(chill electronic music) >> Hello everyone, welcome to theCUBE's presentation of the AWS Startups Showcase, AI and machine learning, the top startups building generative AI on AWS. This is the season three, episode one of the ongoing series covering the exciting startups from the AWS ecosystem, talk about AI and machine learning. Can't believe it's three years and season one. I'm your host, John Furrier. Got a great guest today, we're joined by Joseph Nelson, the co-founder and CEO of Roboflow, doing some cutting edge stuff around computer vision and really at the front end of this massive wave coming around, large language models, computer vision. The next gen AI is here, and it's just getting started. We haven't even scratched a service. Thanks for joining us today. >> Thanks for having me. >> So you got to love the large language model, foundation models, really educating the mainstream world. ChatGPT has got everyone in the frenzy. This is educating the world around this next gen AI capabilities, enterprise, image and video data, all a big part of it. I mean the edge of the network, Mobile World Conference is happening right now, this month, and it's just ending up, it's just continue to explode. Video is huge. So take us through the company, do a quick explanation of what you guys are doing, when you were founded. Talk about what the company's mission is, and what's your North Star, why do you exist? >> Yeah, Roboflow exists to really kind of make the world programmable. I like to say make the world be read and write access. And our North Star is enabling developers, predominantly, to build that future. If you look around, anything that you see will have software related to it, and can kind of be turned into software. The limiting reactant though, is how to enable computers and machines to understand things as well as people can. And in a lot of ways, computer vision is that missing element that enables anything that you see to become software. So in the virtue of, if software is eating the world, computer vision kind of makes the aperture infinitely wide. It's something that I kind of like, the way I like to frame it. And the capabilities are there, the open source models are there, the amount of data is there, the computer capabilities are only improving annually, but there's a pretty big dearth of tooling, and an early but promising sign of the explosion of use cases, models, and data sets that companies, developers, hobbyists alike will need to bring these capabilities to bear. So Roboflow is in the game of building the community around that capability, building the use cases that allow developers and enterprises to use computer vision, and providing the tooling for companies and developers to be able to add computer vision, create better data sets, and deploy to production, quickly, easily, safely, invaluably. >> You know, Joseph, the word in production is actually real now. You're seeing a lot more people doing in production activities. That's a real hot one and usually it's slower, but it's gone faster, and I think that's going to be more the same. And I think the parallel between what we're seeing on the large language models coming into computer vision, and as you mentioned, video's data, right? I mean we're doing video right now, we're transcribing it into a transcript, linking up to your linguistics, times and the timestamp, I mean everything's data and that really kind of feeds. So this connection between what we're seeing, the large language and computer vision are coming together kind of cousins, brothers. I mean, how would you compare, how would you explain to someone, because everyone's like on this wave of watching people bang out their homework assignments, and you know, write some hacks on code with some of the open AI technologies, there is a corollary directly related to to the vision side. Can you explain? >> Yeah, the rise of large language models are showing what's possible, especially with text, and I think increasingly will get multimodal as the images and video become ingested. Though there's kind of this still core missing element of basically like understanding. So the rise of large language models kind of create this new area of generative AI, and generative AI in the context of computer vision is a lot of, you know, creating video and image assets and content. There's also this whole surface area to understanding what's already created. Basically digitizing physical, real world things. I mean the Metaverse can't be built if we don't know how to mirror or create or identify the objects that we want to interact with in our everyday lives. And where computer vision comes to play in, especially what we've seen at Roboflow is, you know, a little over a hundred thousand developers now have built with our tools. That's to the tune of a hundred million labeled open source images, over 10,000 pre-trained models. And they've kind of showcased to us all of the ways that computer vision is impacting and bringing the world to life. And these are things that, you know, even before large language models and generative AI, you had pretty impressive capabilities, and when you add the two together, it actually unlocks these kind of new capabilities. So for example, you know, one of our users actually powers the broadcast feeds at Wimbledon. So here we're talking about video, we're streaming, we're doing things live, we've got folks that are cropping and making sure we look good, and audio/visual all plugged in correctly. When you broadcast Wimbledon, you'll notice that the camera controllers need to do things like track the ball, which is moving at extremely high speeds and zoom crop, pan tilt, as well as determine if the ball bounced in or out. The very controversial but critical key to a lot of tennis matches. And a lot of that has been historically done with the trained, but fallible human eye and computer vision is, you know, well suited for this task to say, how do we track, pan, tilt, zoom, and see, track the tennis ball in real time, run at 30 plus frames per second, and do it all on the edge. And those are capabilities that, you know, were kind of like science fiction, maybe even a decade ago, and certainly five years ago. Now the interesting thing, is that with the advent of of generative AI, you can start to do things like create your own training data sets, or kind of create logic around once you have this visual input. And teams at Tesla have actually been speaking about, of course the autopilot team's focused on doing vision tasks, but they've combined large language models to add reasoning and logic. So given that you see, let's say the tennis ball, what do you want to do? And being able to combine the capabilities of what LLM's represent, which is really a lot of basically, core human reasoning and logic, with computer vision for the inputs of what's possible, creates these new capabilities, let alone multimodality, which I'm sure we'll talk more about. >> Yeah, and it's really, I mean it's almost intoxicating. It's amazing that this is so capable because the cloud scales here, you got the edge developing, you can decouple compute power, and let Moore's law and all the new silicone and the processors and the GPUs do their thing, and you got open source booming. You're kind of getting at this next segment I wanted to get into, which is the, how people should be thinking about these advances of the computer vision. So this is now a next wave, it's here. I mean I'd love to have that for baseball because I'm always like, "Oh, it should have been a strike." I'm sure that's going to be coming soon, but what is the computer vision capable of doing today? I guess that's my first question. You hit some of it, unpack that a little bit. What does general AI mean in computer vision? What's the new thing? Because there are old technology's been around, proprietary, bolted onto hardware, but hardware advances at a different pace, but now you got new capabilities, generative AI for vision, what does that mean? >> Yeah, so computer vision, you know, at its core is basically enabling machines, computers, to understand, process, and act on visual data as effective or more effective than people can. Traditionally this has been, you know, task types like classification, which you know, identifying if a given image belongs in a certain category of goods on maybe a retail site, is the shoes or is it clothing? Or object detection, which is, you know, creating bounding boxes, which allows you to do things like count how many things are present, or maybe measure the speed of something, or trigger an alert when something becomes visible in frame that wasn't previously visible in frame, or instant segmentation where you're creating pixel wise segmentations for both instance and semantic segmentation, where you often see these kind of beautiful visuals of the polygon surrounding objects that you see. Then you have key point detection, which is where you see, you know, athletes, and each of their joints are kind of outlined is another more traditional type problem in signal processing and computer vision. With generative AI, you kind of get a whole new class of problem types that are opened up. So in a lot of ways I think about generative AI in computer vision as some of the, you know, problems that you aimed to tackle, might still be better suited for one of the previous task types we were discussing. Some of those problem types may be better suited for using a generative technique, and some are problem types that just previously wouldn't have been possible absent generative AI. And so if you make that kind of Venn diagram in your head, you can think about, okay, you know, visual question answering is a task type where if I give you an image and I say, you know, "How many people are in this image?" We could either build an object detection model that might count all those people, or maybe a visual question answering system would sufficiently answer this type of problem. Let alone generative AI being able to create new training data for old systems. And that's something that we've seen be an increasingly prominent use case for our users, as much as things that we advise our customers and the community writ large to take advantage of. So ultimately those are kind of the traditional task types. I can give you some insight, maybe, into how I think about what's possible today, or five years or ten years as you sort go back. >> Yes, definitely. Let's get into that vision. >> So I kind of think about the types of use cases in terms of what's possible. If you just imagine a very simple bell curve, your normal distribution, for the longest time, the types of things that are in the center of that bell curve are identifying objects that are very common or common objects in context. Microsoft published the COCO Dataset in 2014 of common objects and contexts, of hundreds of thousands of images of chairs, forks, food, person, these sorts of things. And you know, the challenge of the day had always been, how do you identify just those 80 objects? So if we think about the bell curve, that'd be maybe the like dead center of the curve, where there's a lot of those objects present, and it's a very common thing that needs to be identified. But it's a very, very, very small sliver of the distribution. Now if you go out to the way long tail, let's go like deep into the tail of this imagined visual normal distribution, you're going to have a problem like one of our customers, Rivian, in tandem with AWS, is tackling, to do visual quality assurance and manufacturing in production processes. Now only Rivian knows what a Rivian is supposed to look like. Only they know the imagery of what their goods that are going to be produced are. And then between those long tails of proprietary data of highly specific things that need to be understood, in the center of the curve, you have a whole kind of messy middle, type of problems I like to say. The way I think about computer vision advancing, is it's basically you have larger and larger and more capable models that eat from the center out, right? So if you have a model that, you know, understands the 80 classes in COCO, well, pretty soon you have advances like Clip, which was trained on 400 million image text pairs, and has a greater understanding of a wider array of objects than just 80 classes in context. And over time you'll get more and more of these larger models that kind of eat outwards from that center of the distribution. And so the question becomes for companies, when can you rely on maybe a model that just already exists? How do you use your data to get what may be capable off the shelf, so to speak, into something that is usable for you? Or, if you're in those long tails and you have proprietary data, how do you take advantage of the greatest asset you have, which is observed visual information that you want to put to work for your customers, and you're kind of living in the long tails, and you need to adapt state of the art for your capabilities. So my mental model for like how computer vision advances is you have that bell curve, and you have increasingly powerful models that eat outward. And multimodality has a role to play in that, larger models have a role to play in that, more compute, more data generally has a role to play in that. But it will be a messy and I think long condition. >> Well, the thing I want to get, first of all, it's great, great mental model, I appreciate that, 'cause I think that makes a lot of sense. The question is, it seems now more than ever, with the scale and compute that's available, that not only can you eat out to the middle in your example, but there's other models you can integrate with. In the past there was siloed, static, almost bespoke. Now you're looking at larger models eating into the bell curve, as you said, but also integrating in with other stuff. So this seems to be part of that interaction. How does, first of all, is that really happening? Is that true? And then two, what does that mean for companies who want to take advantage of this? Because the old model was operational, you know? I have my cameras, they're watching stuff, whatever, and like now you're in this more of a, distributed computing, computer science mindset, not, you know, put the camera on the wall kind of- I'm oversimplifying, but you know what I'm saying. What's your take on that? >> Well, to the first point of, how are these advances happening? What I was kind of describing was, you know, almost uni-dimensional in that you have like, you're only thinking about vision, but the rise of generative techniques and multi-modality, like Clip is a multi-modal model, it has 400 million image text pairs. That will advance the generalizability at a faster rate than just treating everything as only vision. And that's kind of where LLMs and vision will intersect in a really nice and powerful way. Now in terms of like companies, how should they be thinking about taking advantage of these trends? The biggest thing that, and I think it's different, obviously, on the size of business, if you're an enterprise versus a startup. The biggest thing that I think if you're an enterprise, and you have an established scaled business model that is working for your customers, the question becomes, how do you take advantage of that established data moat, potentially, resource moats, and certainly, of course, establish a way of providing value to an end user. So for example, one of our customers, Walmart, has the advantage of one of the largest inventory and stock of any company in the world. And they also of course have substantial visual data, both from like their online catalogs, or understanding what's in stock or out of stock, or understanding, you know, the quality of things that they're going from the start of their supply chain to making it inside stores, for delivery of fulfillments. All these are are visual challenges. Now they already have a substantial trove of useful imagery to understand and teach and train large models to understand each of the individual SKUs and products that are in their stores. And so if I'm a Walmart, what I'm thinking is, how do I make sure that my petabytes of visual information is utilized in a way where I capture the proprietary benefit of the models that I can train to do tasks like, what item was this? Or maybe I'm going to create AmazonGo-like technology, or maybe I'm going to build like delivery robots, or I want to automatically know what's in and out of stock from visual input fees that I have across my in-store traffic. And that becomes the question and flavor of the day for enterprises. I've got this large amount of data, I've got an established way that I can provide more value to my own customers. How do I ensure I take advantage of the data advantage I'm already sitting on? If you're a startup, I think it's a pretty different question, and I'm happy to talk about. >> Yeah, what's startup angle on this? Because you know, they're going to want to take advantage. It's like cloud startups, cloud native startups, they were born in the cloud, they never had an IT department. So if you're a startup, is there a similar role here? And if I'm a computer vision startup, what's that mean? So can you share your your take on that, because there'll be a lot of people starting up from this. >> So the startup on the opposite advantage and disadvantage, right? Like a startup doesn't have an proven way of delivering repeatable value in the same way that a scaled enterprise does. But it does have the nimbleness to identify and take advantage of techniques that you can start from a blank slate. And I think the thing that startups need to be wary of in the generative AI enlarged language model, in multimodal world, is building what I like to call, kind of like sandcastles. A sandcastle is maybe a business model or a capability that's built on top of an assumption that is going to be pretty quickly wiped away by improving underlying model technology. So almost like if you imagine like the ocean, the waves are coming in, and they're going to wipe away your progress. You don't want to be in the position of building sandcastle business where, you don't want to bet on the fact that models aren't going to get good enough to solve the task type that you might be solving. In other words, don't take a screenshot of what's capable today. Assume that what's capable today is only going to continue to become possible. And so for a startup, what you can do, that like enterprises are quite comparatively less good at, is embedding these capabilities deeply within your products and delivering maybe a vertical based experience, where AI kind of exists in the background. >> Yeah. >> And we might not think of companies as, you know, even AI companies, it's just so embedded in the experience they provide, but that's like the vertical application example of taking AI and making it be immediately usable. Or, of course there's tons of picks and shovels businesses to be built like Roboflow, where you're enabling these enterprises to take advantage of something that they have, whether that's their data sets, their computes, or their intellect. >> Okay, so if I hear that right, by the way, I love, that's horizontally scalable, that's the large language models, go up and build them the apps, hence your developer focus. I'm sure that's probably the reason that the tsunami of developer's action. So you're saying picks and shovels tools, don't try to replicate the platform of what could be the platform. Oh, go to a VC, I'm going to build a platform. No, no, no, no, those are going to get wiped away by the large language models. Is there one large language model that will rule the world, or do you see many coming? >> Yeah, so to be clear, I think there will be useful platforms. I just think a lot of people think that they're building, let's say, you know, if we put this in the cloud context, you're building a specific type of EC2 instance. Well, it turns out that Amazon can offer that type of EC2 instance, and immediately distribute it to all of their customers. So you don't want to be in the position of just providing something that actually ends up looking like a feature, which in the context of AI, might be like a small incremental improvement on the model. If that's all you're doing, you're a sandcastle business. Now there's a lot of platform businesses that need to be built that enable businesses to get to value and do things like, how do I monitor my models? How do I create better models with my given data sets? How do I ensure that my models are doing what I want them to do? How do I find the right models to use? There's all these sorts of platform wide problems that certainly exist for businesses. I just think a lot of startups that I'm seeing right now are making the mistake of assuming the advances we're seeing are not going to accelerate or even get better. >> So if I'm a customer, if I'm a company, say I'm a startup or an enterprise, either one, same question. And I want to stand up, and I have developers working on stuff, I want to start standing up an environment to start doing stuff. Is that a service provider? Is that a managed service? Is that you guys? So how do you guys fit into your customers leaning in? Is it just for developers? Are you targeting with a specific like managed service? What's the product consumption? How do you talk to customers when they come to you? >> The thing that we do is enable, we give developers superpowers to build automated inventory tracking, self-checkout systems, identify if this image is malignant cancer or benign cancer, ensure that these products that I've produced are correct. Make sure that that the defect that might exist on this electric vehicle makes its way back for review. All these sorts of problems are immediately able to be solved and tackled. In terms of the managed services element, we have solutions as integrators that will often build on top of our tools, or we'll have companies that look to us for guidance, but ultimately the company is in control of developing and building and creating these capabilities in house. I really think the distinction is maybe less around managed service and tool, and more around ownership in the era of AI. So for example, if I'm using a managed service, in that managed service, part of their benefit is that they are learning across their customer sets, then it's a very different relationship than using a managed service where I'm developing some amount of proprietary advantages for my data sets. And I think that's a really important thing that companies are becoming attuned to, just the value of the data that they have. And so that's what we do. We tell companies that you have this proprietary, immense treasure trove of data, use that to your advantage, and think about us more like a set of tools that enable you to get value from that capability. You know, the HashiCorp's and GitLab's of the world have proven like what these businesses look like at scale. >> And you're targeting developers. When you go into a company, do you target developers with freemium, is there a paid service? Talk about the business model real quick. >> Sure, yeah. The tools are free to use and get started. When someone signs up for Roboflow, they may elect to make their work open source, in which case we're able to provide even more generous usage limits to basically move the computer vision community forward. If you elect to make your data private, you can use our hosted data set managing, data set training, model deployment, annotation tooling up to some limits. And then usually when someone validates that what they're doing gets them value, they purchase a subscription license to be able to scale up those capabilities. So like most developer centric products, it's free to get started, free to prove, free to poke around, develop what you think is possible. And then once you're getting to value, then we're able to capture the commercial upside in the value that's being provided. >> Love the business model. It's right in line with where the market is. There's kind of no standards bodies these days. The developers are the ones who are deciding kind of what the standards are by their adoption. I think making that easy for developers to get value as the model open sources continuing to grow, you can see more of that. Great perspective Joseph, thanks for sharing that. Put a plug in for the company. What are you guys doing right now? Where are you in your growth? What are you looking for? How should people engage? Give the quick commercial for the company. >> So as I mentioned, Roboflow is I think one of the largest, if not the largest collections of computer vision models and data sets that are open source, available on the web today, and have a private set of tools that over half the Fortune 100 now rely on those tools. So we're at the stage now where we know people want what we're working on, and we're continuing to drive that type of adoption. So companies that are looking to make better models, improve their data sets, train and deploy, often will get a lot of value from our tools, and certainly reach out to talk. I'm sure there's a lot of talented engineers that are tuning in too, we're aggressively hiring. So if you are interested in being a part of making the world programmable, and being at the ground floor of the company that's creating these capabilities to be writ large, we'd love to hear from you. >> Amazing, Joseph, thanks so much for coming on and being part of the AWS Startup Showcase. Man, if I was in my twenties, I'd be knocking on your door, because it's the hottest trend right now, it's super exciting. Generative AI is just the beginning of massive sea change. Congratulations on all your success, and we'll be following you guys. Thanks for spending the time, really appreciate it. >> Thanks for having me. >> Okay, this is season three, episode one of the ongoing series covering the exciting startups from the AWS ecosystem, talking about the hottest things in tech. I'm John Furrier, your host. Thanks for watching. (chill electronic music)

