(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)

(upbeat music) >> Welcome everyone to theCUBE's presentation of the AWS Startup Showcase AI/ML Top Startups Building Foundation Model Infrastructure. This is season three, episode one of our ongoing series covering exciting startups from the AWS ecosystem to talk about data and analytics. I'm your host, Lisa Martin and today we're excited to be joined by two guests from Astronomer. Steven Hillion joins us, it's Chief Data Officer and Jeff Fletcher, it's director of ML. They're here to talk about machine learning and data orchestration. Guys, thank you so much for joining us today. >> Thank you. >> It's great to be here. >> Before we get into machine learning let's give the audience an overview of Astronomer. Talk about what that is, Steven. Talk about what you mean by data orchestration. >> Yeah, let's start with Astronomer. We're the Airflow company basically. The commercial developer behind the open-source project, Apache Airflow. I don't know if you've heard of Airflow. It's sort of de-facto standard these days for orchestrating data pipelines, data engineering pipelines, and as we'll talk about later, machine learning pipelines. It's really is the de-facto standard. I think we're up to about 12 million downloads a month. That's actually as a open-source project. I think at this point it's more popular by some measures than Slack. Airflow was created by Airbnb some years ago to manage all of their data pipelines and manage all of their workflows and now it powers the data ecosystem for organizations as diverse as Electronic Arts, Conde Nast is one of our big customers, a big user of Airflow. And also not to mention the biggest banks on Wall Street use Airflow and Astronomer to power the flow of data throughout their organizations. >> Talk about that a little bit more, Steven, in terms of the business impact. You mentioned some great customer names there. What is the business impact or outcomes that a data orchestration strategy enables businesses to achieve? >> Yeah, I mean, at the heart of it is quite simply, scheduling and managing data pipelines. And so if you have some enormous retailer who's managing the flow of information throughout their organization they may literally have thousands or even tens of thousands of data pipelines that need to execute every day to do things as simple as delivering metrics for the executives to consume at the end of the day, to producing on a weekly basis new machine learning models that can be used to drive product recommendations. One of our customers, for example, is a British food delivery service. And you get those recommendations in your application that says, "Well, maybe you want to have samosas with your curry." That sort of thing is powered by machine learning models that they train on a regular basis to reflect changing conditions in the market. And those are produced through Airflow and through the Astronomer platform, which is essentially a managed platform for running airflow. So at its simplest it really is just scheduling and managing those workflows. But that's easier said than done of course. I mean if you have 10 thousands of those things then you need to make sure that they all run that they all have sufficient compute resources. If things fail, how do you track those down across those 10,000 workflows? How easy is it for an average data scientist or data engineer to contribute their code, their Python notebooks or their SQL code into a production environment? And then you've got reproducibility, governance, auditing, like managing data flows across an organization which we think of as orchestrating them is much more than just scheduling. It becomes really complicated pretty quickly. >> I imagine there's a fair amount of complexity there. Jeff, let's bring you into the conversation. Talk a little bit about Astronomer through your lens, data orchestration and how it applies to MLOps. >> So I come from a machine learning background and for me the interesting part is that machine learning requires the expansion into orchestration. A lot of the same things that you're using to go and develop and build pipelines in a standard data orchestration space applies equally well in a machine learning orchestration space. What you're doing is you're moving data between different locations, between different tools, and then tasking different types of tools to act on that data. So extending it made logical sense from a implementation perspective. And a lot of my focus at Astronomer is really to explain how Airflow can be used well in a machine learning context. It is being used well, it is being used a lot by the customers that we have and also by users of the open source version. But it's really being able to explain to people why it's a natural extension for it and how well it fits into that. And a lot of it is also extending some of the infrastructure capabilities that Astronomer provides to those customers for them to be able to run some of the more platform specific requirements that come with doing machine learning pipelines. >> Let's get into some of the things that make Astronomer unique. Jeff, sticking with you, when you're in customer conversations, what are some of the key differentiators that you articulate to customers? >> So a lot of it is that we are not specific to one cloud provider. So we have the ability to operate across all of the big cloud providers. I know, I'm certain we have the best developers that understand how best practices implementations for data orchestration works. So we spend a lot of time talking to not just the business outcomes and the business users of the product, but also also for the technical people, how to help them better implement things that they may have come across on a Stack Overflow article or not necessarily just grown with how the product has migrated. So it's the ability to run it wherever you need to run it and also our ability to help you, the customer, better implement and understand those workflows that I think are two of the primary differentiators that we have. >> Lisa: Got it. >> I'll add another one if you don't mind. >> You can go ahead, Steven. >> Is lineage and dependencies between workflows. One thing we've done is to augment core Airflow with Lineage services. So using the Open Lineage framework, another open source framework for tracking datasets as they move from one workflow to another one, team to another, one data source to another is a really key component of what we do and we bundle that within the service so that as a developer or as a production engineer, you really don't have to worry about lineage, it just happens. Jeff, may show us some of this later that you can actually see as data flows from source through to a data warehouse out through a Python notebook to produce a predictive model or a dashboard. Can you see how those data products relate to each other? And when something goes wrong, figure out what upstream maybe caused the problem, or if you're about to change something, figure out what the impact is going to be on the rest of the organization. So Lineage is a big deal for us. >> Got it. >> And just to add on to that, the other thing to think about is that traditional Airflow is actually a complicated implementation. It required quite a lot of time spent understanding or was almost a bespoke language that you needed to be able to develop in two write these DAGs, which is like fundamental pipelines. So part of what we are focusing on is tooling that makes it more accessible to say a data analyst or a data scientist who doesn't have or really needs to gain the necessary background in how the semantics of Airflow DAGs works to still be able to get the benefit of what Airflow can do. So there is new features and capabilities built into the astronomer cloud platform that effectively obfuscates and removes the need to understand some of the deep work that goes on. But you can still do it, you still have that capability, but we are expanding it to be able to have orchestrated and repeatable processes accessible to more teams within the business. >> In terms of accessibility to more teams in the business. You talked about data scientists, data analysts, developers. Steven, I want to talk to you, as the chief data officer, are you having more and more conversations with that role and how is it emerging and evolving within your customer base? >> Hmm. That's a good question, and it is evolving because I think if you look historically at the way that Airflow has been used it's often from the ground up. You have individual data engineers or maybe single data engineering teams who adopt Airflow 'cause it's very popular. Lots of people know how to use it and they bring it into an organization and say, "Hey, let's use this to run our data pipelines." But then increasingly as you turn from pure workflow management and job scheduling to the larger topic of orchestration you realize it gets pretty complicated, you want to have coordination across teams, and you want to have standardization for the way that you manage your data pipelines. And so having a managed service for Airflow that exists in the cloud is easy to spin up as you expand usage across the organization. And thinking long term about that in the context of orchestration that's where I think the chief data officer or the head of analytics tends to get involved because they really want to think of this as a strategic investment that they're making. Not just per team individual Airflow deployments, but a network of data orchestrators. >> That network is key. Every company these days has to be a data company. We talk about companies being data driven. It's a common word, but it's true. It's whether it is a grocer or a bank or a hospital, they've got to be data companies. So talk to me a little bit about Astronomer's business model. How is this available? How do customers get their hands on it? >> Jeff, go ahead. >> Yeah, yeah. So we have a managed cloud service and we have two modes of operation. One, you can bring your own cloud infrastructure. So you can say here is an account in say, AWS or Azure and we can go and deploy the necessary infrastructure into that, or alternatively we can host everything for you. So it becomes a full SaaS offering. But we then provide a platform that connects at the backend to your internal IDP process. So however you are authenticating users to make sure that the correct people are accessing the services that they need with role-based access control. From there we are deploying through Kubernetes, the different services and capabilities into either your cloud account or into an account that we host. And from there Airflow does what Airflow does, which is its ability to then reach to different data systems and data platforms and to then run the orchestration. We make sure we do it securely, we have all the necessary compliance certifications required for GDPR in Europe and HIPAA based out of the US, and a whole bunch host of others. So it is a secure platform that can run in a place that you need it to run, but it is a managed Airflow that includes a lot of the extra capabilities like the cloud developer environment and the open lineage services to enhance the overall airflow experience. >> Enhance the overall experience. So Steven, going back to you, if I'm a Conde Nast or another organization, what are some of the key business outcomes that I can expect? As one of the things I think we've learned during the pandemic is access to realtime data is no longer a nice to have for organizations. It's really an imperative. It's that demanding consumer that wants to have that personalized, customized, instant access to a product or a service. So if I'm a Conde Nast or I'm one of your customers, what can I expect my business to be able to achieve as a result of data orchestration? >> Yeah, I think in a nutshell it's about providing a reliable, scalable, and easy to use service for developing and running data workflows. And talking of demanding customers, I mean, I'm actually a customer myself, as you mentioned, I'm the head of data for Astronomer. You won't be surprised to hear that we actually use Astronomer and Airflow to run all of our data pipelines. And so I can actually talk about my experience. When I started I was of course familiar with Airflow, but it always seemed a little bit unapproachable to me if I was introducing that to a new team of data scientists. They don't necessarily want to have to think about learning something new. But I think because of the layers that Astronomer has provided with our Astro service around Airflow it was pretty easy for me to get up and running. Of course I've got an incentive for doing that. I work for the Airflow company, but we went from about, at the beginning of last year, about 500 data tasks that we were running on a daily basis to about 15,000 every day. We run something like a million data operations every month within my team. And so as one outcome, just the ability to spin up new production workflows essentially in a single day you go from an idea in the morning to a new dashboard or a new model in the afternoon, that's really the business outcome is just removing that friction to operationalizing your machine learning and data workflows. >> And I imagine too, oh, go ahead, Jeff. >> Yeah, I think to add to that, one of the things that becomes part of the business cycle is a repeatable capabilities for things like reporting, for things like new machine learning models. And the impediment that has existed is that it's difficult to take that from a team that's an analyst team who then provide that or a data science team that then provide that to the data engineering team who have to work the workflow all the way through. What we're trying to unlock is the ability for those teams to directly get access to scheduling and orchestrating capabilities so that a business analyst can have a new report for C-suite execs that needs to be done once a week, but the time to repeatability for that report is much shorter. So it is then immediately in the hands of the person that needs to see it. It doesn't have to go into a long list of to-dos for a data engineering team that's already overworked that they eventually get it to it in a month's time. So that is also a part of it is that the realizing, orchestration I think is fairly well and a lot of people get the benefit of being able to orchestrate things within a business, but it's having more people be able to do it and shorten the time that that repeatability is there is one of the main benefits from good managed orchestration. >> So a lot of workforce productivity improvements in what you're doing to simplify things, giving more people access to data to be able to make those faster decisions, which ultimately helps the end user on the other end to get that product or the service that they're expecting like that. Jeff, I understand you have a demo that you can share so we can kind of dig into this. >> Yeah, let me take you through a quick look of how the whole thing works. So our starting point is our cloud infrastructure. This is the login. You go to the portal. You can see there's a a bunch of workspaces that are available. Workspaces are like individual places for people to operate in. I'm not going to delve into all the deep technical details here, but starting point for a lot of our data science customers is we have what we call our Cloud IDE, which is a web-based development environment for writing and building out DAGs without actually having to know how the underpinnings of Airflow work. This is an internal one, something that we use. You have a notebook-like interface that lets you write python code and SQL code and a bunch of specific bespoke type of blocks if you want. They all get pulled together and create a workflow. So this is a workflow, which gets compiled to something that looks like a complicated set of Python code, which is the DAG. I then have a CICD process pipeline where I commit this through to my GitHub repo. So this comes to a repo here, which is where these DAGs that I created in the previous step exist. I can then go and say, all right, I want to see how those particular DAGs have been running. We then get to the actual Airflow part. So this is the managed Airflow component. So we add the ability for teams to fairly easily bring up an Airflow instance and write code inside our notebook-like environment to get it into that instance. So you can see it's been running. That same process that we built here that graph ends up here inside this, but you don't need to know how the fundamentals of Airflow work in order to get this going. Then we can run one of these, it runs in the background and we can manage how it goes. And from there, every time this runs, it's emitting to a process underneath, which is the open lineage service, which is the lineage integration that allows me to come in here and have a look and see this was that actual, that same graph that we built, but now it's the historic version. So I know where things started, where things are going, and how it ran. And then I can also do a comparison. So if I want to see how this particular run worked compared to one historically, I can grab one from a previous date and it will show me the comparison between the two. So that combination of managed Airflow, getting Airflow up and running very quickly, but the Cloud IDE that lets you write code and know how to get something into a repeatable format get that into Airflow and have that attached to the lineage process adds what is a complete end-to-end orchestration process for any business looking to get the benefit from orchestration. >> Outstanding. Thank you so much Jeff for digging into that. So one of my last questions, Steven is for you. This is exciting. There's a lot that you guys are enabling organizations to achieve here to really become data-driven companies. So where can folks go to get their hands on this? >> Yeah, just go to astronomer.io and we have plenty of resources. If you're new to Airflow, you can read our documentation, our guides to getting started. We have a CLI that you can download that is really I think the easiest way to get started with Airflow. But you can actually sign up for a trial. You can sign up for a guided trial where our teams, we have a team of experts, really the world experts on getting Airflow up and running. And they'll take you through that trial and allow you to actually kick the tires and see how this works with your data. And I think you'll see pretty quickly that it's very easy to get started with Airflow, whether you're doing that from the command line or doing that in our cloud service. And all of that is available on our website >> astronomer.io. Jeff, last question for you. What are you excited about? There's so much going on here. What are some of the things, maybe you can give us a sneak peek coming down the road here that prospects and existing customers should be excited about? >> I think a lot of the development around the data awareness components, so one of the things that's traditionally been complicated with orchestration is you leave your data in the place that you're operating on and we're starting to have more data processing capability being built into Airflow. And from a Astronomer perspective, we are adding more capabilities around working with larger datasets, doing bigger data manipulation with inside the Airflow process itself. And that lends itself to better machine learning implementation. So as we start to grow and as we start to get better in the machine learning context, well, in the data awareness context, it unlocks a lot more capability to do and implement proper machine learning pipelines. >> Awesome guys. Exciting stuff. Thank you so much for talking to me about Astronomer, machine learning, data orchestration, and really the value in it for your customers. Steve and Jeff, we appreciate your time. >> Thank you. >> My pleasure, thanks. >> And we thank you for watching. This is season three, episode one of our ongoing series covering exciting startups from the AWS ecosystem. I'm your host, Lisa Martin. You're watching theCUBE, the leader in live tech coverage. (upbeat music)

(uplifting music) >> Hello, welcome back to theCUBE's coverage of CloudNativeSecurityCon, the inaugural event, in Seattle. I'm John Furrier, host of theCUBE, here in the Palo Alto Studios. We're calling it theCUBE Center. It's kind of like our Sports Center for tech. It's kind of remote coverage. We've been doing this now for a few years. We're going to amp it up this year as more events are remote, and happening all around the world. So, we're going to continue the coverage with this segment focusing on the data stack, entrepreneurial opportunities around all things security, and as, obviously, data's involved. And our next guest is a friend of theCUBE, and CUBE alumni from 2013, entrepreneur himself, turned, now, venture capitalist angel investor, with his own firm, Opher Kahane, Managing Director, Sonoma Ventures. Formerly the founder of Origami, sold to Intuit a few years back. Focusing now on having a lot of fun, angel investing on boards, focusing on data-driven applications, and stacks around that, and all the stuff going on in, really, in the wheelhouse for what's going on around security data. Opher, great to see you. Thanks for coming on. >> My pleasure. Great to be back. It's been a while. >> So you're kind of on Easy Street now. You did the entrepreneurial venture, you've worked hard. We were on together in 2013 when theCUBE just started. XCEL Partners had an event in Stanford, XCEL, and they had all the features there. We interviewed Satya Nadella, who was just a manager at Microsoft at that time, he was there. He's now the CEO of Microsoft. >> Yeah, he was. >> A lot's changed in nine years. But congratulations on your venture you sold, and you got an exit there, and now you're doing a lot of investments. I'd love to get your take, because this is really the biggest change I've seen in the past 12 years, around an inflection point around a lot of converging forces. Data, which, big data, 10 years ago, was a big part of your career, but now it's accelerated, with cloud scale. You're seeing people building scale on top of other clouds, and becoming their own cloud. You're seeing data being a big part of it. Cybersecurity kind of has not really changed much, but it's the most important thing everyone's talking about. So, developers are involved, data's involved, a lot of entrepreneurial opportunities. So I'd love to get your take on how you see the current situation, as it relates to what's gone on in the past five years or so. What's the big story? >> So, a lot of big stories, but I think a lot of it has to do with a promise of making value from data, whether it's for cybersecurity, for Fintech, for DevOps, for RevTech startups and companies. There's a lot of challenges in actually driving and monetizing the value from data with velocity. Historically, the challenge has been more around, "How do I store data at massive scale?" And then you had the big data infrastructure company, like Cloudera, and MapR, and others, deal with it from a scale perspective, from a storage perspective. Then you had a whole layer of companies that evolved to deal with, "How do I index massive scales of data, for quick querying, and federated access, et cetera?" But now that a lot of those underlying problems, if you will, have been solved, to a certain extent, although they're always being stretched, given the scale of data, and its utility is becoming more and more massive, in particular with AI use cases being very prominent right now, the next level is how to actually make value from the data. How do I manage the full lifecycle of data in complex environments, with complex organizations, complex use cases? And having seen this from the inside, with Origami Logic, as we dealt with a lot of large corporations, and post-acquisition by Intuit, and a lot of the startups I'm involved with, it's clear that we're now onto that next step. And you have fundamental new paradigms, such as data mesh, that attempt to address that complexity, and responsibly scaling access, and democratizing access in the value monetization from data, across large organizations. You have a slew of startups that are evolving to help the entire lifecycle of data, from the data engineering side of it, to the data analytics side of it, to the AI use cases side of it. And it feels like the early days, to a certain extent, of the revolution that we've seen in transition from traditional databases, to data warehouses, to cloud-based data processing, and big data. It feels like we're at the genesis of that next wave. And it's super, super