back, what is cloud custodian? Cloud custodian is really a cloud orchestrator, a resource orchestrator. What <inaudible> provides on top of that is UI UX, um, policy packs at scale execution, across thousands of accounts, but in the context of an auditor, what we're really providing is here's the policy that we're enforcing. And here's the evidence, the attestation over time. And here's the resource database with history that shows how we, how we got here, where we compliant last year to this policy that we just wrote today. >>So shifting the conversation, you just mentioned operations. One of the larger conversations that I have with CIOs and CTOs is where do I put my people? Like this is a really tough challenge. When you look at moving to something like a SRE model, or, uh, let's say, even focus on the SRE, like what, where does the SRE sit in an organization? How does stack, like if at all, help me make those types of strategic decisions if I'm talking about governance overall. So, >>So I think in terms of personas, if you look at there's a cloud engineer, then SRE, I think that what at its core Stackler and cloud custodian does is a centralized engine, right? So your cost policies, your compliance policies, your security policies are not in a silo anymore. It's one tool. It's one repository that everyone can collaborate on as well. And even engineering, a lot of engineering teams run custodian and, and adopt custodian as well. So in terms of persona stack, it really helps bring it together. All teams have the same simple YAML DSL file that they can write their policies, share their policies and communicate and collaborate better as well. >>Yeah. So I mean, cloud transformation for an enterprise is a deeper topic. Like I think, you know, there's a lot of good breast practices establishing a cloud center of excellence. Um, I, I think, you know, investing in training for people, uh, getting certification so everyone can speak the same language when it comes to cloud is a key aspect. When it comes to the operations aspect, I very much believe that you should have, you know, try to devolve and get the developers writing, uh, some of the DevOps. And so having SREs around for the actual application teams is, is valuable, but you still have a core cloud infrastructure engineering group that's doing potentially any of your core networking, any of your, you know, IM authentication aspects. And so, uh, what we found is that, you know, SLA and cloud custodian get PR primarily get deployed by one of three groups. >>The, uh, you know, you've got the, the CIO buyer within that cloud infrastructure engineering team. And what we found is that group is because they're working with the application teams in a read right way. Uh, they're very much more, um, uh, used to doing and open to doing remediation in real time. Um, and so, and then we also have the CISO teams that want to get to a secure compliance state, be able to do audit and, and validate that all the environments are, um, you know, secure, frankly. And then we get to the CFO groups. Uh, and so, and this sometimes is part of the cloud center of excellence. And so it, it has to be this cross team collaboration. And they're really focused on the, that, that cost optimization, finding the over provision, underutilized things, establishing workloads for dev environments to turn them off at night. Um, and of course, respective of time zones, cause we're all global these days. Uh, and so those are sort of the three groups that we see that sort of really want to engage with us because we can provide value for them to help their accelerate their business goals. >>So that's an expansive view, cost compliance, security operations. That's a lot, I'm thinking about all the tools, all the information that feeds into that, where does cloud custodians start and stop? Like, am I putting cloud custodian agents on servers or, uh, pods, like how, how am I interacting with this? >>So the core clock suiting is just to see lot it's stateless, it's designed to be operationally simple. Um, and so you can run it in Kubernetes, in Jenkins. We've seen people use GitLab. We've seen people run just as a query interactive tool just from, um, investigations perspective on their laptop. But when you write a policy, a policy really consists of, you know, a couple of core elements. Uh, you identify a resource you want to target say an S3 bucket or, uh, a Google cloud VM. And then you say establishes that a filters. I want to look for all the C two instances that are on public subnets with an IM roll attached that has the ability to, uh, create another IM user. And so that, you know, you filter down, you ask the arbitrary questions to filter to the interesting set of things you want, and then you take a set of actions on them. >>So you might take an action, like stop an C two instance, and you might use it as an incident response. Um, you might, uh, use it for off hours in a, in that type of policy. So you get this library of filters and actions that you can combine to form, you know, millions of different types of policies. Now, we also have this notion of an execution mode. So you might say, uh, let's operate in real time. Whenever someone launches this instance, whenever there's an API call, we want to introspect what that API I call is doing and make sure that it's compliant to policy. Now, when you do that, custo will, when you, and you run it with the COI, cause you will actually provision a Lambda function and hook up the event sources to it. Uh, and sorry, Lambda really the serverless we bind into the serverless native capabilities of the underlying cloud provider. So Google cloud function, Azure serverless functions, uh, and native AWS Lambda native us. And so now that policy is effectively hermetically sealed, running, uh, in the Seus runtime of that cloud and responding to API calls in real time, all with, you know, structured outputs and logs and metrics to the native cloud provider capabilities around those. Um, and that really ensures that, uh, you know, it's effectively becomes operation free from the perspective of the user of having to maintain infrastructure >>For it. So let's talk about >>Agent agent list and API based. >>Let's talk about like the a non-developer use case specifically finance. Absolutely. We, you have to deploy the ability to deploy, uh, um, uh, SAP in a, uh, E C two instance, but it's very expensive. Do it only when you absolutely need to do it, but you have the rights to do it. And I wanna run a, uh, a check to see if anyone's doing it like this is this isn't a colder developer, what is their experience? So, >>So primarily we focus on the infrastructure. So low balancers, VMs, you know, encryption and address on discs. Um, when we get into the application workloads running on those instances, we spend, we don't spend that that's on our target focus area. Mm-hmm <affirmative>, we can do it. Uh, and it really depends on the underlying cloud provider's capabilities. So in Amazon, there's a system called systems manager and it runs, and it's basically running an agent on the box. We're not running the agent, but we can communicate with that agent. We can, I inspect the, the inventory that's running on that box. We can send commands to that box, through those serverless functions and through those policies. And so we see it commonly used for like incident response and a security perspective where you might wanna take a memory snapshot of, of, of the instance before, uh, um, yeah, putting it into a forensic cloud and adding >>To that, like these days we're seeing the emerging personas of a fops engineer or a fops director as well, because cost in cloud is