(gentle music) >> Hello everyone. Welcome to this CUBE conversation here in Palo Alto, California. I'm John Furrier, host of theCUBE. We got a great remote guest coming in. Joseph Nelson, co-founder and CEO of RoboFlow hot startup in AI, computer vision. Really interesting topic in this wave of AI next gen hitting. Joseph, thanks for coming on this CUBE conversation. >> Thanks for having me. >> Yeah, I love the startup tsunami that's happening here in this wave. RoboFlow, you're in the middle of it. Exciting opportunities, you guys are in the cutting edge. I think computer vision's been talked about more as just as much as the large language models and these foundational models are merging. You're in the middle of it. What's it like right now as a startup and growing in this new wave hitting? >> It's kind of funny, it's, you know, I kind of describe it like sometimes you're in a garden of gnomes. It's like we feel like we've got this giant headstart with hundreds of thousands of people building with computer vision, training their own models, but that's a fraction of what it's going to be in six months, 12 months, 24 months. So, as you described it, a wave is a good way to think about it. And the wave is still building before it gets to its full size. So it's a ton of fun. >> Yeah, I think it's one of the most exciting areas in computer science. I wish I was in my twenties again, because I would be all over this. It's the intersection, there's so many disciplines, right? It's not just tech computer science, it's computer science, it's systems, it's software, it's data. There's so much aperture of things going on around your world. So, I mean, you got to be batting all the students away kind of trying to get hired in there, probably. I can only imagine you're hiring regiment. I'll ask that later, but first talk about what the company is that you're doing. How it's positioned, what's the market you're going after, and what's the origination story? How did you guys get here? How did you just say, hey, want to do this? What was the origination story? What do you do and how did you start the company? >> Yeah, yeah. I'll give you the what we do today and then I'll shift into the origin. RoboFlow builds tools for making the world programmable. Like anything that you see should be read write access if you think about it with a programmer's mind or legible. And computer vision is a technology that enables software to be added to these real world objects that we see. And so any sort of interface, any sort of object, any sort of scene, we can interact with it, we can make it more efficient, we can make it more entertaining by adding the ability for the tools that we use and the software that we write to understand those objects. And at RoboFlow, we've empowered a little over a hundred thousand developers, including those in half the Fortune 100 so far in that mission. Whether that's Walmart understanding the retail in their stores, Cardinal Health understanding the ways that they're helping their patients, or even electric vehicle manufacturers ensuring that they're making the right stuff at the right time. As you mentioned, it's early. Like I think maybe computer vision has touched one, maybe 2% of the whole economy and it'll be like everything in a very short period of time. And so we're focused on enabling that transformation. I think it's it, as far as I think about it, I've been fortunate to start companies before, start, sell these sorts of things. This is the last company I ever wanted to start and I think it will be, should we do it right, the world's largest in riding the wave of bringing together the disparate pieces of that technology. >> What was the motivating point of the formation? Was it, you know, you guys were hanging around? Was there some catalyst? What was the moment where it all kind of came together for you? >> You know what's funny is my co-founder, Brad and I, we were making computer vision apps for making board games more fun to play. So in 2017, Apple released AR kit, augmented reality kit for building augmented reality applications. And Brad and I are both sort of like hacker persona types. We feel like we don't really understand the technology until we build something with it and so we decided that we should make an app that if you point your phone at a Sudoku puzzle, it understands the state of the board and then it kind of magically fills in that experience with all the digits in real time, which totally ruins the game of Sudoku to be clear. But it also just creates this like aha moment of like, oh wow, like the ability for our pocket devices to understand and see the world as good or better than we can is possible. And so, you know, we actually did that as I mentioned in 2017, and the app went viral. It was, you know, top of some subreddits, top of Injure, Reddit, the hacker community as well as Product Hunt really liked it. So it actually won Product Hunt AR app of the year, which was the same year that the Tesla model three won the product of the year. So we joked that we share an award with Elon our shared (indistinct) But frankly, so that was 2017. RoboFlow wasn't incorporated as a business until 2019. And so, you know, when we made Magic Sudoku, I was running a different company at the time, Brad was running a different company at the time, and we kind of just put it out there and were excited by how many people liked it. And we assumed that other curious developers would see this inevitable future of, oh wow, you know. This is much more than just a pedestrian point your phone at a board game. This is everything can be seen and understood and rewritten in a different way. Things like, you know, maybe your fridge. Knowing what ingredients you have and suggesting recipes or auto ordering for you, or we were talking about some retail use cases of automated checkout. Like anything can be seen and observed and we presume that that would kick off a Cambrian explosion of applications. It didn't. So you fast forward to 2019, we said, well we might as well be the guys to start to tackle this sort of problem. And because of our success with board games before, we returned to making more board game solving applications. So we made one that solves Boggle, you know, the four by four word game, we made one that solves chess, you point your phone at a chess board and it understands the state of the board and then can make move recommendations. And each additional board game that we added, we realized that the tooling was really immature. The process of collecting images, knowing which images are actually going to be useful for improving model performance, training those models, deploying those models. And if we really wanted to make the world programmable, developers waiting for us to make an app for their thing of interest is a lot less efficient, less impactful than taking our tool chain and releasing that externally. And so, that's what RoboFlow became. RoboFlow became the internal tools that we used to make these game changing applications readily available. And as you know, when you give developers new tools, they create new billion dollar industries, let alone all sorts of fun hobbyist projects along the way. >> I love that story. Curious, inventive, little radical. Let's break the rules, see how we can push the envelope on the board games. That's how companies get started. It's a great story. I got to ask you, okay, what happens next? Now, okay, you realize this new tooling, but this is like how companies get built. Like they solve their own problem that they had 'cause they realized there's one, but then there has to be a market for it. So you actually guys knew that this was coming around the corner. So okay, you got your hacker mentality, you did that thing, you got the award and now you're like, okay, wow. Were you guys conscious of the wave coming? Was it one of those things where you said, look, if we do this, we solve our own problem, this will be big for everybody. Did you have that moment? Was that in 2019 or was that more of like, it kind of was obvious to you guys? >> Absolutely. I mean Brad puts this pretty effectively where he describes how we lived through the initial internet revolution, but we were kind of too young to really recognize and comprehend what was happening at the time. And then mobile happened and we were working on different companies that were not in the mobile space. And computer vision feels like the wave that we've caught. Like, this is a technology and capability that rewrites how we interact with the world, how everyone will interact with the world. And so we feel we've been kind of lucky this time, right place, right time of every enterprise will have the ability to improve their operations with computer vision. And so we've been very cognizant of the fact that computer vision is one of those groundbreaking technologies that every company will have as a part of their products and services and offerings, and we can provide the tooling to accelerate that future. >> Yeah, and the developer angle, by the way, I love that because I think, you know, as we've been saying in theCUBE all the time, developer's the new defacto standard bodies because what they adopt is pure, you know, meritocracy. And they pick the best. If it's sell service and it's good and it's got open source community around it, its all in. And they'll vote. They'll vote with their code and that is clear. Now I got to ask you, as you look at the market, we were just having this conversation on theCUBE in Barcelona at recent Mobile World Congress, now called MWC, around 5G versus wifi. And the debate was specifically computer vision, like facial recognition. We were talking about how the Cleveland Browns were using facial recognition for people coming into the stadium they were using it for ships in international ports. So the question was 5G versus wifi. My question is what infrastructure or what are the areas that need to be in place to make computer vision work? If you have developers building apps, apps got to run on stuff. So how do you sort that out in your mind? What's your reaction to that? >> A lot of the times when we see applications that need to run in real time and on video, they'll actually run at the edge without internet. And so a lot of our users will actually take their models and run it in a fully offline environment. Now to act on that information, you'll often need to have internet signal at some point 'cause you'll need to know how many people were in the stadium or what shipping crates are in my port at this point in time. You'll need to relay that information somewhere else, which will require connectivity. But actually using the model and creating the insights at the edge does not require internet. I mean we have users that deploy models on underwater submarines just as much as in outer space actually. And those are not very friendly environments to internet, let alone 5g. And so what you do is you use an edge device, like an Nvidia Jetson is common, mobile devices are common. Intel has some strong edge devices, the Movidius family of chips for example. And you use that compute that runs completely offline in real time to process those signals. Now again, what you do with those signals may require connectivity and that becomes a question of the problem you're solving of how soon you need to relay that information to another place. >> So, that's an architectural issue on the infrastructure. If you're a tactical edge war fighter for instance, you might want to have highly available and maybe high availability. I mean, these are words that mean something. You got storage, but it's not at the edge in real time. But you can trickle it back and pull it down. That's management. So that's more of a business by business decision or environment, right? >> That's right, that's right. Yeah. So I mean we can talk through some specifics. So for example, the RoboFlow actually powers the broadcaster that does the tennis ball tracking at Wimbledon. That runs completely at the edge in real time in, you know, technically to track the tennis ball and point the camera, you actually don't need internet. Now they do have internet of course to do the broadcasting and relay the signal and feeds and these sorts of things. And so that's a case where you have both edge deployment of running the model and high availability act on that model. We have other instances where customers will run their models on drones and the drone will go and do a flight and it'll say, you know, this many residential homes are in this given area, or this many cargo containers are in this given shipping yard. Or maybe we saw these environmental considerations of soil erosion along this riverbank. The model in that case can run on the drone during flight without internet, but then you only need internet once the drone lands and you're going to act on that information because for example, if you're doing like a study of soil erosion, you don't need to be real time. You just need to be able to process and make use of that information once the drone finishes its flight. >> Well I can imagine a zillion use cases. I heard of a use case interview at a company that does computer vision to help people see if anyone's jumping the fence on their company. Like, they know what a body looks like climbing a fence and they can spot it. Pretty easy use case compared to probably some of the other things, but this is the horizontal use cases, its so many use cases. So how do you guys talk to the marketplace when you say, hey, we have generative AI for commuter vision. You might know language models that's completely different animal because vision's like the world, right? So you got a lot more to do. What's the difference? How do you explain that to customers? What can I build and what's their reaction? >> Because we're such a developer centric company, developers are usually creative and show you the ways that they want to take advantage of new technologies. I mean, we've had people use things for identifying conveyor belt debris, doing gas leak detection, measuring the size of fish, airplane maintenance. We even had someone that like a hobby use case where they did like a specific sushi identifier. I dunno if you know this, but there's a specific type of whitefish that if you grew up in the western hemisphere and you eat it in the eastern hemisphere, you get very sick. And so there was someone that made an app that tells you if you happen to have that fish in the sushi that you're eating. But security camera analysis, transportation flows, plant disease detection, really, you know, smarter cities. We have people that are doing curb management identifying, and a lot of these use cases, the fantastic thing about building tools for developers is they're a creative bunch and they have these ideas that if you and I sat down for 15 minutes and said, let's guess every way computer vision can be used, we would need weeks to list all the example use cases. >> We'd miss everything. >> And we'd miss. And so having the community show us the ways that they're using computer vision is impactful. Now that said, there are of course commercial industries that have discovered the value and been able to be out of the gate. And that's where we have the Fortune 100 customers, like we do. Like the retail customers in the Walmart sector, healthcare providers like Medtronic, or vehicle manufacturers like Rivian who all have very difficult either supply chain, quality assurance, in stock, out of stock, anti-theft protection considerations that require successfully making sense of the real world. >> Let me ask you a question. This is maybe a little bit in the weeds, but it's more developer focused. What are some of the developer profiles that you're seeing right now in terms of low-hanging fruit applications? And can you talk about the academic impact? Because I imagine if I was in school right now, I'd be all over it. Are you seeing Master's thesis' being worked on with some of your stuff? Is the uptake in both areas of younger pre-graduates? And then inside the workforce, What are some of the devs like? Can you share just either what their makeup is, what they work on, give a little insight into the devs you're working with. >> Leading developers that want to be on state-of-the-art technology build with RoboFlow because they know they can use the best in class open source. They know that they can get the most out of their data. They know that they can deploy extremely quickly. That's true among students as you mentioned, just as much as as industries. So we welcome students and I mean, we have research grants that will regularly support for people to publish. I mean we actually have a channel inside our internal slack where every day, more student publications that cite building with RoboFlow pop up. And so, that helps inspire some of the use cases. Now what's interesting is that the use case is relatively, you know, useful or applicable for the business or the student. In other words, if a student does a thesis on how to do, we'll say like shingle damage detection from satellite imagery and they're just doing that as a master's thesis, in fact most insurance businesses would be interested in that sort of application. So, that's kind of how we see uptick and adoption both among researchers who want to be on the cutting edge and publish, both with RoboFlow and making use of open source tools in tandem with the tool that we provide, just as much as industry. And you know, I'm a big believer in the philosophy that kind of like what the hackers are doing nights and weekends, the Fortune 500 are doing in a pretty short order period of time and we're experiencing that transition. Computer vision used to be, you know, kind of like a PhD, multi-year investment endeavor. And now with some of the tooling that we're working on in open source technologies and the compute that's available, these science fiction ideas are possible in an afternoon. And so you have this idea of maybe doing asset management or the aerial observation of your shingles or things like this. You have a few hundred images and you can de-risk whether that's possible for your business today. So there's pretty broad-based adoption among both researchers that want to be on the state of the art, as much as companies that want to reduce the time to value. >> You know, Joseph, you guys and your partner have got a great front row seat, ground floor, presented creation wave here. I'm seeing a pattern emerging from all my conversations on theCUBE with founders that are successful, like yourselves, that there's two kind of real things going on. You got the enterprises grabbing the products and retrofitting into their legacy and rebuilding their business. And then you have startups coming out of the woodwork. Young, seeing greenfield or pick a specific niche or focus and making that the signature lever to move the market. >> That's right. >> So can you share your thoughts on the startup scene, other founders out there and talk about that? And then I have a couple questions for like the enterprises, the old school, the existing legacy. Little slower, but the startups are moving fast. What are some of the things you're seeing as startups are emerging in this field? >> I think you make a great point that independent of RoboFlow, very successful, especially developer focused businesses, kind of have three customer types. You have the startups and maybe like series A, series B startups that you're building a product as fast as you can to keep up with them, and they're really moving just as fast as as you are and pulling the product out at you for things that they need. The second segment that you have might be, call it SMB but not enterprise, who are able to purchase and aren't, you know, as fast of moving, but are stable and getting value and able to get to production. And then the third type is enterprise, and that's where you have typically larger contract value sizes, slower moving in terms of adoption and feedback for your product. And I think what you see is that successful companies balance having those three customer personas because you have the small startups, small fast moving upstarts that are discerning buyers who know the market and elect to build on tooling that is best in class. And so you basically kind of pass the smell test of companies who are quite discerning in their purchases, plus are moving so quick they're pulling their product out of you. Concurrently, you have a product that's enterprise ready to service the scalability, availability, and trust of enterprise buyers. And that's ultimately where a lot of companies will see tremendous commercial success. I mean I remember seeing the Twilio IPO, Uber being like a full 20% of their revenue, right? And so there's this very common pattern where you have the ability to find some of those upstarts that you make bets on, like the next Ubers of the world, the smaller companies that continue to get developed with the product and then the enterprise whom allows you to really fund the commercial success of the business, and validate the size of the opportunity in market that's being creative. >> It's interesting, there's so many things happening there. It's like, in a way it's a new category, but it's not a new category. It becomes a new category because of the capabilities, right? So, it's really interesting, 'cause that's what you're talking about is a category, creating. >> I think developer tools. So people often talk about B to B and B to C businesses. I think developer tools are in some ways a third way. I mean ultimately they're B to B, you're selling to other businesses and that's where your revenue's coming from. However, you look kind of like a B to C company in the ways that you measure product adoption and kind of go to market. In other words, you know, we're often tracking the leading indicators of commercial success in the form of usage, adoption, retention. Really consumer app, traditionally based metrics of how to know you're building the right stuff, and that's what product led growth companies do. And then you ultimately have commercial traction in a B to B way. And I think that that actually kind of looks like a third thing, right? Like you can do these sort of funny zany marketing examples that you might see historically from consumer businesses, but yet you ultimately make your money from the enterprise who has these de-risked high value problems you can solve for them. And I selfishly think that that's the best of both worlds because I don't have to be like Evan Spiegel, guessing the next consumer trend or maybe creating the next consumer trend and catching lightning in a bottle over and over again on the consumer side. But I still get to have fun in our marketing and make sort of fun, like we're launching the world's largest game of rock paper scissors being played with computer vision, right? Like that's sort of like a fun thing you can do, but then you can concurrently have the commercial validation and customers telling you the things that they need to be built for them next to solve commercial pain points for them. So I really do think that you're right by calling this a new category and it really is the best of both worlds. >> It's a great call out, it's a great call out. In fact, I always juggle with the VC. I'm like, it's so easy. Your job is so easy to pick the winners. What are you talking about its so easy? I go, just watch what the developers jump on. And it's not about who started, it could be someone in the dorm room to the boardroom person. You don't know because that B to C, the C, it's B to D you know? You know it's developer 'cause that's a human right? That's a consumer of the tool which influences the business that never was there before. So I think this direct business model evolution, whether it's media going direct or going direct to the developers rather than going to a gatekeeper, this is the reality. >> That's right. >> Well I got to ask you while we got some time left to describe, I want to get into this topic of multi-modality, okay? And can you describe what that means in computer vision? And what's the state of the growth of that portion of this piece? >> Multi modality refers to using multiple traditionally siloed problem types, meaning text, image, video, audio. So you could treat an audio problem as only processing audio signal. That is not multimodal, but you could use the audio signal at the same time as a video feed. Now you're talking about multi modality. In