(mellow music) >> Hello everyone, welcome to theCUBE's coverage of AWS Startup Showcase. This is the closing panel session on AI machine learning, the top startups generating generative AI on AWS. It's a great panel. This is going to be the experts talking about riding the wave in generative AI. We got Ankur Mehrotra, who's the director and general manager of AI and machine learning at AWS, and Clem Delangue, co-founder and CEO of Hugging Face, and Ori Goshen, who's the co-founder and CEO of AI21 Labs. Ori from Tel Aviv dialing in, and rest coming in here on theCUBE. Appreciate you coming on for this closing session for the Startup Showcase. >> Thanks for having us. >> Thank you for having us. >> Thank you. >> I'm super excited to have you all on. Hugging Face was recently in the news with the AWS relationship, so congratulations. Open source, open science, really driving the machine learning. And we got the AI21 Labs access to the LLMs, generating huge scale live applications, commercial applications, coming to the market, all powered by AWS. So everyone, congratulations on all your success, and thank you for headlining this panel. Let's get right into it. AWS is powering this wave here. We're seeing a lot of push here from applications. Ankur, set the table for us on the AI machine learning. It's not new, it's been goin' on for a while. Past three years have been significant advancements, but there's been a lot of work done in AI machine learning. Now it's released to the public. Everybody's super excited and now says, "Oh, the future's here!" It's kind of been going on for a while and baking. Now it's kind of coming out. What's your view here? Let's get it started. >> Yes, thank you. So, yeah, as you may be aware, Amazon has been in investing in machine learning research and development since quite some time now. And we've used machine learning to innovate and improve user experiences across different Amazon products, whether it's Alexa or Amazon.com. But we've also brought in our expertise to extend what we are doing in the space and add more generative AI technology to our AWS products and services, starting with CodeWhisperer, which is an AWS service that we announced a few months ago, which is, you can think of it as a coding companion as a service, which uses generative AI models underneath. And so this is a service that customers who have no machine learning expertise can just use. And we also are talking to customers, and we see a lot of excitement about generative AI, and customers who want to build these models themselves, who have the talent and the expertise and resources. For them, AWS has a number of different options and capabilities they can leverage, such as our custom silicon, such as Trainium and Inferentia, as well as distributed machine learning capabilities that we offer as part of SageMaker, which is an end-to-end machine learning development service. At the same time, many of our customers tell us that they're interested in not training and building these generative AI models from scratch, given they can be expensive and can require specialized talent and skills to build. And so for those customers, we are also making it super easy to bring in existing generative AI models into their machine learning development environment within SageMaker for them to use. So we recently announced our partnership with Hugging Face, where we are making it super easy for customers to bring in those models into their SageMaker development environment for fine tuning and deployment. And then we are also partnering with other proprietary model providers such as AI21 and others, where we making these generative AI models available within SageMaker for our customers to use. So our approach here is to really provide customers options and choices and help them accelerate their generative AI journey. >> Ankur, thank you for setting the table there. Clem and Ori, I want to get your take, because the riding the waves, the theme of this session, and to me being in California, I imagine the big surf, the big waves, the big talent out there. This is like alpha geeks, alpha coders, developers are really leaning into this. You're seeing massive uptake from the smartest people. Whether they're young or around, they're coming in with their kind of surfboards, (chuckles) if you will. These early adopters, they've been on this for a while; Now the waves are hitting. This is a big wave, everyone sees it. What are some of those early adopter devs doing? What are some of the use cases you're seeing right out of the gate? And what does this mean for the folks that are going to come in and get on this wave? Can you guys share your perspective on this? Because you're seeing the best talent now leaning into this. >> Yeah, absolutely. I mean, from Hugging Face vantage points, it's not even a a wave, it's a tidal wave, or maybe even the tide itself. Because actually what we are seeing is that AI and machine learning is not something that you add to your products. It's very much a new paradigm to do all technology. It's this idea that we had in the past 15, 20 years, one way to build software and to build technology, which was writing a million lines of code, very rule-based, and then you get your product. Now what we are seeing is that every single product, every single feature, every single company is starting to adopt AI to build the next generation of technology. And that works both to make the existing use cases better, if you think of search, if you think of social network, if you think of SaaS, but also it's creating completely new capabilities that weren't possible with the previous paradigm. Now AI can generate text, it can generate image, it can describe your image, it can do so many new things that weren't possible before. >> It's going to really make the developers really productive, right? I mean, you're seeing the developer uptake strong, right? >> Yes, we have over 15,000 companies using Hugging Face now, and it keeps accelerating. I really think that maybe in like three, five years, there's not going to be any company not using AI. It's going to be really kind of the default to build all technology. >> Ori, weigh in on this. APIs, the cloud. Now I'm a developer, I want to have live applications, I want the commercial applications on this. What's your take? Weigh in here. >> Yeah, first, I absolutely agree. I mean, we're in the midst of a technology shift here. I think not a lot of people realize how big this is going to be. Just the number of possibilities is endless, and I think hard to imagine. And I don't think it's just the use cases. I think we can think of it as two separate categories. We'll see companies and products enhancing their offerings with these new AI capabilities, but we'll also see new companies that are AI first, that kind of reimagine certain experiences. They build something that wasn't possible before. And that's why I think it's actually extremely exciting times. And maybe more philosophically, I think now these large language models and large transformer based models are helping us people to express our thoughts and kind of making the bridge from our thinking to a creative digital asset in a speed we've never imagined before. I can write something down and get a piece of text, or an image, or a code. So I'll start by saying it's hard to imagine all the possibilities right now, but it's certainly big. And if I had to bet, I would say it's probably at least as big as the mobile revolution we've seen in the last 20 years. >> Yeah, this is the biggest. I mean, it's been compared to the Enlightenment Age. I saw the Wall Street Journal had a recent story on this. We've been saying that this is probably going to be bigger than all inflection points combined in the tech industry, given what transformation is coming. I guess I want to ask you guys, on the early adopters, we've been hearing on these interviews and throughout the industry that there's already a set of big companies, a set of companies out there that have a lot of data and they're already there, they're kind of tinkering. Kind of reminds me of the old hyper scaler days where they were building their own scale, and they're eatin' glass, spittin' nails out, you know, they're hardcore. Then you got everybody else kind of saying board level, "Hey team, how do I leverage this?" How do you see those two things coming together? You got the fast followers coming in behind the early adopters. What's it like for the second wave coming in? What are those conversations for those developers like? >> I mean, I think for me, the important switch for companies is to change their mindset from being kind of like a traditional software company to being an AI or machine learning company. And that means investing, hiring machine learning engineers, machine learning scientists, infrastructure in members who are working on how to put these models in production, team members who are able to optimize models, specialized models, customized models for the company's specific use cases. So it's really changing this mindset of how you build technology and optimize your company building around that. Things are moving so fast that I think now it's kind of like too late for low hanging fruits or small, small adjustments. I think it's important to realize that if you want to be good at that, and if you really want to surf this wave, you need massive investments. If there are like some surfers listening with this analogy of the wave, right, when there are waves, it's not enough just to stand and make a little bit of adjustments. You need to position yourself aggressively, paddle like crazy, and that's how you get into the waves. So that's what companies, in my opinion, need to do right now. >> Ori, what's your take on the generative models out there? We hear a lot about foundation models. What's your experience running end-to-end applications for large foundation models? Any insights you can share with the app developers out there who are looking to get in? >> Yeah, I think first of all, it's start create an economy, where it probably doesn't make sense for every company to create their own foundation models. You can basically start by using an existing foundation model, either open source or a proprietary one, and start deploying it for your needs. And then comes the second round when you are starting the optimization process. You bootstrap, whether it's a demo, or a small feature, or introducing new capability within your product, and then start collecting data. That data, and particularly the human feedback data, helps you to constantly improve the model, so you create this data flywheel. And I think we're now entering an era where customers have a lot of different choice of how they want to start their generative AI endeavor. And it's a good thing that there's a variety of choices. And the really amazing thing here is that every industry, any company you speak with, it could be something very traditional like industrial or financial, medical, really any company. I think peoples now start to imagine what are the possibilities, and seriously think what's their strategy for adopting this generative AI technology. And I think in that sense, the foundation model actually enabled this to become scalable. So the barrier to entry became lower; Now the adoption could actually accelerate. >> There's a lot of integration aspects here in this new wave that's a little bit different. Before it was like very monolithic, hardcore, very brittle. A lot more integration, you see a lot more data coming together. I have to ask you guys, as developers come in and grow, I mean, when I went to college and you were a software engineer, I mean, I got a degree in computer science, and software engineering, that's all you did was code, (chuckles) you coded. Now, isn't it like everyone's a machine learning engineer at this point? Because that will be ultimately the science. So, (chuckles) you got open source, you got open software, you got the communities. Swami called you guys the GitHub of machine learning, Hugging Face is the GitHub of machine learning, mainly because that's where people are going to code. So this is essentially, machine learning is computer science. What's your reaction to that? >> Yes, my co-founder Julien at Hugging Face have been having this thing for quite a while now, for over three years, which was saying that actually software engineering as we know it today is a subset of machine learning, instead of the other way around. People would call us crazy a few years ago when we're seeing that. But now we are realizing that you can actually code with machine learning. So machine learning is generating code. And we are starting to see that every software engineer can leverage machine learning through open models, through APIs, through different technology stack. So yeah, it's not crazy anymore to think that maybe in a few years, there's going to be more people doing AI and machine learning. However you call it, right? Maybe you'll still call them software engineers, maybe you'll call them machine learning engineers. But there might be more of these people in a couple of years than there is software engineers today. >> I bring this up as more tongue in cheek as well, because Ankur, infrastructure's co is what made Cloud great, right? That's kind of the DevOps movement. But here the shift is so massive, there will be a game-changing philosophy around coding. Machine learning as code, you're starting to see CodeWhisperer, you guys have had coding companions for a while on AWS. So this is a paradigm shift. How is the cloud playing into this for you guys? Because to me, I've been riffing on some interviews where it's like, okay, you got the cloud going next level. This is an example of that, where there is a DevOps-like moment happening with machine learning, whether you call it coding or whatever. It's writing code on its own. Can you guys comment on what this means on top of the cloud? What comes out of the scale? What comes out of the benefit here? >> Absolutely, so- >> Well first- >> Oh, go ahead. >> Yeah, so I think as far as scale is concerned, I think customers are really relying on cloud to make sure that the applications that they build can scale along with the needs of their business. But there's another aspect to it, which is that until a few years ago, John, what we saw was that machine learning was a data scientist heavy activity. They were data scientists who were taking the data and training models. And then as machine learning found its way more and more into production and actual usage, we saw the MLOps become a thing, and MLOps engineers become more involved into the process. And then we now are seeing, as machine learning is being used to solve more business critical problems, we're seeing even legal and compliance teams get involved. We are seeing business stakeholders more engaged. So, more and more machine learning is becoming an activity that's not just performed by data scientists, but is performed by a team and a group of people with different skills. And for them, we as AWS are focused on providing the best tools and services for these different personas to be able to do their job and really complete that end-to-end machine learning story. So that's where, whether it's tools related to MLOps or even for folks who cannot code or don't know any machine learning. For example, we launched SageMaker Canvas as a tool last year, which is a UI-based tool which data analysts and business analysts can use to build machine learning models. So overall, the spectrum in terms of persona and who can get involved in the machine learning process is expanding, and the cloud is playing a big role in that process. >> Ori, Clem, can you guys weigh in too? 'Cause this is just another abstraction layer of scale. What's it mean for you guys as you look forward to your customers and the use cases that you're enabling? >> Yes, I think what's important is that the AI companies and providers and the cloud kind of work together. That's how you make a seamless experience and you actually reduce the barrier to entry for this technology. So that's what we've been super happy to do with AWS for the past few years. We actually announced not too long ago that we are doubling down on our partnership with AWS. We're excited to have many, many customers on our shared product, the Hugging Face deep learning container on SageMaker. And we are working really closely with the Inferentia team and the Trainium team to release some more exciting stuff in the coming weeks and coming months. So I think when you have an ecosystem and a system where the AWS and the AI providers, AI startups can work hand in hand, it's to the benefit of the customers and the companies, because it makes it orders of magnitude easier for them to adopt this new paradigm to build technology AI. >> Ori, this is a scale on reasoning too. The data's out there and making sense out of it, making it reason, getting comprehension, having it make decisions is next, isn't it? And you need scale for that. >> Yes. Just a comment about the infrastructure side. So I think really the purpose is to streamline and make these technologies much more accessible. And I think we'll see, I predict that we'll see in the next few years more and more tooling that make this technology much more simple to consume. And I think it plays a very important role. There's so many aspects, like the monitoring the models and their kind of outputs they produce, and kind of containing and running them in a production environment. There's so much there to build on, the infrastructure side will play a very significant role. >> All right, that's awesome stuff. I'd love to change gears a little bit and get a little philosophy here around AI and how it's going to transform, if you guys don't mind. There's been a lot of conversations around, on theCUBE here as well as in some industry areas, where it's like, okay, all the heavy lifting is automated away with machine learning and AI, the complexity, there's some efficiencies, it's horizontal and scalable across all industries. Ankur, good point there. Everyone's going to use it for something. And a lot of stuff gets brought to the table with large language models and other things. But the key ingredient will be proprietary data or human input, or some sort of AI whisperer kind of role, or prompt engineering, people are saying. So with that being said, some are saying it's automating intelligence. And that creativity will be unleashed from this. If the heavy lifting goes away and AI can fill the void, that shifts the value to the intellect or the input. And so that means data's got to come together, interact, fuse, and understand each other. This is kind of new. I mean, old school AI was, okay, got a big model, I provisioned it long time, very expensive. Now it's all free flowing. Can you guys comment on where you see this going with this freeform, data flowing everywhere, heavy lifting, and then specialization? >> Yeah, I think- >> Go ahead. >> Yeah, I think, so what we are seeing with these large language models or generative models is that they're really good at creating stuff. But I think it's also important to recognize their limitations. They're not as good at reasoning and logic. And I think now we're seeing great enthusiasm, I think, which is justified. And the next phase would be how to make these systems more reliable. How to inject more reasoning capabilities into these models, or augment with other mechanisms that actually perform more reasoning so we can achieve more reliable results. And we can count on these models to perform for critical tasks, whether it's medical tasks, legal tasks. We really want to kind of offload a lot of the intelligence to these systems. And then we'll have to get back, we'll have to make sure these are reliable, we'll have to make sure we get some sort of explainability that we can understand the process behind the generated results that we received. So I think this is kind of the next phase of systems that are based on these generated models. >> Clem, what's your view on this? Obviously you're at open community, open source has been around, it's been a great track record, proven model. I'm assuming creativity's going to come out of the woodwork, and if we can automate open source contribution, and relationships, and onboarding more developers, there's going to be unleashing of creativity. >> Yes, it's been so exciting on the open source front. We all know Bert, Bloom, GPT-J, T5, Stable Diffusion, that work up. The previous or the current generation of open source models that are on Hugging Face. It has been accelerating in the past few months. So I'm super excited about ControlNet right now that is really having a lot of impact, which is kind of like a way to control the generation of images. Super excited about Flan UL2, which is like a new model that has been recently released and is open source. So yeah, it's really fun to see the ecosystem coming together. Open source has been the basis for traditional software, with like open source programming languages, of course, but also all the great open source that we've gotten over the years. So we're happy to see that the same thing is happening for machine learning and AI, and hopefully can help a lot of companies reduce a little bit the barrier to entry. So yeah, it's going to be exciting to see how it evolves in the next few years in that respect. >> I think the developer productivity angle that's been talked about a lot in the industry will be accelerated significantly. I think security will be enhanced by this. I think in general, applications are going to transform at a radical rate, accelerated, incredible rate. So I think it's not a big wave, it's the water, right? I mean, (chuckles) it's the new thing. My final question for you guys, if you don't mind, I'd love to get each of you to answer the question I'm going to ask you, which is, a lot of conversations around data. Data infrastructure's obviously involved in this. And the common thread that I'm hearing is that every company that looks at this is asking themselves, if we don't rebuild our company, start thinking about rebuilding our business model around AI, we might be dinosaurs, we might be extinct. And it reminds me that scene in Moneyball when, at the end, it's like, if we're not building the model around your model, every company will be out of business. What's your advice to companies out there that are having those kind of moments where it's like, okay, this is real, this is next gen, this is happening. I better start thinking and putting into motion plans to refactor my business, 'cause it's happening, business transformation is happening on the cloud. This kind of puts an exclamation point on, with the AI, as a next step function. Big increase in value. So it's an opportunity for leaders. Ankur, we'll start with you. What's your advice for folks out there thinking about this? Do they put their toe in the water? Do they jump right into the deep end? What's your advice? >> Yeah, John, so we talk to a lot of customers, and customers are excited about what's happening in the space, but they often ask us like, "Hey, where do we start?" So we always advise our customers to do a lot of proof of concepts, understand where they can drive the biggest ROI. And then also leverage existing tools and services to move fast and scale, and try and not reinvent the wheel where it doesn't need to be. That's basically our advice to customers. >> Get it. Ori, what's your advice to folks who are scratching their head going, "I better jump in here. "How do I get started?" What's your advice? >> So I actually think that need to think about it really economically. Both on the opportunity side and the challenges. So there's a lot of opportunities for many companies to actually gain revenue upside by building these new generative features and capabilities. On the other hand, of course, this would probably affect the cogs, and incorporating these capabilities could probably affect the cogs. So I think we really need to think carefully about both of these sides, and also understand clearly if this is a project or an F word towards cost reduction, then the ROI is pretty clear, or revenue amplifier, where there's, again, a lot of different opportunities. So I think once you think about this in a structured way, I think, and map the different initiatives, then it's probably a good way to start and a good way to start thinking about these endeavors. >> Awesome. Clem, what's your take on this? What's your advice, folks out there? >> Yes, all of these are very good advice already. Something that you said before, John, that I disagreed a little bit, a lot of people are talking about the data mode and proprietary data. Actually, when you look at some of the organizations that have been building the best models, they don't have specialized or unique access to data. So I'm not sure that's so important today. I think what's important for companies, and it's been the same for the previous generation of technology, is their ability to build better technology faster than others. And in this new paradigm, that means being able to build machine learning faster than others, and better. So that's how, in my opinion, you should approach this. And kind of like how can you evolve your company, your teams, your products, so that you are able in the long run to build machine learning better and faster than your competitors. And if you manage to put yourself in that situation, then that's when you'll be able to differentiate yourself to really kind of be impactful and get results. That's really hard to do. It's something really different, because machine learning and AI is a different paradigm than traditional software. So this is going to be challenging, but I think if you manage to nail that, then the future is going to be very interesting for your company. >> That's a great point. Thanks for calling that out. I think this all reminds me of the cloud days early on. If you went to the cloud early, you took advantage of it when the pandemic hit. If you weren't native in the cloud, you got hamstrung by that, you were flatfooted. So just get in there. (laughs) Get in the cloud, get into AI, you're going to be good. Thanks for for calling that. Final parting comments, what's your most exciting thing going on right now for you guys? Ori, Clem, what's the most exciting thing on your plate right now that you'd like to share with folks? >> I mean, for me it's just the diversity of use cases and really creative ways of companies leveraging this technology. Every day I speak with about two, three customers, and I'm continuously being surprised by the creative ideas. And the future is really exciting of what can be achieved here. And also I'm amazed by the pace that things move in this industry. It's just, there's not at dull moment. So, definitely exciting times. >> Clem, what are you most excited about right now? >> For me, it's all the new open source models that have been released in the past few weeks, and that they'll keep being released in the next few weeks. I'm also super excited about more and more companies getting into this capability of chaining different models and different APIs. I think that's a very, very interesting development, because it creates new capabilities, new possibilities, new functionalities that weren't possible before. You can plug an API with an open source embedding model, with like a no-geo transcription model. So that's also very exciting. This capability of having more interoperable machine learning will also, I think, open a lot of interesting things in the future. >> Clem, congratulations on your success at Hugging Face. Please pass that on to your team. Ori, congratulations on your success, and continue to, just day one. I mean, it's just the beginning. It's not even scratching the service. Ankur, I'll give you the last word. What are you excited for at AWS? More cloud goodness coming here with AI. Give you the final word. >> Yeah, so as both Clem and Ori said, I think the research in the space is moving really, really fast, so we are excited about that. But we are also excited to see the speed at which enterprises and other AWS customers are applying machine learning to solve real business problems, and the kind of results they're seeing. So when they come back to us and tell us the kind of improvement in their business metrics and overall customer experience that they're driving and they're seeing real business results, that's what keeps us going and inspires us to continue inventing on their behalf. >> Gentlemen, thank you so much for this awesome high impact panel. Ankur, Clem, Ori, congratulations on all your success. We'll see you around. Thanks for coming on. Generative AI, riding the wave, it's a tidal wave, it's the water, it's all happening. All great stuff. This is season three, episode one of AWS Startup Showcase closing panel. This is the AI ML episode, the top startups building generative AI on AWS. I'm John Furrier, your host. Thanks for watching. (mellow music)

(upbeat music) >> Welcome everyone to theCUBE's presentation of the AWS Startup Showcase AI Machine Learning Top Startups Building Generative AI on AWS. This is season 3, episode 1 of the ongoing series covering the exciting startup from the AWS ecosystem to talk about AI and machine learning. I'm your host, John Furrier. I'm joined by two great guests here, Adam Wenchel, who's the CEO of Arthur, and Chief Scientist of Arthur, John Dickerson. Talk about how they help people build better LLM AI systems to get them into the market faster. Gentlemen, thank you for coming on. >> Yeah, thanks for having us, John. >> Well, I got to say I got to temper my enthusiasm because the last few months explosion of interest in LLMs with ChatGPT, has opened the eyes to everybody around the reality of that this is going next gen, this is it, this is the moment, this is the the point we're going to look back and say, this is the time where AI really hit the scene for real applications. So, a lot of Large Language Models, also known as LLMs, foundational models, and generative AI is all booming. This is where all the alpha developers are going. This is where everyone's focusing their business model transformations on. This is where developers are seeing action. So it's all happening, the wave is here. So I got to ask you guys, what are you guys seeing right now? You're in the middle of it, it's hitting you guys right on. You're in the front end of this massive wave. >> Yeah, John, I don't think you have to temper your enthusiasm at all. I mean, what we're seeing every single day is, everything from existing enterprise customers coming in with new ways that they're rethinking, like business things that they've been doing for many years that they can now do an entirely different way, as well as all manner of new companies popping up, applying LLMs to everything from generating code and SQL statements to generating health transcripts and just legal briefs. Everything you can imagine. And when you actually sit down and look at these systems and the demos we get of them, the hype is definitely justified. It's pretty amazing what they're going to do. And even just internally, we built, about a month ago in January, we built an Arthur chatbot so customers could ask questions, technical questions from our, rather than read our product documentation, they could just ask this LLM a particular question and get an answer. And at the time it was like state of the art, but then just last week we decided to rebuild it because the tooling has changed so much that we, last week, we've completely rebuilt it. It's now way better, built on an entirely different stack. And the tooling has undergone a full generation worth of change in six weeks, which is crazy. So it just tells you how much energy is going into this and how fast it's evolving right now. >> John, weigh in as a chief scientist. I mean, you must be blown away. Talk about kid in the candy store. I mean, you must be looking like this saying, I mean, she must be super busy to begin with, but the change, the acceleration, can you scope the kind of change you're seeing and be specific around the areas you're seeing movement and highly accelerated change? >> Yeah, definitely. And it is very, very exciting actually, thinking back to when ChatGPT was announced, that was a night our company was throwing an event at NeurIPS, which is maybe the biggest machine learning conference out there. And the hype when that happened was palatable and it was just shocking to see how well that performed. And then obviously over the last few months since then, as LLMs have continued to enter the market, we've seen use cases for them, like Adam mentioned all over the place. And so, some things I'm excited about in this space are the use of LLMs and more generally, foundation models to redesign traditional operations, research style problems, logistics problems, like auctions, decisioning problems. So moving beyond the already amazing news cases, like creating marketing content into more core integration and a lot of the bread and butter companies and tasks that drive the American ecosystem. And I think we're just starting to see some of that. And in the next 12 months, I think we're going to see a lot more. If I had to make other predictions, I think we're going to continue seeing a lot of work being done on managing like inference time costs via shrinking models or distillation. And I don't know how to make this prediction, but at some point we're going to be seeing lots of these very large scale models operating on the edge as well. So the time scales are extremely compressed, like Adam mentioned, 12 months from now, hard to say. >> We were talking on theCUBE prior to this session here. We had theCUBE conversation here and then the Wall Street Journal just picked up on the same theme, which is the printing press moment created the enlightenment stage of the history. Here we're in the whole nother automating intellect efficiency, doing heavy lifting, the creative class coming back, a whole nother level of reality around the corner that's being hyped up. The question is, is this justified? Is there really a breakthrough here or is this just another result of continued progress with AI? Can you guys weigh in, because there's two schools of thought. There's the, "Oh my God, we're entering a new enlightenment tech phase, of the equivalent of the printing press in all areas. Then there's, Ah, it's just AI (indistinct) inch by inch. What's your guys' opinion? >> Yeah, I think on the one hand when you're down in the weeds of building AI systems all day, every day, like we are, it's easy to look at this as an incremental progress. Like we have customers who've been building on foundation models since we started the company four years ago, particular in computer vision for classification tasks, starting with pre-trained models, things like that. So that part of it doesn't feel real new, but what does feel new is just when you apply these things to language with all the breakthroughs and computational efficiency, algorithmic improvements, things like that, when you actually sit down and interact with ChatGPT or one of the other systems that's out there that's building on top of LLMs, it really is breathtaking, like, the level of understanding that they have and how quickly you can accelerate your development efforts and get an actual working system in place that solves a really important real world problem and makes people way faster, way more efficient. So I do think there's definitely something there. It's more than just incremental improvement. This feels like a real trajectory inflection point for the adoption of AI. >> John, what's your take on this? As people come into the field, I'm seeing a lot of people move from, hey, I've been coding in Python, I've been doing some development, I've been a software engineer, I'm a computer science student. I'm coding in C++ old school, OG systems person. Where do they come in? Where's the focus, where's the action? Where are the breakthroughs? Where are people jumping in and rolling up their sleeves and getting dirty with this stuff? >> Yeah, all over the place. And it's funny you mentioned students in a different life. I wore a university professor hat and so I'm very, very familiar with the teaching aspects of this. And I will say toward Adam's point, this really is a leap forward in that techniques like in a co-pilot for example, everybody's using them right now and they really do accelerate the way that we develop. When I think about the areas where people are really, really focusing right now, tooling is certainly one of them. Like you and I were chatting about LangChain right before this interview started, two or three people can sit down and create an amazing set of pipes that connect different aspects of the LLM ecosystem. Two, I would say is in engineering. So like distributed training might be one, or just understanding better ways to even be able to train large models, understanding better ways to then distill them or run them. So like this heavy interaction now between engineering and what I might call traditional machine learning from 10 years ago where you had to know a lot of math, you had to know calculus very well, things like that. Now you also need to be, again, a very strong engineer, which is exciting. >> I interviewed Swami when he talked about the news. He's ahead of Amazon's machine learning and AI when they announced Hugging Face announcement. And I reminded him how Amazon was easy to get into if you were developing a startup back in 2007,8, and that the language models had that similar problem. It's step up a lot of content and a lot of expense to get provisioned up, now it's easy. So this is the next wave of innovation. So how do you guys see that from where we are right now? Are we at that point where it's that moment where it's that cloud-like experience for LLMs and large language models? >> Yeah, go ahead John. >> I think the answer is yes. We see a number of large companies that are training these and serving these, some of which are being co-interviewed in this episode. I think we're at that. Like, you can hit one of these with a simple, single line of Python, hitting an API, you can boot this up in seconds if you want. It's easy. >> Got it. >> So I (audio cuts out). >> Well let's take a step back and talk about the company. You guys being featured here on the Showcase. Arthur, what drove you to start the company? How'd this all come together? What's the origination story? Obviously you got a big customers, how'd get started? What are you guys doing? How do you make money? Give a quick overview. >> Yeah, I think John and I come at it from slightly different angles, but for myself, I have been a part of a number of technology companies. I joined Capital One, they acquired my last company and shortly after I joined, they asked me to start their AI team. And so even though I've been doing AI for a long time, I started my career back in DARPA. It was the first time I was really working at scale in AI at an organization where there were hundreds of millions of dollars in revenue at stake with the operation of these models and that they were impacting millions of people's financial livelihoods. And so it just got me hyper-focused on these issues around making sure that your AI worked well and it worked well for your company and it worked well for the people who were being affected by it. At the time when I was doing this 2016, 2017, 2018, there just wasn't any tooling out there to support this production management model monitoring life phase of the life cycle. And so we basically left to start the company that I wanted. And John has a his own story. I'll let let you share that one, John. >> Go ahead John, you're up. >> Yeah, so I'm coming at this from a different world. So I'm on leave now from a tenured role in academia where I was leading a large lab focusing on the intersection of machine learning and economics. And so questions like fairness or the response to the dynamism on the underlying environment have been around for quite a long time in that space. And so I've been thinking very deeply about some of those more like R and D style questions as well as having deployed some automation code across a couple of different industries, some in online advertising, some in the healthcare space and so on, where concerns of, again, fairness come to bear. And so Adam and I connected to understand the space of what that might look like in the 2018 20 19 realm from a quantitative and from a human-centered point of view. And so booted things up from there. >> Yeah, bring that applied engineering R and D into the Capital One, DNA that he had at scale. I could see that fit. I got to ask you now, next step, as you guys move out and think about LLMs and the recent AI news around the generative models and the foundational models like ChatGPT, how should we be looking at that news and everyone watching might be thinking the same thing. I know at the board level companies like, we should refactor our business, this is the future. It's that kind of moment, and the tech team's like, okay, boss, how do we do this again? Or are they prepared? How should we be thinking? How should people watching be thinking about LLMs? >> Yeah, I think they really are transformative. And so, I mean, we're seeing companies all over the place. Everything from large tech companies to a lot of our large enterprise customers are launching significant projects at core parts of their business. And so, yeah, I would be surprised, if you're serious about becoming an AI native company, which most leading companies are, then this is a trend that you need to be taking seriously. And we're seeing the adoption rate. It's funny, I would say the AI adoption in the broader business world really started, let's call it four or five years ago, and it was a relatively slow adoption rate, but I think all that kind of investment in and scaling the maturity curve has paid off because the rate at which people are adopting and deploying systems based on this is tremendous. I mean, this has all just happened in the few months and we're already seeing people get systems into production. So, now there's a lot of things you have to guarantee in order to put these in production in a way that basically is added into your business and doesn't cause more headaches than it solves. And so that's where we help customers is where how do you put these out there in a way that they're going to represent your company well, they're going to perform well, they're going to do their job and do it properly. >> So in the use case, as a customer, as I think about this, there's workflows. They might have had an ML AI ops team that's around IT. Their inference engines are out there. They probably don't have a visibility on say how much it costs, they're kicking the tires. When you look at the deployment, there's a cost piece, there's a workflow piece, there's fairness you mentioned John, what should be, I should be thinking about if I'm going to be deploying stuff into production, I got to think about those things. What's your opinion? >> Yeah, I'm happy to dive in on that one. So monitoring in general is extremely important once you have one of these LLMs in production, and there have been some changes versus traditional monitoring that we can dive deeper into that LLMs are really accelerated. But a lot of that bread and butter style of things you should be looking out for remain just as important as they are for what you might call traditional machine learning models. So the underlying environment of data streams, the way users interact with these models, these are all changing over time. And so any performance metrics that you care about, traditional ones like an accuracy, if you can define that for an LLM, ones around, for example, fairness or bias. If that is a concern for your particular use case and so on. Those need to be tracked. Now there are some interesting changes that LLMs are bringing along as well. So most ML models in production that we see are relatively static in the sense that they're not getting flipped in more than maybe once a day or once a week or they're just set once and then not changed ever again. With LLMs, there's this ongoing value alignment or collection of preferences from users that is often constantly updating the model. And so that opens up all sorts of vectors for, I won't say attack, but for problems to arise in production. Like users might learn to use your system in a different way and thus change the way those preferences are getting collected and thus change your system in ways that you never intended. So maybe that went through governance already internally at the company and now it's totally, totally changed and it's through no fault of your own, but you need to be watching over that for sure. >> Talk about the reinforced learnings from human feedback. How's that factoring in to the LLMs? Is that part of it? Should people be thinking about that? Is that a component that's important? >> It certainly is, yeah. So this is one of the big tweaks that happened with InstructGPT, which is the basis model behind ChatGPT and has since gone on to be used all over the place. So value alignment I think is through RLHF like you mentioned is a very interesting space to get into and it's one that you need to watch over. Like, you're asking humans for feedback over outputs from a model and then you're updating the model