totally different. So what custodian and Stackler allows to do is again, using the simple policy files. Even if they have a non-developer background, they can understand this DSL, they can create policies, they can better, uh, target developers, better get them to take actions on policy as well. If they're overspending in the cloud or underspending in the cloud, uh, especially with St. You get, they get a lot of, out of the box dashboards and policy packs too. So say they can really understand how the cost has been consumed. They can have the developers take actions because a lot of the fops finance people complain like my developers does not understand it. Right. How do we get them to take action and make sure we are not over spending? Right. So with custodian policies, they're able to send them, uh, educational messages on slack or open a J ticket and really enforce them to take action as well and start saving cost. Like >>If you, uh, if you imagine cloud custodian as, um, you know, cleaning staff for, for the, your, your cloud environment, like it, it's, uh, you know, if you go to a typical, you know, cloud account, you're gonna see chairs that are 10 feet tall sitting at the table. You're gonna, because it's been over provision and obviously, you know, one can use it. Um, you're gonna find like the trash is overflowing because no one set up a log retention policy on the log group or set up S3, uh, life cycle rules on their buckets. And so you just have this, um, sort of this, uh, this explosion of things that people now, you know, beyond application functioning, like beyond, you know, getting to, you know, high performance, Dr. Capable, uh, SLAs around your application model, you now have to worry about the life cycle of all those resources and helping people manage that life cycle and making sure that they're using the, the, just the resources and consumption that they need, because we're all utilization based, uh, in the cloud. And so getting that to be more in line with what the application actually needs is really where we can help organizations and the CFO cost context. >>So, Emil, you got 10 seconds to tell me why you brought me a comic book. >><laugh> we created this comic book, uh, to explain the concept of governance scored in a simplified fashion. I know Keith, you like comic books, I believe. Uh, so it's a simple way of describing what we do, why it's important for pH ops for SecOps teams. And it talks about custodian and St. It as well. >>Well, I'm more of an Ironman type of guy or Batman cloud governance or governance cloud native governance is a very tough problem. I can't under emphasize how many projects get stalled or fail from a perception perspective, even if you're technically delivered what you've asked to deliver. That's where a lot of these conversations are going. We're gonna talk to a bunch of startups that are solving these tough problems here from Licia Spain, I'm Keith Townsend, and you're watching the cube, the leader in high tech coverage.

>> 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)

>>Live from Boston, Massachusetts. It's the cube covering ifs world conference 2019 brought to you by ifs. >>Well going back to Boston and everybody, this is the cube, the leader in live tech coverage. We're here day one at the ifs world conference at the Hynes convention center in Boston. Cindy shutdown is here. She's the president of America's at ifs and she's joined by to my right, K in Blackwell, who's a controller at PPC partners, one of the divisions of PPC Metro power. And rod is the CIO of PPC partners. Welcome folks. Good to see you. I said, let me start with you. So you were on last year in the cube down at Atlanta. You still kind of set some, set some goals, you're a little competitive with your other brethren within then ifs. We love it. You know, we're Americans. Okay. So how's it going in North America? >>Um, well it's, it's growing well. We've had fantastic growth and it's been, you know, a little bit of competition within ifs, but you know, certainly we were very proud. We were named region of the year last year. So we won the coveted cup, which, uh, means, uh, we, uh, we want to keep that cup. So that's some of the, some of the competition that we've got going, right? >>Yeah. Well, of course, most of us based companies, they'll do, they'll start up 79, you know, 90% of their businesses, U S if not 100%, and then they'll slowly go overseas as some of the opposite. Right? >>Very much. I mean, ifs is a European based company. We've been in the, in the U S for quite awhile and, but we've really been investing in our growth and we've had fantastic growth over the last few years. And I think, you know, one of the reasons for that growth is our customer satisfaction in the fact that we really want to listen to our customers. You know, I, um, I, I travel quite a lot as you can imagine. And when I travel, I always try to make sure I can visit customers and hear what they have to say, you know, and of course we love to hear the good things, but I also like to hear when they can give us some ideas for improvement and um, you know, then that gives us something to work on and to, you know, to keep moving forward. Um, I also think that, you know, the good thing about that is, um, it gives us a chance to listen and um, you know, I heard something really great from one of our customers, they went live two weeks ago and they called up and said, Hey, can we do a customer story? I love things like that. Yeah. >>I always love that. Uh, let me think about it. I'll get back to you. Okay. What's your relationship between ifs and PPC part? >>Well, PPC partners is one of our newer customers in there in the middle of an implementation and they're doing some great things around digital transformation. And when I had this opportunity to be here on the cube, I thought it would be great to invite rod and can with me and to, you know, tell some of the things that they're doing. >>Cool. So I kind of recruited Cindy as my cohost, your, they're going to be the defective coho. So welcome to the queue and then we're going to show you right to the fire. Okay. So, uh, can you describe your, your role, your when one of the divisions of PPC partners, right? So maybe maybe set up sort of PPC partners and then your role. >>Right. Okay. So PPC is a specialty contracting company and we have four subsidiary companies that operate in the upper Midwest and then also the Southeastern United States. And we provide, um, um, customers within a base innovative, innovative solutions in the electrical and mechanical contracting. So there are those four companies. I was one of the controllers, um, of those four companies for a lot of years. And now I'm on the core team. There's four of us, five of us now, um, that are involved in the implement. >>Okay. So you got all the numbers in your head. And then rod, you're the CIO and you guys are a service organization for all the divisions. Is that correct? That is correct. >>We sit at the holding company and we're responsible for technology across all four of those specialty contractor companies that can just mention. >>So I love these segments, Cindy, because you know, we, here you go, we go to a lot of conferences in the cube and um, you hear a lot about digital transformation, but, so I'd like to ask the practitioners, what does that mean for you guys? We've got somebody who's very close to the line of business, like I say, knows the numbers, but at the end of the day you've got to deliver the technology services. So what does digital transformation mean to you? What's the company doing in that regard? So a great question actually. >>Um, you'll find companies