computer vision, multi modality is predominantly happening with images and text. And one of the biggest releases in this space is actually two years old now, was clip, contrastive language image pre-training, which took 400 million image text pairs and basically instead of previously when you do classification, you basically map every single image to a single class, right? Like here's a bunch of images of chairs, here's a bunch of images of dogs. What clip did is used, you can think about it like, the class for an image being the Instagram caption for the image. So it's not one single thing. And by training on understanding the corpora, you basically see which words, which concepts are associated with which pixels. And this opens up the aperture for the types of problems and generalizability of models. So what does this mean? This means that you can get to value more quickly from an existing trained model, or at least validate that what you want to tackle with a computer vision, you can get there more quickly. It also opens up the, I mean. Clip has been the bedrock of some of the generative image techniques that have come to bear, just as much as some of the LLMs. And increasingly we're going to see more and more of multi modality being a theme simply because at its core, you're including more context into what you're trying to understand about the world. I mean, in its most basic sense, you could ask yourself, if I have an image, can I know more about that image with just the pixels? Or if I have the image and the sound of when that image was captured or it had someone describe what they see in that image when the image was captured, which one's going to be able to get you more signal? And so multi modality helps expand the ability for us to understand signal processing. >> Awesome. And can you just real quick, define clip for the folks that don't know what that means? >> Yeah. Clip is a model architecture, it's an acronym for contrastive language image pre-training and like, you know, model architectures that have come before it captures the almost like, models are kind of like brands. So I guess it's a brand of a model where you've done these 400 million image text pairs to match up which visual concepts are associated with which text concepts. And there have been new releases of clip, just at bigger sizes of bigger encoding's, of longer strings of texture, or larger image windows. But it's been a really exciting advancement that OpenAI released in January, 2021. >> All right, well great stuff. We got a couple minutes left. Just I want to get into more of a company-specific question around culture. All startups have, you know, some sort of cultural vibe. You know, Intel has Moore's law doubles every whatever, six months. What's your culture like at RoboFlow? I mean, if you had to describe that culture, obviously love the hacking story, you and your partner with the games going number one on Product Hunt next to Elon and Tesla and then hey, we should start a company two years later. That's kind of like a curious, inventing, building, hard charging, but laid back. That's my take. How would you describe the culture? >> I think that you're right. The culture that we have is one of shipping, making things. So every week each team shares what they did for our customers on a weekly basis. And we have such a strong emphasis on being better week over week that those sorts of things compound. So one big emphasis in our culture is getting things done, shipping, doing things for our customers. The second is we're an incredibly transparent place to work. For example, how we think about giving decisions, where we're progressing against our goals, what problems are biggest and most important for the company is all open information for those that are inside the company to know and progress against. The third thing that I'd use to describe our culture is one that thrives with autonomy. So RoboFlow has a number of individuals who have founded companies before, some of which have sold their businesses for a hundred million plus upon exit. And the way that we've been able to attract talent like that is because the problems that we're tackling are so immense, yet individuals are able to charge at it with the way that they think is best. And this is what pairs well with transparency. If you have a strong sense of what the company's goals are, how we're progressing against it, and you have this ownership mentality of what can I do to change or drive progress against that given outcome, then you create a really healthy pairing of, okay cool, here's where the company's progressing. Here's where things are going really well, here's the places that we most need to improve and work on. And if you're inside that company as someone who has a preponderance to be a self-starter and even a history of building entire functions or companies yourself, then you're going to be a place where you can really thrive. You have the inputs of the things where we need to work on to progress the company's goals. And you have the background of someone that is just necessarily a fast moving and ambitious type of individual. So I think the best way to describe it is a transparent place with autonomy and an emphasis on getting things done. >> Getting shit done as they say. Getting stuff done. Great stuff. Hey, final question. Put a plug out there for the company. What are you going to hire? What's your pipeline look like for people? What jobs are open? I'm sure you got hiring all around. Give a quick plug for the company what you're looking for. >> I appreciate you asking. Basically you're either building the product or helping customers be successful with the product. So in the building product category, we have platform engineering roles, machine learning engineering roles, and we're solving some of the hardest and most impactful problems of bringing such a groundbreaking technology to the masses. And so it's a great place to be where you can kind of be your own user as an engineer. And then if you're enabling people to be successful with the products, I mean you're working in a place where there's already such a strong community around it and you can help shape, foster, cultivate, activate, and drive commercial success in that community. So those are roles that tend themselves to being those that build the product for developer advocacy, those that are account executives that are enabling our customers to realize commercial success, and even hybrid roles like we call it field engineering, where you are a technical resource to drive success within customer accounts. And so all this is listed on roboflow.com/careers. And one thing that I actually kind of want to mention John that's kind of novel about the thing that's working at RoboFlow. So there's been a lot of discussion around remote companies and there's been a lot of discussion around in-person companies and do you need to be in the office? And one thing that we've kind of recognized is you can actually chart a third way. You can create a third way which we call satellite, which basically means people can work from where they most like to work and there's clusters of people, regular onsite's. And at RoboFlow everyone gets, for example, $2,500 a year that they can use to spend on visiting coworkers. And so what's sort of organically happened is team numbers have started to pull together these resources and rent out like, lavish Airbnbs for like a week and then everyone kind of like descends in and works together for a week and makes and creates things. And we call this lighthouses because you know, a lighthouse kind of brings ships into harbor and we have an emphasis on shipping. >> Yeah, quality people that are creative and doers and builders. You give 'em some cash and let the self-governing begin, you know? And like, creativity goes through the roof. It's a great story. I think that sums up the culture right there, Joseph. Thanks for sharing that and thanks for this great conversation. I really appreciate it and it's very inspiring. Thanks for coming on. >> Yeah, thanks for having me, John. >> Joseph Nelson, co-founder and CEO of RoboFlow. Hot company, great culture in the right place in a hot area, computer vision. This is going to explode in value. The edge is exploding. More use cases, more development, and developers are driving the change. Check out RoboFlow. This is theCUBE. I'm John Furrier, your host. Thanks for watching. (gentle music)

(bright upbeat music) >> Hello and welcome to this Cube Conversation. I'm John Furrier, host of theCUBE. We've got a great conversation featuring Arthur AI. I'm your host. I'm excited to have Adam Wenchel who's the Co-Founder and CEO. Thanks for joining us today, appreciate it. >> Yeah, thanks for having me on, John, looking forward to the conversation. >> I got to say, it's been an exciting world in AI or artificial intelligence. Just an explosion of interest kind of in the mainstream with the language models, which people don't really get, but they're seeing the benefits of some of the hype around OpenAI. Which kind of wakes everyone up to, "Oh, I get it now." And then of course the pessimism comes in, all the skeptics are out there. But this breakthrough in generative AI field is just awesome, it's really a shift, it's a wave. We've been calling it probably the biggest inflection point, then the others combined of what this can do from a surge standpoint, applications. I mean, all aspects of what we used to know is the computing industry, software industry, hardware, is completely going to get turbo. So we're totally obviously bullish on this thing. So, this is really interesting. So my first question is, I got to ask you, what's you guys taking? 'Cause you've been doing this, you're in it, and now all of a sudden you're at the beach where the big waves are. What's the explosion of interest is there? What are you seeing right now? >> Yeah, I mean, it's amazing, so for starters, I've been in AI for over 20 years and just seeing this amount of excitement and the growth, and like you said, the inflection point we've hit in the last six months has just been amazing. And, you know, what we're seeing is like people are getting applications into production using LLMs. I mean, really all this excitement just started a few months ago, with ChatGPT and other breakthroughs and the amount of activity and the amount of new systems that we're seeing hitting production already so soon after that is just unlike anything we've ever seen. So it's pretty awesome. And, you know, these language models are just, they could be applied in so many different business contexts and that it's just the amount of value that's being created is again, like unprecedented compared to anything. >> Adam, you know, you've been in this for a while, so it's an interesting point you're bringing up, and this is a good point. I was talking with my friend John Markoff, former New York Times journalist and he was talking about, there's been a lot of work been done on ethics. So there's been, it's not like it's new. It's like been, there's a lot of stuff that's been baking over many, many years and, you know, decades. So now everyone wakes up in the season, so I think that is a key point I want to get into some of your observations. But before we get into it, I want you to explain for the folks watching, just so we can kind of get a definition on the record. What's an LLM, what's a foundational model and what's generative ai? Can you just quickly explain the three things there? >> Yeah, absolutely. So an LLM or a large language model, it's just a large, they would imply a large language model that's been trained on a huge amount of data typically pulled from the internet. And it's a general purpose language model that can be built on top for all sorts of different things, that includes traditional NLP tasks like document classification and sentiment understanding. But the thing that's gotten people really excited is it's used for generative tasks. So, you know, asking it to summarize documents or asking it to answer questions. And these aren't new techniques, they've been around for a while, but what's changed is just this new class of models that's based on new architectures. They're just so much more capable that they've gone from sort of science projects to something that's actually incredibly useful in the real world. And there's a number of companies that are making them accessible to everyone so that you can build on top of them. So that's the other big thing is, this kind of access to these models that can power generative tasks has been democratized in the last few months and it's just opening up all these new possibilities. And then the third one you mentioned foundation models is sort of a broader term for the category that includes LLMs, but it's not just language models that are included. So we've actually seen this for a while in the computer vision world. So people have been building on top of computer vision models, pre-trained computer vision models for a while for image classification, object detection, that's something we've had customers doing for three or four years already. And so, you know, like you said, there are antecedents to like, everything that's happened, it's not entirely new, but it does feel like a step change. >> Yeah, I did ask ChatGPT to give me a riveting introduction to you and it gave me an interesting read. If we have time, I'll read it. It's kind of, it's fun, you get a kick out of it. "Ladies and gentlemen, today we're a privileged "to have Adam Wenchel, Founder of Arthur who's going to talk "about the exciting world of artificial intelligence." And then it goes on with some really riveting sentences. So if we have time, I'll share that, it's kind of funny. It was good. >> Okay. >> So anyway, this is what people see and this is why I think it's exciting 'cause I think people are going to start refactoring what they do. And I've been saying this on theCUBE now for about a couple months is that, you know, there's a scene in "Moneyball" where Billy Beane sits down with the Red Sox owner and the Red Sox owner says, "If people aren't rebuilding their teams on your model, "they're going to be dinosaurs." And it reminds me of what's happening right now. And I think everyone that I talk to in the business sphere is looking at this and they're connecting the dots and just saying, if we don't rebuild our business with this new wave, they're going to be out of business because there's so much efficiency, there's so much automation, not like DevOps automation, but like the generative tasks that will free up the intellect of people. Like just the simple things like do an intro or do this for me, write some code, write a countermeasure to a hack. I mean, this is kind of what people are doing. And you mentioned computer vision, again, another huge field where 5G things are coming on, it's going to accelerate. What do you say to people when they kind of are leaning towards that, I need to rethink my business? >> Yeah, it's 100% accurate and what's been amazing to watch the last few months is the speed at which, and the urgency that companies like Microsoft and Google or others are actually racing to, to do that rethinking of their business. And you know, those teams, those companies which are large and haven't always been the fastest moving companies are working around the clock. And the pace at which they're rolling out LLMs across their suite of products is just phenomenal to watch. And it's not just the big, the large tech companies as well, I mean, we're seeing the number of startups, like we get, every week a couple of new startups get in touch with us for help with their LLMs and you know, there's just a huge amount of venture capital flowing into it right now because everyone realizes the opportunities for transforming like legal and healthcare and content creation in all these different areas is just wide open. And so there's a massive gold rush going on right now, which is amazing. >> And the cloud scale, obviously horizontal scalability of the cloud brings us to another level. We've been seeing data infrastructure since the Hadoop days where big data was coined. Now you're seeing this kind of take fruit, now you have vertical specialization where data shines, large language models all of a set up perfectly for kind of this piece. And you know, as you mentioned, you've been doing it for a long time. Let's take a step back and I want to get into how you started the company, what drove you to start it? Because you know, as an entrepreneur you're probably saw this opportunity before other people like, "Hey, this is finally it, it's here." Can you share the origination story of what you guys came up with, how you started it, what was the motivation and take us through that origination story. >> Yeah, absolutely. So as I mentioned, I've been doing AI for many years. I started my career at DARPA, but it wasn't really until 2015, 2016, my previous company was acquired by Capital One. Then I started working there and shortly after I joined, I was asked to start their AI team and scale it up. And for the first time I was actually doing it, had production models that we were working with, that was at scale, right? And so there was hundreds of millions of dollars of business revenue and certainly a big group of customers who were impacted by the way these models acted. And so it got me hyper-aware of these issues of when you get models into production, it, you know. So I think people who are earlier in the AI maturity look at that as a finish line, but it's really just the beginning and there's this constant drive to make them better, make sure they're not degrading, make sure you can explain what they're doing, if they're impacting people, making sure they're not biased. And so at that time, there really weren't any tools to exist to do this, there wasn't open source, there wasn't anything. And so after a few years there, I really started talking to other people in the industry and there was a really clear theme that this needed to be addressed. And so, I joined with my Co-Founder John Dickerson, who was on the faculty in University of Maryland and he'd been doing a lot of research in these areas. And so we ended up joining up together and starting Arthur. >> Awesome. Well, let's get into what you guys do. Can you explain the value proposition? What are people using you for now? Where's the action? What's the customers look like? What do prospects look like? Obviously you mentioned production, this has been the theme. It's not like people woke up one day and said, "Hey, I'm going to put stuff into production." This has kind of been happening. There's been companies that have been doing this at scale and then yet there's a whole follower model coming on mainstream enterprise and businesses. So there's kind of the early adopters are there now in production. What do you guys do? I mean, 'cause I think about just driving the car off the lot is not, you got to manage operations. I mean, that's a big thing. So what do you guys do? Talk about the value proposition and how you guys make money? >> Yeah, so what we do is, listen, when you go to validate ahead of deploying these models in production, starts at that point, right? So you want to make sure that if you're going to be upgrading a model, if you're going to replacing one that's currently in production, that you've proven that it's going to perform well, that it's going to be perform ethically and that you can explain what it's doing. And then when you launch it into production, traditionally data scientists would spend 25, 30% of their time just manually checking in on their model day-to-day babysitting as we call it, just to make sure that the data hasn't drifted, the model performance hasn't degraded, that a programmer did make a change in an upstream data system. You know, there's all sorts of reasons why the world changes and that can have a real adverse effect on these models. And so what we do is bring the same kind of automation that you have for other kinds of, let's say infrastructure monitoring, application monitoring, we bring that to your AI systems. And that way if there ever is an issue, it's not like weeks or months till you find it and you find it before it has an effect on your P&L and your balance sheet, which is too often before they had tools like Arthur, that was the way they were detected. >> You know, I was talking to Swami at Amazon who I've known for a long time for 13 years and been on theCUBE multiple times and you know, I watched Amazon try to pick up that sting with stage maker about six years ago and so much has happened since then. And he and I were talking about this wave, and I kind of brought up this analogy to how when cloud started, it was, Hey, I don't need a data center. 'Cause when I did my startup that time when Amazon, one of my startups at that time, my choice was put a box in the colo, get all the configuration before I could write over the line of code. So the cloud became the benefit for that and you can stand up stuff quickly and then it grew from there. Here it's kind of the same dynamic, you don't want to have to provision a large language model or do all this heavy lifting. So that seeing companies coming out there saying, you can get started faster, there's like a new way to get it going. So it's kind of like the same vibe of limiting that heavy lifting. >> Absolutely. >> How do you look at that because this seems to be a wave that's going to be coming in and how do you guys help companies who are going to move quickly and start developing? >> Yeah, so I think in the race to this kind of gold rush mentality, race to get these models into production, there's starting to see more sort of examples and evidence that there are a lot of risks that go along with it. Either your model says things, your system says things that are just wrong, you know, whether it's hallucination or just making things up, there's lots of examples. If you go on Twitter and the news, you can read about those, as well as sort of times when there could be toxic content coming out of things like that. And so there's a lot of risks there that you need to think about and be thoughtful about when you're deploying these systems. But you know, you need to balance that with the business imperative of getting these things into production and really transforming your business. And so that's where we help people, we say go ahead, put them in production, but just make sure you have the right guardrails in place so that you can do it in a smart way that's going to reflect well on you and your company. >> Let's frame the challenge for the companies now that you have, obviously there's the people who doing large scale production and then you have companies maybe like as small as us who have large linguistic databases or transcripts for example, right? So what are customers doing and why are they deploying AI right now? And is it a speed game, is it a cost game? Why have some companies been able to deploy AI at such faster rates than others? And what's a best practice to onboard new customers? >> Yeah, absolutely. So I mean, we're seeing across a bunch of different verticals, there are leaders who have really kind of started to solve this puzzle about getting AI models into production quickly and being able to iterate on them quickly. And I think those are the ones that realize that imperative that you mentioned earlier about how transformational this technology is. And you know, a lot of times, even like the CEOs or the boards are very personally kind of driving this sense of urgency around it. And so, you know, that creates a lot of movement, right? And so those companies have put in place really smart infrastructure and rails so that people can, data scientists aren't encumbered by having to like hunt down data, get access to it. They're not encumbered by having to stand up new platforms every time they want to deploy an AI system, but that stuff is already in place. There's a really nice ecosystem of products out there, including Arthur, that you can tap into. Compared to five or six years ago when I was building at a top 10 US bank, at that point you really had to build almost everything yourself and that's not the case now. And so it's really nice to have things like, you know, you mentioned AWS SageMaker and a whole host of other tools that can really accelerate things. >> What's your profile customer? Is it someone who already has a team or can people who are learning just dial into the service? What's the persona? What's the pitch, if you will, how do you align with that customer value proposition? Do people have to be built out with a team and in play or is it pre-production or can you start with people who are just getting going? >> Yeah, people do start using it pre-production for validation, but I think a lot of our customers do have a team going and they're starting to put, either close to putting something into production or about to, it's everything from large enterprises that have really sort of complicated, they have dozens of models running all over doing all sorts of use cases to tech startups that are very focused on a single problem, but that's like the lifeblood of the company and so they need to guarantee that it works well. And you know, we make it