exciting, for me at least, as someone who's sitting more in the coach seat, rather than being on the pitch, and building startups, helping folks as they go through those motions. >> So that's awesome. I want to get into some of these data infrastructure dynamics you mentioned, but before that, talk to the audience around what you're working on now. You've been a successful entrepreneur, you're focused on angel investing, so, super-early seed stage. What kind of deals are you looking at? What's interesting to you? What is Sonoma Ventures looking for, and what are some of the entrepreneurial dynamics that you're seeing right now, from a startup standpoint? >> Cool, so, at a macro level, this is a little bit of background of my history, because it shapes very heavily what it is that I'm looking at. So, I've been very fortunate with entrepreneurial career. I founded three startups. All three of them are successful. Final two were sold, the first one merged and went public. And my third career has been about data, moving data, passing data, processing data, generating insights from it. And, at this phase, I wanted to really evolve from just going and building startup number four, from going through the same motions again. A 10 year adventure, I'm a little bit too old for that, I guess. But the next best thing is to sit from a point whereby I can be more elevated in where I'm dealing with, and broaden the variety of startups I'm focused on, rather than just do your own thing, and just go very, very deep into it. Now, what specifically am I focused on at Sonoma Ventures? So, basically, looking at what I refer to as a data-driven application stack. Anything from the low-level data infrastructure and cloud infrastructure, that helps any persona in the data universe maximize value for data, from their particular point of view, for their particular role, whether it's data analysts, data scientists, data engineers, cloud engineers, DevOps folks, et cetera. All the way up to the application layer, in applications that are very data-heavy. And what are very typical data-heavy applications? FinTech, cyber, Web3, revenue technologies, and product and DevOps. So these are the areas we're focused on. I have almost 23 or 24 startups in the portfolio that span all these different areas. And this is in terms of the aperture. Now, typically, focus on pre-seed, seed. Sometimes a little bit later stage, but this is the primary focus. And it's really about partnering with entrepreneurs, and helping them make, if you will, original mistakes, avoid the mistakes I made. >> Yeah. >> And take it to the next level, whatever the milestone they're driving with. So I'm very, very hands-on with many of those startups. Now, what is it that's happening right now, initially, and why is it so exciting? So, on one hand, you have this scaling of data and its complexity, yet lagging value creation from it, across those different personas we've touched on. So that's one fundamental opportunity which is secular. The other one, which is more a cyclic situation, is the fact that we're going through a down cycle in tech, as is very evident in the public markets, and everything we're hearing about funding going slower and lower, terms shifting more into the hands of typical VCs versus entrepreneur-friendly market, and so on and so forth. And a very significant amount of layoffs. Now, when you combine these two trends together, you're observing a very interesting thing, that a lot of folks, really bright folks, who have sold a startup to a company, or have been in the guts of the large startup, or a large corporation, have, hands-on, experienced all those challenges we've spoken about earlier, in turf, maximizing value from data, irrespective of their role, in a specific angle, or vantage point they have on those challenges. So, for many of them, it's an opportunity to, "Now, let me now start a startup. I've been laid off, maybe, or my company's stock isn't doing as well as it used to, as a large corporation. Now I have an opportunity to actually go and take my entrepreneurial passion, and apply it to a product and experience as part of this larger company." >> Yeah. >> And you see a slew of folks who are emerging with these great ideas. So it's a very, very exciting period of time to innovate. >> It's interesting, a lot of people look at, I mean, I look at Snowflake as an example of a company that refactored data warehouses. They just basically took data warehouse, and put it on the cloud, and called it a data cloud. That, to me, was compelling. They didn't pay any CapEx. They rode Amazon's wave there. So, a similar thing going on with data. You mentioned this, and I see it as an enabling opportunity. So whether it's cybersecurity, FinTech, whatever vertical, you have an enablement. Now, you mentioned data infrastructure. It's a super exciting area, as there's so many stacks emerging. We got an analytics stack, there's real-time stacks, there's data lakes, AI stack, foundational models. So, you're seeing an explosion of stacks, different tools probably will emerge. So, how do you look at that, as a seasoned entrepreneur, now investor? Is that a good thing? Is that just more of the market? 'Cause it just seems like more and more kind of decomposed stacks targeted at use cases seems to be a trend. >> Yeah. >> And how do you vet that, is it? >> So it's a great observation, and if you take a step back and look at the evolution of technology over the last 30 years, maybe longer, you always see these cycles of expansion, fragmentation, contraction, expansion, contraction. Go decentralize, go centralize, go decentralize, go centralize, as manifested in different types of technology paradigms. From client server, to storage, to microservices, to et cetera, et cetera. So I think we're going through another big bang, to a certain extent, whereby end up with more specialized data stacks for specific use cases, as you need performance, the data models, the tooling to best adapt to the particular task at hand, and the particular personas at hand. As the needs of the data analysts are quite different from the needs of an NL engineer, it's quite different from the needs of the data engineer. And what happens is, when you end up with these siloed stacks, you end up with new fragmentation, and new gaps that need to be filled with a new layer of innovation. And I suspect that, in part, that's what we're seeing right now, in terms of the next wave of data innovation. Whether it's in a service of FinTech use cases, or cyber use cases, or other, is a set of tools that end up having to try and stitch together those elements and bridge between them. So I see that as a fantastic gap to innovate around. I see, also, a fundamental need in creating a common data language, and common data management processes and governance across those different personas, because ultimately, the same underlying data these folks need, albeit in different mediums, different access models, different velocities, et cetera, the subject matter, if you will, the underlying raw data, and some of the taxonomies right on top of it, do need to be consistent. So, once again, a great opportunity to innovate, whether it's about semantic layers, whether it's about data mesh, whether it's about CICD tools for data engineers, and so on and so forth. >> I got to ask you, first of all, I see you have a friend you brought into the interview. You have a dog in the background who made a little cameo appearance. And that's awesome. Sitting right next to you, making sure everything's going well. On the AI thing, 'cause I think that's the hot trend here. >> Yeah. >> You're starting to see, that ChatGPT's got everyone excited, because it's kind of that first time you see kind of next-gen functionality, large-language models, where you can bring data in, and it integrates well. So, to me, I think, connecting the dots, this kind of speaks to the beginning of what will be a trend of really blending of data stacks together, or blending of models. And so, as more data modeling emerges, you start to have this AI stack kind of situation, where you have things out there that you can compose. It's almost very developer-friendly, conceptually. This is kind of new, but kind of the same concept's been working on with Google and others. How do you see this emerging, as an investor? What are some of the things that you're excited about, around the ChatGPT kind of things that's happening? 'Cause it brings it mainstream. Again, a million downloads, fastest applications get a million downloads, even among all the successes. So it's obviously hit a nerve. People are talking about it. What's your take on that? >> Yeah, so, I think that's a great point, and clearly, it feels like an iPhone moment, right, to the industry, in this case, AI, and lots of applications. And I think there's, at a high level, probably three different layers of innovation. One is on top of those platforms. What use cases can one bring to the table that would drive on top of a ChatGPT-like service? Whereby, the startup, the company, can bring some unique datasets to infuse and add value on top of it, by custom-focusing it and purpose-building it for a particular use case or particular vertical. Whether it's applying it to customer service, in a particular vertical, applying it to, I don't know, marketing content creation, and so on and so forth. That's one category. And I do know that, as one of my startups is in Y Combinator, this season, winter '23, they're saying that a very large chunk of the YC companies in this cycle are about GPT use cases. So we'll see a flurry of that. The next layer, the one below that, is those who actually provide those platforms, whether it's ChatGPT, whatever will emerge from the partnership with Microsoft, and any competitive players that emerge from other startups, or from the big cloud providers, whether it's Facebook, if they ever get into this, and Google, which clearly will, as they need to, to survive around search. The third layer is the enabling layer. As you're going to have more and more of those different large-language models and use case running on top of it, the underlying layers, all the way down to cloud infrastructure, the data infrastructure, and the entire set of tools and systems, that take raw data, and massage it into useful, labeled, contextualized features and data to feed the models, the AI models, whether it's during training, or during inference stages, in production. Personally, my focus is more on the infrastructure than on the application use cases. And I believe that there's going to be a massive amount of innovation opportunity around that, to reach cost-effective, quality, fair models that are deployed easily and maintained easily, or at least with as little pain as possible, at scale. So there are startups that are dealing with it, in various areas. Some are about focusing on labeling automation, some about fairness, about, speaking about cyber, protecting models from threats through data and other issues with it, and so on and so forth. And I believe that this will be, too, a big driver for massive innovation, the infrastructure layer. >> Awesome, and I love how you mentioned the iPhone moment. I call it the browser moment, 'cause it felt that way for me, personally. >> Yep. >> But I think, from a business model standpoint, there is that iPhone shift. It's not the BlackBerry. It's a whole 'nother thing. And I like that. But I do have to ask you, because this is interesting. You mentioned iPhone. iPhone's mostly proprietary. So, in these machine learning foundational models, >> Yeah. >> you're starting to see proprietary hardware, bolt-on, acceleration, bundled together, for faster uptake. And now you got open source emerging, as two things. It's almost iPhone-Android situation happening. >> Yeah. >> So what's your view on that? Because there's pros and cons for either one. You're seeing a lot of these machine learning laws are very proprietary, but they work, and do you care, right? >> Yeah. >> And then you got open source, which is like, "Okay, let's get some upsource code, and let people verify it, and then build with that." Is it a balance? >> Yes, I think- >> Is it mutually exclusive? What's your view? >> I think it's going to be, markets will drive the proportion of both, and I think, for a certain use case, you'll end up with more proprietary offerings. With certain use cases, I guess the fundamental infrastructure for ChatGPT-like, let's say, large-language models and all the use cases running on top of it, that's likely going to be more platform-oriented and open source, and will allow innovation. Think of it as the equivalent of iPhone apps or Android apps running on top of those platforms, as in AI apps. So we'll have a lot of that. Now, when you start going a little bit more into the guts, the lower layers, then it's clear that, for performance reasons, in particular, for certain use cases, we'll end up with more proprietary offerings, whether it's advanced silicon, such as some of the silicon that emerged from entrepreneurs who have left Google, around TensorFlow, and all the silicon that powers that. You'll see a lot of innovation in that area as well. It hopefully intends to improve the cost efficiency of running large AI-oriented workloads, both in inference and in learning stages. >> I got to ask you, because this has come up a lot around Azure and Microsoft. Microsoft, pretty good move getting into the ChatGPT >> Yep. >> and the open AI, because I was talking to someone who's a hardcore Amazon developer, and they said, they swore they would never use Azure, right? One of those types. And they're spinning up Azure servers to get access to the API. So, the developers are flocking, as you mentioned. The YC class is all doing large data things, because you can now program with data, which is amazing, which is amazing. So, what's your take on, I know you got to be kind of neutral 'cause you're an investor, but you got, Amazon has to respond, Google, essentially, did all the work, so they have to have a solution. So, I'm expecting Google to have something very compelling, but Microsoft, right now, is going to just, might run the table on developers, this new wave of data developers. What's your take on the cloud responses to this? What's Amazon, what do you think AWS is going to do? What should Google be doing? What's your take? >> So, each of them is coming from a slightly different angle, of course. I'll say, Google, I think, has massive assets in the AI space, and their underlying cloud platform, I think, has been designed to support such complicated workloads, but they have yet to go as far as opening it up the same way ChatGPT is now in that Microsoft partnership, and Azure. Good question regarding Amazon. AWS has had a significant investment in AI-related infrastructure. Seeing it through my startups, through other lens as well. How will they respond to that higher layer, above and beyond the low level, if you will, AI-enabling apparatuses? How do they elevate to at least one or two layers above, and get to the same ChatGPT layer, good question. Is there an acquisition that will make sense for them to accelerate it, maybe. Is there an in-house development that they can reapply from a different domain towards that, possibly. But I do suspect we'll end up with acquisitions as the arms race around the next level of cloud wars emerges, and it's going to be no longer just about the basic tooling for basic cloud-based applications, and the infrastructure, and the cost management, but rather, faster time to deliver AI in data-heavy applications. Once again, each one of those cloud suppliers, their vendor is coming with different assets, and different pros and cons. All of them will need to just elevate the level of the fight, if you will, in this case, to the AI layer. >> It's going to be very interesting, the different stacks on the data infrastructure, like I mentioned, analytics, data lake, AI, all happening. It's going to be interesting to see how this turns into this AI cloud, like data clouds, data operating systems. So, super fascinating area. Opher, thank you for coming on and sharing your expertise with us. Great to see you, and congratulations on the work. I'll give you the final word here. Give a plugin for what you're looking for for startup seats, pre-seeds. What's the kind of profile that gets your attention, from a seed, pre-seed candidate or entrepreneur? >> Cool, first of all, it's my pleasure. Enjoy our chats, as always. Hopefully the next one's not going to be in nine years. As to what I'm looking for, ideally, smart data entrepreneurs, who have come from a particular domain problem, or problem domain, that they understand, they felt it in their own 10 fingers, or millions of neurons in their brains, and they figured out a way to solve it. Whether it's a data infrastructure play, a cloud infrastructure play, or a very, very smart application that takes advantage of data at scale. These are the things I'm looking for. >> One final, final question I have to ask you, because you're a seasoned entrepreneur, and now coach. What's different about the current entrepreneurial environment right now, vis-a-vis, the past decade? What's new? Is it different, highly accelerated? What advice do you give entrepreneurs out there who are putting together their plan? Obviously, a global resource pool now of engineering. It might not be yesterday's formula for success to putting a venture together to get to that product-market fit. What's new and different, and what's your advice to the folks out there about what's different about the current environment for being an entrepreneur? >> Fantastic, so I think it's a great question. So I think there's a few axes of difference, compared to, let's say, five years ago, 10 years ago, 15 years ago. First and foremost, given the amount of infrastructure out there, the amount of open-source technologies, amount of developer toolkits and frameworks, trying to develop an application, at least at the application layer, is much faster than ever. So, it's faster and cheaper, to the most part, unless you're building very fundamental, core, deep tech, where you still have a big technology challenge to deal with. And absent that, the challenge shifts more to how do you manage my resources, to product-market fit, how are you integrating the GTM lens, the go-to-market lens, as early as possible in the product-market fit cycle, such that you reach from pre-seed to seed, from seed to A, from A to B, with an optimal amount of velocity, and a minimal amount of resources. One big difference, specifically as of, let's say, beginning of this year, late last year, is that money is no longer free for entrepreneurs, which means that you need to operate and build startup in an environment with a lot more constraints. And in my mind, some of the best startups that have ever been built, and some of the big market-changing, generational-changing, if you will, technology startups, in their respective industry verticals, have actually emerged from these times. And these tend to be the smartest, best startups that emerge because they operate with a lot less money. Money is not as available for them, which means that they need to make tough decisions, and make verticals every day. What you don't need to do, you can kick the cow down the road. When you have plenty of money, and it cushions for a lot of mistakes, you don't have that cushion. And hopefully we'll end up with companies with a more agile, more, if you will, resilience, and better cultures in making those tough decisions that startups need to make every day. Which is why I'm super, super excited to see the next batch of amazing unicorns, true unicorns, not just valuation, market rising with the water type unicorns that emerged from this particular era, which we're in the beginning of. And very much enjoy working with entrepreneurs during this difficult time, the times we're in. >> The next 24 months will be the next wave, like you said, best time to do a company. Remember, Airbnb's pitch was, "We'll rent cots in apartments, and sell cereal." Boy, a lot of people passed on that deal, in that last down market, that turned out to be a game-changer. So the crazy ideas might not be that bad. So it's all about the entrepreneurs, and >> 100%. >> this is a big wave, and it's certainly happening. Opher, thank you for sharing. Obviously, data is going to change all the markets. Refactoring, security, FinTech, user experience, applications are going to be changed by data, data operating system. Thanks for coming on, and thanks for sharing. Appreciate it. >> My pleasure. Have a good one. >> Okay, more coverage for the CloudNativeSecurityCon inaugural event. Data will be the key for cybersecurity. theCUBE's coverage continues after this break. (uplifting 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)

(upbeat music) >> Welcome back to Vegas. Lisa Martin and Dave Vellante here with theCUBE live on the Venetian Expo Hall Floor, talking all things AWS re:Invent 2022. This is the first full day of coverage. It is jam-packed here. People are back. They are ready to hear all the new innovations from AWS. Dave, how does it feel to be back yet again in Vegas? >> Yeah, Vegas. I think it's my 10th time in Vegas this year. So, whatever. >> This year alone. You must have a favorite steak restaurant then. >> There are several. The restaurants in Vegas are actually really good. >> You know? >> They are good. >> They used to be terrible. But I'll tell you. My favorite? The place that closed. >> Oh! >> Yeah, closed. In between where we are in the Wynn and the Venetian. Anyway. >> Was it CUT? >> No, I forget what the name was. >> Something else, okay. >> It was like a Greek sort of steak place. Anyway. >> Now, I'm hungry. >> We were at Pure Accelerate a couple years ago. >> Yes, we were. >> When they announced Cloud Block Store. >> That's right. >> Pure was the first- >> In Austin. >> To do that. >> Yup. >> And then they made the acquisition of Portworx which was pretty prescient given that containers have been going through the roof. >> Yeah. >> So I'm sort of excited to have these guys on and talk about that. >> We're going to unpack all of this. We've got one of our alumni back with us, Venkat Ramakrishna, VP of Product, Portworx by Pure Storage. And Dan Kogan joins us for the first time, VP of Product Management and Product Marketing, FlashArray at Pure Storage. Guys, welcome to the program. >> Thank you. >> Hey, guys. >> Dan: Thanks for having us. >> Do you have a favorite steak restaurant in Vegas? Dave said there's a lot of good choices. >> There's a lot of good steak restaurants here. >> I like SDK. >> Yeah, that's a good one. >> That's the good one. >> That's a good one. >> Which one? >> SDK. >> SDK. >> Where's that? >> It's, I think, in Cosmopolitan. >> Ooh. >> Yeah. >> Oh, yeah, yeah, yeah. >> It's pretty good, yeah. >> There's one of the Western too that's pretty. >> I'm an Herbs and Rye guy. Have you ever been there? >> No. >> No. >> Herbs and Rye is off strip, but it's fantastic. It's kind of like a locals joint. >> I have to dig through all of this great stuff today and then check that out. Talk to me. This is our first day, obviously. First main day. I want to get both of your perspectives. Dan, we'll start with you since you're closest to me. How are you finding this year's event so far? Obviously, tons of people. >> Busy. >> Busy, yeah. >> Yeah, it is. It is old times. Bigger, right? Last re:Invent I was at was 2019 right before everything shut down and it's probably half the size of this which is a different trend than I feel like most other tech conferences have gone where they've come back, but a little bit smaller. re:Invent seems to be the IT show. >> It really does. Venkat, are you finding the same? In terms of what you're experiencing so far on day one of the events? >> Yeah, I mean... There's tremendous excitement. Overall, I think it's good to be back. Very good crowd, great turnout, lot of excitement around some of the new offerings we've announced. The booth traffic has been pretty good. And just the quality of the conversations, the customer meetings, have been really good. There's very interesting use cases shaping up and customers really looking to solve real large scale problems. Yeah, it's been a phenomenal first day. >> Venkat, talk a little bit about, and then we'll get to you Dan as well, the relationship that Portworx by Pure Storage has with AWS. Maybe some joint customers. >> Yeah, so we... Definitely, we have been a partner of AWS for quite some time, right? Earlier this year, we signed what is called a strategic investment letter with AWS where we kind of put some joint effort together like to better integrate our products. Plus, kind of get in front of our customers more together and educate them on how going to how they can deploy and build vision critical apps on EKS and EKS anywhere and Outpost. So that partnership has grown a lot over the last year. We have a lot of significant mutual customer wins together both on the public cloud on EKS as well as on EKS anywhere, right? And there are some exciting use cases around Edge and Edge deployments and different levels of Edge as well with EKS anywhere. And there are pretty good wins on the Outpost as well. So that partnership I think is kind of like growing across not just... We started off with the one product line. Now our Portworx backup as a service is also available on EKS and along with the Portworx Data Services. So, it is also expanded across the product lanes as well. >> And then Dan, you want to elaborate a bit on AWS Plus Pure? >> Yeah, it's for kind of what we'll call the core Pure business or the traditional Pure business. As Dave mentioned, Cloud Block Store is kind of where things started and we're seeing that move and evolve from predominantly being a DR site and kind of story into now more and more production applications being lifted and shifted and running now natively in AWS honor storage software. And then we have a new product called Pure Fusion which is our storage as code automation product essentially. It takes you from moving and managing of individual arrays, now obfuscates a fleet level allows you to build a very cloud-like backend and consume storage as code. Very, very similar to how you do with AWS, with an EBS. That product is built in AWS. So it's a SaaS product built in AWS, really allowing you to turn your traditional Pure storage into an AWS-like experience. >> Lisa: Got it. >> What changed with Cloud Block Store? 