like ours that have been on the same platform for quite a while, uh, 50 plus years, uh, five zero five, six, zero, uh, probably North of five zero, but we'll go with five zero. Uh, and what happens over time is just, you know, with the system can't grow with the organizations, you resort to a lot of manual paper pushing a lot of file flinging, lots of Excel. And so there's just a ton of duplication of effort and those types of things going on. So from a technology standpoint, that's really the stuff that I come in and see and go, you know. Um, but overall I think that getting to the ifs platform, getting a lot of those redundant processes, a lot of the file flinging out of there, it's just going to be beneficial for all of them. >>Okay. So you guys have had to make the business, you're in the middle of the implementation, right? Is that correct? So she had to go through the business case. Um, it sounds like the business case was, you know, we're, we're basically struggling with running our business because, you know, data's all over the place. We don't have a single view of our business, our customers, et cetera. So we have to come to grips with that. But, but, so what was the business case like? I presume that you were involved as well. >>Right. So I've was really involved in building the software that we've used for that 40 plus years though I haven't used it all of them two years. Um, and, and it was really. It was built by accountants. We, you know, intended for it to meet the needs of the whole, the whole organization. But really it was built by accountants. So, um, we've found that we just really weren't able to keep up with meeting the needs of all of the users. Um, so when we started looking at that, we also had, we were running on a couple of different, um, I'm going to call them boxes. We run it on IBM. So, um, we were not able to look across the entire organization and see a consolidated view of the whole organization. So that was one of the things that we were looking to do, was to really bring all four companies under one umbrella and be able to get a picture of the whole mainframe or, yes, we had a couple of mainframes and all of that software was internally written. Um, and it was good. It was, it was good, but it met, you know, just the needs that those of us within the company saw. Um, so I think we were missing a whole lot of opportunity, um, to really, you know, see what else was out there and see new things and really get outside of our sphere of understanding, you know, >>so PPC, >>no, I was going to say as SKM pointed out and the sort of running joke within the companies is the system we have today does numbers really well. Words not so much because it was designed by accountants for accounting, tracking the financials primarily. Yeah. >>In PPC you do construction of course, or construction club, but you also do some service as well, right? You've got people out in the field that are, that are doing, doing service. So when you were looking, um, I'm assuming that you were trying to find a system that could do both, both solutions. Yeah. Did. >>Absolutely. Uh, one of the things that's been concerning to the entire core team is it's great to go out and find a system and there's plenty of them that can handle your back office. Most systems do that fairly well. But what about you feel services, uh, any in our particular industry, electrical contracting, you might have residential, you know, we could very well be working on the buck stadium or a military installation or even the school, you know, those folks have to be able to process invoices, do all sorts of things from a handheld, et cetera, et cetera. That was a big, big driving factor for us. So has a lot of COBOL code running? Is that, is there right here? So you said 50 years, I mean, um, so now I'm interested in the, in the, in the migration and, and you know what that looks like. >>Yeah, I'll bet. So do you, do you have to freeze the existing sort of systems and then sort of bring the other ones up to speed? Is this cloud-based? What does that all look like? That great question. So, uh, we are, uh, we subscribe to the managed cloud solution. Um, you know, for most construction companies, electrical contracting companies like ours, you know, technology is important, but it is not what really makes our wheels turn. It's a con. It's a competitive advantage if you use it wisely. And so, um, you know, for us it was very important to think about this holistically and try to figure out if we're gonna bring in a solution, what does that solution need to look like and will it work for all of our companies, not just one, not just residential, commercial, et cetera. Okay. All right. So, so w w what's that journey look like? I mean, um, when did, when did it start? What's your >>sort of timeline? So about two and a half years ago, we really started looking at what we had in on hand now and what we had in place and thinking about did we really want to make a move? And so, um, we had a team that came together about 15 people across the organization from operations and also the back office to really evaluate what we had evaluated our needs. Um, we decided, yes, we needed something new. And then we actually brought in a second team, um, that started looking at what that new thing would be. We had a consultant assisting us with that and uh, we kinda narrowed it down to two players if you will. And ifs was one of those. Um, and we, even though, um, one of the things that we liked was the fact that that ifs had, um, a broad reach over different types of industries and we felt like that would give us, um, something in addition to a construct and centric view know domain expertise. Yeah, >>exactly. You know, and you know, with our core industries, you know, construction is a big part of that. But one of the things that we're seeing in the construction industry today is the trend to go to what we call prefabrication. The fact that you know, you can really speed up a project if you aren't trying to build everything on site and you can also do it much more cheaper. McKinsey has a study out and they believe that over time if, if of comp of construction company will engage with prefabrication, they can reduce the project timelines 20 to 50% and lower the cost up to 20% and with ifs is heritage in manufacturing. It's really a perfect marriage for construction companies because construction companies need the project management, the installation, you know, the change management that goes along with some of those back-office things. They also a lot of time have to do service. But if you really want to get that competitive advantage, if you can take advantage of the prefab, which is really manufacturing high, if this is heritage, he could really have a, a full, complete S, you know, solution from one supplier. >>There's a huge trend in home-building actually. You would, you see, you know, modular homes and kind of the future of it. But uh, so how does that affect you guys? I mean you, you prefab something that resonates with you, is that sort of more of a generic statement across the customer base or >>it's certainly an area where we're focusing on more. Um, we also have an automation, uh, division that really focuses on, um, automation for industries. And that's an area that it's kind of a manufacturing type of thing. They build panels and those sorts of things. So we're definitely seeing it >>well. So, okay. So I got to ask you, so when you pulled out the Gartner magic quadrant, I said, okay, it always is. Ifs isn't the leader that, that, that, that might've helped. Right. Okay. So you don't get fired now, but choose the leader, but then you started