really easy to get started, especially if you're using one of the common model development platforms, you can just kind of turn key, get going and make sure that you have a nice feedback loop. So then when your models are out there, it's pointing out, areas where it's performing well, areas where it's performing less well, giving you that feedback so that you can make improvements, whether it's in training data or futurization work or algorithm selection. There's a number of, you know, depending on the symptoms, there's a number of things you can do to increase performance over time and we help guide people on that journey. >> So Adam, I have to ask, since you have such a great customer base and they're smart and they got teams and you're on the front end, I mean, early adopters is kind of an overused word, but they're killing it. They're putting stuff in the production's, not like it's a test, it's not like it's early. So as the next wave comes of fast followers, how do you see that coming online? What's your vision for that? How do you see companies that are like just waking up out of the frozen, you know, freeze of like old IT to like, okay, they got cloud, but they're not yet there. What do you see in the market? I see you're in the front end now with the top people really nailing AI and working hard. What's the- >> Yeah, I think a lot of these tools are becoming, or every year they get easier, more accessible, easier to use. And so, you know, even for that kind of like, as the market broadens, it takes less and less of a lift to put these systems in place. And the thing is, every business is unique, they have their own kind of data and so you can use these foundation models which have just been trained on generic data. They're a great starting point, a great accelerant, but then, in most cases you're either going to want to create a model or fine tune a model using data that's really kind of comes from your particular customers, the people you serve and so that it really reflects that and takes that into account. And so I do think that these, like the size of that market is expanding and its broadening as these tools just become easier to use and also the knowledge about how to build these systems becomes more widespread. >> Talk about your customer base you have now, what's the makeup, what size are they? Give a taste a little bit of a customer base you got there, what's they look like? I'll say Capital One, we know very well while you were at there, they were large scale, lot of data from fraud detection to all kinds of cool stuff. What do your customers now look like? >> Yeah, so we have a variety, but I would say one area we're really strong, we have several of the top 10 US banks, that's not surprising, that's a strength for us, but we also have Fortune 100 customers in healthcare, in manufacturing, in retail, in semiconductor and electronics. So what we find is like in any sort of these major verticals, there's typically, you know, one, two, three kind of companies that are really leading the charge and are the ones that, you know, in our opinion, those are the ones that for the next multiple decades are going to be the leaders, the ones that really kind of lead the charge on this AI transformation. And so we're very fortunate to be working with some of those. And then we have a number of startups as well who we love working with just because they're really pushing the boundaries technologically and so they provide great feedback and make sure that we're continuing to innovate and staying abreast of everything that's going on. >> You know, these early markups, even when the hyperscalers were coming online, they had to build everything themselves. That's the new, they're like the alphas out there building it. This is going to be a big wave again as that fast follower comes in. And so when you look at the scale, what advice would you give folks out there right now who want to tee it up and what's your secret sauce that will help them get there? >> Yeah, I think that the secret to teeing it up is just dive in and start like the, I think these are, there's not really a secret. I think it's amazing how accessible these are. I mean, there's all sorts of ways to access LLMs either via either API access or downloadable in some cases. And so, you know, go ahead and get started. And then our secret sauce really is the way that we provide that performance analysis of what's going on, right? So we can tell you in a very actionable way, like, hey, here's where your model is doing good things, here's where it's doing bad things. Here's something you want to take a look at, here's some potential remedies for it. We can help guide you through that. And that way when you're putting it out there, A, you're avoiding a lot of the common pitfalls that people see and B, you're able to really kind of make it better in a much faster way with that tight feedback loop. >> It's interesting, we've been kind of riffing on this supercloud idea because it was just different name than multicloud and you see apps like Snowflake built on top of AWS without even spending any CapEx, you just ride that cloud wave. This next AI, super AI wave is coming. I don't want to call AIOps because I think there's a different distinction. If you, MLOps and AIOps seem a little bit old, almost a few years back, how do you view that because everyone's is like, "Is this AIOps?" And like, "No, not kind of, but not really." How would you, you know, when someone says, just shoots off the hip, "Hey Adam, aren't you doing AIOps?" Do you say, yes we are, do you say, yes, but we do differently because it's doesn't seem like it's the same old AIOps. What's your- >> Yeah, it's a good question. AIOps has been a term that was co-opted for other things and MLOps also has people have used it for different meanings. So I like the term just AI infrastructure, I think it kind of like describes it really well and succinctly. >> But you guys are doing the ops. I mean that's the kind of ironic thing, it's like the next level, it's like NextGen ops, but it's not, you don't want to be put in that bucket. >> Yeah, no, it's very operationally focused platform that we have, I mean, it fires alerts, people can action off them. If you're familiar with like the way people run security operations centers or network operations centers, we do that for data science, right? So think of it as a DSOC, a Data Science Operations Center where all your models, you might have hundreds of models running across your organization, you may have five, but as problems are detected, alerts can be fired and you can actually work the case, make sure they're resolved, escalate them as necessary. And so there is a very strong operational aspect to it, you're right. >> You know, one of the things I think is interesting is, is that, if you don't mind commenting on it, is that the aspect of scale is huge and it feels like that was made up and now you have scale and production. What's your reaction to that when people say, how does scale impact this? >> Yeah, scale is huge for some of, you know, I think, I think look, the highest leverage business areas to apply these to, are generally going to be the ones at the biggest scale, right? And I think that's one of the advantages we have. Several of us come from enterprise backgrounds and we're used to doing things enterprise grade at scale and so, you know, we're seeing more and more companies, I think they started out deploying AI and sort of, you know, important but not necessarily like the crown jewel area of their business, but now they're deploying AI right in the heart of things and yeah, the scale that some of our companies are operating at is pretty impressive. >> John: Well, super exciting, great to have you on and congratulations. I got a final question for you, just random. What are you most excited about right now? Because I mean, you got to be pretty pumped right now with the way the world is going and again, I think this is just the beginning. What's your personal view? How do you feel right now? >> Yeah, the thing I'm really excited about for the next couple years now, you touched on it a little bit earlier, but is a sort of convergence of AI and AI systems with sort of turning into AI native businesses. And so, as you sort of do more, get good further along this transformation curve with AI, it turns out that like the better the performance of your AI systems, the better the performance of your business. Because these models are really starting to underpin all these key areas that cumulatively drive your P&L. And so one of the things that we work a lot with our customers is to do is just understand, you know, take these really esoteric data science notions and performance and tie them to all their business KPIs so that way you really are, it's kind of like the operating system for running your AI native business. And we're starting to see more and more companies get farther along that maturity curve and starting to think that way, which is really exciting. >> I love the AI native. I haven't heard any startup yet say AI first, although we kind of use the term, but I guarantee that's going to come in all the pitch decks, we're an AI first company, it's going to be great run. Adam, congratulations on your success to you and the team. Hey, if we do a few more interviews, we'll get the linguistics down. We can have bots just interact with you directly and ask you, have an interview directly. >> That sounds good, I'm going to go hang out on the beach, right? So, sounds good. >> Thanks for coming on, really appreciate the conversation. Super exciting, really important area and you guys doing great work. Thanks for coming on. >> Adam: Yeah, thanks John. >> Again, this is Cube Conversation. I'm John Furrier here in Palo Alto, AI going next gen. This is legit, this is going to a whole nother level that's going to open up huge opportunities for startups, that's going to use opportunities for investors and the value to the users and the experience will come in, in ways I think no one will ever see. So keep an eye out for more coverage on siliconangle.com and theCUBE.net, thanks for watching. (bright upbeat music)

>> Welcome back to the program. My name is Dave Valante and in this session, we're going to explore one of the more interesting topics of the day. IoT for Smart Factories. And with me are, Todd Edmunds,the Global CTO of Smart Manufacturing Edge and Digital Twins at Dell Technologies. That is such a cool title. (chuckles) I want to be you. And Dr. Aditi Banerjee, who's the Vice President, General Manager for Aerospace Defense and Manufacturing at DXC Technology. Another really cool title. Folks, welcome to the program. Thanks for coming on. >> Thanks Dave. >> Thank you. Great to be here. >> Nice to be here. >> Todd, let's start with you. We hear a lot about Industry 4.0, Smart Factories, IIoT. Can you briefly explain, what is Industry 4.0 all about and why is it important for the manufacturing industry? >> Yeah. Sure, Dave. You know, it's been around for quite a while and it's gone by multiple different names, as you said. Industry 4.0, Smart Manufacturing, Industrial IoT, Smart Factory. But it all really means the same thing, its really applying technology to get more out of the factories and the facilities that you have to do your manufacturing. So, being much more efficient, implementing really good sustainability initiatives. And so, we really look at that by saying, okay, what are we going to do with technology to really accelerate what we've been doing for a long, long time? So it's really not- it's not new. It's been around for a long time. What's new is that manufacturers are looking at this, not as a one-of, two-of individual Use Case point of view but instead they're saying, we really need to look at this holistically, thinking about a strategic investment in how we do this. Not to just enable one or two Use Cases, but enable many many Use Cases across the spectrum. I mean, there's tons of them out there. There's Predictive maintenance and there's OEE, Overall Equipment Effectiveness and there's Computer Vision and all of these things are starting to percolate down to the factory floor, but it needs to be done in a little bit different way and really to really get those outcomes that they're looking for in Smart Factory or Industry 4.0 or however you want to call it. And truly transform, not just throw an Industry 4.0 Use Case out there but to do the digital transformation that's really necessary and to be able to stay relevant for the future. I heard it once said that you have three options. Either you digitally transform and stay relevant for the future or you don't and fade into history. Like, 52% of the companies that used to be on the Fortune 500 since 2000. Right? And so, really that's a key thing and we're seeing that really, really being adopted by manufacturers all across the globe. >> Yeah. So, Aditi, it's like digital transformation is almost synonymous with business transformation. So, is there anything you'd add to what Todd just said? >> Absolutely. Though, I would really add that what really drives Industry 4.0 is the business transformation. What we are able to deliver in terms of improving the manufacturing KPIs and the KPIs for customer satisfaction, right? For example, improving the downtime or decreasing the maintenance cycle of the equipments or improving the quality of products, right? So, I think these are lot of business outcomes that our customers are looking at while using Industry 4.0 and the technologies of Industry 4.0 to deliver these outcomes. >> So, Aditi, I wonder if I could stay with you and maybe this is a bit esoteric but when I first first started researching IoT and Industrial IoT 4.0, et cetera, I felt, well, there could be some disruptions in the ecosystem. I kind of came to the conclusion that large manufacturing firms, Aerospace Defense companies the firms building out critical infrastructure actually had kind of an incumbent advantage and a great opportunity. Of course, then I saw on TV somebody now they're building homes with 3D printers. It like blows your mind. So that's pretty disruptive. But, so- But they got to continue, the incumbents have to continue to invest in the future. They're well-capitalized. They're pretty good businesses, very good businesses but there's a lot of complexities involved in kind of connecting the old house to the new addition that's being built, if you will, or this transformation that we're talking about. So, my question is, how are your customers preparing for this new era? What are the key challenges that they're facing in the the blockers, if you will? >> Yeah, I mean the customers are looking at Industry 4.0 for Greenfield Factories, right? That is where the investments are going directly into building the factories with the new technologies, with the new connectivities, right? For the machines, for example, Industrial IoT having the right type of data platforms to drive computational analytics and outcomes, as well as looking at Edge versus Cloud type of technologies, right? Those are all getting built in the Greenfield Factories. However, for the Install-Based Factories, right? That is where our customers are looking at how do I modernize these factories? How do I connect the existing machine? And that is where some of the challenges come in on the legacy system connectivity that they need to think about. Also, they need to start thinking about cybersecurity and operation technology security because now you are connecting the factories to each other. So, cybersecurity becomes top of mind, right? So, there is definitely investment that is involved. Clients are creating roadmaps for digitizing and modernizing these factories and investments in a very strategic way. So, perhaps they start with the innovation program and then they look at the business case and they scale it up, right? >> Todd, I'm glad you did brought up security, because if you think about the operations technology folks, historically they air-gaped the systems, that's how they created security. That's changed. The business came in and said, 'Hey, we got to connect. We got to make it intelligence.' So, that's got to be a big challenge as well. >> It absolutely is, Dave. And, you know, you can no longer just segment that because really to get all of those efficiencies that we talk about, that IoT and Industrial IoT and Industry 4.0 promise, you have to get data out of the factory but then you got to put data back in the factory. So, no longer is it just firewalling everything is really the answer. So, you really have to have a comprehensive approach to security, but you also have to have a comprehensive approach to the Cloud and what that means. And does it mean a continuum of Cloud all the way down to the Edge, right down to the factory? It absolutely does. Because no one approach has the answer to everything. The more you go to the Cloud the broader the attack surface is. So, what we're seeing is a lot of our customers approaching this from kind of that hybrid right ones run anywhere on the factory floor down to the Edge. And one of the things we're seeing too, is to help distinguish between what is the Edge and bridge that gap between, like, Dave, you talked about IT and OT and also help what Aditi talked about is the Greenfield Plants versus the Brownfield Plants that they call it, that are the legacy ones and modernizing those. It's great to kind of start to delineate what does that mean? Where's the Edge? Where's the IT and the OT? We see that from a couple of different ways. We start to think about really two Edges in a manufacturing floor. We talk about an Industrial Edge that sits... or some people call it a Far Edge or a Thin Edge, sits way down on that plant, consists of industrial hardened devices that do that connectivity. The hard stuff about how do I connect to this obsolete legacy protocol and what do I do with it? And create that next generation of data that has context. And then we see another Edge evolving above that, which is much more of a data and analytics and enterprise grade application layer that sits down in the factory itself; that helps figure out where we're going to run this? Does it connect to the Cloud? Do we run Applications On-Prem? Because a lot of times that On-Prem Application it needs to be done. 'Cause that's the only way that it's going to work because of security requirements, because of latency requirements performance and a lot of times, cost. It's really helpful to build that Multiple-Edge strategy because then you kind of, you consolidate all of those resources, applications, infrastructure, hardware into a centralized location. Makes it much, much easier to really deploy and manage that security. But it also makes it easier to deploy new Applications, new Use Cases and become the foundation for DXC'S expertise and Applications that they deliver to our customers as well. >> Todd, how complex are these projects? I mean, I feel like it's kind of the the digital equivalent of building the Hoover Dam. I mean, its.. so yeah. How long does a typical project take? I know it varies, but what are the critical success factors in terms of delivering business value quickly? >> Yeah, that's a great question in that we're- you know, like I said at the beginning, this is not new. Smart Factory and Industry 4.0 is not new. It's been, it's people have been trying to implement the Holy Grail of Smart Factory for a long time. And what we're seeing is a switch, a little bit of a switch or quite a bit of a switch to where the enterprises and the IT folks are having a much bigger say and they have a lot to offer to be able to help that complexity. So, instead of deploying a computer here and a Gateway there and a Server there, I mean, you go walk into any manufacturing plant and you can see Servers sitting underneath someone's desk or a PC in a closet somewhere running a critical production application. So, we're seeing the enterprise have a much bigger say at the table, much louder voice at the table to say, we've been doing this enterprise all the time. We know how to really consolidate, bring Hyper-Converged Applications, Hyper-Converged Infrastructure to really accelerate these kind of applications. Really accelerate the outcomes that are needed to really drive that Smart Factory and start to bring that same capabilities down into the Mac on the factory floor. That way, if you do it once to make it easier to implement, you can repeat that. You can scale that. You can manage it much easily and you can then bring that all together because you have the security in one centralized location. So, we're seeing manufacturers that first Use Case may be fairly difficult to implement and we got to go down in and see exactly what their problems are. But when the infrastructure is done the correct way when that- Think about how you're going to run that and how are you going to optimize the engineering. Well, let's take that what you've done in that one factory and then set. Let's make that across all the factories including the factory that we're in, then across the globe. That makes it much, much easier. You really do the hard work once and then repeat. Almost like cookie cutter. >> Got it. Thank you. >> Aditi, what about the skillsets available to apply these to these projects? You got to have knowledge of digital, AI, Data, Integration. Is there a talent shortage to get all this stuff done? >> Yeah, I mean, definitely. Lot different types of skillsets are needed from a traditional manufacturing skillset, right? Of course, the basic knowledge of manufacturing is important. But the digital skillsets like IoT, having a skillset in in different Protocols for connecting the machines, right? That experience that comes with it. Data and Analytics, Security, Augmented Virtual Reality Programming. Again, looking at Robotics and the Digital Twin. So, the... It's a lot more connectivity software, data-driven skillsets that are needed to Smart Factory to life at scale. And, you know, lots of firms are recruiting these types of resources with these skill sets to accelerate their Smart Factory implementation, as well as consulting firms like DXC Technology and others. We recruit, we train our talent to provide these services. >> Got it. Aditi, I wonder if we could stay on you. Let's talk about the partnership between DXC and Dell. What are you doing specifically to simplify the move to Industry 4.0 for customers? What solutions are you offering? How are you working together, Dell and DXC to bring these to market? >> Yeah, Dell and DXC have a very strong partnership and we work very closely together to create solutions, to create strategies and how we are going to jointly help our clients, right? So, areas that we have worked closely together is Edge Compute, right? How that impacts the Smart Factory. So, we have worked pretty closely in that area. We're also looked at Vision Technologies. How do we use that at the Edge to improve the quality of products, right? So, we have several areas that we collaborate in and our approaches that we want to bring solutions to our client and as well as help them scale those solutions with the right infrastructure, the right talent and the right level of security. So, we bring a comprehensive solution to our clients. >> So, Todd, last question. Kind of similar but different, you know. Why Dell, DXC, pitch me? What's different about this partnership? Where are you confident that you're going to be to deliver the best value to customers? >> Absolutely. Great question. You know, there's no shortage of Bespoke Solutions that are out there. There's hundreds of people that can come in and do individual Use Cases and do these things and just, and that's where it ends. What Dell and DXC Technology together bring to the table is we do the optimization of the engineering of those previously Bespoke Solutions upfront, together. The power of our scalable enterprise grade structured industry standard infrastructure, as well as our expertise in delivering package solutions that really accelerate with DXC's expertise and reputation as a global trusted advisor. Be able to really scale and repeat those solutions that DXC is so really, really good at. And Dell's infrastructure and our, 30,000 people across the globe that are really, really good at that scalable infrastructure to be able to repeat. And then it really lessens the risk that our customers have and really accelerates those solutions. So it's again, not just one individual solutions it's all of the solutions that not just drive Use Cases but drive outcomes with those solutions. >> Yeah, you're right. The partnership has gone, I mean I first encountered it back in, I think it was 2010. May of 2010. We had guys both on the, I think you were talking about converged infrastructure and I had a customer on, and it was actually the manufacturing customer. It was quite interesting. And back then it was how do we kind of replicate what's coming in the Cloud? And you guys have obviously taken it into the digital world. Really want to thank you for your time today. Great conversation and love to have you back. >> Thank you so much. It was a pleasure speaking with you. I agree. >> All right, keep it right there for more discussions that educate and inspire on "The Cube."