'Cause if I recall, am I right that you basically did it on S3 originally? >> S3 is a big... It's a number of components. >> And you had a high performance EC2 instances. >> Dan: Yup, that's right. >> On top of lower cost object store. Is that still the case? >> That's still the architecture. Yeah, at least for AWS. It's a different architecture in Azure where we leverage their disc storage more. But in AWS were just based on essentially that backend. >> And then what's the experience when you go from, say, on-prem to AWS to sort of a cross cloud? >> Yeah, very, very simple. It's our replication technology built in. So our sync rep, our async rep, our active cluster technology is essentially allowing you to move the data really, really seamlessly there and then again back to Fusion, now being that kind of master control plan. You can have availability zones, running Cloud Block Store instances in AWS. You can be running your own availability zones in your data centers wherever those may happen to be, and that's kind of a unification layer across it all. >> It looks the same to the customer. >> To the customer, at the end of the day, it's... What the customer sees is the purity operating system. We have FlashArray proprietary hardware on premises. We have AWS's hardware that we run it on here. But to the customer, it's just the FlashArray. >> That's a data super cloud actually. Yeah, it's a data super cloud. >> I'd agree. >> It spans multiple clouds- >> Multiple clouds on premises. >> It extracts all the complexity of the underlying muck and the primitives and presents a common experience. >> Yeah, and it's the same APIs, same management console. >> Dave: Yeah, awesome. >> Everything's the same. >> See? It's real. It's a thing, On containers, I have a question. So we're in this environment, everybody wants to be more efficient, what's happening with containers? Is there... The intersection of containers and serverless, right? You think about all the things you have to do to run containers in VMs, configure everything, configure the memory, et cetera, and then serverless simplifies all that. I guess Knative in between or I guess Fargate. What are you seeing with customers between stateless apps, stateful apps, and how it all relates to containers? >> That's a great question, right? I think that one of the things that what we are seeing is that as people run more and more workloads in the cloud, right? There's this huge movement towards being the ability to bring these applications to run anywhere, right? Not just in one public cloud, but in the data centers and sometimes the Edge clouds. So there's a lot of portability requirements for the applications, right? I mean, yesterday morning I was having breakfast with a customer who is a big AWS customer but has to go into an on-prem air gap deployment for one of their large customers and is kind of re-platforming some other apps into containers in Kubernetes because it makes it so much easier for them to deploy. So there is no longer the debate of, is it stateless versus it stateful, it's pretty much all applications are moving to containers, right? And in that, you see people are building on Kubernetes and containers is because they wanted multicloud portability for their applications. Now the other big aspect is cost, right? You can significantly run... You know, like lower cost by running with Kubernetes and Portworx and by on the public cloud or on a private cloud, right? Because it lets you get more out of your infrastructure. You're not all provisioning your infrastructure. You are like just deploying the just-enough infrastructure for your application to run with Kubernetes and scale it dynamically as your application load scales. So, customers are better able to manage costs. >> Does serverless play in here though? Right? Because if I'm running serverless, I'm not paying for the compute the whole time. >> Yeah. >> Right? But then stateless and stateful come into play. >> Serverless has a place, but it is more for like quick event-driven decision. >> Dave: The stateless apps. >> You know, stuff that needs to happen. The serverless has a place, but majority of the applications have need compute and more compute to run because there's like a ton of processing you have to do, you're serving a whole bunch of users, you're serving up media, right? Those are not typically good serverless apps, right? The several less apps do definitely have a place. There's a whole bunch of minor code snippets or events you need to process every now and then to make some decisions. In that, yeah, you see serverless. But majority of the apps are still requiring a lot of compute and scaling the compute and scaling storage requirements at a time. >> So what Venkat was talking about is cost. That is probably our biggest tailwind from a cloud adoption standpoint. I think initially for on-premises vendors like Pure Storage or historically on-premises vendors, the move to the cloud was a concern, right? In that we're getting out the data center business, we're going all in on the cloud, what are you going to do? That's kind of why we got ahead of that with Cloud Block Store. But as customers have matured in their adoption of cloud and actually moved more applications, they're becoming much more aware of the costs. And so anywhere you can help them save money seems to drive adoption. So they see that on the Kubernetes side, on our side, just by adding in things that we do really well: Data reduction, thin provisioning, low cost snaps. Those kind of things, massive cost savings. And so it's actually brought a lot of customers who thought they weren't going to be using our storage moving forward back into the fold. >> Dave: Got it. >> So cost saving is great, huge business outcomes potentially for customers. But what are some of the barriers that you're helping customers to overcome on the storage side and also in terms of moving applications to Kubernetes? What are some of those barriers that you could help us? >> Yeah, I mean, I can answer it simply from a core FlashArray side, it's enabling migration of applications without having to refactor them entirely, right? That's Kubernetes side is when they think about changing their applications and building them, we'll call quote unquote more cloud native, but there are a lot of customers that can't or won't or just aren't doing that, but they want to run those applications in the cloud. So the movement is easier back to your data super cloud kind of comment, and then also eliminating this high cost associated with it. >> I'm kind of not a huge fan of the whole repatriation narrative. You know, you look at the numbers and it's like, "Yeah, there's something going on." But the one use case that looks like it's actually valid is, "I'm going to test in the cloud and I'm going to deploy on-prem." Now, I dunno if that's even called repatriation, but I'm looking to help the repatriation narrative because- >> Venkat: I think it's- >> But that's a real thing, right? >> Yeah, it's more than repatriation, right? It's more about the ability to run your app, right? It's not just even test, right? I mean, you're going to have different kinds of governance and compliance and regulatory requirements have to run your apps in different kinds of cloud environments, right? There are certain... Certain regions may not have all of the compliance and regulatory requirements implemented in that cloud provider, right? So when you run with Kubernetes and containers, I mean, you kind of do the transformation. So now you can take that app and run an infrastructure that allows you to deliver under those requirements as well, right? So that portability is the major driver than repatriation. >> And you would do that for latency reasons? >> For latency, yeah. >> Or data sovereign? >> Data sovereignty. >> Data sovereignty. >> Control. >> I mean, yeah. Availability of your application and data just in that region, right? >> Okay, so if the capability is not there in the cloud region, you come in and say, "Hey, we can do that on-prem or in a colo and get you what you need to comply to your EDX." >> Yeah, or potentially moves to a different cloud provider. It's just a lot more control that you're providing on customer at the end of the day. >> What's that move like? I mean, now you're moving data and everybody's going to complain about egress fees. >> Well, you shouldn't be... I think it's more of a one-time move. You're probably not going to be moving data between cloud providers regularly. But if for whatever reasons you decide that I'm going to stop running in X Cloud and I'm going to move to this cloud, what's the most seamless way to do? >> So a customer might say, "Okay, that's certification's not going to be available in this region or gov cloud or whatever for a year, I need this now." >> Yeah, or various commercial. Whatever it might be. >> "And I'm going to make the call now, one-way door, and I'm going to keep it on-prem." And then worry about it down the road. Okay, makes sense. >> Dan, I got to talk to you about the sustainability element there because it's increasingly becoming a priority for organizations in every industry where they need to work with companies that really have established sustainability programs. What are some of the factors that you talk with customers about as they have choice in all FlashArray between Pure and competitors where sustainability- >> Yeah, I mean we've leaned very heavily into that from a marketing standpoint recently because it has become so top of mind for so many customers. But at the end of the day, sustainability was built into the core of the Purity operating system in FlashArray back before it was FlashArray, right? In our early generation of products. The things that drive that sustainability of high density, high data reduction, small footprint, we needed to build that for Pure to exist as a company. And we are maybe kind of the last all-flash vendor standing that came ground up all-flash, not just the disc vendor that's refactored, right? And so that's sort of engineering from the ground up that's deeply, deeply into our software as a huge sustainability payout now. And we see that and that message is really, really resonating with customers. >> I haven't thought about that in a while. You actually are. I don't think there's any other... Nobody else made it through the knothole. And you guys hit escape velocity and then some. >> So we hit escape velocity and it hasn't slowed down, right? Earnings will be tomorrow, but the last many quarters have been pretty good. >> Yeah, we follow you pretty closely. I mean, there was one little thing in the pandemic and then boom! It's just kept cranking since, so. >> So at the end of the day though, right? We needed that level to be economically viable as a flash bender going against disc. And now that's really paying off in a sustainability equation as well because we consume so much less footprint, power cooling, all those factors. >> And there's been some headwinds with none pricing up until recently too that you've kind of blown right through. You know, you dealt with the supply issues and- >> Yeah, 'cause the overall... One, we've been, again, one of the few vendors that's been able to navigate supply really well. We've had no major delays in disruptions, but the TCO argument's real. Like at the end of the day, when you look at the cost of running on Pure, it's very, very compelling. >> Adam Selipsky made the statement, "If you're looking to tighten your belt, the cloud is the place to do it." Yeah, okay. It might be that, but... Maybe. >> Maybe, but you can... So again, we are seeing cloud customers that are traditional Pure data center customers that a few years ago said, "We're moving these applications into the cloud. You know, it's been great working with you. We love Pure. We'll have some on-prem footprint, but most of everything we're going to do is in the cloud." Those customers are coming back to us to keep running in the cloud. Because again, when you start to factor in things like thin provisioning, data reduction, those don't exist in the cloud. >> So, it's not repatriation. >> It's not repatriation. >> It's we want Pure in the cloud. >> Correct. We want your software. So that's why we built CBS, and we're seeing that come all the way through. >> There's another cost savings is on the... You know, with what we are doing with Kubernetes and containers and Portworx Data Services, right? So when we run Portworx Data Services, typically customers spend a lot of money in running the cloud managed services, right? Where there is obviously a sprawl of those, right? And then they end up spending a lot of item costs. So when we move that, like when they run their data, like when they move their databases to Portworx Data Services on Kubernetes, because of all of the other cost savings we deliver plus the licensing costs are a lot lower, we deliver 5X to 10X savings to our customers. >> Lisa: Significant. >> You know, significant savings on cloud as well. >> The operational things he's talking about, too. My Fusion engineering team is one of his largest customers from Portworx Data Services. Because we don't have DBAs on that team, it's just developers. But they need databases. They need to run those databases. We turn to PDS. >> This is why he pays my bills. >> And that's why you guys have to come back 'cause we're out of time, but I do have one final question for each of you. Same question. We'll start with you Dan, the Venkat we'll go to you. Billboard. Billboard or a bumper sticker. We'll say they're going to put a billboard on Castor Street in Mountain View near the headquarters about Pure, what does it say? >> The best container for containers. (Dave and Lisa laugh) >> Venkat, Portworx, what's your bumper sticker? >> Well, I would just have one big billboard that goes and says, "Got PX?" With the question mark, right? And let people start thinking about, "What is PX?" >> I love that. >> Dave: Got Portworx, beautiful. >> You've got a side career in marketing, I can tell. >> I think they moved him out of the engineering. >> Ah, I see. We really appreciate you joining us on the program this afternoon talking about Pure, Portworx, AWS. Really compelling stories about how you're helping customers just really make big decisions and save considerable costs. We appreciate your insights. >> Awesome. Great. Thanks for having us. >> Thanks, guys. >> Thank you. >> For our guests and for Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live enterprise and emerging tech coverage. (upbeat music)

>>Hey everyone. Welcome to Las Vegas. The Cube is here, live at the Venetian Expo Center for AWS Reinvent 2022. Amazing attendance. This is day one of our coverage. Lisa Martin here with Day Ante. David is great to see so many people back. We're gonna be talk, we've been having great conversations already. We have a wall to wall coverage for the next three and a half days. When we talk to companies, customers, every company has to be a data company. And one of the things I think we learned in the pandemic is that access to real time data and real time analytics, no longer a nice to have that is a differentiator and a competitive all >>About data. I mean, you know, I love the topic and it's, it's got so many dimensions and such texture, can't get enough of data. >>I know we have a great guest joining us. One of our alumni is back, Evan Kaplan, the CEO of Influx Data. Evan, thank you so much for joining us. Welcome back to the Cube. >>Thanks for having me. It's great to be here. So here >>We are, day one. I was telling you before we went live, we're nice and fresh hosts. Talk to us about what's new at Influxed since the last time we saw you at Reinvent. >>That's great. So first of all, we should acknowledge what's going on here. This is pretty exciting. Yeah, that does really feel like, I know there was a show last year, but this feels like the first post Covid shows a lot of energy, a lot of attention despite a difficult economy. In terms of, you know, you guys were commenting in the lead into Big data. I think, you know, if we were to talk about Big Data five, six years ago, what would we be talking about? We'd been talking about Hadoop, we were talking about Cloudera, we were talking about Hortonworks, we were talking about Big Data Lakes, data stores. I think what's happened is, is this this interesting dynamic of, let's call it if you will, the, the secularization of data in which it breaks into different fields, different, almost a taxonomy. You've got this set of search data, you've got this observability data, you've got graph data, you've got document data and what you're seeing in the market and now you have time series data. >>And what you're seeing in the market is this incredible capability by developers as well and mostly open source dynamic driving this, this incredible capability of developers to assemble data platforms that aren't unicellular, that aren't just built on Hado or Oracle or Postgres or MySQL, but in fact represent different data types. So for us, what we care about his time series, we care about anything that happens in time, where time can be the primary measurement, which if you think about it, is a huge proportion of real data. Cuz when you think about what drives ai, you think about what happened, what happened, what happened, what happened, what's going to happen. That's the functional thing. But what happened is always defined by a period, a measurement, a time. And so what's new for us is we've developed this new open source engine called IOx. And so it's basically a refresh of the whole database, a kilo database that uses Apache Arrow, par K and data fusion and turns it into a super powerful real time analytics platform. It was already pretty real time before, but it's increasingly now and it adds SQL capability and infinite cardinality. And so it handles bigger data sets, but importantly, not just bigger but faster, faster data. So that's primarily what we're talking about to show. >>So how does that affect where you can play in the marketplace? Is it, I mean, how does it affect your total available market? Your great question. Your, your customer opportunities. >>I think it's, it's really an interesting market in that you've got all of these different approaches to database. Whether you take data warehouses from Snowflake or, or arguably data bricks also. And you take these individual database companies like Mongo Influx, Neo Forge, elastic, and people like that. I think the commonality you see across the volume is, is many of 'em, if not all of them, are based on some sort of open source dynamic. So I think that is an in an untractable trend that will continue for on. But in terms of the broader, the broader database market, our total expand, total available tam, lots of these things are coming together in interesting ways. And so the, the, the wave that will ride that we wanna ride, because it's all big data and it's all increasingly fast data and it's all machine learning and AI is really around that measurement issue. That instrumentation the idea that if you're gonna build any sophisticated system, it starts with instrumentation and the journey is defined by instrumentation. So we view ourselves as that instrumentation tooling for understanding complex systems. And how, >>I have to follow quick follow up. Why did you say arguably data bricks? I mean open source ethos? >>Well, I was saying arguably data bricks cuz Spark, I mean it's a great company and it's based on Spark, but there's quite a gap between Spark and what Data Bricks is today. And in some ways data bricks from the outside looking in looks a lot like Snowflake to me looks a lot like a really sophisticated data warehouse with a lot of post-processing capabilities >>And, and with an open source less >>Than a >>Core database. Yeah. Right, right, right. Yeah, I totally agree. Okay, thank you for that >>Part that that was not arguably like they're, they're not a good company or >>No, no. They got great momentum and I'm just curious. Absolutely. You know, so, >>So talk a little bit about IOx and, and what it is enabling you guys to achieve from a competitive advantage perspective. The key differentiators give us that scoop. >>So if you think about, so our old storage engine was called tsm, also open sourced, right? And IOx is open sourced and the old storage engine was really built around this time series measurements, particularly metrics, lots of metrics and handling those at scale and making it super easy for developers to use. But, but our old data engine only supported either a custom graphical UI that you'd build yourself on top of it or a dashboarding tool like Grafana or Chronograph or things like that. With IOCs. Two or three interventions were important. One is we now support, we'll support things like Tableau, Microsoft, bi, and so you're taking that same data that was available for instrumentation and now you're using it for business intelligence also. So that became super important and it kind of answers your question about the expanded market expands the market. The second thing is, when you're dealing with time series data, you're dealing with this concept of cardinality, which is, and I don't know if you're familiar with it, but the idea that that it's a multiplication of measurements in a table. And so the more measurements you want over the more series you have, you have this really expanding exponential set that can choke a database off. And the way we've designed IIS to handle what we call infinite cardinality, where you don't even have to think about that design point of view. And then lastly, it's just query performance is dramatically better. And so it's pretty exciting. >>So the unlimited cardinality, basically you could identify relationships between data and different databases. Is that right? Between >>The same database but different measurements, different tables, yeah. Yeah. Right. Yeah, yeah. So you can handle, so you could say, I wanna look at the way, the way the noise levels are performed in this room according to 400 different locations on 25 different days, over seven months of the year. And that each one is a measurement. Each one adds to cardinality. And you can say, I wanna search on Tuesdays in December, what the noise level is at 2:21 PM and you get a very quick response. That kind of instrumentation is critical to smarter systems. How are >>You able to process that data at at, in a performance level that doesn't bring the database to its knees? What's the secret sauce behind that? >>It's AUM database. It's built on Parque and Apache Arrow. But it's, but to say it's nice to say without a much longer conversation, it's an architecture that's really built for pulling that kind of data. If you know the data is time series and you're looking for a time measurement, you already have the ability to optimize pretty dramatically. >>So it's, it's that purpose