peeling the onion. He had to do due diligence. So what kinds of things did you look at? What kind of tires did you kick? Piers, did you talk to and be, I'm interested in what your, what you learned. Well, I'll touch on one key element and >>we can get in as many sub elements as you like. The selection process for us took several months. Um, I think initially we really pared it down to about eight packages that we were seriously considering. Then down to four and then eventually down to two. And what really, really intrigued us about ifs was the fact that they are not construction centric. So we really had a big decision to make internally, which was do we want to just get on the bandwagon and do what everyone else in construction is doing or do we really wanna you know, risk versus reward and go after something special. So ifs, they are in, you name it, manufacturing is obviously key. Aerospace engineering, race cars I saw today, I didn't know that. So that was a big selling point for us. And the plan is to retire your mainframe and go into the cloud. >>Yes, yes, yes. So IBM got you in a headlock. >>We've been friends for a long time. Good company. Um, w what's that been like just to sort of, uh, that the thought of, you know, going to the cloud. W how, how is, you know, the it folks you know, responded to that. Um, how has that changed their sort of role brokers versus all? Again, I think in construction organizations, technology is important, but it is not what makes the wheels turn. So I'm trying to bring in all of that iron and infrastructure and build it out and configure it ourselves and then maintain it for the long haul. Just not something that was value added for us. In addition, um, if you've ever worked with Oracle, which is a close partner of ifs, but there is a lot of licensing caveats and a lot of things you've got to worry about if you're going to go it alone by going with the managed cloud solution, we're sort of partnering and trusting ifs to take that on for us so we can focus on taking care of our companies, our customers, and doing what we do best. Right? So, okay, so you're still going to be an Oracle. You just won't be, it won't be as visible. We use Oracle too. We're a Salesforce customer, so Hey, Oracle is behind there, but no offense. >>Ah, I know you guys did >>for the distinction as well, right? Because even if you are going to have portions of Oracle that are running your system, you've got to have some Oracle experts on staff. You know, if you're going to have all of the infrastructure, you gotta have infrastructure folks who understand how it all ties together. So on the surface it could seem like a simple decision to do it in house or go to the cloud. Far from it. >>Yeah. You know, I think certainly one of the things that we see in a lot of different industries, but certainly in construction, the plant had always been that you bring together different, different solutions and you try to both and together and then some of that becomes a lot more concerning. You know, some of the technology behind it. But one of the things that with the ifs solution is the fact that from one provider you can do, you know, do the whole life cycle. So then some of the have it in the managed cloud where we take care of it for you. So then that takes away some of those technology issues and then you can focus on your core competencies. So Rhonda would agree generally >>with what you're saying. I mean some probably say that for most companies that you know, the technology is not the core differentiator. Obviously this for Google, sure. For Amazon, for Facebook, but for CIO is I talked to, they go people process, technology, technology is the least of my problems. It's like I was going to come and go, it's going to change. I can deal with that. It's the, if the people in the process issues. So having said that, I'm still interested in how concerned you were about peeling the onion on the cloud, what's behind it, the security model, all that stuff in terms of your due diligence, you know, with any cloud based solution, there's some concern obviously. But, but in working with ifs, we, we asked a ton of questions and they gave us a ton of answers. So the comfort level was there. Um, the industry's been going to the cloud now for quite some time. And to be brutally honest, if you're not going there, um, you need to be strongly considered >>in Microsoft is our partner with the cloud. We're on, you know, using Microsoft Azure. So it's not like, you know, it's one of the largest cloud provider. So it's not like, you know, it's, it's something that you have to worry about. You've got the, you know, the backstop of Microsoft behind you as well. You know, I'm sorry, go, go, go. I was going to say, I think one of the things that's interesting is you talk about all your different divisions and you're really trying to bring a lot of different companies together on one system. And one of the things that I, you know, as I've seen the things that's change management becomes really something that you really have to consider. I mean, how have you seen that part of the implementation going? Has there been stepping in the easy piece for you? It's not been an easy piece and that's one of the pieces that we're still working on. >>Um, I don't know if any organization that says that they're really, really good at change. Um, but we've recognized that really the, our organization is a group of entrepreneurs and we've encouraged people to have their own business, but we're really trying to streamline and get some consistency across the organization. That's a little bit of a culture shift for us. So that change management piece is a piece that we're really trying to get our arms around now and prepare, um, the organization for that team. Just trying to get my head around your software still. You guys do change management? I TSM. Well, you'll change management is really some of the, um, consulting that goes along with it and certainly ifs and AR, we've got many partners who can, you know, help our customers go through that. Because when you're going through a digital transformation, you know, you're taking people who have been using something for 50 years, being out, especially out in the field doing those things. And now you're trying to figure out what are the right processes to put in place to get what the business needs. And in some cases they might have to do things differently. So you really have to think that through and how you're going to roll those out. >>So now, is this your first ifs world? Yes, it is. It is. What final thoughts, you know, things you've, you've taken away or you're going to bring back to your teams? >>Well, yeah, Boston is a favorite city of mine. I was just glad to be here just for that. But, and we've just been here a little bit. I've already picked up some things on leadership. I was involved the um, >>Oh, the women's leadership breakfast this morning. So there's already been some things that I think we can take back to users and share with them, particularly around change management and trying to get people comfortable and understanding why they're uncomfortable with change. You know? So it, rod, you're next on the line. So I'm sure you were taking notes, pretty attentive in the sessions and just getting started, right? >>No, you know, I have, and one of the things for me that was most, I guess rewarding is, is the partner network. All of the vendors. There's a number of things with our implementation that we're still trying to sort out OCR for example, being one of them. Are we going to go there or are we gonna wait until later? Just different technologies and maybe add ons that we may want to take advantage of. All you've got to do is walk down the hallways and there's, there's people ready to talk to you about it. So that's, that's been kind of intriguing. >>Okay. Excellent. Well yeah, I said earlier I was, I was surprised and impressed at the sort of size of the ecosystem and its great. Well good luck to you guys. Really wish you the best and thanks so much for coming on the cube and sharing your story Cindy. Great to see you. Always pleasure. All right, take care. Thank you for watching everybody. We're back with our next guest right after this short break. You're watching the cube from Boston ifs world 2019 right back.