>> If you really want to make an impact to your business, it takes more than just moving your workloads into the cloud. So-called lift and shift is fine to reduce data center footprints and associated costs, but to really drive change, you don't want to simply "pave the cow path," as the saying goes. Rather, you need to think about the operating model, and that requires more comprehensive systems thinking. In other words, how will changes in technology affect business productivity? Or, you know what? Even flip that. What changes in my business process could lower cost, cut elapse times, and accelerate time to market, increase user productivity, and lower operational risks? And what role can technology play in supporting these mandates through modernization, automation, machine intelligence, and business resilience? And that's what we're here to discuss today. Welcome to Driving Business Results with Cloud Transformation, made Possible by Dell and DXC. My name is Dave Vellante, and today we're going to zoom out and explore many aspects of cloud transformation that leading organizations are acting on today. Yeah, sure, we're going to look at optimizing infrastructure, but we'll also dig deeper into cloud considerations, governance, compliance, and security angles, as well as the impact of emerging opportunities around edge and Industry 4.0. Our focus will be on how to remove barriers and help you achieve business outcomes. And to do this, our program features the long-term partnership between Dell and DXC. And we bring to this program six experts in three separate sessions, who are working directly with top organizations in virtually every industry to achieve high impact results. We're going to start with a conversation about cloud, the cloud operating model, and transforming key aspects of your infrastructure. And then we'll look into governance, security, and business resilience. And in our third session, we'll discuss exciting transformations that are occurring in smart manufacturing and facilities innovations. So let's get right into it with our first session. Enjoy the program. (bright music) Hello, and welcome to what is sure to be an insightful conversation about getting business results with cloud transformation. My name is Dave Vellante, and I'm here with James Miller, Chief Technologist for Cloud and Infrastructure Services, and Jay Dowling, Americas Sales Lead for Cloud and Infrastructure Services, both with DXC Technology. Gentlemen, thanks for your time today. Welcome to theCube. >> Great. Thanks for having us. >> Thank you Dave. Appreciate it. >> So let's get right into it. You know, I've talked to a lot of practitioners who've said, "Look, if you really want to drop zeros, like a lot of zeros to the bottom line, you can't just lift and shift." You really got to think about modernizing, the application portfolio. You got to think about your business model, and really think about transforming your business, particularly the operating model. So my first question, Jim, is, What role does the cloud play in modernization? >> Well, there are really three aspects that the, the cloud plays in modernization. You mentioned multiple zeros. One is cost optimization, and that can be achieved through business operations, through environmental, social, and governance. Also being more efficient with your IT investments. But that's not the only aspect. There's also agility and innovation. And that can be achieved through automation and productivity, speed to market for new features and functions, improvements in the customer experience, and the capability to metabolize a great deal more data in your environment, which the end result is an improvement in releasing of new things to the field. And finally, there's resilience. And I'm not really talking about IT resilience, but more of business resilience, to be able, to be able to handle operational risk, improve your securities and controls, deal with some of the talent gap that's in the industry, and also protect your brand reputation. So modernization is really about balancing these three aspects, cost optimization, agility and innovation, and resilience. >> So, so thank you for that. So Jay, I got to ask you, in the current climate, everybody's, you know, concerned, and there's not great visibility on the macro. So, Jim mentioned cost optimization. That seems to be one of the top areas that customers are focused on. The two I hear a lot are consolidating redundant vendors and optimizing cloud costs. So that's, you know, top of mind today. I think everybody really, you know, understands the innovation and, and, and agility piece, at least at a high level, maybe realizing it is different. And then the business resilience piece is really interesting because, you know, prior to the pandemic people, you know, they had a DR strategy, but they realized, "Wow, my business might not be that resilient." So Jay, my question to you is, What are you hearing when you talk to customers? What's the priority today? >> Yeah, the priority is an often overused term of digital transformation. You know, people want to get ready for next generation environments, customer experience, making sure they're improving, you know, how they engage with their clients and what their branding is. And what we find is a lot of clients don't have the underlying infrastructure in place today to get to where they want to get to. So cloud becomes an important element of that. But, you know, with DXC's philosophy, not everything goes to, not everything necessarily needs to go to cloud to be cost optimized, for instance. In many cases, you can run applications, you know, in your own data center, or on-prem, or in other environments, in a hybrid environment, or multi-cloud environment, and, and still be very optimized from a cost spend standpoint and also put yourself in position for modernization and for be able to do the, bring the things to the business that the clients are, you know, that their clients are looking for, like the CMO and the CFO, et cetera. Trying to use IT as a lever to drive business and to drive, you know, business acceleration and drive profitability, frankly. So there's a lot of dependency on infrastructure, but there's a lot of elements to it. And, and we advocate for, you know, there's not a single answer to that. We like to evaluate clients' environments and work with them to get them to an optimal target operating model, you know, so that they can really deliver on what the promises are for their departments. >> So if, let's talk about some of the, the barriers to realizing value in, in a context of modernization. We talked about cost optimization, agility, and, and, and resilience. But there's a business angle, and there's a technical angle here. 'Cause we always talk about people, process, and technology. Technology, oftentimes, CIOs will tell us, "Well, that's the easy part. We'll figured that out," whether it's true or not. But I agree, people and process is sometimes the tough one. So Jay, why don't you start. What do you see as the barriers, particularly from a business standpoint? >> I think people need to let their guard down and be open to the ideas that are, that are out there in the market from, you know, the, the standards that are being built by, you know, best in class models. And, and there's many people that have gone on, you know, cloud journeys and been very successful with it. There's others that have set high expectations with their business leaders that haven't necessarily met the goals that they need to meet or maybe haven't met them as quickly as they promised. So there's a, you know, there's a change management aspect that you'd need to look at with the, you know, with the environments. There's a, you know, there's a skillset set environment that they need to be prepared for. Do they have the people, you know, to deliver with the, you know, with the tools and the skills and the, and the models that that they're putting themselves in place for in the future versus where they are now? There's just a lot of, you know, there's a lot of different elements. It's not just a, "This price is better," or, "This can operate better than one environment over the other." I think we like to try to look at things holistically and make sure that, you know, we're being, you know, as much of a consultative advocate for the client, for where they want to go, what their destiny is, and based on what we've learned with other clients. You know, and we can bring those best practices forward because we've worked, you know, across such a broad spectra of clients versus them being somewhat contained and sometimes can't see outside of their own, you know, their own challenges, if you would. So they need, they need advocacy to help, you know, bring them to the next level. And we like to translate that through, you know, technology advances, which, you know, Jim's really good at doing for us. >> Yeah, Jim, is, is it, is it a, is the big barrier a skills issue, you know, bench strength? Are there other considerations from your perspective? >> Well, we, we've identified a number of factors that inhibit success of, of customers. One is thinking it's only a technology change in moving to cloud when it's much broader than that. There are changes in governance, changes in process that need to take place. The other is evaluating the cloud providers on their current pricing structure and performance. And, and we see pricing and structure changing dramatically every few months between the various cloud providers. And you have to be flexible enough to, to determine which providers you want. And it may not be feasible to just have a single cloud provider in this world. The other thing is a big bang approach to transformation, "I want to move everything, and I want to move it all at once." That's not necessarily the best approach. A well thought out cloud journey and strategy and timing your investments are really important to get at maximizing your business return on the journey to the cloud. And finally, not engaging stakeholders early and continuously. You have to manage expectations in moving to cloud on what business factors will get affected, how you will achieve your cost savings, and, and how you will achieve the business impact over the journey and reporting out on that with very strict metrics to all of the stakeholders. >> You know, mentioned multi-cloud just then. We had, in January 17th, we had our Supercloud 2 event. And Supercloud is basically, it's really multi, what multi-cloud should have been, I, I like to say. So it's this creating a common experience across clouds. And you guys were talking about, you know, there's different governance, there's different security, there's different pricing. So, and, and one of the takeaways from this event in talking to customers and practitioners and technologists is, you can't go it alone. So I wonder if you could talk about your partnership strategy, what do partners bring to the table, and what is, what is DXC's, you know, unique value? >> I'd be happy to lead with that if you'd like. >> Great. >> I, you know, we've got a vast partner ecosystem at DXC, given the size and, and the history of the company. I could use several examples. One of the larger partners in my particular space is Dell Technology, right? They're a great, you know, partner for us across many different areas of the business. It's not just a storage and compute play anymore. They're, they're on the edge. They're, you know, they're, they've got intelligence in their networking devices now. And they've really brought, you know, a lot of value to us as a partner. And, you know, there, there's somebody, you could look at Dell technology as somebody that might, you know, have a victim, you know, effect because of all the hyperscale activity and all the cloud activity. But they've really taken an outstanding attitude with this and say, "Listen, not all things are destined for cloud, or not all things would operate better in a cloud environment." And they like to be part of those discussions to see how they can, you know, how we can bring a multi-cloud environment, you know, both private and public, you know, to clients. And let's look at the applications and the infrastructure and, and what's, you know, what's the best optimal running environment, you know, for us to be able to bring, you know, the greatest value to the business with speed, with security, with, you know. And, you know, the things that they want to keep closest to the business are often things that you want to kind of, you know, keep on your premise or keep in your own data center. So they're, they're an ideal model of somebody that's resourced us well, partners with us well in the market. And, and we continue to grow that relationship day in and day out with those guys. And we really appreciate, you know, their support of our strategy, and, and we like to also compliment their strategy and work, you know, work together hand in hand in front of our clients. >> Yeah, you know, Jim, Matt Baker, who's the head of strategic planning at Dell talks about, "It's not a zero sum game." And I think, you know, you're right, Jay. I think initially people felt like, "Oh wow, it's, it is a zero sum game." But it's clearly not, and this idea of of, whether you call it supercloud or ubercloud or multicloud, clearly Dell is headed in in that direction. And I, you know, look at some of their future projects. There's their narrative. I'm curious from a technology standpoint, Jim, what your role is. Is it to make it all work? Is it to, you know, end to end? I wonder if you could help, you know, us understand that. >> Help us figure this out, Jim, here. (group laughing) >> Glad to expand on that. One of my key roles is developing our product roadmap for DXC offerings. And we do that roadmap in conjunction with our partners where we can leverage the innovation that our partners bring to the table. And we often utilize engineering resources from our partners to help us jointly build those offerings that adapt to changes in the market and also adapt to many of our customers changing needs over time. So my primary role is to look at the market, talk to our customers, and work with our partners to develop a product roadmap for delivering DXC products and services to our clients so that they can get the return on investment on their technology journeys. >> You know, we've been working with these two firms for a while now. Even predates, you know, the, the name DXC and that, that transformation. I'm curious as to what's, how you would respond to, "What's unique?" You know, you hear a lot about partnerships. You guys got a lot of competition. Dell has a lot of competition. What's specifically unique about this combination? >> I think, go ahead, Jim. >> I would say our unique approach, we call it cloud right. And that, that approach is making the right investments, at the right time, and on the right platforms. And our partners play a, play a key role in that. So we, we encourage our customers to not necessarily have a cloud first approach, but a cloud right approach where they place the workloads in the environment that is best suited from a technology perspective, a business perspective, and even a security and governance perspective. And, and the right approach might include mainframe. It might include an on-premises infrastructure. It could include private cloud, public cloud, and SaaS components all integrated together to deliver that value. >> Yeah, Jay, please. >> If you were... >> That is a complicated situation for a lot of customers. Chime in here. (Jay chuckles) >> And now, if you were speaking specifically to Dell here, like they, they also walk the talk, right? They invest in DXC as a partnership. They put people on the ground that their only purpose in life is to help DXC succeed with Dell in, you know, arm in arm in front of clients. And it's not, you know, it's not a winner take all thing at all. It's really a true partnership. They, they, they've brought solution resources. We have an account CTO. We've got executive sponsorship. We do regular QBR meetings. We have regular executive touchpoint meetings. It's really important that you keep a high level of intimacy with the client, with the partners, you know, and, and the, and the GSI community. And I, I've been with several GSIs, and, and this is an exceptional example of true partnership and commitment to success with Dell technology. I'm really extremely impressed on, on the engagement level that we've had there and, you know, continue to show a lot of support, you know, both for them. You know, there's other OEM partners, of course, in the market. There's always going to be other technology solutions for certain clients, but this has been a particularly strong element for us in our partnership and in our go-to-market strategy. >> Well, I think too, just my observation, is a lot of it's about trust. You guys have both earned the trust, the kind of, over the, over the years taking your arrows, you know, of over decades. And, and you know, that just doesn't happen overnight. So guys, I appreciate it. Thanks for your time. It's all about getting cloud right, isn't it? >> That's right. (chuckles) (Dave chuckles) >> Thank you Dave. Appreciate it very much. >> Dave, thank you. >> Jay, Jim, great to have you on. Keep it right there for more action on theCube. Be right back. (upbeat guitar music) (keyboard clicks) Welcome back to the program. My name is Dave Vellante, and in this session we're going to explore one of the more interesting topics of the day. IoT for smart factories and with me are Todd Edmunds, the Global CTO of Smart Manufacturing Edge and Digital Twins at Dell Technologies. That is such a cool title. (Todd chuckles) I want to be you. And Dr. Aditi Banerjee who's the Vice President, General Manager for Aerospace Defense and Manufacturing at DXC Technology. Another really cool title. Folks, welcome to the program. Thanks for coming on. >> Thank you. >> Thanks, Dave. Great to be here. >> Nice to be here. So, Todd, let's start with you. We hear a lot about Industry 4.0, smart factories, IIoT. Can you briefly explain like what is Industry 4.0 all about, and why is it important for the manufacturing industry? >> Yeah, sure, Dave. You know, it's been around for quite a while. And it's got, it's gone by multiple different names, as you said, Industry 4.0, smart manufacturing, industrial IoT, smart factory, but it all really means the same thing. Its really applying technology to get more out of the factories and the facilities that you have to do your manufacturing. So being much more efficient, implementing really good sustainability initiatives. And so we really look at that by saying, "Okay, what are we going to do with technology to really accelerate what we've been doing for a long, long time?" So it's really not, it's not new. It's been around for a long time. What's new is that manufacturers are looking at this not as a one-off, two-off, individual use case point of view. But instead they're saying, "We really need to look at this holistically, thinking about a strategic investment in how we do this, not to just enable one or two use cases, but enable many, many use cases across the spectrum." I mean, there's tons of them out there. There's predictive maintenance, and there's OEE, overall equipment effectiveness, and there's computer vision. And all of these things are starting to percolate down to the factory floor. But it needs to be done in a little bit different way. And, and, and really, to really get those outcomes that they're looking for in smart factory, or Industry 4.0, or however you want to call it, and truly transform. Not just throw an Industry 4.0 use case out there, but to do the digital transformation that's really necessary and to be able to stay relevant for the future. You know, I heard it once said that you have three options. Either you digitally transform and stay relevant for the future, or you don't and fade into history like 52% of the companies that used to be on the Fortune 500 since 2000, right? And so really that's a key thing, and we're seeing that really, really being adopted by manufacturers all across the globe. >> Yeah so, Aditi, that's like digital transformation is almost synonymous with business transformation. So is there anything you'd add to what Todd just said? >> Absolutely. Though, I would really add that what really drives Industry 4.0 is the business transformation, what we are able to deliver in terms of improving the manufacturing KPIs and the KPIs for customer satisfaction, right? For example, improving the downtime, you know, or decreasing the maintenance cycle of the equipments, or improving the quality of products, right? So I think these are a lot of business outcomes that our customers are looking at while using Industry 4.0 and the technologies of Industry 4.0 to deliver these outcomes. >> So Aditi, I wonder if I could stay with you. And maybe this is a bit esoteric. But when I first started researching IoT and, and, and Industrial IoT 4.0, et cetera, I felt, you know, while there could be some disruptions in the ecosystem, I kind of came to the conclusion that large manufacturing firms, aerospace defense companies, the firms building out critical infrastructure, actually had kind of an incumbent advantage in a great opportunity. Of course, then I saw on TV, somebody now they're building homes with 3D printers. Its like, blows your