(upbeat instrumental) >> Hello and welcome to this CUBE conversation. I'm John Furrier, host of theCUBE, here in Palo Alto, California. Got a great conversation with Robert Nishihara who's the co-founder and CEO of Anyscale. Robert, great to have you on this CUBE conversation. It's great to see you. We did your first Ray Summit a couple years ago and congratulations on your venture. Great to have you on. >> Thank you. Thanks for inviting me. >> So you're first time CEO out of Berkeley in Data. You got the Databricks is coming out of there. You got a bunch of activity coming from Berkeley. It's like a, it really is kind of like where a lot of innovations going on data. Anyscale has been one of those startups that has risen out of that scene. Right? You look at the success of what the Data lakes are now. Now you've got the generative AI. This has been a really interesting innovation market. This new wave is coming. Tell us what's going on with Anyscale right now, as you guys are gearing up and getting some growth. What's happening with the company? >> Yeah, well one of the most exciting things that's been happening in computing recently, is the rise of AI and the excitement about AI, and the potential for AI to really transform every industry. Now of course, one of the of the biggest challenges to actually making that happen is that doing AI, that AI is incredibly computationally intensive, right? To actually succeed with AI to actually get value out of AI. You're typically not just running it on your laptop, you're often running it and scaling it across thousands of machines, or hundreds of machines or GPUs, and to, so organizations and companies and businesses that do AI often end up building a large infrastructure team to manage the distributed systems, the computing to actually scale these applications. And that's a, that's a, a huge software engineering lift, right? And so, one of the goals for Anyscale is really to make that easy. To get to the point where, developers and teams and companies can succeed with AI. Can build these scalable AI applications, without really you know, without a huge investment in infrastructure with a lot of, without a lot of expertise in infrastructure, where really all they need to know is how to program on their laptop, how to program in Python. And if you have that, then that's really all you need to succeed with AI. So that's what we've been focused on. We're building Ray, which is an open source project that's been starting to get adopted by tons of companies, to actually train these models, to deploy these models, to do inference with these models, you know, to ingest and pre-process their data. And our goals, you know, here with the company are really to make Ray successful. To grow the Ray community, and then to build a great product around it and simplify the development and deployment, and productionization of machine learning for, for all these businesses. >> It's a great trend. Everyone wants developer productivity seeing that, clearly right now. And plus, developers are voting literally on what standards become. As you look at how the market is open source driven, a lot of that I love the model, love the Ray project love the, love the Anyscale value proposition. How big are you guys now, and how is that value proposition of Ray and Anyscale and foundational models coming together? Because it seems like you guys are in a perfect storm situation where you guys could get a real tailwind and draft off the the mega trend that everyone's getting excited. The new toy is ChatGPT. So you got to look at that and say, hey, I mean, come on, you guys did all the heavy lifting. >> Absolutely. >> You know how many people you are, and what's the what's the proposition for you guys these days? >> You know our company's about a hundred people, that a bit larger than that. Ray's been going really quickly. It's been, you know, companies using, like OpenAI uses Ray to train their models, like ChatGPT. Companies like Uber run all their deep learning you know, and classical machine learning on top of Ray. Companies like Shopify, Spotify, Netflix, Cruise, Lyft, Instacart, you know, Bike Dance. A lot of these companies are investing heavily in Ray for their machine learning infrastructure. And I think it's gotten to the point where, if you're one of these, you know type of businesses, and you're looking to revamp your machine learning infrastructure. If you're looking to enable new capabilities, you know make your teams more productive, increase, speed up the experimentation cycle, you know make it more performance, like build, you know, run applications that are more scalable, run them faster, run them in a more cost efficient way. All of these types of companies are at least evaluating Ray and Ray is an increasingly common choice there. I think if they're not using Ray, if many of these companies that end up not using Ray, they often end up building their own infrastructure. So Ray has been, the growth there has been incredibly exciting over the, you know we had our first in-person Ray Summit just back in August, and planning the next one for, for coming September. And so when you asked about the value proposition, I think there's there's really two main things, when people choose to go with Ray and Anyscale. One reason is about moving faster, right? It's about developer productivity, it's about speeding up the experimentation cycle, easily getting their models in production. You know, we hear many companies say that they, you know they, once they prototype a model, once they develop a model, it's another eight weeks, or 12 weeks to actually get that model in production. And that's a reason they talk to us. We hear companies say that, you know they've been training their models and, and doing inference on a single machine, and they've been sort of scaling vertically, like using bigger and bigger machines. But they, you know, you can only do that for so long, and at some point you need to go beyond a single machine and that's when they start talking to us. Right? So one of the main value propositions is around moving faster. I think probably the phrase I hear the most is, companies saying that they don't want their machine learning people to have to spend all their time configuring infrastructure. All this is about productivity. >> Yeah. >> The other. >> It's the big brains in the company. That are being used to do remedial tasks that should be automated right? I mean that's. >> Yeah, and I mean, it's hard stuff, right? It's also not these people's area of expertise, and or where they're adding the most value. So all of this is around developer productivity, moving faster, getting to market faster. The other big value prop and the reason people choose Ray and choose Anyscale, is around just providing superior infrastructure. This is really, can we scale more? You know, can we run it faster, right? Can we run it in a more cost effective way? We hear people saying that they're not getting good GPU utilization with the existing tools they're using, or they can't scale beyond a certain point, or you know they don't have a way to efficiently use spot instances to save costs, right? Or their clusters, you know can't auto scale up and down fast enough, right? These are all the kinds of things that Ray and Anyscale, where Ray and Anyscale add value and solve these kinds of problems. >> You know, you bring up great points. Auto scaling concept, early days, it was easy getting more compute. Now it's complicated. They're built into more integrated apps in the cloud. And you mentioned those companies that you're working with, that's impressive. Those are like the big hardcore, I call them hardcore. They have a good technical teams. And as the wave starts to move from these companies that were hyper scaling up all the time, the mainstream are just developers, right? So you need an interface in, so I see the dots connecting with you guys and I want to get your reaction. Is that how you see it? That you got the alphas out there kind of kicking butt, building their own stuff, alpha developers and infrastructure. But mainstream just wants programmability. They want that heavy lifting taken care of for them. Is that kind of how you guys see it? I mean, take us through that. Because to get crossover to be democratized, the automation's got to be there. And for developer productivity to be in, it's got to be coding and programmability. >> That's right. Ultimately for AI to really be successful, and really you know, transform every industry in the way we think it has the potential to. It has to be easier to use, right? And that is, and being easier to use, there's many dimensions to that. But an important one is that as a developer to do AI, you shouldn't have to be an expert in distributed systems. You shouldn't have to be an expert in infrastructure. If you do have to be, that's going to really limit the number of people who can do this, right? And I think there are so many, all of the companies we talk to, they don't want to be in the business of building and managing infrastructure. It's not that they can't do it. But it's going to slow them down, right? They want to allocate their time and their energy toward building their product, right? To building a better product, getting their product to market faster. And if we can take the infrastructure work off of the critical path for them, that's going to speed them up, it's going to simplify their lives. And I think that is critical for really enabling all of these companies to succeed with AI. >> Talk about the customers you guys are talking to right now, and how that translates over. Because I think you hit a good thread there. Data infrastructure is critical. Managed services are coming online, open sources continuing to grow. You have these people building their own, and then if they abandon it or don't scale it properly, there's kind of consequences. 'Cause it's a system you mentioned, it's a distributed system architecture. It's not as easy as standing up a monolithic app these days. So when you guys go to the marketplace and talk to customers, put the customers in buckets. So you got the ones that are kind of leaning in, that are pretty peaked, probably working with you now, open source. And then what's the customer profile look like as you go mainstream? Are they looking to manage service, looking for more architectural system, architecture approach? What's the, Anyscale progression? How do you engage with your customers? What are they telling you? >> Yeah, so many of these companies, yes, they're looking for managed infrastructure 'cause they want to move faster, right? Now the kind of these profiles of these different customers, they're three main workloads that companies run on Anyscale, run with Ray. It's training related workloads, and it is serving and deployment related workloads, like actually deploying your models, and it's batch processing, batch inference related workloads. Like imagine you want to do computer vision on tons and tons of, of images or videos, or you want to do natural language processing on millions of documents or audio, or speech or things like that, right? So the, I would say the, there's a pretty large variety of use cases, but the most common you know, we see tons of people working with computer vision data, you know, computer vision problems, natural language processing problems. And it's across many different industries. We work with companies doing drug discovery, companies doing you know, gaming or e-commerce, right? Companies doing robotics or agriculture. So there's a huge variety of the types of industries that can benefit from AI, and can really get a lot of value out of AI. And, but the, but the problems are the same problems that they all want to solve. It's like how do you make your team move faster, you know succeed with AI, be more productive, speed up the experimentation, and also how do you do this in a more performant way, in a faster, cheaper, in a more cost efficient, more scalable way. >> It's almost like the cloud game is coming back to AI and these foundational models, because I was just on a podcast, we recorded our weekly podcast, and I was just riffing with Dave Vellante, my co-host on this, were like, hey, in the early days of Amazon, if you want to build an app, you just, you have to build a data center, and then you go to now you go to the cloud, cloud's easier, pay a little money, penny's on the dollar, you get your app up and running. Cloud computing is born. With foundation models in generative AI. The old model was hard, heavy lifting, expensive, build out, before you get to do anything, as you mentioned time. So I got to think that you're pretty much in a good position with this foundational model trend in generative AI because I just looked at the foundation map, foundation models, map of the ecosystem. You're starting to see layers of, you got the tooling, you got platform, you got cloud. It's filling out really quickly. So why is Anyscale important to this new trend? How do you talk to people when they ask you, you know what does ChatGPT mean for Anyscale? And how does the financial foundational model growth, fit into your plan? >> Well, foundational models are hugely important for the industry broadly. Because you're going to have these really powerful models that are trained that you know, have been trained on tremendous amounts of data. tremendous amounts of computes, and that are useful out of the box, right? That people can start to use, and query, and get value out of, without necessarily training these huge models themselves. Now Ray fits in and Anyscale fit in, in a number of places. First of all, they're useful for creating these foundation models. Companies like OpenAI, you know, use Ray for this purpose. Companies like Cohere use Ray for these purposes. You know, IBM. If you look at, there's of course also open source versions like GPTJ, you know, created using Ray. So a lot of these large language models, large foundation models benefit from training on top of Ray. And, but of course for every company training and creating these huge foundation models, you're going to have many more that are fine tuning these models with their own data. That are deploying and serving these models for their own applications, that are building other application and business logic around these models. And that's where Ray also really shines, because Ray you know, is, can provide common infrastructure for all of these workloads. The training, the fine tuning, the serving, the data ingest and pre-processing, right? The hyper parameter tuning, the and and so on. And so where the reason Ray and Anyscale are important here, is that, again, foundation models are large, foundation models are compute intensive, doing you know, using both creating and using these foundation models requires tremendous amounts of compute. And there there's a big infrastructure lift to make that happen. So either you are using Ray and Anyscale to do this, or you are building the infrastructure and managing the infrastructure yourself. Which you can do, but it's, it's hard. >> Good luck with that. I always say good luck with that. I mean, I think if you really need to do, build that hardened foundation, you got to go all the way. And I think this, this idea of composability is interesting. How is Ray working with OpenAI for instance? Take, take us through that. Because I think you're going to see a lot of people talking about, okay I got trained models, but I'm going to have not one, I'm going to have many. There's big debate that OpenAI is going to be the mother of all LLMs, but now, but really people are also saying that to be many more, either purpose-built or specific. The fusion and these things come together there's like a blending of data, and that seems to be a value proposition. How does Ray help these guys get their models up? Can you take, take us through what Ray's doing for say OpenAI and others, and how do you see the models interacting with each other? >> Yeah, great question. So where, where OpenAI uses Ray right now, is for the training workloads. Training both to create ChatGPT and models like that. There's both a supervised learning component, where you're pre-training this model on doing supervised pre-training with example data. There's also a reinforcement learning component, where you are fine-tuning the model and continuing to train the model, but based on human feedback, based on input from humans saying that, you know this response to this question is better than this other response to this question, right? And so Ray provides the infrastructure for scaling the training across many, many GPUs, many many machines, and really running that in an efficient you know, performance fault tolerant way, right? And so, you know, open, this is not the first version of OpenAI's infrastructure, right? They've gone through iterations where they did start with building the infrastructure themselves. They were using tools like MPI. But at some point, you know, given the complexity, given the scale of what they're trying to do, you hit a wall with MPI and that's going to happen with a lot of other companies in this space. And at that point you don't have many other options other than to use Ray or to build your own infrastructure. >> That's awesome. And then your vision on this data interaction, because the old days monolithic models were very rigid. You couldn't really interface with them. But we're kind of seeing this future of data fusion, data interaction, data blending at large scale. What's your vision? How do you, what's your vision of where this goes? Because if this goes the way people think. You can have this data chemistry kind of thing going on where people are integrating all kinds of data with each other at large scale. So you need infrastructure, intelligence, reasoning, a lot of code. Is this something that you see? What's your vision in all this? Take us through. >> AI is going to be used everywhere right? It's, we see this as a technology that's going to be ubiquitous, and is going to transform every business. I mean, imagine you make a product, maybe you were making a tool like Photoshop or, or whatever the, you know, tool is. The way that people are going to use your tool, is not by investing, you know, hundreds of hours into learning all of the different, you know specific buttons they need to press and workflows they need to go through it. They're going to talk to it, right? They're going to say, ask it to do the thing they want it to do right? And it's going to do it. And if it, if it doesn't know what it's want, what it's, what's being asked of it. It's going to ask clarifying questions, right? And then you're going to clarify, and you're going to have a conversation. And this is going to make many many many kinds of tools and technology and products easier to use, and lower the barrier to entry. And so, and this, you know, many companies fit into this category of trying to build products that, and trying to make them easier to use, this is just one kind of way it can, one kind of way that AI will will be used. But I think it's, it's something that's pretty ubiquitous. >> Yeah. It'll be efficient, it'll be efficiency up and down the stack, and will change the productivity equation completely. You just highlighted one, I don't want to fill out forms, just stand up my environment for me. And then start coding away. Okay well this is great stuff. Final word for the folks out there watching, obviously new kind of skill set for hiring. You guys got engineers, give a plug for the company, for Anyscale. What are you looking for? What are you guys working on? Give a, take the last minute to put a plug in for the company. >> Yeah well if you're interested in AI and if you think AI is really going to be transformative, and really be useful for all these different industries. We are trying to provide the infrastructure to enable that to happen, right? So I think there's the potential here, to really solve an important problem, to get to the point where developers don't need to think about infrastructure, don't need to think about distributed systems. All they think about is their application logic, and what they want their application to do. And I think if we can achieve that, you know we can be the foundation or the platform that enables all of these other companies to succeed with AI. So that's where we're going. I think something like this has to happen if AI is going to achieve its potential, we're looking for, we're hiring across the board, you know, great engineers, on the go-to-market side, product managers, you know people who want to really, you know, make this happen. >> Awesome well congratulations. I know you got some good funding behind you. You're in a good spot. I think this is happening. I think generative AI and foundation models is going to be the next big inflection point, as big as the pc inter-networking, internet and smartphones. This is a whole nother application framework, a whole nother set of things. So this is the ground floor. Robert, you're, you and your team are right there. Well done. >> Thank you so much. >> All right. Thanks for coming on this CUBE conversation. I'm John Furrier with theCUBE. Breaking down a conversation around AI and scaling up in this new next major inflection point. This next wave is foundational models, generative AI. And thanks to ChatGPT, the whole world's now knowing about it. So it really is changing the game and Anyscale is right there, one of the hot startups, that is in good position to ride this next wave. Thanks for watching. (upbeat instrumental)