with respect to that human feedback. And now you've thrown humans into the loop here in a way that is just going to complicate things. And it certainly helps in many ways. You can ask humans to, let's say that you're deploying an internal chat bot at an enterprise, you could ask humans to align that LLM behind the chatbot to, say company values. And so you're listening feedback about these company values and that's going to scoot that chatbot that you're running internally more toward the kind of language that you'd like to use internally on like a Slack channel or something like that. Watching over that model I think in that specific case, that's a compliance and HR issue as well. So while it is part of the greater LLM stack, you can also view that as an independent bit to watch over. >> Got it, and these are important factors. When people see the Bing news, they freak out how it's doing great. Then it goes off the rails, it goes big, fails big. (laughing) So these models people see that, is that human interaction or is that feedback, is that not accepting it or how do people understand how to take that input in and how to build the right apps around LLMs? This is a tough question. >> Yeah, for sure. So some of the examples that you'll see online where these chatbots go off the rails are obviously humans trying to break the system, but some of them clearly aren't. And that's because these are large statistical models and we don't know what's going to pop out of them all the time. And even if you're doing as much in-house testing at the big companies like the Go-HERE's and the OpenAI's of the world, to try to prevent things like toxicity or racism or other sorts of bad content that might lead to bad pr, you're never going to catch all of these possible holes in the model itself. And so, again, it's very, very important to keep watching over that while it's in production. >> On the business model side, how are you guys doing? What's the approach? How do you guys engage with customers? Take a minute to explain the customer engagement. What do they need? What do you need? How's that work? >> Yeah, I can talk a little bit about that. So it's really easy to get started. It's literally a matter of like just handing out an API key and people can get started. And so we also offer alternative, we also offer versions that can be installed on-prem for models that, we find a lot of our customers have models that deal with very sensitive data. So you can run it in your cloud account or use our cloud version. And so yeah, it's pretty easy to get started with this stuff. We find people start using it a lot of times during the validation phase 'cause that way they can start baselining performance models, they can do champion challenger, they can really kind of baseline the performance of, maybe they're considering different foundation models. And so it's a really helpful tool for understanding differences in the way these models perform. And then from there they can just flow that into their production inferencing, so that as these systems are out there, you have really kind of real time monitoring for anomalies and for all sorts of weird behaviors as well as that continuous feedback loop that helps you make make your product get better and observability and you can run all sorts of aggregated reports to really understand what's going on with these models when they're out there deciding. I should also add that we just today have another way to adopt Arthur and that is we are in the AWS marketplace, and so we are available there just to make it that much easier to use your cloud credits, skip the procurement process, and get up and running really quickly. >> And that's great 'cause Amazon's got SageMaker, which handles a lot of privacy stuff, all kinds of cool things, or you can get down and dirty. So I got to ask on the next one, production is a big deal, getting stuff into production. What have you guys learned that you could share to folks watching? Is there a cost issue? I got to monitor, obviously you brought that up, we talked about the even reinforcement issues, all these things are happening. What is the big learnings that you could share for people that are going to put these into production to watch out for, to plan for, or be prepared for, hope for the best plan for the worst? What's your advice? >> I can give a couple opinions there and I'm sure Adam has. Well, yeah, the big one from my side is, again, I had mentioned this earlier, it's just the input data streams because humans are also exploring how they can use these systems to begin with. It's really, really hard to predict the type of inputs you're going to be seeing in production. Especially, we always talk about chatbots, but then any generative text tasks like this, let's say you're taking in news articles and summarizing them or something like that, it's very hard to get a good sampling even of the set of news articles in such a way that you can really predict what's going to pop out of that model. So to me, it's, adversarial maybe isn't the word that I would use, but it's an unnatural shifting input distribution of like prompts that you might see for these models. That's certainly one. And then the second one that I would talk about is, it can be hard to understand the costs, the inference time costs behind these LLMs. So the pricing on these is always changing as the models change size, it might go up, it might go down based on model size, based on energy cost and so on, but your pricing per token or per a thousand tokens and that I think can be difficult for some clients to wrap their head around. Again, you don't know how these systems are going to be used after all so it can be tough. And so again that's another metric that really should be tracked. >> Yeah, and there's a lot of trade off choices in there with like, how many tokens do you want at each step and in the sequence and based on, you have (indistinct) and you reject these tokens and so based on how your system's operating, that can make the cost highly variable. And that's if you're using like an API version that you're paying per token. A lot of people also choose to run these internally and as John mentioned, the inference time on these is significantly higher than a traditional classifi, even NLP classification model or tabular data model, like orders of magnitude higher. And so you really need to understand how that, as you're constantly iterating on these models and putting out new versions and new features in these models, how that's affecting the overall scale of that inference cost because you can use a lot of computing power very quickly with these profits. >> Yeah, scale, performance, price all come together. I got to ask while we're here on the secret sauce of the company, if you had to describe to people out there watching, what's the secret sauce of the company? What's the key to your success? >> Yeah, so John leads our research team and they've had a number of really cool, I think AI as much as it's been hyped for a while, it's still commercial AI at least is really in its infancy. And so the way we're able to pioneer new ways to think about performance for computer vision NLP LLMs is probably the thing that I'm proudest about. John and his team publish papers all the time at Navs and other places. But I think it's really being able to define what performance means for basically any kind of model type and give people really powerful tools to understand that on an ongoing basis. >> John, secret sauce, how would you describe it? You got all the action happening all around you. >> Yeah, well I going to appreciate Adam talking me up like that. No, I. (all laughing) >> Furrier: Robs to you. >> I would also say a couple of other things here. So we have a very strong engineering team and so I think some early hires there really set the standard at a very high bar that we've maintained as we've grown. And I think that's really paid dividends as scalabilities become even more of a challenge in these spaces, right? And so that's not just scalability when it comes to LLMs, that's scalability when it comes to millions of inferences per day, that kind of thing as well in traditional ML models. And I think that's compared to potential competitors, that's really... Well, it's made us able to just operate more efficiently and pass that along to the client. >> Yeah, and I think the infancy comment is really important because it's the beginning. You really is a long journey ahead. A lot of change coming, like I said, it's a huge wave. So I'm sure you guys got a lot of plannings at the foundation even for your own company, so I appreciate the candid response there. Final question for you guys is, what should the top things be for a company in 2023? If I'm going to set the agenda and I'm a customer moving forward, putting the pedal to the metal, so to speak, what are the top things I should be prioritizing or I need to do to be successful with AI in 2023? >> Yeah, I think, so number one, as we talked about, we've been talking about this entire episode, the things are changing so quickly and the opportunities for business transformation and really disrupting different applications, different use cases, is almost, I don't think we've even fully comprehended how big it is. And so really digging in to your business and understanding where I can apply these new sets of foundation models is, that's a top priority. The interesting thing is I think there's another force at play, which is the macroeconomic conditions and a lot of places are, they're having to work harder to justify budgets. So in the past, couple years ago maybe, they had a blank check to spend on AI and AI development at a lot of large enterprises that was limited primarily by the amount of talent they could scoop up. Nowadays these expenditures are getting scrutinized more. And so one of the things that we really help our customers with is like really calculating the ROI on these things. And so if you have models out there performing and you have a new version that you can put out that lifts the performance by 3%, how many tens of millions of dollars does that mean in business benefit? Or if I want to go to get approval from the CFO to spend a few million dollars on this new project, how can I bake in from the beginning the tools to really show the ROI along the way? Because I think in these systems when done well for a software project, the ROI can be like pretty spectacular. Like we see over a hundred percent ROI in the first year on some of these projects. And so, I think in 2023, you just need to be able to show what you're getting for that spend. >> It's a needle moving moment. You see it all the time with some of these aha moments or like, whoa, blown away. John, I want to get your thoughts on this because one of the things that comes up a lot for companies that I talked to, that are on my second wave, I would say coming in, maybe not, maybe the front wave of adopters is talent and team building. You mentioned some of the hires you got were game changing for you guys and set the bar high. As you move the needle, new developers going to need to come in. What's your advice given that you've been a professor, you've seen students, I know a lot of computer science people want to shift, they might not be yet skilled in AI, but they're proficient in programming, is that's going to be another opportunity with open source when things are happening. How do you talk to that next level of talent that wants to come in to this market to supplement teams and be on teams, lead teams? Any advice you have for people who want to build their teams and people who are out there and want to be a coder in AI? >> Yeah, I've advice, and this actually works for what it would take to be a successful AI company in 2023 as well, which is, just don't be afraid to iterate really quickly with these tools. The space is still being explored on what they can be used for. A lot of the tasks that they're used for now right? like creating marketing content using a machine learning is not a new thing to do. It just works really well now. And so I'm excited to see what the next year brings in terms of folks from outside of core computer science who are, other engineers or physicists or chemists or whatever who are learning how to use these increasingly easy to use tools to leverage LLMs for tasks that I think none of us have really thought about before. So that's really, really exciting. And so toward that I would say iterate quickly. Build things on your own, build demos, show them the friends, host them online and you'll learn along the way and you'll have somebody to show for it. And also you'll help us explore that space. >> Guys, congratulations with Arthur. Great company, great picks and shovels opportunities out there for everybody. Iterate fast, get in quickly and don't be afraid to iterate. Great advice and thank you for coming on and being part of the AWS showcase, thanks. >> Yeah, thanks for having us on John. Always a pleasure. >> Yeah, great stuff. Adam Wenchel, John Dickerson with Arthur. Thanks for coming on theCUBE. I'm John Furrier, your host. Generative AI and AWS. Keep it right there for more action with theCUBE. Thanks for watching. (upbeat music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> OpenAI The company, and ChatGPT have taken the world by storm. Microsoft reportedly is investing an additional 10 billion dollars into the company. But in our view, while the hype around ChatGPT is justified, we don't believe OpenAI will lock up the market with its first mover advantage. Rather, we believe that success in this market will be directly proportional to the quality and quantity of data that a technology company has at its disposal, and the compute power that it could deploy to run its system. Hello and welcome to this week's Wikibon CUBE insights, powered by ETR. In this Breaking Analysis, we unpack the excitement around ChatGPT, and debate the premise that the company's early entry into the space may not confer winner take all advantage to OpenAI. And to do so, we welcome CUBE collaborator, alum, Sarbjeet Johal, (chuckles) and John Furrier, co-host of the Cube. Great to see you Sarbjeet, John. Really appreciate you guys coming to the program. >> Great to be on. >> Okay, so what is ChatGPT? Well, actually we asked ChatGPT, what is ChatGPT? So here's what it said. ChatGPT is a state-of-the-art language model developed by OpenAI that can generate human-like text. It could be fine tuned for a variety of language tasks, such as conversation, summarization, and language translation. So I asked it, give it to me in 50 words or less. How did it do? Anything to add? >> Yeah, think it did good. It's large language model, like previous models, but it started applying the transformers sort of mechanism to focus on what prompt you have given it to itself. And then also the what answer it gave you in the first, sort of, one sentence or two sentences, and then introspect on itself, like what I have already said to you. And so just work on that. So it it's self sort of focus if you will. It does, the transformers help the large language models to do that. >> So to your point, it's a large language model, and GPT stands for generative pre-trained transformer. >> And if you put the definition back up there again, if you put it back up on the screen, let's see it back up. Okay, it actually missed the large, word large. So one of the problems with ChatGPT, it's not always accurate. It's actually a large language model, and it says state of the art language model. And if you look at Google, Google has dominated AI for many times and they're well known as being the best at this. And apparently Google has their own large language model, LLM, in play and have been holding it back to release because of backlash on the accuracy. Like just in that example you showed is a great point. They got almost right, but they missed the key word. >> You know what's funny about that John, is I had previously asked it in my prompt to give me it in less than a hundred words, and it was too long, I said I was too long for Breaking Analysis, and there it went into the fact that it's a large language model. So it largely, it gave me a really different answer the, for both times. So, but it's still pretty amazing for those of you who haven't played with it yet. And one of the best examples that I saw was Ben Charrington from This Week In ML AI podcast. And I stumbled on this thanks to Brian Gracely, who was listening to one of his Cloudcasts. Basically what Ben did is he took, he prompted ChatGPT to interview ChatGPT, and he simply gave the system the prompts, and then he ran the questions and answers into this avatar builder and sped it up 2X so it didn't sound like a machine. And voila, it was amazing. So John is ChatGPT going to take over as a cube host? >> Well, I was thinking, we get the questions in advance sometimes from PR people. We should actually just plug it in ChatGPT, add it to our notes, and saying, "Is this good enough for you? Let's ask the real question." So I think, you know, I think there's a lot of heavy lifting that gets done. I think the ChatGPT is a phenomenal revolution. I think it highlights the use case. Like that example we showed earlier. It gets most of it right. So it's directionally correct and it feels like it's an answer, but it's not a hundred percent accurate. And I think that's where people are seeing value in it. Writing marketing, copy, brainstorming, guest list, gift list for somebody. Write me some lyrics to a song. Give me a thesis about healthcare policy in the United States. It'll do a bang up job, and then you got to go in and you can massage it. So we're going to do three quarters of the work. That's why plagiarism and schools are kind of freaking out. And that's why Microsoft put 10 billion in, because why wouldn't this be a feature of Word, or the OS to help it do stuff on behalf of the user. So linguistically it's a beautiful thing. You can input a string and get a good answer. It's not a search result. >> And we're going to get your take on on Microsoft and, but it kind of levels the playing- but ChatGPT writes better than I do, Sarbjeet, and I know you have some good examples too. You mentioned the Reed Hastings example. >> Yeah, I was listening to Reed Hastings fireside chat with ChatGPT, and the answers were coming as sort of voice, in the voice format. And it was amazing what, he was having very sort of philosophy kind of talk with the ChatGPT, the longer sentences, like he was going on, like, just like we are talking, he was talking for like almost two minutes and then ChatGPT was answering. It was not one sentence question, and then a lot of answers from ChatGPT and yeah, you're right. I, this is our ability. I've been thinking deep about this since yesterday, we talked about, like, we want to do this segment. The data is fed into the data model. It can be the current data as well, but I think that, like, models like ChatGPT, other companies will have those too. They can, they're democratizing the intelligence, but they're not creating intelligence yet, definitely yet I can say that. They will give you all the finite answers. Like, okay, how do you do this for loop in Java, versus, you know, C sharp, and as a programmer you can do that, in, but they can't tell you that, how to write a new algorithm or write a new search algorithm for you. They cannot create a secretive code for you to- >> Not yet. >> Have competitive advantage. >> Not yet, not yet. >> but you- >> Can Google do that today? >> No one really can. The reasoning side of the data is, we talked about at our Supercloud event, with Zhamak Dehghani who's was CEO of, now of Nextdata. This next wave of data intelligence is going to come from entrepreneurs that are probably cross discipline, computer science and some other discipline. But they're going to be new things, for example, data, metadata, and data. It's hard to do reasoning like a human being, so that needs more data to train itself. So I think the first gen of this training module for the large language model they have is a corpus of text. Lot of that's why blog posts are, but the facts are wrong and sometimes out of context, because that contextual reasoning takes time, it takes intelligence. So machines need to become intelligent, and so therefore they need to be trained. So you're going to start to see, I think, a lot of acceleration on training the data sets. And again, it's only as good as the data you can get. And again, proprietary data sets will be a huge winner. Anyone who's got a large corpus of content, proprietary content like theCUBE or SiliconANGLE as a publisher will benefit from this. Large FinTech companies, anyone with large proprietary data will probably be a big winner on this generative AI wave, because it just, it will eat that up, and turn that back into something better. So I think there's going to be a lot of interesting things to look at here. And certainly productivity's going to be off the charts for vanilla and the internet is going to get swarmed with vanilla content. So if you're in the content business, and you're an original content producer of any kind, you're going to be not vanilla, so you're going to be better. So I think there's so much at play Dave (indistinct). >> I think the playing field has been risen, so we- >> Risen and leveled? >> Yeah, and leveled to certain extent. So it's now like that few people as consumers, as consumers of AI, we will have a advantage and others cannot have that advantage. So it will be democratized. That's, I'm sure about that. But if you take the example of calculator, when the calculator came in, and a lot of people are, "Oh, people can't do math anymore because calculator is there." right? So it's a similar sort of moment, just like a calculator for the next level. But, again- >> I see it more like open source, Sarbjeet, because like if you think about what ChatGPT's doing, you do a query and it comes from somewhere the value of a post from ChatGPT is just a reuse of AI. The original content accent will be come from a human. So if I lay out a paragraph from ChatGPT, did some heavy lifting on some facts, I check the facts, save me about maybe- >> Yeah, it's productive. >> An hour writing, and then I write a killer two, three sentences of, like, sharp original thinking or critical analysis. I then took that body of work, open source content, and then laid something on top of it. >> And Sarbjeet's example is a good one, because like if the calculator kids don't do math as well anymore, the slide rule, remember we had slide rules as kids, remember we first started using Waze, you know, we were this minority and you had an advantage over other drivers. Now Waze is like, you know, social traffic, you know, navigation, everybody had, you know- >> All the back roads are crowded. >> They're car crowded. (group laughs) Exactly. All right, let's, let's move on. What about this notion that futurist Ray Amara put forth and really Amara's Law that we're showing here, it's, the law is we, you know, "We tend to overestimate the effect of technology in the short run and underestimate it in the long run." Is that the case, do you think, with ChatGPT? What do you think Sarbjeet? >> I think that's true actually. There's a lot of, >> We don't debate this. >> There's a lot of awe, like when people see the results from ChatGPT, they say what, what the heck? Like, it can do this? But then if you use it more and more and more, and I ask the set of similar question, not the same question, and it gives you like same answer. It's like reading from the same bucket of text in, the interior read (indistinct) where the ChatGPT, you will see that in some couple of segments. It's very, it sounds so boring that the ChatGPT is coming out the same two sentences every time. So it is kind of good, but it's not as good as people think it is right now. But we will have, go through this, you know, hype sort of cycle and get realistic with it. And then in the long term, I think it's a great thing in the short term, it's not something which will (indistinct) >> What's your counter point? You're saying it's not. >> I, no I think the question was, it's hyped up in the short term and not it's underestimated long term. That's what I think what he said, quote. >> Yes, yeah. That's what he said. >> Okay, I think that's wrong with this, because this is a unique, ChatGPT is a unique kind of impact and it's very generational. People have been comparing it, I have been comparing to the internet, like the web, web browser Mosaic and Netscape, right, Navigator. I mean, I clearly still remember the days seeing Navigator for the first time, wow. And there weren't not many sites you could go to, everyone typed in, you know, cars.com, you know. >> That (indistinct) wasn't that overestimated, the overhyped at the beginning and underestimated. >> No, it was, it was underestimated long run, people thought. >> But that Amara's law. >> That's what is. >> No, they said overestimated? >> Overestimated near term underestimated- overhyped near term, underestimated long term. I got, right I mean? >> Well, I, yeah okay, so I would then agree, okay then- >> We were off the charts about the internet in the early days, and it actually exceeded our expectations. >> Well there were people who were, like, poo-pooing it early on. So when the browser came out, people were like, "Oh, the web's a toy for kids." I mean, in 1995 the web was a joke, right? So '96, you had online populations growing, so you had structural changes going on around the browser, internet population. And then that replaced other things, direct mail, other business activities that were once analog then went to the web, kind of read only as you, as we always talk about. So I think that's a moment where the hype long term, the smart money, and the smart industry experts all get the long term. And in this case, there's more poo-pooing in the short term. "Ah, it's not a big deal, it's just AI." I've heard many people poo-pooing ChatGPT, and a lot of smart people saying, "No this is next gen, this is different and it's only going to get better." So I think people are estimating a big long game on this one. >> So you're saying it's bifurcated. There's those who say- >> Yes. >> Okay, all right, let's get to the heart of the premise, and possibly the debate for today's episode. Will OpenAI's early entry into the market confer sustainable competitive advantage for the company. And if you look at the history of tech, the technology industry, it's kind of littered with first mover failures. Altair, IBM, Tandy, Commodore, they and Apple even, they were really early in the PC game. They took a backseat to Dell who came in the scene years later with a better business model. Netscape, you were just talking about, was all the rage in Silicon Valley, with the first browser, drove up all the housing prices out here. AltaVista was the first search engine to really, you know, index full text. >> Owned by Dell, I mean DEC. >> Owned by Digital. >> Yeah, Digital Equipment >> Compaq bought it. And of course as an aside, Digital, they wanted to showcase their hardware, right? Their super computer stuff. And then so Friendster and MySpace, they came before Facebook. The iPhone certainly wasn't the first mobile device. So lots of failed examples, but there are some recent successes like AWS and cloud. >> You could say smartphone. So I mean. >> Well I know, and you can, we can parse this so we'll debate it. Now Twitter, you could argue, had first mover advantage. You kind of gave me that one John. Bitcoin and crypto clearly had first mover advantage, and sustaining that. Guys, will OpenAI make it to the list on the right with ChatGPT, what do you think? >> I think categorically as a company, it probably won't, but as a category, I think what they're doing will, so OpenAI as a company, they get funding, there's power dynamics involved. Microsoft put a billion dollars in early on, then they just pony it up. Now they're reporting 10 billion more. So, like, if the browsers, Microsoft had competitive advantage over Netscape, and used monopoly power, and convicted by the Department of Justice for killing Netscape with their monopoly, Netscape should have had won that battle, but Microsoft killed it. In this case, Microsoft's not killing it, they're buying into it. So I think the embrace extend Microsoft power here makes OpenAI vulnerable for that one vendor solution. So the AI as a company might not make the list, but the category of what this is, large language model AI, is probably will be on the right hand side. >> Okay, we're going to come back to the government intervention and maybe do some comparisons, but what are your thoughts on this premise here? That, it will basically set- put forth the premise that it, that ChatGPT, its early entry into the market will not confer competitive advantage to >> For OpenAI. >> To Open- Yeah, do you agree with that? >> I agree with that actually. It, because Google has been at it, and they have been holding back, as John said because of the scrutiny from the Fed, right, so- >> And privacy too. >> And the privacy and the accuracy as well. But I think Sam Altman and the company on those guys, right? They have put this in a hasty way out there, you know, because it makes mistakes, and there are a lot of questions around the, sort of, where the content is coming from. You saw that as your example, it just stole the content, and without your permission, you know? >> Yeah. So as quick this aside- >> And it codes on people's behalf and the, those codes are wrong. So there's a lot of, sort of, false information it's putting out there. So it's a very vulnerable thing to do what Sam Altman- >> So even though it'll get better, others will compete. >> So look, just side note, a term which Reid Hoffman used a little bit. Like he said, it's experimental launch, like, you know, it's- >> It's pretty damn good. >> It is clever because according to Sam- >> It's more than clever. It's good. >> It's awesome, if you haven't used it. I mean you write- you read what it writes and you go, "This thing writes so well, it writes so much better than you." >> The human emotion drives that too. I think that's a big thing. But- >> I Want to add one more- >> Make your last point. >> Last one. Okay. So, but he's still holding back. He's conducting quite a few interviews. If you want to get the gist of it, there's an interview with StrictlyVC interview from yesterday with Sam Altman. Listen to that one it's an eye opening what they want- where they want to take it. But my last one I want to make it on this point is that Satya Nadella yesterday did an interview with Wall Street Journal. I think he was doing- >> You were not impressed. >> I was not impressed because he was pushing it too much. So Sam Altman's holding back so there's less backlash. >> Got 10 billion reasons to push. >> I think he's almost- >> Microsoft just laid off 10000 people. Hey ChatGPT, find me a job. You know like. (group laughs) >> He's overselling it to an extent that I think it will backfire on Microsoft. And he's over promising a lot of stuff right now, I think. I don't know why he's very jittery about all these things. And he did the same thing during Ignite as well. So he said, "Oh, this AI will write code for you and this and that." Like you called him out- >> The hyperbole- >> During your- >> from Satya Nadella, he's got a lot of hyperbole. (group talks over each other) >> All right, Let's, go ahead. >> Well, can I weigh in on the whole- >> Yeah, sure. >> Microsoft thing on whether OpenAI, here's the take on this. I think it's more like the browser moment to me, because I could relate to that experience with ChatG, personally, emotionally, when I saw that, and I remember vividly- >> You mean that aha moment (indistinct). >> Like this is obviously the future. Anything else in the old world is dead, website's going to be everywhere. It was just instant dot connection for me. And a lot of other smart people who saw this. Lot of people by the way, didn't see it. Someone said the web's a toy. At the company I was worked for at the time, Hewlett Packard, they like, they could have been in, they had invented HTML, and so like all this stuff was, like, they just passed, the web was just being passed over. But at that time, the browser got better, more websites came on board. So the structural advantage there was online web usage was growing, online user population. So that was growing exponentially with the rise of the Netscape browser. So OpenAI could stay on the right side of your list as durable, if they leverage the category that they're creating, can get the scale. And if they can get the scale, just like Twitter, that failed so many times that they still hung around. So it was a product that was always successful, right? So I mean, it should have- >> You're right, it was terrible, we kept coming back. >> The fail whale, but it still grew. So OpenAI has that moment. They could do it if Microsoft doesn't meddle too much with too much power as a vendor. They could be the Netscape Navigator, without the anti-competitive behavior of somebody else. So to me, they have the pole position. So they have an opportunity. So if not, if they don't execute, then there's opportunity. There's not a lot of barriers to entry, vis-a-vis say the CapEx of say a cloud company like AWS. You can't replicate that, Many have tried, but I think you can replicate OpenAI. >> And we're going to talk about that. Okay, so real quick, I want to bring in some ETR data. This isn't an ETR heavy segment, only because this so new, you know, they haven't coverage yet, but they do cover AI. So basically what we're seeing here is a slide on the vertical axis's net score, which is a measure of spending momentum, and in the horizontal axis's is presence in the dataset. Think of it as, like, market presence. And in the insert right there, you can see how the dots are plotted, the two columns. And so, but the key point here that we want to make, there's a bunch of companies on the left, is he like, you know, DataRobot and C3 AI and some others, but the big whales, Google, AWS, Microsoft, are really dominant in this market. So that's really the key takeaway that, can we- >> I notice IBM is way low. >> Yeah, IBM's low, and actually bring that back up and you, but then you see Oracle who actually is injecting. So I guess that's the other point is, you're not necessarily going to go buy AI, and you know, build your own AI, you're going to, it's going to be there and, it, Salesforce is going to embed it into its platform, the SaaS companies, and you're going to purchase AI. You're not necessarily going to build it. But some companies obviously are. >> I mean to quote IBM's general manager Rob Thomas, "You can't have AI with IA." information architecture and David Flynn- >> You can't Have AI without IA >> without, you can't have AI without IA. You can't have, if you have an Information Architecture, you then can power AI. Yesterday David Flynn, with Hammersmith, was on our Supercloud. He was pointing out that the relationship of storage, where you store things, also impacts the data and stressablity, and Zhamak from Nextdata, she was pointing out that same thing. So the data problem factors into all this too, Dave. >> So you got the big cloud and internet giants, they're all poised to go after this opportunity. Microsoft is investing up to 10 billion. Google's code red, which was, you know, the headline in the New York Times. Of course Apple is there and several alternatives in the market today. Guys like Chinchilla, Bloom, and there's a company Jasper and several others, and then Lena Khan looms large and the government's around the world, EU, US, China, all taking notice before the market really is coalesced around a single player. You know, John, you mentioned Netscape, they kind of really, the US government was way late to that game. It was kind of game over. And Netscape, I remember Barksdale was like, "Eh, we're going to be selling software in the enterprise anyway." and then, pshew, the company just dissipated. So, but it looks like the US government, especially with Lena Khan, they're changing the definition of antitrust and what the cause is to go after people, and they're really much more aggressive. It's only what, two years ago that (indistinct). >> Yeah, the problem I have with the federal oversight is this, they're always like late to the game, and they're slow to catch up. So in other words, they're working on stuff that should have been solved a year and a half, two years ago around some of the social networks hiding behind some of the rules around open web back in the days, and I think- >> But they're like 15 years late to that. >> Yeah, and now they got this new thing on top of it. So like, I just worry about them getting their fingers. >> But there's only two years, you know, OpenAI. >> No, but the thing (indistinct). >> No, they're still fighting other battles. But the problem with government is that they're going to label Big Tech as like a evil thing like Pharma, it's like smoke- >> You know Lena Khan wants to kill Big Tech, there's no question. >> So I think Big Tech is getting a very seriously bad rap. And I think anything that the government does that shades darkness on tech, is politically motivated in most cases. You can almost look at everything, and my 80 20 rule is in play here. 80% of the government activity around tech is bullshit, it's politically motivated, and the 20% is probably relevant, but off the mark and not organized. >> Well market forces have always been the determining factor of success. The governments, you know, have been pretty much failed. I mean you look at IBM's antitrust, that, what did that do? The market ultimately beat them. You look at Microsoft back in the day, right? Windows 95 was peaking, the government came in. But you know, like you said, they missed the web, right, and >> so they were hanging on- >> There's nobody in government >> to Windows. >> that actually knows- >> And so, you, I think you're right. It's market forces that are going to determine this. But Sarbjeet, what do you make of Microsoft's big bet here, you weren't impressed with with Nadella. How do you think, where are they going to apply it? Is this going to be a Hail Mary for Bing, or is it going to be applied elsewhere? What do you think. >> They are saying that they will, sort of, weave this into their products, office products, productivity and also to write code as well, developer productivity as well. That's a big play for them. But coming back to your antitrust sort of comments, right? I believe the, your comment was like, oh, fed was late 10 years or 15 years earlier, but now they're two years. But things are moving very fast now as compared to they used to move. >> So two years is like 10 Years. >> Yeah, two years is like 10 years. Just want to make that point. (Dave laughs) This thing is going like wildfire. Any new tech which comes in that I think they're going against distribution channels. Lina Khan has commented time and again that the marketplace model is that she wants to have some grip on. Cloud marketplaces are a kind of monopolistic kind of way. >> I don't, I don't see this, I don't see a Chat AI. >> You told me it's not Bing, you had an interesting comment. >> No, no. First of all, this is great from Microsoft. If you're Microsoft- >> Why? >> Because Microsoft doesn't have the AI chops that Google has, right? Google is got so much core competency on how they run their search, how they run their backends, their cloud, even though they don't get a lot of cloud market share in the enterprise, they got a kick ass cloud cause they needed one. >> Totally. >> They've invented SRE. I mean Google's development and engineering chops are off the scales, right? Amazon's got some good chops, but Google's got like 10 times more chops than AWS in my opinion. Cloud's a whole different story. Microsoft gets AI, they get a playbook, they get a product they can render into, the not only Bing, productivity software, helping people write papers, PowerPoint, also don't forget the cloud AI can super help. We had this conversation on our Supercloud event, where AI's going to do a lot of the heavy lifting around understanding observability and managing service meshes, to managing microservices, to turning on and off applications, and or maybe writing code in real time. So there's a plethora of use cases for Microsoft to deploy this. combined with their R and D budgets, they can then turbocharge more research, build on it. So I think this gives them a car in the game, Google may have pole position with AI, but this puts Microsoft right in the game, and they already have a lot of stuff going on. But this just, I mean everything gets lifted up. Security, cloud, productivity suite, everything. >> What's under the hood at Google, and why aren't they talking about it? I mean they got to be freaked out about this. No? Or do they have kind of a magic bullet? >> I think they have the, they have the chops definitely. Magic bullet, I don't know where they are, as compared to the ChatGPT 3 or 4 models. Like they, but if you look at the online sort of activity and the videos put out there from Google folks, Google technology folks, that's account you should look at if you are looking there, they have put all these distinctions what ChatGPT 3 has used, they have been talking about for a while as well. So it's not like it's a secret thing that you cannot replicate. As you said earlier, like in the beginning of this segment, that anybody who has more data and the capacity to process that data, which Google has both, I think they will win this. >> Obviously living in Palo Alto where the Google founders are, and Google's headquarters next town over we have- >> We're so close to them. We have inside information on some of the thinking and that hasn't been reported by any outlet yet. And that is, is that, from what I'm hearing from my sources, is Google has it, they don't want to release it for many reasons. One is it might screw up their search monopoly, one, two, they're worried about the accuracy, 'cause Google will get sued. 