mind. So that's pretty disruptive, but, so, but they got to continue. The incumbents have to continue to invest in the future. They're well capitalized. They're pretty good businesses, very good businesses. But there's a lot of complexities involved in kind of connecting the old house to the new addition that's being built, if you will, or this transformation that we're talking about. So my question is, How are your customers preparing for this new era? What are the key challenges that they're facing and the, the blockers, if you will? >> Yeah, I mean the customers are looking at Industry 4.0 for greenfield factories, right? That is where the investments are going directly into building the factories with the new technologies, with the new connectivities, right, for the machines. For example, industrial IoT, having the right type of data platforms to drive computational analytics and outcomes, as well as looking at edge versus cloud type of technologies, right? Those are all getting built in the greenfield factories. However, for the install-based factories, right, that is where our customers are looking at, "How do I modernize these factories? How do I connect the existing machine?" And that is where some of the challenges come in on, you know, the legacy system connectivity that they need to think about. Also, they need to start thinking about cybersecurity and operation technology security, right, because now you are connecting the factories to each other, right? So cybersecurity becomes top of mind, right? So there is definitely investment that is involved. Clients are creating roadmaps for digitizing and modernizing these factories and investments in a very strategic way, right? So perhaps they start with the innovation program, and then they look at the business case, and they scale it up, right? >> Todd, I'm glad Aditi brought up security. Because if you think about the operations technology, you know, folks, historically, they air gapped, you know, the systems. That's how they created security. That's changed. The business came in and said, "Hey, we got to, we got to connect. We got to make it intelligent." So that's, that's got to be a big challenge as well. >> It, it, it absolutely is Dave. And, and you know, you can no longer just segment that because really, to get all of those efficiencies that we talk about, that IoT and Industrial IoT and Industry 4.0 promise, you have to get data out of the factory. But then you got to put data back in the factory. So no longer is it just firewalling everything is really the answer. So you really have to have a comprehensive approach to security, but you also have to have a comprehensive approach to the cloud and what that means. And does it mean a continuum of cloud all the way down to the edge, right down to the factory? It absolutely does because no one approach has the answer to everything. The more you go to the cloud, the broader the attack surface is. So what we're seeing is a lot of our customers approaching this from a, kind of that, that hybrid, you know, "write once, run anywhere" on the factory floor down to the edge. And one of the things we're seeing, too, is to help distinguish between what is the edge, and that, and, and bridge that gap between, like Dave, you talked about IT and OT. And also help that, what Aditi talked about, is the greenfield plants versus the brownfield plants that they call it, that are the legacy ones and modernizing those. Is, it's great to kind of start to delineate. What does that mean? Where's the edge? Where's the IT and the OT? We see that from a couple of different ways. We start to think about really two edges in a manufacturing floor. We talk about an industrial edge that sits, or some people call it a far edge or a thin edge, sits way down on that plan. It consists of industrial hardened devices that do that connectivity. The hard stuff about, "How do I connect to this obsolete legacy protocol and what do I do with it?" And create that next generation of data that has context. And then we see another edge evolving above that, which is much more of a data and analytics and enterprise grade application layer that sits down in the factory itself that helps figure out where we're going to run this. Does it connect to the cloud? Do we run applications on-prem? Because a lot of times that on-prem application is, is, needs to be done because that's the only way that its going to, it's going to work because of security requirements, because of latency requirements, performance, and a lot of times cost. It's really helpful to build that multiple edge strategy because then you kind of, you consolidate all of those resources, applications, infrastructure, hardware, into a centralized location. Makes it much, much easier to really deploy and manage that security. But it also makes it easier to deploy new applications, new use cases, and become the foundation for DXC's expertise and applications that they deliver to our customers as well. >> Todd, how complex are these projects? I mean, I feel like it's kind of the, the digital equivalent of building the Hoover Dam. I mean, it, it, it's, (chuckles) it, it, so. Yeah, how long does a typical project take? I know it varies, but what, you know, what are the critical success factors in terms of delivering business value quickly? >> Yeah, that's a great question in that, in that we're, you know, like I said at the beginning, we, this is not new. Smart factory and Industry 4.0 is not new. It's been, it's, people have been trying to implement the holy grail of smart factory for a long time. And what we're seeing is a switch, a little bit of a switch, or quite a bit of a switch, to where the enterprise and the IT folks are having a much bigger say and have a lot to offer to be able to help that complexity. So instead of deploying a computer here, and a gateway there, and a server there, I mean, you go walk into any manufacturing plant and you can see servers sitting underneath someone's desk or a, or a PC in a closet somewhere running a critical production application. So we're seeing the enterprise have a much bigger say at the table, much louder voice at the table to say, "We've been doing this at enterprise all the time. We, we know how to really consolidate, bring hyper-converged applications, hyper-converged infrastructure, to really accelerate these kind of applications, really accelerate the outcomes that are needed to really drive that smart factory, and start to bring that same capabilities down into the, on the factory floor." That way, if you do it once to make it easier to implement, you can repeat that. You can scale that. You can manage it much easily. And you can then bring that all together because you have the security in one centralized location. So we're seeing manufacturers, yeah, that first use case may be fairly difficult to implement and we got to go down in and see exactly what their problems are. But when the infrastructure is done the correct way, when that, think about how you're going to run that and how are you going to optimize the engineering. Well, let's take that, what you've done in that one factory, and then set. Let's that, make that across all the factories, including the factory that we're in, but across the globe. That makes it much, much easier. You really do the hard work once and then repeat, almost like a cookie cutter. >> Got it. Thank you. Aditi, what about the skillsets available to apply these, to these projects? You got to have knowledge of digital, AI, data, integration. Is there a talent shortage to get all this stuff done? >> Yeah, I mean definitely, a lot. Different types of skillsets are needed from a traditional manufacturing skillset, right? Of course, the basic knowledge of manufacturing is, is important. But the, the digital skillset sets like, you know, IoT, having a skillset in different protocols for connecting the machines, right, that experience that comes with it, data and analytics, security, augmented virtual reality programming. You know, again, looking at robotics and the digital twin. So you know, it's a lot more connectivity software, data driven skillsets that are needed to smart factory to life at scale. And, you know, lots of firms are, you know, recruiting these types of skill, resources with these skillsets to, you know, accelerate their smart factory implementation, as well as consulting firms like DXC Technology and others. We, we, we recruit. We, we train our talent to, to provide these services. >> Got it. Aditi, I wonder if we could stay on you. Let's talk about the partnership between DXC and Dell. What are you doing specifically to simplify the move to Industry 4.0 for customers? What solutions are you offering? How are you working together, Dell and DXC, to, to bring these to market? >> Yeah, Dell and DXC have a very strong partnership. You know, and we work very closely together to, to create solutions, to create strategies, and how we, we are going to jointly help our clients, right? So areas that we have worked closely together is edge compute, right, how that impacts the smart factory. So we have worked pretty closely in that area. We're also looked at vision technologies, you know. How do we use that at the edge to improve the quality of products, right? So we have several areas that we collaborate in. And our approach is that we, we want to bring solutions to our client, and as well as help them scale those solutions with the right infrastructure, the right talent, and the right level of security. So we bring a comprehensive solution to our clients. >> So, Todd, last question, kind of similar but different. You know, why Dell DXC? Pitch me. What's different about this partnership? You know, where do you, are you confident that, you know, you're going to be, deliver the best value to, to customers? >> Absolutely. Great question. You know, there's no shortage of bespoke solutions that are out there. There's hundreds of people that can come in and do individual use cases and do these things. And just, and, and, and that's, that's where it ends. What Dell and DXC Technology together bring to the table is, we do the optimization, the optimization of the engineering of those previously bespoke solutions upfront, together, right? The power of our scalables, enterprise-grade, structured, you know, industry standard infrastructure, as well as our expertise in delivering package solutions that really accelerate with DXC's expertise and reputation as a global, trusted, trusted advisor. Be able to really scale and repeat those solutions that DXC is so really, really good at. And, and Dell's infrastructure, and our, what, 30,000 people across the globe that are really, really good at that, at that scalable infrastructure, to be able to repeat. And then it really lessens the risk that our customers have and really accelerates those solutions. So it's again, not just one individual solutions, it's all of the solutions that not just drive use cases, but drive outcomes with those solutions. >> Yeah, the, you're right, the partnership has gone, I mean, I first encountered it back in, I think it was 2010, May of 2010, we had you, you guys both on theCube. I think you were talking about converged infrastructure. And I had a customer on, and it was, actually a manufacturing customer, was quite interesting. And back then it was, "How do we kind of replicate what's coming in the cloud?" And, and you guys have obviously taken it into the digital world. Really want to thank you for your time today. Great conversation, and love to have you back. >> Thank you so much. >> Absolutely. >> It was a pleasure speaking with you. >> I agree. >> All right, keep it right there for more discussions that educate and inspire on theCube. (bright music) Welcome back to the program and we're going to dig into the number one topic on the minds of every technology organization. That's cybersecurity. You know, survey data from ETR, our data partner, shows that among CIOs and IT decision makers, cybersecurity continues to rank as the number one technology priority to be addressed in the coming year. That's ahead of even cloud migration and analytics. And with me to discuss this critical topic area are Jim Shook, who's the Global Director of Cybersecurity and Compliance Practice at Dell Technologies, and he's joined by Andrew Gonzalez, who focuses on Cloud and Infrastructure consulting at DXC Technology. Gents, welcome. Good to have you. >> Thanks Dave. Great to be here. >> Thank you. >> Jim, let's start with you. What are you seeing from the front lines in terms of the attack surface, and, and how are customers responding these days? >> It's always up and down and back and forth. The bad actors are smart. They adapt to everything that we do. So we're seeing more and more kind of living off the land. They're not necessarily deploying malware. Makes it harder to find what they're doing. And I think though, Dave, we've, we've adapted, and this whole notion of cyber resilience really helps our customers figure this out. And the idea there goes beyond cybersecurity, it's, "Let's protect as much as possible, so we keep the bad actors out as much as we can. But then, let's have the ability to adapt to and recover to the extent that the bad actors are successful." So we're recognizing that we can't be perfect a hundred percent of the time against a hundred percent of the bad actors. Let's keep out what we can, but then recognize and have that ability to recover when necessary. >> Yeah, thank you. So Andrew, you know, I like what Jim was saying about living off the land, of course, meaning using your own tooling against you, kind of hiding in plain sight, if you will. But, and, and as Jim is saying, you, you can't be perfect. But, so given that, what's your perspective on what good cybersecurity hygiene looks like? >> Yeah, so you have to understand what your crown jewel data looks like, what a good copy of a recoverable asset looks like. When you look at an attack, if it were to occur, right, how you get that copy of data back into production. And not only that, but what that golden image actually entails. So, whether it's networking, storage, some copy of a source code, intellectual property, maybe CMBD data, or an active directory, or DNS dump, right? Understanding what your data actually entails so that you can protect it and that you can build out your recovery plan for it. >> So, and where's that live? Where's that gold copy? You put on a yellow sticky? No, it's got to be, (chuckles) you got to be somewhere safe, right? So you have to think about that chain as well, right? >> Absolutely. Yeah. You, so, a lot of folks have not gone through the exercise of identifying what that golden copy looks like. Everyone has a DR scenario, everyone has a DR strategy, but actually identifying what that golden crown jewel data, let's call it, actually entails is one aspect of it. And then where to put it, how to protect it, how to make it immutable and isolated, that's the other portion of it. >> You know, if I go back to sort of earlier part of last decade, you know, cybersecurity was kind of a checkoff item. And as you got toward the middle part of the decade, and I'd say clearly by 2016, it, security became a boardroom issue. It was on the agenda, you know, every quarter at the board meetings. So compliance is no longer the driver, is, is my point. The driver is business risk, real loss of reputation or data, you know, it's, or money, et cetera. What are the business implications of not having your cyber house in order today? >> They're extreme, Dave. I mean the, you know, the bad actors are good at what they do. These losses by organizations, tens, hundreds of millions into the billions sometimes, plus the reputational damage that's difficult to, to really measure. There haven't been a lot of organizations that have actually been put out of business by an attack, at least not directly on, if they're larger organizations. But that's also on the table, too. So you can't just rely on, "Oh we need to do, you know, A, B and C because our regulators require it." You need to look at what the actual risk is to the business, and then come up with a strategy from there. >> You know, Jim, staying with you, one of the most common targets we hear of attackers is to go after the backup corpus. So how should customers think about protecting themselves from that tactic? >> Well, Dave, you hit on it before, right? Everybody's had the backup and DR strategies for a long time going back to requirements that we had in place for physical disaster or human error. And that's a great starting point for resilience capability. But that's all it is, is a starting point. Because the bad actors will, they also understand that you have those capabilities, and, and they've adapted to that. In every sophisticated attack that we see, the backup is a target. The bad actors want to take it out, or corrupt it, or do something else to that backup so that it's not available to you. That's not to say they're always successful, and it's still a good control to have in place because maybe it will survive. But you have to plan beyond that. So the capabilities that we talk about with resilience, let's harden that backup infrastructure. You've already got it in place. Let's use the capabilities that are there like immutability and other controls to make it more difficult for the bad actors to get to. But then as Andrew said, that gold copy, that critical systems, you need to protect that in something that's more secure, which commonly we, we might say a cyber vault. Although, there's a lot of different capabilities for cyber vaulting, some far better than others, and that's some of the things that we focus on. >> You know, it's interesting, but I've talked to a lot of CIOs about this, is prior to the pandemic, they, you know, had their, as you're pointing out, Jim, they had their DR strategy in place, but they felt like they weren't business resilient. And they realized that when we had the forced march to digital. So Andrew, are there solutions out there to help with this problem? Do you guys have an answer to this? >> Yeah, absolutely. So I'm glad you brought up resiliency. We, we take a position that to be cyber resilient, it includes operational resiliency. It includes understanding at the C level what the implication of an attack means, as we stated, and then, how to recover back into production. When you look at protecting that data, not only do you want to put it into what we call a vault, which is a Dell technology that is an offline immutable copy of your crown jewel data, but also how to recover it in real time. So DXC offers a, I don't want to call it a turnkey solution since we architect these specific to each client needs, right, when we look at what client data entails, their recovery point, objectives, recovery time objectives, what we call quality of the restoration. But when we architect these out, we look at not only how to protect the data, but how to alert and monitor for attacks in real time, how to understand what we should do when a breach is in progress, putting together with our security operations centers, a forensic and recovery plan and a runbook for the client, and then being able to cleanse and remediate so that we can get that data back into production. These are all services that DXC offers in conjunction with the Dell solution to protect, and recover, and keep bad actors out. And if we can't keep them out to ensure that we are back into production in short order. >> You know, this, this discussion we've been having about DR kind of versus resilience, and, and you were just talking about RPO and RTO. I mean, it used to be that a lot of firms wouldn't even test their recovery 'cause it was too risky. Or, you know, maybe they tested it on, you know, July 4th or something like that. But, but it, I'm inferring that's changed. I wonder if we could, you know, double click on recovery? How hard is it to, to, to test that recovery, and, and how quickly are you seeing organizations recover from attacks? >> So it depends, right, on the industry vertical, what kind of data. Again, a financial services client compared to a manufacturing client are going to be two separate conversations. We've seen it as quickly as being able to recover in six hours, in 12 hours. In some instances we have the grace period of a day to a couple of days. We do offer the ability to run scenarios once a quarter where we can stand up in our systems the production data that we are protecting to ensure that we have a good recoverable copy. But it depends on the client. >> I really like the emphasis here, Dave, that you're raising and that Andrew's talking about. It's not on the technology of how the data gets protected. It's focused on the recovery. That's all that we want to do. And so the solution with DXC really focuses on generating that recovery for customers. I think where people get a little bit twisted up on their testing capability is, you have to think about different scenarios. So there are scenarios where the attack might be small. It might be limited to a database or an application. It might be really broadly based like the NotPetya attacks from a few years ago. The regulatory environment, we call those attacks severe but plausible. So you can't necessarily test everything with the infrastructure, but you can test some things with the infrastructure. Others, you might sit around on a tabletop exercise or walk through what