(melodious music) >> Voiceover: From around the globe. It's theCUBE with digital coverage of IBM Think 2021. Brought to you by IBM. >> Hi and welcome back to theCUBE's coverage of IBM Think 2021 virtual. I'm John Furrier, host of theCUBE. We're a virtual this year. But we'll be in real life soon, right around the corner, as we come out of COVID. We got a great guest, a CUBE alumni, Jerry Cuomo, IBM Fellow VP, CTO for IBM automation. Jerry, great to see you. Been on since almost since the early days of theCUBE. Good to see you >> Yeah, John. Thrilled to be back again. Thank you. >> What I love about our conversations, one is you're super technical. You've got patents under your belt. You're on the cutting edge. You've been involved in web services and web technologies for a long, long time. You're constantly riding the wave. And also you're a creator of a great podcast called "The Art of Automation", which is the subject of this discussion. As automation becomes central in cloud operations and Hybrid Cloud, which is the main theme of this event this year and the industry. So great to see you. First give us a little background for the folks that may not know you, about your history with IBM and who you are. >> Yeah. So thanks John. So I'm Jerry Cuomo. I've been with IBM for about three decades and I started my career at IBM research in Yorktown at the dawn of the internet. And I've been incredibly fortunate as you mentioned, to be on the forefront of many technology trends over the last three decades, internet software, middleware, including being one of the founding fathers of WebSphere software. I recently helped launch the IBM blockchain initiative and now all about AI powered automation. Which actually brings me back to my roots of studying AI in graduate school. So it's kind of come full circle for me, really enjoying the topic. >> It's funny you mentioned AI in graduate school. I was really kind of into AI when I was an undergraduate and get a master's degree in Computer Science. I kind of went the MBA route. But if you think about what was going on in the eighties during those systems times, a lot of the concepts of systems programming and cloud operations kind of gel well together. So you got this confluence of computer science and engineering AKA now DevOps, DevSecOps, coming together. This is actually a really unique time to bring back the best of the best concepts. Whether it's AI and systems and computer science and engineering into automation. Could you share your view on this? Because you're in a unique position, you've been there done that, now you're on the cutting edge. What's your thoughts? >> Yeah, absolutely John. And just when you think of automation and time, automation is not new. That literally, if you go into Wikipedia and you look up automation, you see patents and references to like steam engine regulators at the dawn of the industrial era, right? So automation has been around and in its simplest form, automation whether it was back then, whether it was in the eighties or today it's about applying technology that uses technology software to perform tasks that were once exclusively done by us, humans. But now what we're seeing is AI coming into the picture and changing the landscape in an interesting way. But I think at its essence, automation is this two-step dance of both eliminating repetitive mundane tasks that help reduce errors and free up our time. So we get back the gift of time, but also helps. It's not about taking jobs away at that point, it's a sentence of two-step dance. That's step one. But if you stop there, you're not getting the full value. Step two is to augment our skills. And to use automation, to help augment our skills. And we get speed, we get quality, we get lower costs, we get improved user experience. So whether it was back in the steam engine times or today with AI, automation is evolving with technology. >> And it's interesting too, as a student of the history of the computer industry, as you are and now a creator with your podcast, which we'll get to in a second. You're starting to see the intersection of these concerts and not bespoke as much as they used to be. You got transformation, digital transformation and innovation are connected and scale. If you think about those three concepts, they don't stand alone anymore. They can stand alone, but they work better together. Transformation. And it is the innovation, innovation provides cloud scale. So if you think about automation, automation is powering this dynamic of taking all that undifferentiated heavy lifting and moving the creativity and the skillset into higher integrated areas. Can you share your thoughts? >> Yeah, no, right on there. When you talk about transformation, jeez, look around us. The pandemic has made, transformation and specifically digital transformation, the default. So everything is digital. Whether it's ordering a pizza, visiting a doctor through telemedicine, or this zoom WebEx based workplace that we live in. But picture a telemedicine environment, talking about transformation and going digital. With 10 X more users, they can't hire 10 X more support staff. And think about it. I forgot my password, does this work on my version of the Apple iPhone or all of that kind of stuff? So their support desks are lit up, right? So as they scale digitally, automation is the relief that that comes into play, which is just in time. So the digital transformation needs automation. And John, I think about it like this, businesses like cars have become computers. So they're programmable. So automation software just like in the cars, it makes the car self-driving. I think about the Tesla model three, which I recently test drove. So with this digital acceleration, digital opens the door for automation. And now we can muse about self-driving business. We can muse about maybe that's step one, right? That's the remove repetitive work, but maybe we can actually augment business to have an autopilot. So it doesn't need us there all the time to drive. And that's the scale that you talked about. That's the scale we need. So automation is really like the peanut butter and chocolate. Digital is the peanut butter automation is the chocolate. They go well together and they produce amazing tastes. >> Yeah. That's a really interesting insight. And I was just put an exclamation point on that because you mentioned self-driving business you're implying, you said the business is a computer. So if you just think about that mind blowing concept for a second. If it's a computer, what's the operating system and what's the suite of applications that are on top of it? So, okay. Let's go in the old days you had a windows machine and you had office, which was a system software, applications software construct. If you map that to the entire company, you're talking about Red Hat and IBM will kind of come working together. Kind of connects the dots a little bit on what Red Hat could, because they're not breaking system company. So if Hybrid Cloud is the system, edge, hybrid, then you got the application suite is all software for the business. >> That's right. That's right. And if you listen to anything these days about what IBM stands for, it's Hybrid Cloud and think Red Hat as kind of the core element of that with OpenShift and AI. And both of those really matter in terms of automation and maybe I'll come back to the Hybrid Cloud or Red Hat thing in a second, but let's just talk about Watson and AI, which is the application. You mentioned scale, which I'm so glad you did. AI could help scale automation. And the trick is, is that automation sometimes gets stuck. It gets stuck when it's working with data that is noisy or unstructured. So there's a lot of structured data in your organization. With that, we can breeze through automation. But if there is more ambiguous data, unstructured, noisy, you need a human in the loop. And when you get a human in the loop, it slows things down. So what AI can start to do, AI and its subordinates, machine learning, natural language processing, computer vision. We can start to make sense of both unstructured and structured data together and we can make a big deal going forward. So that's the AI part. You mentioned Red Hat and, and Hybrid Cloud Park. Well, think about it this way, when you shop, how many stores do you... You don't just shop in one store, right? You go to specialty stores to pick up that special catsup, I don't know or mustard. (audio cuts) In one store and maybe do shopping in another store. Customers using clouds John, aren't very different. They have their specialty places to go. Maybe they're going to be running workloads in Google, involving search and AI related to search. And they're going to be using other clouds for more specialty things. From that perspective, that's a view of hybrid. Customers today, take that shopping analogy. They're going to be using Salesforce or Servicenow, IBM cloud, they have a private cloud. So when you think about automating that world, it's the real world. It's how we shop, whether it's for groceries or for cloud. The Hybrid Cloud is a reality. And how do you make sense of that? Because when an average customer has five clouds, how do you deal with five things? How do you make it easy, normalize? And that's what Red hat really does. >> It's interesting. I'll just share with you though. When I interviewed Arvin, who is now the CEO of IBM when he was at Red Hat summit 2019 in San Francisco. Before he made the acquisition, I was peppering him with questions. Like, you need to get this cloud and he loves cloud, you know, he loves cloud. So he was smiling. He just wanted to say it, he wanted to just say it. And I think Red hat brings that operating kind of mindset where the clouds are just subsystems in the OS of the middleware, which is now software which is software defined business. And this kind of is the talk of your views. Now you have a podcast called "Art of Automation". We want to get that in there, for the folks watching. Search for the podcast, "Art of Automation". This is the stories that you tell. Tell us some stories, from this phenomena. What's the impact of automation for the holistic picture? >> Well, it starts with a lot of, I guess it starts with customers. The stories start with the customer. So we're hearing from customers that AI and automation is where they're investing in 2021. For all the reasons we briefly mentioned, and IBM has a lot to offer there. So we've made AI powered automation a priority. But John, in the pursuit of making it a priority, I've started talking with many of our subject matter experts and was floored by their knowledge, their energy, their passion, and their stories. And I said, we can't keep this to ourselves. We can't keep this locked away. We have to share it. We have to let it out. So basically this is what started the podcast around that. And since then, we've had many industry luminaries from IBM and outside. Starting with customers. We had Klaus Jensen who is the CIO of Memorial Clones Kettering Hospital to talk about automation in healthcare. And he shared great stories. You need to listen to them, about automation is not going to take the place of doctors. But automation will help better read x-rays and look at those shades of gray on the x-ray and interpret it much better than we can. And be able to ingest all of the up-to-date medical research to provide pointers and make connections that the human may not be able to do in that moment. So the two working together are better than any individual. Carol Polson recently joined me to talk, and she's the CIO for Cooperators, to talk about automation and insurance. And she had some great stories too. So John, with that, a bunch of IBM, great IBM fellows like Rama Akkiraju, who is one of Forbes top 20 women in AI research. Talking about AI ops. And also Ruchir Puri talking, and Ruchir has been working on Watson since jeopardy to tell stories about ultimately now how we're teaching AI to code and all the modern programming languages. And really automating application modernization and the like. Four keyed episodes in, we have those under our belt. About 6,000 downloads so far. So it's coming along pretty well. Thanks for asking, John. >> The key is you're a content creator now, as well as a fellow. And this is the democratization, as we say direct to audience, share those stories. Also here, I think you released an ebook. Tell us a little quickly about that. We've got one minute left, give a quick plug for the ebook. >> The book echoes the podcast. Chapters relate to the episodes of the book. We're dropping the first five chapters plus forward for free on the IBM website. Other chapters will become available and drop as they become available. The book makes the content searchable on the internet. We go into more detail with advice on how to get started. You get to hear the topics and the voice of those subject matter experts. And I really suggest you go out and check it out. >> All right, Jerry Cuomo. IBM fellow VP, CTO, IBM automation. Also a content creator, podcast "Art of Automation." Jerry, we're going to list it out on our Silicon angle and our cube sites, gets you some extra love on that. Love the podcast, love the focus on sharing from experts in the field. Thanks for coming on. >> Yeah. Thank you so much for having me again, John. >> Okay, I'm John Furrier with theCUBE. Here for IBM Think 2021. Thanks for watching.

>>from around the globe. It's the >>cube >>With digital coverage of IBM think 2021 brought to you by IBM. Hello and welcome back to the cubes coverage of IBM Think 2021 virtual. I'm john for a year host of the cube. We're virtual this year in real life. Soon, right around the corner as we come out of code, we've got a great guest cube alumni jerry, cuomo IBM fellow V P C T O for IBM automation jerry, Great to see you uh nonsense got almost since the early days of the cube. Good to see you, >>john thrilled to be back again. Thank you >>what I love about our conversations. One is your super technical, you've got patents under your belt during the cutting edge. You've been involved in web services and web technologies for a long, long time. You constantly riding the wave and also your creator of a great podcast called the art of automation, which is the subject of this discussion as automation becomes central in cloud operations and hybrid cloud, which is the main theme of this event this year and the industry so great to see you. Uh First team is a little background for the folks that may not know you about your history with IBM and who you are. >>Yeah, so thanks john, So I'm I'm jerry Carrillo, I've been with IBM for about three decades and I started my career at IBM research in Yorktown at the dawn of the internet and I've been incredibly fortunate, as you mentioned to be on the forefront of many technology trends over the last three decades. Internet software middleware, including being one of the founding fathers of web sphere software, uh I recently helped launch the IBM Blockchain initiative and now all about aI powered automation, which actually brings me back to my roots of studying AI and graduate school. So it's kind of come full circle for me, you know, really you know, enjoying the topic. >>You know, these funny, you mentioned aI in graduate school, I was really kind of into a I when I was an undergraduate and get a masters degree in computer science, I kind of went the NBA route. But if you think about what was going on in the eighties during those systems times, a lot of the concepts of systems programming and cloud operations kind of gel well together. So you've got this confluence of computer science and engineering A. K A. Now devops sec cops coming together. This is actually a really unique time to bring back the best of the best concepts, whether it's A I and systems and computer science and engineering into the automation. Could you share your view on this because you're in a unique position, you've been there, done that now. You're on the cutting edge with your thoughts. >>Yeah, absolutely, john And just when you think of automation and time, automation is not new, literally, if you go into Wikipedia and you look up automation, you see patents and references to like steam engine regulators at the dawn of the industrial era. Right? So automation has been around and and in the simplest form automation, whether it was back then, um whether it was in the 80s or today, it's about applying technology and that that performs, that uses like technology software to perform tasks that were once exclusively done by us humans. Right? So, but now what we're seeing is a I coming into the picture and and changing the landscape in an interesting way. But I think at its essence, you know, automation is this two step dance of both eliminating repetitive, mundane tasks. That helped reduce errors and free up our time. So we get back the gift of time but also helps. It's not about taking jobs away at that point, as I said, it's a two step dance, that's step one. But if you stop there, you're not getting the full value. Step two is to augment our skills right? And and to use automation to help augment our skills and we get speed, we get quality, we get lower costs, we get improved user experience. So whether it was back in the steam engine times or today with a I automation is evolving with technology >>and it's interesting to its you know, as a student of the history of the computer industry as you are and now a creator with your podcast which we'll get to in a second, you're starting to see the intersection of these concepts are not bespoke as much as they used to be. You got transformation. Digital, transformation and innovation are connected and scale. If you think about those three concepts they don't stand alone anymore. They can stand alone but they work better together transformation. And is the innovation innovation provides cloud scale. So if you think about automation, automation is powering this dynamic of taking all that undifferentiated heavy lifting