(upbeat music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CUBE conversation. >> Hello everyone, welcome to Palo Altos Cube Studios for CUBE Conversation. I'm John Furrier, co-host of theCUBE. We're with Kevin Ackroyd, CEO of Cision, CUBE Alumni. He's been on before. Building one of the most compelling companies that's disrupting and changing the game in Comms, advertising, PR, with Cloud technologies. Kevin, great to see you again, thanks for coming in. >> Likewise John, It's really good to be back. >> So, we haven't chatted in two years. You've been busy. Our last conversation was the beginning of 2017. Cision's done a lot of interesting things. You've got a lot of M and A under your belt. You're putting this portfolio together with Cloud technologies. Really been interesting. I really got to say I think you cracked the code on I think a new reality, a new economic reality. Also new capabilities for comms folks. Congratulations. >> Thank you, it's been a fun ride. >> So give us the update. So two years since we talked, how many deals, companies have you bought? What's the headcount, what's the revenue? Give us an update. >> In the four years, 12 acquisitions, seven of which have happened since I've been here. Up to 4,500 employees in over 40 countries. Customer count has grown to over 50,000 customers globally. Revenue's kind of gone from 500s to just shy of 800 million. A lot of leadership changes, and as you just mentioned, pretty seismic change, finally. We've certainly been the catalyst and the cattle prod for that seismic change around tech, data, measurement and analytics finally becoming mature and adopted inside this line of business like the Chief Communication Officer, the earn media folks. To say that they were not tech savvy a few years ago would be an understatement. So, a lot's been going on. >> Yeah, and certainly the trend is your friend, in my opinion, for you. But I think the reality is not yet upon people's general mindset. It's coming quickly, so if you look at some of the big trends out there. Look at fake news, look at Facebook, look at the Google effect. Elizabeth Warren wants to break up Big Tech, Amazon. Cloud computing, in that time period that you were, prior to just going to Cision, you had Oracle Cloud, done a lot of great things on the Marketing Cloud side. But the timing of Cloud computing, the timing of how media has changed. There's not many journalists anymore. We had Andy Cunningham, a legendary industry veteran, formerly of Cunningham Communications. He did the PR for Steve Jobs. You said, there's no more journalists, a few left, but you got to tell your story direct to the consumer. >> You do. >> This is now a new marketing phenomenon. This is a tailwind for you at Cision because you guys, although put these cubbies together, have a unique vision around bringing brand value advertising at PR economics. >> Yeah, that's a good way to put it. >> Tell us the vision of Cision and specifically the shift that's happening. Why are you guys important? What wave are you riding? >> So, there's a couple shifts, John. You and I have talked about this in previous programs There's this shift of the line of business, having to work in a whole bunch of non-integrated point solutions. The CFO used to live in 17 different applications from 17 vendors. That's all squished together. Now I buy from one Cloud platform, right, from Oracle or SAP. Same thing happened in Human Capital Management. 22 things squished into the Cloud, one from Workday, right. Same thing happened, you had 25 different things for sales and service. That all squished together, into one CRM in the Cloud, I buy from Salesforce, right. And our last rodeo, the early part of this stack, it was me and Adobe battling it out for the right to go squish the entire the LUMAscape into a marketing cloud, right, so there could be one ring to rule them all for the CMO. So, it happens in every single category. It just hasn't had over here, happened on the earned media side and the Chief Communications Officer. So, bringing the tech stack so that now we are for the CCO what Adobe is for the CMO what Salesforce is for the CRO, Workday is for the CHRO. That has to happen. You can't do, you can't manage it this way without sophisticated tech, without automation, without integration, you can't do it. The second thing that had to happen, especially in marketing and advertising, they all figured out how to get revenue credit. Advertising was a slow single-digit CAGR industry for 50 years. And then something happened. After 5% CAGR for 50 years, and then something happened over the next 10 years. Digital paid went from like 15 billion to 150 billion. And what happened is that old, I know half my advertising is wasted on this one half. That went bye-bye. Now I know immediately, down to the page, down the ad unit, down to this, exactly what worked, right. When I was able to put Pixels on ads, John, you'd go to that page, Pixel would go on you, It would follow you around If you ended up putting something in the e-commerce shop that ad got credit. I'm not saying that's right, I'm just saying that's how the entire-- >> But that's how the infrastructure would let you, allowed you, it enabled you to do that. Then again, paid advertising, paid search, paid advertising, that thing has created massive value in here. >> Massive value. But my buyer, right, so the person that does the little ad on the most regional tech page got credit. My buyer that got Bob Evans, the Cloud King, to write an article about why Microsoft is going to beat AWS, he's a credible third party influencer, writing objectively. That article's worth triple platinum and has more credibility than 20,000 Microsoft sales reps. We've never, until Cision, well let's Pixel that, let's go figure out how many of those are the target audience. Let's ride that all the way down to the lead form that's right. Basically it's super simple. Nobody's ever tracked the press releases, the articles or any of the earned media content, the way people have tracked banner ads or e-commerce emails. Therefore this line of business never get revenue credit. It stayed over here in the OpEx pile where things like commerce and advertising got dumped onto the revenue pile. Well, you saw the crazy investment shift. So, that's really the more important one, is Comms is finally getting quantified ROI and business's attribution like their commerce and advertising peers for the first time ever in 2018 via what Cision's rolled out. That's the exciting piece. >> I think, I mean, I guess what I hear you saying is that for the first time, the PR actually can be measured, similar to how advertising >> You got it. >> Couldn't be measured then be measured. Now PR or communications can be measured. >> They get measured the same way. And then one other thing. That ad, that press release, down to the business event. This one had $2 million dollars of ad spend, this one had no ad spend. When it goes to convert, in CRM or it goes to convert on a website, this one came from