'Cause a lot of people are jamming on this ChatGPT as, "Oh it does everything for me." when it's clearly not a hundred percent accurate all the time. >> So Lina Kahn is looming, and so Google's like be careful. >> Yeah so Google's just like, this is the third, could be a third rail. >> But the first thing you said is a concern. >> Well no. >> The disruptive (indistinct) >> What they will do is do a Waymo kind of thing, where they spin out a separate company. >> They're doing that. >> The discussions happening, they're going to spin out the separate company and put it over there, and saying, "This is AI, got search over there, don't touch that search, 'cause that's where all the revenue is." (chuckles) >> So, okay, so that's how they deal with the Clay Christensen dilemma. What's the business model here? I mean it's not advertising, right? Is it to charge you for a query? What, how do you make money at this? >> It's a good question, I mean my thinking is, first of all, it's cool to type stuff in and see a paper get written, or write a blog post, or gimme a marketing slogan for this or that or write some code. I think the API side of the business will be critical. And I think Howie Xu, I know you're going to reference some of his comments yesterday on Supercloud, I think this brings a whole 'nother user interface into technology consumption. I think the business model, not yet clear, but it will probably be some sort of either API and developer environment or just a straight up free consumer product, with some sort of freemium backend thing for business. >> And he was saying too, it's natural language is the way in which you're going to interact with these systems. >> I think it's APIs, it's APIs, APIs, APIs, because these people who are cooking up these models, and it takes a lot of compute power to train these and to, for inference as well. Somebody did the analysis on the how many cents a Google search costs to Google, and how many cents the ChatGPT query costs. It's, you know, 100x or something on that. You can take a look at that. >> A 100x on which side? >> You're saying two orders of magnitude more expensive for ChatGPT >> Much more, yeah. >> Than for Google. >> It's very expensive. >> So Google's got the data, they got the infrastructure and they got, you're saying they got the cost (indistinct) >> No actually it's a simple query as well, but they are trying to put together the answers, and they're going through a lot more data versus index data already, you know. >> Let me clarify, you're saying that Google's version of ChatGPT is more efficient? >> No, I'm, I'm saying Google search results. >> Ah, search results. >> What are used to today, but cheaper. >> But that, does that, is that going to confer advantage to Google's large language (indistinct)? >> It will, because there were deep science (indistinct). >> Google, I don't think Google search is doing a large language model on their search, it's keyword search. You know, what's the weather in Santa Cruz? Or how, what's the weather going to be? Or you know, how do I find this? Now they have done a smart job of doing some things with those queries, auto complete, re direct navigation. But it's, it's not entity. It's not like, "Hey, what's Dave Vellante thinking this week in Breaking Analysis?" ChatGPT might get that, because it'll get your Breaking Analysis, it'll synthesize it. There'll be some, maybe some clips. It'll be like, you know, I mean. >> Well I got to tell you, I asked ChatGPT to, like, I said, I'm going to enter a transcript of a discussion I had with Nir Zuk, the CTO of Palo Alto Networks, And I want you to write a 750 word blog. I never input the transcript. It wrote a 750 word blog. It attributed quotes to him, and it just pulled a bunch of stuff that, and said, okay, here it is. It talked about Supercloud, it defined Supercloud. >> It's made, it makes you- >> Wow, But it was a big lie. It was fraudulent, but still, blew me away. >> Again, vanilla content and non accurate content. So we are going to see a surge of misinformation on steroids, but I call it the vanilla content. Wow, that's just so boring, (indistinct). >> There's so many dangers. >> Make your point, cause we got to, almost out of time. >> Okay, so the consumption, like how do you consume this thing. As humans, we are consuming it and we are, like, getting a nicely, like, surprisingly shocked, you know, wow, that's cool. It's going to increase productivity and all that stuff, right? And on the danger side as well, the bad actors can take hold of it and create fake content and we have the fake sort of intelligence, if you go out there. So that's one thing. The second thing is, we are as humans are consuming this as language. Like we read that, we listen to it, whatever format we consume that is, but the ultimate usage of that will be when the machines can take that output from likes of ChatGPT, and do actions based on that. The robots can work, the robot can paint your house, we were talking about, right? Right now we can't do that. >> Data apps. >> So the data has to be ingested by the machines. It has to be digestible by the machines. And the machines cannot digest unorganized data right now, we will get better on the ingestion side as well. So we are getting better. >> Data, reasoning, insights, and action. >> I like that mall, paint my house. >> So, okay- >> By the way, that means drones that'll come in. Spray painting your house. >> Hey, it wasn't too long ago that robots couldn't climb stairs, as I like to point out. Okay, and of course it's no surprise the venture capitalists are lining up to eat at the trough, as I'd like to say. Let's hear, you'd referenced this earlier, John, let's hear what AI expert Howie Xu said at the Supercloud event, about what it takes to clone ChatGPT. Please, play the clip. >> So one of the VCs actually asked me the other day, right? "Hey, how much money do I need to spend, invest to get a, you know, another shot to the openAI sort of the level." You know, I did a (indistinct) >> Line up. >> A hundred million dollar is the order of magnitude that I came up with, right? You know, not a billion, not 10 million, right? So a hundred- >> Guys a hundred million dollars, that's an astoundingly low figure. What do you make of it? >> I was in an interview with, I was interviewing, I think he said hundred million or so, but in the hundreds of millions, not a billion right? >> You were trying to get him up, you were like "Hundreds of millions." >> Well I think, I- >> He's like, eh, not 10, not a billion. >> Well first of all, Howie Xu's an expert machine learning. He's at Zscaler, he's a machine learning AI guy. But he comes from VMware, he's got his technology pedigrees really off the chart. Great friend of theCUBE and kind of like a CUBE analyst for us. And he's smart. He's right. I think the barriers to entry from a dollar standpoint are lower than say the CapEx required to compete with AWS. Clearly, the CapEx spending to build all the tech for the run a cloud. >> And you don't need a huge sales force. >> And in some case apps too, it's the same thing. But I think it's not that hard. >> But am I right about that? You don't need a huge sales force either. It's, what, you know >> If the product's good, it will sell, this is a new era. The better mouse trap will win. This is the new economics in software, right? So- >> Because you look at the amount of money Lacework, and Snyk, Snowflake, Databrooks. Look at the amount of money they've raised. I mean it's like a billion dollars before they get to IPO or more. 'Cause they need promotion, they need go to market. You don't need (indistinct) >> OpenAI's been working on this for multiple five years plus it's, hasn't, wasn't born yesterday. Took a lot of years to get going. And Sam is depositioning all the success, because he's trying to manage expectations, To your point Sarbjeet, earlier. It's like, yeah, he's trying to "Whoa, whoa, settle down everybody, (Dave laughs) it's not that great." because he doesn't want to fall into that, you know, hero and then get taken down, so. >> It may take a 100 million or 150 or 200 million to train the model. But to, for the inference to, yeah to for the inference machine, It will take a lot more, I believe. >> Give it, so imagine, >> Because- >> Go ahead, sorry. >> Go ahead. But because it consumes a lot more compute cycles and it's certain level of storage and everything, right, which they already have. So I think to compute is different. To frame the model is a different cost. But to run the business is different, because I think 100 million can go into just fighting the Fed. >> Well there's a flywheel too. >> Oh that's (indistinct) >> (indistinct) >> We are running the business, right? >> It's an interesting number, but it's also kind of, like, context to it. So here, a hundred million spend it, you get there, but you got to factor in the fact that the ways companies win these days is critical mass scale, hitting a flywheel. If they can keep that flywheel of the value that they got going on and get better, you can almost imagine a marketplace where, hey, we have proprietary data, we're SiliconANGLE in theCUBE. We have proprietary content, CUBE videos, transcripts. Well wouldn't it be great if someone in a marketplace could sell a module for us, right? We buy that, Amazon's thing and things like that. So if they can get a marketplace going where you can apply to data sets that may be proprietary, you can start to see this become bigger. And so I think the key barriers to entry is going to be success. I'll give you an example, Reddit. Reddit is successful and it's hard to copy, not because of the software. >> They built the moat. >> Because you can, buy Reddit open source software and try To compete. >> They built the moat with their community. >> Their community, their scale, their user expectation. Twitter, we referenced earlier, that thing should have gone under the first two years, but there was such a great emotional product. People would tolerate the fail whale. And then, you know, well that was a whole 'nother thing. >> Then a plane landed in (John laughs) the Hudson and it was over. >> I think verticals, a lot of verticals will build applications using these models like for lawyers, for doctors, for scientists, for content creators, for- >> So you'll have many hundreds of millions of dollars investments that are going to be seeping out. If, all right, we got to wrap, if you had to put odds on it that that OpenAI is going to be the leader, maybe not a winner take all leader, but like you look at like Amazon and cloud, they're not winner take all, these aren't necessarily winner take all markets. It's not necessarily a zero sum game, but let's call it winner take most. What odds would you give that open AI 10 years from now will be in that position. >> If I'm 0 to 10 kind of thing? >> Yeah, it's like horse race, 3 to 1, 2 to 1, even money, 10 to 1, 50 to 1. >> Maybe 2 to 1, >> 2 to 1, that's pretty low odds. That's basically saying they're the favorite, they're the front runner. Would you agree with that? >> I'd say 4 to 1. >> Yeah, I was going to say I'm like a 5 to 1, 7 to 1 type of person, 'cause I'm a skeptic with, you know, there's so much competition, but- >> I think they're definitely the leader. I mean you got to say, I mean. >> Oh there's no question. There's no question about it. >> The question is can they execute? >> They're not Friendster, is what you're saying. >> They're not Friendster and they're more like Twitter and Reddit where they have momentum. If they can execute on the product side, and if they don't stumble on that, they will continue to have the lead. >> If they say stay neutral, as Sam is, has been saying, that, hey, Microsoft is one of our partners, if you look at their company model, how they have structured the company, then they're going to pay back to the investors, like Microsoft is the biggest one, up to certain, like by certain number of years, they're going to pay back from all the money they make, and after that, they're going to give the money back to the public, to the, I don't know who they give it to, like non-profit or something. (indistinct) >> Okay, the odds are dropping. (group talks over each other) That's a good point though >> Actually they might have done that to fend off the criticism of this. But it's really interesting to see the model they have adopted. >> The wildcard in all this, My last word on this is that, if there's a developer shift in how developers and data can come together again, we have conferences around the future of data, Supercloud and meshs versus, you know, how the data world, coding with data, how that evolves will also dictate, 'cause a wild card could be a shift in the landscape around how developers are using either machine learning or AI like techniques to code into their apps, so. >> That's fantastic insight. I can't thank you enough for your time, on the heels of Supercloud 2, really appreciate it. All right, thanks to John and Sarbjeet for the outstanding conversation today. Special thanks to the Palo Alto studio team. My goodness, Anderson, this great backdrop. You guys got it all out here, I'm jealous. And Noah, really appreciate it, Chuck, Andrew Frick and Cameron, Andrew Frick switching, Cameron on the video lake, great job. And Alex Myerson, he's on production, manages the podcast for us, Ken Schiffman as well. Kristen Martin and Cheryl Knight help get the word out on social media and our newsletters. Rob Hof is our editor-in-chief over at SiliconANGLE, does some great editing, thanks to all. Remember, all these episodes are available as podcasts. All you got to do is search Breaking Analysis podcast, wherever you listen. Publish each week on wikibon.com and siliconangle.com. Want to get in touch, email me directly, david.vellante@siliconangle.com or DM me at dvellante, or comment on our LinkedIn post. And by all means, check out etr.ai. They got really great survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, We'll see you next time on Breaking Analysis. (electronic music)

>> From the CUBE Studios in Palo Alto in Boston, bringing you data driven insights from the Cube and ETR. This is Breaking Analysis with Dave Vellante. >> A better than expected earnings report in late August got people excited about Snowflake again, but the negative sentiment in the market is weighed heavily on virtually all growth tech stocks and Snowflake is no exception. As we've stressed many times the company's management is on a long term mission to dramatically simplify the way organizations use data. Snowflake is tapping into a multi hundred billion dollar total available market and continues to grow at a rapid pace. In our view, Snowflake is embarking on its third major wave of innovation data apps, while its first and second waves are still bearing significant fruit. Now for short term traders focused on the next 90 or 180 days, that probably doesn't matter. But those taking a longer view are asking, "Should we still be optimistic about the future of this high flyer or is it just another over hyped tech play?" Hello and welcome to this week's Wiki Bond Cube Insights powered by ETR. Snowflake's Quarter just ended. And in this breaking analysis we take a look at the most recent survey data from ETR to see what clues and nuggets we can extract to predict the near term future in the long term outlook for Snowflake which is going to announce its earnings at the end of this month. Okay, so you know the story. If you've been investor in Snowflake this year, it's been painful. We said at IPO, "If you really want to own this stock on day one, just hold your nose and buy it." But like most IPOs we said there will be likely a better entry point in the future, and not surprisingly that's been the case. Snowflake IPOed a price of 120, which you couldn't touch on day one unless you got into a friends and family Delio. And if you did, you're still up 5% or so. So congratulations. But at one point last year you were up well over 200%. That's been the nature of this volatile stock, and I certainly can't help you with the timing of the market. But longer term Snowflake is targeting 10 billion in revenue for fiscal year 2028. A big number. Is it achievable? Is it big enough? Tell you what, let's come back to that. Now shorter term, our expert trader and breaking analysis contributor Chip Simonton said he got out of the stock a while ago after having taken a shot at what turned out to be a bear market rally. He pointed out that the stock had been bouncing around the 150 level for the last few months and broke that to the downside last Friday. So he'd expect 150 is where the stock is going to find resistance on the way back up, but there's no sign of support right now. He said maybe at 120, which was the July low and of course the IPO price that we just talked about. Now, perhaps earnings will be a catalyst, when Snowflake announces on November 30th, but until the mentality toward growth tech changes, nothing's likely to change dramatically according to Simonton. So now that we have that out of the way, let's take a look at the spending data for Snowflake in the ETR survey. Here's a chart that shows the time series breakdown of snowflake's net score going back to the October, 2021 survey. Now at that time, Snowflake's net score stood at a robust 77%. And remember, net score is a measure of spending velocity. It's a proprietary network, and ETR derives it from a quarterly survey of IT buyers and asks the respondents, "Are you adopting the platform new? Are you spending 6% or more? Is you're spending flat? Is you're spending down 6% or worse? Or are you leaving the platform decommissioning?" You subtract the percent of customers that are spending less or churning from those that are spending more and adopting or adopting and you get a net score. And that's expressed as a percentage of customers responding. In this chart we show Snowflake's in out of the total survey which ranges... The total survey ranges between 1,200 and 1,400 each quarter. And the very last column... Oh sorry, very last row, we show the number of Snowflake respondents that are coming in the survey from the Fortune 500 and the Global 2000. Those are two very important Snowflake constituencies. Now what this data tells us is that Snowflake exited 2021 with very strong momentum in a net score of 82%, which is off the charts and it was actually accelerating from the previous survey. Now by April that sentiment had flipped and Snowflake came down to earth with a 68% net score. Still highly elevated relative to its peers, but meaningfully down. Why was that? Because we saw a drop in new ads and an increase in flat spend. Then into the July and most recent October surveys, you saw a significant drop in the percentage of customers that were spending more. Now, notably, the percentage of customers who are contemplating adding the platform is actually staying pretty strong, but it is off a bit this past survey. And combined with a slight uptick in planned churn, net score is now down to 60%. That uptick from 0% and 1% and then 3%, it's still small, but that net score at 60% is still 20 percentage points higher than our highly elevated benchmark of 40% as you recall from listening to earlier breaking analysis. That 40% range is we consider a milestone. Anything above that is actually quite strong. But again, Snowflake is down and coming back to churn, while 3% churn is very low, in previous quarters we've seen Snowflake 0% or 1% decommissions. Now the last thing to note in this chart is the meaningful uptick in survey respondents that are citing, they're using the Snowflake platform. That's up to 212 in the survey. So look, it's hard to imagine that Snowflake doesn't feel the softening in the market like everyone else. Snowflake is guiding for around 60% growth in product revenue against the tough compare from a year ago with a 2% operating margin. So like every company, the reaction of the street is going to come down to how accurate or conservative the guide is from their CFO. Now, earlier this year, Snowflake acquired a company called Streamlit for around $800 million. Streamlit is an open source Python library and it makes it easier to build data apps with machine learning, obviously a huge trend. And like Snowflake, generally its focus is on simplifying the complex, in this case making data science easier to integrate into data apps that business people can use. So we were excited this summer in the July ETR survey to see that they added some nice data and pick on Streamlit, which we're showing here in comparison to Snowflake's core business on the left hand side. That's the data warehousing, the Streamlit pieces on the right hand side. And we show again net score over time from the previous survey for Snowflake's core database and data warehouse offering again on the left as compared to a Streamlit on the right. Snowflake's core product had 194 responses in the October, 22 survey, Streamlit had an end of 73, which is up from 52 in the July survey. So significant uptick of people responding that they're doing business in adopting Streamlit. That was pretty impressive to us. And it's hard to see, but the net scores stayed pretty constant for Streamlit at 51%. It was 52% I think in the previous quarter, well over that magic 40% mark. But when you blend it with Snowflake, it does sort of bring things down a little bit. Now there are two key points here. One is that the acquisition seems to have gained exposure right out of the gate as evidenced by the large number of responses. And two, the spending momentum. Again while it's lower than Snowflake overall, and when you blend it with Snowflake it does pull it down, it's very healthy and steady. Now let's do a little pure comparison with some of our favorite names in this space. This chart shows net score or spending velocity in the Y-axis, an overlap or presence, pervasiveness if you will, in the data set on the X-axis. That red dotted line again is that 40% highly elevated net score that we like to talk about. And that table inserted informs us as to how the companies are plotted, where the dots set up, the net score, the ins. And we're comparing a number of database players, although just a caution, Oracle includes all of Oracle including its apps. But we just put it in there for reference because it is the leader in database. Right off the bat, Snowflake jumps out with a net score of 64%. The 60% from the earlier chart, again included Streamlit. So you can see its core database, data warehouse business actually is higher than the total company average that we showed you before 'cause the Streamlit is blended in. So when you separate it out, Streamlit is right on top of data bricks. Isn't that ironic? Only Snowflake and Databricks in this selection of names are above the 40% level. You see Mongo and Couchbase, they know they're solid and Teradata cloud actually showing pretty well compared to some of the earlier survey results. Now let's isolate on the database data platform sector and see how that shapes up. And for this analysis, same XY dimensions, we've added the big giants, AWS and Microsoft and Google. And notice that those three plus Snowflake are just at or above the 40% line. Snowflake continues to lead by a significant margin in spending momentum and it keeps creeping to the right. That's that end that we talked about earlier. Now here's an interesting tidbit. Snowflake is often asked, and I've asked them myself many times, "How are you faring relative to AWS, Microsoft and Google, these big whales with Redshift and Synapse and Big Query?" And Snowflake has been telling folks that 80% of its business comes from AWS. And when Microsoft heard that, they said, "Whoa, wait a minute, Snowflake, let's partner up." 'Cause Microsoft is smart, and they understand that the market is enormous. And if they could do better with Snowflake, one, they may steal some business from AWS. And two, even if Snowflake is winning against some of the Microsoft database products, if it wins on Azure, Microsoft is going to sell more compute and more storage, more AI tools, more other stuff to these customers. Now AWS is really aggressive from a partnering standpoint with Snowflake. They're openly negotiating, not openly, but they're negotiating better prices. They're realizing that when it comes to data, the cheaper that you make the offering, the more people are going to consume. At scale economies and operating leverage are really powerful things at volume that kick in. Now Microsoft, they're coming along, they obviously get it, but Google is seemingly resistant to that type of go to market partnership. Rather than lean into Snowflake as a great partner Google's field force is kind of fighting fashion. Google itself at Cloud next heavily messaged what they call the open data cloud, which is a direct rip off of Snowflake. So what can we say about Google? They continue to be kind of behind the curve when it comes to go to market. Now just a brief aside on the competitive posture. I've seen Slootman, Frank Slootman, CEO of Snowflake in action with his prior companies and how he depositioned the competition. At Data Domain, he eviscerated a company called Avamar with their, what he called their expensive and slow post process architecture. I think he actually called it garbage, if I recall at one conference I heard him speak at. And that sort of destroyed BMC when he was at ServiceNow, kind of positioning them as the equivalent of the department of motor vehicles. And so it's interesting to hear how Snowflake openly talks about the data platforms of AWS, Microsoft, Google, and data bricks. I'll give you this sort of short bumper sticker. Redshift is just an on-prem database that AWS morphed to the cloud, which by the way is kind of true. They actually did a brilliant job of it, but it's basically a fact. Microsoft Excel, a collection of legacy databases, which also kind of morphed to run in the cloud. And even Big Query, which is considered cloud native by many if not most, is being positioned by Snowflake as originally an on-prem database to support Google's ad business, maybe. And data bricks is for those people smart enough to get it to Berkeley that love complexity. And now Snowflake doesn't, they don't mention Berkeley as far as I know. That's my addition. But you get the point. And the interesting thing about Databricks and Snowflake is a while ago in the cube I said that there was a new workload type emerging around data where you have AWS cloud, Snowflake obviously for the cloud database and Databricks data for the data science and EML, you bring those things together and there's this new workload emerging that's going to be very powerful in the future. And it's interesting to see now the aspirations of all three of these platforms are colliding. That's quite a dynamic, especially when you see both Snowflake and Databricks putting venture money and getting their hooks into the loyalties of the same companies like DBT labs and Calibra. Anyway, Snowflake's posture is that we are the pioneer in cloud native data warehouse, data sharing and now data apps. And our platform is designed for business people that want simplicity. The other guys, yes, they're formidable, but we Snowflake have an architectural lead and of course we run in multiple clouds. So it's pretty strong positioning or depositioning, you have to admit. Now I'm not sure I agree with the big query knockoffs completely. I think that's a bit of a stretch, but snowflake, as we see in the ETR survey data is winning. So in thinking about the longer term future, let's talk about what's different with Snowflake, where it's headed and what the opportunities are for the company. Snowflake put itself on the map by focusing on simplifying data analytics. What's interesting about that is the company's founders are as you probably know from Oracle. And rather than focusing on transactional data, which is Oracle's sweet spot, the stuff they worked on when they were at Oracle, the founder said, "We're going to go somewhere else. We're going to attack the data warehousing problem and the data analytics problem." And they completely re-imagined the database and how it could be applied to solve those challenges and reimagine what was possible if you had virtually unlimited compute and storage capacity. And of course Snowflake became famous for separating the compute from storage and being able to completely shut down compute so you didn't have to pay for it when you're not using it. And the ability to have multiple clusters hit the same data without making endless copies and a consumption/cloud pricing model. And then of course everyone on the planet realized, "Wow, that's a pretty good idea." Every venture capitalist in Silicon Valley has been funding companies to copy that move. And that today has pretty much become mainstream in table stakes. But I would argue that Snowflake not only had the lead, but when you look at how others are approaching this problem, it's not necessarily as clean and as elegant. Some of the startups, the early startups I think get it and maybe had an advantage of starting later, which can be a disadvantage too. But AWS is a good example of what I'm saying here. Is its version of separating compute from storage was an afterthought and it's good, it's... Given what they had it was actually quite clever and customers like it, but it's more of a, "Okay, we're going to tier to storage to lower cost, we're going to sort of dial down the compute not completely, we're not going to shut it off, we're going to minimize the compute required." It's really not true as separation is like for instance Snowflake has. But having said that, we're talking about competitors with lots of resources and cohort offerings. And so I don't want to make this necessarily all about the product, but all things being equal architecture matters, okay? So that's the cloud S-curve, the first one we're showing. Snowflake's still on that S-curve, and in and of itself it's got legs, but it's not what's going to power the company to 10 billion. The next S-curve we denote is the multi-cloud in the middle. And now while 80% of Snowflake's revenue is AWS, Microsoft is ramping up and Google, well, we'll see. But the interesting part of that curve is data sharing, and this idea of data clean rooms. I mean it really should be called the data sharing curve, but I have my reasons for calling it multi-cloud. And this is all about network effects and data gravity, and you're seeing this play out today, especially in industries like financial services and healthcare and government that are highly regulated verticals where folks are super paranoid about compliance. There not going to share data if they're going to get sued for it, if they're going to be in the front page of the Wall Street Journal for some kind of privacy breach. And what Snowflake has done is said, "Put all the data in our cloud." Now, of course now that triggers a lot of people because it's a walled garden, okay? It is. That's the trade off. It's not the Wild West, it's not Windows, it's Mac, it's more controlled. But the idea is that as different parts of the organization or even partners begin to share data that they need, it's got to be governed, it's got to be secure, it's got to be compliant, it's got to be trusted. So Snowflake introduced the idea of, they call these things stable edges. I think that's the term that they use. And they track a metric around stable edges. And so a stable edge, or think of it as a persistent edge is an ongoing relationship between two parties that last for some period of time, more than a month. It's not just a one shot deal, one a done type of, "Oh guys shared it for a day, done." It sent you an FTP, it's done. No, it's got to have trajectory over time. Four weeks or six weeks or some period of time that's meaningful. And that metric is growing. Now I think sort of a different metric that they track. I think around 20% of Snowflake customers are actively sharing data today and then they track the number of those edge relationships that exist. So that's something that's unique. Because again, most data sharing is all about making copies of data. That's great for storage companies, it's bad for auditors, and it's bad for compliance officers. And that trend is just starting out, that middle S-curve, it's going to kind of hit the base of that steep part of the S-curve and it's going to have legs through this decade we think. And then finally the third wave that we show here is what we call super cloud. That's why I called it multi-cloud before, so it could invoke super cloud. The idea that you've built a PAS layer that is purpose built for a specific objective, and in this case it's building data apps that are cloud native, shareable and governed. And is a long-term trend that's going to take some time to develop. I mean, application development platforms can take five to 10 years to mature and gain significant adoption, but this one's unique. This is a critical play for Snowflake. If it's going to compete with the big cloud players, it has to have an app development framework like Snowpark. It has to accommodate new data types like transactional data. That's why it announced this thing called UniStore last June, Snowflake a summit. And the pattern that's forming here is Snowflake is building layer upon layer with its architecture at the core. It's not currently anyway, it's not going out and saying, "All right, we're going to buy a company that's got to another billion dollars in revenue and that's how we're going to get to 10 billion." So it's not buying its way into new markets through revenue. It's actually buying smaller companies that can complement Snowflake and that it can turn into revenue for growth that fit in to the data cloud. Now as to the 10 billion by fiscal year 28, is that achievable? That's the question. Yeah, I think so. Would the momentum resources go to market product and management prowess that Snowflake has? Yes, it's definitely achievable. And one could argue to $10 billion is too conservative. Indeed, Snowflake CFO, Mike Scarpelli will fully admit his forecaster built on existing offerings. He's not including revenue as I understand it from all the new stuff that's in the pipeline because he doesn't know what it's going to look like. He doesn't know what the adoption is going to look like. He doesn't have data on that adoption, not just yet anyway. And now of course things can change quite dramatically. It's possible that is forecast for existing businesses don't materialize or competition picks them off or a company like Databricks actually is able in the longer term replicate the functionality of Snowflake with open source technologies, which would be a very competitive source of innovation. But in our view, there's plenty of room for growth, the market is enormous and the real key is, can and will Snowflake deliver on the promises of simplifying data? Of course we've heard this before from data warehouse, the data mars and data legs and master data management and ETLs and data movers and data copiers and Hadoop and a raft of technologies that have not lived up to expectations. And we've also, by the way, seen some tremendous successes in the software business with the likes of ServiceNow and Salesforce. So will Snowflake be the next great software name and hit that 10 billion magic mark? I think so. Let's reconnect in 2028 and see. Okay, we'll leave it there today. I want to thank Chip Simonton for his input to today's episode. Thanks to Alex Myerson who's on production and manages the podcast. Ken Schiffman as well. Kristin Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hove is our Editor in Chief over at Silicon Angle. He does some great editing for us. Check it out for all the news. Remember all these episodes are available as podcasts. Wherever you listen, just search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com. Or you can email me to get in touch David.vallante@siliconangle.com. DM me @dvellante or comment on our LinkedIn post. And please do check out etr.ai, they've got the best survey data in the enterprise tech business. This is Dave Vellante for the CUBE Insights, powered by ETR. Thanks for watching, thanks for listening and we'll see you next time on breaking analysis. (upbeat music)

(gentle music) >> Welcome back to theCUBE's virtual program, covering VMware Explorer, 2022. The first time since 2019 that the VMware ecosystem is gathered in person. But in the post isolation economy, hybrid is the new format, cube plus digital, we call it. And so we're really happy to welcome Cal Al-Dhubaib who's the founder and CEO and AI strategist of Pandata. And Jonathan Seckler back in theCUBE, the senior director of product marketing at Dell Technologies. Guys, great to see you, thanks for coming on. >> Yeah, thanks a lot for having us. >> Yeah, thank you >> Cal, Pandata, cool name, what's it all about? >> Thanks for asking. Really excited to share our story. I'm a data scientist by training and I'm based here in Cleveland, Ohio. And Pandata is a company that helps organizations design and develop machine learning and AI technology. And when I started this here in Cleveland six years ago, I had people react to me with, what? So we help demystify AI and make it practical. And we specifically focus on trustworthy AI. So we work a lot in regulated industries like healthcare. And we help organizations navigate the complexities of building machine learning and AI technology when data's hard to work with, when there's risk on the potential outcomes, or high cost in the consequences. And that's what we do every day. >> Yeah, yeah timing is great given all the focus on privacy and what you're seeing with big tech and public policy, so we're going to get into that. Jonathan, I understand you guys got some hard news. What's your story around AI and AutoML? Share that with us. >> Yeah, thanks. So having the opportunity to speak with Cal today is really important because one of the hardest things that we find that our customers have is making that transition of experimenting with AI to making it really useful in real life. >> What is the tech underneath that? Are we talking VxRail here? Are you're talking servers? What do you got? >> Yeah, absolutely. So the Dell validated design for AI is a reference framework that is based on the optimized set of hardware for a given outcome. That includes it could be VxRail, VMware, vSphere and Nvidia GPUs and Nvidia software to make all of that happen. And for today, what we're working with is H2O.ai's solution to develop automatic machine learning. So take just that one more step to make it easier for customers to bring AI into production. >> Cool. >> So it's a full stack of software that includes automated machine learning, it includes NVIDIA's AI enterprise for deployment and development, and it's all built on an engineering validated set of hardware, including servers and storage and whatever else you need >> AI out of the box, I don't have to worry about cobbling it all together. >> Exactly. >> Cal, I want to come back to this trusted AI notion. A lot of people don't trust AI just by the very nature of it. I think about, okay, well how does it know it's a cat? And then you can never explain, it says black box. And so I'm like, what are they do with my data? And you mentioned healthcare, financial services, the government, they know everything about me. I just had to get a real ID and Massachusetts, I had to give all my data away. I don't trust it. So what is trusted AI? >> Well, so let me take a step back and talk about sobering statistics. There's a lot of different sources that report on this, but anywhere you look, you'll hear somewhere between 80 to 90% of AI projects fail to yield a return. That's pretty scary, that's a disappointing industry. And why is that? AI is hard. Versus traditional software, you're programming rules hard and fast. If I click this button, I expect A, B, C to happen. And we're talking about recognizing and reacting to patterns. It's not, will it be wrong? It's, when it's wrong, how wrong will it be? And what are it cost to accept related to that? So zooming back in on this lens of trustworthy AI, much of the last 10 years the development in AI has looked like this. Let's get the data, let's race to build the warehouses, okay we did that, no problem. Next was race to build the algorithms. Can we build more sophisticated models? Can we work with things like documents and images? And it used to be the exclusive domain of deep tech companies. You'd have to have teams of teams building the software, building the infrastructure, working on very specific components in this pipeline. And now we have this explosion of technologies, very much like what Jonathan was talking about with validated designs. So it removes the complexities of the infrastructure, it removes the complexities of being able to access the right data. And we have a ton of modeling capabilities and tools out there, so we can build a lot of things. Now, this is when we start to encounter risk in machine learning and AI. If you think about the models that are being used to replicate or learn from language like GPT-3 to create new content, it's training data set is everything that's on the internet. And if you haven't been on the internet recently, it's not all good. So how do you go about building technology to recognize specific patterns, pick up patterns that are desirable, and avoid unintended consequences? And no one's immune to this. So the discipline of trustworthy AI is building models that are easier to interrogate, that are useful for humans, and that minimize the risk of unintended consequences. >> I would add too, one of the good things about the Pandata solution is how it tries to enforce fairness and transparency in the models. We've done some studies recently with IDC, where we've tried to compare leaders in AI technology versus those who are just getting started. And I have to say, one of the biggest differences between a leader in AI and the rest of us is often that the leaders have a policy in place to deal with the risks and the ethics of using data through some kind of machine oriented model. And it's a really important part of making AI usable for the masses. >> You certainly hear a lot about, AI ultimately, there's algorithms which are built by humans. Although of course, there's algorithms to build algorithms, we know that today. >> Right, exactly. >> But humans are biased, there's inherent bias, and so this is a big problem. Obviously Dell, you have a giant observation space in terms of customers. But I wonder, Cal, if you can share with us how you're working with your customers at Pandata? What kind of customers are you working with? What are they asking? What problems are they asking you to solve? And how does it manifest itself? >> So when I like to talk about AI and where it's useful, it usually has to do with taking a repetitive task that humans are tasked with, but they're starting to act more like machines than humans. There's not much creativity in the process, it's handling something that's fairly routine, and it ends up being a bottleneck to scaling. And just a year ago even, we'd have to start approaching our clients with conversations around trustworthy AI, and now they're starting to approach us. Really example, this actually just happened earlier today, we're partnering with one of our clients that basically scans medical claims from insurance providers. And what they're trying to do is identify members that qualify for certain government subsidies. And this isn't as straightforward as it seems because there's a lot of complexities in how the rules are implemented, how judges look at these cases. Long story short, we help them build machine learning to identify these patients that qualify. And a question that comes up, and that we're starting to hear from the insurance companies they serve is how do you go about making sure that your decisions are fair and you're not selecting certain groups of individuals over others to get this assistance? And so clients are starting to wise up to that and ask questions. Other things that we've done include identifying potential private health information that's contained in medical images so that you can create curated research data sets. We've helped organizations identify anomalies in cybersecurity logs. And go from an exploration space of billions of eventual events to what are the top 100 that I should look at today? And so it's all about, how do you find these routine processes that humans are bottlenecked from getting to, we're starting to act more like machines and insert a little bit of outer recognition intelligence to get them to spend more time on the creative side. >> Can you talk a little bit more about how? A lot of people talk about augmented AI. AI is amazing. My daughter the other day was, I'm sure as an AI expert, you've seen it, where the machine actually creates standup comedy which it's so hilarious because it is and it isn't. Some of the jokes are actually really funny. Some of them are so funny 'cause they're not funny and they're weird. So it really underscored the gap. And so how do you do it? Is it augmented? Is it you're focusing on the mundane things that you want to take humans out of the loop? Explain how. >> So there's this great Wall Street Journal article by Jennifer Strong that she published I think four years ago now. And she says, "For AI to become more useful, it needs to become more boring." And I really truly believe in that. So you hear about these cutting edge use cases. And there's certainly some room for these generative AI applications inspiring new designs, inspiring new approaches. But the reality is, most successful use cases that we encounter in our business have to do with augmenting human decisions. How do you make arriving at a decision easier? How do you prioritize from millions of options, hundreds of thousands of options down to three or four that a human can then take the last stretch and really consider or think about? So a really cool story, I've been playing around with DALL.E 2. And for those of you who haven't heard, it's this algorithm that can create images from props. And they're just painting I really wish I had bought when I was in Paris a few years ago. And I gave it a description, skyline of the Sacre-Coeur Church in Montmartre with pink and white hues. And it came up with a handful of examples that I can now go take to an artist and say paint me this. So at the end of the day, automation, it's not really, yes, there's certain applications where you really are truly getting to that automated AI in action. But in my experience, most of the use cases have to do with using AI to make humans more effective, more creative, more valuable. >> I'd also add, I think Cal, is that the opportunity to make AI real here is to automate these things and simplify the languages so that can get what we call citizen data scientists out there. I say ordinary, ordinary employees or people who are at the front line of making these decisions, working with the data directly. We've done this with customers who have done this on farms, where the growers are able to use AI to monitor and to manage the yield of crops. I think some of the other examples that you had mentioned just recently Cal I think are great. The other examples is where you can make this technology available to anyone. And maybe that's part of the message of making it boring, it's making it so simple that any of us can use it. >> I love that. John Furrier likes to say that traditionally in IT, we solve complexity with more complexity. So anything that simplifies things is goodness. So how do you use automated machine learning at Pandata? Where does that fit in here? >> So really excited that the connection here through H2O that Jonathan had mentioned earlier. So H2O.ai is one of the leading AutoML platforms. And what's really cool is if you think about the traditional way you would approach machine learning, is you need to have data scientists. These patterns might exist in documents or images or boring old spreadsheets. And the way you'd approach this is, okay, get these expensive data scientists, and 80% of what they do is clean up the data. And I'm yet to encounter a situation where there isn't cleaning data. Now, I'll get through the cleaning up the data step, you actually have to consider, all right, am I working with language? Am I working with financial forecasts? What are the statistical modeling approaches I want to use? And there's a lot of creativity involved in that. And you have to set up a whole experiment, and that takes a lot of time and effort. And then you might test one, two or three models because you know to use those or those are the go to for this type of problem. And you see which one performs best and you iterate from there. The AutoML framework basically allows you to cut through all of that. It can reduce the amount of time you're spending on those steps to 1/10 of the time. You're able to very quickly profile data, understand anomalies, understand what data you want to work with, what data you don't want to work with. And then when it comes to the modeling steps, instead of iterating through three or four AutoML is throwing the whole kitchen sink at it. Anything that's appropriate to the task, maybe you're trying to predict a category or label something, maybe you're trying to predict a value like a financial forecast or even generate test. And it tests all of the models that it has at its disposal that are appropriate to the task and says, here are the top 10. You can use features like let me make this more explainable, let me make the model more accurate. I don't necessarily care about interrogating the results because the risk here is low, I want to a model that predicts things with a higher accuracy. So you can use these dials instead of having to approach it from a development perspective. You can approach it from more of an experimental mindset. So you still need that expertise, you still need to understand what you're looking at, but it makes it really quick. And so you're not spending all that expensive data science time cleaning up data. >> Makes sense. Last question, so Cal, obviously you guys go deep into AI, Jonathan Dell works with every customer on the planet, all sizes, all industries. So what are you hearing and doing with customers that are best practices that you can share for people that want to get into it, that are concerned about AI, they want to simplify it? What would you tell them? Go ahead, Cal. >> Okay, you go first, Cal. >> And Jonathan, you're going to bring us home. >> Sure. >> This sounds good. So as far as where people get scared, I see two sides of it. One, our data's not clean enough, not enough quality, I'm going to stay away from this. So one, I combat that with, you've got to experiment, you got to iterate, And that's the only way your data's going to improve. Two, there's organizations that worry too much about managing the risk. We don't have the data science expertise that can help us uncover potential biases we have. We are now entering a new stage of AI development and machine learning development, And I use those terms interchangeably anymore. I know some folks will differentiate between them. But machine learning is the discipline driving most of the advances. The toolkits that we have at our disposal to quickly profile and manage and mitigate against the risk that data can bring to the table is really giving organizations more comfort, should give organizations more comfort to start to build mission critical applications. The thing that I would encourage organizations to look for, is organizations that put trustworthy AI, ethical AI first as a consideration, not as an afterthought or not as a we're going to sweep this on the carpet. When you're intentional with that, when you bring that up front and you make it a part of your design, it sets you up for success. And we saw this when GDPR changed the IT world a few years ago. Organizations that built for privacy first to begin with, adapting to GDPR was relatively straightforward. Organizations that made that an afterthought or had that as an afterthought, it was a huge lift, a huge cost to adapt and adjust to those changes. >> Great example. All right, John, I said bring us home, put a bow on this. >> Last bit. So I think beyond the mechanics of how to make a AI better and more workable, one of the big challenges with the AI is this concern that you're going to isolate and spend too much effort and dollars on the infrastructure itself. And that's one of the benefits that Dell brings to the table here with validated designs. Is that our AI validated design is built on a VMware vSphere architecture. So your backup, your migration, all of the management and the operational tools that IT is most comfortable with can be used to maintain and develop and deploy artificial intelligence projects without having to create unique infrastructure, unique stacks of hardware, and then which potentially isolates the data, potentially makes things unavailable to the rest of the organization. So when you run it all in a VMware environment, that means you can put it in the cloud, you can put it in your data center. Just really makes it easier for IT to build AI into their everyday process >> Silo busting. All right, guys, thanks Cal, John. I really appreciate you guys coming on theCUBE. >> Yeah, it's been a great time, thanks. >> All right. And thank you for watching theCUBE's coverage of VMware Explorer, 2022. Keep it right there for more action from the show floor with myself, Dave Velante, John Furrier, Lisa Martin and David Nicholson, keep it right there. (gentle music)