and moving the creativity and the skill set into higher integrated areas. Can you share >>your john Yeah no right on there when you talk about transformation, jeez look around us, the pandemic has made transformation and specifically digital transformation the default so everything is digital. You know whether it's ordering a pizza, you know visiting a doctor through telemedicine or or this zoom webex based workplace that we live in. But picture of telemedicine environment right? Talking about transformation and going digital With 10 x more users. They can't hire 10 x more support staff and think about it I forgot my password. Um Does does this work on my version of the Apple iphone or all of that kind of stuff? So their support desks or lit up? Right. So uh as they scale digitally automation is the relief that that comes into play which is which is just in time. Right? So the digital transformation needs automation and john I think about it like this um businesses like cars are have become computers right? So they are programmable. So automation software just like in the cars it makes you know the car self driving? I think about the Tesla model three which I recently test drove. Um so with this digital acceleration digital opens the door for automation And now we can use about a self driving business. We can use about uh maybe that's step one, right? That's the um remove repetitive work, but maybe we can actually augment business to have an autopilot so it doesn't eat us there all the time to drive. And that's the scale that you talked about. That's the scale we need. So automation is really like the peanut butter and chocolate Digital is the peanut butter, automation is the chocolate. They go well together and they produce amazing tastes. >>You know, that's a really, that's a really interesting insight and I will just put an exclamation point on that because you mentioned self driving business, you're implying, you said the computer, the business is a computer. So if you just just think about that mind blowing concept for a second, if it's a computer, what's the operating system and what's the suite of applications that are on top of it? So, Okay, let's go in the old days at a Windows machine and you had office, which is a system software, applications, software construct. Okay, If you map that to the entire company, you're talking about Red hat and IBM kind of come working together. Kind of connects the dots a little bit on what Red Hackett because they're not bring system company. So if hybrid cloud is the system mm hybrid, then you got the applications suite is all software for the That's >>right. That's right. And if you, you know, if you listen to anything these days about what IBM stands words, hybrid cloud and and think red hat as as, you know, kind of the core element of that with open shift in a I right. And both of those really matter in terms of automation and maybe I'll come back to the hybrid cloud and red hat thing in a second. Let's just talk about you know Watson and Ai, you know, which is the application and you mentioned scale, which I'm so glad you did. You know a I could help scale automation. And the trick is is that ai automation sometimes gets stuck right? It gets stuck when it's working with data that is noisy or unstructured. Right? So there's a lot of structured data in your organization and it it with that we can breeze through automation. But if there is more ambiguous data unstructured noisy, you need a human in the loop. And when you get a human in the loop, it slows things down. So what a I can start to do a I. And its subordinates, machine learning, natural language processing, computer vision. We can start to make sense of both unstructured and structured data together and we can make a big deal going forward. Right? So that's that's the way I part you mentioned Red hat and and hybrid cloud part. We'll think about it this way. When you shop, how many stores do you don't just shop in one store? Right. You you go to specialty stores to pick up that special uh ketchup, I don't know or must store and maybe do shopping another store, customers using clouds john aren't very different. You know, they have their specialty places to go. Maybe they're going to be running workloads and google involving search and a I related to search, right? And they're going to be using other clouds for more specialty things. Right? So from that perspective, that's a view of hybrid, you know, customers today, you know, take that shopping analogy, they're going to be using sales force or service Now, IBM cloud, they have a private cloud, right? So, when you think about automating that world, All right. It's the real world. It's how we shop, whether it's for groceries or for cloud, right? So the hybrid cloud is a reality. Um and how do you make sense of a high of that? Right, Because when when an average customer has five clouds, How do you deal with five things? Right. How do you make it easy normalize? And that's what red hat really >>does. It's interesting. I just just share with you the and I interviewed Arvin um who is now the ceo of IBM when he was at Red Hat some in 2019 in SAn Francisco before he made the acquisition here that I was, I was peppered with questions like you know, you need to get this cloud and he loves cloud, you know, he loves clouds. So so he was smiling, he just wanted to say it, I wanted to just say it and I think Red Hat brings that operating kind of mindset where the clouds are just subsystems in the Os >>yes of the middle >>where which is now software which is software to find business. And this kind of is the talk of your, your your views. Now you have a podcast called Art of automation. Want to get that in there for the folks watching uh search for the podcast, Art of automation. This is the stories that you tell. Tell us some stories from this phenomenon. What's the impact of automation for the holistic picture? >>Well, it starts with a lot of, I guess it starts with customers. The stories start with the customers. So we're hearing from customers that Ai and automation is where they're investing in 2021. Um for all the reasons we briefly mentioned and and IBM has a lot to offer there. So we've made a I powered automation of priority but john in the pursuit of making it a priority. I've started talking with many of our subject matter experts and was floored by their knowledge, their energy, their passion and their stories. And I said we can't keep this to ourselves, we can't keep this locked away, we have to share it, we have to let it out. So, so basically this is what started the podcast around that. And since then we've had many industry luminaries from IBM and outside, starting with customers, we had claus Jensen who is the ceo of memorial clones Kettering hospital to talk about automation and health care. And he shared great stories. You need to listen to them about. Automation is not going to take the place of doctors, but automation will help better read um x rays and look at those shades of gray on the X ray and interpret it much better than we can and be able to ingest all of the up to date medical research to provide pointers and make connections that the human may not be able to do in that moment. Right. So the two working together or better than any individual carol Poulsen recently joined me to talk and she's the C. I. O. For cooperators to talk about automation and insurance. And she had some great stories to uh so john with that a bunch of IBM great IBM fellows like Rama Agra jew, who is one of Forbes top 20 women in AI research, talking about Ai ops and also Russia near pori talking and Russia has been working on Watson since jeopardy to tell stories about ultimately now how we're teaching ai to code and all the modern programming languages and really automating application modernization and the like uh, 14 episodes in we have those under our belt, About 6000 downloads so far. So it's it's coming along pretty well. Thanks. >>Thanks for being done. Yeah. The key is your your content creator now as well as a fellow and this is the democratization, as we say, direct to audience, share those stories also here. And thank you released an e book. Tell us a little quickly about that. We've got one minute left, give a quick plug. >>The book echoes the podcast chapters relate to the, to the episodes of the book. We're dropping the first five chapters plus forward for free on the IBM website. Other chapters will become available um, and drop as they become available. The book makes the content searchable on the internet. We go into more detail with advice on how to get started. You get to hear the topics and the voice of those subject matter experts and uh I really, you know, suggest you go out and check it out. >>Alright, jerry, cuomo IBM fellow VPC T IBM automation um also a content creator podcast, art of automation, jerry. We're gonna lift it listed on our silicon angle and our cube sites. Get you some extra love on that. Love the podcast. Love the focus on sharing from experts in the field. Thanks for coming on. >>Thank you so much for having me again, john >>Okay. I'm John Fryer with the Cube here for IBM think 2021. Thanks for watching.

>>from around the globe. It's the Q. With digital coverage >>of exa scale day made possible by Hewlett Packard Enterprise. We're back at the celebration of Exa Scale Day. This is Dave Volant, and I'm pleased to welcome to great guests Brian Dance Berries Here. Here's what The ISS Program Science office at the Johnson Space Center. And Dr Mark Fernandez is back. He's the Americas HPC technology officer at Hewlett Packard Enterprise. Gentlemen, welcome. >>Thank you. Yeah, >>well, thanks for coming on. And, Mark, Good to see you again. And, Brian, I wonder if we could start with you and talk a little bit about your role. A T. I s s program Science office as a scientist. What's happening these days? What are you working on? >>Well, it's been my privilege the last few years to be working in the, uh, research integration area of of the space station office. And that's where we're looking at all of the different sponsors NASA, the other international partners, all the sponsors within NASA, and, uh, prioritizing what research gets to go up to station. What research gets conducted in that regard. And to give you a feel for the magnitude of the task, but we're coming up now on November 2nd for the 20th anniversary of continuous human presence on station. So we've been a space faring society now for coming up on 20 years, and I would like to point out because, you know, as an old guy myself, it impresses me. That's, you know, that's 25% of the US population. Everybody under the age of 20 has never had a moment when they were alive and we didn't have people living and working in space. So Okay, I got off on a tangent there. We'll move on in that 20 years we've done 3000 experiments on station and the station has really made ah, miraculously sort of evolution from, ah, basic platform, what is now really fully functioning national lab up there with, um, commercially run research facilities all the time. I think you can think of it as the world's largest satellite bus. We have, you know, four or five instruments looking down, measuring all kinds of things in the atmosphere during Earth observation data, looking out, doing astrophysics, research, measuring cosmic rays, X ray observatory, all kinds of things, plus inside the station you've got racks and racks of experiments going on typically scores, you know, if not more than 50 experiments going on at any one time. So, you know, the topic of this event is really important. Doesn't NASA, you know, data transmission Up and down, all of the cameras going on on on station the experiments. Um, you know, one of one of those astrophysics observatory's you know, it has collected over 15 billion um uh, impact data of cosmic rays. And so the massive amounts of data that that needs to be collected and transferred for all of these experiments to go on really hits to the core. And I'm glad I'm able toe be here and and speak with you today on this. This topic. >>Well, thank you for that, Bryan. A baby boomer, right? Grew up with the national pride of the moon landing. And of course, we've we've seen we saw the space shuttle. We've seen international collaboration, and it's just always been something, you know, part of our lives. So thank you for the great work that you guys were doing their mark. You and I had a great discussion about exa scale and kind of what it means for society and some of the innovations that we could maybe expect over the coming years. Now I wonder if you could talk about some of the collaboration between what you guys were doing and Brian's team. >>Uh, yeah, so yes, indeed. Thank you for having me early. Appreciate it. That was a great introduction. Brian, Uh, I'm the principal investigator on Space Born computer, too. And as the two implies, where there was one before it. And so we worked with Bryant and his team extensively over the past few years again high performance computing on board the International Space Station. Brian mentioned the thousands of experiments that have been done to date and that there are currently 50 orm or going on at any one time. And those experiments collect data. And up until recently, you've had to transmit that data down to Earth for processing. And that's a significant amount of bandwidth. Yeah, so with baseball and computer to we're inviting hello developers and others to take advantage of that onboard computational capability you mentioned exa scale. We plan to get the extra scale next year. We're currently in the era that's called PETA scale on. We've been in the past scale era since 2000 and seven, so it's taken us a while to make it that next lead. Well, 10 years after Earth had a PETA scale system in 2017 were able to put ah teraflop system on the International space station to prove that we could do a trillion calculations a second in space. That's where the data is originating. That's where it might be best to process it. So we want to be able to take those capabilities with us. And with H. P. E. Acting as a wonderful partner with Brian and NASA and the space station, we think we're able to do that for many of these experiments. >>It's mind boggling you were talking about. I was talking about the moon landing earlier and the limited power of computing power. Now we've got, you know, water, cool supercomputers in space. I'm interested. I'd love to explore this notion of private industry developing space capable computers. I think it's an interesting model where you have computer companies can repurpose technology that they're selling obviously greater scale for space exploration and apply that supercomputing technology instead of having government fund, proprietary purpose built systems that air. Essentially, you use case, if you will. So, Brian, what are the benefits of that model? The perhaps you wouldn't achieve with governments or maybe contractors, you know, kind of building these proprietary systems. >>Well, first of all, you know, any any tool, your using any, any new technology that has, you know, multiple users is going to mature quicker. You're gonna have, you know, greater features, greater capabilities, you know, not even talking about computers. Anything you're doing. So moving from, you know, governor government is a single, um, you know, user to off the shelf type products gives you that opportunity to have things that have been proven, have the technology is fully matured. Now, what had to happen is we had to mature the space station so that we had a platform where we could test these things and make sure they're gonna work in the high radiation environments, you know, And they're gonna be reliable, because first, you've got to make sure that that safety and reliability or taken care of so that that's that's why in the space program you're gonna you're gonna be behind the times in terms of the computing power of the equipment up there because, first of all and foremost, you needed to make sure that it was reliable and say, Now, my undergraduate degree was in aerospace engineering and what we care about is aerospace engineers is how heavy is it, how big and bulky is it because you know it z expensive? You know, every pound I once visited Gulfstream Aerospace, and they would pay their employees $1000 that they could come up with a way saving £1 in building that aircraft. That means you have more capacity for flying. It's on the orders of magnitude. More important to do that when you're taking payloads to space. So you know, particularly with space born computer, the opportunity there to use software and and check the reliability that way, Uh, without having to make the computer, you know, radiation resistance, if you will, with heavy, you know, bulky, um, packaging to protect it from that radiation is a really important thing, and it's gonna be a huge advantage moving forward as we go to the moon and on to Mars. >>Yeah, that's interesting. I mean, your point about cots commercial off the shelf technology. I mean, that's something that obviously governments have wanted to leverage for a long, long time for many, many decades. But but But Mark the issue was always the is. Brian was just saying the very stringent and difficult requirements of space. Well, you're obviously with space Born one. You got to the point where you had visibility of the economics made sense. It made commercial sense for companies like Hewlett Packard Enterprise. And now we've sort of closed that gap to the point where you're sort of now on that innovation curve. What if you could talk about that a little bit? >>Yeah, absolutely. Brian has some excellent points, you know, he said, anything we do today and requires computers, and that's absolutely correct. So I tell people that when you go to the moon and when you go to the Mars, you probably want to go with the iPhone 10 or 11 and not a flip phone. So before space born was sent up, you went with 2000 early two thousands computing technology there which, like you said many of the people born today weren't even around when the space station began and has been occupied so they don't even know how to program or use that type of computing. Power was based on one. We sent the exact same products that we were shipping to customers today, so they are current state of the art, and we had a mandate. Don't touch the hardware, have all the protection that you can via software. So that's what we've done. We've got several philosophical ways to do that. We've implemented those in software. They've been successful improving in the space for one, and now it's space born to. We're going to begin the experiments so that the rest of the community so that the rest of the community can figure out that it is economically viable, and it will accelerate their research and progress in space. I'm most excited about that. Every venture into space as Brian mentioned will require some computational capability, and HP has figured out that the economics air there we need to bring the customers through space ball into in order for them to learn that we are reliable but current state of the art, and that we could benefit them and all of humanity. >>Guys, I wanna ask you kind of a two part question. And, Brian, I'll start with you and it z somewhat philosophical. Uh, I mean, my understanding was and I want to say this was probably around the time of the Bush administration w two on and maybe certainly before that, but as technology progress, there was a debate about all right, Should we put our resource is on moon because of the proximity to Earth? Or should we, you know, go where no man has gone before and or woman and get to Mars? Where What's the thinking today, Brian? On that? That balance between Moon and Mars? >>Well, you know, our plans today are are