built aspect of it. It's the >>Purpose built aspect. You couldn't take Postgres and do the same >>Thing. Right? Because a lot of vendors say, oh yeah, we have time series now. Yeah. Right. So yeah. Yeah. Right. >>And they >>Do. Yeah. But >>It's not, it's not, the founding of the company came because Paul Dicks was working on Wall Street building time series databases on H base, on MyQ, on other platforms and realize every time we do it, we have to rewrite the code. We build a bunch of application logic to handle all these. We're talking about, we have customers that are adding hundreds of millions to billions of points a second. So you're talking about an ingest level. You know, you think about all those data points, you're talking about ingest level that just doesn't, you know, it just databases aren't designed for that. Right? And so it's not just us, our competitors also build good time series databases. And so the category is really emergent. Yeah, >>Sure. Talk about a favorite customer story they think really articulates the value of what Influx is doing, especially with IOx. >>Yeah, sure. And I love this, I love this story because you know, Tesla may not be in favor because of the latest Elon Musker aids, but, but, but so we've had about a four year relationship with Tesla where they built their power wall technology around recording that, seeing your device, seeing the stuff, seeing the charging on your car. It's all captured in influx databases that are reporting from power walls and mega power packs all over the world. And they report to a central place at, at, at Tesla's headquarters and it reports out to your phone and so you can see it. And what's really cool about this to me is I've got two Tesla cars and I've got a Tesla solar roof tiles. So I watch this date all the time. So it's a great customer story. And actually if you go on our website, you can see I did an hour interview with the engineer that designed the system cuz the system is super impressive and I just think it's really cool. Plus it's, you know, it's all the good green stuff that we really appreciate supporting sustainability, right? Yeah. >>Right, right. Talk about from a, what's in it for me as a customer, what you guys have done, the change to IOCs, what, what are some of the key features of it and the key values in it for customers like Tesla, like other industry customers as well? >>Well, so it's relatively new. It just arrived in our cloud product. So Tesla's not using it today. We have a first set of customers starting to use it. We, the, it's in open source. So it's a very popular project in the open source world. But the key issues are, are really the stuff that we've kind of covered here, which is that a broad SQL environment. So accessing all those SQL developers, the same people who code against Snowflake's data warehouse or data bricks or Postgres, can now can code that data against influx, open up the BI market. It's the cardinality, it's the performance. It's really an architecture. It's the next gen. We've been doing this for six years, it's the next generation of everything. We've seen how you make time series be super performing. And that's only relevant because more and more things are becoming real time as we develop smarter and smarter systems. The journey is pretty clear. You instrument the system, you, you let it run, you watch for anomalies, you correct those anomalies, you re instrument the system. You do that 4 billion times, you have a self-driving car, you do that 55 times, you have a better podcast that is, that is handling its audio better, right? So everything is on that journey of getting smarter and smarter. So >>You guys, you guys the big committers to IOCs, right? Yes. And how, talk about how you support the, develop the surrounding developer community, how you get that flywheel effect going >>First. I mean it's actually actually a really kind of, let's call it, it's more art than science. Yeah. First of all, you you, you come up with an architecture that really resonates for developers. And Paul Ds our founder, really is a developer's developer. And so he started talking about this in the community about an architecture that uses Apache Arrow Parque, which is, you know, the standard now becoming for file formats that uses Apache Arrow for directing queries and things like that and uses data fusion and said what this thing needs is a Columbia database that sits behind all of this stuff and integrates it. And he started talking about it two years ago and then he started publishing in IOCs that commits in the, in GitHub commits. And slowly, but over time in Hacker News and other, and other people go, oh yeah, this is fundamentally right. >>It addresses the problems that people have with things like click cows or plain databases or Coast and they go, okay, this is the right architecture at the right time. Not different than original influx, not different than what Elastic hit on, not different than what Confluent with Kafka hit on and their time is you build an audience of people who are committed to understanding this kind of stuff and they become committers and they become the core. Yeah. And you build out from it. And so super. And so we chose to have an MIT open source license. Yeah. It's not some secondary license competitors can use it and, and competitors can use it against us. Yeah. >>One of the things I know that Influx data talks about is the time to awesome, which I love that, but what does that mean? What is the time to Awesome. Yeah. For developer, >>It comes from that original story where, where Paul would have to write six months of application logic and stuff to build a time series based applications. And so Paul's notion was, and this was based on the original Mongo, which was very successful because it was very easy to use relative to most databases. So Paul developed this commitment, this idea that I quickly joined on, which was, hey, it should be relatively quickly for a developer to build something of import to solve a problem, it should be able to happen very quickly. So it's got a schemaless background so you don't have to know the schema beforehand. It does some things that make it really easy to feel powerful as a developer quickly. And if you think about that journey, if you feel powerful with a tool quickly, then you'll go deeper and deeper and deeper and pretty soon you're taking that tool with you wherever you go, it becomes the tool of choice as you go to that next job or you go to that next application. And so that's a fundamental way we think about it. To be honest with you, we haven't always delivered perfectly on that. It's generally in our dna. So we do pretty well, but I always feel like we can do better. >>So if you were to put a bumper sticker on one of your Teslas about influx data, what would it >>Say? By the way, I'm not rich. It just happened to be that we have two Teslas and we have for a while, we just committed to that. The, the, so ask the question again. Sorry. >>Bumper sticker on influx data. What would it say? How, how would I >>Understand it be time to Awesome. It would be that that phrase his time to Awesome. Right. >>Love that. >>Yeah, I'd love it. >>Excellent time to. Awesome. Evan, thank you so much for joining David, the >>Program. It's really fun. Great thing >>On Evan. Great to, you're on. Haven't Well, great to have you back talking about what you guys are doing and helping organizations like Tesla and others really transform their businesses, which is all about business transformation these days. We appreciate your insights. >>That's great. Thank >>You for our guest and Dave Ante. I'm Lisa Martin, you're watching The Cube, the leader in emerging and enterprise tech coverage. We'll be right back with our next guest.

>> 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)

(atmospheric music) >> Welcome to theCUBE Coverage of Data Citizens 2022 Collibra's Customer event. My name is Dave Vellante. With us is Kirk Haslbeck, who's the Vice President of Data Quality of Collibra. Kirk, good to see you, welcome. >> Thanks for having me, Dave. Excited to be here. >> You bet. Okay, we're going to discuss data quality, observability. It's a hot trend right now. You founded a data quality company, OwlDQ, and it was acquired by Collibra last year. Congratulations. And now you lead data quality at Collibra. So we're hearing a lot about data quality right now. Why is it such a priority? Take us through your thoughts on that. >> Yeah, absolutely. It's definitely exciting times for data quality which you're right, has been around for a long time. So why now? And why is it so much more exciting than it used to be? I think it's a bit stale, but we all know that companies use more data than ever before, and the variety has changed and the volume has grown. And while I think that remains true there are a couple other hidden factors at play that everyone's so interested in as to why this is becoming so important now. And I guess you could kind of break this down simply and think about if Dave you and I were going to build a new healthcare application and monitor the heartbeat of individuals, imagine if we get that wrong, what the ramifications could be, what those incidents would look like. Or maybe better yet, we try to build a new trading algorithm with a crossover strategy where the 50 day crosses the 10 day average. And imagine if the data underlying the inputs to that is incorrect. We will probably have major financial ramifications in that sense. So, kind of starts there, where everybody's realizing that we're all data companies, and if we are using bad data we're likely making incorrect business decisions. But I think there's kind of two other things at play. I bought a car not too long ago and my dad called and said, "How many cylinders does it have?" And I realized in that moment, I might have failed him cause I didn't know. And I used to ask those types of questions about any lock breaks and cylinders, and if it's manual or automatic. And I realized, I now just buy a car that I hope works. And it's so complicated with all the computer chips. I really don't know that much about it. And that's what's happening with data. We're just loading so much of it. And it's so complex that the way companies consume them in the IT function is that they bring in a lot of data and then they syndicate it out to the business. And it turns out that the individuals loading and consuming all of this data for the company actually may not know that much about the data itself and that's not even their job anymore. So, we'll talk more about that in a minute, but that's really what's setting the foreground for this observability play and why everybody's so interested. It's because we're becoming less close to the intricacies of the data and we just expect it to always be there and be correct. >> You know, the other thing too about data quality, and for years we did the MIT, CDO, IQ event. We didn't do it last year at COVID, messed everything up. But the observation I would make there, your thoughts is, data quality used to be information quality, used to be this back office function, and then it became sort of front office with financial services, and government and healthcare, these highly regulated industries. And then the whole chief data officer thing happened and people were realizing, well they sort of flipped the bit from sort of a data as a risk to data as an asset. And now as we say, we're going to talk about observability. And so it's really become front and center, just the whole quality issue because data's so fundamental, hasn't it? >> Yeah, absolutely. I mean, let's imagine we pull up our phones right now and I go to my favorite stock ticker app, and I check out the Nasdaq market cap. I really have no idea if that's the correct number. I know it's a number, it looks large, it's in a numeric field. And that's kind of what's going on. There's so many numbers and they're coming from all of these different sources, and data providers, and they're getting consumed and passed along. But there isn't really a way to tactically put controls on every number and metric across every field we plan to monitor, but with the scale that we've achieved in early days, even before Collibra. And what's been so exciting is, we have these types of observation techniques, these data monitors that can actually track past performance of every field at scale. And why that's so interesting, and why I think the CDO is listening right intently nowadays to this topic is, so maybe we could surface all of these problems with the right solution of data observability and with the right scale, and then just be alerted on breaking trends. So we're sort of shifting away from this world of must write a condition and then when that condition breaks that was always known as a break record. But what about breaking trends and root cause analysis? And is it possible to do that with less human intervention? And so I think most people are seeing now that it's going to have to be a software tool and a computer system. It's not ever going to be based on one or two domain experts anymore. >> So how does data observability relate to data quality? Are they sort of two sides of the same coin? Are they cousins? What's your perspective on that? >> Yeah, it's super interesting. It's an emerging market. So the language is changing, a lot of the topic and areas changing. The way that I like to say it or break it down because the lingo is constantly moving, as a target on the space is really breaking records versus breaking trends. And I could write a condition when this thing happens it's wrong, and when it doesn't it's correct. Or I could look for a trend and I'll give you a good example. Everybody's talking about fresh data and stale data, and why would that matter? Well, if your data never arrived, or only part of it arrived, or didn't arrive on time, it's likely stale, and there will not be a condition that you could write that would show you all the good and the bads. That was kind of your traditional approach of data quality break records. But your modern day approach is you lost a significant portion of your data, or it did not arrive on time to make that decision accurately on time. And that's a hidden concern. Some people call this freshness, we call it stale data. But it all points to the same idea of the thing that you're observing may not be a data quality condition anymore. It may be a breakdown in the data pipeline. And with thousands of data pipelines in play for every company out there, there's more than a couple of these happening every day. >> So what's the Collibra angle on all this stuff? Made the acquisition, you got data quality, observability coming together. You guys have a lot of expertise in this area, but you hear providence of data. You just talked about stale data, the whole trend toward realtime. How is Collibra approaching the problem and what's unique about your approach? >> Well I think where we're fortunate is with our background. Myself and team, we sort of lived this problem for a long time in the Wall Street days about a decade ago. And we saw it from many different angles. And what we came up with, before it was called data observability or reliability, was basically the underpinnings of that. So we're a little bit ahead of the curve there when most people evaluate our solution. It's more advanced than some of the observation techniques that currently exist. But we've also always covered data quality and we believe that people want to know more, they need more insights. And they want to see break records and breaking trends together, so they can correlate the root cause. And we hear that all the time. "I have so many things going wrong just show me the big picture. Help me find the thing that if I were to fix it today would make the most impact." So we're really focused on root cause analysis, business impact, connecting it with lineage and catalog metadata. And as that grows you can actually achieve total data governance. At this point with the acquisition of what was a Lineage company years ago, and then my company OwlDQ, now Collibra Data Quality. Collibra may be the best positioned for total data governance and intelligence in the space. >> Well, you mentioned financial services a couple of times and some examples, remember the flash crash in 2010. Nobody had any idea what that was. They would just say, "Oh, it's a glitch." So they didn't understand the root cause of it. So this is a really interesting topic to me. So we know at Data Citizens 22 that you're announcing, you got to announce new products, right? It is your yearly event. What's new? Give us a sense as to what products are coming out but specifically around data quality and observability. >> Absolutely. There's this, there's always a next thing on the forefront. And the one right now is these hyperscalers in the cloud. So you have databases like Snowflake and BigQuery, and Databricks, Delta Lake and SQL Pushdown. And ultimately what that means is a lot of people are storing in loading data even faster in a SaaS like model. And we've started to hook into these databases, and while we've always worked with the same databases in the past they're supported today. We're doing something called Native Database pushdown, where the entire compute and data activity happens in the database. And why that is so interesting and powerful now? Is everyone's concerned with something called Egress. Did my data that I've spent all this time and money with my security team securing ever leave my hands, did it ever leave my secure VPC as they call it? And with these native integrations that we're building and about to unveil here as kind of a sneak peak for next week at Data Citizens, we're now doing all compute and data operations in databases like Snowflake. And what that means is with no install and no configuration you could log into the Collibra data quality app and have all of your data quality running inside the database that you've probably already picked as your go forward team selection secured database of choice. So we're really excited about that. And I think if you look at the whole landscape of network cost, egress cost, data storage and compute, what people are realizing is it's extremely efficient to do it in the way that we're about to release here next week. >> So this is interesting because what you just described, you mentioned Snowflake, you mentioned Google, oh actually you mentioned yeah, Databricks. You know, Snowflake has the data cloud. If you put everything in the data cloud, okay, you're cool. But then Google's got the open data cloud. If you heard, Google next. And now Databricks doesn't call it the data cloud, but they have like the open source data cloud. So you have all these different approaches and there's really no way, up until now I'm hearing, to really understand the relationships between all those and have confidence across, it's like yamarket AMI, you should just be a note on the mesh. I don't care if it's a data warehouse or a data lake, or where it comes from, but it's a point on that mesh and I need tooling to be able to have confidence that my data is governed and has the proper lineage, providence. And that's what you're bringing to the table. Is that right? Did I get that right? >> Yeah, that's right. And it's, for us, it's not that we haven't been working with those great cloud databases, but it's the fact that we can send them the instructions now we can send them the operating ability to crunch all of the calculations, the governance, the quality, and get the answers. And what that's doing, it's basically zero network cost, zero egress cost, zero latency of time. And so when you were to log into BigQuery tomorrow using our tool, or say Snowflake for example, you have instant data quality metrics, instant profiling, instant lineage in access, privacy controls, things of that nature that just become less onerous. What we're seeing is there's so much technology out there just like all of the major brands that you mentioned but how do we make it easier? The future is about less clicks, faster time to value, faster scale, and eventually lower cost. And we think that this positions us to be the leader there. >> I love this example because, we've got talks about well the cloud guys you're going to own the world. And of course now we're seeing that the ecosystem is finding so much white space to add value connect across cloud. Sometimes we call it super cloud and so, or inter clouding. Alright, Kirk, give us your final thoughts on the trends that we've talked about and data Citizens 22. >> Absolutely. Well I think, one big trend is discovery and classification. Seeing that across the board, people used to know it was a zip code and nowadays with the amount of data that's out there they want to know where everything is, where their sensitive data is, if it's redundant, tell me everything inside of three to five seconds. And with that comes, they want to know in all of these hyperscale databases how fast they can get controls and insights out of their tools. So I think we're going to see more one click solutions, more SaaS based solutions, and solutions that hopefully prove faster time to value on all of these modern cloud platforms. >> Excellent. All right, Kirk Haslbeck, thanks so much for coming on theCUBE and previewing Data Citizens 22. Appreciate it. >> Thanks for having me, Dave. >> You're welcome. All right. And thank you for watching. Keep it right there for more coverage from theCUBE. (atmospheric music)