(light hearted music) >> Okay, welcome back to Supercloud '22. I'm John Furrier, host of theCUBE. We got Ali Ghodsi here, co-founder and CEO of Databricks. Ali, Great to see you. Thanks for spending your valuable time to come on and talk about Supercloud and the future of all the structural change that's happening in cloud computing. >> My pleasure, thanks for having me. >> Well, first of all, congratulations. We've been talking for many, many years, and I still go back to the video that we have in archive, you talking about cloud. And really, at the beginning of the big reboot, I called the post Hadoop, a revitalization of data. Congratulations, you've been cloud-first, now on multiple clouds. Congratulations to you and your team for achieving what looks like a billion dollars in annualized revenue as reported by the Wall Street Journal, so first, congratulations. >> Thank you so much, appreciate it. >> So I was talking to some young developers and I asked a random poll, what do you think about Databricks? Oh, we love those guys, they're AI and ML-native, and that's their advantage over the competition. So I pressed why. I don't think they knew why, but that's an interesting perspective. This idea of cloud native, AI/ML-native, ML Ops, this has been a big trend and it's continuing. This is a big part of how this change and this structural change is happening. How do you react to that? And how do you see Databricks evolving into this new Supercloud-like multi-cloud environment? >> Yeah, look, I think it's a continuum. It starts with having data, but they want to clean it, you know, and they want to get insights out of it. But then, eventually, you'd like to start asking questions, doing reports, maybe ask questions about what was my revenue yesterday, last week, but soon you want to start using the crystal ball, predictive technology. Okay, but what will my revenue be next week? Next quarter? Who's going to churn? And if you can finally automate that completely so that you can act on the predictions, right? So this credit card that got swiped, the AI thinks it's fraud, we're going to deny it. That's when you get real value. So we're trying to help all these organizations move through this data AI maturity curve, all the way to that, the prescriptive, automated AI machine learning. That's when you get real competitive advantage. And you know, we saw that with the fans, right? I mean, Google wouldn't be here today if it wasn't for AI. You know, we'd be using AltaVista or something. We want to help all organizations to be able to leverage data and AI that way that the fans did. >> One of the things we're looking at with supercloud and why we call it supercloud versus other things like multi-cloud is that today a lot of the successful companies have started in the cloud have been successful, but have realized and even enterprises who have gotten by accident, and maybe have done nothing with cloud have just some cloud projects on multiple clouds. So, people have multiple cloud operational things going on but it hasn't necessarily been a strategy per se. It's been more of kind of a default reaction to things but the ones that are innovating have been successful in one native cloud because the use cases that drove that got scale got value, and then they're making that super by bringing it on premise, putting in a modern data stack, for the modern application development, and kind of dealing with the things that you guys are in the middle of with data bricks is that, that is where the action is, and they don't want to go, lose the trajectory in all the economies of scale. So we're seeing another structural change where the evolutionary nature of the cloud has solved a bunch of use cases, but now other use cases are emerging that's on premises and edge that have been driven by applications because of the developer boom, that's happening. You guys are in the middle of it. What is happening with this structural change? Are people looking for the modern data stack? Are they looking for more AI? What's the, what's your perspective on this supercloud kind of position? >> Look, it started with not AR on multiple clouds, right? So multi-cloud has been a thing. It became a thing 70, 80% of our customers when you ask them, they're more than one cloud. But then soon to start realizing that, hey, you know, if I'm on multiple clouds, this data stuff is hard enough as it is. Do I want to redo it again and again with different proprietary technologies, on each of the clouds. And that's when I started thinking about let's standardize this, let's figure out a way which just works across them. That's where I think open source comes in, becomes really important. Hey, can we leverage open standards because then we can make it work in these different environments, as we said so that we can actually go super, as you said, that's one. The second thing is, can we simplify it? You know, and I think today, the data landscape is complicated. Conceptually it's simple. You have data which is essentially customer data that you have, maybe employee data. And you want to get some kind of insights from that. But how you do that is very complicated. You have to buy data warehouse, hire data analysts. You have to buy, store stuff in the Delta Lake you know, get your data engineers. If you want streaming real time thing that's another complete different set of technologies you have to buy. And then you have to stitch all these together, and you have to do again and again on every cloud. So they just want simplification. So that's why we're big believers in this Delta Lakehouse concept. Which is an open standard to simplifying this data stack and help people to just get value out of their data in any environment. So they can do that in this sort of supercloud as you call it. >> You know, we've been talking about that in previous interviews, do the heavy lifting let them get the value. I have to ask you about how you see that going forward, Because if I'm a customer, I have a lot of operational challenges. Cause the developers are are kicking butt right now. We see that clearly. Open sources growing at, and continue to be great. But ops and security teams they really care about this stuff. And most companies don't want to spin up multiple ops teams to deal with different stacks. This is one big problem that I think that's leading into the multi-cloud viability. How do you guys deal with that? How do you talk to customers when they say, I want to have less complications on operations? >> Yeah, you're absolutely right. You know, it's easy for a developer to adopt all these technologies and new things are coming out all the time. The ops teams are the ones that have to make sure this works. Doing that in multiple different environments is super hard. especially when there's a proprietary stack in each environment that's different. So they just want standardization. They want open source, that's super important. We hear that all the time from them. They want open the source technologies. They believe in the communities around it. You know, they know that source code is open. So you can also see if there's issues with it. If there's security breaches, those kind of things that they can have a community around it. So they can actually leverage that. So they're the ones that are really pushing this, and we're seeing it across the board. You know, it starts first with the digital natives you know, the companies that are, but slowly it's also now percolating to the other organizations, we're hearing across the board. >> Where are we, Ali on the innovation strategies for customers? Where are they on the trajectory around how they're building out their teams? How are they looking at the open source? How are they extending the value proposition of Databricks, and data at scale, as they start to build out their teams and operations, because some are like kind of starting, crawl, walk, run, kind of vibe. Some are big companies, they're dealing with data all the time. Where are they in their journey? What's the core issues that they're solving? What are some of the use cases that you see that are most pressing in customer? >> Yeah, what I've seen, that's really exciting about this Delta Lakehouse concept is that we're now seeing a lot of use cases around real time. So real time fraud detection, real time stock ticker pricing, anyone that's doing trading, they want that to work real time. Lots of use cases around that. Lots of use cases around how do we in real time drive more engagement on our web assets if we're a media company, right? We have all these assets how do we get people to get engaged? Stay on our sites. Continue engaging with the material we have. Those are real time use cases. And the interesting thing is, they're real time. So, you know, it's really important that you that now you don't want to recommend someone, hey, you should go check out this restaurant if they just came from that restaurant, half an hour ago. So you want it to be real time, but B, that it's also all based on machine learning. These are a lot of this is trying to predict what you want to see, what you want to do, is it fraudulent? And that's also interesting because basically more and more machine learning is coming in. So that's super exciting to see, the combination of real time and machine learning on the Lakehouse. And finally, I would say the Lakehouse is really important for this because that's where the data is flowing in. If they have to take that data that's flowing into the lake and actually copy it into a separate warehouse, that delays the real time use cases. And then it can't hit those real time deadlines. So that's another catalyst for this Lakehouse pattern. >> Would that be an example of how the metrics are changing? Cause I've been looking at some people saying, well you can tell if someone's doing well there's a lot of data being transferred. And then I was saying, well, wait a minute. Data transfer costs money, right? And time. So this is interesting dynamic, in a way you don't want to have a lot of movement, right? >> Yeah, movement actually decreases for a lot of these real time use cases. 'Cause what we saw in the past was that they would run a batch processing to process all the data. So once they process all the data. But actually if you look at the things that have changed since the data that we have yesterday it's actually not that much. So if you can actually incrementally process it in real time, you can actually reduce the cost of transfers and storage and processing. So that's actually a great point. That's also one of the main things that we're seeing with the use cases, the bill shrinks and the cost goes down, and they can process less. >> Yeah, and it'd be interesting to see how those KPIs evolve into industry metrics down the road around the supercloud of evolution. I got to ask you about the open source concept of data platforms. You guys have been a pioneer in there doing great work, kind of picking the baton off where the Hadoop World left off as Dave Vellante always points out. But if working across clouds is super important. How are you guys looking at the ability to work across the different clouds with data bricks? Are you going to build that abstraction yourself? Does data sharing and model sharing kind of come into play there? How do you see this data bricks capability across the clouds? >> Yeah, I mean, let me start by saying, we just we're big fans of open source. We think that open source is a force in software. That's going to continue for, decades, hundreds of years, and it's going to slowly replace all proprietary code in its way. We saw that, it could do that with the most advanced technology. Windows, you know proprietary operating system, very complicated, got replaced with Linux. So open source can pretty much do anything. And what we're seeing with the Delta Lakehouse is that slowly the open source community is building a replacement for the proprietary data warehouse, Delta Lake, machine learning, real time stack in open source. And we're excited to be part of it. For us, Delta Lake is a very important project that really helps you standardize how you layout your data in the cloud. And when it comes a really important protocol called data sharing, that enables you in a open way actually for the first time ever share large data sets between organizations, but it uses an open protocol. So the great thing about that is you don't need to be a Databricks customer. You don't need to even like Databricks, you just need to use this open source project and you can now securely share data sets between organizations across clouds. And it actually does so really efficiently just one copy of the data. So you don't have to copy it if you're within the same cloud. >> So you're playing the long game on open source. >> Absolutely. I mean, this is a force it's going to be there if if you deny it, before you know it there's going to be, something like Linux, that is going to be a threat to your propriety. >> I totally agree by the way. I was just talking to somebody the other day and they're like hey, the software industry someone made the comment, the software industry, the software industry is open source. There's no more software industry, it's called open source. It's integrations that become interesting. And I was looking at integrations now is really where the action is. And we had a panel with the Clouderati we called it, the people have been around for a long time. And it was called the innovator's dilemma. And one of the comments was it's the integrator's dilemma, not the innovator's dilemma. And this is a big part of this piece of supercloud. Can you share your thoughts on how cloud and integration need to be tightened up to really make it super? >> Actually that's a great point. I think the beauty of this is, look the ecosystem of data today is vast, there's this picture that someone puts together every year of all the different vendors and how they relate, and it gets bigger and bigger and messy and messier. So, we see customers use all kinds of different aspects of what's existing in the ecosystem and they want it to be integrated in whatever you're selling them. And that's where I think the power of open source comes in. Open source, you get integrations that people will do without you having to push it. So us, Databricks as a vendor, we don't have to go tell people please integrate with Databricks. The open source technology that we contribute to, automatically, people are integrating with it. Delta Lake has integrations with lots of different software out there and Databricks as a company doesn't have to push that. So I think open source is also another thing that really helps with the ecosystem integrations. Many of these companies in this data space actually have employees that are full-time dedicated to make sure make sure our software works well with Spark. Make sure our software works well with Delta and they contribute back to that community. And that's the way you get this sort of ecosystem to further sort of flourish. >> Well, I really appreciate your time. And I, my final question for you is, as we're kind of unpack and and kind of shape and frame supercloud for the future, how would you see a roadmap or architecture or outcome for companies that are going to clearly be in the cloud where it's open source is going to be dominating. Integrations has got to be seamless and frictionless. Abstraction layer make things super easy and take away the complexity. What is supercloud to them? What does the outcome look like? How would you define a supercloud environment for an enterprise? >> Yeah, for me, it's the simplification that you get where you standardize an open source. You get your data in one place, in one format in one standardized way, and then you can get your insights from it, without having to buy lots of different idiosyncratic proprietary software from different vendors. That's different in each environment. So it's this slow standardization that's happening. And I think it's going to happen faster than we think. And I think in a couple years it's going to be a requirement that, does your software work on all these different departments? Is it based on open source? Is it using this Delta Lake house pattern? And if it's not, I think they're going to demand it. >> Yeah, I feel like we're close to some sort of defacto standard coming and you guys are a big part of it, once that clicks in, it's going to highly accelerate in the open, and I think it's going to be super valuable. Ali, thank you so much for your time, and congratulations to you and your team. Like we've been following you guys since the beginning. Remember the early days and look how far it's come. And again, you guys are really making a big difference in making a super cool environment out there. Thanks for coming on sharing. >> Thank you so much John. >> Okay, this is supercloud 22. I'm John Furrier stay with more for more coverage and more commentary after this break. (light hearted music)

(upbeat music) >> Welcome everyone, this is Stephanie Chan with theCUBE. Recently analyst Dave Ross RADIO entitled, Pat Gelsinger has a vision. It just needs the time, the cash and a miracle where he highlights why he thinks Intel is years away from reversing position in the semiconductor industry. Welcome Dave. >> Hey thanks, Stephanie. Good to see you. >> So, Dave you been following the company closely over the years. If you look at Wall Street Journal most analysts are saying to hold onto Intel. can you tell us why you're so negative on it? >> Well, you know, I'm not a stock picker Stephanie, but I've seen the data there are a lot of... some buys some sells, but most of the analysts are on a hold. I think they're, who knows maybe they're just hedging their bets they don't want to a strong controversial call that kind of sitting in the fence. But look, Intel still an amazing company they got tremendous resources. They're an ICON and they pay a dividend. So, there's definitely an investment case to be made to hold onto the stock. But I would generally say that investors they better be ready to hold on to Intel for a long, long time. I mean, Intel's they're just not the dominant player that it used to be. And the challenges have been mounting for a decade and look competitively Intel's fighting a five front war. They got AMD in both PCs and the data center the entire Arm Ecosystem` and video coming after with the whole move toward AI and GPU they're dominating there. Taiwan Semiconductor is by far the leading fab in the world with terms of output. And I would say even China is kind of the fifth leg of that stool, long term. So, lot of hurdles to jump competitively. >> So what are other sources of Intel's trouble sincere besides what you just mentioned? >> Well, I think they started when PC volumes peaked which was, or David Floyer, Wikibon wrote back in 2011, 2012 that he tells if it doesn't make some moves, it's going to face some trouble. So, even though PC volumes have bumped up with the pandemic recently, they pair in comparison to the wafer volume that are coming out of the Arm Ecosystem, and TSM and Samsung factories. The volumes of the Arm Ecosystem, Stephanie they dwarf the output of Intel by probably 10 X in semiconductors. I mean, the volume in semiconductors is everything. And because that's what costs down and Intel they just knocked a little cost manufacture any anymore. And in my view, they may never be again, not without a major change in the volume strategy, which of course Gelsinger is doing everything he can to affect that change, but they're years away and they're going to have to spend, north of a 100 billion dollars trying to get there, but it's all about volume in the semiconductor game. And Intel just doesn't have it right now. >> So you mentioned Pat Gelsinger he was a new CEO last January. He's a highly respected CEO and in truth employed more than four decades, I think he has knowledge and experience. including 30 years at Intel where he began his career. What's your opinion on his performance thus far besides the volume and semiconductor industry position of Intel? >> Well, I think Gelsinger is an amazing executive. He's a technical visionary, he's an execution machine, he's doing all the right things. I mean, he's working, he was at the state of the union address and looking good in a suit, he's saying all the right things. He's spending time with EU leaders. And he's just a very clear thinker and a super strong strategist, but you can't change Physics. The thing about Pat is he's known all along what's going on with Intel. I'm sure he's watched it from not so far because I think it's always been his dream to run the company. So, the fact that he's made a lot of moves. He's bringing in new management, he's repairing some of the dead wood at Intel. He's launched, kind of relaunched if you will, the Foundry Business. But I think they're serious about that. You know, this time around, they're spinning out mobile eye to throw off some cash mobile eye was an acquisition they made years ago to throw off some more cash to pay for the fabs. They have announced things like; a fabs in Ohio, in the Heartland, Ze in Heartland which is strikes all the right chords with the various politicians. And so again, he's doing all the right things. He's trying to inject. He's calling out his best Andrew Grove. I like to say who's of course, The Iconic CEO of Intel for many, many years, but again you can't change Physics. He can't compress the cycle any faster than the cycle wants to go. And so he's doing all the right things. It's just going to take a long, long time. >> And you said that competition is better positioned. Could you elaborate on why you think that, and who are the main competitors at this moment? >> Well, it's this Five Front War that I talked about. I mean, you see what's happened in Arm changed everything, Intel remember they passed on the iPhone didn't think it could make enough money on smartphones. And that opened the door for Arm. It was eager to take Apple's business. And because of the consumer volumes the semiconductor industry changed permanently just like the PC volume changed the whole mini computer business. Well, the smartphone changed the economics of semiconductors as well. Very few companies can afford the capital expense of building semiconductor fabrication facilities. And even fewer can make cutting edge chips like; five nanometer, three nanometer and beyond. So companies like AMD and Invidia, they don't make chips they design them and then they ship them to foundries like TSM and Samsung to manufacture them. And because TSM has such huge volumes, thanks to large part to Apple it's further down or up I guess the experience curve and experience means everything in terms of cost. And they're leaving Intel behind. I mean, the best example I can give you is Apple would look at the, a series chip, and now the M one and the M one ultra, I think about the traditional Moore's law curve that we all talk about two X to transistor density every two years doubling. Intel's lucky today if can keep that pace up, let's assume it can. But meanwhile, look at Apple's Arm based M one to M one Ultra transition. It occurred in less than two years. It was more like, 15 or 18 months. And it went from 16 billion transistors on a package to over a 100 billion. And so we're talking about the competition Apple in this case using Arm standards improving it six to seven X inside of a two year period while Intel's running it two X. And that says it all. So Intel is on a curve that's more expensive and slower than the competition. >> Well recently, until what Lujan Harrison did with 5.4 billion So it can make more check order companies last February I think the middle of February what do you think of that strategic move? >> Well, it was designed to help with Foundry. And again, I said left that out of my things that in Intel's doing, as Pat's doing there's a long list actually and many more. Again I think, it's an Israeli based company they're a global company, which is important. One of the things that Pat stresses is having a a presence in Western countries, I think that's super important, he'd like to get the percentage of semiconductors coming out of Western countries back up to at least maybe not to where it was previously but by the end of the decade, much more competitive. And so that's what that acquisition was designed to do. And it's a good move, but it's, again it doesn't change Physics. >> So Dave, you've been putting a lot of content out there and been following Intel for years. What can Intel do to go back on track? >> Well, I think first it needs great leadership and Pat Gelsinger is providing that. Since we talked about it, he's doing all the right things. He's manifesting his best. Andrew Grove, as I said earlier, splitting out the Foundry business is critical because we all know Moore's law. This is Right Law talks about volume in any business not just semiconductors, but it's crucial in semiconductors. So, splitting out a separate Foundry business to make chips is important. He's going to do that. Of course, he's going to ask Intel's competitors to allow Intel to manufacture their chips which they very well may well want to do because there's such a shortage right now of supply and they need those types of manufacturers. So, the hope is that that's going to drive the volume necessary for Intel to compete cost effectively. And there's the chips act. And it's EU cousin where governments are going to possibly put in some money into the semiconductor manufacturing to make the west more competitive. It's a key initiative that Pat has put forth and a challenge. And it's a good one. And he's making a lot of moves on the design side and committing tons of CapEx in these new fabs as we talked about but maybe his best chance is again the fact that, well first of all, the market's enormous. It's a trillion dollar market, but secondly there's a very long term shortage in play here in semiconductors. I don't think it's going to be cleared up in 2022 or 2023. It's just going to be keep being an explotion whether it's automobiles and factory devices and cameras. I mean, virtually every consumer device and edge device is going to use huge numbers of semiconductor chip. So, I think that's in Pat's favor, but honestly Intel is so far behind in my opinion, that I hope by the end of this decade, it's going to be in a position maybe a stronger number two position, and volume behind TSM maybe number three behind Samsung maybe Apple is going to throw Intel some Foundry business over time, maybe under pressure from the us government. And they can maybe win that account back but that's still years away from a design cycle standpoint. And so again, maybe in the 2030's, Intel can compete for top dog status, but that in my view is the best we can hope for this national treasure called Intel. >> Got it. So we got to leave it right there. Thank you so much for your time, Dave. >> You're welcome Stephanie. Good to talk to you >> So you can check out Dave's breaking analysis on theCUBE.net each Friday. This is Stephanie Chan for theCUBE. We'll see you next time. (upbeat music)

(slow music) >> Welcome back to theCUBE's ongoing coverage of VMworld 2021, the second year in a row we've done this virtually. My name is Dave Vellante and long-time VMware technologist and new CTO Kit Colbert is here. Kit, welcome. Good to see you again. >> Thanks, Dave. Super excited to be here. >> So let's talk about your new role. You've been at VMware. You've touched all the bases so to speak (Kit chuckles) and, you know, love the career evolution. You're ready for this job. So tell us about that role. >> Well, I hope so. I don't know. It's definitely a big step up. Been here at VMware for 18 years now, which, if know Silicon Valley, you know that's a long time. It's probably like four or five normal Silicon Valley lifetime's in terms of stints at a company. But I love it. I love the company. I love the culture. I love the technology and I'm super passionate, super excited about it. And so, you know, previously I was CTO for one of our business groups and focused on a specific set of our products and services. But now, as the corporate CTO, I really am overseeing all of VMware R&D. In the sense of really trying to drive a whole bunch of core engineering transformations, right, where we've talked a lot about our shift toward becoming a SaaS company. So, you know, a cloud services company. And so there's a lot of changes we got to make internally. Technologies, platform services we need to build out, you know, the sort of culture aspects of it again. And so, you know, I'm kind of sitting at the center of that and, I'll be honest, it's big, there's a lot of stuff to go and do, but I am just super excited about it. Wake up every day, really excited to meet a whole bunch of new people across the organization and to learn all the cool things we're doing. Well, you know, I'll say it again, like the level of innovation happening inside VMware is just insane. And it's really cool now that I get kind of more of a front and center row to see everything that's happening. >> And when I was preparing for the interview with Raghu, you know, I've been following VMware for a long time, and I sort of noted that it's like the fourth, you know, wave of executive management and I sort of went back and said, okay, yes, we know it started with, you know, Workstation. Okay, fine. But then really quickly went into really changing the way in which we think about servers, and server utilization, and driving. I remember the first time I ever saw a demo, I said, "Wow, this is going to be completely game-changing." And then thought about the era of the software-defined data center, fine-tuning the cloud strategy, and then this explosion of innovation, whether it was this sort of NSX piece, the acquisitions you've made around security, again, more cloud expansion. And now you're laying out sort of this Switzerland from Multi-Cloud combined with, as you're pointing out, this as a service model. So when you think about the technical vision of the company transforming into a cloud and subscription model, what does that mean from a sort of architectural standpoint >> Yeah. >> Or a mindset perspective? >> Oh yeah. Both great questions and both sort of key focus areas for me, and by the way, it's something I've been thinking about for quite a while, right? Yeah, so you're right. Like we are on our third or fourth lap of the track depending on how you count. But I also think that this notion of getting into Multi-Cloud, of becoming a real cloud services company is going to be probably the biggest one for us. And the biggest transformation that we're going to have to make, you know, we did extend from core compute virtualization to network and storage with the software-defined data center. But now these things I think are a bit more fundamental. So, you know, how are we thinking about it? Well, we're thinking about it in a few different ways. I do think, as you mentioned, the mindset is definitely the most important thing. This notion that, you know, we no longer really have product teams purely, they should be thinking of themselves as service teams and the idea being that they are operating and accountable for the availability of their cloud service. And so this means we really needed to step up our game, and we have in terms of the types of tooling that we built, but really it's about getting these developers engaged with that, to know that, hey, like what matters most of all right now is that service availability, in addition to things like security, compliance, et cetera. But we have monitoring systems to tell you, hey, like there's a problem. And that you need to go jump on those things immediately. This is not like, you know, a normal bug that comes in, oh, I'll get to it tomorrow or whatever. It's like, no, no, you got to step up and really get there immediately. And so there is that big mindset shift and that's something we've been driving for the past few years, but we need to continue to push there. And as part of that, you know, what we've seen is that a lot of our individual teams have gone out and build like really great cloud services, but what we really want to build to enable us to accelerate that, is a platform, a true, you know, SaaS platform and leveraging all these great capabilities that we have to help all of our teams go faster. And so it gets to things like standardization and really raising the bar across the board to allow all these teams to focus on what makes their products or services unique and differentiated rather than, you know, just doing the basic blocking and tackling. So those are couple of things I'm really focused on. Both driving the mindset shift. You know, as I was taking on this role, I did a lot of reading on other CTOs and, you know, how do they view their roles within their companies? And one of the things I did hear there was that the CTO is kind of the, I don't know if the keeper is the right word, but the keeper of the engineering culture, right, that you want to really be a steward for that to help take it forward in the right sort of directions that aligned with the strategic direction of the business. And so that's a big aspect for what I'm thinking about. And the second one in the SaaS platform, one of the really interesting things about this reorg that we've done internally is, that traditionally CTO is kind of focused, you know, outbound, maybe a little bit inbound, but typically don't have large engineering organizations, but here, what we want to do, because the SaaS platform is so important to us. We did centralize it within the office of the CTO. And so now, you know, my customers, from an engineering standpoint, are all the internal business units. So a lot of really big changes inside VMware, but I think this is the sort of stuff we need to do to help us really accelerate toward the multi-cloud vision that we're painting. >> Well, VMware has always had a superstrong engineering culture, and I liked the way you phrase that, "The steward of the engineering culture," when you think about a product mindset, 'course correct me, if I'm off here, but when you're building a product and you're making that thing rock-solid, you know, Maritz used to talk about the hardened top. And so it seems to me that the services mindset expands the mind a little bit in terms of what other services can I integrate to make my service better, whether that's a machine, intelligence service, or a security service or, you know, the dozens of other services that you guys are now building, the combination of that innovation has like a step function and a lever on top of the sort of traditional product mindset. >> Yeah, I think you're absolutely right there's a ton of like really fundamental mental mindset shifts, right? That are a part of that. And the integration piece you mentioned, super critical, but I also think it's actually taking a step back and looking at the life cycle more holistically. When you're thinking about a product, you're thinking about, okay, I'ma get the bits together, I'm going to ship it out. But then it's really up to the customer to go deploy that, to operate it, to, you know, deal with problems and bugs that come up. And when you're delivering a cloud service, those are all problems that you, as the application creator, have to deal with. And so you've got to be on top of all those things. And, you know, if you design something in such a way that it becomes kind of hard to debug at runtime, well, that's going to directly impact your availability, that might have, you know, contractual obligations with an SLA impact to a customer. So there's some really big implications there that I think traditionally product teams didn't always fully think through, but now that they sort of have to with like a cloud service. The other point, I think that's really important there, is the notion of simplicity and ease of use. Experience is always important, right? Customer experience, user experience, but it gets even more magnified in a SaaS type of environment because the idea is that you shouldn't have to talk to anybody. You, as a user, should be able to go and call an API and start using this thing, right, and swipe a credit card and you're good to go. And so, you know, that sort of maniacal focus on how you just remove roadblocks, remove any unnecessary things between that customer and getting the value that they're looking for. So in general, the thing that I really love about SaaS and cloud services is that they really align incentives very well. What you want to do, as an application builder, as a solution builder, really aligns well with what customers are looking for. And you can get that feedback very, very rapidly, which allows for much quicker evolution of the underlying product and application. >> So one of the other things I learned from my interview with Raghu, and I couldn't go deep into it, I did a little bit with Sumit, but I wonder if I get your perspectives as well. I always talk about this abstraction layer across clouds, hybrid, multi-cloud, edge, abstracting, you know, the underlying complexity, and Raghu, it's nuance, but he said, "Okay, but the thing is, we're not trying to limit access to the primitives. We want to allow developers to go there to the extent." And my takeaway was okay, but the abstraction is you want to be that single management layer with access to the deep primitives and APIs of the respective clouds. But simplify, to your point, across those estates at the management layer, maybe you could add some color to that. >> Yeah, you know, it's a really interesting question. But let me tell you about how we think about it because you're right. In that, you know, the abstractions can sometimes find the underlying primitives and capabilities. And so Raghu getting at, hey, like we don't necessarily force you one way or the other. And here's the way to think about it, is that it's really about delivering optionality. And we do that through offering these abstractions at different layers. So to your point, Dave, like we have a management capabilities that can enable you to manage consistently across all types of clouds, public, private, edge, et cetera, irrespective of what that underlying infrastructure is. And so you'll look at things that are like our vRealize suite of products, or CloudHealth, or Tanzu, Tanzu Mission Control is really focused on that one as well. But then we also have our infrastructure layer. That's what we're doing with VMware Cloud. And this notion of delivering consistent infrastructure. Now, even though the core, sort of IIS layer, is more consistent, you still get great flexibility in terms of the higher-level services. If you want to use a database from one of the public clouds, or a messaging system, or streaming service, or, you know, AI, whatever it is, you still got that sort of optionality as well. And so the reason that we offer these different things is because customers are just in different places. As a matter of fact, a single customer may have all of those different use cases, right? They may have some apps where they're moving from on-prem into the cloud. They want to do that very quickly. So, boom, we can just do it really fast with VMware Cloud, consistent infrastructure. We can VMotion that thing up in the Cloud, great. But for other ones, maybe a modern app they're building, and maybe a team has chosen to use native AWS for that, but they want to leverage Kubernetes. So there you could put in a Tanzu Mission Control to give them that, you know, consistent management across sites, or leverage CloudHealth to understand costs and to really enable the application teams to manage costs on their own. So, you know, I always go back to that concept of optionality, like we offer sort of these different levels of abstraction, and it really depends on what the use case is because the reality is, especially for a complex enterprise, they're likely going to have all of those use cases. >> You know, I want to stay on optionality for a moment because you're essentially becoming a cloud company. I'm expanding the definition of cloud, which I think is appropriate 'cause the cloud is expanding. It's going on-prem, it's going out to the edge, there's hybrid connections, across clouds, et cetera. And when you look at the public cloud players, they all are deep into what I'll call data management. I'm not even sure what that term means anymore sometimes, but certainly they all own, own, databases, but they also offer databases from folks. I go back to something Maritz said with the software mainframe that we want to be able to run any workload, you know, anywhere and have high