to get back to the moon by 2024. That's the Artemus program. Uh, it's exciting. It makes sense from, you know, an engineering standpoint. You take, you know, you take baby steps as you continue to move forward. And so you have that opportunity, um, to to learn while you're still, you know, relatively close to home. You can get there in days, not months. If you're going to Mars, for example, toe have everything line up properly. You're looking at a multi year mission you know, it may take you nine months to get there. Then you have to wait for the Earth and Mars to get back in the right position to come back on that same kind of trajectory. So you have toe be there for more than a year before you can turn around and come back. So, you know, he was talking about the computing power. You know, right now that the beautiful thing about the space station is, it's right there. It's it's orbiting above us. It's only 250 miles away. Uh, so you can test out all of these technologies. You can rely on the ground to keep track of systems. There's not that much of a delay in terms of telemetry coming back. But as you get to the moon and then definitely is, you get get out to Mars. You know, there are enough minutes delay out there that you've got to take the computing power with you. You've got to take everything you need to be able to make those decisions you need to make because there's not time to, um, you know, get that information back on the ground, get back get it back to Earth, have people analyze the situation and then tell you what the next step is to do. That may be too late. So you've got to think the computing power with you. >>So extra scale bring some new possibilities. Both both for, you know, the moon and Mars. I know Space Born one did some simulations relative. Tomorrow we'll talk about that. But But, Brian, what are the things that you hope to get out of excess scale computing that maybe you couldn't do with previous generations? >>Well, you know, you know, market on a key point. You know, bandwidth up and down is, of course, always a limitation. In the more computing data analysis you can do on site, the more efficient you could be with parsing out that that bandwidth and to give you ah, feel for just that kind of think about those those observatory's earth observing and an astronomical I was talking about collecting data. Think about the hours of video that are being recorded daily as the astronauts work on various things to document what they're doing. They many of the biological experiments, one of the key key pieces of data that's coming back. Is that video of the the microbes growing or the plants growing or whatever fluid physics experiments going on? We do a lot of colloids research, which is suspended particles inside ah liquid. And that, of course, high speed video. Is he Thio doing that kind of research? Right now? We've got something called the I s s experience going on in there, which is basically recording and will eventually put out a syriza of basically a movie on virtual reality recording. That kind of data is so huge when you have a 360 degree camera up there recording all of that data, great virtual reality, they There's still a lot of times bringing that back on higher hard drives when the space six vehicles come back to the Earth. That's a lot of data going on. We recorded videos all the time, tremendous amount of bandwidth going on. And as you get to the moon and as you get further out, you can a man imagine how much more limiting that bandwidth it. >>Yeah, We used to joke in the old mainframe days that the fastest way to get data from point a to Point B was called C Tam, the Chevy truck access method. Just load >>up a >>truck, whatever it was, tapes or hard drive. So eso and mark, of course space born to was coming on. Spaceport one really was a pilot, but it proved that the commercial computers could actually work for long durations in space, and the economics were feasible. Thinking about, you know, future missions and space born to What are you hoping to accomplish? >>I'm hoping to bring. I'm hoping to bring that success from space born one to the rest of the community with space born to so that they can realize they can do. They're processing at the edge. The purpose of exploration is insight, not data collection. So all of these experiments begin with data collection. Whether that's videos or samples are mold growing, etcetera, collecting that data, we must process it to turn it into information and insight. And the faster we can do that, the faster we get. Our results and the better things are. I often talk Thio College in high school and sometimes grammar school students about this need to process at the edge and how the communication issues can prevent you from doing that. For example, many of us remember the communications with the moon. The moon is about 250,000 miles away, if I remember correctly, and the speed of light is 186,000 miles a second. So even if the speed of light it takes more than a second for the communications to get to the moon and back. So I can remember being stressed out when Houston will to make a statement. And we were wondering if the astronauts could answer Well, they answered as soon as possible. But that 1 to 2 second delay that was natural was what drove us crazy, which made us nervous. We were worried about them in the success of the mission. So Mars is millions of miles away. So flip it around. If you're a Mars explorer and you look out the window and there's a big red cloud coming at you that looks like a tornado and you might want to do some Mars dust storm modeling right then and there to figure out what's the safest thing to do. You don't have the time literally get that back to earth have been processing and get you the answer back. You've got to take those computational capabilities with you. And we're hoping that of these 52 thousands of experiments that are on board, the SS can show that in order to better accomplish their missions on the moon. And Omar, >>I'm so glad you brought that up because I was gonna ask you guys in the commercial world everybody talks about real time. Of course, we talk about the real time edge and AI influencing and and the time value of data I was gonna ask, you know, the real time, Nous, How do you handle that? I think Mark, you just answered that. But at the same time, people will say, you know, the commercial would like, for instance, in advertising. You know, the joke the best. It's not kind of a joke, but the best minds of our generation tryingto get people to click on ads. And it's somewhat true, unfortunately, but at any rate, the value of data diminishes over time. I would imagine in space exploration where where you're dealing and things like light years, that actually there's quite a bit of value in the historical data. But, Mark, you just You just gave a great example of where you need real time, compute capabilities on the ground. But but But, Brian, I wonder if I could ask you the value of this historic historical data, as you just described collecting so much data. Are you? Do you see that the value of that data actually persists over time, you could go back with better modeling and better a i and computing and actually learn from all that data. What are your thoughts on that, Brian? >>Definitely. I think the answer is yes to that. And, you know, as part of the evolution from from basically a platform to a station, we're also learning to make use of the experiments in the data that we have there. NASA has set up. Um, you know, unopened data access sites for some of our physical science experiments that taking place there and and gene lab for looking at some of the biological genomic experiments that have gone on. And I've seen papers already beginning to be generated not from the original experimenters and principal investigators, but from that data set that has been collected. And, you know, when you're sending something up to space and it to the space station and volume for cargo is so limited, you want to get the most you can out of that. So you you want to be is efficient as possible. And one of the ways you do that is you collect. You take these earth observing, uh, instruments. Then you take that data. And, sure, the principal investigators air using it for the key thing that they designed it for. But if that data is available, others will come along and make use of it in different ways. >>Yeah, So I wanna remind the audience and these these these air supercomputers, the space born computers, they're they're solar powered, obviously, and and they're mounted overhead, right? Is that is that correct? >>Yeah. Yes. Space borne computer was mounted in the overhead. I jokingly say that as soon as someone could figure out how to get a data center in orbit, they will have a 50 per cent denser data station that we could have down here instead of two robes side by side. You can also have one overhead on. The power is free. If you can drive it off a solar, and the cooling is free because it's pretty cold out there in space, so it's gonna be very efficient. Uh, space borne computer is the most energy efficient computer in existence. Uh, free electricity and free cooling. And now we're offering free cycles through all the experimenters on goal >>Eso Space born one exceeded its mission timeframe. You were able to run as it was mentioned before some simulations for future Mars missions. And, um and you talked a little bit about what you want to get out of, uh, space born to. I mean, are there other, like, wish list items, bucket bucket list items that people are talking about? >>Yeah, two of them. And these air kind of hypothetical. And Brian kind of alluded to them. Uh, one is having the data on board. So an example that halo developers talk to us about is Hey, I'm on Mars and I see this mold growing on my potatoes. That's not good. So let me let me sample that mold, do a gene sequencing, and then I've got stored all the historical data on space borne computer of all the bad molds out there and let me do a comparison right then and there before I have dinner with my fried potato. So that's that's one. That's very interesting. A second one closely related to it is we have offered up the storage on space borne computer to for all of your raw data that we process. So, Mr Scientist, if if you need the raw data and you need it now, of course, you can have it sent down. But if you don't let us just hold it there as long as they have space. And when we returned to Earth like you mentioned, Patrick will ship that solid state disk back to them so they could have a new person, but again, reserving that network bandwidth, uh, keeping all that raw data available for the entire duration of the mission so that it may have value later on. >>Great. Thank you for that. I want to end on just sort of talking about come back to the collaboration between I S s National Labs and Hewlett Packard Enterprise, and you've got your inviting project ideas using space Bourne to during the upcoming mission. Maybe you could talk about what that's about, and we have A We have a graphic we're gonna put up on DSM information that you can you can access. But please, mark share with us what you're planning there. >>So again, the collaboration has been outstanding. There. There's been a mention off How much savings is, uh, if you can reduce the weight by a pound. Well, our partners ice s national lab and NASA have taken on that cost of delivering baseball in computer to the international space station as part of their collaboration and powering and cooling us and giving us the technical support in return on our side, we're offering up space borne computer to for all the onboard experiments and all those that think they might be wanting doing experiments on space born on the S s in the future to take advantage of that. So we're very, very excited about that. >>Yeah, and you could go toe just email space born at hp dot com on just float some ideas. I'm sure at some point there'll be a website so you can email them or you can email me david dot volonte at at silicon angle dot com and I'll shoot you that that email one or that website once we get it. But, Brian, I wanna end with you. You've been so gracious with your time. Uh, yeah. Give us your final thoughts on on exa scale. Maybe how you're celebrating exa scale day? I was joking with Mark. Maybe we got a special exa scale drink for 10. 18 but, uh, what's your final thoughts, Brian? >>Uh, I'm going to digress just a little bit. I think I think I have a unique perspective to celebrate eggs a scale day because as an undergraduate student, I was interning at Langley Research Center in the wind tunnels and the wind tunnel. I was then, um, they they were very excited that they had a new state of the art giant room size computer to take that data we way worked on unsteady, um, aerodynamic forces. So you need a lot of computation, and you need to be ableto take data at a high bandwidth. To be able to do that, they'd always, you know, run their their wind tunnel for four or five hours. Almost the whole shift. Like that data and maybe a week later, been ableto look at the data to decide if they got what they were looking for? Well, at the time in the in the early eighties, this is definitely the before times that I got there. They had they had that computer in place. Yes, it was a punchcard computer. It was the one time in my life I got to put my hands on the punch cards and was told not to drop them there. Any trouble if I did that. But I was able thio immediately after, uh, actually, during their run, take that data, reduce it down, grabbed my colored pencils and graph paper and graph out coefficient lift coefficient of drag. Other things that they were measuring. Take it back to them. And they were so excited to have data two hours after they had taken it analyzed and looked at it just pickled them. Think that they could make decisions now on what they wanted to do for their next run. Well, we've come a long way since then. You know, extra scale day really, really emphasizes that point, you know? So it really brings it home to me. Yeah. >>Please, no, please carry on. >>Well, I was just gonna say, you know, you talked about the opportunities that that space borne computer provides and and Mark mentioned our colleagues at the I S s national lab. You know, um, the space station has been declared a national laboratory, and so about half of the, uh, capabilities we have for doing research is a portion to the national lab so that commercial entities so that HP can can do these sorts of projects and universities can access station and and other government agencies. And then NASA can focus in on those things we want to do purely to push our exploration programs. So the opportunities to take advantage of that are there marks opening up the door for a lot of opportunities. But others can just Google S s national laboratory and find some information on how to get in the way. Mark did originally using s national lab to maybe get a good experiment up there. >>Well, it's just astounding to see the progress that this industry is made when you go back and look, you know, the early days of supercomputing to imagine that they actually can be space born is just tremendous. Not only the impacts that it can have on Space six exploration, but also society in general. Mark Wayne talked about that. Guys, thanks so much for coming on the Cube and celebrating Exa scale day and helping expand the community. Great work. And, uh, thank you very much for all that you guys dio >>Thank you very much for having me on and everybody out there. Let's get the XO scale as quick as we can. Appreciate everything you all are >>doing. Let's do it. >>I've got a I've got a similar story. Humanity saw the first trillion calculations per second. Like I said in 1997. And it was over 100 racks of computer equipment. Well, space borne one is less than fourth of Iraq in only 20 years. So I'm gonna be celebrating exa scale day in anticipation off exa scale computers on earth and soon following within the national lab that exists in 20 plus years And being on Mars. >>That's awesome. That mark. Thank you for that. And and thank you for watching everybody. We're celebrating Exa scale day with the community. The supercomputing community on the Cube Right back

>> connecting with thought leaders all around the world, this is a CUBE Conversation. Hi, everybody this is Dave Vellante of theCUBE, and welcome back to my CXO series. I've been running this through really since the start of the COVID-19 crisis to really understand how leaders are dealing with this pandemic. Sri Ambati is here, he's the CEO and founder of H20. Sri, it's great to see you again, thanks for coming on. >> Thank you for having us. >> Yeah, so this pandemic has obviously given people fits, no question, but it's also given opportunities for companies to kind of reassess where they are. Automation is a huge watchword, flexibility, business resiliency and people who maybe really hadn't fully leaned into things like the cloud and AI and automation are now realizing, wow, we have no choice, it's about survival. Your thought as to what you're seeing in the marketplace. >> Thanks for having us. I think first of all, kudos to the frontline health workers who have been ruthlessly saving lives across the country and the world, and what you're really doing is a fraction of what we could have done or should be doing to stay away the next big pandemic. But that apart I think, I usually tend to say BC is before COVID. So if the world was thinking about going digital after COVID-19, they have been forced to go digital and as a result, you're seeing tremendous transformation across our customers, and a lot of application to kind of go in and reinvent their business models that allow them to scale as effortlessly as they could using the digital means. >> So, think about, doctors and diagnosis machines, in some cases, are helping doctors make diagnoses, they're sometimes making even better diagnosis, (mumbles) is informing. There's been a lot of talk about the models, you know how... Yeah, I know you've been working with a lot of healthcare organizations, you may probably familiar with that, you know, the Medium post, The Hammer and the Dance, and if people criticize the models, of course, they're just models, right? And you iterate models and machine intelligence can help us improve. So, in this, you know, you talk about BC and post C, how have you seen the data and in machine intelligence informing the models and proving that what we know about this pandemic, I mean, it changed literally daily, what are you seeing? >> Yeah, and I think it started with Wuhan and we saw the best application of AI in trying to trace, literally from Alipay, to WeChat, track down the first folks who were spreading it across China and then eventually the rest of the world. I think contact tracing, for example, has become a really interesting problem. supply chain has been disrupted like never before. We're beginning to see customers trying to reinvent their distribution mechanisms in the second order effects of the COVID, and the the prime center is hospital staffing, how many ventilator, is the first few weeks so that after COVID crisis as it evolved in the US. We are busy predicting working with some of the local healthcare communities to predict how staffing in hospitals will work, how many PPE and ventilators will be needed and so henceforth, but that quickly and when the peak surge will be those with the beginning problems, and many of our customers have begin to do these models and iterate and improve and kind of educate the community to practice social distancing, and that led to a lot of flattening the curve and you're talking flattening the curve, you're really talking about data science and analytics in public speak. That led to