(bright upbeat music) >> Welcome to theCUBE's Coverage of Data Citizens 2022 Collibra's Customer event. My name is Dave Vellante. With us is Kirk Hasselbeck, who's the Vice President of Data Quality of Collibra. Kirk, good to see you. Welcome. >> Thanks for having me, Dave. Excited to be here. >> You bet. Okay, we're going to discuss data quality, observability. It's a hot trend right now. You founded a data quality company, OwlDQ and it was acquired by Collibra last year. Congratulations! And now you lead data quality at Collibra. So we're hearing a lot about data quality right now. Why is it such a priority? Take us through your thoughts on that. >> Yeah, absolutely. It's definitely exciting times for data quality which you're right, has been around for a long time. So why now, and why is it so much more exciting than it used to be? I think it's a bit stale, but we all know that companies use more data than ever before and the variety has changed and the volume has grown. And while I think that remains true, there are a couple other hidden factors at play that everyone's so interested in as to why this is becoming so important now. And I guess you could kind of break this down simply and think about if Dave, you and I were going to build, you know a new healthcare application and monitor the heartbeat of individuals, imagine if we get that wrong, what the ramifications could be? What those incidents would look like? Or maybe better yet, we try to build a new trading algorithm with a crossover strategy where the 50 day crosses the 10 day average. And imagine if the data underlying the inputs to that is incorrect. We'll probably have major financial ramifications in that sense. So, it kind of starts there where everybody's realizing that we're all data companies and if we are using bad data, we're likely making incorrect business decisions. But I think there's kind of two other things at play. I bought a car not too long ago and my dad called and said, "How many cylinders does it have?" And I realized in that moment, I might have failed him because 'cause I didn't know. And I used to ask those types of questions about any lock brakes and cylinders and if it's manual or automatic and I realized I now just buy a car that I hope works. And it's so complicated with all the computer chips. I really don't know that much about it. And that's what's happening with data. We're just loading so much of it. And it's so complex that the way companies consume them in the IT function is that they bring in a lot of data and then they syndicate it out to the business. And it turns out that the individuals loading and consuming all of this data for the company actually may not know that much about the data itself and that's not even their job anymore. So, we'll talk more about that in a minute but that's really what's setting the foreground for this observability play and why everybody's so interested, it's because we're becoming less close to the intricacies of the data and we just expect it to always be there and be correct. >> You know, the other thing too about data quality and for years we did the MIT CDOIQ event we didn't do it last year at COVID, messed everything up. But the observation I would make there love thoughts is it data quality used to be information quality used to be this back office function, and then it became sort of front office with financial services and government and healthcare, these highly regulated industries. And then the whole chief data officer thing happened and people were realizing, well, they sort of flipped the bit from sort of a data as a a risk to data as an asset. And now, as we say, we're going to talk about observability. And so it's really become front and center, just the whole quality issue because data's fundamental, hasn't it? >> Yeah, absolutely. I mean, let's imagine we pull up our phones right now and I go to my favorite stock ticker app and I check out the NASDAQ market cap. I really have no idea if that's the correct number. I know it's a number, it looks large, it's in a numeric field. And that's kind of what's going on. There's so many numbers and they're coming from all of these different sources and data providers and they're getting consumed and passed along. But there isn't really a way to tactically put controls on every number and metric across every field we plan to monitor. But with the scale that we've achieved in early days, even before Collibra. And what's been so exciting is we have these types of observation techniques, these data monitors that can actually track past performance of every field at scale. And why that's so interesting and why I think the CDO is listening right intently nowadays to this topic is so maybe we could surface all of these problems with the right solution of data observability and with the right scale and then just be alerted on breaking trends. So we're sort of shifting away from this world of must write a condition and then when that condition breaks, that was always known as a break record. But what about breaking trends and root cause analysis? And is it possible to do that, with less human intervention? And so I think most people are seeing now that it's going to have to be a software tool and a computer system. It's not ever going to be based on one or two domain experts anymore. >> So, how does data observability relate to data quality? Are they sort of two sides of the same coin? Are they cousins? What's your perspective on that? >> Yeah, it's super interesting. It's an emerging market. So the language is changing a lot of the topic and areas changing the way that I like to say it or break it down because the lingo is constantly moving as a target on this space is really breaking records versus breaking trends. And I could write a condition when this thing happens it's wrong and when it doesn't, it's correct. Or I could look for a trend and I'll give you a good example. Everybody's talking about fresh data and stale data and why would that matter? Well, if your data never arrived or only part of it arrived or didn't arrive on time, it's likely stale and there will not be a condition that you could write that would show you all the good and the bads. That was kind of your traditional approach of data quality break records. But your modern day approach is you lost a significant portion of your data, or it did not arrive on time to make that decision accurately on time. And that's a hidden concern. Some people call this freshness, we call it stale data but it all points to the same idea of the thing that you're observing may not be a data quality condition anymore. It may be a breakdown in the data pipeline. And with thousands of data pipelines in play for every company out there there, there's more than a couple of these happening every day. >> So what's the Collibra angle on all this stuff made the acquisition you got data quality observability coming together, you guys have a lot of expertise in this area but you hear providence of data you just talked about stale data, the whole trend toward real time. How is Collibra approaching the problem and what's unique about your approach? >> Well, I think where we're fortunate is with our background, myself and team we sort of lived this problem for a long time in the Wall Street days about a decade ago. And we saw it from many different angles. And what we came up with before it was called data observability or reliability was basically the underpinnings of that. So we're a little bit ahead of the curve there when most people evaluate our solution. It's more advanced than some of the observation techniques that currently exist. But we've also always covered data quality and we believe that people want to know more, they need more insights and they want to see break records and breaking trends together so they can correlate the root cause. And we hear that all the time. I have so many things going wrong just show me the big picture. Help me find the thing that if I were to fix it today would make the most impact. So we're really focused on root cause analysis, business impact connecting it with lineage and catalog, metadata. And as that grows, you can actually achieve total data governance. At this point, with the acquisition of what was a lineage company years ago and then my company OwlDQ, now Collibra Data Quality, Collibra may be the best positioned for total data governance and intelligence in the space. >> Well, you mentioned financial services a couple of times and some examples, remember the flash crash in 2010. Nobody had any idea what that was, they just said, "Oh, it's a glitch." So they didn't understand the root cause of it. So this is a really interesting topic to me. So we know at Data Citizens '22 that you're announcing you got to announce new products, right? Your yearly event, what's new? Give us a sense as to what products are coming out but specifically around data quality and observability. >> Absolutely. There's always a next thing on the forefront. And the one right now is these hyperscalers in the cloud. So you have databases like Snowflake and Big Query and Data Bricks, Delta Lake and SQL Pushdown. And ultimately what that means is a lot of people are storing in loading data even faster in a salike model. And we've started to hook in to these databases. And while we've always worked with the same databases in the past they're supported today we're doing something called Native Database pushdown, where the entire compute and data activity happens in the database. And why that is so interesting and powerful now is everyone's concerned with something called Egress. Did my data that I've spent all this time and money with my security team securing ever leave my hands? Did it ever leave my secure VPC as they call it? And with these native integrations that we're building and about to unveil here as kind of a sneak peek for next week at Data Citizens, we're now doing all compute and data operations in databases like Snowflake. And what that means is with no install and no configuration you could log into the Collibra Data Quality app and have all of your data quality running inside the database that you've probably already picked as your your go forward team selection secured database of choice. So we're really excited about that. And I think if you look at the whole landscape of network cost, egress cost, data storage and compute, what people are realizing is it's extremely efficient to do it in the way that we're about to release here next week. >> So this is interesting because what you just described you mentioned Snowflake, you mentioned Google, oh actually you mentioned yeah, the Data Bricks. Snowflake has the data cloud. If you put everything in the data cloud, okay, you're cool but then Google's got the open data cloud. If you heard Google Nest and now Data Bricks doesn't call it the data cloud but they have like the open source data cloud. So you have all these different approaches and there's really no way up until now I'm hearing to really understand the relationships between all those and have confidence across, it's like (indistinct) you should just be a note on the mesh. And I don't care if it's a data warehouse or a data lake or where it comes from, but it's a point on that mesh and I need tooling to be able to have confidence that my data is governed and has the proper lineage, providence. And that's what you're bringing to the table. Is that right? Did I get that right? >> Yeah, that's right. And for us, it's not that we haven't been working with those great cloud databases, but it's the fact that we can send them the instructions now we can send them the operating ability to crunch all of the calculations, the governance, the quality and get the answers. And what that's doing, it's basically zero network cost, zero egress cost, zero latency of time. And so when you were to log into Big BigQuery tomorrow using our tool or let or say Snowflake, for example, you have instant data quality metrics, instant profiling, instant lineage and access privacy controls things of that nature that just become less onerous. What we're seeing is there's so much technology out there just like all of the major brands that you mentioned but how do we make it easier? The future is about less clicks, faster time to value faster scale, and eventually lower cost. And we think that this positions us to be the leader there. >> I love this example because every talks about wow the cloud guys are going to own the world and of course now we're seeing that the ecosystem is finding so much white space to add value, connect across cloud. Sometimes we call it super cloud and so, or inter clouding. Alright, Kirk, give us your final thoughts and on the trends that we've talked about and Data Citizens '22. >> Absolutely. Well I think, one big trend is discovery and classification. Seeing that across the board people used to know it was a zip code and nowadays with the amount of data that's out there, they want to know where everything is where their sensitive data is. If it's redundant, tell me everything inside of three to five seconds. And with that comes, they want to know in all of these hyperscale databases, how fast they can get controls and insights out of their tools. So I think we're going to see more one click solutions, more SAS-based solutions and solutions that hopefully prove faster time to value on all of these modern cloud platforms. >> Excellent, all right. Kurt Hasselbeck, thanks so much for coming on theCUBE and previewing Data Citizens '22. Appreciate it. >> Thanks for having me, Dave. >> You're welcome. All right, and thank you for watching. Keep it right there for more coverage from theCUBE.

>>The transformation of Dell into Dell emc. And now Dell Technologies has been one of the most remarkable stories in the history of the enterprise technology industry. The company has gone from a Wall Street darling rocket ship PC company to a Midling enterprise player, forced to go private to a debt laden powerhouse that controlled one of the most valuable assets in enterprise tech i e VMware, and now is a hundred billion dollar giant with a low margin business. A strong balance sheet in the broadest hardware portfolio in the industry and financial magic that Dell went through would make anyone's head spin. The last lever of Dell EMC of the Dell EMC deal was detailed in Michael Dell's book Play Nice But Win in a captivating chapter called Harry You and the Bolt from the Blue Michael Dell described how he and his colleagues came up with the final straw of how to finance the deal. >>If you haven't read it, you should. And of course, after years of successfully integrating EMC and becoming VMware's number one distribution channel, all of this culminated in the spin out of VMware from Dell and a massive wealth creation milestone pending, of course the Broadcom acquisition of VMware. So where's that leave Dell and what does the future look like for this technology powerhouse? Hello and welcome to the Cube's exclusive coverage of Dell Technology Summit 2022. My name is Dave Ante and I'll be hosting the program. Now today in conjunction with the Dell Tech Summit, we're gonna hear from four of Dell's senior executives, Tom Sweet, who's the CFO of Dell Technologies. He's gonna share his views on the company's position and opportunities going forward. He's gonna answer the question, why is Dell a good long-term investment? Then we'll hear from Jeff Boudreau, who's the president of Dell's ISG business. >>That unit is the largest profit driver of Dell. He's gonna talk about the product angle and specifically how Dell is thinking about solving the multi-cloud challenge. And then Sam Groot, who is the senior vice president of marketing, will come on the program and give us the update on Apex, which is Dell's as a service offering, and then the new Edge platform called Project Frontier. Now it's also cyber security Awareness month that we're gonna see if Sam has, you know, anything to say about that. Then finally, for a company that's nearly 40 years old, Dell actually has some pretty forward thinking philosophies when it comes to its culture and workforce. And we're gonna speak with Jen Vera, who's Dell's chief Human Resource Resource Officer about hybrid work and how Dell is thinking about the future of work. However, before we get into all this, I wanna share our independent perspectives on the company and some research that we'll introduce to frame the program. >>Now, as you know, we love data here at the cube and one of our partners, ETR has what we believe is the best spending intentions data for enterprise tech. So here's a graphic that shows ET R'S proprietary net score methodology in the vertical access. That's a measure of spending velocity. And on the X axis, his overlap of pervasiveness in the data sample, this is a cut for just the server, the storage, and the client sectors within the ETR taxonomy. So you can see Dell CSG products, laptops in particular are dominant on both the X and the Y dimensions. CSG is the client solutions group and accounts for nearly 60% of Dell's revenue and about half of its operating income. And then the arrow signifies that dot, that represents Dell's ISG business that we're gonna talk to Jeff Boudro about. That's the infrastructure solutions group. Now, ISG accounts for the bulk of of the remainder of Dell's business, and it is, it's, as I said, it's most profitable from a margin standpoint. >>It comprises the EMC storage business as well as the Dell server business and Dell's networking portfolio. And as a note, we didn't include networking in that cut had we done. So Cisco would've dominated the graphic. And frankly, Dell's networking business isn't industry leading in the same way that PCs, servers and storage are. And as you can see, the data confirms the leadership position Dell has in its client side, its server and its storage sectors. But the nuance is look at that red dotted line at 40% on the vertical axis that represents a highly elevated net score, and every company in the sector is below that line. Now we should mention that we also filtered the data for those companies with more than a hundred mentions in the survey, but the point remains the same. This is a mature business that generally is lower margin storage is the exception, but cloud has put pressure on margins even in that business in addition to the server space. >>The last point on this graphic is we put a box around VMware and it's prominently present on both the X and Y dimensions. VMware participates with purely software defined high margin offerings in this, in these spaces, and it gives you a sense of what might have been had Dell chosen to hold onto that asset or spin it into the company. But let's face it, the alternatives from Michael Dell were just too attractive and it's unlikely that a spin in would've unlocked the value in the way a spinout did, at least not in the near future. So let's take a look at the snapshot of Dell's financials. To give you a sense of where the company stands today, Dell is a company with over a hundred billion in revenue. Last quarter, it did more than 26 billion in revenue and grew at a quite amazing 9% rate for a company that size. >>But because it's a hardware company, primarily its margins are low with operating income, 10% of revenue, and at 21% gross margin with VMware on Dell's income statement before the spin, its gross margins. Were in the low thirties. Now, Dell only spends about 2% of revenue on r and d because because it's so big, it's still a lot of money. And you can see it is cash flow positive. Dell's free cash flow over the trailing 12 month period is 3.7 billion, but that's only 3.5% of trailing 12 month revenue. Dell's Apex, and of course it's hardware maintenance business is recurring revenue and that is only about 5 billion in revenue and it's growing at 8% annually. Now having said that, it's the equivalent of service now's total revenue. Of course, service now is 23% operating margin and 16% free cash flow margin and more than 5 billion in cash on the balance sheet and an 85 billion market cap. >>That's what software will do for you. Now Dell, like most companies, is staring at a challenging macro environment with FX headwinds, inflation, et cetera. You've heard the story and hence it's conservative and contracting revenue guidance. But the balance sheet transformation has been quite amazing. Thanks to VMware's cash flow, Michael Dell and his partners from Silver Lake at all, they put up around $4 billion of their own cash to buy EMC for 67 billion, and of course got VMware in the process. Most of that financing was debt that Dell put on its balance sheet to do the transaction to the tune of 46 billion. It added to the, to the balance sheet debt. Now Dell's debt, the core debt net of its financing operation is now down to 16 billion and it has 7 billion in cash in the balance sheet. So dramatic delta from just a few years ago. So pretty good picture. >>But Dell a hundred billion company is still only valued at 28 billion or around 26 cents on the revenue dollar H HP's revenue multiple is around 60 cents on the revenue dollar. HP Inc. Dell's, you know, laptop and PC competitor is around 45 cents. IBM's revenue multiple is almost two times. By the way, IBM has more than 50 billion in debt thanks to the Red Hat acquisition. And Cisco has a revenue multiple, it's over three x, about 3.3 x currently. So is Dell undervalued? Well, based on these comparisons with its peers, I'd say yes and no. Dell's performance relative to its peers in the market is very strong. It's winning and has an extremely adept go to market machine, but it's lack of software content and it's margin profile leads. One to believe that if it can continue to pull some valuation levers while entering new markets, it can get its valuation well above where it is today. >>So what are some of those levers and what might that look like going forward? Despite the fact that Dell doesn't have a huge software revenue component since spinning out VMware and it doesn't own a cloud, it plays in virtually every part of the hardware market and it can provide infrastructure for pr pretty much any application in any use case and pretty much any industry and pretty much any geography in the world and it can serve those customers. So its size is an advantage. However, the history for hardware heavy companies that try to get bigger has some notable failures, namely hp, which had to split into two businesses, HP Inc. And hp E and ibm, which has had in abysmal decade from a performance standpoint and has had to shrink to grow again and obviously do a massive 34 billion acquisition of Red Hat. So why will Dell do any better than these two? >>Well, it has a fantastic supply chain. It's a founder led company, which makes a cultural difference in our view, and it's actually comfortable with a low margin software, light business model. Most certainly, IBM wasn't comfortable with that and didn't have these characteristics, and HP was kind of just incomprehensible at the end. So Dell in my opinion, is a much better chance of doing well at a hundred billion or over, but we'll see how it navigates through the current headwinds as it's guiding down. Apex is essentially Dell's version of the cloud. Now remember, Dell got started late. HPE is further along from a model standpoint with GreenLake, but Dell has a larger portfolio, so they're gonna try to play on that advantage. But at the end of the day, these as a service offerings are simply ways to bring a utility model to existing customers and generate recurring revenue. >>And that's a good thing because customers will be loyal to an incumbent if it can deliver as a service and reduce risk for for customers. But the real opportunity lies ahead, specifically Dell is embracing the cloud model. It took a while, but they're on board as Matt Baker Dell's senior vice president of corporate strategy likes to say it's not a zero sum game. What it means by that is just because Dell doesn't own its own cloud, it doesn't mean Dell can't build value on top of hyperscale clouds, what we call super cloud. And that's Dell's strategy to take advantage of public cloud CapEx and connect on-prem to the cloud, create a unified experience across clouds and out to the edge that's ambitious and technically it's non-trivial. But listen to Dell's vice chairman and Coco, Jeff Clark, explain this vision, please play the clip. >>You said also technology and business models are tied together and enabler. That's if, if you believe that, then you have to believe that it's a business operating system that they want, They want to leverage whatever they can, and at the end of the day there's, they have to differentiate what they do. Well that, that's >>Exactly right. If I take that and what, what Dave was saying and and I, and I summarize it the following way, if we can take these cloud assets and capabilities, combine them in an orchestrated way to delivery a distributed platform, game over, >>Eh, pretty interesting, right? John Freer called it a business operating system. Essentially, I think of it sometimes as a cloud operating system or cloud operating environment to drive new business value on top of the hyperscale CapEx. Now, is it really game over? As Jeff Clark said, if Dell can do that, I'd say if it had that today, it might be game over for the competition, but this vision will take years to play out. And of course it's gotta be funded and now it's gonna take time. And in this industry it tends to move. Companies tend to move in lockstep. So as often as the case, it's gonna come down to execution and Dell's ability to enter new markets that are ideally, at least from my perspective, higher margin data management, extending data protection into cyber security as an adjacency and of course edge at telco slash 5G opportunities. >>All there for the taking. I mean, look, even if Dell doesn't go after more higher margin software content, it can thrive with a lower margin model just by penetrating new markets and throwing off cash from those markets. But by keeping close to customers and maybe through Tuck in acquisitions, it might be able to find the next nugget beyond today's cloud and on-prem models. And the last thing I'll call out is ecosystem. I say here ecosystem, ecosystem, ecosystem. Because a defining characteristic of a cloud player is ecosystem, and if Apex is Dell's cloud, it has the opportunity to expand that ecosystem dramatically. This is one of the company's biggest opportunities and challenges. At the same time, in my view, it's just scratching the surface on its partner ecosystem. And it's ecosystem today is is both reseller heavy and tech partner heavy. And that's not a bad thing, but in a, but it's starting to evolve more rapidly. >>The snowflake deal is an example of up to stack evolution, but I'd like to see much more out of that snowflake relationship and more relationships like that. Specifically I'd like to see more momentum with data and database. And if we live at a data heavy world, which we do, where the data and the database and data management offerings, you know, coexist and are super important to customers, like to see that inside of Apex, like to see that data play beyond storage, which is really where it is today and it's early days. The point is with Dell's go to market advantage, which which company wouldn't treat Dell like the on-prem hybrid edge super cloud player that I wanna partner with to drive more business. You'd be crazy not to, but Dell has a lot on its plate and we'd like to see some serious acceleration on the ecosystem front. In other words, Dell as both a selling partner and a business enabler with its platform, its programmable infrastructure as a service. And that is a moving target that will rapidly involve. And of course we'll be here watching and reporting. So thanks for watching this preview of Dell Technology Summit 2022. I'm Dave Vte. We hope you enjoy the rest of the program.