banner ad, this one came from credible third party content. Guess which one, not only had zero ad spend instead of $2 million in ad spend. Guess which one from which source actually converts better. It's the guy that chose to read credible third-party article. He's going to convert in the marketing system way better that somebody who just clicked on the ad. >> Well certainly, I'm biased-- >> So all the way down the funnel, we're talking about real financial impact based on capturing earned media ID, which is pretty exciting. >> Well, I think the more exciting thing is that you're basically taking a value that is unfunded quote by the advertising firm, has no budget basically, or thin budgets, trying to hit an organic, credible outlet which is converting in progression to a buyer, an outcome. That progression is now tracked. But let's just talk about the economics because you're talking about $2 million in spend, it could be $20 million. The ratio between ad spend and conversion to this new element you mentioned is different. You're essentially talking about the big mega trend, which is organic content. Meaning connecting to sources. >> That's right. >> That flow. Of course, we believe and we, at the Cube, everyone's been seeing that with our business. Let's talk about that dynamic because this is not a funded operationalized piece yet, so we've been seeing, in the industry, PR and comms becoming more powerful. So, the Chief Communication Officer isn't just rolling out press releases, although they have to do that to communicate. You've got medium posts now, you've got multiple channels. A lot of places to put the story. So the Chief Communication Officer really is the Chief Storyteller Officer, Not necessarily the CMO. >> Emphatically. >> The Martech Stack kind of tracking. So talk about that dynamic. How is the Chief Communication Officer role change or changing? Why is that important and what should people be thinking about, if they are a Chief Communication Officer? >> You know, it's interesting. There's a, I'm just going to call it an actual contradiction on this front. When you and I were getting out of our undergrad, 7 out of 10 times that CCO, the Chief Communication Officer, worked for the CEO and 30% of time other. Yet the role was materially narrow. The role has exploded. You just said it pretty eloquently. This role has really exploded and widened its aperture. Right now though 7 out of 10 of them actually do work for the CMO, which is a pretty interesting contradiction. And only 30% of them work for the CEO. Despite the fact that from an organizational stand point, that kind of counter intuitive org move has been made. It doesn't really matter because, so much of what you just said too, you was in marketing's purview or around brand or around reputation or around telling the story or around even owning the key assets. Key assets isn't that beautiful Budweiser frog commercial they played on Super Bowl anymore. The key assets are what's getting done over in the communications, in part. So, from a storytelling standpoint, from an ownership of the narrative, from a, not just a product or a service or promotion, but the whole company, the whole brand reputation, the goodwill, all of that is comms. Therefore you're seeing comms take the widest amount of real estate around the boardroom table than they've ever had. Despite the fact that they don't sit in the chair as much. I mentioned that just because I find it very interesting. Comms has never been more empowered, never had a wider aperture. >> But budget wise, they're not really that loaded up with funding. >> And to my earlier point, it's because they couldn't show. Super strategic. Showing ROI. >> So, showing ROI is critical. >> Not the quality of clippings. >> It was the Maslow of Hierarchy of Needs if you can just show me that I put a quarter in and I got a dollar out. Like the ads and the e-commerce folks do. It simply drives the drives me. >> So take us through some of those analytics because people who know about comms, the old school comms people who are doing this, they should really be thinking about what their operation is because, can I get an article in the Wall Street Journal? Can Silicon Angle write about us? I've got to get more clippings. That tend to be the thing. Did we get the press release out on time? They're not really tied into some of the key marketing mix pieces. They tend to be kind of a narrow scope. Those metrics were pretty clear. What are the new metrics? What's the new operational playbook.? >> Yeah, we call those Vanity Metrics. I cared about theoretical reach. Hey, Yahoo tells me I reached 222 billion people, so I plug in 222 billion people. I reached more people than there are on the planet with this PR campaign. I needed to get to the basic stuff like how many people did I actually reach, number one. But they don't, they do theoretical reach. They work in things like sentiment. Well, I'm going to come up with, 100 reporters wrote about me. I'm going to come up with, how many of them I thought were positive, negative, neutral. Sentiment analysis, they measure number of reporters or hits versus their competitors and say, Proctor and Gamble rolled out this diaper product, how did I do this five days? How much did Proctor and Gamble diapers get written about versus Craft diapers versus Unilever's. Share a voice. Not irrelevant metrics. But not metrics the CEO and the CFO are going to invest in. >> Conversion to brand or sales, those kind of things? >> They never just never existed. Those never existed. Now when we can introduce the same exact metrics that the commerce and the ad folks do and say, I can tell you exactly how many people. I can tell you exactly who they were, demographic, firmographic, lifestyle, you name it. I can tell you who the audience is you're reaching. I can tell you exactly what they do. When those kind of people read those kind of articles or those kind of people read those kind of press releases, they go to these destinations, they take these behaviors. And because I can track that all the way down to whatever that success metric is, which could be a lead form if I'm B2B for pipe. It could be a e-commerce store from B2C. It could be a rating or review or a user generation content gourd. It could be a sign up and register, if I'm trying to get database names. Whatever the business metric is. That's what the commerce and the ad people do all day every day. That's why they are more funded than ever. The fact that press releases, articles, tweets, blogs, the fact that the earned media stuff has never been able to do those things is why they just continue to suffer and have had a real lack of investment prices going on for the last 20 year. >> Talk about the trend around-- >> It's simple stuff. >> I know, if you improve the ROI, you get more budget. >> It really is that simple. >> That's been the challenge. I think PR is certainly becoming, comms is becoming more powerful. People know I talk about it all the time. I think comms is the new CMO I think command and control and organic content work together in the organic. We've seen it first hand in our business. But, it's an issue of tech savviness and also vision. A lot of people just are uncomfortable shifting to the new realities. >> That's for sure. >> What are some of the people tech savvy look at when they look at say revamping comms platform or strategy versus say old school? >> I'll give you two answers on that, John. Here is one thing that is good for us, that 7 out of 10 to the CCOs work for the CMO. Because when I was in this seat starting to light that fire under the CMO for the first time, which was not that long ago, and they