kind of the next level, now that we have somewhat brought a semblance of order to the reaction to COVID, I think what we are beginning to figure out is, is there going to be a second surge, what elective procedures that were postponed, will be top of the mind for customers, and so this is the kind of things that hospitals are beginning to plan out for the second half of the year, and as businesses try to open up, certain things were highly correlated to surgeon cases, such as cleaning supplies, for example, the obvious one or pantry buying. So retailers are beginning to see what online stores are doing well, e-commerce, online purchases, electronic goods, and so everyone essentially started working from home, and so homes needed to have the same kind of bandwidth that offices and commercial enterprises needed to have, and so a lot of interesting, as one side you saw airlines go away, this side you saw the likes of Zoom and video take off. So you're kind of seeing a real divide in the digital divide and that's happening and AI is here to play a very good role to figure out how to enhance your profitability as you're looking about planning out the next two years. >> Yeah, you know, and obviously, these things they get, they get partisan, it gets political, I mean, our job as an industry is to report, your job is to help people understand, I mean, let the data inform and then let public policy you know, fight it out. So who are some of the people that you're working with that you know, as a result of COVID-19. What's some of the work that H2O has done, I want to better understand what role are you playing? >> So one of the things we're kind of privileged as a company to come into the crisis, with a strong balance and an ability to actually have the right kind of momentum behind the company in terms of great talent, and so we have 10% of the world's top data scientists in the in the form of Kaggle Grand Masters in the company. And so we put most of them to work, and they started collecting data sets, curating data sets and making them more qualitative, picking up public data sources, for example, there's a tremendous amount of job loss out there, figuring out which are the more difficult kind of sectors in the economy and then we started looking at exodus from the cities, we're looking at mobility data that's publicly available, mobility data through the data exchanges, you're able to find which cities which rural areas, did the New Yorkers as they left the city, which places did they go to, and what's to say, Californians when they left Los Angeles, which are the new places they have settled in? These are the places which are now busy places for the same kind of items that you need to sell if you're a retailer, but if you go one step further, we started engaging with FEMA, we start engaging with the universities, like Imperial College London or Berkeley, and started figuring out how best to improve the models and automate them. The SEER model, the most popular SEER model, we added that into our Driverless AI product as a recipe and made that accessible to our customers in testing, to customers in healthcare who are trying to predict where the surge is likely to come. But it's mostly about information right? So the AI at the end of it is all about intelligence and being prepared. Predictive is all about being prepared and that's kind of what we did with general, lots of blogs, typical blog articles and working with the largest health organizations and starting to kind of inform them on the most stable models. What we found to our not so much surprise, is that the simplest, very interpretable models are actually the most widely usable, because historical data is actually no longer as effective. You need to build a model that you can quickly understand and retry again to the feedback loop of back testing that model against what really happened. >> Yeah, so I want to double down on that. So really, two things I want to understand, if you have visibility on it, sounds like you do. Just in terms of the surge and the comeback, you know, kind of what those models say, based upon, you know, we have some advanced information coming from the global market, for sure, but it seems like every situation is different. What's the data telling you? Just in terms of, okay, we're coming into the spring and the summer months, maybe it'll come down a little bit. Everybody says it... We fully expect it to come back in the fall, go back to college, don't go back to college. What is the data telling you at this point in time with an understanding that, you know, we're still iterating every day? >> Well, I think I mean, we're not epidemiologists, but at the same time, the science of it is a highly local response, very hyper local response to COVID-19 is what we've seen. Santa Clara, which is just a county, I mean, is different from San Francisco, right, sort of. So you beginning to see, like we saw in Brooklyn, it's very different, and Bronx, very different from Manhattan. So you're seeing a very, very local response to this disease, and I'm talking about US. You see the likes of Brazil, which we're worried about, has picked up quite a bit of cases now. I think the silver lining I would say is that China is up and running to a large degree, a large number of our user base there are back active, you can see the traffic patterns there. So two months after their last research cases, the business and economic activity is back and thriving. And so, you can kind of estimate from that, that this can be done where you can actually contain the rise of active cases and it will take masking of the entire community, masking and the healthy dose of increase in testing. One of our offices is in Prague, and Czech Republic has done an incredible job in trying to contain this and they've done essentially, masked everybody and as a result they're back thinking about opening offices, schools later this month. So I think that's a very, very local response, hyper local response, no one country and no one community is symmetrical with other ones and I think we have a unique situation where in United States you have a very, very highly connected world, highly connected economy and I think we have quite a problem on our hands on how to safeguard our economy while also safeguarding life. >> Yeah, so you can't just, you can't just take Norway and apply it or South Korea and apply it, every situation is different. And then I want to ask you about, you know, the economy in terms of, you know, how much can AI actually, you know, how can it work in this situation where you have, you know, for example, okay, so the Fed, yes, it started doing asset buys back in 2008 but still, very hard to predict, I mean, at this time of this interview you know, Stock Market up 900 points, very difficult to predict that but some event happens in the morning, somebody, you know, Powell says something positive and it goes crazy but just sort of even modeling out the V recovery, the W recovery, deep recession, the comeback. You have to have enough data, do you not? In order for AI to be reasonably accurate? How does it work? And how does at what pace can you iterate and improve on the models? >> So I think that's exactly where I would say, continuous modeling, instead of continuously learning continuous, that's where the vision of the world is headed towards, where data is coming, you build a model, and then you iterate, try it out and come back. That kind of rapid, continuous learning would probably be needed for all our models as opposed to the typical, I'm pushing a model to production once a year, or once every quarter. I think what we're beginning to see is the kind of where companies are beginning to kind of plan out. A lot of people lost their jobs in the last couple of months, right, sort of. And so up scaling and trying to kind of bring back these jobs back both into kind of, both from the manufacturing side, but also lost a lot of jobs in the transportation and the kind of the airlines slash hotel industries, right, sort of. So it's trying to now bring back the sense of confidence and will take a lot more kind of testing, a lot more masking, a lot more social empathy, I think well, some of the things that we are missing while we are socially distant, we know that we are so connected as a species, we need to kind of start having that empathy for we need to wear a mask, not for ourselves, but for our neighbors and people we may run into. And I think that kind of, the same kind of thinking has to kind of parade, before we can open up the economy in a big way. The data, I mean, we can do a lot of transfer learning, right, sort of there are new methods, like try to model it, similar to the 1918, where we had a second bump, or a lot of little bumps, and that's kind of where your W shaped pieces, but governments are trying very well in seeing stimulus dollars being pumped through banks. So some of the US case we're looking for banks is, which small medium business in especially, in unsecured lending, which business to lend to, (mumbles) there's so many applications that have come to banks across the world, it's not just in the US, and banks are caught up with the problem of which and what's growing the concern for this business to kind of, are they really accurate about the number of employees they are saying they have? Do then the next level problem or on forbearance and mortgage, that side of the things are coming up at some of these banks as well. So they're looking at which, what's one of the problems that one of our customers Wells Fargo, they have a question which branch to open, right, sort of that itself, it needs a different kind of modeling. So everything has become a very highly good segmented models, and so AI is absolutely not just a good to have, it has become a must have for most of our customers in how to go about their business. (mumbles) >> I want to talk a little bit about your business, you have been on a mission to democratize AI since the beginning, open source. Explain your business model, how you guys make money and then I want to help people understand basic theoretical comparisons and current affairs. >> Yeah, that's great. I think the last time we spoke, probably about at the Spark Summit. I think Dave and we were talking about Sparkling Water and H2O our open source platforms, which are premium platforms for democratizing machine learning and math at scale, and that's been a tremendous brand for us. Over the last couple of years, we have essentially built a platform called Driverless AI, which is a license software and that automates machine learning models, we took the best practices of all these data scientists, and combined them to essentially build recipes that allow people to build the best forecasting models, best fraud prevention models or the best recommendation engines, and so we started augmenting traditional data scientists with this automatic machine learning called AutoML, that essentially allows them to build models without necessarily having the same level of talent as these great Kaggle Grand Masters. And so that has democratized, allowed ordinary companies to start producing models of high caliber and high quality that would otherwise have been the pedigree of Google, Microsoft or Amazon or some of these top tier AI houses like Netflix and others. So what we've done is democratize not just the algorithms at the open source level. Now, we've made it easy for kind of rapid adoption of AI across every branch inside a company, a large organization, also across smaller organizations which don't have the access to the same kind of talent. Now, third level, you know, what we've brought to market, is ability to augment data sets, especially public and private data sets that you can, the alternative data sets that can increase the signal. And that's where we've started working on a new platform called Q, again, more license software, and I mean, to give you an idea there from business models endpoint, now majority of our software sales is coming from closed source software. And sort of so, we've made that transition, we still make our open source widely accessible, we continue to improve it, a large chunk of the teams are improving and participating in building the communities but I think from a business model standpoint as of last year, 51% of our revenues are now coming from closed source software and that change is continuing to grow. >> And this is the point I wanted to get to, so you know, the open source model was you know, Red Hat the one company that, you know, succeeded wildly and it was, put it out there open source, come up with a service, maintain the software, you got to buy the subscription okay, fine. And everybody thought that you know, you were going to do that, they thought that Databricks was going to do and that changed. But I want to take two examples, Hortonworks which kind of took the Red Hat model and Cloudera which does IP. And neither really lived up to the expectation, but now there seems to be sort of a new breed I mentioned, you guys, Databricks, there are others, that seem to be working. You with your license software model, Databricks with a managed service and so there's, it's becoming clear that there's got to be some level of IP that can be licensed in order to really thrive in the open source community to be able to fund the committers that you have to put forth to open source. I wonder if you could give me your thoughts on that narrative. >> So on Driverless AI, which is the closest platform I mentioned, we opened up the layers in open source as recipes. So for example, different companies build their zip codes differently, right, the domain specific recipes, we put about 150 of them in open source again, on top of our Driverless AI platform, and the idea there is that, open source is about freedom, right? It is not necessarily about, it's not a philosophy, it's not a business model, it allows freedom for rapid adoption of a platform and complete democratization and commodification of a space. And that allows a small company like ours to compete at the level of an SaaS or a Google or a Microsoft because you have the same level of voice as a very large company and you're focused on using code as a community building exercise as opposed to a business model, right? So that's kind of the heart of open source, is allowing that freedom for our end users and the customers to kind of innovate at the same level of that a Silicon Valley company or one of these large tech giants are building software. So it's really about making, it's a maker culture, as opposed to a consumer culture around software. Now, if you look at how the the Red Hat model, and the others who have tried to replicate that, the difficult part there was, if the product is very good, customers are self sufficient and if it becomes a standard, then customers know how to use it. If the product is crippled or difficult to use, then you put a lot of services and that's where you saw the classic Hadoop companies, get pulled into a lot of services, which is a reasonably difficult business to scale. So I think what we chose was, instead, a great product that builds a fantastic brand, that makes AI, even when other first or second.ai domain, and for us to see thousands of companies which are not AI and AI first, and even more companies adopting AI and talking about AI as a major way that was possible because of open source. If you had chosen close source and many of your peers did, they all vanished. So that's kind of how the open source is really about building the ecosystem and having the patience to build a company that takes 10, 20 years to build. And what we are expecting unfortunately, is a first and fast rise up to become unicorns. In that race, you're essentially sacrifice, building a long ecosystem play, and that's kind of what we chose to do, and that took a little longer. Now, if you think about the, how do you truly monetize open source, it takes a little longer and is much more difficult sales machine to scale, right, sort of. Our open source business actually is reasonably positive EBITDA business because it makes more money than we spend on it. But trying to teach sales teams, how to sell open source, that's a much, that's a rate limiting step. And that's why we chose and also explaining to the investors, how open source is being invested in as you go closer to the IPO markets, that's where we chose, let's go into license software model and scale that as a regular business. >> So I've said a few times, it's kind of like ironic that, this pandemic is as we're entering a new decade, you know, we've kind of we're exiting the era, I mean, the many, many decades of Moore's law being the source of innovation and now it's a combination of data, applying machine intelligence and being able to scale and with cloud. Well, my question is, what did we expect out of AI this decade if those are sort of the three, the cocktail of innovation, if you will, what should we expect? Is it really just about, I suggest, is it really about automating, you know, businesses, giving them more agility, flexibility, you know, etc. Or should we should we expect more from AI this decade? >> Well, I mean, if you think about the decade of 2010 2011, that was defined by software is eating the world, right? And now you can say software is the world, right? I mean, pretty much almost all conditions are digital. And AI is eating software, right? (mumbling) A lot of cloud transitions are happening and are now happening much faster rate but cloud and AI are kind of the leading, AI is essentially one of the biggest driver for cloud adoption for many of our customers. So in the enterprise world, you're seeing rebuilding of a lot of data, fast data driven applications that use AI, instead of rule based software, you're beginning to see patterned, mission AI based software, and you're seeing that in spades. And, of course, that is just the tip of the iceberg, AI has been with us for 100 years, and it's going to be ahead of us another hundred years, right, sort of. So as you see the discovery rate at which, it is really a fundamentally a math, math movement and in that math movement at the beginning of every century, it leads to 100 years of phenomenal discovery. So AI is essentially making discoveries faster, AI is producing, entertainment, AI is producing music, AI is producing choreographing, you're seeing AI in every walk of life, AI summarization of Zoom meetings, right, you beginning to see a lot of the AI enabled ETF peaking of stocks, right, sort of. You're beginning to see, we repriced 20,000 bonds every 15 seconds using H2O AI, corporate bonds. And so you and one of our customers is on the fastest growing stock, mostly AI is powering a lot of these insights in a fast changing world which is globally connected. No one of us is able to combine all the multiple dimensions that are changing and AI has that incredible opportunity to be a partner for every... (mumbling) For a hospital looking at how the second half will look like for physicians looking at what is the sentiment of... What is the surge to expect? To kind of what is the market demand looking at the sentiment of the customers. AI is the ultimate money ball in business and then I think it's just showing its depth at this point. >> Yeah, I mean, I think you're right on, I mean, basically AI is going to convert every software, every application, or those tools aren't going to have much use, Sri we got to go but thanks so much for coming to theCUBE and the great work you guys are doing. Really appreciate your insights. stay safe, and best of luck to you guys. >> Likewise, thank you so much. >> Welcome, and thank you for watching everybody, this is Dave Vellante for the CXO series on theCUBE. We'll see you next time. All right, we're clear. All right.