(upbeat music) >> The transformation of Dell into Dell EMC, and now Dell Technologies, has been one of the most remarkable stories in the history of the enterprise technology industry. The company has gone from a Wall Street darling rocketship PC company, to a middling enterprise player, forced to go private, to a debt-laden powerhouse that controlled one of the most valuable assets in enterprise tech, i.e., VMware. And now is a $100 billion dollar giant with a low-margin business, a strong balance sheet, and the broadest hardware portfolio in the industry. The financial magic that Dell went through would make anyone's head spin. The last lever of the Dell EMC deal was detailed in Michael Dell's book "Play Nice But Win," in a captivating chapter called "Harry You and the Bolt from the Blue." Michael Dell described how he and his colleagues came up with the final straw of how to finance the deal. If you haven't read it, you should. And of course, after years of successfully integrating EMC and becoming VMware's number-one distribution channel, all of this culminated in the spin-out of VMware from Dell, and a massive wealth-creation milestone, pending, of course, the Broadcom acquisition of VMware. So where's that leave Dell, and what does the future look like for this technology powerhouse? Hello, and welcome to theCUBE's exclusive coverage of Dell Technologies Summit 2022. My name is Dave Vellante, and I'll be hosting the program. Now, today in conjunction with the Dell Tech Summit, we're going to hear from four of Dell's senior executives. Tom Sweet, who's the CFO of Dell Technologies. He's going to share his views on the company's position and opportunities going forward. He's going to answer the question, why is Dell a good long-term investment? Then we'll hear from Jeff Boudreau, who's the President of Dell's ISG business. That unit is the largest profit driver of Dell. He's going to talk about the product angle, and specifically, how Dell is thinking about solving the multi-cloud challenge. And then Sam Grocott, who's the Senior Vice President of Marketing, will come on the program and give us the update on APEX, which is Dell's as-a-Service offering, and then the new edge platform called Project Frontier. Now, it's also Cybersecurity Awareness Month, that we're going to see if Sam has, you know, anything to say about that. Then finally, for a company that's nearly 40 years old, Dell actually has some pretty forward-thinking philosophies when it comes to its culture and workforce. And we're going to speak with Jenn Saavedra, who's Dell's Chief Human Resource Officer, about hybrid work, and how Dell is thinking about the future of work. However, before we get into all this, I want to share our independent perspectives on the company, and some research that we'll introduce to frame the program. Now, as you know, we love data here at theCUBE, and one of our partners, ETR, has what we believe is the best spending intentions data for enterprise tech. So here's a graphic that shows ETR's proprietary Net Score methodology on the vertical axis, that's a measure of spending velocity, and on the x-axis is overlap or pervasiveness in the data sample. This is a cut for just the server, the storage, and the client sectors within the ETR taxonomy. So you can see Dell's CSG products, laptops in particular, are dominant on both the x and the y dimensions. CSG is the Client Solutions Group, and accounts for nearly 60% of Dell's revenue, and about half of its operating income. And then the arrow signifies that dot that represents Dell's ISG business, that we're going to talk to Jeff Boudreau about. That's the Infrastructure Solutions Group. Now, ISG accounts for the bulk of the remainder of Dell's business, and it is its, as I said, its most profitable from a margin standpoint. It comprises the EMC storage business, as well as the Dell server business, and Dell's networking portfolio. And as a note, we didn't include networking in that cut. Had we done so, Cisco would've dominated the graphic. And frankly, Dell's networking business isn't industry leading in the same way that PCs, servers, and storage are. And as you can see, the data confirms the leadership position Dell has in its client side, its server, and its storage sectors. But the nuance is, look at that red dotted line at 40% on the vertical axis. That represents a highly elevated Net Score, and every company in the sector is below that line. Now, we should mention that we also filtered the data for those companies with more than a hundred mentions in the survey, but the point remains the same. This is a mature business that generally is lower margin. Storage is the exception, but cloud has put pressure on margins even in that business, in addition to the server space. The last point on this graphic is, we put a box around VMware, and it's prominently present on both the x and y dimensions. VMware participates with purely software-defined high-margin offerings in these spaces, and it gives you a sense of what might have been, had Dell chosen to hold onto that asset or spin it into the company. But let's face it, the alternatives for Michael Dell were just too attractive, and it's unlikely that a spin-in would've unlocked the value in the way a spin-out did, at least not in the near future. So let's take a look at the snapshot of Dell's financials, to give you a sense of where the company stands today. Dell is a company with over $100 billion dollars in revenue. Last quarter, it did more than 26 billion in revenue, and grew at a quite amazing 9% rate, for a company that size. But because it's a hardware company, primarily, its margins are low, with operating income 10% of revenue, and at 21% gross margin. With VMware on Dell's income statement before the spin, its gross margins were in the low 30s. Now, Dell only spends about 2% of revenue on R&D, but because it's so big, it's still a lot of money. And you can see it is cash-flow positive. Dell's free cash flow over the trailing 12-month period is 3.7 billion, but that's only 3.5% of trailing 12-month revenue. Dell's APEX, and of course its hardware maintenance business, is recurring revenue, and that is only about 5 billion in revenue, and it's growing at 8% annually. Now, having said that, it's the equivalent of ServiceNow's total revenue. Of course, ServiceNow has 23% operating margin and 16% free cash-flow margin, and more than $5 billion in cash on the balance sheet, and an $85 billion market cap. That's what software will do for you. Now Dell, like most companies, is staring at a challenging macro environment, with FX headwinds, inflation, et cetera. You've heard the story. And hence it's conservative, and contracting revenue guidance. But the balance sheet transformation has been quite amazing, thanks to VMware's cash flow. Michael Dell and his partners from Silver Lake et al., they put up around $4 billion of their own cash to buy EMC for 67 billion, and of course got VMware in the process. Most of that financing was debt that Dell put on its balance sheet to do the transaction, to the tune of $46 billion it added to the balance sheet debt. Now, Dell's debt, the core debt, net of its financing operation, is now down to 16 billion, and it has $7 billion in cash on the balance sheet. So a dramatic delta from just a few years ago. So, pretty good picture. But Dell, a $100 billion company, is still only valued at 28 billion, or around 26 cents on the revenue dollar. HPE's revenue multiple is around 60 cents on the revenue dollar. HP Inc., Dell's laptop and PC competitor, is around 45 cents. IBM's revenue multiple is almost two times. By the way, IBM has more than $50 billion in debt thanks to the Red Hat acquisition. And Cisco has a revenue multiple that's over 3x, about 3.3x currently. So is Dell undervalued? Well, based on these comparisons with its peers, I'd say yes, and no. Dell's performance, relative to its peers in the market, is very strong. It's winning, and has an extremely adept go-to-market machine, but its lack of software content and its margin profile leads one to believe that if it can continue to pull some valuation levers while entering new markets, it can get its valuation well above where it is today. So what are some of those levers, and what might that look like, going forward? Despite the fact that Dell doesn't have a huge software revenue component since spinning out VMware, and it doesn't own a cloud, it plays in virtually every part of the hardware market. And it can provide infrastructure for pretty much any application in any use case, in pretty much any industry, in pretty much any geography in the world. And it can serve those customers. So its size is an advantage. However, the history for hardware-heavy companies that try to get bigger has some notable failures, namely HP, which had to split into two businesses, HP Inc. and HPE, and IBM, which has had an abysmal decade from a performance standpoint, and has had to shrink to grow again, and obviously do a massive $34 billion acquisition of Red Hat. So why will Dell do any better than these two? Well, it has a fantastic supply chain. It's a founder-led company, which makes a cultural difference, in our view. And it's actually comfortable with a low-margin software-light business model. Most certainly, IBM wasn't comfortable with that, and didn't have these characteristics, and HP was kind of just incomprehensible at the end. So Dell in my opinion, has a much better chance of doing well at 100 billion or over, but we'll see how it navigates through the current headwinds as it's guiding down. APEX is essentially Dell's version of the cloud. Now, remember, Dell got started late. HPE is further along from a model standpoint with GreenLake, but Dell has a larger portfolio, so they're going to try to play on that advantage. But at the end of the day, these as-a-Service offerings are simply ways to bring a utility model to existing customers, and generate recurring revenue. And that's a good thing, because customers will be loyal to an incumbent if it can deliver as-a-Service and reduce risk for customers. But the real opportunity lies ahead. Specifically, Dell is embracing the cloud model. It took a while, but they're on board. As Matt Baker, Dell's Senior Vice President of Corporate Strategy, likes to say, it's not a zero-sum game. What he means by that is, just because Dell doesn't own its own cloud, it doesn't mean Dell can't build value on top of hyperscale clouds. What we call supercloud. And that's Dell's strategy, to take advantage of public cloud capex, and connect on-prem to the cloud, create a unified experience across clouds, and out to the edge. That's ambitious, and technically it's nontrivial. But listen to Dell's Vice Chairman and Co-COO, Jeff Clarke, explain this vision. Please play the clip. >> You said also, technology and business models are tied together, and an enabler. >> That's right. >> If you believe that, then you have to believe that it's a business operating system that they want. They want to leverage whatever they can, and at the end of the day, they have to differentiate what they do. >> Well, that's exactly right. If I take that and what Dave was saying, and I summarize it the following way: if we can take these cloud assets and capabilities, combine them in an orchestrated way to deliver a distributed platform, game over. >> Eh, pretty interesting, right? John Furrier called it a "business operating system." Essentially, I think of it sometimes as a cloud operating system, or cloud operating environment, to drive new business value on top of the hyperscale capex. Now, is it really game over, as Jeff Clarke said, if Dell can do that? Uh, (sucks in breath) I'd say if it had that today, it might be game over for the competition, but this vision will take years to play out. And of course, it's got to be funded. And that's going to take time, and in this industry, it tends to move, companies tend to move in lockstep. So, as often is the case, it's going to come down to execution and Dell's ability to enter new markets that are ideally, at least from my perspective, higher margin. Data management, extending data protection into cybersecurity as an adjacency, and of course, edge and telco/5G opportunities. All there for the taking. I mean, look, even if Dell doesn't go after more higher-margin software content, it can thrive with a lower-margin model just by penetrating new markets and throwing off cash from those markets. But by keeping close to customers, and maybe through tuck-in acquisitions, it might be able to find the next nugget beyond today's cloud and on-prem models. And the last thing I'll call out is ecosystem. I say here, "Ecosystem, ecosystem, ecosystem," because a defining characteristic of a cloud player is ecosystem, and if APEX is Dell's cloud, it has the opportunity to expand that ecosystem dramatically. This is one of the company's biggest opportunities and challenges at the same time, in my view. It's just scratching the surface on its partner ecosystem. And its ecosystem today is both reseller heavy and tech partner heavy. And that's not a bad thing, but it's starting to evolve more rapidly. The Snowflake deal is an example of up-the-stack evolution, but I'd like to see much more out of that Snowflake relationship, and more relationships like that. Specifically, I'd like to see more momentum with data and database. And if we live in a data-heavy world, which we do, where the data and the database and data management offerings, you know, coexist and are super important to customers, I'd like to see that inside of APEX. I'd like to see that data play beyond storage, which is really where it is today, in its early days. The point is, with Dell's go-to-market advantage, which company wouldn't treat Dell like the on-prem, hybrid, edge, supercloud player that I want to partner with to drive more business? You'd be crazy not to. But Dell has a lot on its plate, and we'd like to see some serious acceleration on the ecosystem front. In other words, Dell as both a selling partner and a business enabler with its platform, its programmable Infrastructure-as-a-Service. And that is a moving target that will rapidly evolve. And of course, we'll be here watching and reporting. So thanks for watching this preview of Dell Technologies Summit 2022. I'm Dave Vellante, we hope you enjoy the rest of the program. (upbeat music)

foreign welcome back everyone to our special presentation here at thecube with Horizon 3.a I'm John Furrier host thecube here in Palo Alto back it's niho and Tony CEO and co-founder of horizon 3 for deep dive on going under the hood around the big news and also the platform autonomous pen testing changing the game and security great to see you welcome back thank you John I love what you guys have been doing with the cube huge fan been here a bunch of times and yeah looking forward to the conversation let's get into it all right so what what's the market look like and how do you see it evolving we're in a down Market relative to startups some say our data we're reporting on siliconangle in the cube that yeah there might be a bit of downturn in the economy with inflation but the tech Market is booming because the hyperscalers are still pumping out massive scale and still innovating so so you know for the first time in history this is a recession or downturn where there's now Cloud scale players that are an economic engine what's your view on this where's the market heading relative to the downturn and how are you guys navigating that so um I think about it one the there's a lot of belief out there that we're going to hit a downturn and we started to see that we started to see deals get longer and longer to close back in May across the board in the industry we continue to see deals get at least backloaded in the quarter as people understand their procurement how much money they really have to spend what their earnings are going to be so we're seeing this across the board one is quarters becoming lumpier for tech companies and we think that that's going to become kind of the norm over the next over the next year but what's interesting in our space of security testing is a very basic supply and demand problem the demand for security testing has skyrocketed when I was a CIO eight years ago I only had to worry about my on-prem attack surface my perimeter and Insider threat those are my primary threat vectors now if I was a CIO I have to include multiple clouds all of the data in my SAS offerings my Salesforce account and so on as well as work from home threat vectors and other pieces and I've got Regulatory Compliance in Europe in Asia in in the U.S tons of demand for testing and there's just not enough Supply there's only 5 000 certified pen testers in the United States so I think for starters you have a fundamental supply and demand problem that plays to our strength because we're able to bring a tremendous amount of pen testing supply to the table but now let's flip to if you are the CEO of a large security company or whether it's a Consulting shop or so on you've got a whole bunch of deferred revenue in your business model around security testing services and what we've done in our past in previous companies I worked at is if we didn't think we were going to make the money the quarter with product Revenue we would start to unlock some of that deferred Services Revenue to make the number to hit what we expected Wall Street to hit what Wall Street expected of us in testing that's not possible because there's not enough Supply except us so if I'm the CEO of an mssp or a large security company and I need I see a huge backlog of security testing revenue on the table the easy button to convert that to recognized revenue is Horizon 3. and when I think about the next six months and the amount of Revenue misses we're going to see in security shops especially those that can't fulfill their orders I think there's a ripe opportunity for us to win yeah one of the few opportunities where on any Market you win because the forces will drive your flywheel that's exactly right very basic supply and demand forces that are only increasing with pressure and there's no way it takes 10 years just to build a master hacker just it's a very hard complex space we become the easy button to address that supply problem yeah and this and the autonomous aspect makes appsec reviews as new things get pushed with Cloud native developers they're shifting left but still the security policies need to stay Pace as these new vectors threat vectors appear yeah I mean because that's what's happening a new new thing makes a vector possible that's exactly right I think there's two aspects one is the as you in increase change in your environment you need to increase testing they are absolutely correlated the second thing though is you know for 20 years we focused on remote code execution or rces as an industry what was the latest rce that gave an attacker access to my environment but if you look over the past few years that entire mindset has shifted credentials are the new code execution what I mean by that is if I have a large organization with a hundred a thousand ten thousand employees all it takes is one of them to have a password I can crack in credential spray and gain access to as an attacker and once I've gained access to a single user I'm going to systematically snowball that into something of consequence and so I think that the attackers have shifted away from looking for code execution and looked more towards harvesting credentials and cascading credentials from a regular domain user into an admin this brings up the conversation I would like to do it more Deep dive now shift into more of like the real kind of landscape of the market and your positioning and value proposition in that and that is managed services are becoming really popular as we move into this next next wave of super cloud and multi-cloud and hybrid Cloud because I mean multi-cloud and hybrid hybrid than multi-cloud sounds good on paper but the security Ops become big and one of the things we're reporting with here on the cube and siliconangle the past six months is devops has made the developer the IT team because they've essentially run it now in CI CD pipeline as they say that means it's replaced by data Ops or AI Ops or security Ops and data and security kind of go hand in hand so I can see that playing out do you believe that to be true that that's kind of the new operational kind of beach head that's critical and if so secure if data is part of security that makes security the new it yeah I I think that if you think about organizations hell even for Horizon 3 right now I don't need to hire a CIO I'll have a CSO and that CSO will own it and governance risk and compliance and security operations because at the end of the day the most pressing question for me to answer as a CEO is my security posture IIT is a supporting function of that security posture and we see that at say or a growth stage company like Horizon 3 but when I thought about my time at GE Capital we really shifted to this mindset of security by Design architecture as code and it was very much security driven conversation and I think that is the norm going forward and how do you view the idea that you have to enable a managed service provider with security also managing comp and which then manages the company to enable them to have agile security um security is code because what you're getting at is this autonomous layer that's going to be automated away to make the next talented layer whether it's coder or architect scale so the question is what is abstracted away at at automation seems to be the conversation that's coming out of this big cloud native or super cloud next wave of cloud scale I think there's uh there's two Dimensions to that and honestly I think the more interesting Dimension is not the technical side of it but rather think of the Equifax hack a bunch of years ago had Equifax used a managed security services provider would the CEO have been fired after the breach and the answer is probably not I think the CEO would have transferred enough reputational risk in operational risk to the third party mssp to save his job from being you know from him being fired you can look at that across the board I think that if if I were a CIO again I would be hard-pressed to build my own internal security function because I'm accepting that risk as an executive and we saw what just happened at Uber there's a ton of risk coming with that with the with accepting that as a security person so I think in the future the role of the mssp becomes more significant as a mechanism for transferring enough reputational and operational and legal risk to a third party so that you as the Core Company are able to protect yourself and your people now then what you think is a super cloud printables and Concepts being applied at mssp scale and I think that becomes really interesting talk about the talent opportunity because I think the managed service providers point to markets that are growing and changing also having managed service means that the customers can't always hire Talent hence they go to a Channel or a partner this seems to be a key part of the growth in your area talk about the talent aspect of it yeah um think back to what we saw in Cloud so as as Cloud picked up we saw IBM HP other Hardware companies sell more servers but to fewer customers Amazon Google and others right and so I think something similar is going to happen in the security space where I think you're going to see security tools providers selling more volume but to fewer customers that are just