reliability, recovery, you know, lowest costs, et cetera. So you're going to run those workloads. Project Monterey is about supporting new workloads, but it doesn't seem like you have aspirations to own sort of the database layer, for example, what's your philosophy around that? >> Yeah. Not generally. I mean, we do have some solutions like Greenplum, for instance, that play in that space, more of a data warehouse solution, but generally speaking, you're absolutely right. You know, VMware success was built through tight partnerships. We have a very, very broad partner network. And of course, we see hyperscalers as great partners as well. And so, I think if we get back to like, what's the core of VMware, it really is providing those powerful abstractions in the right places, at the infrastructure level, at the management level, and so forth. But yeah, we're not trying to necessarily compete with everyone, reinvent the world. And by the way, if I just take a step back, when we talk to customers, what really drives them toward using multiple clouds is the fact that they want to get after these, what we call, best of breed cloud services, that many of the different public clouds offer databases and AI and ML systems. And for each app team, the exact one that perfectly meets their needs may be different, right? Maybe on one conference is another cloud. And so that is really the optionality that we want to optimize for when we talk to those customers. They want the easiest way of getting that app onto that cloud, so we can take advantage of that cloud service, but what they worry about is the lack of consistency there. And that goes across the board. You know, if something fails at 2:00 am, and you have to wake up and go fix it. Do you have like the right sort of tooling in place, if it's fails on one cloud versus another, do you have to like, you know, scramble to figure out which tools to go use, you know, which dashboard to look at? It's like, no, that you want kind of a consistent one. When you think about, from a security perspective, how do you drive a secure software supply chain? How do you prevent the types of attacks that we've seen in the past few years? Where people insert malicious code into your supply chain and now you're running with hack code out there. And if you have different teams doing different things across different clouds, well, that's going to just open up sort of a can of worm of different possibilities there for hackers to get in. So that's why this consistency is so important. And so, you know, I guess, if we refine the optionality a little bit, that point, it's about getting optionality around cloud services and then like those are the things that really differentiate. And so, you know, we're not trynna compete with that. We're saying, hey, like we want to bring customers to those and give them the best experience that they can, irrespective of whether that's in the public cloud, or on-prem, or even at the edge. >> And that's a huge technical challenge and amazing value for customers. I want to ask you, there's a lot of talk about ESG today. How does that fit into the CTO mindset? >> Yeah. >> Is it a bolt-on, is it a fundamental component? >> Yeah. Yeah, so ESG is talking about environment, sustainability, and governance. And so, you know, it's not an environment, excuse me, equity, (Kit chuckles) equity, sustainability, and governance. Getting my acronyms wrong, which as the technologist, really a faux pas, but any case, equity, sustainability, and governance. And the idea there is that if we look at the core values for VMware, this is something that's hugely important. And something that we've actually been focused on for quite a while. We now have a whole team focused on this, really being a force multiplier to help keep us honest across VMware, to help ensure equity, and in many different ways, that we have or continue to increase, for instance, the amount of female representation within our organization, or underrepresented minorities or communities, ensuring that, you know, pay is equal across the company. You know, these different sorts of things, but also around sustainability. They actually have a number of folks working very closely with our teams to drive sustainability into our products. You know, vSphere is great because it reduces the amount of physical servers you need. So by definition reduces the carbon footprint there. But now, you know, taking a step further. We have cloud partners that we're working with to ensure that they have net-zero carbon emissions, you know, using 100% renewables by 2030. And in fact, that's something that, we ourselves, have signed up for, you know, today we are carbon-neutral, but what we want to get to is to be net carbon zero by 2030, which is an absolutely huge lift. And that's, by the way, not just for VMware, our operations, our offices, but also for our supply chain as well. And so, you know, when you look across, you know, as well as efforts around diversity and inclusion, this is something that is very core to what we do as a company, but it's also a personal passion of mine. The ESG office actually lives within my organization. And it does that because what I view the office of the CTO as being is really a force multiplier, as I said before, like, yes, the team is located here, but their purview is across all of engineering. And in fact, all of VMware. So I think, you know, when we look at this, it's about getting the best talent we have, very diverse talent, increasing our ability to deliver innovative products, but also doing so in a way that's good for the planet, that is sustainable. And that is giving back to the community. >> You know, by the way, I don't think that was faux pas. (Kit laughs) 'Cause a lot of times, people use environmental, social, and governance, and your equity piece would fall into the S in that equation, the social responsibility, you know, components. So I think you've just done an interesting twist on the acronym. So no mistake there. (Dave chuckles) Just another way to look at it. >> Yup, yup, yup. >> So you're now deep into the CTO role. What should we look for in the, you know, coming months and years? How should we >> Hmm. >> Kind of evaluate progress? What are those sort of milestones that we should be looking at? >> Yeah, so about a month or so into the job now, and so still getting my arms wrapped around, but, you know, I'm looking at measuring success in a few different ways. First of all, as I said before, the ESG component and in diversity, equity inclusion in particular, in terms of our workforce, extraordinarily important to me and something we're going to be really pushing hard on, you know, as we all know, you know, women, underrepresented minorities, not very well represented, in general, in Silicon Valley. So something that we all need to step up on. And so we're going to be putting a lot of effort in there, and that will actually help drive, as I said before, all of these innovations, this fundamental shift in mindset, I mean, that requires diverse perspectives. It requires pushing us out of our comfort zone, but the net result of that, so that what you're going to see, is a much faster cadence of releases of innovation coming from VMware. So there's some just insanely exciting things (Kit laughs) that are happening in the labs right now that we're cooking up. But, you know, as we start making this shift, we're going to be delivering those faster and faster to our customers and our partners. >> You know, I'm interested to hear that it's a passion of yours. There was an article, I think it was last week, in "The Wall Street Journal," it was an insert section on "Women in the Workforce," and there was a stat in there, which I thought was pretty interesting. I'll run it by and you see what you think, you know, it was talking about COVID, and post COVID,and the stresses. And it's interesting to me because a lot of executives, and pfft, you know, I'm with them, said, "Hey, work from home. This a beautiful thing. It's good for business too, because, you know, everybody's more productive," but you have this perpetual workday now. It's like we never sleep. It bleeds in the weekends. And the stat from Qualtrics, which was published in the journal, I think it said, "30% of working women said that their mental health has declined since COVID." And that number was only 15% for working men, is still notable, but half. And so, you know, one has to question maybe that perpetual work week and, you know, maybe there's a benefit from business productivity, but then there's the other side of that as well. And a lot of women have left the workforce, a lot of previously working moms. And so there's an untapped labor pool there, and there's this huge labor shortage. And so these are important issues, but they're not easy ones to solve, are they? >> No, no, no. It's something we've been putting a lot of thought into at VMware. So we do have a flexible program that we're rolling out in terms of work. People can come into the office if they want to, of course, you know, where we have offices where it's safe to do so, where the government has allowed that, and people can have an actual desk there, or sometimes they can say, "Hey, I only want to come in once or twice a week." And then we say, "Okay, we'll have some floating desks that you can take." And others are saying, "I want to be fully remote." So we give people a pretty broad range in terms of how they want to address that. But I do think, to your point though, and this is something I've been really trying to do already is to create a more inclusive environment by doing a number of different things. And so it's being thoughtful around when you're sending emails. 'Cause like my sort of schedule is, I do tend to like fire off emails late at night after the kids are in bed, I get a little quiet time, some thinking time, but I make it very clear that I'm not expecting an immediate response. Don't worry about it. This is my work time. Doesn't have to be your work time. And so really setting those, I guess, boundaries, if you will, explicitly and kind of the expectations maybe is a better term, setting that explicitly, trying to schedule meetings, not at times where you're going to have to drop the kids off at school or pick them (indistinct) and to take over your life. And so we really try to emphasize boundaries and really setting those things appropriately. But honestly, it's something that we're still working on and I'm still learning. And so I'd love to get feedback from folks, but those are some of the early thinkings. But I would say that we at VMware are taking it very, very seriously and really supporting our employees in terms of navigating that work-life balance. >> Well Kit, congratulations on the new role and it's great to see you again. I hope next year we can be face-to-face, always a pleasure to have you on theCUBE. >> Thanks, Dave. Appreciated being here. >> All right, and thank you for watching theCUBE's continuous coverage of VMworld 2021, the virtual edition. Keep it right there for more right after this. (slow music)

(bright music) >> Welcome back to HPE Discover 2021. My name is Dave Vellante and you're watching theCUBE's virtual coverage of HPE's big customer event. Of course, the virtual edition and we're going to dig into transformations, the role of technology and the role of senior technology leadership. Look, let's face it, HPE has gone through a pretty dramatic transformation itself in the past few years so it makes a great example in case study and with me is Rashmi Kumar who is the senior vice president and CIO at HPE, Rashmi welcome come on inside theCUBE. >> Hi Dave nice to be here. >> Well it's been almost a year since COVID you know changed the world as we know it. How would you say the role of the CIO specifically in generally IT has changed? I mean you got digital, zero trust has gone from buzzword to mandate, digital, everybody was you know complacent about digital in many ways and now it's really accelerated, remote work, hybrid, how do you see it? >> Absolutely, as I said in the last Discover that COVID has been the biggest reason to accelerate digital transformation in the companies. I see CIO's role has changed tremendously in the last 15 months. It's no more just keep the operations running, that's become a table stake. Our roles have become not only to create digital customer experience, engage with our customers in different ways, but also to transform the company operations from inside out to be able to give that digital experience from beginning to end of the customer engagement going forward. We have also become responsible for switching our strategies around the companies as the COVID hit in different parts of the world at different times and how companies structured their operations to go from one region to another, a global company like HPE had to look into its supply chain differently, had to look into strategies to mitigate the risk that was created because of the supply chain disruptions, as well as you go to taking care of our employees. How do you create this digital collaboration experience where teams can still come together and make the work happen for our end customers? How do we think about future employee engagement when people are not coming into these big buildings and offices and working together, but how do you create the same level of collaboration, coordination, as well as delivery of faster, good and services which is enabled by technology going forward. So CIO and IT's role has gone from giving a different level of customer experience to different level of employee experience, as well as enabling day-to-day operations of the companies. CEOs have realized that digital is the way to go forward, it does not matter what industry you are in and now CIOs have their seat at the table to define what the future of every company now which is a technology company irrespective you are in oil and gas, or mining, or a technical product, or a car or a mobility company, end of the day you have to act and behave like a technology company. >> So I want to ask you about that because you've been a CIO at a leading technology provider now for the last three years and you've had previous roles and were, you know non-technical, technology, you know, selling to IT companies and as you point out those worlds are coming together. Everybody's a technology company today. How do you think that changes the role of the CIO because it would always seem to me that there was a difference between a CIO at a tech company you know what I mean by that and a CIO at sort of every other company is, are those two worlds converging? >> Absolutely and it's interesting you pointed out that I have worked in many different industries from healthcare and pharma, to entertainment, to utilities and now at a technology company. End of the day the issues that IT deals with are pretty similar across the organization. What is different here is now my customers are people like me in other industries and I have little bit of an advantage because just having the experience across various ecosystem even that HPE look I was fortunate at HPE because of Antonio's leadership we had top-down mandate to transform how we did business and I talked about my NextGEN IT program in last year's CUBE interview. But at the same time while we were changing our customer, partner's experience from ordering, to order processing, to supply chain, to finance, we decided this pivot of becoming as a service company. And if you think about that pivot, it's pretty common. If it was a technology company or non-technology company. At HPE we were very used to selling a product and coming back three years later at the time of refresh of infrastructure or hardware. That's no more true for us. Now we are becoming an as a service or a subscription company and IT played a major role to enable that quote-to-cash experience which is very different than the traditional experience, around how we stay connected with our customer, how we proactively understand their behavior. I always talk about this term digital exhaust which results into data, which can result into better insight and you can not only upsell, cross-sell because now you have more data about your product usage, but first and foremost give what your customer wants in a much better way because you can proactively understand their needs and wants because you are providing a digital product versus a physical product. So this is the change that most of the companies are now going through. If you look at Domino's transition, they are pizza sellers but they did better because they had better digital experience. If you look at Chipotle, these are food service companies. Ikea which is a furniture manufacturer, across the board we have helped our customers and industries to understand how to become a more digital provider. And remember when HPE says edge to cloud platform as a service, edge is the product, the customers is what we deal with and how do we get that, help them get that data, understand how the product is behaving and then get the information to cloud for further analysis and understanding from the data that comes out of the products that they sell. >> I think you've been at HPE now I think around three years and I've been watching of course for decades, you know HPE, well HP then HPE is, I feel like it's entering now that sort of third phase of its transformation, your phase one was okay we got to figure out how to deal or operate as separate companies, okay, that took some time and then it was okay, now how do we align our resources? And you know what are the waves that we're going to ride? And how do we take our human capital, our investments and what bets do we place? And you're all in on as a service and now it's like okay, you know how do we deliver on all those promises? So pretty massive transformations. You talked about edge to cloud as a service so you've got this huge pivot in your business. What's the technology strategy to support that transformation? >> Yeah, that's a great question. So as I mentioned first, your second phase which was becoming a stand-alone company was the NextGEN IT program where we brought in S4 and 60 related ecosystem application where even in the traditional business there was a realization that we were 120 billion company, we are a 30 billion company, we need different types of technologies as well as more integrated across our product line, across the globe and we, I'm very happy to report that we are the last leg of NextGEN IT transformation. Where we have brought in new customer experience through low-touch or no-touch order processing, a very strong S4 capabilities where we are now able to run all global orders across all our hardware and services business together and I'm happy to report that we have been able to successfully run through the transformation which a typical company of our size would take five or six years to do in around close to three years. But at the same time while we were building this foundation and the capabilities to be able to do order management supply chain and data and analytics platforms, we also made the pivot to go to as a service. Now for as a service and subscription selling, it needs a very different quote-to-cash experience for our customers. And that's where we had bring in platforms like BRIM to do subscription billing, convergent charging and a whole different way to address. But we were lucky to have this transformation completed on which we could bolt on this new capability and we had the data analytics platform built which now these as a service products can also use to drive better insight into our customer behavior as well as how they're using our product real time for our operations teams. >> Well they say follow the money, in theCUBE we love to say follow the data. I mean data is obviously a crucial component of competitive advantage, business value, so talk a little bit more about the role of data, I'm interested in where IT fits. You know a lot of companies they'll have a chief data officer, or a CIO, sometimes they're separate sometimes they work, you know for each other, or CDO works for CIO, how do you guys approach the whole data conversation? >> Yeah that's a great question and has been top of the mind of a lot of CEOs, CIOs, chief digital officers in many different companies. The way we have set it up here is we do have a chief data officer and we do have a head of technology and platform and data lake within IT. Look the way I see is that I call the term data torture. If they have multiple data lakes, if they have multiple data locations and the data is not coming together at one place at the first time that it comes out to the source system, we end up with data swamps and it's very difficult to drive insights, it's very difficult to have single version of truth. So HPE had two-pronged approach. First one was as part of this NextGEN IT transformation we embarked upon the journey first of all to define our customers and products in a very uniform way across the globe. It's called entity master data and product master data program. These were very, very difficult program. We are now happy to report that we can understand the customer from cold stage to servicing stage beginning to end across all our system. It's been a tough journey but it was effort well spent. At the same time while we were building this master data capability we also invested time in our analytics platform. Because we are generating so much data now globally as one footprint, how do we link our data lake to our SAP and Salesforce and all these systems where our customer data flows through and create analytics and insight from it from our customers or our operations team. At the same time we also created a chief data officer role where the responsibility is really to drive business from understanding what decision making and analytics they need around product, around customer, around their usage around their experience to be able to drive better alignment with our customers and products going forward. So this creates efficiencies in the organization. If you have a leader who is taking care of your platforms and data, building single source of truth and you have a leader who is propagating this mature notion of handling data as enterprise data and driving that focus on understanding the metrics and the insight that the businesses need to drive better customer alignment, that's when we gain those efficiencies and behind the scenes the chief data officer and the data leader within my organization work very, very closely to understand each other needs, sometimes art of the possible, where do we need the data processing? Is it at the edge? Is it in the cloud? What's the best way to drive the technology and the platform forward? And they kind of rely on each other's knowledge and intelligence to give us superior results. And I have done data analytics in many different companies, this model works. Where you have focus on insight and analytics without, because data without insight is of no value. But at the same time you need clean data, you need efficient, fast platforms to process that insight at the functional non-functional requirement that our business partners have. And that's how we have established in here and we have seen many successes recently as of now. >> I want to ask you a kind of a harder, maybe it's not a harder question it's a weird question around single version of the truth. 'Cause it's clearly a challenge for organizations and there's many applications, workloads that require that single version of the truth, the operational systems, the transaction systems, the HR, the Salesforce and clearly you have to have a single version of the truth. I feel like, however we're on the cusp of a new era where business lines see an opportunity for whatever, their own truth to work with a partner to create some kind of new data product. And it's early days in that but I wonder, maybe not the right question for HPE but I wonder if you see it with in your ecosystems where it's yes, single version of truth is sort of one class of data and analytics got to have that nailed down, data quality, everything else. But then there's this sort of artistic version of the data where business people need more freedom, they need more latitude to create. Are you seeing that? Maybe you can help me put that into context. >> That's a great question Dave and I'm glad you asked it so. I think Tom Davenport, who is known in the data space talks about the offensive and the defensive use cases of leveraging data. I think the piece that you talked about where it's clean, it's pristine, it's quality, it's all that, most of those offer the offensive use cases where you are improving companies' operations incrementally because you have very clean data, you have very good understanding of how my territories are doing, how my customers are doing, how my products are doing, how am I meeting my SLAs or how my financials are looking, there's no room for failure in that area. The other area is though which works on the same set of data. It's not a different set of data but the need is more around finding needles in the haystack to come up with new needs, new wants in customers or new business models that we go with. The way we have done it is we do take this data, take out what's not allowed for everybody to be seen and then what we call is a private space but that's this entire data available to our business leader not real time, because the need is not as real time because they are doing more, what we call this predictive analytics to be able to leverage the same data set and run their analytics. And we work very closely with business units, we educate them, we tell them how to leverage this data set and use it and gather their feedback to understand what they need in that space to continue to run with their analytics. I think as we talk about hindsight, insight and foresight, hindsight and insight happens more from this clean data lakes where you have authenticity, you have quality and then most of the foresight happens in a different space where the users have more leverage to use data in many different ways to drive analytics and insights which is not readily available. >> Great thank you for that. That's an interesting discussion. You know digital transformation it's a journey and it's going to take you know many years. I know a lot of ways, not a lot of ways, 2020 was a forced march to digital you know. If you weren't a digital business you were out of business and so you really didn't have much time to plan. So now organizations are stepping back saying, okay, let's really lean into our strategy, the journey and along the way, there's going to be blind spots, there's bumps in the road, when you look out what are the potential disruptions that you see maybe in terms of how companies are currently approaching their digital transformations? >> That's a great question Dave and I'm going to take a little bit more longer-term view on this topic, right? And what's top of my mind recently is the whole topic of ESG, environmental, social and governance. Most of the companies have governance in place right? Because they are either public companies, or they're under some kind of scrutiny from different regulatory bodies or whatnot even if you're a startup you need to do things with our customers and whatnot. It has been there for companies, it continues to be there. We the public companies are very good at making sure that we have the right compliance, right privacy, right governance in place. Now we'll talk about cybersecurity I think that creates a whole new challenge in that governance space, however we have the setup within our companies to be able to handle that challenge. Now, when we go to social, what happened last year was really important. And now as each and every company we need to think about what are we doing from our perspective to play our part in that and not only the bigger companies, leaders at our level I would say that between last March and this year I have hired more than 400 people during pandemic which was all virtual, but me and my team have made sure that we are doing the right thing to drive inclusion and diversity which is also very big objective for HPE and Antonio himself has been very active in various round tables in US at the World Economic Forum level and I think it's really important for companies to create that opportunity, remove that disparity that's there for the underserved communities. If we want to continue to be successful in this world to create innovative product and services we need to sell it to the broader cross section of populations and to be able to do that we need to bring them in our fold and enable them to create that equal consumption capabilities across different sets of people. HPE has taken many initiatives and so are many companies. I feel like the momentum that companies have now created around the topic of equality is very important. I'm also very excited to see that a lot of startups are now coming up to serve that 99% versus just the shiny ones as you know in the Bay Area to create better delivery methods of food or products right? But the third piece which is environmental is extremely important as well. As we have seen recently in many companies and where even the dollar or the economic value is flowing are around the companies which are serious about environmental. HPE recently published it's a Living Progress Report, we have been in the forefront of innovation to reduce carbon emissions, we help our customers through those processes. Again, if we don't, if our planet is on fire none of us will exist right? So we all have to do that every little part to be able to do better. And I'm happy to report I myself as a person solar panels, battery, electric cars, whatever I can do. But I think something more needs to happen right? Where as an individual I need to pitch in but maybe utilities will be so green in the future that I don't need to put panels on my roof which again creates a different kind of race going forward. So when you ask me about disruptions, I personally feel that successful company like ours have to have ESG top of their mind and think of product and services from that perspective, which creates equal opportunity for people, which creates better environment sustainability going forward and you know our customers, our investors are very interested in seeing what we are doing to be able to serve that cause for bigger cross section of companies. And I'm most of the time very happy to share with my CIO cohort around how our HPEFS capabilities creates or feeds into the circular economy, how much e-waste we have recycled or kept it off of landfills, our green lake capabilities, how it reduces the e-waste going forward, as well as our sustainability initiatives which can help other CIOs to be more carbon neutral going forward as well. >> You know that's a great answer Rashmi thank you for that 'cause I got to tell you I hear a lot of mumbo jumbo about ESG but that was a very substantive, thoughtful response that I think tech companies in particular are, have to lead and are leading in this area. So I really appreciate that sentiment. I want to end with a very important topic which is cyber it's, obviously you know escalated in the news the last several months, it's always in the news but, you know 10 or 15 years ago there was this mentality of failure equals fire. And now we realize, hey they're going to get in, it's how you handle it. Cyber has become a board-level topic. You know years ago there was a lot of discussion, oh you can't have the SecOps team working for the CIO because that's like the fox watching the hen house that's changed. It's been a real awakening, a kind of a rude awakening so the world is now more virtual, you've got a secure physical assets. I mean any knucklehead can now become a ransomware attacker, they can buy ransomware as a service in the dark web so that's something we've never seen before. You're seeing supply chains get hacked and self-forming malware I mean it's a really scary time. So you've got these intellectual assets it's a top priority for organizations. Are you seeing a convergence of the CISO role, the CIO role, the line of business roles relative to sort of prior years in terms of driving security throughout organizations? >> Yeah this is a great question and this was a big discussion at my public board meeting a couple of days ago. It's, as I talk about many topics, if you think digital, if you think data, if you think ESG, it's no more one organization's business, it's now everybody's responsibility. I saw a Wall Street Journal article a couple of days ago where somebody has compared cyber to 9/11 type scenario that if it happens for a company that's the level of impact you feel on your operations. So, you know all models are going to change where CISO reports to CIO, at HPE we are also into product security and that's why CISO is a peer of mine who I work with very closely, who also worked with product teams where we are saving our customers from lot of pain in this space going forward and HPE itself is investing enormous amount of efforts and time in coming out of products which are secure and are not vulnerable to these types of attacks. The way I see it is CISO role has become extremely critical in every company and a big part of that role is to make people understand that cybersecurity is also everybody's responsibility. That's why an IT we propagate DevSecOps, as we talk about it we are very, very careful about picking the right products and services. This is one area where companies cannot shy away from investing. You have to continuously looking at cybersecurity architecture, you have to continuously look at and understand where the gaps are and how do we switch our product or service that we use from the providers to make sure our companies stay secure. The training not only for individual employees around anti-phishing or what does cybersecurity mean, but also to the executive committee and to the board around what cyber security means, what zero trust means, but at the same time doing drive-ins. We did it for business continuity and disaster recovery before, now it is time we do it for a ransomware attack and stay prepared. As you mentioned and we all say in tech community, it's always if not when. No company can take them their chest and say, "oh we are fully secure," because something can happen going forward. But what is the readiness for something that can happen? It has to be handled at the same risk level as a pandemic, or a earthquake, or a natural disaster and assume that it's going to happen and how as a company we will behave when something like this happens. So I'm huge believer in the framework of protect, detect, govern and respond as these things happen. So we need to have exercises within the company to ensure that everybody's aware of the part that they play day to day but at the same time when some event happen and making sure we do very periodic reviews of IT and cyber practices across the company, there is no more differentiation between IT and OT. That was 10 years ago. I remember working with different industries where OT was totally out of reach of IT and guess what happened? WannaCry and Petya and XP machines were still running your supply chains and they were not protected. So, if it's a technology it needs to be protected. That's the mindset people need to go with. Invest in education, training, awareness of your employees, your management committee, your board and do frequent exercises to understand how to respond when something like this happen. See it's a big responsibility to protect our customer data, our customer's operations and we all need to be responsible and accountable to be able to provide all our product and services to our customers when something unforeseen like this happens. >> Rashmi you're very generous with your time thank you so much for coming back in theCUBE it was great to have you again. >> Thank you Dave, it was really nice chatting with you. >> And thanks for being with us for our ongoing coverage of HPE Discover '21. This is Dave Vellante you're watching the virtual CUBE, the leader in digital tech coverage we'll be right back. (bright music)

>>Welcome back to HP discover 2021 My name is Dave Volonte and you're watching the cubes, virtual coverage of H. P. S. Big customer event. Of course, the virtual edition, we're gonna dig into transformations the role of technology in the role of senior technology leadership. Look, let's face it, H P. E. Has gone through a pretty dramatic transformation itself in the past few years. So it makes a great example in case study and with me is rashmi kumari who is the senior vice president and C. I. O. At HP rashmi welcome come on inside the cube. >>Dave Nice to be here. >>Well, it's been almost a year since Covid changed the world as we know it. How would you say the role of the CEO specifically and generally it has changed. I mean you got digital Zero Trust has gone from buzzword to >>mandate >>digital. Everybody was complacent about digital in many ways and now it's really accelerated remote work hybrid. How do you see it? >>Absolutely. As I said in the last discover that Covid has been the biggest reason to accelerate digital transformation in the company's I. C. C. I O. S role has changed tremendously in the last 15 months. It's no more just keep the operations running that's become a table stick. Our roles have become not only to create digital customer experience engaged with our customers in different ways, but also to transform the company operations from inside out to be able to give that digital experience from beginning to end off the customer engagement going forward. We have also become responsible for switching our strategies around the companies as the Covid. Covid hit in different parts of the world at different times and how companies structured their operations to go from one region to another. A global company like H. B had to look into its supply chain differently. Had to look into strategies to mitigate the risk that was created because of the supply chain disruptions as well as you go to taking care of our employees. How do you create this digital collaboration experience where teams can still come together and make the work happen for our end customers? How do we think about future employee engagement when people are not coming into these big buildings and offices and working together, But how to create the same level of collaboration coordination as well as delivery or faster uh goods and services which is enabled by technology going forward. So see I. O. And I. T. S. Role has gone from giving a different level of customer experience to a different level of employee experience as well as enabling day to day operations of the company's. Ceos have realized that digital is the way to go forward. It does not matter what industry you are in and now see a as have their seat at the table to define what the future of every company now, which is a technology company respective you are in oil and gas or mining or a technical product or a card or a mobility company. End of the day you have to act and behave like a technology company. >>So I want to ask you about that because you've you've been a Ceo and uh you know, leading technology provider now for the last three years and you've had previous roles and where you know non technical technology, you know, selling to I. T. Companies and as you point out those worlds are coming together, everybody is a technology company today. How do you think that changes the role of the C. I. O. Because it would always seem to me that there was a difference between A C. I. O. And a tech company. You know what I mean by that? And the C. I. O. It's sort of every other company is those two worlds converging. >>Absolutely. And it's interesting you pointed out that I have worked in many different industries from healthcare and pharma to entertainment to utilities. Um And now at a technology company end of the day um The issues that I. T. Deals with are pretty similar across the organization. What is different here is now my customers are people like me in other industries and I have a little bit of an advantage because just having the experience across various ecosystem. Even at H. B. Look I was fortunate um at H. B. Because of Antonio's leadership, we have topped out mandate to transform how we did business. And I talked about my next gen IT program in last year's cube interview. But at the same time while we were changing our customer partners experience from ordering to order processing to supply chain to finance. Uh We decided this pivot of becoming as a service company. And if you think about that pivot it's pretty common if it was a technology company or non technology company at HP. We were very used to selling a product and coming back three years later at the time of refresh of infrastructure or hardware. That's no more true for us now we are becoming as a service or a subscription company and I. T. Played a major role to enable that quote to cash experience. Which is very different than the traditional experience around how we stay connected with our customer, how we proactively understand their behavior. I always talk about this term. Um Digital exhaust which results into data which can result into better insight and you can not only Upsell cross l because now you have more data about your product usage, but first and the foremost give what your customer wants in a much better way because you can proactively understand their needs and wants because you are providing a digital product versus a physical product. So this is the change that most of the companies are now going through. If you look at Domino's transition, there are pills a sellers but they did better because they had better digital experience. If you look at Chipotle, these are food service companies I. K which is a furniture manufacturer across the board. We have helped our customers and industries to understand how to become a more digital provider. And and remember when uh hp says edge to cloud platform as a service edges the product, the customers who we deal with and how do we get that? Help them get their data to understand how the product is behaving and then get the information to cloud for further analysis. Um and understanding from the data that comes out of the products that gets up, >>I think you've been HP now think around three years and I've been watching of course for decades. Hp. Hp then HP is I feel like it's entering now the sort of third phase of its transformation, your phase one was okay, we gotta figure out how to deal or or operate as a separate companies. Okay. That took some time and then it was okay. Now how do we align our resources and you know, what are the waves that we're gonna ride? And how do we how do we take our human capital, our investments and what bets do we place and and all in on as a service. And now it's like okay how do we deliver on all those promises? So pretty massive transformations. You talked about edge to cloud as a service so you've got this huge pivot in your in your business. What's the technology strategy to support that transformation? >>Yeah that's a that's a great question. So as I mentioned first your second phase which was becoming a stand alone company was the next N. I. T. Program very broad and um S. Four and 60 related ecosystem application. We're even in the traditional business there was a realization that we were 100 20 billion company. We are 30 billion company. We need different types of technologies as well as more integrated across our product line across the globe. And um we I'm very happy to report that we are the last leg of next in I. T. Transformation where we have brought in new customer experience through low touch or not touch order pressing. A very strong as four capabilities. Where we are now able to run all global orders across all our hardware and services business together. And I'm happy to report that we have been able to successfully run through the transformation which a typical company of our size would take five or six years to do in around close to three years. But at the same time while we were building this foundation and the capabilities to be able to do other management, supply chain and data and analytics platforms. We also made the pivot to go to as a service now for as a service and subscription selling. It needs a very different quote to Kazakh cash experience for our customers and that's where we had to bring in um platforms like brim to do um subscription building, convergent charging and a whole different way to address. But we were lucky to have this transformation completed on which we could bolt on this new capability and we had the data and another X platform built which now these as a service products can also use to drive better insight into our customer behavior um as well as how they're using our product a real time for our operations teams. >>Well they say follow the money in the cube. We love to say follow the day to day is obviously a crucial component of competitive advantage business value. So you talk a little bit more about the role of data. I'm interested I'm interested in where I. T. Fits uh you know a lot of companies that have a Chief data officer or Ceo sometimes they're separate. Sometimes they they work you know for each other or Cdo works for C. I. O. How do you guys approach the whole data conversation? >>Yeah that's a that's a great question and has been top of the mind