>> Starting the record, Dave in five, four, three. Hi, everybody this is Dave Vellante, theCUBE, and welcome back to my CXO series. I've been running this through really since the start of the COVID-19 crisis to really understand how leaders are dealing with this pandemic. Sri Ambati is here, he's the CEO and founder of H20. Sri, it's great to see you again, thanks for coming on. >> Thank you for having us. >> Yeah, so this pandemic has obviously given people fits, no question, but it's also given opportunities for companies to kind of reassess where they are. Automation is a huge watchword, flexibility, business resiliency and people who maybe really hadn't fully leaned into things like the cloud and AI and automation are now realizing, wow, we have no choice, it's about survival. Your thought as to what you're seeing in the marketplace. >> Thanks for having us. I think first of all, kudos to the frontline health workers who have been ruthlessly saving lives across the country and the world, and what you're really doing is a fraction of what we could have done or should be doing to stay away the next big pandemic. But that apart I think, I usually tend to say BC is before COVID. So if the world was thinking about going digital after COVID-19, they have been forced to go digital and as a result, you're seeing tremendous transformation across our customers, and a lot of application to kind of go in and reinvent their business models that allow them to scale as effortlessly as they could using the digital means. >> So, think about, doctors and diagnosis machines, in some cases, are helping doctors make diagnoses, they're sometimes making even better diagnosis, (mumbles) is informing. There's been a lot of talk about the models, you know how... Yeah, I know you've been working with a lot of healthcare organizations, you may probably familiar with that, you know, the Medium post, The Hammer and the Dance, and if people criticize the models, of course, they're just models, right? And you iterate models and machine intelligence can help us improve. So, in this, you know, you talk about BC and post C, how have you seen the data and in machine intelligence informing the models and proving that what we know about this pandemic, I mean, it changed literally daily, what are you seeing? >> Yeah, and I think it started with Wuhan and we saw the best application of AI in trying to trace, literally from Alipay, to WeChat, track down the first folks who were spreading it across China and then eventually the rest of the world. I think contact tracing, for example, has become a really interesting problem. supply chain has been disrupted like never before. We're beginning to see customers trying to reinvent their distribution mechanisms in the second order effects of the COVID, and the the prime center is hospital staffing, how many ventilator, is the first few weeks so that after COVID crisis as it evolved in the US. We are busy predicting working with some of the local healthcare communities to predict how staffing in hospitals will work, how many PPE and ventilators will be needed and so henceforth, but that quickly and when the peak surge will be those with the beginning problems, and many of our customers have begin to do these models and iterate and improve and kind of educate the community to practice social distancing, and that led to a lot of flattening the curve and you're talking flattening the curve, you're really talking about data science and analytics in public speak. That led to kind of the next level, now that we have somewhat brought a semblance of order to the reaction to COVID, I think what we are beginning to figure out is, is there going to be a second surge, what elective procedures that were postponed, will be top of the mind for customers, and so this is the kind of things that hospitals are beginning to plan out for the second half of the year, and as businesses try to open up, certain things were highly correlated to surgeon cases, such as cleaning supplies, for example, the obvious one or pantry buying. So retailers are beginning to see what online stores are doing well, e-commerce, online purchases, electronic goods, and so everyone essentially started working from home, and so homes needed to have the same kind of bandwidth that offices and commercial enterprises needed to have, and so a lot of interesting, as one side you saw airlines go away, this side you saw the likes of Zoom and video take off. So you're kind of seeing a real divide in the digital divide and that's happening and AI is here to play a very good role to figure out how to enhance your profitability as you're looking about planning out the next two years. >> Yeah, you know, and obviously, these things they get, they get partisan, it gets political, I mean, our job as an industry is to report, your job is to help people understand, I mean, let the data inform and then let public policy you know, fight it out. So who are some of the people that you're working with that you know, as a result of COVID-19. What's some of the work that H2O has done, I want to better understand what role are you playing? >> So one of the things we're kind of privileged as a company to come into the crisis, with a strong balance and an ability to actually have the right kind of momentum behind the company in terms of great talent, and so we have 10% of the world's top data scientists in the in the form of Kaggle Grand Masters in the company. And so we put most of them to work, and they started collecting data sets, curating data sets and making them more qualitative, picking up public data sources, for example, there's a tremendous amount of job loss out there, figuring out which are the more difficult kind of sectors in the economy and then we started looking at exodus from the cities, we're looking at mobility data that's publicly available, mobility data through the data exchanges, you're able to find which cities which rural areas, did the New Yorkers as they left the city, which places did they go to, and what's to say, Californians when they left Los Angeles, which are the new places they have settled in? These are the places which are now busy places for the same kind of items that you need to sell if you're a retailer, but if you go one step further, we started engaging with FEMA, we start engaging with the universities, like Imperial College London or Berkeley, and started figuring out how best to improve the models and automate them. The SaaS model, the most popular SaaS model, we added that into our Driverless AI product as a recipe and made that accessible to our customers in testing, to customers in healthcare who are trying to predict where the surge is likely to come. But it's mostly about information right? So the AI at the end of it is all about intelligence and being prepared. Predictive is all about being prepared and that's kind of what we did with general, lots of blogs, typical blog articles and working with the largest health organizations and starting to kind of inform them on the most stable models. What we found to our not so much surprise, is that the simplest, very interpretable models are actually the most widely usable, because historical data is actually no longer as effective. You need to build a model that you can quickly understand and retry again to the feedback loop of back testing that model against what really happened. >> Yeah, so I want to double down on that. So really, two things I want to understand, if you have visibility on it, sounds like you do. Just in terms of the surge and the comeback, you know, kind of what those models say, based upon, you know, we have some advanced information coming from the global market, for sure, but it seems like every situation is different. What's the data telling you? Just in terms of, okay, we're coming into the spring and the summer months, maybe it'll come down a little bit. Everybody says it... We fully expect it to come back in the fall, go back to college, don't go back to college. What is the data telling you at this point in time with an understanding that, you know, we're still iterating every day? >> Well, I think I mean, we're not epidemiologists, but at the same time, the science of it is a highly local response, very hyper local response to COVID-19 is what we've seen. Santa Clara, which is just a county, I mean, is different from San Francisco, right, sort of. So you beginning to see, like we saw in Brooklyn, it's very different, and Bronx, very different from Manhattan. So you're seeing a very, very local response to this disease, and I'm talking about US. You see the likes of Brazil, which we're worried about, has picked up quite a bit of cases now. I think the silver lining I would say is that China is up and running to a large degree, a large number of our user base there are back active, you can see the traffic patterns there. So two months after their last research cases, the business and economic activity is back and thriving. And so, you can kind of estimate from that, that this can be done where you can actually contain the rise of active cases and it will take masking of the entire community, masking and the healthy dose of increase in testing. One of our offices is in Prague, and Czech Republic has done an incredible job in trying to contain this and they've done essentially, masked everybody and as a result they're back thinking about opening offices, schools later this month. So I think that's a very, very local response, hyper local response, no one country and no one community is symmetrical with other ones and I think we have a unique situation where in United States you have a very, very highly connected world, highly connected economy and I think we have quite a problem on our hands on how to safeguard our economy while also safeguarding life. >> Yeah, so you can't just, you can't just take Norway and apply it or South Korea and apply it, every situation is different. And then I want to ask you about, you know, the economy in terms of, you know, how much can AI actually, you know, how can it work in this situation where you have, you know, for example, okay, so the Fed, yes, it started doing asset buys back in 2008 but still, very hard to predict, I mean, at this time of this interview you know, Stock Market up 900 points, very difficult to predict that but some event happens in the morning, somebody, you know, Powell says something positive and it goes crazy but just sort of even modeling out the V recovery, the W recovery, deep recession, the comeback. You have to have enough data, do you not? In order for AI to be reasonably accurate? How does it work? And how does at what pace can you iterate and improve on the models? >> So I think that's exactly where I would say, continuous modeling, instead of continuously learning continuous, that's where the vision of the world is headed towards, where data is coming, you build a model, and then you iterate, try it out and come back. That kind of rapid, continuous learning would probably be needed for all our models as opposed to the typical, I'm pushing a model to production once a year, or once every quarter. I think what we're beginning to see is the kind of where companies are beginning to kind of plan out. A lot of people lost their jobs in the last couple of months, right, sort of. And so up scaling and trying to kind of bring back these jobs back both into kind of, both from the manufacturing side, but also lost a lot of jobs in the transportation and the kind of the airlines slash hotel industries, right, sort of. So it's trying to now bring back the sense of confidence and will take a lot more kind of testing, a lot more masking, a lot more social empathy, I think well, some of the things that we are missing while we are socially distant, we know that we are so connected as a species, we need to kind of start having that empathy for we need to wear a mask, not for ourselves, but for our neighbors and people we may run into. And I think that kind of, the same kind of thinking has to kind of parade, before we can open up the economy in a big way. The data, I mean, we can do a lot of transfer learning, right, sort of there are new methods, like try to model it, similar to the 1918, where we had a second bump, or a lot of little bumps, and that's kind of where your W shaped pieces, but governments are trying very well in seeing stimulus dollars being pumped through banks. So some of the US case we're looking for banks is, which small medium business in especially, in unsecured lending, which business to lend to, (mumbles) there's so many applications that have come to banks across the world, it's not just in the US, and banks are caught up with the problem of which and what's growing the concern for this business to kind of, are they really accurate about the number of employees they are saying they have? Do then the next level problem or on forbearance and mortgage, that side of the things are coming up at some of these banks as well. So they're looking at which, what's one of the problems that one of our customers Wells Fargo, they have a question which branch to open, right, sort of that itself, it needs a different kind of modeling. So everything has become a very highly good segmented models, and so AI is absolutely not just a good to have, it has become a must have for most of our customers in how to go about their business. (mumbles) >> I want to talk a little bit about your business, you have been on a mission to democratize AI since the beginning, open source. Explain your business model, how you guys make money and then I want to help people understand basic theoretical comparisons and current affairs. >> Yeah, that's great. I think the last time we spoke, probably about at the Spark Summit. I think Dave and we were talking about Sparkling Water and H2O or open source platforms, which are premium platforms for democratizing machine learning and math at scale, and that's been a tremendous brand for us. Over the last couple of years, we have essentially built a platform called Driverless AI, which is a license software and that automates machine learning models, we took the best practices of all these data scientists, and combined them to essentially build recipes that allow people to build the best forecasting models, best fraud prevention models or the best recommendation engines, and so we started augmenting traditional data scientists with this automatic machine learning called AutoML, that essentially allows them to build models without necessarily having the same level of talent as these Greek Kaggle Grand Masters. And so that has democratized, allowed ordinary companies to start producing models of high caliber and high quality that would otherwise have been the pedigree of Google, Microsoft or Amazon or some of these top tier AI houses like Netflix and others. So what we've done is democratize not just the algorithms at the open source level. Now, we've made it easy for kind of rapid adoption of AI across every branch inside a company, a large organization, also across smaller organizations which don't have the access to the same kind of talent. Now, third level, you know, what we've brought to market, is ability to augment data sets, especially public and private data sets that you can, the alternative data sets that can increase the signal. And that's where we've started working on a new platform called Q, again, more license software, and I mean, to give you an idea there from business models endpoint, now majority of our software sales is coming from closed source software. And sort of so, we've made that transition, we still make our open source widely accessible, we continue to improve it, a large chunk of the teams are improving and participating in building the communities but I think from a business model standpoint as of last year, 51% of our revenues are now coming from closed source software and that change is continuing to grow. >> And this is the point I wanted to get to, so you know, the open source model was you know, Red Hat the one company that, you know, succeeded wildly and it was, put it out there open source, come up with a service, maintain the software, you got to buy the subscription okay, fine. And everybody thought that you know, you were going to do that, they thought that Databricks was going to do and that changed. But I want to take two examples, Hortonworks which kind of took the Red Hat model and Cloudera which does IP. And neither really lived up to the expectation, but now there seems to be sort of a new breed I mentioned, you guys, Databricks, there are others, that seem to be working. You with your license software model, Databricks with a managed service and so there's, it's becoming clear that there's got to be some level of IP that can be licensed in order to really thrive in the open source community to be able to fund the committers that you have to put forth to open source. I wonder if you could give me your thoughts on that narrative. >> So on Driverless AI, which is the closest platform I mentioned, we opened up the layers in open source as recipes. So for example, different companies build their zip codes differently, right, the domain specific recipes, we put about 150 of them in open source again, on top of our Driverless AI platform, and the idea there is that, open source is about freedom, right? It is not necessarily about, it's not a philosophy, it's not a business model, it allows freedom for rapid adoption of a platform and complete democratization and commodification of a space. And that allows a small company like ours to compete at the level of an SaaS or a Google or a Microsoft because you have the same level of voice as a very large company and you're focused on using code as a community building exercise as opposed to a business model, right? So that's kind of the heart of open source, is allowing that freedom for our end users and the customers to kind of innovate at the same level of that a Silicon Valley company or one of these large tech giants are building software. So it's really about making, it's a maker culture, as opposed to a consumer culture around software. Now, if you look at how the the Red Hat model, and the others who have tried to replicate that, the difficult part there was, if the product is very good, customers are self sufficient and if it becomes a standard, then customers know how to use it. If the product is crippled or difficult to use, then you put a lot of services and that's where you saw the classic Hadoop companies, get pulled into a lot of services, which is a reasonably difficult business to scale. So I think what we chose was, instead, a great product that builds a fantastic brand, that makes AI, even when other first or second.ai domain, and for us to see thousands of companies which are not AI and AI first, and even more companies adopting AI and talking about AI as a major way that was possible because of open source. If you had chosen close source and many of your peers did, they all vanished. So that's kind of how the open source is really about building the ecosystem and having the patience to build a company that takes 10, 20 years to build. And what we are expecting unfortunately, is a first and fast rise up to become unicorns. In that race, you're essentially sacrifice, building a long ecosystem play, and that's kind of what we chose to do, and that took a little longer. Now, if you think about the, how do you truly monetize open source, it takes a little longer and is much more difficult sales machine to scale, right, sort of. Our open source business actually is reasonably positive EBITDA business because it makes more money than we spend on it. But trying to teach sales teams, how to sell open source, that's a much, that's a rate limiting step. And that's why we chose and also explaining to the investors, how open source is being invested in as you go closer to the IPO markets, that's where we chose, let's go into license software model and scale that as a regular business. >> So I've said a few times, it's kind of like ironic that, this pandemic is as we're entering a new decade, you know, we've kind of we're exiting the era, I mean, the many, many decades of Moore's law being the source of innovation and now it's a combination of data, applying machine intelligence and being able to scale and with cloud. Well, my question is, what did we expect out of AI this decade if those are sort of the three, the cocktail of innovation, if you will, what should we expect? Is it really just about, I suggest, is it really about automating, you know, businesses, giving them more agility, flexibility, you know, etc. Or should we should we expect more from AI this decade? >> Well, I mean, if you think about the decade of 2010 2011, that was defined by software is eating the world, right? And now you can say software is the world, right? I mean, pretty much almost all conditions are digital. And AI is eating software, right? (mumbling) A lot of cloud transitions are happening and are now happening much faster rate but cloud and AI are kind of the leading, AI is essentially one of the biggest driver for cloud adoption for many of our customers. So in the enterprise world, you're seeing rebuilding of a lot of data, fast data driven applications that use AI, instead of rule based software, you're beginning to see patterned, mission AI based software, and you're seeing that in spades. And, of course, that is just the tip of the iceberg, AI has been with us for 100 years, and it's going to be ahead of us another hundred years, right, sort of. So as you see the discovery rate at which, it is really a fundamentally a math, math movement and in that math movement at the beginning of every century, it leads to 100 years of phenomenal discovery. So AI is essentially making discoveries faster, AI is producing, entertainment, AI is producing music, AI is producing choreographing, you're seeing AI in every walk of life, AI summarization of Zoom meetings, right, you beginning to see a lot of the AI enabled ETF peaking of stocks, right, sort of. You're beginning to see, we repriced 20,000 bonds every 15 seconds using H2O AI, corporate bonds. And so you and one of our customers is on the fastest growing stock, mostly AI is powering a lot of these insights in a fast changing world which is globally connected. No one of us is able to combine all the multiple dimensions that are changing and AI has that incredible opportunity to be a partner for every... (mumbling) For a hospital looking at how the second half will look like for physicians looking at what is the sentiment of... What is the surge to expect? To kind of what is the market demand looking at the sentiment of the customers. AI is the ultimate money ball in business and then I think it's just showing its depth at this point. >> Yeah, I mean, I think you're right on, I mean, basically AI is going to convert every software, every application, or those tools aren't going to have much use, Sri we got to go but thanks so much for coming to theCUBE and the great work you guys are doing. Really appreciate your insights. stay safe, and best of luck to you guys. >> Likewise, thank you so much. >> Welcome, and thank you for watching everybody, this is Dave Vellante for the CXO series on theCUBE. We'll see you next time. All right, we're clear. All right.