really big mssps so that is the the path forward and I think that the underlying Talent issue gives us economies at scale and that's what we saw this with Cloud we're going to see the same thing in the mssp space I've got a density of Talent Plus a density of automation plus a density of of relationships and ecosystem that give mssps a huge economies of scale advantage over everybody else I mean I want to get into the mssp business sounds like I make a lot of money yeah definitely it's profitable no doubt about it like that I got to ask more on the more of the burden side of it because if you're a partner I don't need another training class I don't need another tool I don't need someone saying this is the highest margin product I need to actually downsize my tools so right now there's hundreds of tools that mssps have all the time dealing with and does the customer so tools platforms we've kind of teased this out in previous conversations together but more more relevant to the mssp is what they do to the customers so talk about this uh burden of tools and the socks out there in the in in the landscape how do you how do you view that and what's the conversation like on average an organization has 130 different cyber security tools installed none of those tools were designed to work together none of those tools are from the same vendor and in fact oftentimes they're from vendors that have competing products and so what we don't have and they're still getting breached in the industry we don't have a tools problem we have an Effectiveness problem we have to reduce the number of tools we have get more out of out of the the effectiveness out of the existing infrastructure build muscle memory you know how to detect and respond to a breach and continuously verify that posture I think that's what the the most successful security organizations have mastered the fundamentals and they mastered that by making sure they were effective in detection and response not mastering it by buying the next shiny AI tool on the defensive side okay so you mentioned supply and demand early since you're brought up economics we'll get into the economic equations here when you have great profits that's going to attract more entrance into the marketplace so as more mssps enter the market you're going to start to see a little bit of competition maybe some fud maybe some price competitive price penetration all kinds of different Tactics get out go on there um how does that impact you because now does that impact your price or are you now part of them just competing on their own value what's that mean for the channel as more entrants come in hey you know I can compete against that other one does that create conflict is that an opportunity does are you neutral on that what's the position it's a great question actually I think the way it plays out is one we are neutral two the mssp has to stand on their own with their own unique value proposition otherwise they're going to become commoditized we saw this in the early cloud provider days the cloud providers that were just basically wrapping existing Hardware with with a race to the bottom pricing model didn't survive those that use the the cloud infrastructure as a starting point to build higher value capabilities they're the ones that have succeeded to this day the same Mo I think will occur in mssps which is there's a base level of capability that they've got to be able to deliver and it is the burden of the mssp to innovate effectively to elevate their value problem it's interesting Dynamic and I brought it up mainly because if you believe that this is going to be a growing New Market price erosion is more in mature markets so it's interesting to see that Dynamic come up and we'll see how that handles on the on the economics and just the macro side of it getting more into kind of like the next gen autonomous pen testing is a leading indicator that a new kind of security assessment is here um if I said that to you how do you respond to that what is this new security assessment mean what does that mean for the customer and to the partner and that that relationship down that whole chain yeah um back to I'm wearing a CIO hat right now don't tell me we're secure in PowerPoint show me we're secure Today Show me where we're secure tomorrow and then show me we're secure again next week because that's what matters to me if you can show me we're secure I can understand the risk I'm accepting and articulate it up to my board to my Regulators up until now we've had a PowerPoint tell me where secure culture and security and I just don't think that's going to last all that much longer so I think the future of security testing and assessment is this shift from a PowerPoint report to truly showing me that my I'm secure enough you guys auto-generate those statements now you mentioned that earlier that's exactly right because the other part is you know the classic way to do security reports was garbage in garbage out you had a human kind of theoretically fill out a spreadsheet that magically came up with the risk score or security posture that doesn't work that's a check the box mentality what you want to have is an accurate High Fidelity understanding of your blind spots your threat vectors what data is at risk what credentials are at risk you want to look at those results over time how quickly did I find problems how quickly did I fix them how often did they reoccur and that is how you get to a show me where secure culture whether I'm a company or I'm a channel partner working with Horizon 3.ai I have to put my name on the line and say Here's a service level agreement I'm going to stand behind there's levels of compliance you mentioned that earlier how do you guys help that area because that becomes I call the you know below the line I got to do it anyway usually it's you know they grind out the work but it has to be fundamental because if the threats vectors are increasing and you're handling it like you say you are the way it is real time today tomorrow the next day you got to have that other stuff flow into it can you describe how that works under the hood yeah there's there's two parts to it the first part is that attackers don't have to hack in with zero days they log in with credentials that they found but often what attackers are doing is chaining together different types of problems so if you have 10 different tactics you can chain those together a number of different ways it's not just 10 to the 10th it's it's actually because you don't you don't have to use all the tactics at once this is a very large number of combinations that an attacker can apply upon you is what it comes down to and so at the base level what you want to have is what are the the primary tactics that are being used and those tactics are always being added to and evolving what are the primary outcomes that an attacker is trying to achieve steal your data disrupt your systems become a domain admin and borrow and now what you have is it actually looks more like a chess game algorithm than it does any sort of hard-coded automation or anything else which is based on the pieces on the board the the it infrastructure I've discovered what is the next best action to become a domain admin or steal your data and that's the underlying innovation in IP we've created which is next best action Knowledge Graph analytics and adaptiveness to figure out how to combine different problems together to achieve an objective that an attacker cares about so the 3D chess players out there I'd say that's more like 3D chess are the practitioners implementing it but when I think about compliance managers I don't see 3D chess players I see back office accountants in my mind like okay are they actually even understand what comes out of that so how do you handle the compliance side do you guys just check the boxes there is it not part of it is it yeah I I know I don't Envision the compliance guys on the front lines identifying vectors do you know what it doesn't even know what it means yeah it's a great question when you think about uh the market segmentation I think there are we've seen are three basic types of users you've got the the really mature high frequency security testing purple team type folks and for them we are the the force multiplier for them to secure the environment you then have the middle group where the IT person and the security person are the same individual they are barely Treading Water they don't know what their attack surface is and they don't know what to focus on we end up that's actually where we started with the barely Treading Water Persona and that's why we had a product that helped those Network Engineers become superheroes the third segment are those that view security and compliance as synonymous and they don't really care about continuous they care about running and checking the box for PCI and forever else and those customers while they use us they are better served by our partner ecosystem and that's really so the the first two categories tend to use us directly self-service pen tests as often as they want that compliance-minded folks end up going through our partners because they're better served there steel great to have you on thanks for this deep dive on um under the hood section of the interview appreciate it and I think autonomous is is an indicator Beyond pen testing pen testing has become like okay penetration security but this is not going away where do you see this evolving what's next what's next for Horizon take a minute to give a plug for what's going on with copy how do you see it I know you got good margins you're raising Capital always raising money you're not yet public um looking good right now as they say yeah yeah well I think the first thing is our company strategy is in three chapters chapter one is become the best security testing platform in the industry period that's it and be very good at helping you find and fix your security blind spots that's chapter one we've been crushing it there with great customer attraction great partner traction chapter two which we've started to enter is look at our results over time to help that that GRC officer or auditor accurately assess the security posture of an organization and we're going to enter that chapter about this time next year longer term though the big Vision I have is how do I use offense to inform defense so for me chapter three is how do I get away from just security testing towards autonomous security overall where you can use our security testing platform to identify ways to attack that informs defensive tools exactly where to focus how to adjust and so on and now you've got offset and integrated learning Loop between attack and defense that's the future never been done before Master the art of attack to become a better Defender is the bigger vision of the company love the new paradigm security congratulations been following you guys we will continue to follow you thanks for coming on the Special Report congratulations on the new Market expansion International going indirect that a big way congratulations thank you John appreciate it okay this is a special presentation with the cube and Horizon 3.ai I'm John Furrier your host thanks for watching thank you

(upbeat music) >> The transformation of Dell into Dell EMC and now Dell Technologies has been one of the most remarkable stories in the history of the enterprise technology industry. The company has gone from a Wall Street darling rocket ship PC company, to a middling enterprise player forced to go private, to a debt-laden powerhouse that controlled one of the most valuable assets in enterprise tech i.e VMware. And now is a 100 billion dollar giant with a low margin business, a strong balance sheet, and the broadest hardware portfolio in the industry. Financial magic that Dell went through would make anyone's head spin. The last lever of Dell EMC, of the Dell EMC deal was detailed in Michael Dell's book, "Play Nice But Win." In a captivating chapter called Harry You and the Bolt from the Blue, Michael Dell described how he and his colleagues came up with the final straw of how to finance the deal. If you haven't read it, you should. And, of course, after years of successfully integrating EMC and becoming VMware's number one distribution channel, all of this culminated in the spin out of VMware from Dell in a massive wealth creation milestone. Pending, of course, the Broadcom acquisition of VMware. So where's that leave Dell and what does the future look like for this technology powerhouse? Hello, and welcome to theCUBE's exclusive coverage of Dell Technology Summit 2022. My name is Dave Vellante and I'll be hosting the program. Now, today in conjunction with the Dell Tech Summit, we're going to hear from four of Dell's senior executives Tom Sweet, who's the CFO of Dell Technologies. He's going to share his views on the company's position and opportunities going forward. He's going to answer the question, why is Dell a good long-term investment? Then we'll hear from Jeff Boudreau who's the president of Dell's ISG business. That unit is the largest profit driver of Dell. He's going to talk about the product angle and specifically, how Dell is thinking about solving the multi-cloud challenge. And then Sam Grocott who is the senior vice president of marketing will come on the program and give us the update on Apex, which is Dell's as-a-service offering, and then the new edge platform called Project Frontier. Now, it's also Cyber Security Awareness month that we're going to see if Sam has anything to say about that. Then finally, for a company that's nearly 40 years old, Dell actually has some pretty forward-thinking philosophies when it comes to its culture and workforce. And we're going to speak with Jennifer Saavedra who's Dell's chief human resource officer about hybrid work and how Dell is thinking about the future of work. However, before we get into all this, I want to share our independent perspectives on the company and some research that will introduce to frame the program. Now, as you know, we love data here at theCUBE and one of our partners, ETR has what we believe is the best spending intentions data for enterprise tech. So here's a graphic that shows ETR's proprietary net score methodology in the vertical axis. That's a measure of spending velocity. And on the x-axis is overlap of pervasiveness in the data sample. This is a cut for just the server, the storage, and the client sectors within the ETR taxonomy. So you can see Dell CSG products, laptops in particular are dominant on both the X and the Y dimensions. CSG is the client solutions group and accounts for nearly 60% of Dell's revenue and about half of its operating income. And then the arrow signifies that dot that represents Dell's ISG business that we're going to talk to Jeff Boudreau about. That's the infrastructure solutions group. Now, ISG accounts for the bulk of the remainder of Dell's business and it is, as I said, it's most profitable from a margin standpoint. It comprises the EMC storage business as well as the Dell server business and Dell's networking portfolio. And as a note, we didn't include networking in that cut. Had we done so, SISCO would've dominated the graphic. And frankly, Dell's networking business is an industry-leading in the same way that PCs, servers, and storage are. And as you can see, the data confirms the leadership position Dell has in its client side, its server and its storage sectors. But the nuance is look at that red dotted line at 40% on the vertical axis. That represents a highly elevated net score and every company in the sector is below that line. Now, we should mention that we also filtered the data for those companies with more than a 100 mentions in the survey, but the point remains the same. This is a mature business that generally is lower margin. Storage is the exception but cloud has put pressure on margins even in that business in addition to the server space. The last point on this graphic is we put a box around VMware and it's prominently present on both the X and Y dimensions. VMware participates with purely software-defined high margin offerings in these spaces, and it gives you a sense of what might have been had Dell chosen to hold onto that asset or spin it into the company. But let's face it, the alternatives from Michael Dell were just too attractive and it's unlikely that a spin in would've unlocked the value in the way a spin-out did, at least not in the near future. So let's take a look at the snapshot of Dell's financials to give you a sense of where the company stands today. Dell is a company with over a 100 billion dollars in revenue. Last quarter, it did more than 26 billion in revenue and grew at a quite amazing 9% rate for a company that size. But because it's a hardware company primarily, its margins are low with operating income 10% of revenue and at 21% gross margin. With VMware on Dell's income statement, before the spin its gross margins were in the low 30s. Now, Dell only spends about 2% of revenue on R&D because because it's so big, it's still a lot of money. And you can see it is cash flow positive, Dell's free cash flow over the trailing 12-month period is 3.7 billion but that's only 3.5% of trailing 12-month revenue. Dell's Apex and of course it's hardware maintenance business is recurring revenue and that is only about 5 billion in revenue and it's growing at 8% annually. Now having said that, it's the equivalent of Service now's total revenue. Of course, Service now has 23% operating margin and 16% free cash flow margin and more than $5 billion in cash on the balance sheet and an 85 billion dollar market cap. That's what software will do for you. Now, Dell, like most companies, is staring at a challenging macro environment with FX headwinds, inflation, et cetera. You've heard the story, and hence it's conservative and contracting revenue guidance. But the balance sheet transformation has been quite amazing thanks to VMware's cash flow. Michael Dell and his partners from Silver Lake et al, they put up around $4 billion of their own cash to buy EMC for $67 billion and of course got VMware in the process. Most of that financing was debt that Dell put on its balance sheet to do the transaction to the tune of $46 billion it added to the balance sheet debt. Now, Dell's debt, the core debt, net of its financing operation is now down to 16 billion and it has 7 billion in cash in the balance sheet. So dramatic delta from just a few years ago. So pretty good picture. But Dell, a 100 billion company, is still only valued at 28 billion or around 26 cents on the revenue dollar. HPE's revenue multiple is around 60 cents on the revenue dollar. HP Inc, Dell's laptop and PC competitor, is around 45 cents. IBM's revenue multiple is almost two times. By the way, IBM has more than $50 billion in debt thanks to the Red Hat acquisition. And Cisco has a revenue multiple, it's over 3X, about 3.3X currently. So is Dell undervalued? Well, based on these comparisons with its peers, I'd say yes and no. Dell's performance relative to its peers in the market is very strong. It's winning and has an extremely adept go to market machine. But it's lack of software content and it's margin profile leads one to believe that if it can continue to pull some valuation levers while entering new markets, it can get its valuation well above where it is today. So what are some of those levers and what might that look like going forward? Despite the fact that Dell doesn't have a huge software revenue component, since spinning out VMware, and it doesn't own a cloud, it plays in virtually every part of the hardware market. And it can provide infrastructure for pretty much any application, in any use case, in pretty much any industry, in pretty much any geography in the world and it can serve those customers. So its size is an advantage. However, the history for hardware-heavy companies that try to get bigger has some notable failures. Namely HP which had to split into two businesses, HP Inc and HPE, and IBM which has had in abysmal decade from a performance standpoint and has had to shrink to grow again and obviously do a massive $34 billion acquisition of Red Hat. So why will Dell do any better than these two? Well, it has a fantastic supply chain. It's a founder-led company which makes a cultural difference, in our view, and it's actually comfortable with a low margin software light business model. Most certainly, IBM wasn't comfortable with that and didn't have these characteristics and HP was kind of just incomprehensible at the end. So Dell in my opinion is a much better chance of doing well at a 100 billion or over, but we'll see how it navigates through the current headwinds as it's guiding down. Apex is essentially Dell's version of the cloud. Now remember, Dell got started late. HPE is further along from a model standpoint with GreenLake. But Dell has a larger portfolio so they're going to try to play on that advantage. But at the end of the day, these as-a-service offerings are simply ways to bring a utility model to existing customers and generate recurring revenue. And that's a good thing because customers will be loyal to an incumbent if it can deliver as-a-service and reduce risk for customers. But the real opportunity lies ahead, specifically Dell is embracing the cloud model. It took a while, but they're on board. As Matt Baker, Dell's senior vice president of corporate strategy likes to say, it's not a zero sum game. What he means by that is just because Dell doesn't own its own cloud, it doesn't mean Dell can't build value on top of hyperscale clouds, what we call super cloud. And that's Dell's strategy to take advantage of public cloud CapEx and connect on-prem to the cloud, create a unified experience across clouds and out to the edge. That's ambitious and technically it's non-trivial. But listen to Dell's vice chairman and co-COO Jeff Clarke explain this vision. Please play the clip. >> You said also technology and business models are tied together and enabler. If you believe that, then you have to believe that it's a business operating system that they want. They want to leverage whatever they can and at the end of the day, they have to differentiate what they do. >> No, that's exactly right. If I take that and what Dave was saying and I summarize it the following way. If we can take these cloud assets and capabilities, combine them in an orchestrated way to deliver a distributed platform, game over. >> Yeah, pretty interesting, right? John Freer called it a business operating system. Essentially, I think of it sometimes as a cloud operating system or cloud operating environment to drive new business value on top of the hyperscale CapEx. Now, is it really game over as Jeff Clarke said, if Dell can do that? I'd say if it had that today, it might be game over for the competition but this vision will take years to play out, and of course it's got to be funded. And now it's going to take time and in this industry, it tends to move, companies tend to move in lockstep. So as often as the case, it's going to come down to execution and Dell's ability to enter new markets that are ideally, at least from my perspective, higher margin. Data management, extending data protection into cyber security as an adjacency and, of course, edge at Telco slash 5G opportunities. All there for the taking. I mean, look, even if Dell doesn't go after more higher margin software content, it can thrive with a lower margin model just by penetrating new markets and throwing off cash from those markets. But by keeping close to customers and maybe through tuck in acquisitions, it might be able to find the next nugget beyond today's cloud and on-prem models. And the last thing I'll call out is ecosystem. I say here ecosystem, ecosystem, ecosystem. Because a defining characteristic of a cloud player is ecosystem and if Apex is Dell's cloud, it has the opportunity to expand that ecosystem dramatically. This is one of the company's biggest opportunities and challenges at the same time, in my view. It's just scratching the surface on its partner ecosystem. And it's ecosystem today is is both reseller heavy and tech partner heavy. And that's not a bad thing, but it's starting to evolve more rapidly. The snowflake deal is an example of up to stack evolution. But I'd like to see much more out of that Snowflake relationship and more relationships like that. Specifically, I'd like to see more momentum with data and database. And if we live at a data heavy world, which we do, where the data and the database and data management offerings coexist and are super important to customers, I'd like to see that inside of Apex. I'd like to see that data play beyond storage which is really where it is today and it's early days. The point is, with Dell's go to market advantage, which company wouldn't treat Dell like the on-prem, hybrid, edge, super cloud player, that I want to partner with to drive more business? You'd be crazy not to. But Dell has a lot on its plate and we'd like to see some serious acceleration on the ecosystem front. In other words, Dell as both a selling partner and a business enabler with its platform. Its programmable infrastructure as-a-service. And that is a moving target that will rapidly involve. And, of course, we'll be here watching and reporting. So thanks for watching this preview of Dell Technology Summit 2022. I'm Dave Vellante, we hope you enjoy the rest of the program. (upbeat music)