of a lot of C E O C I O S. Chief digital officers in many different companies. The way we have set it up here is do we do have a chief data officer and we do have a head of uh technology and platform and data within I. T. Look. The way I see is that I call the term data torture if we have multiple data lakes, if we have multiple data locations and the data is not coming together at one place at the first time that it comes out of the source system, we end up with data swamps and it's very difficult to drive insights. It's very difficult to have a single version of truth. So HP had two pronged approach. First one was as part of this next gen i. T. Transformation we embarked upon the journey first of all to define our customers and products in a very uniform way across the globe. It's called entity Master Data and Product Master Data Program. These were very very difficult program. We are now happy to report that we can understand the customer from code stage to servicing stage beginning to end across all our system. It's been a tough journey but it was a effort well spent at the same time while we were building this message capability, we also invest the time in our analytics platform because we are generating so much data now globally as one footprint. How do we link our data link to R. S. A. P. And Salesforce and all these systems where our customer data flows through and create analytics and insight from it from our customers or our operations team. At the same time, we also created a chief data officer role where the responsibility is really to drive business from understanding what decision making an analytics they need around product, around customer, around their usage, around their experience to be able to drive better alignment with our customers and products going forward. So this creates efficiencies in the organization. If you have a leader who is taking care of your platforms and data building single source of truth and you have a leader who is propagating this mature notion of handling data as enterprise data and driving that focus on understanding the metrics and the insight that the businesses need to drive better customer alignment. That's when we gain those efficiencies and behind the scenes, the chief data officer and the data leader within my organization worked very, very closely to understand each other needs sometimes out of the possible where do we need the data processing? Is it at the edge? Is it in the cloud? What's the best way to drive the technology and the platform forward? And they kind of rely on each other's knowledge and intelligence to give us give us superior results. And I have done data analytics in many different companies. This model works where you have focused on insight and analytics without because data without insight is of no value, but at the same time you need clean data. You need efficient, fast platforms to process that insight at the functional nonfunctional requirements that are business partners have and that's how we have established in here and we have seen many successes recently. As of now, >>I want to ask you a kind of a harder maybe it's not harder question. It's a weird question around single version of the truth because it's clearly a challenge for organizations and there's many applications workloads that require that single version of the truth. The operational systems, the transaction systems, the HR the salesforce. Clearly you have to have a single version of the truth. I feel like however we're on the cusp of a new era where business lines see an opportunity for whatever their own truth to work with a partner to create some kind of new data product. And it's early days in that. But I want to and maybe not the right question for HP. But I wonder if you see it with in your ecosystems where where it's it's yes, single version of truth is sort of one class of data and analytics gotta have that nail down data quality, everything else. But then there's this sort of artistic version of the data where business people need more freedom. They need more latitude to create. Are you seeing that? And maybe you can help me put that into context. >>Uh, that's a great question. David. I'm glad you asked it. So I think tom Davenport who is known in the data space talks about the offensive and the defensive use cases of leveraging data. I think the piece that you talked about where it's clean, it's pristine, it's quality. It's all that most of those offer the offensive use cases where you are improving company's operations incrementally because you have very clean that I have very good understanding of how my territories are doing, how my customers are doing how my products are doing. How am I meeting my sls or how my financials are looking? There's no room for failure in that area. The other area is though, which works on the same set of data. It's not a different set of data, but the need is more around finding needles in the haystack to come up with new needs, new ones and customers or new business models that we go with. The way we have done it is we do take this data take out what's not allowed for everybody to be seen and then what we call is a private space. But that's this entire data available to our business leader, not real time because the need is not as real time because they're doing more what we call this predictive analytics to be able to leverage the same data set and run their analytics. And we work very closely with business in its we educate them. We tell them how to leverage this data set and use it and gather their feedback to understand what they need in that space to continue to run with their with their analytics. I think as we talk about hindsight insight and foresight hindsight and insight happens more from this clean data lakes where you have authenticity, you have quality and then most of the foresight happens in a different space where the users have more leverage to use data in many different ways to drive analytics and insights which is not readily available. >>Thank you for that. That's interesting discussion. You know digital transformation. It's a journey and it's going to take many years. A lot of ways, not a lot of ways 2020 was a forced March to digital. If you weren't a digital business, you were out of business and you really didn't have much time to plan. So now organizations are stepping back saying, okay let's really lean into our strategy the journey and along the way there's gonna be blind spots, there's bumps in the road when you look out what are the potential disruptions that you see maybe in terms of how companies are currently approaching their digital transformations? That's a great question. >>Dave and I'm going to take a little bit more longer term view on this topic. Right in what's top of my mind um recently is the whole topic of E. S. G. Environmental, social and governance. Most of the companies have governance in place, right? Because they are either public companies or they're under some kind of uh scrutiny from different regulatory bodies or what not. Even if you're a startup, you need to do things with our customers and what not. It has been there for companies. It continues to be there. We the public companies are very good at making sure that we have the right compliance, right privacy, right governance in in in place. Now we'll talk about cyber security. I think that creates a whole new challenge in that governance space. However, we have the set up within our companies to be able to handle that challenge. Now, when we go to social, what happened last year was really important. And now as each and every company, we need to think about what are we doing from our perspective to play our part in that. And not only the bigger companies leaders at our level, I would say that Between last March and this year, I have hired more than 400 people during pandemic, which was all virtual, but me and my team have made sure that we are doing the right thing to drive inclusion and diversity, which is also very big objective for h P E. And Antonio himself has been very active in various round tables in us at the world Economic forum level and I think it's really important for companies to create that opportunity, remove that disparity that's there for the underserved communities. If we want to continue to be successful in this world too, create innovative products and services, we need to sell it to the broader cross section of populations and to be able to do that, we need to bring them in our fold and enable them to create that um, equal consumption capabilities across different sets of people. Hp has taken many initiatives and so are many companies. I feel like uh, The momentum that companies have now created around the topic of equality is very important. I'm also very excited to see that a lot of startups are now coming up to serve that 99% versus just the shiny ones, as you know, in the bay area to create better delivery methods of food or products. Right. The third piece, which is environmental, is extremely important as well as we have seen recently in many companies and where even the dollar or the economic value is flowing are around the companies which are serious about environmental HP recently published its living Progress report. We have been in the forefront of innovation to reduce carbon emissions, we help our customers, um, through those processes. Again, if we do, if our planet is on fire, none of us will exist, right. So we all have to do that every little part to be able to do better. And I'm happy to report, I myself as a person, solar panels, battery electric cars, whatever I can do, but I think something more needs to happen right where as an individual I need to pitch in, but maybe utilities will be so green in the future that I don't need to put panels on my roof, which again creates a different kind of uh waste going forward. So when you ask me about disruptions, I personally feel that successful company like ours have to have E. S. G. Top of their mind and think of products and services from that perspective, which creates equal opportunity for people, which creates better environment sustainability going forward. And, you know, our customers are investors are very interested in seeing what we are doing to be able to serve that cause uh for for bigger cross section of companies, and I'm most of the time very happy to share with my C I. O cohort around how are H. P E F s capabilities creates or feeds into the circular economy, how much e waste we have recycled or kept it off of landfills are green capabilities, How it reduces the evils going forward as well as our sustainability initiatives, which can help other, see IOS to be more um carbon neutral going forward as well. >>You know, that's a great answer, rashmi, thank you for that because I gotta tell you hear a lot of mumbo jumbo about E S G. But that was a very substantive, thoughtful response that I think, I think tech companies in particular are have to lead in our leading in this area. So I really appreciate that sentiment. I want to end with a very important topic which is cyber. It's obviously, you know, escalated in, in the news the last several months. It's always in the news, but You know, 10 or 15 years ago there was this mentality of failure equals fire. Now we realize, hey, they're gonna get in, it's how you handle it. Cyber has become a board level topic, you know? Years ago there was a lot of discussion, oh, you can't have the sec ops team working for the C. I. O. Because that's like the Fox watching the Henhouse, that's changed. Uh it's been a real awakening, a kind of a rude awakening. So the world is now more virtual, you've gotta secure physical uh assets. I mean, any knucklehead can now become a ransomware attack, er they can, they can, they can buy ransomware as a services in the dark, dark web. So that's something we've never seen before. You're seeing supply chains get hacked and self forming malware. I mean, it's a really scary time. So you've got these intellectual assets, it's a top priority for organizations. Are you seeing a convergence of the sea? So roll the C. I. O. Roll the line of business roles relative to sort of prior years in terms of driving security throughout organizations. >>This is a great question. And this was a big discussion at my public board meeting a couple of days ago. It's as as I talk about many topics, if you think digital, if you think data, if you think is you, it's no more one organizations, business, it's now everybody's responsibility. I saw a Wall Street Journal article a couple of days ago where Somebody has compared cyber to 9-11-type scenario that if it happens for a company, that's the level of impact you feel on your on your operations. So, you know, all models are going to change where C so reports to see IO at H P E. We are also into products or security and that's why I see. So is a peer of mine who I worked with very closely who also worked with product teams where we are saving our customers from a lot of pain in this space going forward. And H. B. E. Itself is investing enormous amount of efforts in time in coming out of products which are which are secured and are not vulnerable to these types of attacks. The way I see it is see So role has become extremely critical in every company and the big part of that role is to make people understand that cybersecurity is also everybody's responsibility. That's why in I. T. V. Propagate def sec ups. Um As we talk about it, we are very very careful about picking the right products and services. This is one area where companies cannot shy away from investing. You have to continuously looking at cyber security architecture, you have to continuously look at and understand where the gaps are and how do we switch our product or service that we use from the providers to make sure our companies stay secure The training, not only for individual employees around anti phishing or what does cybersecurity mean, but also to the executive committee and to the board around what cybersecurity means, what zero trust means, but at the same time doing drive ins, we did it for business continuity and disaster recovery. Before now at this time we do it for a ransomware attack and stay prepared as you mentioned. And we all say in tech community, it's always if not when no company can them their chest and say, oh, we are fully secured because something can happen going forward. But what is the readiness for something that can happen? It has to be handled at the same risk level as a pandemic or earthquake or a natural disaster. And assume that it's going to happen and how as a company we will behave when when something like this happen. So I'm here's believer in the framework of uh protect, detect, govern and respond um as these things happen. So we need to have exercises within the company to ensure that everybody is aware of the part that they play day today but at the same time when some event happen and making sure we do very periodic reviews of I. T. And cyber practices across the company. There is no more differentiation between I. T. And O. T. That was 10 years ago. I remember working with different industries where OT was totally out of reach of I. T. And guess what happened? Wanna cry and Petra and XP machines were still running your supply chains and they were not protected. So if it's a technology it needs to be protected. That's the mindset. People need to go with invest in education, training, um awareness of your employees, your management committee, your board and do frequent exercises to understand how to respond when something like this happen. See it's a big responsibility to protect our customer data, our customers operations and we all need to be responsible and accountable to be able to provide all our products and services to our customers when something unforeseen like this happens, >>Russian, very generous with your time. Thank you so much for coming back in the CUBA is great to have you again. >>Thank you. Dave was really nice chatting with you. Thanks >>for being with us for our ongoing coverage of HP discover 21 This is Dave Volonte, you're watching the virtual cube, the leader in digital tech coverage. Be right back. >>Mm hmm, mm.

>> From theCUBE studios in Palo Alto and Boston, Bringing you data-driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vellante. >> Recent survey data from ETR shows that enterprise tech spending is tracking with projected US GDP growth at six to 7% this year. Many markers continue to point the way to a strong recovery, including hiring trends and the loosening of frozen IT Project budgets. However skills shortages are blocking progress at some companies which bodes well for an increased reliance on external IT services. Moreover, while there's much talk about the rotation out of work from home plays and stocks such as video conferencing, VDI, and other remote worker tech, we see organizations still trying to figure out the ideal balance between funding headquarter investments that have been neglected and getting hybrid work right. In particular, the talent gap combined with a digital mandate, means companies face some tough decisions as to how to fund the future while serving existing customers and transforming culturally. Hello everyone, and welcome to this week's Wikibon CUBE's Insights powered by ETR. In this "Breaking Analysis", we welcome back Erik Porter Bradley of ETR who will share fresh data, perspectives and insights from the latest survey data. Erik, great to see you. Welcome. >> Thank you very much, Dave. Always good to see you and happy to be on the show again. >> Okay, we're going to share some macro data and then we're going to dig into some highlights from ETR's most recent March COVID survey and also the latest April data. So Erik, the first chart that we want to show, it shows CIO and IT buyer responses to expected IT spend for each quarter of 2021 versus 2020, and you can see here a steady quarterly improvement. Erik, what are the key takeaways, from your perspective? >> Sure, well, first of all, for everyone out there, this particular survey had a record-setting number of participation. We had a 1,500 IT decision makers participate and we had over half of the Fortune 500 and over a fifth of the Global 1000. So it was a really good survey. This is seventh iteration of the COVID Impact Survey specifically, and this is going to transition to an overlarge macro survey going forward so we can continue it. And you're 100% right, what we've been tracking here since March of last year was, how is spending being impacted because of COVID? Where is it shifting? And what we're seeing now finally is that there is a real re-acceleration in spend. I know we've been a little bit more cautious than some of the other peers out there that just early on slapped an eight or a 9% number, but what we're seeing is right now, it's at a midpoint of over six, about 6.7% and that is accelerating. So, we are still hopeful that that will continue, and really, that spending is going to be in the second half of the year. As you can see on the left part of this chart that we're looking at, it was about 1.7% versus 3% for Q1 spending year-over-year. So that is starting to accelerate through the back half. >> I think it's prudent to be cautious (indistinct) 'cause normally you'd say, okay, tech is going to grow a couple of points higher than GDP, but it's really so hard to predict this year. Okay, the next chart here that we want to show you is we asked respondents to indicate what strategies they're employing in the short term as a result of coronavirus and you can see a few things that I'll call out and then I'll ask Erik to chime in. First, there's been no meaningful change of course, no surprise in tactics like remote work and holding travel, however, we're seeing very positive trends in other areas trending downward, like hiring freezes and freezing IT deployments, a downward trend in layoffs, and we also see an increase in the acceleration of new IT deployments and in hiring. Erik, what are your key takeaways? >> Well, first of all, I think it's important to point out here that we're also capturing that people believe remote work productivity is still increasing. Now, the trajectory might be coming down a little bit, but that is really key, I think, to the backdrop of what's happening here. So people have a perception that productivity of remote work is better than hybrid work and that's from the IT decision makers themselves, but what we're seeing here is that, most importantly, these organizations are citing plans to increase hiring, and that's something that I think is really important to point out. It's showing a real following, and to your point right in the beginning of the intro, we are seeing deployments stabilize versus prior survey levels, which means early on, they had no plans to launch new tech deployments, then they said, "Nope, we're going to start." and now that stalling, and I think it's exactly right, what you said, is there's an IT skills shortage. So people want to continue to do IT deployments 'cause they have to support work from home and a hybrid back return to the office, but they just don't have the skills to do so, and I think that's really probably the most important takeaway from this chart, is that stalling and to really ask why it's stalling. >> Yeah, so we're going to get into that for sure, and I think that's a really key point, is that accelerating IT deployments, it looks like it's hit a wall in the survey, but before we get deep into the skills, let's take a look at this next chart, and we're asking people here how our return to the new normal, if you will, and back to offices is going to change spending with on-prem architectures and applications. And so the first two bars, they're Cloud-friendly, if you add them up, it's 63% of the respondents, say that either they'll stay in the Cloud for the most part, or they're going to lower their on-prem spend when they go back to the office. The next three bars are on-prem friendly. If you add those up it's 29% of the respondents say their on-prem spend is going to bounce back to pre-COVID levels or actually increase, and of course, 12% of that number, by the way, say they've never altered their on-prem spend. So Erik, no surprise, but this bodes well for Cloud, but isn't it also a positive for on-prem? We've had this dual funding premise, meaning Cloud continues to grow, but neglected data center spend also gets a boost. What's your thoughts? >> Really, it's interesting. It's people are spending on all fronts. You and I were talking in the prep, it's like we're in battle and I've got naval, I've got air, I've got land, I've got to spend on Cloud and digital transformation, but I also have to spend for on-prem. The hybrid work is here and it needs to be supported. So this is spending is going to increase. When you look at this chart, you're going to see though, that roughly 36% of all respondents say that their spending is going to remain mostly on Cloud. So that is still the clear direction, digital transformation is still happening, COVID accelerated it greatly, you and I, as journalists and researchers already know this is where the puck is going, but spend has always lagged a little bit behind 'cause it just takes some time to get there. Inversely, 27% said that their on-prem spending will decrease. So when you look at those two, I still think that the trend is the friend for Cloud spending, even though, yes, they do have to continue spending on hybrid, some of it's been neglected, there are refresh cycles coming up, so, overall it just points to more and more spending right now. It really does seem to be a very strong backdrop for IT growth. >> So I want to talk a little bit about the ETR taxonomy before we bring up the next chart. We get a lot of questions about this, and of course, when you do a massive survey like you're doing, you have to have consistency for time series, so you have to really think through what the buckets look like, if you will. So this next chart takes a look at the ETR taxonomy and it breaks it down into simple-to-understand terms. So the green is the portion of spending on a vendor's tech within a category that is accelerating, and the red is the portion that is decelerating. So Erik, what are the key messages in this data? >> Well, first of all, Dave, thank you so much for pointing that out. We used to do, just what we call a Net score. It's a proprietary formula that we use to determine the overall velocity of spending. Some people found it confusing. Our data scientists decided to break this sector, break down into what you said, which is really more of a mode analysis. In that sector, how many of the vendors are increasing versus decreasing? So again, I just appreciate you bringing that up and allowing us to explain the reasoning behind our analysis there. But what we're seeing here goes back to something you and I did last year when we did our predictions, and that was that IT services and consulting was going to have a true rebound in 2021, and that's what this is showing right here. So in this chart, you're going to see that consulting and services are really continuing their recovery, 2020 had a lot of the clients and they have the biggest sector year-over-year acceleration sector wise. The other thing to point out on this, which we'll get to again later, is that the inverse analysis is true for video conferencing. We will get to that, so I'm going to leave a little bit of ammunition behind for that one, but what we're seeing here is IT consulting services being the real favorable and video conferencing having a little bit more trouble. >> Great, okay, and then let's take a look at that services piece, and this next chart really is a drill down into that space and emphasizes, Erik, what you were just talking about. And we saw this in IBM's earnings, where still more than 60% of IBM's business comes from services and the company beat earnings, in part, due to services outperforming expectations, I think it had a somewhat easier compare and some of this pent-up demand that we've been talking about bodes well for IBM and other services companies, it's not just IBM, right, Erik? >> No, it's not, but again, I'm going to point out that you and I did point out IBM in our predictions when we did in late December, so, it is nice to see. One of the reasons we don't have a more favorable rating on IBM at the moment is because they are in the process of spinning out this large unit, and so there's a little bit of a corporate action there that keeps us off on the sideline. But I would also want to point out here, Tata, Infosys and Cognizant 'cause they're seeing year-over-year acceleration in both IT consulting and outsourced IT services. So we break those down separately and those are the three names that are seeing acceleration in both of those. So again, at the Tata, Infosys and Cognizant are all looking pretty well positioned as well. >> So we've been talking a little bit about this skills shortage, and this is what's, I think, so hard for forecasters, is that in the one hand, There's a lot of pent up demand, Scott Gottlieb said it's like Woodstock coming out of the COVID, but on the other hand, if you have a talent gap, you've got to rely on external services. So there's a learning curve, there's a ramp up, it's an external company, and so it takes time to put those together. So this data that we're going to show you next, is really important in my view and ties what we were saying at the top. It asks respondents to comment on their staffing plans. The light blue is "We're increasing staff", the gray is "No change" and the magenta or whatever, whatever color that is that sort of purplish color, anyway, that color is decreasing, and the picture is very positive across the board. Full-time staff, offshoring, contract employees, outsourced professional services, all up trending upwards, and this Erik is more evidence of the services bounce back. >> Yeah, it's certainly, yes, David, and what happened is when we caught this trend, we decided to go one level deeper and say, all right, we're seeing this, but we need to know why, and that's what we always try to do here. Data will tell you what's happening, it doesn't always tell you why, and that's one of the things that ETR really tries to dig in with through the insights, interviews panels, and also going direct with these more custom survey questions. So in this instance, I think the real takeaway is that 30% of the respondents said that their outsourced and managed services are going to increase over the next three months. That's really powerful, that's a large portion of organizations in a very short time period. So we're capturing that this acceleration is happening right now and it will be happening in real time, and I don't see it slowing down. You and I are speaking about we have to increase Cloud spend, we have to increase hybrid spend, there are refresh cycles coming up, and there's just a real skills shortage. So this is a long-term setup that bodes very well for IT services and consulting. >> You know, Erik, when I came out of college, somebody told me, "Read, read, read, read as much as you can." And then they said, "Read the Wall Street Journal every day." and so I did it, and I would read the tech magazines and back then it was all paper, and what happens is you begin to connect the dots. And so the reason I bring that up is because I've now taken a bath in the ETR data for the better part of two years and I'm beginning to be able to connect the dots. The data is not always predictive, but many, many times it is. And so this next data gets into the fun stuff where we name names. A lot of times people don't like it because they're either marketing people at organizations, say, "Well, data's wrong." because that's the first thing they do, is attack the data. But you and I know, we've made some really great calls, work from home, for sure, you're talking about the services bounce back. We certainly saw the rise of CrowdStrike, Okta, Zscaler, well before people were talking about that, same thing with video conferencing. And so, anyway, this is the fun stuff and it looks at positive versus negative sentiment on companies. So first, how does ETR derive this data and how should we interpret it, and what are some of your takeaways? >> Sure, first of all, how we derive the data, are systematic survey responses that we do on a quarterly basis, and we standardize those responses to allow for time series analysis so we can do trend analysis as well. We do find that our data, because it's talking about forward-looking spending intentions, is really more predictive because we're talking about things that might be happening six months, three months in the future, not things that a lot of other competitors and research peers are looking at things that already happened, they're looking in the past, ETR really likes to look into the future and our surveys are set up to do so. So thank you for that question, It's a enjoyable lead in, but to get to the fun stuff, like you said, what we do here is we put ratings on the datasets. I do want to put the caveat out there that our spending intentions really only captures top-line revenue. It is not indicative of profit margin or any other line items, so this is only to be viewed as what we are rating the data set itself, not the company, that's not what we're in the game of doing. So I think that's very important for the marketing and the vendors out there themselves when they take a look at this. We're just talking about what we can control, which is our data. We're going to talk about a few of the names here on this highlighted vendors list. One, we're going to go back to that you and I spoke about, I guess, about six months ago, or maybe even earlier, which was the observability space. You and I were noticing that it was getting very crowded, a lot of new entrants, there was a lot of acquisition from more of the legacy or standard players in the space, and that is continuing. So I think in a minute, we're going to move into that observability space, but what we're seeing there is that it's becoming incredibly crowded and we're possibly seeing signs of them cannibalizing each other. We're also going to move on a little bit into video conferencing, where we're capturing some spend deceleration, and then ultimately, we're going to get into a little bit of a storage refresh cycle and talk about that. But yeah, these are the highlighted vendors for April, we usually do this once a quarter and they do change based on the data, but they're not usually whipsawed around, the data doesn't move that quickly. >> Yeah, so you can see some of the big names in the left-hand side, some of the SAS companies that have momentum. Obviously, ServiceNow has been doing very, very well. We've talked a lot about Snowflake, Okta, CrowdStrike, Zscaler, all very positive, as well as several others. I guess I'd add some things. I mean, I think if thinking about the next decade, it's Cloud, which is not going to be like the same Cloud as the last decade, a lot of machine learning and deep learning and AI and the Cloud is extending to the edge and the data center. Data, obviously, very important, data is decentralized and distributed, so data architectures are changing. A lot of opportunities to connect across Clouds and actually create abstraction layers, and then something that we've been covering a lot is processor performance is actually accelerating relative to Moore's law. It's probably instead of doubling every two years, it's quadrupling every two years, and so that is a huge factor, especially as it relates to powering AI and AI inferencing at the edge. This is a whole new territory, custom Silicon is really becoming in vogue and so something that we're watching very, very closely. >> Yeah, I completely, agree on that and I do think that the next version of Cloud will be very different. Another thing to point out on that too, is you can't do anything that you're talking about without collecting the data and organizations are extremely serious about that now. It seems it doesn't matter what industry they're in, every company is a data company, and that also bodes well for the storage goal. We do believe that there is going to just be a huge increase in the need for storage, and yes, hopefully that'll become portable across multi-Cloud and hybrid as well. >> Now, as Erik said, the ETR data, it's really focused on that top-line spend. So if you look on the right side of that chart, you saw NetApp was kind of negative, was very negative, right? But it is a company that's in transformation now, they've lowered expectations and they've recently beat expectations, that's why the stock has been doing better, but at the macro, from a spending standpoint, it's still stout challenged. So you have big footprint companies like NetApp and Oracle is another one. Oracle's stock is at an all time high, but the spending relative to sort of previous cycles are relative to, like for instance, Snowflake, much, much smaller, not as high growth, but they're managing expectations, they're managing their transition, they're managing profitability. Zoom is another one, Zoom looking negative, but Zoom's got to use its market cap now to transform and increase its TAM. And then Splunk is another one we're going to talk about. Splunk is in transition, it acquired SignalFX, It just brought on this week, Teresa Carlson, who was the head of AWS Public Sector. She's the president and head of sales, so they've got a go-to-market challenge and they brought in Teresa Carlson to really solve that, but Splunk has been trending downward, we called that several quarters ago, Erik, and so I want to bring up the data on Splunk, and this is Splunk, Erik, in analytics, and it's not trending in the right direction. The green is accelerating spend, the red is in the bars is decelerating spend, the top blue line is spending velocity or Net score, and the yellow line is market share or pervasiveness in the dataset. Your thoughts. >> Yeah, first I want to go back. There's a great point, Dave, about our data versus a disconnect from an equity analysis perspective. I used to be an equity analyst, that is not what we do here. And the main word you said is expectations, right? Stocks will trade on how they do compare to the expectations that are set, whether that's buy-side expectations, sell-side expectations or management's guidance themselves. We have no business in tracking any of that, what we are talking about is the top-line acceleration or deceleration. So, that was a great point to make, and I do think it's an important one for all of our listeners out there. Now, to move to Splunk, yes, I've been capturing a lot of negative commentary on Splunk even before the data turns. So this has been a about a year-long, our analysis and review on this name and I'm dating myself here, but I know you and I are both rock and roll fans, so I'm going to point out a Led Zeppelin song and movie, and say that the song remains the same for Splunk. We are just seeing recent spending attentions are taking yet another step down, both from prior survey levels, from year ago levels. This, we're looking at in the analytics sector and spending intentions are decelerating across every single group, and we went to one of our other slide analysis on the ETR+ platform, and you do by customer sub-sample, in analytics, it's dropping in every single vertical. It doesn't matter which one. it's really not looking good, unfortunately, and you had mentioned this is an analytics and I do believe the next slide is an information security. >> Yeah, let's bring that up. >> And unfortunately it's not doing much better. So this is specifically Fortune 500 accounts and information security. There's deep pockets in the Fortune 500, but from what we're hearing in all the insights and interviews and panels that I personally moderate for ETR, people are upset, that they didn't like the strong tactics that Splunk has used on them in the past, they didn't like the ingestion model pricing, the inflexibility, and when alternatives came along, people are willing to look at the alternatives, and that's what we're seeing in both analytics and big data and also for their SIM and security. >> Yeah, so I think again, I pointed Teresa Carlson. She's got a big job, but she's very capable. She's going to meet with a lot of customers, she's a go-to-market pro, she's going to to have to listen hard, and I think you're going to see some changes there. Okay, so sorry, there's more bad news on Splunk. So (indistinct) bring this up is Net score for Splunk and Elastic accounts. This is for analytics, so there's 106 Elastic accounts in the dataset that also have Splunk and it's trending downward for Splunk, that's why it's green for Elastic. And Erik, the important call out from ETR here is how Splunk's performance in Elastic accounts compares with its performance overall. The ELK stack, which obviously Elastic is a big part of that, is causing pain for Splunk, as is Datadog, and you mentioned the pricing issue, well, is it pricing in your assessment or is it more fundamental? >> It's multi-level based on the commentary we get from our ITDMs teams that take the survey. So yes, you did a great job with this analysis. What we're looking at is the spending within shared accounts. So if I have Splunk already, how am I spending? I'm sorry if I have Elastic already, how am I spending on Splunk? And what you're seeing here is it's down to about a 12% Net score, whereas Splunk overall, has a 32% Net score among all of its customers. So what you're seeing there is there is definitely a drain that's happening where Elastic is draining spend from Splunk and usage from them. The reason we used Elastic here is because all observabilities, the whole sector seems to be decelerating. Splunk is decelerating the most, but Elastic is the only one that's actually showing resiliency, so that's why we decided to choose these two, but you pointed out, yes, it's also Datadog. Datadog is Cloud native. They're more dev ops-oriented. They tend to be viewed as having technological lead as compared to Splunk. So a really good point. Dynatrace also is expanding their abilities and Splunk has been making a lot of acquisitions to push their Cloud services, they are also changing their pricing model, right? They're trying to make things a little bit more flexible, moving off ingestion and moving towards consumption. So they are trying, and the new hires, I'm not going to bet against them because the one thing that Splunk has going for them is their market share in our survey, they're still very well entrenched. So they do have a lot of accounts, they have their foothold. So if they can find a way to make these changes, then they will be able to change themselves, but the one thing I got to say across the whole sector is competition is increasing, and it does appear based on commentary and data that they're starting to cannibalize themselves. It really seems pretty hard to get away from that, and you know there are startups in the observability space too that are going to be even more disruptive. >> I think I want to key on the pricing for a moment, and I've been pretty vocal about this. I think the old SAS pricing model where you essentially lock in for a year or two years or three years, pay up front, or maybe pay quarterly if you're lucky, that's a one-way street and I think it's a flawed model. I like what Snowflake's doing, I like what Datadog's doing, look at what Stripe is doing, look at what Twilio is doing, you mentioned it, it's consumption-based pricing, and if you've got a great product, put it out there and damn, the torpedoes, and I think that is a game changer. I look at, for instance, HPE with GreenLake, I look at Dell with Apex, they're trying to mimic that model and apply it to infrastructure, it's much harder with infrastructure 'cause you've got to deploy physical infrastructure, but that is a model that I think is going to change, and I think all of the traditional SAS pricing is going to come under disruption over the next better part of the decades, but anyway, let's move on. We've been covering the APM space pretty extensively, application performance management, and this chart lines up some of the big players here. Comparing Net score or spending momentum from the April 20th survey, the gray is, sorry, the gray is the April 20th survey, the blue is Jan 21 and the yellow is April 21, and not only are Elastic and Datadog doing well relative to Splunk, Erik, but everything is down from last year. So this space, as you point out, is undergoing a transformation. >> Yeah, the pressures are real and it's sort of that perfect storm where it's not only the data that's telling us that, but also the direct feedback we get from the community. Pretty much all the interviews I do, I've done a few panels specifically on this topic, for anyone who wants to dive a little bit deeper. We've had some experts talk about this space and there really is no denying that there is a deceleration in spend and it's happening because that spend is getting spread out among different vendors. People are using a Datadog for certain aspects, they are using Elastic where they can 'cause it's cheaper. They're using Splunk because they have to, but because it's so expensive, they're cutting some of the things that they're putting into Splunk, which is dangerous, particularly on the security side. If I have to decide what to put in and whatnot, that's not really the right way to have security hygiene. So this space is just getting crowded, there's disruptive vendors coming from the emerging space as well, and what you're seeing here is the only bit of positivity is Elastic on a survey-over-survey basis with a slight, slight uptick. Everywhere else, year-over-year and survey-over-survey, it's showing declines, it's just hard to ignore. >> And then you've got Dynatrace who, based on the interviews you do in the (indistinct), one-on-one, or one-on-five, the private interviews that I've been invited to, Dynatrace gets very high scores for their roadmap. You've got New Relic, which has been struggling financially, but they've got a really good product and a purpose-built database just for this APM space, and then of course, you've got Cisco with AppD, which is a strong business for them, and then as you mentioned, you've got startups coming in, you got ChaosSearch, which Ed Walsh is now running, leave the data in place in AWS and really interesting model, Honeycomb is getting really disruptive, Jeremy Burton's company, Observed. So this space is it's becoming jumped ball. >> Yeah, there's a great line that came out of one of them, and that was that the lines are blurring. It used to be that you knew exactly that AppDynamics, what they were doing, it was APM only, or it was logging and monitoring only, and a lot of what I'm hearing from the ITDM experts is that the lines are blurring amongst all of these names. They all have functionality that kind of crosses over each other. And the other interesting thing is it used to be application versus infrastructure monitoring, but as you know, infrastructure is becoming code more and more and more, and as infrastructure becomes code, there's really no difference between application and infrastructure monitoring. So we're seeing a convergence and a blurring of the lines in this space, which really doesn't bode well, and a great point about New Relic, their tech gets good remarks. I just don't know if their enterprise level service and sales is up to snuff right now. As one