>> From the KIPP studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> IBM's new CEO has an opportunity to reset the direction of the company. Outgoing CEO Ginni Rometty, inherited a strategy that was put in place over two decades. It became fossilized in a lower-margin services-led model that she helped architect. Ginni spent a large portion of her tenure, shrinking the company so it could grow. But unfortunately, she ran out of time. For decades, IBM has missed opportunities to aggressively invest in the key waves that are now powering the tech economy. Instead, IBM really tried to balance investing innovation with placating Wall Street. We believe IBM has an opportunity to return to the Big Blue status that set the standard for the tech industry. But several things have to change, some quite dramatically. So we're going to talk about what it's going to take for IBM to succeed in this endeavor. Welcome to this special Wikibon CUBE Insights powered by ETR. In this breaking analysis, we're going to address our view of the future of IBM and try to accomplish three things. First, I want to review IBM's most recent earnings, the very first one under new CEO Arvind Krishna, and we'll discuss IBM's near-term prospects. Next, we'll look at how IBM got to where we are today. We want to review some of the epic decisions that it has made over the past several years and even decades. Finally, we'll look at some of the opportunities that we see for IBM to essentially remake itself and return to that tech titan that was revered by customers and feared by competitors. First, I want to look at the comments from new CEO Arvind Krishna. And let's try to decode them a bit. Arvind in the first earnings call that he held, and in interviews as well, and also internal memos, he's given some clues as to how he's thinking. This slide addresses a few of the key points. Arvind has clearly stated that he's committed to growing the IBM company, and of course, increasing its value. This is no surprise, as you know, every IBM CEO has been under pressure to do the same. And we'll look at that further a little later on in the segment. Arvind, also stated that he wants the company, he said it this way, "To lead with a technical approach." Now as we reported in January when Krishna was appointed to CEO. We're actually very encouraged that the IBM board chose a technical visionary to lead the company. Arvind's predecessors did not have the technical vision needed to make the bold decisions that we believe are now needed to power the company's future. As a technologist, we believe his decisions will be more focused on bigger tactical bets that can pay bigger returns, potentially with more risk. Now, as a point of just tactical commentary, I want to point out that IBM noted that it was doing well coming into the March month, but software deals especially came to a halt as customers focused on managing the pandemic and other parts of the business were okay. Now, this chart pulls some of the data from IBM's quarter. And let me make a few comments here. Now, what was weird here, IBM cited modest revenue growth on this chart, this was pulled from their slides. But revenue was down 2% for the quarter relative to last year. So I guess that's modest growth. Cloud revenue for the past 12 months, the trailing 12 months, was 22 billion and grew 23%. We're going to unpack that in a minute. Red Hat showed good growth, Stu Miniman and I talked about this last week. And IBM continues to generate a solid free cash flow. Now IBM, like many companies, they prudently suspended forward guidance. Some investors bristled at that, but I really have no problem with it. I mean, just way too much uncertainty right now. So I think that was a smart move by IBM. And basically, everybody's doing it. Now, let's take a look at IBM's business segments and break those down and make a few comments there. As you can see, in this graph, IBM's 17 plus billion dollar quarter comprises their four reporting segments. Cloud and cognitive software, which is, of course, its highest margin and highest growth business at 7%. You can see its gross margin is really, really nice. But it only comprises 30% of the pie. Services, the Global Business Services and GTS global technology services are low-growth or no growth businesses that are relatively low margin operations. But together they comprise more than 60% of IBM's revenue in the quarter and consistently throughout the last several years. Systems, by the way, grew nicely on the strength of the Z15 product cycles, it was up by 60% and dragged storage with it. But unfortunately power had a terrible quarter and hence the 4% growth. But decent margins compared to services of 50%. IBM's balance sheet looks pretty good. It took an advantage of some low rates recently and took out another $4 billion in corporate debt. So it's okay, I'm not too concerned about its debt related to the Red Hat acquisition. Now, welcome back to cloud at 22 billion for the past 12 months and growing at 23%. What, you say? That sounds very large, I don't understand. It's understandable that you don't understand. But let me explain with this next graphic. What this shows is the breakdown of IBM's cloud revenue by segment from fiscal year 19. As you can see, the cloud and cognitive segments, or segment which includes Red Hat comprises only 20% of IBM's cloud business. I know, kind of strange. Professional services accounts for 2/3 of IBM's Cloud revenue with systems at 14%. So look, IBM is defining cloud differently than most people. I mean, actually, that's 1% of the cloud business of AWS, Azure and Google Cloud come from professional services and on-prem hardware. This just doesn't have real meaning. And I think frankly, it hurts IBM's credibility as it hides the ball on cloud. Nobody really believes this number. So, I mean, it's really not much else I can say there. But look, why don't we bring in the customer angle, and let's look at some ETR data. So what this chart shows is the results of an ETR survey. That survey ran, we've been reporting on this, ran from mid March to early April. And more than 1200 respondents and almost 800 IBM customers are in there. If this chart shows the percentage of customers spending more on IBM products by various product segments that we chose with three survey samples April last year, January 2020, and the most recent April 2020 survey. So the good news here is the container platforms, OpenShift, Ansible, the Staples of Red Hat are showing strength, even though they're notably down from previous surveys. But that's the part of IBM's business that really is promising. AI and machine learning and cloud, they're right there in the mix, and even outsourcing and consulting and really across the board, you can see a pretty meaningful and respectable number or percent of customers are actually planning on spending more. So that's good, especially considering that the survey was taken right during the middle of the COVID-19 pandemic. But, if you look at the next chart, the net scores across IBM's portfolio, they're not so rosy. Remember, net score is a measure of spending momentum. It's derived by essentially subtracting the percent of customers that are spending less from those that are spending more. It's a nice simple metric. Kind of like NPS and ETR surveys, every quarter with the exact same methodology for consistency so we can do some comparisons over time series, it's quite nice. And you can see here that Red Hat remains the strongest part of IBM's portfolio. But generally in my experience as net scores starts to dip below 25% and kind of get into the red zone, that so called danger zone. And you can see many parts of IBM's portfolio are showing softness as we measure in net score. And even though you see here, the outsourcing and consulting businesses are up relative to last year, if you slice the data by large companies, as we showed you with Sagar Kadakia last week, that services business is showing deceleration, same thing we saw for Accenture, EY, Deloitte, etc. So here's the takeaway. Red Hat, of course, is where all the action is, and that's where IBM is going to invest in our opinion, and we'll talk a little bit more about that and drill into that kind of investment scenario a bit later. But what I want to do now is I want to come back to Arvind Krishna. Because he has a chance to pull off a Satya Nadella like move. Maybe it's different, but there are definite similarities. I mean, you have an iconic brand, a great company, that's in many technology sectors, and yes, there are differences, IBM doesn't have the recurring software revenue that Microsoft had, it didn't have the monopoly and PCs. But let's move on. Arvind has cited four enduring platforms for IBM, mainframes, services, middleware, and the newest hybrid cloud. He says that IBM must win the architectural battle for hybrid cloud. Now, I'm going to really share later what we think that means. There's a lot in that statement, including the role of AI in the edge. Both of which we'll address later on in this breaking analysis. But before we get there, I want to understand from a historical perspective where we think Arvind is going to take IBM. And to do that, we want to look back over the modern history of IBM, modern meaning of the post mainframe dominance era, which really started in 1993 when Louis Gerstner took over. Look, it's been well documented how Louis Gerstner pivoted into services. He wrote his own narrative with the book, "Who Says Elephants Can't Dance". And you know, look, you can't argue with his results. The graphic here shows IBM's rank in the fortune 500, that's the green line over time. IBM was sixth under Gerstner, today it's number 38. The blue area chart on the Insert, it shows IBM's market cap. Now, look, Gerstner was a hero to Wall Street. And IBM's performance under his tenure was pretty stellar. But his decision to pivot to services set IBM on a path that to this day marks company's greatest strength, and in my view, its greatest vulnerability. Name a product under the mainframes in which IBM leads. Again, middleware, I guess WebSphere, okay. But you know, IBM used to be the leader in the all important database market, semiconductors, storage servers, even PCs back in the day. So, I don't want to beat on this too much, I can say it's been well documented. And I said earlier, Ginni essentially inherited a portfolio that she had to unwind, and hence the steep revenue declines as you see here, and it's 'cause she had to jettison the so called non-strategic businesses. But the real issue is R&D, and how IBM has used it's free cash. And this chart shows IBM's breakdown of cash use between 2007 and 2019. Blue is cash return to shareholders, orange is research and development, and gray is CapEx. Now I chose these years because I think we can all agree that this was the period of tech defined by cloud. And you can see, during those critical early formative years, IBM consistently returned well over 50%, and often 60% plus of its free cash flow to shareholders in the form of dividends and stock buybacks. Now, while the orange appears to grow, it's because of what you see in this chart. The point is the absolute R&D spend really didn't change too much. It pretty much hovered, if you look back around 5 1/2 to $6 billion annually, the percentage grew because IBM's revenue declined. Meanwhile, IBM's competitors were spending on R&D and CapEx, what were they doing? Well, they were building up the cloud. Now, let me give you some perspective on this. In 2007 IBM spent $6.2 billion on R&D, Microsoft spent 7 billion that same year, Intel 5.8 billion, Amazon spent 800 million, that's it. Google spent 2.1 billion that year. And that same year, IBM returned nearly $21 billion to shareholders. In 2012 IBM spent $6.3 billion on R&D, Microsoft that year 9.8 billion, Intel 10 billion, Amazon 4.6 billion, less than IBM, Google 6.1 billion, about the same as IBM. That year IBM returned almost $16 billion to shareholders. Today, IBM spends about the same 6 billion on R&D, about the same as Cisco and Oracle. Meanwhile, Microsoft and Amazon are spending nearly $17 billion each. Sorry, Amazon 23 billion, and IBM could only return $7 billion to shareholders last year. So while IBM was returning cash to its shareholders, its competitors were investing in the future and are now reaping the rewards. Now IBM suspended its stock buybacks after the Red Hat deal, which is good, in my opinion. Buybacks have been a poor use of cash for IBM, in my view. Recently, IBM raised its dividend by a penny. It did this so it could say that it has increased its dividend 25 years in a row. Okay, great, not expensive. So I'm glad that that investors were disappointed with that move. But since 2007, IBM has returned more than $175 billion to shareholders. And somehow Arvind has to figure out how to tell Wall Street to expect less while he invests in the future. So let's talk about that a little bit. Now, as I've reported before, here is the opportunity. This chart shows data from ETR. It plots cloud landscape and is a proxy for multi-cloud and hybrid cloud. It plots net score or spending momentum on the y-axis, and market share, which really isn't market share, as we've talked about, it's a measure of pervasiveness in the data set, that's plotted on the x-axis. So, the point is, IBM has presence, it's pervasive in the marketplace, Red Hat and OpenShift, they have relevance, they have momentum with higher net scores. Arvind's opportunity is to really plug OpenShift into IBM's, large install base, and increase Red Hat's pervasiveness, while at the same time lifting IBM momentum. This, in my view, as Stu Miniman and I reported last week at the Red Hat Summit, puts IBM in a leading position to go after multi and hybrid cloud and the edge. So let's break that down a little bit further. When Arvind talks about winning the architectural battle for hybrid cloud, what does he mean by that? Here's our interpretation. We think IBM can create the de facto standard for cloud and hybrid cloud. And this includes on-prem, public cloud, cross clouds, or multi cloud, and importantly, the edge. Here's the opportunity, is to have OpenShift run natively, natively everywhere, on-premises in the AWS cloud, in the Azure Cloud, GCP, Alibaba, and the IBM Cloud and the Oracle Cloud, everywhere natively, so we can take advantage of the respective services within all those clouds. Same thing for on-prem, same thing for edge opportunities. Now I'll talk a little bit more about that in a moment. But what we're talking about here is the entire IT stack running natively, if I haven't made that point on OpenShift. The control plane, the security plane, the transport, the data management plane, the network plane, the recovery plane, every plane, a Red Hat lead stack with a management of resources is 100% identical, everywhere the same cloud experience. That's how IBM is defining cloud. Okay, I'll give them a mulligan on that one. IBM can be the independent broker of this open source standard covering as many use cases and workloads as possible. Here's the rub, this is going to require an enormous amount of R&D. Just think about all the startups that are building cloud native services and imagine IBM building or buying to fill out that IT stack. Now I don't have enough time to go in too deep to all other areas, but I do want to address the edge, the opportunity there and weave in AI. Beyond what I said above, which I want to stress, the points I made above about hybrid, multi-cloud include edge, the edge is a huge opportunity. But IBM and in many other, if not most other traditional players, we think are kind of missing the boat on that. I'll talk about that in a minute. Here's the opportunity, AI inference is going to run at the edge in real-time. This is going to be incredibly challenging. We think about this, a car running inference AI generates a billion pixels per second today, in five years, it'll be 15 times that. The pressure for real-time analysis at the edge is going to be enormous, and will require a new architecture with new processing models that are likely going to be ARM-based in our opinion. IBM has the opportunity to build end-to-end solutions powered by Red Hat to automate the data pipeline from factory to data center to cloud and everywhere. Anywhere there's instruments, IBM has an opportunity to automate them. Now rather than toss traditional Intel-based IT hardware over the fence to the edge, which is what IBM and most people are doing right now, IBM can develop specialized systems and make new silicon investments that can power the edge with very low cost and efficient systems that process data in real-time. Hey look, I'm out of time, but some other things I want you to consider, IBM transitioning to a recurring revenue model. Interestingly, Back to the Future, right? IBM used to have a massive rental revenue stream before it converted that base to sales. But if Arvind can recreate a culture of innovation and win the day with developers via its Red Hat relationships, as I said recently, he will be CEO of the decade. But he has to transform the portfolio by investing more in R&D. He's got to convince the board to stop pouring money back to investors for a number of years, not just a couple of quarters and do Whatever they have to do to protect the company from corporate raiders. This is not easy, but with the right leader, IBM, a company that has shown resilience through the decades, I think it can be done. All right, well, thanks for watching this episode of the Wikibon CUBE Insights powered by ETR. This is Dave Vellante. And don't forget, these episodes are available as podcasts, wherever you listen, I publish weekly on siliconangle.com, where you'll find all the news, I publish on wikibon.com which is our research site. Please comment on my LinkedIn posts, check out etr.plus, that's where all the data lives. And thanks for watching everybody. This is Dave Vellante for Breaking Analysis, we'll see you next time. (soft music)

>> From theCUBE studios in Palo Alto in Boston, connecting with our leaders all around the world, this is theCUBE Conversation. >> Hello everyone, and welcome to this week's Wiki Bond CUBE Insights powered by ETR. In this breaking analysis, we're changing the format a little bit, we're going right to the new data from ETR. You might recall that last week, ETR received survey results from over 1000 CIOs and IT practitioners. And they made a call at that time, which said that actually surprisingly, a large number of respondents about 40% said they didn't expect a change in their 2020 IT spending. At the same time about 20% of the survey said they're going to spend more largely related to Work From Home infrastructure. ETR was really the first to report on this. And it wasn't just collaboration tool like zoom and video conferencing. It was infrastructure around that security, network bandwidth and other types of infrastructure to support Work From Home like desktop virtualization. ETR made the call at that time, that it looked like budgets, were going to be flat for 2020. Now, you also might recall consensus estimates for 2020 came into the year at about 4%, slightly ahead of GDP. Obviously, that's all is changed. Last week, ETR took the forecast down, and we're going to update you today. We're now gone slightly negative. And with me to talk about that again, is Sagar Kadakia, who's the Director of Research at ETR. Sagar, great to see you again, thank you for coming on. >> Thanks for having me again David, really appreciate it. >> Let's get right into it. I mean, if you look at the time series chart that we showed last week, you can see how sentiment changed over time. That blue line was basically people who responded to the survey starting at 3/11. Now you've updated that, that forecast, really tracking after the COVID-19 really kicked in. Can you explain what we're seeing here in this chart? >> Yeah, no problem. The last time we spoke, we were around an N or sample size of about 1000. And we were right around that zero percent growth rate. One of the unique things that we've done is we've left this survey open. And so what that allows us to do is really track the impact on annual IP growth, essentially daily. And so as things have progressed, as you look at that blue line, you can really see the growth rate has continued to trend downwards. And as of just a day or two ago, we're now below zero. And so I think because of what's occurring right now, the overall current climate continues to slightly deteriorate. You're seeing that in a lot of the CIOs responses. >> If you bring that slide back up Andrew, I want to just sort of stay on this for a second. What I really like about what you guys are doing is you're essentially bringing event analysis in this. So if you see that blue line, you see on 3/13, a national emergency was declared and that's really when the blue line started to decline. What ETR has done is kind of reset that, reset the data since 3/13. Because it's now a more accurate reflection of what's actually happening happening in the market. Notice in the upper right, it says the US approved... The Senate last night approved a stimulus package. Actually, they're calling it an Aid Package. It's really not a stimulus package. It's an aid package that they're injecting to help. A number of our workers actually sounds like existing workers and small businesses and even large businesses like Boeing. Boeing was up significantly yesterday powering the Dow and potentially airlines. As you can see ETR is going to continue to monitor the impact, and roll this out. Really ETR is the only