(upbeat electronic music) >> Welcome back to everyone, to "theCUBE's" coverage here in Seattle, Washington for Amazon Web Services Partner Marketplace Seller Conference, combining their partner network with Marketplace forming a new organization called AWS Partner Organization. This is "theCUBE" coverage. I'm John Furrier, your host. We've got great "Cube" alumni here from Zscaler, a very successful cloud company doing great work. Stelio D'Alo, senior director of cloud business development and Raveesh Chugh, VP of Public Cloud Partnerships at Zscaler. Welcome back to "theCUBE." Good to see you guys. Thanks for coming on. >> Thank you. >> Thanks having us, John. >> So we've been doing a lot of coverage of Zscaler, what a great success story. I mean, the numbers are great. The business performance, it's in the top two, three, one, two, three in all metrics on public companies, SaaS. So you guys, check. Good job. >> Yes, thank you. >> So you guys have done a good job. Now you're here, selling through the Marketplace. You guys are a world class performing company in cloud SaaS, so you're in the front lines doing well. Now, Marketplace is a procurement front end opportunity for people to buy. Hey, self-service, buy and put things together. Sounds novel, what a great concept. Great cloud life. >> Yes. >> You guys are participating and now sellers are coming together. The merger of the public, the partner network with Marketplace. It feels like this is a second act for AWS to go to the next level. They got their training wheels done with partners. Now they're going to the next level. What do you guys think about this? >> Well, I think you're right, John. I think it is very much something that is in keeping with the way AWS does business. Very Amazonian, they're working back from the customer. What we're seeing is, our customers and in general, the market is gravitating towards purchase mechanisms and route to market that just are lower friction. So in the same way that companies are going through their digital transformations now, really modernizing the way they host applications and they reach the internet. They're also modernizing on the purchasing side, which is super exciting, because we're all motivated to help customers with that agility. >> You know, it's fun to watch and again I'm being really candid and props to you guys as a company. Now, everyone else is kind of following that. Okay, lift and shift, check, doing some things. Now they go, whoa, I can really build on this. People are building their own apps for their companies. Going to build their own stuff. They're going to use piece parts. They're going to put it together in a really scalable way. That's the new normal. Okay, so now they go okay, I'm going to just buy through the market, I get purchasing power. So you guys have been a real leader with AWS. Can you share what you guys are doing in the Marketplace? I think you guys are a nice example of how to execute the Marketplace. Take us through. What are you guys offering there? What's the contract look like? Is it multi-pronged? What's the approach? What do customers get if they go to the marketplace for Zscaler? >> Yeah, so it's been a very exciting story and been a very pleasing one for us with AWS marketplace. We see a huge growth potentially. There are more than 350,000 customers that are actively buying through Marketplace today. We expect that number to grow to around a million customers by the next, I would say, five to ten years and we want to be part of this wave. We see AWS Marketplace to be a channel where not only our resalers or our channel partners can come and transact, but also our GSIs like Accenture want to transact through this channel. We are doing a lot, in terms of bringing new customers through Marketplace, who want to not only close their deals, but close it in the next few hours. That's the beauty of Marketplace, the agility, the flexibility in terms of pricing that it provides to ISVs like us. If a customer wants to delay their payments by a couple of quarters, Marketplace supports that. If a customer wants to do monthly payments, Marketplace supports that. We are seeing lot of customers, big customers, that have signed EDPs, enterprise discount plans with AWS. These are multi-year cloud commits coming to us and saying we can retire our EDPs with AWS if we transact your solution through AWS Marketplace. So what we have done, as of today, we have all of our production services enabled through AWS Marketplace. What that means for customers, they can now retire their EDPs by buying Zscaler products through AWS Marketplace and in return get the full benefit of maximizing their EDP commits with AWS. >> So you guys are fully committed, no toe on the water, as we heard. You guys are all in. >> Absolutely, that's exactly the way to put it. We're all in, all of our solutions are available in the marketplace. As you mentioned, we're a SaaS provider. So we're one of the vendors in the Marketplace that have SaaS solutions. So unlike a lot of customers and even the market in general, associate the Marketplace for historical reasons, the way it started with a lot of monthly subscriptions and just dipping your toe in it from a consumer perspective. Whereas we're doing multimillion dollar, multi-year SaaS contracts. So the most complicated kinds of transactions you'd normally associate with enterprise software, we're doing in very low friction ways. >> On the Zscaler side going in low friction. >> Yep, yeah, that's right. >> How about the customer experience? >> So it is primarily the the customer that experiences. >> Driving it? >> Yeah, they're driving it and it's because rather than traditional methods of going through paperwork, purchase orders- >> What are some of the things that customers are saying about this, bcause I see two benefits, I'll say that. The friction, it's a channel, okay, for Zscaler. Let's be clear, but now you have a customer who's got a lot of Amazon. They're a trusted partner too. So why wouldn't they want to have one point of contact to use their purchasing power and you guys are okay with that. >> We're absolutely okay with it. The reason being, we're still doing the transaction and we can do the transaction with our... We're a channel first company, so that's another important distinction of how people tend to think of the Marketplace. We go through channel. A lot of our transactions are with traditional channel partners and you'd be surprised the kinds of, even the Telcos, carrier providers, are starting to embrace Marketplace. So from a customer perspective, it's less paperwork, less legal work. >> Yeah, I'd love to get your reaction to something, because I think this highlights to me what we've been reporting on with "theCUBE" with super cloud and other trends that are different in a good way. Taking it to the next level and that is that if you look at Zscaler, SaaS, SaaS is self-service, the scale, there's efficiencies. Marketplace first started out as a self-service catalog, a website, you know, click and choose, but now it's a different. He calls it a supply chain, like the CICD pipeline of buying software. He mentions that, there's also services. He put the Channel partners can come in. The GSIs, global system integrators can come in. So it's more than just a catalog now. It's kind of self-service procurement more than it is just a catalog of buy stuff. >> Yes, so yeah, I feel CEOs, CSOs of today should understand what Marketplace brings to the bear in terms of different kinds of services or Zscaler solutions that they can acquire through Marketplace and other ISV solutions, for that matter. I feel like we are at a point, after the pandemic, where there'll be a lot of digital exploration and companies can do more in terms of not just Marketplace, but also including the channel partners as part of deals. So you talked about channel conflict. AWS addressed this by bringing a program called CPPO in the picture, Channel Partner Private Offers. What that does is, we are not only bringing all our channel partners into deals. For renewals as well, they're the partner of record and they get paid alongside with the customer. So AWS does all the heavy lifting, in terms of disbursements of payments to us, to the channel partner, so it's a win-win situation for all. >> I mean, private offers and co-sale has been very popular. >> It has been, and that is our bread and butter in the Marketplace. Again, we do primarily three year contracts and so private offers work super well. A nice thing for us as a vendor is it provides a great amount of flexibility. Private Offer gives you a lot of optionality, in terms of how the constructs of the deal and whether or not you're working with a partner, how the partner is utilizing as well to resell to the end user. So, we've always talked about AWS giving IT agility. This gives purchasing and finance business agility. >> Yeah, and I think this comes up a lot. I just noticed this happening a lot more, where you see dedicated sessions, not just on DevOps and all the goodies of the cloud, financial strategy. >> Yeah. >> Seeing a lot more conversation around how to operationalize the business transactions in the cloud. >> Absolutely. >> This is the new, I mean it's not new, it's been thrown around, but not at a tech conference. You don't see that. So I got to ask you guys, what's the message to the CISOs and executives watching the business people about Zscaler in the Marketplace? What should they be looking at? What is the pitch for Zscaler for the Marketplace buyer? >> So I would say that we are a cloud-delivered network security service. We have been in this game for more than a decade. We have years of early head start with lots of features and functionality versus our competitors. If customers were to move into AWS Cloud, they can get rid of their next-gen firewalls and just have all the traffic routed through our Zscaler internet access and use Zscaler private access for accessing their private applications. We feel we have done everything in our capacity, in terms of enabling customers through Marketplace and will continue to participate in more features and functionality that Marketplace has to offer. We would like these customers to take advantage of their EDPs as well as their retirement and spend for the multi-commit through AWS Marketplace. Learn about what we have to offer and how we can really expedite the motion for them, if they want to procure our solutions through Marketplace >> You know, we're seeing an ability for them to get more creative, more progressive in terms of the purchasing. We're also doing, we're really excited about the ability to serve multiple markets. So we've had an immense amount of success in commercial. We also are seeing increasing amount of public sector, US federal government agencies that want to procure this way as well for the same reasons. So there's a lot of innovation going on. >> So you have the FedRAMP going on, you got all those certifications. >> Exactly right. So we are the first cloud-native solution to provide IL5 ATO, as well as FedRAMP pie and we make that all available, GSA schedule pricing through the AWS Marketplace, again through FSIs and other resellers. >> Public private partnerships have been a big factor, having that span of capability. I got to ask you about, this is a cool conversation, because now you're like, okay, I'm selling through the Marketplace. Companies themselves are changing how they operate. They don't just buy software that we used to use. So general purpose, bundled stuff. Oh yeah, I'm buying this product, because this has got a great solution and I have to get forced to use this firewall, because I bought this over here. That's not how companies are architecting and developing their businesses. It's no longer buying IT. They're building their company digitally. They have to be the application. So they're not sitting around, saying hey, can I get a solution? They're building and architecting their solution. This is kind of like the new enterprise that no one's talking about. They kind of, got to do their own work. >> Yes. >> There's no general purpose solution that maps every company. So they got to pick the best piece parts and integrate them. >> Yes and I feel- >> Do you guys agree with that? >> Yeah, I agree with that and customers don't want to go for point solutions anymore. They want to go with a platform approach. They want go with a vendor that can not only cut down their vendors from multi-dozens to maybe a dozen or less and that's where, you know, we kind of have pivoted to the platform-centric approach, where we not only help customers with Cloud Network Security, but we also help customers with Cloud Native Application Protection Platform that we just recently launched. It's going by the name of the different elements, including Cloud Security Posture Management, Cloud Identity Event Management and so we are continuously doing more and more on the configuration and vulnerability side space. So if a customer has an AWS S3 bucket that is opened it can be detected and can be remediated. So all of those proactive steps we are taking, in terms of enhancing our portfolio, but we have come a long way as a company, as a platform that we have evolved in the Marketplace. >> What's the hottest product? >> The hottest product? >> In Marketplace right now. >> Well, the fastest growing products include our digital experience products and we have new Cloud Protection. So we've got Posture and Workload Protection as well and those are the fastest growing. For AWS customers a strong affinity also for ZPA, which provides you zero trust access to your workloads on AWS. So those are all the most popular in Marketplace. >> Yeah. >> So I would like to add that we recently launched and this has been a few years, a couple of years. We launched a product called Zscaler Digital X, the ZDX. >> Mm-hmm. >> What that product does is, let's say you're making a Zoom call and your WiFi network is laggy or it's a Zoom server that's laggy. It kind of detects where is the problem and it further tells the IT department you need to fix either the server on which Zoom is running, or fix your home network. So that is the beauty of the product. So I think we are seeing massive growth with some of our new editions in the portfolio, which is a long time coming. >> Yeah and certainly a lot of growth opportunities for you guys, as you come in. Where do you see Zscaler's big growth coming from product-wise? What's the big push? Actually, this is great upside for you here. >> Yeah. >> On the go to market side. Where's the big growth for Zscaler right now? So I think we are focused as a company on zero trust architecture. We want to securely connect users to apps, apps to apps, workloads to workloads and machines to machines. We want to give customers an experience where they have direct access to the apps that's hidden from the outside world and they can securely connect to the apps in a very succinct fashion. The user experience is second to none. A lot of customers use us on the Microsoft Office 365 side, where they see a lag in connecting to Microsoft Office 365 directly. They use the IE service to securely connect. >> Yeah, latency kills. >> Microsoft Office 365. >> Latency kills, as we always say, you know and security, you got to look at the pattern, you want to see that data. >> Yeah, and emerging use cases, there is an immense amount of white space and upside for us as well in emerging use cases, like OT, 5G, IOT. >> Yeah. >> Federal government, DOD. >> Oh god, tactical edge government. >> Security at the edge, absolutely, yeah. >> Where's the big edge? What's the edge challenge right now, if you have to put your finger on the edge, because right now that's the hot area, we're watching that. It's going to be highly contested. It's not yet clear, I mean certainly hybrid is the operating model, cloud, distributing, computing, but edge has got unique things that you can't really point to on premises that's the same. It's highly dynamic, you need high bandwidth, low latency, compute at the edge. The data has to be processed right there. What's the big thing at the edge right now? >> Well, so that's probably an emerging answer. I mean, we're working with our customers, they're inventing and they're kind of finding the use cases for those edge, but one of the good things about Zscaler is that we are able to, we've got low latency at the edge. We're able to work as a computer at the edge. We work on Outpost, Snowball, Snowcone, the Snow devices. So we can be wherever our customers need us. Mobile devices, there are a lot of applications where we've got to be either on embedded devices, on tractors, providing security for those IOT devices. So we're pretty comfortable with where we are being the- >> So that's why you guys are financially doing so well, performance wise. I got to ask you though, because I think that brings up the great point. If this is why I like the Marketplace, if I'm a customer, the edge is highly dynamic. It's changing all the time. I don't want to wait to buy something. If I got my solution architects on a product, I need to know I'm going to have zero trust built in and I need to push the button on Zscaler. I don't want to wait. So how does the procurement side impact? What have you guys seen? Share your thoughts on how Marketplace is working from the procurement standpoint, because it seems to me to be fast. Is that right, or is it still slow on their side? On the buyer side, because this to me would be a benefit to developers, if we say, hey, the procurement can just go really fast. I don't want to go through a bunch of PO approvals or slow meetings. >> It can be, that manifests itself in several ways, John. It can be, for instance, somebody wants to do a POC and traditionally you could take any amount of time to get budget approval, take it through. What if you had a pre-approved cloud budget and that was spent primarily through AWS Marketplace, because it's consolidated data on your AWS invoice. The ability to purchase a POC on the Marketplace could be done literally within minutes of the decision being made to go forward with it. So that's kind of a front end, you know, early stage use case. We've got examples we didn't talk about on our recent earnings call of how we have helped customers bring in their procurement with large million dollar, multimillion dollar deals. Even when a resaler's been involved, one of our resaler partners. Being able to accelerate deals, because there's so much less legal work and traditional bureaucratic effort. >> Agility. >> That agility purchasing process has allowed our customers to pull into the quarter, or the end of month, or end of quarter for them, deals that would've otherwise not been able to be done. >> So this is a great example of where you can set policy and kind of create some guard rails around innovation and integration deals, knowing if it's something that the edge is happening, say okay, here's some budget. We approved it, or Amazon gives credits and partnership going on. Then I'd say, hey, well green light this, not to exceed a million dollars, or whatever number in their range and then let people have the freedom to execute. >> You're absolutely right, so from the purchasing side, it does give them that agility. It eliminates a lot of the processes that would push out a purchase in actual execution past when the business decision is made and quite frankly, to be honest, AWS has been very accommodative. They're a great partner. They've invested a lot in Marketplace, Marketplace programs, to help customers do the right thing and do it more quickly as well as vendors like us to help our customers make the decisions they need to. >> Rising tide, a rising tide floats all boats and you guys are a great example of an independent company. Highly successful on your own. >> Yep. >> Certainly the numbers are clear. Wall Street loves Zscaler and economics are great. >> Our customer CSAT numbers are off the scale as well. >> Customers are great and now you've got the Marketplace. This is again, a new normal. A new kind of ecosystem is developing where it's not like the old monolithic ecosystems. The value creation and extraction is happening differently now. It's kind of interesting. >> Yes and I feel we have a long way to go, but what I can tell you is that Zscaler is in this for the long run. We are seeing some of the competitors erupt in the space as well, but they have a long way to go. What we have built requires years worth of R&D and features and thousands of customer's use cases which kind of lead to something what Zscaler has come up with today. What we have is very unique and is going to continuously be an innovation in the market in the years to come. In terms of being more cloud-savvy or more cloud-focused or more cloud-native than what the market has seen so far in the form of next-gen firewalls. >> I know you guys have got a lot of AI work. We've had many conversations with Howie over there. Great stuff and really appreciate you guys participating in our super cloud event we had and we'll see more of that where we're talking about the next generation clouds, really enabling that new disruptive, open-spanning capabilities across multiple environments to run cloud-native modern applications at scale and secure. Appreciate your time to come on "theCUBE". >> Thank you. >> Thank you very much. >> Thanks for having us. >> Thanks, I totally appreciate it. Zscaler, leading company here on "theCUBE" talking about their relationship with Marketplace as they continue to grow and succeed as technology goes to the next level in the cloud. Of course "theCUBE's" covering it here in Seattle. I'm John Furrier, your host. Thanks for watching. (peaceful electronic music)