of my experts said, a CTO of a very large public online hospitality company essentially said that he would be shocked that within 18 months if all of these players are still standalone, that there needs to be some M and A or convergence in this space. >> Okay, now we're going to call out some of the data that really has jumped out to ETR in the latest survey, and some of the names that are getting the most queries from ETR clients, many of which are investor clients. So let's start by having a look at one of the most important and prominent work from home names, Zoom. Let's look at this. Erik is the ride over for Zoom? >> Ah, I've been saying it for a little bit of a time now actually. I do believe it is, and we'll get into it, but again, pointing out, great, Dave, the reason we're presenting today Splunk, Elastic and Zoom, they are the most viewed on the ETR+ platform. Trailing behind that only slightly is F5, I decided not to bring F5 to the table today 'cause we don't have a rating on the data set. So then I went one deep, one below that and it's pure. So the reason we're presenting these to you today is that these are the ones that our clients and our community are most interested in, which is hopefully going to gain interest to your viewers as well. So to get to Zoom, yeah, I call Zoom the pandemic bull market baby. This was really just one that had a meteoric ride. You look back, January in 2020, the stock was at $60 and 10 months later, it was like 580, that's in 10 months. That's cooled down a little bit into the mid-300s, and I believe that cooling down should continue, and the reason why is because we are seeing huge deceleration in our spending intentions. They're hitting all-time lows, it's really just a very ugly dataset. More importantly than the spending intentions, for the first time, we're seeing customer growth in our survey flatten. In the past, we knew that the deceleration of spend was happening, but meanwhile, their new customer growth was accelerating, so it was kind of hard to really make any call based on that. This is the first time we're seeing flattening customer growth trajectory, and that in tandem with just dominance from Microsoft in every sector they're involved in, I don't care if it's IP telephony, productivity apps or the core video conferencing, Microsoft is just dominating. So there's really just no way to ignore this anymore. The data and the commentary state that Zoom is facing some headwinds. >> Well, plus you've pointed out to me that a lot of your private conversations with buyers says that, "Hey, we're, we're using the freebie version of Zoom, and we're not paying them." And that combined with Teams, I mean, it's... I think, look, Zoom, they've got to figure out how to use their elevated market cap to transform and expand their TAM, but let's move on. Here's the data on Pure Storage and we've highlighted a number of times this company is showing elevated spending intentions. Pure announced it's earnings in May, IBM just announced storage, it was way down actually. So still, Pure, more positive, but I'll on that comment in a moment, but what does this data tell you, Erik? >> Yeah, right now we started seeing this data last survey in January, and that was the first time we really went positive on the data set itself, and it's just really continuing. So we're seeing the strongest year-over-year acceleration in the entire survey, which is a really good spot to be. Pure is also a leading position among its sector peers, and the other thing that was pretty interesting from the data set is among all storage players, Pure has the highest positive public Cloud correlation. So what we can do is we can see which respondents are accelerating their public Cloud spend and then cross-reference that with their storage spend and Pure is best positioned. So as you and I both know, digital transformation Cloud spending is increasing, you need to be aligned with that. And among all storage sector peers, Pure is best positioned in all of those, in spending intentions and adoptions and also public Cloud correlation. So yet again, to start another really strong dataset, and I have an anecdote about why this might be happening, because when I saw the data, I started asking in my interviews, what's going on here? And there was one particular person, he was a director of Cloud operations for a very large public tech company. Now, they have hybrid, but their data center is in colo, So they don't own and build their own physical building. He pointed out that during COVID, his company wanted to increase storage, but he couldn't get into his colo center due to COVID restrictions. They weren't allowed. You had 250,000 square feet, right, but you're only allowed to have six people in there. So it's pretty hard to get to your rack and get work done. He said he would buy storage, but then the colo would say, "Hey, you got to get it out of here. It's not even allowed to sit here. We don't want it in our facility." So he has all this pent up demand. In tandem with pent up demand, we have a refresh cycle. The SSD depreciation cycle is ending. SSDs are moving on and we're starting to see a new technology in that space, NVMe sorry, technology increasing in that space. So we have pent up demand and we have new technology and that's really leading to a refresh cycle, and this particular ITDM that I spoke to and many of his peers think this has a long tailwind that storage could be a good sector for some time to come. >> That's really interesting, thank you for that extra metadata. And I want to do a little deeper dive on storage. So here's a look at storage in the industry in context and some of the competitive. I mean, it's been a tough market for the reasons that we've highlighted, Cloud has been eating away that flash headroom. It used to be you'd buy storage to get more spindles and more performance and we're sort of forced to buy more, flash, gave more headroom, but it's interesting what you're saying about the depreciation cycle. So that's good news. So ETR combines, just for people's benefit here, combines primary and secondary storage into a single category. So you have companies like Pure and NetApp, which are really pure play primary storage companies, largely in the sector, along with Veeam, Cohesity and Rubrik, which are kind of secondary data or data protection. So my quick thoughts here that Pure is elevated and remains what I call the one-eyed man in the land of the blind, but that's positive tailwinds there, so that's good news. Rubrik is very elevated but down, it's big competitor, Cohesity is way off its highs, and I have to say to me, Veeam is like the Steady Eddy consistent player here. They just really continue to do well in the data protection business, and the highs are steady, the lows are steady. Dell is also notable, they've been struggling in storage. Their ISG business, which comprises servers and storage, it's been softer in COVID, and during even this new product rollout, so it's notable with this new mid range they have in particular, the uptick in Dell, this survey, because Dell is so large, a small uptick can be very good for Dell. HPE has a big announcement next month in storage, so that might improve based on a product cycle. Of course, the Nimble brand continues to do well, IBM, as I said, just announced a very soft quarter, down double digits again, and they're in a product cycle shift. And NetApp, it looks bad in the ETR data from a spending momentum standpoint, but their management team is transforming the company into a Cloud play, which Erik is why it was interesting that Pure has the greatest momentum in Cloud accounts, so that is sort of striking to me. I would have thought it would be NetApp, so that's something that we want to pay attention to, but I do like a lot of what NetApp is doing, and other than Pure, they're the only big kind of pure play in primary storage. So long-winded, intro there, Erik, but anything you'd add? >> No, actually I appreciate it as long-winded. I'm going to be honest with you, storage is not my best sector as far as a researcher and analyst goes, but I actually think that a lot of what you said is spot on. We do capture a lot of large organizations spend, we don't capture much mid and small, so I think when you're talking about these large, large players like NetApp not looking so good, all I would state is that we are capturing really big organization spending attention, so these are names that should be doing better to be quite honest, in those accounts, and at least according to our data, we're not seeing it in. It's longterm depression, as you can see, NetApp now has a negative spending velocity in this analysis. So, I can go dig around a little bit more, but right now the names that I'm hearing are Pure, Cohesity. I'm hearing a little bit about Hitachi trying to reinvent themselves in the space, but I'll take a wait-and-see approach on that one, but pure Cohesity are the ones I'm hearing a lot from our community. >> So storage is transforming to Cloud as a service. You've seen things like Apex in GreenLake from Dell and HPE and container storage. A little, so not really a lot of people paying attention to it, but Pure bought a company called Portworx which really specializes in container storage, and there's many startups there, they're trying to really change the way. David Flynn, has a startup in that space, he's the guy who started Fusion-io. So a lot of transformations happening here. Okay, I know it's been a long segment, we have to summarize, and let me go through a summary and then I'll give you the last word, Erik. So tech spending appears to be tracking US GDP at 6 to 7%. This talent shortage could be a blocker to accelerating IT deployments, so that's kind of good news actually for services companies. Digital transformation, it remains a priority, and that bodes, well, not only for services, but automation. UiPath went public this week, we profiled that extensively, that went public last Wednesday. Organizations that sit at the top face some tough decisions on how to allocate resources. They're running the business, growing the business, transforming the business, and we're seeing a bifurcation of spending and some residual effects on vendors, and that remains a theme that we're watching. Erik, your final thoughts. >> Yeah, I'm going to go back quickly to just the overall macro spending, 'cause there's one thing I think is interesting to point out and we're seeing a real acceleration among mid and small. So it seems like early on in the COVID recovery or COVID spending, it was the deep pockets that moved first, right? Fortune 500 knew they had to support remote work, they started spending first. Around that in the Fortune 500, we're only seeing about 5% spend, but when you get into mid and small organizations, that's creeping up to eight, nine. So I just think it's important to point out that they're playing catch up right now. I also would point out that this is heavily skewed to North America spending. We're seeing laggards in EMEA, they just don't seem to be spending as much. They're in a very different place in their recovery, and I do think that it's important to point that out. Lastly, I also want to mention, I know you do such a great job on following a lot of the disruptive vendors that you just pointed out, with Pure doing container storage, we also have another bi-annual survey that we do called Emerging Technology, and that's for the private names. That's going to be launching in May, for everyone out there who's interested in not only the disruptive vendors, but also private equity players. Keep an eye out for that. We do that twice a year and that's growing in its respondents as well. And then lastly, one comment, because you mentioned the UiPath IPO, it was really hard for us to sit on the sidelines and not put some sort of rating on their dataset, but ultimately, the data was muted, unfortunately, and when you're seeing this kind of hype into an IPO like we saw with Snowflake, the data was resoundingly strong. We had no choice, but to listen to what the data said for Snowflake, despite the hype. We didn't see that for UiPath and we wanted to, and I'm not making a large call there, but I do think it's interesting to juxtapose the two, that when snowflake was heading to its IPO, the data was resoundingly positive, and for UiPath, we just didn't see that. >> Thank you for that, and Erik, thanks for coming on today. It's really a pleasure to have you, and so really appreciate the collaboration and look forward to doing more of these. >> Yeah, we enjoy the partnership greatly, Dave. We're very happy to have you on the ETR family and looking forward to doing a lot, lot more with you in the future. >> Ditto. Okay, that's it for today. Remember, these episodes are all available as podcasts wherever you listen. All you have to do is search "Breaking Analysis" podcast, and please subscribe to the series. Check out ETR website it's etr.plus. We also publish a full report every week on wikibon.com and siliconangle.com. You can email me, david.vellante@siliconangle.com, you can DM me on Twitter @dvellante or comment on our LinkedIn posts. I could see you in Clubhouse. This is Dave Vellante for Erik Porter Bradley for the CUBE Insights powered by ETR. Have a great week, stay safe, be well and we'll see you next time. (bright music)

>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> UiPath has had a long, strange trip to IPO. How so you ask? Well, the company was started in 2005. But it's culture, is akin to a frenetic startup. The firm shunned conventions and instead of focusing on a narrow geographic area to prove its product market fit before it started to grow, it aggressively launched international operations prior to reaching unicorn status. Well prior, when it had very little revenue, around a million dollars. Today, more than 60% of UiPath business is outside of the United States. Despite its headquarters being in New York city. There's more, according to recent SEC filings, UiPath total revenue grew 81% last year. But it's free cash flow, is actually positive, modestly. Wait, there's more. The company raised $750 million in a Series F in early February, at a whopping $35 billion valuation. Yet, the implied back of napkin valuation, based on the number of shares outstanding after the offering multiplied by the proposed maximum offering price per share yields evaluation of just under 26 billion. (Dave chuckling) And there's even more to this crazy story. Hello everyone, and welcome to this week's Wikibon CUBE Insights, Powered by ETR. In this Breaking Analysis we'll share our learnings, from sifting through hundreds of pages (paper rustling) of UiPath's red herring. So you didn't have to, we'll share our thoughts on its market, its competitive position and its outlook. Let's start with a question. Mark Roberge, is a venture capitalist. He's a managing director at Stage 2 Capital and he's also a teacher, a professor at the B-School in Harvard. One of his favorite questions that he asks his students and others, is what's the best way to grow a company? And he uses this chart to answer that question. On the vertical axis is customer retention and the horizontal axis is growth to growth rate and you can see he's got modest and awesome and so forth. Now, so I want to let you look at it for a second. What's the best path to growth? Of course you want to be in that green circle. Awesome retention of more than 90% and awesome growth but what's the best way to get there? Should you blitz scale and go for the double double, triple, triple blow it out and grow your go to market team on the horizontal axis or should be more careful and focus on nailing retention and then, and only then go for growth? What do you think? What do you think most VCs would say? What would you say? When you want to maybe run the table, capture the flag before your competitors could get there or would you want to take a more conservative approach? What would Daniel Dines say the CEO of UiPath? Again, I'll let you think about that for a second. Let's talk about UiPath. What did they do? Well, I shared at the top that the company shunned conventions and expanded internationally, very rapidly. Well before it hit escape velocity and they grew like crazy and it got out of control and he had to reign it in, plug some holes, but the growth didn't stop, go. So very clearly based on it's performance and reading through the S1, the company has great retention. It uses a metric called gross retention rate which is at 96 or 97%, very high. Says customers are sticking with it. So maybe that's the right formula go for growth and grow like crazy. Let chaos reign, then reign in the chaos as Andy Grove would say. Go fast horizontally, and you can go vertically. Let me tell you what I think Mark Roberge would say, he told me you can do that. But churn is the silent killer of SaaS companies and perhaps the better path is to nail product market fit. And then your retention metrics, before you go into hyperbolic growth mode. There's all science behind this, which may be antithetical to the way many investors want to roll the dice and go for super growth, like go fast or die. Well, it worked for UiPath you might say, right. Well, no. And this is where the story gets even more interesting and long and strange for UiPath. As we shared earlier, UiPath was founded in 2005 out of Bucharest Romania. The company actually started as a software outsourcing startup. It called the company, DeskOver and it built automation libraries and SDKs for companies like Microsoft, IBM and Google and others. It also built automation scripts and developed importantly computer vision technology which became part of its secret sauce. In December 2015, DeskOver changed its name to UiPath and became a Delaware Corp and moved its headquarters to New York City a couple of years later. So our belief is that UiPath actually took the preferred path of Mark Roberge, five ticks North, then five more East. They slow-cooked for the better part of 10 years trying to figure out what market to serve. And they spent that decade figuring out their product market fit. And then they threw gas in the fire. Pretty crazy. All right, let's take a peak (chuckling) at the takeaways from the UiPath S1 the numbers are impressive. 580 million ARR with 65% growth. That asterisk is there because like you, we thought ARR stood for annual recurring revenue. It really stands for annualized renewal run rate. annualized renewal run rate is a metric that is one of UiPath's internal KPIs and are likely communicate that publicly over time. We'll explain that further in a moment. UiPath has a very solid customer base. Nearly 8,000, I've interviewed many of them. They're extremely happy. They have very high retention. They get great penetration into the fortune 500, around 63% of the fortune 500 has UiPath. Most of UiPath business around 70% comes from existing customers. I always say you're going to get more money out of existing customers than new customers but everybody's trying to go out and get new customers. But UiPath I think is taking a really interesting approach. It's their land and expand and they didn't invent that term but I'll come back to that. It kind of reminds me of the early days of Tableau. Actually I think Tableau is an interesting example. Like UiPath, Tableau started out as pretty much a point tool and it had, but it had very passionate customers. It was solving problems. It was simplifying things. And it would have bid into a company and grow and grow. Now the market fundamentals for UiPath are very good. Automation is super hot right now. And the pandemic has created an automation mandate to date and I'll share some data there as well. UiPath is a leader. I'm going to show you the Gartner Magic Quadrant for RPA. That's kind of a good little snapshot. UiPath pegs it's TAM at 60 billion dollars based on some bottoms up calculations and some data from Bain. Pre-pandemic, we pegged it at over 30 billion and we felt that was conservative. Post-pandemic, we think the TAM is definitely higher because of that automation mandate, it's been accelerated. Now, according to the S1, UiPath is going to raise around 1.2 billion. And as we said, if that's an implied valuation that is lower than the Series F, so we suspect the Series F investors have some kind of ratchet in there. UiPath needed the cash from its Series F investors. So it took in 750 million in February and its balance sheet in the S1 shows about 474 million in cash and equivalent. So as I say, it needed that cash. UiPath has had significant expense reductions that we'll show you in some detail. And it's brought in some fresh talent to provide some adult supervision around 70% of its executive leadership team and outside directors came to the company after 2019 and the company's S1, it disclosed that it's independent accounting firm identified last year what it called the "material weakness in our internal controls over financial report relating to revenue recognition for the fiscal year ending 2018, caused by a lack of oversight and technical competence within the finance department". Now the company outlined the steps it took to remediate the problem, including hiring new talent. However, we said that last year, we felt UiPath wasn't quite ready to go public. So it really had to get its act together. It was not as we said at the time, the well-oiled machine, that we said was Snowflake under Mike Scarpelli's firm operating guidance. The guy's the operational guru, but we suspect the company wants to take advantage of this mock market. It's a good time to go public. It needs the cash to bolster its balance sheet. And the public offering is going to give it cache in a stronger competitive posture relative to its main new competitor, autumn newbie competitor Automation Anywhere and the big whales like Microsoft and others that aspire and are watching what UiPath is doing and saying, hey we want a piece of that action. Now, one other note, UiPath's CEO Daniel Dines owns 100% of the class B shares of the company and has a 35 to one voting power. So he controls the company, subject of course to his fiduciary responsibilities but if UiPath, let's say it gets in trouble financially, he has more latitude to do secondary offerings. And at the same time, it's insulated from activist shareholders taking over his company. So lots of detail in the S1 and we just wanted to give you some of those highlights. Here are the pretty graphs. If whoever wrote this F1 was a genius. It's just beautiful. As we said, ARR, annualized renewal run rate all it does is it annualizes the invoice amount from subscriptions in the maintenance portion of the revenue. In other words, the parts that are recurring revenue, it excludes revenue from support and perpetual license. Like one-time licenses and services is just kind of the UiPath's and maybe that's some sort of legacy there. It's future is that recurring revenue. So it's pretty similar to what we think of as ARR, but it's not exact. Lots of customers with a growing number of six and seven figure accounts and a dollar-based net retention of 145%. This figure represents the rate of net expansion of the UiPath ARR, from existing listing customers over a 12 month period. Translation. This says UiPath's existing customers are spending more with the company, land and expand and we'll share some data from ETR on that. And as you can see, the growth of 86% CAGR over the past nine quarters, very impressive. Let's talk about some of the fundamentals of UiPath's business. Here's some data from the Brookings Institute and the OECD that shows productivity statistics for the US. The smaller charts in the right are for Germany and Japan. And I've shared some similar data before the US showed in the middle there. Showed productivity improvements with the personal productivity boom in the mid to late 90s. And it spilled into the early 2000s. But since then you can see it's dropped off quite significantly. Germany and Japan are also under pressure as are most developed countries. China's labor productivity might show declines but it's level, is at level significantly higher than these countries, April 16th headline of the Wall Street Journal says that China's GDP grew 18% this quarter. So, we've talked about the snapback in post-COVID and the post-isolation economy, but these are kind of one time bounces. But anyway, the point is we're reaching the limits of what humans can do alone to solve some of the world's most pressing challenges. And automation is one key to shifting labor away from these more mundane tasks toward more productive and more important activities that can deliver lasting benefits. This according to UiPath, is its stated purpose to accelerate human achievement, big. And the market is ready to be automated, for the most part. Now the post-isolation economy is increasingly going to focus on automation to drive toward activity as we've discussed extensively, I got to share the RPA Magic Quadrant where nearly everyone's a winner, many people are of course happy. Many companies are happy, just to get into the Magic Quadrant. You can't just, you have to have certain criteria. So that's good. That's what I mean by everybody wins. We've reported extensively on UiPath and Automation Anywhere. Yeah, we think we might shuffle the deck a little bit on this picture. Maybe creating more separation between UiPath and Automation Anywhere and the rest. And from our advantage point, UiPath's IPO is going to either force Automation Anywhere to respond. And I don't know what its numbers are. I don't know if it's ready. I suspect it's not, we'd see that already but I bet you it's trying to get there. Or if they don't, UiPath is going to extend its lead even further, that would be our prediction. Now personally, I would have Pegasystems higher on the vertical. Of course they're not an IPO, RPA specialist, so I kind of get what Gartner is doing there but I think they're executing well. And I'd probably, in a broader context I'd probably maybe drop blue prism down a little bit, even though last year was a pretty good year for the company. And I would definitely have Microsoft looming larger up in the upper left as a challenger more than a visionary in my opinion, but look, Gartner does good work and its analysts are very deep into this stuff, deeper than I am. So I don't want to discount that. It's just how I see it. Let's bring in the ETR data and show some of the backup here. This is a candlestick chart that shows the components of net score, which is spending momentum, however, ETR goes out every quarter. Says you're spending more, you're spending less. They subtract the lesses from the mores and that's net score. It's more complicated than that, but that's that blue line that you see in the top and yes it's trending downward but it's still highly elevated. We'll talk about that. The market share is in the yellow line at the bottom there. That green represents the percentage of customers that are spending more and the reds are spending less or replacing. That gray is flat. And again, even though UiPath's net score is declining, it's that 61%, that's a very elevated score. Anything over 40% in our view is impressive. So it's, UiPath's been holding in the 60s and 70s percents over the past several years. That's very good. Now that yellow line market share, yes it dips a bit, but again it's nuanced. And this is because Microsoft is so pervasive in the data stat. It's got so many mentions that it tends to somewhat overwhelm and skew these curves. So let's break down net score a little bit. Here's another way to look at this data. This is a wheel chart we show this often it shows the components of net score and what's happening here is that bright red is defection. So look at it, it's very small that wouldn't be churn. It's tiny. Remember that it's churn is the killer for software companies. And so that forest green is existing customers spending more at 49%, that's big. That lime green is new customers. So again, it's from the S1, 70% of UiPath's revenue comes from existing customers. And this really kind of underscores that. Now here's more evidence in the ETR data in terms of land and expand. This is a snapshot from the January survey and it lines up UiPath next to its competitors. And it cuts the data just on those companies that are increasing spending. It's so that forest green that we saw earlier. So what we saw in Q1 was the pace of new customer acquisition for UiPath was decelerating from previous highs. But UiPath, it shows here is outpacing its competition in terms of increasing spend from existing customers. So we think that's really important. UiPath gets very high scores in terms of customer satisfaction. There's, I've talked to many in theCUBE. There's places on the web where we have customer ratings. And so you want to check that out, but it'll confirm that the churn is low, satisfaction is high. Yeah, they get dinged sometimes on pricing. They get dinged sometimes, lately on service cause they're growing so fast. So, maybe they've taken the eye off the ball in a couple of counts, but generally speaking clients are leaning in, they're investing heavily. They're creating centers of excellence around RPA and automation, and UiPath is very focused on that. Again, land and expand. Now here's further evidence that UiPath has a strong account presence, even in accounts where its competitors are presence. In the 149 shared accounts from the Q1 survey where UiPath, Automation Anywhere and Microsoft have a presence, UiPath's net score or spending velocity is not only highly elevated, it's relative momentum, is accelerating compared to last year. So there's some really good news in the numbers but some other things stood out in the S1 that are concerning or at least worth paying attention to. So we want to talk about that. Here is the income statement and look at the growth. The company was doing like 1 million dollars in 2015 like I said before. And when it started to expand internationally it surpassed 600 million last year. It's insane growth. And look at the gross profit. Gross margin is almost 90% because revenue grew so rapidly. And last year, its cost went down in some areas like its services, less travel was part of that. Now jump down to the net loss line. And normally you would expect a company growing at this rate to show a loss. The street wants growth and UiPath is losing money, but it's net loss went from 519 million, half a billion down to only 92 million. And that's because the operating expenses went way down. Now, again, typically a company growing at this rate would show corresponding increases in sales and marketing expense, R&D and even G&A but all three declined in the past 12 months. Now reading the notes, there was definitely some meaningful savings from no travel and canceled events. UiPath has great events around the world. In fact theCUBE, Knock Wood is going to be at its event in October, in Las Vegas at the Bellagio . So we're stoked for that. But, to drop expenses that precipitously with such high growth, is kind of strange. Go look at Snowflake's income statement. They're in hyper-growth as well. We like to compare it to Snowflake is a very well-run company and it's in hyper-growth mode, but it's sales and marketing and R&D and G&A expense lines. They're all growing along with that revenue. Now, perhaps they're growing at a slower rate. Perhaps the percent of revenue is declining as it should as they achieve operating leverage but they're not shrinking in absolute dollar terms as shown in the UiPath S1. So either UiPath has applied some magic automation mojo to it's business (chuckling). Like magic beans or magic grits with my cousin Vinny. Maybe it has found the Holy grail of operating leverage. It's a company that's all about automation or the company was running way too hot on the expense side and had a cut and clean up its income statement for the IPO and conserve some cash. Our guess is the latter but maybe there's a combination there. We'll give him the benefit of the doubt. And just to add a bit more to this long, strange trip. When have you seen an explosive growth company just about to go public, show positive cashflow? Maybe it's happened, but it's rare in the tech and software business these days. Again, go look at companies like Snowflake. They're not showing positive cashflow, not yet anyway. They're growing and trying to run the table. So you have to ask why is UiPath operating this way? And we think it's because they were so hot and burning cash that they had to reel things in a little bit and get ready to IPO. It's going to be really interesting to see how this stock reacts when it does IPO. So here's some things that we want you to pay attention to. We have to ask. Is this IPO, is it window dressing? Or did UiPath again uncover some new productivity and operating leverage model. I doubt there's anything radically new here. This company doesn't want to miss the window. So I think it said, okay, let's do this. Let's get ready for IPO. We got to cut expenses. It had a lot of good advisors. It surrounded itself with a new board. Extended that board, new management, and really want to take advantage of this because it needs the cash. In addition, it really does want to maintain its lead. It's got Automation Anywhere competing with it. It's got Microsoft looming large. And so it wants to continue to lead. It's made some really interesting acquisitions. It's got very strong vision as you saw in the Gartner Magic Quadrant and obviously it's executing well but it's really had to tighten things up. So we think it's used the IPO as a fortune forcing function to really get its house in order. Now, will the automation mandate sustain? We think it will. The forced match to digital worked, it was effective. It wasn't pleasant, but even in a downturn we think it will confer advantage to automation players and particularly companies like UiPath that have simplified automation in a big way and have done a great job of putting in training, great freemium model and has a culture that is really committed to the future of humankind. It sounds ambitious and crazy but talk to these people, you'll see it's true. Pricing, UiPath had to dramatically expand or did dramatically expand its portfolio and had to reprice everything. And I'm not so worried about that. I think it'll figure that pricing out for that portfolio expansion. My bigger concern is for SaaS companies in general. I don't like SaaS pricing that has been popularized by Workday and ServiceNow, and Salesforce and DocuSign and all these companies that essentially lock you in for a year or two and basically charge you upfront. It's really is a one-way street. You can't dial down. You can only dial up. It's not true Cloud pricing. You look at companies like Stripe and Datadog and Snowflake. It is true Cloud pricing. It's consumption pricing. I think the traditional SaaS pricing model is flawed. It's very unfairly weighted toward the vendors and I think it's going to change. Now, the reason we put cloud on the chart is because we think Cloud pricing is the right way to price. Let people dial up and dial down, let them cancel anytime and compete on the basis of your product excellence. And yeah, give them a price concession if they do lock in. But the starting point we think should be that flexibility, pay by the drink. Cancel anytime. I mentioned some companies that are doing that as well. If you look at the modern SaaS startups and the forward-thinking VCs they're really pushing their startups to this model. So we think over time that the term lock-in model is going to give way to true consumption-based pricing and at the clients option, allow them to lock-in for a better price, way better model. And UiPath's Cloud revenue today is minimal but over time, we think it's going to continue to grow that cloud. And we think it will force a rethink in pricing and in revenue recognition. So watch for that. How is the street going to react to Daniel Dines having basically full control of the company? Generally, we feel that that solid execution if UiPath can execute is going to outweigh those concerns. In fact, I'm very confident that it will. We'll see, I kind of like what the CEO says has enough mojo to say (chuckling) you know what, I'm not going to let what happened to for instance, EMC happen to me. You saw Michael Dell do that. You saw just this week they're spinning out VMware, he's maintaining his control. VMware Dell shareholders get get 40.44 shares for every Dell share they're holding. And who's the biggest shareholder? Michael Dell. So he's, you got two companies, one chairman. He's controlling the table. Michael Dell beat the great Icahn. Who beats Carl Icahn? Well, Michael Dell beats Carl Icahn. So Daniel Dines has looked at that and says, you know what? I'm not just going to give up my company. And the reason I like that with an if, is that we think will allow the company to focus more on the long-term. The if is, it's got to execute otherwise it's so much pressure and look, the bottom line is that UiPath has really favorable market momentum and fundamentals. But it is signing up for the 90 day short clock. The fact that the CEO has control again means they can look more long term and invest accordingly. Oftentimes that's easier said than done. It does come down to execution. So it is going to be fun to watch (chuckling). That's it for now, thanks to the community for your comments and insights and really always appreciate your feedback. Remember, I publish each week on Wikibon.com and siliconangle.com and these episodes are all available as podcasts. All you got to do is search for the Breaking Analysis podcast. You can always connect with me on Twitter @dvellante or email me at david.vellante@siliconangle.com or comment on my LinkedIn posts. And we'll see you in clubhouse. Follow me and get notified when we start a room, which we've been doing with John Furrier and Sarbjeet Johal and others. And we love to riff on these topics and don't forget, please check out etr.plus for all the survey action. This is Dave Vellante, for theCUBE Insights Powered by ETR. Be well everybody. And we'll see you next time. (gentle upbeat music)

>>from around the globe. It's the Cube with digital coverage of Dell Technologies. World Digital Experience Brought to You by Dell Technologies. >>Welcome to the Cubes Coverage of Dell Technologies World 2020. The digital coverage Find Lisa Martin And then we started to be talking with one of Dell Technologies customers. Earth Camp. Joining Me is built sharp, the senior VP of product development and strategy from Earth Camp Phil, Welcome to the Cube. >>Thank you so much. >>So talk to me a little bit. About what Earth Cam does this very interesting Web can technology? You guys have tens of thousands of cameras and sensors all over the globe give her audience and understanding of what you guys are all about. >>Sure thing. The world's leading provider of Webcam technologies and mentioned content services were leaders and live streaming time lapse imaging primary focus in the vertical construction. So a lot of these, the most ambitious, largest construction projects around the world, you see, these amazing time lapse movies were capturing all of that imagery. You know, basically, around the clock of these cameras are are sending all of that image content to us when we're generating these time lapse movies from it. >>You guys, you're headquartered in New Jersey and I was commenting before we went live about your great background. So you're actually getting to be on site today? >>Yes, Yes, that's where lives from our headquarters in Upper Saddle River, New Jersey. >>Excellent. So in terms of the types of information that you're capturing. So I was looking at the website and see from a construction perspective or some of the big projects you guys have done the Hudson Yards, the Panama Canal expansion, the 9 11 Museum. But you talked about one of the biggest focus is that you have is in the construction industry in terms of what type of data you're capturing from all of these thousands of edge devices give us a little bit of insight into how much data you're capturing high per day, how it gets from the edge, presumably back to your court data center for editing. >>Sure, and it's not just construction were also in travel, hospitality, tourism, security, architectural engineering, basically, any any industry that that need high resolution visualization of their their projects or their their performance or of their, you know, product flow. So it's it's high resolution documentation is basically our business. There are billions of files in the isil on system right now. We are ingesting millions of images a month. We are also creating very high resolution panoramic imagery where we're taking hundreds and sometimes multiple hundreds of images, very high resolution images and stitching these together to make panoramas that air up to 30 giga pixel, sometimes typically around 1 to 2 giga pixel. But that composite imagery Eyes represents millions of images per per month coming into the storage system and then being, uh, stitched together to those those composites >>the millions of images coming in every month. You mentioned Isil on talk to me a little bit about before you were working with Delhi, EMC and Power Scale. How are you managing this massive volume of data? >>Sure we had. We've used a number of other enterprise storage systems. It was really nothing was as easy to manage Azazel on really is there was there was a lot of a lot of problems with overhead, the amount of time necessary from a systems administrator resource standpoint, you to manage that, uh, and and it's interesting with the amount of data that we handle. This is being billions of relatively small files there there, you know, half a megabyte to a couple of megabytes each. It's an interesting data profile, which, which isil on really is well suited for. >>So if we think about some of the massive changes that we've all been through the last in 2020 what are some of the changes that that Earth Kemp has seen with respect to the needs for organizations? Or you mentioned other industries, like travel hospitality? Since none of us could get to these great travel destinations, Have you seen a big drive up in the demand and the need to process data more data faster? >>Yeah, that's an injury interesting point with with the Pandemic. Obviously we had to pivot and move a lot of people toe working from home, which we were able to do pretty quickly. But there's also an interesting opportunity that arose from this, where so many of our customers and other people also have to do the same. And there is an increased demand for our our technology so people can remotely collaborate. They can. They can work at a distance. They can stay at home and see what's going on in these projects sites. So we really so kind of an uptick in the in the need for our products and services. And we've also created Cem basically virtual travel applications. We have an application on the Amazon Fire TV, which is the number one app in the travel platform of people can kind of virtually travel when they can't really get out there. So it's, uh, we've been doing kind of giving back Thio to people that are having having some issues with being able to travel around. We've done the fireworks of the Washington Mall around the Statue of Liberty for the July 4th, and this year will be Webcasting and New Year's in Times Square for our 25th year, actually. So again, helping people travel virtually and be, uh, maintain can be collectivity with with each other and with their projects, >>which is so essential during these times, where for the last 67 months everyone is trying to get a sense of community, and most of us just have the Internet. So I also heard you guys were available on Apple TV, someone to fire that up later and maybe virtually travel. Um, but tell me a little bit about how working in conjunction with Delta Technologies and Power Cell How is that enabled you to manage this massive volume change you've experienced this year? Because, as you said, it's also about facilitating collaboration, which is largely online these days. >>Yeah, I mean, the the great things they're working with Dell has been just our confidence in this infrastructure. Like I said, the other systems we worked with in the past we've always found ourselves kind of second guessing. Obviously, resolutions are increasing. The camera performance is increasing. Streaming video is everything is is constantly getting bigger and better, faster. Maurits And we're always innovating. We found ourselves on previous storage platforms having to really kind of go back and look at the second guess we're at with it With with this, this did L infrastructure. That's been it's been fantastic. We don't really have to think about that as much. We just continue innovating everything scales as we needed to dio. It's it's much easier to work with, >>so you've got power scale at your core data center in New Jersey. Tell me a little bit about how data gets from thes tens of thousands of devices at the edge, back to your editors for editing and how power scale facilitates faster editing, for example. >>Basically, you imagine every one of these cameras on It's not just camera. We have mobile applications. We have fixed position of robotic cameras. There's all these different data acquisition systems were integrating with weather sensors and different types of telemetry. All of that data is coming back to us over