were not tech savvy, and they were not sophisticated. They didn't know how to do this stuff either. That was a good 10 year journey to get the CMO from not sophisticated to very sophisticated. Now they're one of the more sophisticated lines of business in the world. But that was a slog. >> So are we going to see a Comms Stack? Like Martech, ComTech. >> ComTech is the decision communication Cloud, is ComTech. So we did it. We've built the Cloud stack. Again like I said, just like Adobe has the tech stack for marketing, Cision has the tech stack for comms, and we've replicated that. But because the CCO works for the CMO and the CMO's already been through this. Been through this with Ad Techs, been through this with MarTech, been through this with eCommerce, been through this with Web. You know, I've got a three or four year sophistication path this time just because >> The learnings are there >> The company's already done it everywhere else. The boss has already done it everywhere else. >> So the learnings are there from the MarTech so it's a pretty easy leap to take? >> That's exactly right. >> It's just-- >> How CommTech works is shocking. Incredibly similar to how MarTech and AdTech work. A lot of it is the same technology, just being applied different. >> That's good news >> So, the adoption curve for us is a fantastic thing. It's a really good thing for us that 70% of them work for CMOs because the CMO is the most impatient person on the planet, to get this over because the CMO is sick of doing customer journeys or omni channel across just paid and owned. They recognize that the most influential thing to influence you, it's not their emails, it's not their push notifications, It's not their ads. It's recognizing which credible third-party content you read, getting them into that, so that they're influencing you. >> It's kind of like Google PageRank in the old days. This source is more relevant than that one, give it more weight. >> And now all of a sudden if I have my Cision ID, I can plug in the more weight stuff under your profile. I want to let him go across paid and owned too, I materially improve the performance of the paid and owned because I'm putting in the really important signal versus what's sitting over there in the DMP or the CDP, which is kind of garbage. That's really important. >> I really think. >> I thinks you've got a home run here. I think you've really cracked the code on this. I think you are absolutely right on the money with comms and CommsTech. I see it all the time. In my years of experiences, it's so obvious. Then again, the tailwind is that they've been through the MarTech. The question I have for you is cultural shift. That's a big one. So, I'm out evangelizing all the time about the CUBE Cloud and some of the things we're doing. I run into the deer in the headlights on one side, what do you mean? And then people like, I believe, I totally understand. The believers and the non believers. What's the cultural shift? Because some chief comms op, they're very savvy, progressive, we've got to make the shift. How do they get the ship to turn? What are some of the cultural challenges? >> And boy is that right. I felt the same thing, getting more doing it with the CMO. A lot of people kept their head in the sand until they got obsoleted. They didn't know. Could they not see the train coming? They didn't want to see the train coming. Now you go look at the top 100 CMOs in the world today. Pretty different bunch than who those top 100 CMOs were 10 years ago. Really different bunch. History's repeating itself over here too. You've got the extremely innovative CCOs that are driving that change and transformation. You've got the deer in the headlight, okay, I know I need to do this, but I'm not sure how, and you do have your typical, you know, nope, I've got my do not disturb sign and police tape over my office. I won't even let you in my door. I don't want to hear about it. You've got all flavors. The good news is we are well past the half point where the innovators are starting actually to deploy and show results, the deer in the headlights are starting to innovate, and these folks are at least opening up the door and taking down some tape. >> Is there pressure on the agency side now? A lot of agencies charge a lot of monthly billings for these clients, the old school thing. Some are trying to be progressive and do more services. Have you seen, with the Cision Cloud and things that you're doing, that you're enabling, those agencies seem to be more productive? >> Yes. >> Are the client's putting pressure on those agencies so they see more value? Talk about the agency dynamic. >> That's also a virtuous cycle too, right? That cycle goes from, it's a Bell Curve. At the beginning of the bell curve, customers have no clue about the communications. They go to their agencies for advice. So, you have to educate the agencies on how to say nice things about you. By the time you're at the Bell Curve, the client's know about the tech or they've adopted the tech, and the agencies realize, oh, I can monetize the hell out of this. They need strategy and services and content and creative and campaign. This is yet another good old fashioned >> High gross profit. >> A buck for the tech means six bucks for me as the service agency. At the bottom, over here, I'll never forget this when we did our modern marketing experiences, Erik, the CMO of Clorox said, hey, to all you agencies out there, now that we're mature, you know, we choose our our agency based on their fluency around our tech stack. So it goes that violently and therefore, the agencies really do need to try to get fluent. The ones that do, really reap rewards because there is a blatant amount of need as the line of business customer tries to get from here to here. And the agency is the is the very first place that that customer is going to go to. >> So, basically the agency-- >> The customer has first right of refusal to go provide these services and monetize them. >> So, the agency has to keep up. >> They certainly do. >> Because, if the game gets changed by speed, it's accelerated >> If they keep up, yup. >> Value is created. If they don't have their running shoes on, they're out. >> If they keep up and they stay fluent, then they're going to be great. The last thing back in the things. We've kind of hit this. This is one of those magic points I've been talking about for 20 years. When the CFO or the CEO or the CMO walk down to the CCOs office and say, where are we on this, 'cause it's out in the wild now, there are over 1200 big brands doing this measurement, Cision ID, CommsTech stuff. It's getting written about by good old fashioned media. Customer says, wow, I couldn't do this for 50 years, now I am, and look what I just did to my Comms program. That gets read. The world's the same place as it always has been. You and I read that. We go down to our comms department and say, wow, I didn't know that was possible, where are we on this? So the Where Are We On This wave is coming to communications, which is an accelerant. >> It's an accountability-- >> Now it's accountability, and therefore, the urgency to get fluent and changed. So now they're hiring up quantums and operations and statisticians and database people just like the marketers did. The anatomy of a communications department is starting to like half science half art, just like happened in marketing. Whereas before that, it was 95% art and 5% science. But it's getting to be 50/50. >> Do you have any competition? >> We have, just like always. >> You guys pretty much have PR Newswire, a lot of big elements there. >> We do. >> You've got a good foothold. >> This is just an