that looks like to really get that, that recovery kind of muscle, muscle memory so that people know what to do when those things occur. But the key to it, as Andrew said before, have to focus down, "What are those critical applications? What do we need, what's most important? What has to come back first?" And that really will go a long way towards having the right recovery points and recovery times from a cyber disaster. >> Yeah, makes sense. Understanding the value of that data is going to inform you how to, how to respond and how to prioritize. Andrew, one of the things that we hear a lot on theCube, especially lately, is around, you know, IOT, IIOT, Industry 4.0, the whole OT security piece of it. And the problem being that, you know, traditionally, operations technologies have been air gapped, often by design. But as businesses, increasingly they're driving initiatives like Industry 4.0, and they're connecting these OT systems to IT systems. They're, you know, driving efficiency, preventative maintenance, et cetera. So a lot of data flowing through the pipes, if you will. What are you seeing in terms of the threats to critical infrastructure and how should customers think about addressing these issues? >> Yeah, so bad actors, you know, can come in many forms. We've seen instances of social engineering. We've seen, you know, a USB stick dropped in a warehouse. That data that is flowing through the IoT device is as sensitive now as your core mainframe infrastructure data. So when you look at it from a protection standpoint, conceptually, it's not dissimilar from what we've been been talking about where you want to understand, again, what the most critical data is. Looking at IoT data and applications is no different than your core systems now, right? Depending on what your, your business is, right? So when, when we're looking at protecting these, yes, we want firewalls, yes, we want air gap solutions, yes, we want front end protection, but we're looking at it from a resiliency perspective. Putting that data, understanding what what data entails to put in the vault from an IoT perspective is just as critical as as it is for your core systems. >> Jim, anything you can add to this topic? >> Yeah, I think you hit on the, the key points there. Everything is interconnected. So even in the days where maybe people thought the OT systems weren't online, oftentimes the IT systems are talking to them, or controlling them, SCADA systems, or perhaps supporting them. Think back to the pipeline attack of last year. All the public testimony was that the OT systems didn't get attacked directly. But there was uncertainty around that, and the IT systems hadn't been secured. So that caused the OT systems to have to shut down. It certainly is a different recovery when you're shutting them down on your own versus being attacked, but the outcome was the same that the business couldn't operate. So you really have to take all of those into account. And I think that does go back to exactly what Andrew's saying, understanding your critical business services, and then the applications and data and other components that support those and drive those, and making sure those are protected. You understand them, you have the ability to recover them if necessary. >> So guys, I mean, you made the point. I mean, you're right. The adversary is highly capable. They're motivated 'cause the ROI is so, it's so lucrative. It's like this never ending battle that cybersecurity pros, you know, go through. It really is kind of frontline sort of technical heroes, if you will. And so, but sometimes it just feels daunting. Why are you optimistic about the future of, of cyber from the good guy's perspective? >> I think we're coming at the problem the right way, Dave. So that, that focus, I'm so pleased with the idea that we are planning that the systems aren't going to be hundred percent capable every single time, and let's figure that out, right? That's, that's real world stuff. So just as the bad actors continue to adapt and expand, so do we. And I think the differences there, the common criminals, it's getting harder and harder for them. The more sophisticated ones, they're tough to beat all the time. And of course, you've raised the question of some nation states and other activities. But there's a lot more information sharing. There's a lot more focus from the business side of the house and not just the IT side of the house that we need to figure these things out. >> Yeah, to, to add to that, I think furthering education for the client base is important. You, you brought up a point earlier. It used to be a boardroom conversation due to compliance reasons. Now, as we have been in the market for a while, we continue to mature the offerings. It's further education for not only the business itself, but for the IT systems and how they interconnect, and working together so that these systems can be protected and continue to be evolved and continue to be protected through multiple frameworks as opposed to seeing it as another check the box item that the board has to adhere to. >> All right, guys, we got to go. Thank you so much. Great conversation on a, on a really important topic. Keep up the good work. Appreciate it. >> Thanks Dan. >> Thank you. >> All right, and thank you for watching. Stay tuned for more excellent discussions around the partnership between Dell Technologies and DXC Technology. We're talking about solving real world problems, how this partnership has evolved over time, really meeting the changing enterprise landscape challenges. Keep it right there. (bright music) Okay, we hope you enjoyed the program and learned some things about cloud transformation and modernizing your business that will inspire you to action. Now if you want to learn more, go to the Dell DXC partner page shown here, or click on the URL in the description. Thanks for watching everybody and on behalf of our supporters, Dell and DXC, good luck. And as always, get in touch if we can be of any assistance. (bright music)

>> Welcome back to the program. My name is Dave Vellante and in this session we're going to explore one of the more interesting topics of the day. IoT for smart factories and with me are Todd Edmunds, the global CTO of Smart Manufacturing, Edge and Digital Twins, at Dell Technologies. That is such a cool title. (Todd laughs) I want to be you. And Dr. Aditi Banerjee, who's the Vice President General Manager for Aerospace Defense and Manufacturing at DXC Technology. Another really cool title. Folks, welcome to the program. Thanks for coming on. >> Thanks Dave. >> Thank you. Great to be here. >> Well- >> Nice to be here. >> Todd, let's start with you. We hear a lot about Industry 4.0, smart factories, IIoT. Can you briefly explain, like, what is Industry 4.0 all about and why is it important for the manufacturing industry? >> Yeah, sure Dave. You know, it's been around for quite a while and it's got, it's gone by multiple different names. As you said, Industry 4.0, smart manufacturing, industrial IoT, smart factory. But it all really means the same thing. It's really applying technology to get more out of the factories and the facilities that you have to do your manufacturing. So being much more efficient. Implementing really good sustainability initiatives. And so we really look at that by saying, "Okay, what are we going to do with technology to really accelerate what we've been doing for a long, long time"? So it's really not, it's not new. It's been around for a long time. What's new is that manufacturers are looking at this, not as a one-off, two off individual use case point of view, but instead they're saying, "We really need to look at this holistically, thinking about a strategic investment in how we do this." Not to just enable one or two use cases, but enable many, many use cases across the spectrum. I mean, there's tons of 'em out there. There's predictive maintenance and there's OEE, overall equipment effectiveness, and there's computer vision. And all of these things are starting to percolate down to the factory floor, but it needs to be done in a little bit different way. And really to to really get those outcomes that they're looking for in smart factory, or Industry 4.0, or however you want to call it. And truly transform. Not just throw an Industry 4.0 use case out there, but to do the digital transformation that's really necessary and to be able to stay relevant for the future. You know, I heard it once said that you have three options. Either you digitally transform and stay relevant for the future or you don't and fade into history like 52% of the companies that used to be on the Fortune 500 since 2000, right. And so really that's a key thing and we're seeing that really, really being adopted by manufacturers all across the globe. >> Yeah, so Aditi, that's like digital transformation is almost synonymous with business transformation. So is there anything you'd add to what Todd just said? >> Absolutely, though, I would really add that what really drives Industry 4.0 is the business transformation. What we are able to deliver in terms of improving the manufacturing KPIs and the KPIs for customer satisfaction, right. For example, improving the downtime, you know, or decreasing the maintenance cycle of the equipments or improving the quality of products, right. So I think these are lot of business outcomes that our customers are looking at while using Industry 4.0 and the technologies of Industry 4.0 to deliver these outcomes. >> So Aditi, one, if I could stay with you and maybe this is a bit esoteric, but when I first started researching IoT and Industrial IoT 4.0, et cetera, I felt, you know, while there could be some disruptions in the ecosystem, I kind of came to the conclusion that large manufacturing firms, aerospace defense companies, the firms building out critical infrastructure, actually had kind of an incumbent advantage and a great opportunity. Of course, then I saw on TV, somebody now, they're building homes with 3D printers. It like blows your mind. So that's pretty disruptive. But. So, but they got to continue, the incumbents have to continue to invest in the future. They're well capitalized. They're pretty good businesses. Very good businesses. But there's a lot of complexities involved in kind of connecting the old house to the new addition that's being built, if you will. Or there's transformation that we're talking about. So my question is how are your customers preparing for this new era? What are the key challenges that they're facing in the blockers, if you will? >> Yeah, I mean the customers are looking at Industry 4.0 for greenfield factories, right. That is where the investments are going directly into building the factories with the new technologies with the new connectivities, right, for the machines, for example. Industry IoT, Having the right type of data platforms to drive computational analytics and outcomes, as well as looking at edge versus cloud type of technologies, right. Those are all getting built in the greenfield factories. However, for the install-based factories, right, that is where our customers are looking at how do I modernize, right. These factories. How do I connect the existing machine? And that is where some of the challenges come in on, you know, the legacy system connectivity that they need to think about. Also, they need to start thinking about cybersecurity and operation technology security, right, because now you are connecting the factories to each other, right. So cybersecurity becomes top of mind, right. So there is definitely investment that is involved. Clients are creating roadmaps for digitizing and modernizing these factories and investments in a very strategic way, right. So perhaps they start with the innovation program. And then they look at the business case and they scale it up, right. >> Todd, I'm glad Aditi brought up security because if you think about the operations technology, you know folks, historically they air gapped, you know, the systems. That's how they created security. That's changed. The business came in and said, "Hey, we got to connect. We got to make it intelligent." So that's got to be a big challenge as well. >> It absolutely is Dave. And, you know, you can no longer just segment that because really to get all of those efficiencies that we talk about, that IOT and industrial IoT and Industry 4.0 promise, you have to get data out of the factory but then you got to put data back in the factory. So no longer is it just firewalling everything is really the answer. So you really have to have a comprehensive approach to security, but you also have to have a comprehensive approach to the cloud and what that means. And does it mean a continuum of cloud all the way down to the edge, right down to the factory? It absolutely does because no one approach has the answer to everything. The more you go to the cloud, the broader the attack surface is. So what we're seeing is a lot of our customers approaching this from, kind of, that hybrid, you know, write once, run anywhere on the factory floor down to the edge. And one of things we're seeing too is to help distinguish between what is the edge and that. And bridge that gap between, like Dave, you talked about IT and OT, and also help that what Aditi talked about is the greenfield plants versus the brownfield plants, that they call it, that are the legacy ones and modernizing those, is it's great to kind of start to delineate. What does that mean? Where's the edge? Where's the IT and the OT? We see that from a couple of different ways. We start to think about, really, two edges in a manufacturing floor. We talk about an industrial edge that sits, or some people call it a far edge or a thin edge, sits way down on that plant. Consists of industrial hardened devices that do that connectivity, the hard stuff, about how do I connect to this obsolete legacy protocol and what do I do with it? And create that next generation of data that has context. And then we see another edge evolving above that which is much more of a data and analytics and enterprise grade application layer that sits down in the factory itself that helps figure out where we're going to run this. Is... Does it connect to the cloud? Do we run applications on-prem? Because a lot of times that on-prem application is needs to be done because that's the only way it's going to work. Because of security requirements. Because of latency requirements, performance, and a lot of times, cost. It's really helpful to build that multiple edge strategy because then you consolidate all of those resources, applications, infrastructure, hardware, into a centralized location. Makes it much, much easier to really deploy and manage that security. But it also makes it easier to deploy new applications, new use cases, and become the foundation for DXC's expertise in applications that they deliver to our customers as well. >> Todd, how complex are these projects? I mean, I feel like it's kind of the digital equivalent of building the Hoover Dam. I mean, it... So, yeah, how long does a typical project take? I know it varies, but what, you know, what are the critical success factors in terms of delivering business value quickly? >> Yeah, that's a great question in that we're, you know, like I said at the beginning, this is not new smart factory and Industry 4.0 is not new. It's been... It's people have been trying to implement the holy grail of smart factory for a long time. And what we're seeing is a switch, a little bit of a switch or quite a bit of a switch, to where the enterprise and the IT folks are having a much bigger say and have a lot to offer to be able to help that complexity. So instead of deploying a computer here and a gateway there and a server there. I mean, you go walk into any manufacturing plant and you can see servers sitting underneath someone's desk or a PC in a closet somewhere running a a critical production application. So we're seeing the enterprise have a much bigger say at the table. Much louder voice at the table to say, "We've been doing this enterprise all the time. We know how to really consolidate, bring hyper-converged applications, hyper-converged infrastructure, to really accelerate these kind of applications. Really accelerate the outcomes that are needed to really drive that smart factory." And start to bring that same capabilities down into the Mac on the factory floor. That way, if you do it once to make it easier to implement you can repeat that. You can scale that. You can manage it much easily. And you can then bring that all together because you have the security in one centralized location. So we're seeing manufacturers... Yeah, that first use case may be fairly difficult to implement and we got to go down in and see exactly what their problems are. But when the infrastructure is done the correct way, when that... Think about how you're going to run that and how are you going to optimize the engineering. Well, let's take that what you've done in that one factory and then set. Let's that, make that across all the factories including the factory that we're in, but across the globe. That makes it much, much easier. You really do the hard work once and then repeat almost like a cookie cutter. >> Got it, thank you. Aditi, what about the skillsets available to apply these to these projects? You got to have knowledge of digital, AI, data, integration. Is there a talent shortage to get all this stuff done? >> Yeah, I mean, definitely. Different types of skillsets are needed from a traditional manufacturing skillset, right. Of course, the basic knowledge of manufacturing is important. But the digital skillsets, like, you know, IoT. Having a skillset in different protocols for connecting the machines, right. That experience that comes with it. Data and analytics, security, augmented virtual reality, programming. You know, again, looking at robotics and the digital twin. So, you know, it's a lot more connectivity software data-driven skillsets that are needed to smart factory to life at scale. And, you know, lots of firms are, you know, recruiting these types of resources with these skillsets to, you know, accelerate their smart factory implementation as well as consulting firms like DXC technology and others. We recruit. We train our talent to provide these services. >> Got it. Aditi, I wonder if we could stay on you. Let's talk about the partnership between DXC and Dell. What are you doing specifically to simplify the move to industry 4.0 for customers? What solutions are you offering? How are you working together, Dell and DXC, to bring these to market? >> Yeah, I... Dell and DXC have a very strong partnership, you know, and we work very closely together to create solutions, to create strategies, and how we are going to jointly help our clients, right. So. Areas that we have worked closely together is edge compute, right. How that impacts the smart factory. So we have worked pretty closely in that area. We're also looked at vision technologies, you know. How do we use that at the edge to improve the quality of products, right. So we have several areas that we collaborate in and our approach is that we want to bring solutions to our client and as well as help them scale those solutions with the right infrastructure, the right talent, and the right level of security. So we bring a comprehensive solution to our clients. >> So, Todd, last question. Kind of similar but different. You know, why Dell DXC? Pitch me. What's different about this partnership? You know, where are you confident that, you know, you're going to deliver the best value to customers? >> Absolutely, great question. You know, there's no shortage of bespoke solutions that are out there. There's hundreds of people that can come in and do individual use cases and do these things and just... And that's where it ends. What Dell and DXC Technology together bring to the table is we do the optimization of the engineering of those previously bespoke solutions upfront, together. Right. The power of our scalables, enterprise grade, structured, you know, industry standard infrastructure as well as our expertise in delivering package solutions that really accelerate with DXC's expertise and reputation as a global trusted advisor. Be able to really scale and repeat those solutions that DXC is so really, really good at. And Dell's infrastructure and our, what, 30,000 people across the globe that are really, really good at that scalable infrastructure to be able to repeat. And then it really lessens the risk that our customers have and really accelerates those solutions. So it's, again, not just one individual solutions. It's all of the solutions that not just drive use cases but drive outcomes with those solutions. >> Yeah, you're right. The partnership has gone... I mean, I first encountered it back in, I think, it was 2010, May of 2010. We had you guys both on the queue... I think we were talking about converged infrastructure and I had a customer on, and it was actually manufacturing customer. Was quite interesting. And back then it was how do we kind of replicate what's coming in the cloud? And you guys have obviously taken it into the digital world. Really want to thank you for your time today. Great conversation. And love to have you back. >> Thank you so much. >> Absolutely. >> It was a pleasure speaking with you. >> I agree. >> All right, keep it right there for more discussions that educate and inspire on theCUBE.