the Internet, so these are all endpoints in our network. Eso that's that's constantly being ingested into our network and say WTO. I salon the big the big thing that's really been a timesaver Working with the video editors is, instead of having to take that content, move it into an editing environment where we have we have a whole team of award winning video editors. Creating these time lapse is we don't need to keep moving that around. We're working natively on Iselin clusters. They're doing their editing, their subsequent edits. Anytime we have to update or change these movies as a project evolves, that's all it happened right there on that live environment on the retention. Is there if we have to go back later on all of our customers, data is really kept within that 11 area. It's consolidated, its secure. >>I was looking at the Del Tech website. There's a case study that you guys did earth campaign with Deltek saying that the video processing time has been reduced 20%. So that's a pretty significant increase. I could imagine what the volumes changing so much now but on Li not only is huge for your business, but to the demands that your customers have as well, depending on where there's demands are coming from >>absolutely and and just being able to do that a lot faster and be more nimble allows us to scale. We've added actually against speaking on this pandemic, we've actually added person who we've been hiring people. A lot of those people are working remotely, as as we've stated before on it's just with the increase in business. We have to continue to keep building on that on this storage environments been been great. >>Tell me about what you guys really kind of think about with respect to power scale in terms of data management, not storage management and what that difference means to your business. >>Well, again, I mean number number one was was really eliminating the amount of resource is amount of time we have to spend managing it. We've almost eliminated any downtime of any of any kind. We have greater storage density, were able to have better visualization on how our data is being used, how it's being access so as thes as thes things, a revolving. We really have good visibility on how the how the storage system is being used in both our production and our and also in our backup environments. It's really, really easy for us Thio to make our business decisions as we innovate and change processes, having that continual visibility and really knowing where we stand. >>And you mentioned hiring folks during the pandemic, which is fantastic but also being able to do things much in a much more streamlined way with respect to managing all of this data. But I am curious in terms of of innovation and new product development. What have you been able to achieve because you've got more resource is presumably to focus on being more innovative rather than managing storage >>well again? It's were always really pushing the envelope of what the technology can do. As I mentioned before, we're getting things into, you know, 20 and 30 Giga pixel. You know, people are talking about megapixel images were stitching hundreds of these together. We've we're just really changing the way imagery is used, uh, both in the time lapse and also just in archival process. Ah, lot of these things we've done with the interior. You know, we have this virtual reality product where you can you can walk through and see in the 3 60 bubble. We're taking that imagery, and we're combining it with with these been models who are actually taking the three D models of the construction site and combining it with the imagery. And we can start doing things to visualize progress and different things that are happening on the site. Look for clashes or things that aren't built like they're supposed to be built, things that maybe aren't done on the proper schedule or things that are maybe ahead of schedule, doing a lot of things to save people, time and money on these construction sites. We've also introduced a I machine learning applications into directly into the workflow in this in the storage environment. So we're detecting equipment and people and activities in the site where a lot of that would have been difficult with our previous infrastructure, it really is seamless and working with YSL on now. >>Imagine, by being able to infuse AI and machine learning, you're able to get insight faster to be ableto either respond faster to those construction customers, for example, or alert them. If perhaps something isn't going according to plan. >>A lot of it's about schedule. It's about saving money about saving time and again, with not as many people traveling to the sites, they really just have have constant visualization of what's going on. Day to day, we're detecting things like different types of construction equipment and things that are happening on the side. We're partnering with people that are doing safety analytics and things of that nature. So these these are all things that are very important to construction sites. >>What are some of the things as we are rounding out the calendar year 2020? What are some of the things that you're excited about going forward in 2021? That Earth cam is going to be able to get into and to deliver >>it, just MAWR and more people really, finally seeing the value. I mean, I've been doing this for 20 years, and it's just it's it's It's amazing how we're constantly seeing new applications and more people understanding how valuable these visual tools are. That's just a fantastic thing for us because we're really trying to create better lives through visual information. We're really helping people with things they can do with this imagery. That's what we're all about that's really exciting to us in a very challenging environment right now is that people are are recognizing the need for this technology and really starting to put it on a lot more projects. >>Well, it's You can kind of consider an essential service, whether or not it's a construction company that needs to manage and oversee their projects, making sure they're on budget on schedule, as you said, Or maybe even just the essential nous of helping folks from any country in the world connect with a favorite favorite travel location or sending the right to help. From an emotional perspective, I think the essential nous of what you guys are delivering is probably even more impactful now, don't you think? >>Absolutely and again about connecting people and when they're at home. And recently we we webcast the president's speech from the Flight 93 9 11 observation from the memorial. There was something where the only the immediate families were allowed to travel there. We webcast that so people could see that around the world we have documented again some of the biggest construction projects out there. The new rate years greater stadium was one of the recent ones, uh, is delivering this kind of flagship content. Wall Street Journal is to use some of our content recently to really show the things that have happened during the pandemic in Times Square's. We have these cameras around the world. So again, it's really bringing awareness of letting people virtually travel and share and really remain connected during this this challenging time on and again, we're seeing a really increase demand in the traffic in those areas as well. >>I can imagine some of these things that you're doing that you're achieving now are going to become permanent, not necessarily artifacts of Cove in 19 as you now have the opportunity to reach so many more people and probably the opportunity to help industries that might not have seen the value off this type of video to be able to reach consumers that they probably could never reach before. >>Yeah, I think the whole nature of business and communication and travel on everything is really going to be changed from this point forward. It's really people are looking at things very, very differently and again, seeing the technology really can help with so many different areas that, uh, that it's just it's gonna be a different kind of landscape out there we feel on that's really, you know, continuing to be seen on the uptick in our business and how many people are adopting this technology. We're developing a lot more. Partnerships with other companies were expanding into new industries on again. You know, we're confident that the current platform is going to keep up with us and help us, you know, really scale and evolved as thes needs air growing. >>It sounds to me like you have the foundation with Dell Technologies with power scale to be able to facilitate the massive growth that you're saying and the skill in the future like you've got that foundation. You're ready to go? >>Yeah, we've been We've been We've been using the system for five years already. We've already added capacity. We can add capacity on the fly, Really haven't hit any limits. And what we can do, It's It's almost infinitely scalable, highly redundant. Gives everyone a real sense of security on our side. And, you know, we could just keep innovating, which is what we do without hitting any any technological limits with with our partnership. >>Excellent. Well, Bill, I'm gonna let you get back to innovating for Earth camp. It's been a pleasure talking to you. Thank you so much for your time today. >>Thank you so much. It's been a pleasure >>for Bill Sharp and Lisa Martin. You're watching the cubes. Digital coverage of Dell Technologies World 2020. Thanks for watching. Yeah,

>> Announcer: From around the globe, it's theCUBE! With digital coverage of Dell Technologies World, digital experience. Brought to you by Dell Technologies. >> Welcome to theCUBE's coverage of Dell Technologies World 2020, the digital coverage. I'm Lisa Martin, and I'm excited to be talking with one of Dell Technologies' customers EarthCam. Joining me is Bill Sharp, the senior VP of product development and strategy from EarthCam. Bill, welcome to theCUBE. >> Thank you so much. >> So talk to me a little bit about what EarthCam does. This is very interesting webcam technology. You guys have tens of thousands of cameras and sensors all over the globe. Give our audience an understanding of what you guys are all about. >> Sure thing. The world's leading provider of webcam technologies, you mentioned content and services, we're leaders in live streaming, time-lapse imaging, primary focus in the vertical construction. So with a lot of these, the most ambitious, largest construction projects around the world that you see these amazing time-lapse movies, we're capturing all of that imagery basically around the clock, these cameras are sending all of that image content to us and we're generating these time-lapse movies from it. >> You guys are headquartered in New Jersey. I was commenting before we went live about your great background. So you're actually getting to be onsite today? >> Yes, yes. We're live from our headquarters in upper Saddle River, New Jersey. >> Excellent, so in terms of the types of information that you're capturing, so I was looking at the website, and see from a construction perspective, some of the big projects you guys have done, the Hudson Yards, the Panama Canal expansion, the 9/11 museum. But you talked about one of the biggest focuses that you have is in the construction industry. In terms of what type of data you're capturing from all of these thousands of edge devices, give us a little bit of an insight into how much data you're capturing per day, how it gets from the edge, presumably, back to your core data center for editing. >> Sure, and it's not just construction. We're also in travel, hospitality, tourism, security, architecture, engineering, basically any industry that need high resolution visualization of their projects or their performance or their product flow. So it's high resolution documentation is basically our business. There are billions of files in the Isilon system right now. We are ingesting millions of images a month. We are also creating very high resolution panoramic imagery where we're taking hundreds and sometimes multiple hundreds of images, very high resolution images and stitching these together to make panoramas that are up to 30 gigapixel sometimes. Typically around one to two gigapixel but that composite imagery represents millions of images per month coming into the storage system and then being stitched together to those composites. >> So millions of images coming in every month, you mentioned Isilon. Talk to me a little bit about before you were working with Dell EMC and PowerScale, how were you managing this massive volume of data? >> Sure, we've used a number of other enterprise storage systems. It was really nothing was as easy to manage as Isilon really is. There was a lot of problems with overhead, the amount of time necessary from a systems administrator resource standpoint, to manage that. And it's interesting with the amount of data that we handle, being billions of relatively small files. They're, you know, a half a megabyte to a couple of megabytes each. It's an interesting data profile which Isilon really is well suited for. >> So if we think about some of the massive changes that we've all been through in the last, in 2020, what are some of the changes that EarthCam hasn't seen with respect to the needs for organizations, or you mentioned other industries like travel, hospitality, since none of us can get to these great travel destinations, have you seen a big drive up in the demand and the need to process more data faster? >> Yeah, that's an interesting point with the pandemic. I mean, obviously we had to pivot and move a lot of people to working from home, which we were able to do pretty quickly, but there's also an interesting opportunity that arose from this where so many of our customers and other people also have to do the same. And there is an increased demand for our technology. So people can remotely collaborate. They can work at a distance, they can stay at home and see what's going on in these project sites. So we really saw kind of an uptick in the need for our products and services. And we've also created some basically virtual travel applications. We have an application on the Amazon Fire TV which is the number one app in the travel platform, and people can kind of virtually travel when they can't really get out there. So it's, we've been doing kind of giving back to people that are having some issues with being able to travel around. We've done the fireworks at the Washington Mall around the Statue of Liberty for July 4th. And this year we'll be webcasting New Years in Times Square for our 25th year, actually. So again, helping people travel virtually and maintain connectivity with each other, and with their projects. >> Which is so essential during these times where for the last six, seven months, everyone is trying to get a sense of community and most of us just have the internet. So I also heard you guys were available on the Apple TV, someone should fire that up later and maybe virtually travel. But tell me a little bit about how working in conjunction with Dell Technologies and PowerScale. How has that enabled you to manage this massive volume change that you've experienced this year? Because as you said, it's also about facilitating collaboration which is largely online these days. >> Yeah, and I mean, the great things of working with Dell has been just our confidence in this infrastructure. Like I said, the other systems we've worked with in the past we've always found ourselves kind of second guessing. We're constantly innovating. Obviously resolutions are increasing. The camera performance is increasing, streaming video is, everything is constantly getting bigger and better, faster, more, and we're always innovating. We found ourselves on previous storage platforms having to really kind of go back and look at them, second guess where we're at with it. With the Dell infrastructure it's been fantastic. We don't really have to think about that as much. We just continue innovating, everything scales as we need it to do. It's much easier to work with. >> So you've got PowerScale at your core data center in New Jersey. Tell me a little bit about how data gets from these tens of thousands of devices at the edge, back to your editors for editing, and how PowerScale facilitates faster editing, for example. >> Well, basically you can imagine every one of these cameras, and it's not just cameras. It's also, you know, we have 360 virtual reality kind of bubble cameras. We have mobile applications, we have fixed position and robotic cameras. There's all these different data acquisition systems we're integrating with weather sensors and different types of telemetry. All of that data is coming back to us over the internet. So these are all endpoints in our network. So that's constantly being ingested into our network and saved to Isilon. The big thing that's really been a time saver working with the video editors is instead of having to take that content, move it into an editing environment where we have a whole team of award-winning video editors creating these time lapses. We don't need to keep moving that around. We're working natively on Isilon clusters. They're doing their editing there, and subsequent edits. Anytime we have to update or change these movies as a project evolves, that's all, can happen right there on that live environment. And the retention is there. If we have to go back later on, all of our customers' data is really kept within that one area, it's consolidated and it's secure. >> I was looking at the Dell Tech website, and there's a case study that you guys did, EarthCam did with Dell Tech saying that the video processing time has been reduced 20%. So that's a pretty significant increase. I can imagine with the volumes changing so much now, not only is huge to your business but to the demands that your customers have as well, depending on where those demands are coming from. >> Absolutely. And just being able to do that a lot faster and be more nimble allows us to scale. We've added actually, again, speaking of during this pandemic, we've actually added personnel, we've been hiring people. A lot of those people are working remotely as we've stated before. And it's just with the increase in business, we have to continue to keep building on that, and this storage environment's been great. >> Tell me about what you guys really kind of think about with respect to PowerScale in terms of data management, not storage management, and what that difference means to your business. >> Well, again, I mean, number one was really eliminating the amount of resources. The amount of time we have to spend managing it. We've almost eliminated any downtime of any kind. We have greater storage density, we're able to have better visualization on how our data is being used, how it's being accessed. So as these things are evolving, we really have good visibility on how the storage system is being used in both our production and also in our backup environments. It's really, really easy for us to make our business decisions as we innovate and change processes, having that continual visibility and really knowing where we stand. >> And you mentioned hiring folks during the pandemic, which is fantastic, but also being able to do things in a much more streamlined way with respect to managing all of this data. But I am curious in terms of innovation and new product development, what have you been able to achieve? Because you've got more resources presumably to focus on being more innovative rather than managing storage. >> Well, again, it's, we're always really pushing the envelope of what the technology can do. As I mentioned before, we're getting things into, you know, 20 and 30 gigapixels, people are talking about megapixel images, we're stitching hundreds of these together. We're just really changing the way imagery is used both in the time lapse and also just in archival process. A lot of these things we've done with the interior, we have this virtual reality product where you can walk through and see in a 360 bubble, we're taking that imagery and we're combining it with these BIM models. So we're actually taking the 3D models of the construction site and combining it with the imagery. And we can start doing things to visualize progress, and different things that are happening on the site, look for clashes or things that aren't built like they're supposed to be built, things that maybe aren't done on the proper schedule or things that are maybe ahead of schedule, doing a lot of things to save people time and money on these construction sites. We've also introduced AI and machine learning applications directly into the workflow in the storage environment. So we're detecting equipment and people and activities in the site where a lot of that would have been difficult with our previous infrastructure. It really is seamless and working with Isilon now. >> I imagine by being able to infuse AI and machine learning, you're able to get insights faster, to be able to either respond faster to those construction customers, for example, or alert them if perhaps something isn't going according to plan. >> Yeah, a lot of it's about schedule, it's about saving money, about saving time. And again, with not as many people traveling to these sites, they really just have to have constant visualization of what's going on day to day. We're detecting things like different types of construction equipment and things that are happening on the site. We're partnering with people that are doing safety analytics and things of that nature. So these are all things that are very important to construction sites. >> What are some of the things as we are rounding out the calendar year 2020, what are some of the things that you're excited about going forward in 2021, that EarthCam is going to be able to get into and to deliver? >> Just more and more people really finally seeing the value. I mean I've been doing this for 20 years and it's just, it's amazing how we're constantly seeing new applications and more people understanding how valuable these visual tools are. That's just a fantastic thing for us because we're really trying to create better lives through visual information. We're really helping people with the things they can do with this imagery. That's what we're all about. And that's really exciting to us in a very challenging environment right now is that people are recognizing the need for this technology and really starting to put it on a lot more projects. >> Well, you can kind of consider it an essential service whether or not it's a construction company that needs to manage and oversee their projects, making sure they're on budget, on schedule, as you said, or maybe even just the essentialness of helping folks from any country in the world connect with a favorite travel location, or (indistinct) to help from an emotional perspective. I think the essentialness of what you guys are delivering is probably even more impactful now, don't you think? >> Absolutely. And again about connecting people when they're at home, and recently we webcast the president's speech from the Flight 93 9/11 observation from the memorial, there was something where only the immediate families were allowed to travel there. We webcast that so people could see that around the world. We've documented, again, some of the biggest construction projects out there, the new Raiders stadium was one of the recent ones, just delivering this kind of flagship content. Wall Street Journal has used some of our content recently to really show the things that have happened during the pandemic in Times Square. We have these cameras around the world. So again, it's really bringing awareness. So letting people virtually travel and share and really remain connected during this challenging time. And again, we're seeing a real increased demand in the traffic in those areas as well. >> I can imagine some of these things that you're doing that you're achieving now are going to become permanent not necessarily artifacts of COVID-19, as you now have the opportunity to reach so many more people and probably the opportunity to help industries that might not have seen the value of this type of video to be able to reach consumers that they probably could never reach before. >> Yeah, I think the whole nature of business and communication and travel and everything is really going to be changed from this point forward. It's really, people are looking at things very, very differently. And again, seeing that the technology really can help with so many different areas that it's just, it's going to be a different kind of landscape out there we feel. And that's really continuing to be seen as on the uptick in our business and how many people are adopting this technology. We're developing a lot more partnerships with other companies, we're expanding into new industries. And again, you know, we're confident that the current platform is going to keep up with us and help us really scale and evolve as these needs are growing. >> It sounds to me like you have the foundation with Dell Technologies, with PowerScale, to be able to facilitate the massive growth that you were saying and the scale in the future, you've got that foundation, you're ready to go. >> Yeah, we've been using the system for five years already. We've already added capacity. We can add capacity on the fly, really haven't hit any limits in what we can do. It's almost infinitely scalable, highly redundant. It gives everyone a real sense of security on our side. And you know, we can just keep innovating, which is what we do, without hitting any technological limits with our partnership. >> Excellent, well, Bill, I'm going to let you get back to innovating for EarthCam. It's been a pleasure talking to you. Thank you so much for your time today. >> Thank you so much. It's been a pleasure. >> For Bill Sharp, I'm Lisa Martin, you're watching theCUBE's digital coverage of Dell Technologies World 2020. Thanks for watching. (calm music)

>>from around the globe. It's the cue with digital coverage of out systems. Next Step 2020 Brought to you by out systems. I'm stupid, man. And welcome back to the cubes Coverage of out systems Next step course. One of the items that we've been talking a lot in the industry is about how artificial intelligence, machine learning or helping people is. We go beyond what really human scale can do and we need to be ableto do things more machine scale. Help us really dig into this topic. Happy to welcome to the program First time guest Antonio Alegria. He is the head of artificial intelligence at out systems. Tonio, thanks so much for joining us. >>Thank you. So I'm really happy to be here and and really talk a little bit about what? We're doing it out systems to help our customers and our leverage eai to get to those goals. >>Wonderful. So I I saw ahead of the event a short video that you did and talked about extreme agility with no limits. So, you know, before we drink, dig into the product itself. Maybe if you could just how should we be thinking about a I you know, there's broad spectrum. Is that machine learning that there's various components in there? Listen to the big analyst firms. You know, the journey. It's big steps and something that that is pretty broad. So when we're talking about A I, you know, what does that mean to you? What does that mean to your customers? >>Eso So AI out systems really speaks to division and the core strategy we have for our product, which is, you know, if you saw the keynote, no, we talk about no, really enabling every company, even those that you know, that existed for decades, perhaps have a lot of legacy to become. You know, leading elite cloud software development companies and really can develop digital solutions at scale really easily. But one thing we see and then this is a big statistic. One of the things that limits limits CEOs the most nowadays is really the lack of town lack of engineering, a softer engineering, you know, ability and people that that that could do that. And there's a statistic that was reported by The Wall Street Journal. I saw it recently, perhaps last year, that said that according to federal jobs dating the U. S. By the end of 2. 2020 there would be about a million unfilled I E. T s after development jobs available. Right? So there's this big problem All of these companies really need to scale, really need to invest in digital systems and so horribly fed out systems. We've already been abstracting and we've been focusing automating as much as possible the softer development tools and applications that use. We've already seen amazing stories of people coming from different backgrounds really starting to develop, really leading edge applications. And we want to take this to the next level. And we believe that artificial intelligence with machine learning but also with other AI technologies that were also taking advantage of can really help us get to a next stage of productivity. So from 10 x productivity to 100 x productivity and we believe AI plays a rolling three ways. We believe II by learning from all of this data that we not collect in terms of, you know, projects are being developed. We're essentially trying to embed a tech lead, so to speak, inside a product and attack Lee that can help developers by guiding them got in the most junior ones by automating some of the boring, repetitive tasks were by validating their work. Making sure that they're using the best practice is making sure that it helps them as they scale to re factor on their code to automatically designed architectures. Things like that >>Wonderful. Antonio Gonzalo stated it quite clearly in the interview that I had with him. It's really about enabling that next you know, 10 million developers. We know that there is that skill gap, as you said, and you know everybody right now how can I do more? How can I react faster? Eso that's where you know, the machine learning artificial intelligence should be able to help. So bring us inside. I know the platform itself has had, you know, guidance and and the whole movement. You know, what we used to call low code was about simplifying things and allowing people to, you know, build faster. So bring us inside the product. You know what? The enhancements? One of the new pieces. Some of the key key items, >>Yes, So 11 interesting thing. And I think one thing that I think out system is really proud of being able to achieve is if you look at how out system has been using a AI within the platform. We started with introducing AI assistance within the Our Software Development Environment Service studio. Right? And so this capability, we've been generating it a lot. We've been evolving it, and now it's really able to accelerate significantly and guide novices, but also help pros dealing through software development process and coding by essentially trying to infer understanding their context and trying to infer their intent and then automating the steps afterwards. And we do this by suggesting you the most likely let's say function or or code p sexual one you need. But then, at the next step, which we're introducing this year, even better, which is we're trying to auto fill most of them. Let's see the variables and all of that in the data flow that you need to collect. And so you get a very delightful frictionless experience as you are coating, so you're closer to the business value even more than before. Now this is the This was just the first step, what you're seeing now and what we're announcing, and we're showing up at this next step that we show that the keynote is that we're trying to fuse starting to fuse AI across the out systems products and across this after development life cycle. So he took this core technology that we used to guide developers and assistant automate their work. Um, and we use the same capability to help developers. Tech leads an architect's to analyze the code, learning from the bad patterns that exist, learning from and receiving runtime information about crashes and performance and inside the product recall architecture, dashboard were really able to give recommendations to these architects and tech leads. Where should they evolve and improve their code? And we're using AI refusing AI in this product into very specific ways. Now that we're releasing today, which is one is to automatically collect and design and defined the architecture. So we call this automated architecture discovery. So if you have a very large factory, you can imagine, you know have lots of different modules, lots of different applications, and if you need to go and manually have to label everything so this is ah, front, and this is the back end. That would take a lot of time. So we use machine learning, learning from what architects have already done in the past, classifying their architecture. And we can map out your architecture completely automatically, which is really powerful. Then we also use our AI engine to analyze your factory and weaken detect the best opportunities for re factoring. Sorry. Factoring is one of the top problems in the top smells and technical depth problems that large factories have. Right, So we can completely identify and pinpoint. What are these opportunities for re factory and we guide you through it, which held you okay, all of these hundreds of functions and logic patterns that we see in your code Could you re factor this into a single function and you can save a lots and lots of code because, as you know, the best code the fastest coast easiest to maintain is the Cody. Don't ride. You don't have. So we're trying to really eliminate Kurt from these factories with these kids ability. >>Well, it's fascinating. You're absolutely right. I'm curious. You know, I think back to some of the earliest interactions I had with things that give you guys spell checkers. Grammar check. How much does the AI that you work on. Does it learn what specific for my organization in my preferences? Is there any community learning over time? Because there are industry breast pack that best practices out there that are super valuable. But, you know, we saw in the SAS wave when I can customize things myself were learned over time. So how does that play into kind of today in the road map for a I that you're building >>that? That's a good question. So our AI let's say technology that we use it actually uses to two different big kinds of AI. So we use machine learning definitely to learn from the community. What are the best practices and what are the most common pattern that people use? So we use that to guide developers, but also to validate and analyze their code. But then we also use automated reasoning. So this is more logic based reasoning based AI and repair these two technologies to really create a system that is able to learn from data but also be able to reason at a higher order about what are good practices and kind of reach conclusions from there and learn new things from there now. We started by applying these technologies to more of the community data and kind of standard best practices. But our vision is to more and more start learning specifically and allowing tech leads an architect even in the future. To Taylor. These engines of AI, perhaps to suggest these are the best practices for my factory. These patterns perhaps, are good best practices in general. But in my factory, I do not want to use them because I have some specificities for compliance or something like that. And our vision is that architects and techniques can just provide just a few examples of what they like and what they don't like in the engine just automatically learns and gets tailor to their own environment. >>So important that you're able to, uh, you know, have the customers move things forward in the direction that makes sense on their end. I'm also curious. You talk about, um, you know what what partnerships out systems has out there, you know, being able to tie into things like what the public cloud is doing. Lots of industry collaboration. So how does health system fit into the kind of the broader ai ecosystem. >>Yes. So one thing I did not mention and to your point is eso were have kind of to, um Teoh Complementary visions and strategies for a I. So one of them is we really want to improve our own product, improve the automation in the product in the abstraction by using AI together with great user experience and the best programming language for software on automation. Right, So that's one. That's what we generally call AI assisted development. And if using AI across this software development life cycle, the other one is We also believe that you know, true elite cloud software companies that create frictionless experiences. One of the things that they used to really be super competitive and create this frictionless experiences is that they can themselves use AI and machine learning to to automate processes created really, really delightful experiences. So we're also investing and we've shown and we're launching, announcing that next step we just showed this at at the keynote one tool that we call the machine learning builder ml builder. So this essentially speaks to the fact that you know, a lot of companies do not have access to data science talent. They really struggle to adopt machine learning. Like just one out of 10 companies are able to go and put a I in production. So we're essentially abstracting also that were also increasing the productivity for you for customers to implement an AI and machine learning we use. We use partners behind the scenes and cloud providers for the core technology with automated machine learning and all of that. But we abstract all of the experience so developers can essentially just pick of the data they have already in the inside the all systems platform, and they want to just select. I want to trade this machine learning model to predict this field, just quickly click and it runs dozens of experiments, selects the best algorithms, transforms that the data for you without you needing to have a lot of data science experience. And then you can just drag and drop in the platform integrating your application. And you're good to go. >>Well, it sounds comes Ah, you know, phenomenal. You mentioned data scientists. We talked about that. The skill gap. Do you have any statistics? You know? Is this helping people you know? Higher, Faster. Lower the bar the entry for people to get on board, you know, increased productivity. What kind of hero numbers do your customers typically, you know, how do they measure success? >>Yes, So we know that in for machine learning adoption at cos we know that. Sorry, This is one of the top challenges that they have, right? So companies do not. It's not only that they do not have the expertise to implement machine learning at in their products in their applications. They don't even have a good understanding of what are the use cases in or out of the technology opportunities for them to apply. Right? So this has been listed by lots of different surveys that this is the top problem. These other 22 of the top problems that companies have to adopt a ice has access to skilled. They decided skill, understanding of the use case. And that's exactly what we're trying to kind of package up in a very easy to use product where you can see the use cases you have available, we just select your data, you just click train. You do not need to know that many greedy details and for us, a measure of success is that we've seen customers that are starting to experiment with ML Builder is that in just a day or a few days that can iterating over several machine learning models and put them in production. We have customers that have, you know, no machine learning models and production ever, and they just now have to, and they're starting to automate processes. They're starting to innovate with business. And that, for us, is we've seen it's kind of the measure of success for businesses initially, what they want to do is they want to do. POC is and they want to experiment and they want to get to production stopped. Getting to field for it and generate from >>a product standpoint, is the A. I just infused in or there's there additional licensing, how to customers, you know to take advantage of it. What's the impact on that from the relationship without systems? >>Yes. So for for for a I in machine learning that is fused into our product and for automation, validation and guidance, there's no extra charge is just part of the product. It's what we believe is kind of a core building block in a course service for everything we do in our product for machine learning services and components that customers can use to in their own applications. We allow you to integrate with cloud providers, and the building is is done separately on. That's something that that we're working towards and building great technical partnerships and exploring other avenues for deeper integration so that developers and customers do not really have to worry about those things. Well, >>it's it's It's such a great way to really democratize the use of this technology platform that they're used to. They start doing it. What's general feedback from your customers? Did they just like, Oh, it's there. I start playing with it. It's super easy. It makes it better there any concerns or push back. Have we gotten beyond that? What? What? What do you hear any any good customer examples you can share us toe general adoption? >>Yes. So, as I said, as we re reduce the friction for adopting these technologies, we've seen one thing that's very interesting. So we have a few customers that are present more in the logistics site of industry and vertical, and so they they have a more conservative management, like take time to adopt and more of a laggard in adopting these kinds of technologies, the businesses more skeptical. But I want to spend a lot of time playing around right and whence they saw. Once they saw what they could do with a platform, they quickly did a proof of concept. They show to the business and the business had lots of ideas. So they just started interacting a lot more with I t, which is something we see without systems platform not just for a I machine learning, but generally in the jib. Digital transformation is when the I teak and can start really being very agile in iterating and innovating, and they start collaborating a lot with the business. And so what we see is customers asking us for even more so customers want more use cases to be supported like this. Customers also the ones that are more mature than already, have their centers of excellence and they have their data scientists, for example. They want to understand how they can also bring in perhaps their use of very specialized tool talking in it. Integrate that into the platform so that you know, for certain use cases. Developer scan very quickly trained their own models. But so specialized data science teams can also bring in. And developers can integrate their models easily and put them into production, which is one of the big barriers we see in a lot of companies people working on yearlong projects. They develop the models that they struggle to get them to production. And so we really want to focus on the whole into in journey. Either you're building everything within the octopus platform or you're bringing it from a specialized pro tool. We want to make that whole journey frictionless in school. >>And Tony a final question I have for you. Of course, this space we're seeing maturing, you know, rapid Ah, new technologies out there gives a little look forward. What should we be expecting to see from out systems or things even a little broader? If you look at your your partner ecosystem over kind of the next 6, 12 18 months, >>Yes. So, um, what you're going to continues to see a trend, I think, from from the closer providers of democratization of the AI services. So this is during that just starting to advanced and accelerate as these providers started packaging. It's like what out systems also doing, starting to packaging Cem some specific, well defined use cases and then making the journey for training these models and deploying Super super simple. That's one thing that's continued to ramp up, and we're going to move from A I services more focused on cognitive, pre trained models, right, that which is kind of the status quo to custom ai models based on your data. That's kind of the train we're going to start seeing in that out systems also pushing forward generally from the AI and machine learning application and technology side of thing. I think one thing that we're leading leading on is that you know, machine learning and deep learning is definitely one of the big drivers for the innovation that we're seeing in a I. But you're start seeing more and more what is called hybrid I, which is taking machine learning and data based artificial intelligence with more logic based automated reasoning techniques, impairing these two to really create systems that are able to operate at a really higher level, higher cognitive level of which is what out systems investing internally in terms of research and development and with partnerships with institutions like Carnegie Mellon University and >>rely Antonio, who doesn't want, you know, a tech experts sitting next to them helping get rid of some of the repetitive, boring things or challenges. Thank you so much for sharing the update. Congratulations. Definitely Look forward to hearing war in the future. >>Thank you. Do have a good day >>Stay tuned for more from out systems. Next step is to minimum and thank you for watching.