company that I know of anyway, that can track this stuff on a daily basis. So Sagar, that event analysis is really key, and you're going to be watching the impact of this stimulus slash aid packet. >> Yeah, so here's what we're doing on that chart. If you look at that yellow line again, effectively what you're seeing is, if we remove the first I think six or seven 100 respondents that took the survey and start tracking how budgets are changing as a 3/13, that's when the US declared a national emergency. We can recalculate the growth rate. And we can see it's around... It's almost negative one and a half. And so the beauty of doing this, really polling daily, is it allows us to be just as dynamic, as a lot of these organizations are. I think one of the things we talked about the last time was some of these budget changes are going to be temporary. And organizations are figuring out what they're doing day by day. And a lot of that is dictated based on government actions. And so uniquely here, what we're able to do is kind of give people a range and also say, "based on these events, "this is how things are changing."" And so I think we think the first biggest event was on 3/13, where the US effectively declared a national emergency over COVID-19. And now what we're going to start tracking between today and over the weekend, and Monday is: Are people getting more positive? Is there no change? Or is there further deterioration because of this aid package that got passed this morning? >> Now I want to share with our audience. I've been down to ETR's headquarters in New York, it's staffed with a number of data scientists and statistical experts. The ends here are well over 1000. I think we're over 1100 now, is that correct? What is the end that we're at today? >> That's right. Yeah, we're we're pushing right over 1200. And we're going to expect a few more hundred respondents. The good thing is it's balanced, which is important. All these events that are occurring, we want to make sure that we have at least a few hundred more CIOs and IT executives answering. And so every week as we kind of continue to do some of these breaking analysis, there are going to be a few more hundred CIOs. And we'll really be able to zero in or hone in on what they're saying. The growth rate on the IT side, it's going to continue to fluctuate. It's going to continue to be dynamic over the next few weeks, but right now versus (murmurs). We are in negative territory now. >> I want to also explain I mean, the end is important. But in and of itself, it's not the be all end all, what's important about the end, the larger it is, the more cuts you can make. And I want to share... You guys have been doing this for the better part of a decade. And so you have firm level data. And you've got indicators and markers that you've tracked over the years. For example, one of the things that ETR tracks is Giant Public and Private GDP we call it. And that's for example, I'm not saying that, that Mars is one of the companies but Mars is a huge private company, UPS before they went public, huge private company. ETR tracks firm level data, they of course anonymize that, but they can see markers and trackers and trends, and probably have, I don't know dozens of those types of segments. So the bigger the end is, the more... The higher the end within those buckets, and the better the confidence interval. And you guys are experts at really digging into that in trying to understand and read the tea leaves. >> That's right. The key to this survey is, it's not anonymous, we know who is taking the survey. Now to your point, we do anonymize and aggregate it when we display those results. But one of the unique capabilities is we're able to see all of these trend lines. The entire drill down survey that we did on COVID-19 through the lenses of different verticals so we can take a look at industrials materials manufacturing, healthcare, pharma, airlines, delivery services, health, and all these other verticals and get a feel for which ones are deteriorating the most, which ones look stable. And, we talked about last week and it continues to remain true this week. And again, the ends have gone up on all these verticals on the supply chain side. Industrials, materials manufacturing, healthcare, pharma, they continue and they also anticipate to see these things in the next few months, broken supply chains and on the demand side, it's really retail consumer airlines delivery services. That's coming down quite substantial. And I think, based on what United and some of these other airlines have done these last few days in terms of cutting capacity, that's just a reflection of what we're seeing. >> Let's dig into the data a little bit more and bring up the next chart. Last week, we're about 40% actually, exactly 40% where that gray line that said: CIOs and IT practitioners said, "no change." They're like the budget of the green. The green was actually at about 20 21%. So it's slightly up now at 22%. And you can see, most of the the green is in that one to 10% range. And you can see in the left hand side, it's obviously changing. Now we're at 37% in the gray line, slightly up in the green, and a little bit more down and in the red. So take us through what's changed Sagar. >> Yeah, to reiterate what we were talking about last week, and then I'll kind of talk about some of the change is, I think the market and a lot of our clients, they were expecting the growth rate to be more negative. Last week when we talked about zero percent. The reason that, it wasn't more negative is because we saw all these organizations accelerating spend because they had to keep employees productive. They don't want to catastrophe in productivity. And so you saw this acceleration, as you mentioned earlier in the interview around Work From Home tools, like collaboration tools, increasing bandwidth on the VPN networking side, laptops, MDM, so forth and so on. That continues to hold true today. Again, if we use the same example that we talked about last week, (mumbles) organizations, they have 40 50 60,000 employees or more working from home. You have to be able to support these individuals and that's why we're actually seeing some organizations accelerate spend and the majority organizations even though they are declining spend, some of that is still being offset by having to spend more on what we're calling kind of this Work From Home infrastructure. But I will say this: you are seeing more organizations versus last week, which is why the growth rate has come down, moving more and more towards the negative buckets. Again, there is some offset there. But the offset we talked about last week, Work From Home infrastructure is not a one-for-one when it comes to taking down your IT budget, and that continues to hold true. >> Let's talk a little bit about some of the industries retail, airlines, industrials, pharma, healthcare, what are you seeing in terms of the industry impact, particularly when it relates to supply chains, but other industry data that went through? >> I think the biggest takeaway is that healthcare pharma, industry materials, manufacturing organizations, they've indicated the highest levels of broken supply chains today. And they think in three months from now, it's actually going to get worse. And so we spoke about this last time, I don't think this is going to be a V shaped recovery from the standpoint of things are going to get better in the next few weeks or the next month or two. CIOs are indicating that they expect conditions to worsen over the next three months on the supply chain side and even demand the ones that are getting hit the hardest on the retail consumer side airlines, delivery services, they are again indicating that they anticipate demand to be worse three months from now. The goal is to continue serving and pulling these individuals over the next few weeks and months and to see if we can get a better timeline as we get into two edge but for the next few months, conditions look like they're going to get worse. >> I want to highlight some of the industries and let's make some comments here. Retail... You guys called out retail airlines, delivery services, industrials, materials, manufacturing, pharma and healthcare, there's some of the highest impact. I'll just make a few comments here. I think retail really, this accelerates the whole digital transformation. We already saw this starting, I think you'll see further consolidation and some permanence in the way in which companies are pivoting to digital. Obviously, the big guys like Walmart and the like are competing very effectively with Amazon. But, there's going to be some more consolidation there. I would say potentially the same thing in airlines that really are closely watching what the government is going to do. But, do we need this this many airlines? Do we need all this capacity? Maybe yes, maybe no. So watching that. And of course, healthcare right now, as I said last week in the braking analysis, they're just too distracted right now to buy anything. And they're overwhelmed. Now, of course, pharma, they're manufacturing, so they've got disruptions in supply chain and obviously the business. But there could be an upside down the road as COVID-19 vaccines come to the market. >> On the upside, I think you kind of hit it, right on the nail. When you get these type of events that occur. Sometimes it speeds up digital transformation. one of the things that the team and I have been talking about internally is: this is not your father's Keep The Lights On strategy so to speak. Organizations are very focused on maintaining productivity versus significantly cutting costs. What does that mean? Maybe three to five years ago, if this had occurred, you would have seen a lot of infrastructure as a service platform, as a service... A lot of these cloud providers, you'd have seen those projects decline as organization spent more on on plan. And we're not seeing that. We're seeing continued elevated budgets on the Cloud side and Micron just reported this morning and again, cited strong demand on the Cloud and data center side. That just goes to show that organizations are trying to maintain productivity. They want to continue these IT roadmaps and they're going to cut budgets where they can, but it's not going to be on the Cloud side. >> You know what, that's a really important point. This is not post Y2K, not 2008, 2007, 2008, 2009 because we've, pretended but a 10 year bull market, companies are doing pretty well, balance sheets are generally strong. They somewhat in whether, it was used to stronger companies, whether they're so they're not focused right now anyway, on cut cut cut as it was in the last few downturns. Let's go into some of the vendor data and some of the sector data, Andrew if you'd bring up the next chart. What we're showing here is really comparing the the blue is the January survey to the current survey in the yellow, and you're seeing some of the sectors that are up taking. You've identified mobile device management, big data and Cloud, some of the productivity, you mentioned DocuSign, Adobe zoom, Citrix, even VMware with the desktop virtualization. We've talked about security, you've got marketing and LinkedIn, my LinkedIn inbound is going through the roof as people are probably signing up for a LinkedIn premium. Let's talk about this a little bit. What you're seeing... Help us interpret this data. >> Yeah, sure. One of the things that everybody wants to know is, okay, so Work From Home infrastructures getting more spend for the vendors that are benefiting the most. One of the unique things that we can do is because we're kind of collecting all the DNA, from a tech stack aside from these organizations, we can overlap, how they're spending on these vendors. And also with the data that they provide in terms of whether they are increasing or decelerating their IT budgets because of COVID-19. What you're looking at here, is we isolated to all of those organizations and customers that indicated that they're increasing their budgets because of COVID-19. Because of the Work From Home infrastructure. And what we're doing is we're then isolating to vendors that are getting the most upticks in spend. This actually really nicely aligns with a lot of the themes that we were talking about collaboration tools. You see that VMware, they're all right on the virtualization side, MDM with Microsoft. And you're seeing a lot of other vendors with Citrix and Zoom and Adobe. These are the ones that we think are going to benefit from this kind of Work From home infrastructure movement. And again, it's all very... It's not just the qualitative and the commentary. This is all analytics, we really went in and analyzed every single one of these organizations that were increasing their budgets and tried to pinpoint using different data analysis techniques, and to see which vendors were really getting the majority or the largest, pie of that span. >> We had Sanjay Poonen, who's the CEO of VMware on yesterday and he was very sensitive but not trying to hear as your ambulance chasing because obviously they do desktop virtualization and VDI big workload. At the same time. I think he was also being cautious because there's probably portions of their business that are going to get hit, Michael Dell similarly, I think he was quoted in CRN as saying, "hey, are we seeing momentum in our laptop "business in our mobile business?" But as you guys pointed out, the flip side of that is their on prem business is probably going to suffer somewhat. It's a kind of like the Work From Home is a partial offset, but it's not a total offset. You're seeing that with a lot of these companies. Obviously, Microsoft, AWS, a lot of the cloud companies are very well positioned, how about some of the guys that are going to get impacted? Obviously, as I said that the on-prem folks, you guys talked about earlier it's not your father's Keep Your Lights On strategy. Okay but this... You asked the question, is this a reprieve for the legacy guys? Not quite, was your conclusion. What did you mean by that? >> I think a lot of times when you have these sub-events, the clients a lot of the market think okay, "some of the legacy vendors are going to do well "because, we're in malicious times, "and we don't want to keep on this kind "of next generation strategy." We're not seeing that and to the point that you highlighted earlier. There are... Even though these companies like Dell, like Cisco, where they're seeing some products accelerate, there are products to your point that are not doing as well The desktops, right? As an example for Dell or the storage. On the negative side or the legacy side where we're just not seeing any traction, the IBM's the Oracle on-prem, Symantec, which got acquired by Broadcom, checkpoint MicroStrategy. And there's another half dozen other vendors that we're seeing where they are not capitalizing. There is no reprieve for these legacy names. And we don't anticipate them getting additional spend, because of this Work From Home infrastructure kind of movement. >> Let's unpack that a little bit. It's interesting Symantec and checkpoint in security, security you think would get an uplift there, but what you're seeing here is... Let me just tell the audience who you called out. Symantec Teradata MicroStrategy, NET app Checkpoint Oracle and IBM, and I know there are others. But I would say this: These are companies that are getting impacted in a big way by the Cloud. Particularly like Symantec and checkpoint. That's a Cloud security companies are actually probably still doing pretty well. You take Teradata, their data is getting impact by the Cloud from folks like Snowflake and Redshift, MicroStrategy a lot of modern BI coming out. NetApp here's a company that's embraced the Cloud, but the vast majority of the business changess to be on-prem. I think IBM and Oracle are interesting. They're somewhat different. Actually a lot different IBM has services exposure, and you guys call that out, particularly around outsourcing. At the same time, it's going to be interesting to see IBM is going to get a lot of resources. Going to be interesting to see if they start coming out with corona virus related services. So watching for that, and then Oracle, their whole story is, "okay, we got Gen 2 Cloud and Mission Critical in the Cloud, but they're on-prem businesses, I think clearly going to be affected here is kind of what you guys pointed out, and I would agree with your thoughts. >> I think what we're seeing is organizations they had a Cloud roadmap, and that roadmap is continuing. The one thing that is changing in some of that roadmap is we need to be able to support employees as they work from home as we achieve this roadmap. And so that's why we're not seeing a reprieve on the legacy side. But we are seeing upticks and spin where we just wouldn't anticipate them right on maybe on Citrix, on Dell laptops, Adobe and a few other areas. Now, in terms of security side, some of the next gen security vendors like CrowdStrike APi, which is an MFA, those vendors are doing well. It makes sense, where you have more people working from home, you have more devices that are connecting to data applications. Just a component itself. And so you would expect spend to continue going up as you need more authentication, more Endpoint Protection. Cisco Meraki they do Cloud Networking. That piece is looking very good, even though Hardware networking is not looking very good at all. The Cloud Networking is looking good, which again makes sense, as you're increasing bandwidth on that side. >> Definitely stories of two sides of that coin. >> That's right >> I want to... Andrew, if you want to... If you wouldn't mind bringing up the next job, we're going to go back to the first one that we showed you with the time series. This is a very important point. Again, we can't stress it enough. We want to understand the impact of the stimulus or aid package. And ETR is going to continue to track that. What can we expect from you guys over the next week or so? >> The goal is to determine whether or not the stimulus is having an impact on how people are responding to our survey as a relates to how they're changing their budgets. The next four or five days, if we start seeing an uptick in this yellow and blue lines here, I think that's a positive. I think that shows that people are kind of wrapping their heads around, great government is taking action here. There is a roadmap in place to help us get out of this. But if the line continues coming down, it just may be that the last few weeks or the last month or so, there was just so much damage. There's not really... There's no coming back from this at least in the near term. So we are kind of watching out for that. >> Well, the Fed is definitely active. >> They're doing right what they can, they're pushing liquidity into the marketplace. People think out of bullets. I don't agree with the Fed. Fed has a quite a bit of of headroom and some dry powder, (murmurs) which is awesome. But the Fed itself, can't do it. You needed to have this fiscal stimulus. So we're excited to see that come to market. I think what I would say to our audiences, my concern is uncertainty. The markets don't like uncertainty and right now there's a lot of uncertainty. If you saw the piece on medium of The Hammer And The Dance it lays out some scenarios about what could happen to the healthcare system. You see people who say, "hey, we should shut down for 10 weeks." The president saying, "hey, we want "to get back to work by by April." The big concern that I have is: okay, maybe we can stamp it out in the near term and get back to work by late April, early May. But then what happens? Are people going to start traveling again? Are people going to start holding events again? And I think there's going to be some real question marks around that. That uncertainty I think, is something that we obviously have to watch. I think there is light at the end of the tunnel, when you look at China and some of the other things that are happening around the world, but we still don't know how long that tunnel is. I'll give you final thoughts before we wrap. >> I think and that's the biggest thing here is the uncertainty, which is why we're doing a lot of this event analysis. We're trying to figure out: after each one of these big events, is there more certainty in people's responses? And just we were talking about, sectors and verticals and vendors that are not doing well. Because the uncertainty we're seeing a lot of down ticks and spend amongst outsource IT and IT consulting vendors. And as long as the uncertainty continues, you're going to see more and more IT projects frozen, less and less spend on those outsource IT and IT consulting vendors and others. And until there's something really in place here where people feel comfortable, you're going to probably see budgets remain where they are, which right now they're negative. >> Folks as we said last week, Sagar and I, ETR is committed, theCUBE is committed to keep you updated on a regular basis. Right now on a weekly cadence. As we have new information, we will bring it to you. Sagar, thanks so much for coming on and supporting us. >> You're welcome and thanks for having me again. >> You're welcome. Thank you for watching this CUBE Insights powered by ETR. And remember all these breaking analysis available on podcast, go to etr.plus that's where all the action is in terms of the survey work. siliconangle.comm covers these breaking analysis and I published weekly on wikibond.com. Thanks for watching everybody. Stay safe. And we'll see you next time.