example. Even though Marketo is part of Adobe, giant. And Eloqua is part of Oracle, giant and Pardot is part of Salesforce. You've got three goliaths in marketing automation. Hubspot's still sticking around. PeerPlay, marketing Automation. You can just picture it. CRM giants, Microsoft and Salesforce have eaten the world Zendesk's still kicking around. It's a little PeerPlay. That equivalent exists. I have nobody that's even one fifth as big as I am, or as global or complete. But I do have some small, point specific solution providers. They're still hanging out there. >> The thing is, one, first you're a great leader. You've seen the moving on the marking tech side. You've got waves of experience under your belt. But I think what's interesting is that like the Web 1.0, having websites and webpages, Web 2.0 and social networks. That was about the first generation. Serve information, create Affiliate programs, all kind of coded tracking. You mentioned all that. I over-simplified it, but you get the idea. Now, every company needs a new capability. They need to stand up media infra structure. What does that mean? They're going to throw a podcast, they're going to take their content, put them into multiple channels. That's a comms function. Now comms is becoming the new CMO-like capability in this earned channel. So, your Cloud becomes that provisioning entity for companies to stand up capabilities without waiting. Is that the vision? >> You've nailed it. And that is one of the key reasons why you have to have a tech stack. That's a spot on one, another one. Early in my career, the 20 influences that mattered, they were all newspaper reporters or TV folks. There was only 20 of them. I had a Rolodex. so I could take each one of them out for a three Martini lunch, they'd write something good about me. >> Wish is was that easy now. >> Now, you have thousands of influencers across 52 channels, and they change in real time, and they're global in nature. It's another example of where, well, if you don't automate that with tech and by the way. >> You're left behind. >> If you send out digital content they talk back to you in real time. You have to actually not only do influencer identification, outreach and curation, you've got to do real time engagement. >> There's no agility. >> There's none. >> Zero agility. >> None, exactly. >> There's no like Dev Ops mindset in there at all. >> Then the speed with which, it's no longer okay for comms to call the agency and say, give me a ClipBook, I've got to get it to my CEO by Friday. That whole start the ClipBook on Tuesday, I've got to have the ClipBook, the physical ClipBook on the CEO as an example. Nope, if I'm not basically streaming my senior executives in real time, curated and analyzed as to what's important and what it means, I can't do that without a tech stack. >> Well, Andy Cunningham was on the Cube. >> This whole thing has been forced to get modernized by cloud technology and transformation >> Andy Cunningham, a legend in the comms business who did all Steve Jobs comms, legend. She basically said on The Cube, it's not about waiting for the clips to create the ClipBook, create your own ClipBook and get it out there. Then evaluate and engage. This is the new command and control with digital assets. >> Now, it's become the real-time, curated feed that never stops. It sure as hell better not. Because comms is in trouble if it does. >> Well this is a great topic. But let's have you in this, I can go deep on this. I think this is a really important shift, and you guys are the only ones that are on it at this level. I don't think the Salesforce and the Adobe yet, I don't think they're nimble enough to go after this wave. I think they're stuck on their wave and they're making a lot of money. >> You know John, paid media and owned media. The Google Marketing Cloud, that SAP Marketing Cloud, Adobe, Oracle, Salesforce Marketing Clouds. They don't do anything in earned. Nothing. This is one of the reasons I jumped because I knew this needed to happen. But, you know, they're also chasing much bigger pots of money. Marketing and Advertising is still a lot more money. We're working on it to grow the pie for comms. But, bottom line is, they're chasing the big markets as I was at Oracle. And they're still pretty much in a violent arms race against each other. Salesforce is still way more focused on what Adobe's doing. >> You're just on a different wave. >> So, we're just over here doing this, building a billion dollar cloud leader, that is mission critical to everyone of their customers. They're going to end up being some pretty import partners to us, because they've been too focused on the big arms race against each other, in paid and owned and have not had the luxury to even go here. >> Well I think this wave that you're on is going to be really big. I think they don't see it, in my opinion, or can't get there. With the right surfboard, to use a surfing analogy, there's going to be a big wave. Thanks for sharing your insights. >> Absolutely. >> While you're here, get the plug in for Cision. What's going on, what's next? What's the big momentum? Get the plug in for the company. What are you guys still going to do? >> Plugin for the company. The company has acquired a couple of companies in January. You might see, one of which is Falcon. Basically Falcon is one of the big four in the land of Hootsuite, Sprinklr, Spredfast. Cloud companies do this. Adobe has Creative Cloud, Document Cloud, Parking Cloud. Salesforce has Sales Cloud, Service Cloud, Marketing Cloud. Cision has just become a multi cloud company. We now have the Cision Social Cloud and the Cision Communications Cloud. And we're going to go grab a couple hundred million dollars of stuff away from Sprinklr, Hootsuite and collapse social into this. Most of social is earned as well. So, look for a wing spread, into another adjacent market. I think that's number one. Then look for publishing of the data. That's probably going to be the most exciting thing because we just talked about, again our metrics and capabilities you can buy But, little teaser. If we can say, in two months here's the average click through on a Google ad, YouTube ad, a banner ad, I'll show it to you on a Blog, a press release, an article. Apples to apples. Here is the conversion rate. If I can start becoming almost like an eMarketer or publisher on what happens when people read earned, there's going to be some unbelievable stats and they're going to be incredibly telling, and it's going to drive where are we on that. So this is going to be the year. >> It's a new digital advertising format. It's a new format. >> That's exactly right. >> It's a new digital advertising format. >> And its one when the CEO understands that he or she can have it for earned now, the way he's had it for marketing and advertising, that little conversation walking down the hall. In thousands of companies where the CCO or the VP of PR looks up and the CEO is going where are we on that? That's the year that that can flip switches, which I'm excited about. >> Every silo function is now horizontally connected with data, now measured, fully instrumented. The value will be there and whoever can bring the value gets the budget. That's the new model. Kevin Ackroyd, CEO of Cision, changing the game in the shift around the Chief Communications Officer and how that is becoming more tech savvy. Really disrupting the business by measuring earned media. A big wave that's coming. Of course, it's early, but it's going to be a big one. Kevin, thanks for coming on. >> My pleasure, John, thank you. >> So, CUBE conversation here in Palo Alto Thanks for watching. >> Thanks John. (upbeat music)