(upbeat music) >> Hey everyone. Welcome to theCUBE's special program series "Women of the Cloud", brought to you by AWS. I'm your host for the program, Lisa Martin. Very pleased to welcome back one of our alumni to this special series, Dr. Tendu Yogurtcu joins us, the CTO of Precisely. >> Lisa: Tendu, it's great to see you, it's been a while, but I'm glad that you're doing so well. >> Geez, it's so great seeing you too, and thank you for having me. >> My pleasure. I want the audience to understand a little bit about you. Talk to me a little bit about you, about your role and what are some of the great things that you're doing at Precisely. >> Of course. As CTO, my current role is driving technology vision and innovation, and also coming up with expansion strategies for Precisely's future growth. Precisely is the leader in data integrity. We deliver data with trust, with maximum accuracy, consistency, and also with context. And as a CTO, keeping an eye on what's coming in the business space, what's coming up with the emerging challenges is really key for me. Prior to becoming CTO, I was General Manager for the Syncsort big data business. And previously I had several engineering and R&D leadership roles. I also have a bit of academia experience. I served as a part-time faculty in computer science department in a university. And I am a person who is very tuned to giving back to my community. So I'm currently serving as a advisory board member in the same university. And I'm also serving as a advisory board member for a venture capital firm. And I take pride in being a dedicated advocate for STEM education and STEM education for women in particular, and girls in the underserved areas. >> You have such a great background. The breadth of your background, the experience that you have in the industry as well in academia is so impressive. I've known you a long time. I'd love the audience to get some recommendations from you. For those of the audience looking to grow and expand their careers in technology, what are some of the things that you that you've experienced that you would recommend people do? >> First, stay current. What is emerging today is going to be current very quickly. Especially now we are seeing more change and change at the increased speed than ever. So keeping an eye on on what's happening in the market if you want to be marketable. Now, some of the things that I will say, we have shortage of skills with data science, data engineering with security cyber security with cloud, right? We are here talking about cloud in particular. So there is a shortage of skills in the emerging technologies, AI, ML, there's a shortage of skills also in the retiring technologies. So we are in this like spectrum of skills shortage. So stay tuned to what's coming up. That's one. And on the second piece is that the quicker you tie what you are doing to the goals of the business, whether that's revenue growth whether that's customer retention or cost optimization you are more likely to grow in your career. You have to be able to articulate what you are doing and how that brings value to business to your boss, to your customers. So that becomes an important one. And then third one is giving back. Do something for the women in technology while being a woman in technology. Give back to your community whether that's community is gender based or whether it's your alumni, whether it's your community social community in your neighborhood or in your country or ethnicity. Give back to your community. I think that's becoming really important. >> I think so too. I think that paying it forward is so critical. I'm sure that you have a a long list of mentors and sponsors that have guided you along the way. Giving back to the community paying it forward I think is so important. For others who might be a few years behind us or even maybe have been in tech for the same amount of time that are looking to grow and expand their career having those mentors and sponsors of women who've been through the trenches is inspiring. It's so helpful. And it really is something that we need to do from a diversity perspective alone, right? >> Correct. Correct. And we have seen that, we have seen, for example Covid impact in women in particular. Diverse studies done by girls who quote on Accenture that showed that actually 50% of the women above age 35 were actually dropping out of the technology. And those numbers are scary. However, on the other side we have also seen incredible amount of technology innovation during that time with cloud adoption increasing with the ability to actually work remotely if you are even living in not so secure areas, for example that created more opportunities for women to come back to workforce as well. So we can turn the challenges to opportunities and watch out for those. I would say tipping points. >> I love that you bring up such a great point. There are so, so the, the data doesn't lie, right? The data shows that there's a significant amount of churn for women in technology. But to your point, there are so many opportunities. You mentioned a minute ago the skills gap. One of the things we talk about often on theCUBE and we're talking about cybersecurity which is obviously it's a global risk for companies in every industry, is that there's massive opportunity for people of, of any type to be able to grow their skills. So knowing that there's trend, but there's also so much opportunity for women in technology to climb the ladder is kind of exciting. I think. >> It is. It is exciting. >> Talk to me a little bit about, I would love for the audience to understand some of your hands-on examples where you've really been successful helping organizations navigate digital transformation and their entry and success with cloud computing. What are some of those success stories that you're really proud of? >> Let me think about, first of all what we are seeing is with the digital transformation in general, every single business every single vertical is becoming a technology company. Telecom companies are becoming a technology company. Financial services are becoming a technology company and manufacturing is becoming a technology company. So every business is becoming technology driven. And data is the key. Data is the enabler for every single business. So when we think about the challenges, one of the examples that I give a big challenge for our customers is I can't find the critical data, I can't access it. What are my critical data elements? Because I have so high volumes growing exponentially. What are the critical data elements that I should care and how do I access that? And we work at Precisely with 99 of Fortune 100. So we have two 12,000 customers in over a hundred countries which means we have customers whose businesses are purely built on cloud, clean slate. We also have businesses who have very complex set of data platforms. They have financial services, insurance, for example. They have critical transactional workloads still running on mainframes, IBM i servers, SAP systems. So one of the challenges that we have, and I work with key customers, is on how do we make data accessible for advanced analytics in the cloud? Cloud opens up a ton of open source tools, AI, ML stack lots of tools that actually the companies can leverage for that analytics in addition to elasticity in addition to easy to set up infrastructure. So how do we make sure the data can be actually available from these transactional systems, from mainframes at the speed that the business requires. So it's not just accessing data at the speed the business requires. One of our insurance customers they actually created this data marketplace on Amazon Cloud. And the, their challenge was to make sure they can bring the fresh data on a nightly basis initially and which became actually half an hour, every half an hour. So the speed of the business requirements have changed over time. We work with them very closely and also with the Amazon teams on enabling bringing data and workloads from the mainframes and executing in the cloud. So that's one example. Another big challenge that we see is, can I trust my data? And data integrity is more critical than ever. The quality of data, actually, according to HBR Harvard Business Review survey, 47% of every new record of data has at least one critical data error, 47%. So imagine, I was talking with the manufacturing organization couple of weeks ago and they were giving me an example. They have these three letter quotes for parts and different chemicals they use in the manufacturing. And the single letter error calls a shutdown of the whole manufacturing line. >> Wow. >> So that kind of challenge, how do I ensure that I can actually have completeness of data cleanness of data and consistency in that data? Moreover, govern that on a continuous basis becomes one of the use cases that we help customers. And in that particular case actually we help them put a data governance framework and data quality in their manufacturing line. It's becoming also a critical for, for example ESG, environment, social and governance, supply chain, monitoring the supply chain, and assessing ESG metrics. We see that again. And then the third one, last one. I will give an example because I think it's important. Hybrid cloud becoming critical. Because there's a purest view for new companies. However, facilitating flexible deployment models and facilitating cloud and hybrid cloud is also where we really we can help our customers. >> You brought up some amazingly critical points where it comes to data. You talked about, you know, a minute ago, every company in every industry has to become a technology company. You could also say every company across every industry has to become a data company. They have to become a software company. But to your point, and what it sounds like precisely is really helping organizations to do is access the data access data that has high integrity data that is free of errors. Obviously that's business critical. You talked about the high percentage of errors that caused manufacturing shutdown. Businesses can't, can't have that. That could potentially be life-ending for an organization. So it sounds like what you're talking about data accessibility, data integrity data governance and having that all in real time is table stakes for businesses. Whether it's your grocery store, your local coffee shop a manufacturing company, and e-commerce company. It's table stakes globally these days. >> It is, and you made a very good point actually, Lisa when you talked about the local coffee shop or the retail. One other interesting statistic is that almost 80% of every data has a location attribute. So when we talk about data integrity we no longer talk about just, and consistency of data. We also talk about context, right? When you are going, for example, to a new town you are probably getting some reminders about where your favorite coffee shop is or what telecom company has an office in that particular town. Or if you're an insurance company and a hurricane is hitting southern Florida. Then you want to know how the path of that hurricane is going to impact your customers and predict the claims before they happen. Also understand the propensity of the potential customers that you don't yet have. So location and context, those additional attributes of demographics, visitations are creating actually more confident business insights. >> Absolutely. And and as the consumer we're becoming more and more demanding. We want to be able to transact things so easily whether it's in our personal life at the grocery store, at that cafe, or in our business life. So those demands from the customer are also really influencing the direction that companies need to go. And it's actually, I think it's quite exciting that the amount of personalization the location data that you talk about that comes in there and really helps companies in every industry deliver these the cloud can, these amazing, unique personalized experiences that really drive business forward. We could talk about that all day long. I have no problem. But I want to get in our final minutes here, Tendu. What do you see as in your crystal ball as next for the cloud? How do you see your role as CTO evolving? >> Sure. For what we are seeing in the cloud I think we will start seeing more and more focus on sustainability. Sustainable technologies and governance. Obviously cloud migrations cloud modernizations are helping with that. And we, we are seeing many of our customers they started actually assessing the ESG supply chain and reporting on metrics whether it's the percentage of face or energy consumption. Also on the social metrics on diversity age distribution and as well as compliance piece. So sustainability governance I think that will become one area. Second, security, we talked about IT security and data privacy. I think we will see more and more investments around those. Cybersecurity in particular. And ethical data access and ethics is becoming center to everything we are doing as we have those personalized experiences and have more opportunities in the cloud. And the third one is continued automation with AI, ML and more focus on automation because cloud enables that at scale. And the work that we need to do is too time-intensive and too manual with the amount of data. Data is powering every business. So automation is going to be an increased focus how my role evolves with that. So I have this unique combination. I have been open to non-linear career paths throughout my growth. So I have an understanding of how to innovate and build products that solve real business problems. I also have an understanding of how to sell them build partnerships that combined with the the scale of growth, the hyper growth that we have absorbed in precisely 10 times growth within the last 10 years through a combination of organic innovation and acquisitions really requires the speed of change. So change, implementing change at scale as well as at speed. So taking those and bringing them to the next challenge is the evolution of my role. How do I bring those and tackle keep an eye on what's coming as a challenge in the industry and how they apply those skills that I have developed throughout my career to that next challenge and evolve with it, bring the innovation to data to cloud and the next challenge that we are going to see. >> There's so much on the horizon. It's, there are certainly challenges, you know within technology, but there's so much opportunity. You've done such a great job highlighting your career path the, the big impact that you're helping organizations make leveraging cloud and the opportunity that's there for the rest of us to really get in there get our hands dirty and solve problems. Tendu, I always love our conversations. It's been such a pleasure having you back, back on theCUBE. Thank you for joining us on this special program series today. >> Thank you Lisa. And also thanks to AWS for the opportunity. >> Absolutely. This is brought, brought to us by AWS. For Dr.Tendu, you are good to go. I'm Lisa Martin. You're watching theCUBE special program series Women of the Cloud. We thank you so much for watching and we'll see you soon. (upbeat music)

(upbeat music beginning) >> From the Cube Studios in Palo Alto and Boston, bringing you data-driven insights from the Cube and ETR, this is "Breaking Analysis" with Dave Vellante. >> Making predictions about the future of enterprise tech is more challenging if you strive to lay down forecasts that are measurable. In other words, if you make a prediction, you should be able to look back a year later and say, with some degree of certainty, whether the prediction came true or not, with evidence to back that up. Hello and welcome to this week's Wikibon Cube Insights, powered by ETR. In this breaking analysis, we aim to do just that, with predictions about the macro IT spending environment, cost optimization, security, lots to talk about there, generative AI, cloud, and of course supercloud, blockchain adoption, data platforms, including commentary on Databricks, snowflake, and other key players, automation, events, and we may even have some bonus predictions around quantum computing, and perhaps some other areas. To make all this happen, we welcome back, for the third year in a row, my colleague and friend Eric Bradley from ETR. Eric, thanks for all you do for the community, and thanks for being part of this program. Again. >> I wouldn't miss it for the world. I always enjoy this one. Dave, good to see you. >> Yeah, so let me bring up this next slide and show you, actually come back to me if you would. I got to show the audience this. These are the inbounds that we got from PR firms starting in October around predictions. They know we do prediction posts. And so they'll send literally thousands and thousands of predictions from hundreds of experts in the industry, technologists, consultants, et cetera. And if you bring up the slide I can show you sort of the pattern that developed here. 40% of these thousands of predictions were from cyber. You had AI and data. If you combine those, it's still not close to cyber. Cost optimization was a big thing. Of course, cloud, some on DevOps, and software. Digital... Digital transformation got, you know, some lip service and SaaS. And then there was other, it's kind of around 2%. So quite remarkable, when you think about the focus on cyber, Eric. >> Yeah, there's two reasons why I think it makes sense, though. One, the cybersecurity companies have a lot of cash, so therefore the PR firms might be working a little bit harder for them than some of their other clients. (laughs) And then secondly, as you know, for multiple years now, when we do our macro survey, we ask, "What's your number one spending priority?" And again, it's security. It just isn't going anywhere. It just stays at the top. So I'm actually not that surprised by that little pie chart there, but I was shocked that SaaS was only 5%. You know, going back 10 years ago, that would've been the only thing anyone was talking about. >> Yeah. So true. All right, let's get into it. First prediction, we always start with kind of tech spending. Number one is tech spending increases between four and 5%. ETR has currently got it at 4.6% coming into 2023. This has been a consistently downward trend all year. We started, you know, much, much higher as we've been reporting. Bottom line is the fed is still in control. They're going to ease up on tightening, is the expectation, they're going to shoot for a soft landing. But you know, my feeling is this slingshot economy is going to continue, and it's going to continue to confound, whether it's supply chains or spending. The, the interesting thing about the ETR data, Eric, and I want you to comment on this, the largest companies are the most aggressive to cut. They're laying off, smaller firms are spending faster. They're actually growing at a much larger, faster rate as are companies in EMEA. And that's a surprise. That's outpacing the US and APAC. Chime in on this, Eric. >> Yeah, I was surprised on all of that. First on the higher level spending, we are definitely seeing it coming down, but the interesting thing here is headlines are making it worse. The huge research shop recently said 0% growth. We're coming in at 4.6%. And just so everyone knows, this is not us guessing, we asked 1,525 IT decision-makers what their budget growth will be, and they came in at 4.6%. Now there's a huge disparity, as you mentioned. The Fortune 500, global 2000, barely at 2% growth, but small, it's at 7%. So we're at a situation right now where the smaller companies are still playing a little bit of catch up on digital transformation, and they're spending money. The largest companies that have the most to lose from a recession are being more trepidatious, obviously. So they're playing a "Wait and see." And I hope we don't talk ourselves into a recession. Certainly the headlines and some of their research shops are helping it along. But another interesting comment here is, you know, energy and utilities used to be called an orphan and widow stock group, right? They are spending more than anyone, more than financials insurance, more than retail consumer. So right now it's being driven by mid, small, and energy and utilities. They're all spending like gangbusters, like nothing's happening. And it's the rest of everyone else that's being very cautious. >> Yeah, so very unpredictable right now. All right, let's go to number two. Cost optimization remains a major theme in 2023. We've been reporting on this. You've, we've shown a chart here. What's the primary method that your organization plans to use? You asked this question of those individuals that cited that they were going to reduce their spend and- >> Mhm. >> consolidating redundant vendors, you know, still leads the way, you know, far behind, cloud optimization is second, but it, but cloud continues to outpace legacy on-prem spending, no doubt. Somebody, it was, the guy's name was Alexander Feiglstorfer from Storyblok, sent in a prediction, said "All in one becomes extinct." Now, generally I would say I disagree with that because, you know, as we know over the years, suites tend to win out over, you know, individual, you know, point products. But I think what's going to happen is all in one is going to remain the norm for these larger companies that are cutting back. They want to consolidate redundant vendors, and the smaller companies are going to stick with that best of breed and be more aggressive and try to compete more effectively. What's your take on that? >> Yeah, I'm seeing much more consolidation in vendors, but also consolidation in functionality. We're seeing people building out new functionality, whether it's, we're going to talk about this later, so I don't want to steal too much of our thunder right now, but data and security also, we're seeing a functionality creep. So I think there's further consolidation happening here. I think niche solutions are going to be less likely, and platform solutions are going to be more likely in a spending environment where you want to reduce your vendors. You want to have one bill to pay, not 10. Another thing on this slide, real quick if I can before I move on, is we had a bunch of people write in and some of the answer options that aren't on this graph but did get cited a lot, unfortunately, is the obvious reduction in staff, hiring freezes, and delaying hardware, were three of the top write-ins. And another one was offshore outsourcing. So in addition to what we're seeing here, there were a lot of write-in options, and I just thought it would be important to state that, but essentially the cost optimization is by and far the highest one, and it's growing. So it's actually increased in our citations over the last year. >> And yeah, specifically consolidating redundant vendors. And so I actually thank you for bringing that other up, 'cause I had asked you, Eric, is there any evidence that repatriation is going on and we don't see it in the numbers, we don't see it even in the other, there was, I think very little or no mention of cloud repatriation, even though it might be happening in this in a smattering. >> Not a single mention, not one single mention. I went through it for you. Yep. Not one write-in. >> All right, let's move on. Number three, security leads M&A in 2023. Now you might say, "Oh, well that's a layup," but let me set this up Eric, because I didn't really do a great job with the slide. I hid the, what you've done, because you basically took, this is from the emerging technology survey with 1,181 responses from November. And what we did is we took Palo Alto and looked at the overlap in Palo Alto Networks accounts with these vendors that were showing on this chart. And Eric, I'm going to ask you to explain why we put a circle around OneTrust, but let me just set it up, and then have you comment on the slide and take, give us more detail. We're seeing private company valuations are off, you know, 10 to 40%. We saw a sneak, do a down round, but pretty good actually only down 12%. We've seen much higher down rounds. Palo Alto Networks we think is going to get busy. Again, they're an inquisitive company, they've been sort of quiet lately, and we think CrowdStrike, Cisco, Microsoft, Zscaler, we're predicting all of those will make some acquisitions and we're thinking that the targets are somewhere in this mess of security taxonomy. Other thing we're predicting AI meets cyber big time in 2023, we're going to probably going to see some acquisitions of those companies that are leaning into AI. We've seen some of that with Palo Alto. And then, you know, your comment to me, Eric, was "The RSA conference is going to be insane, hopping mad, "crazy this April," (Eric laughing) but give us your take on this data, and why the red circle around OneTrust? Take us back to that slide if you would, Alex. >> Sure. There's a few things here. First, let me explain what we're looking at. So because we separate the public companies and the private companies into two separate surveys, this allows us the ability to cross-reference that data. So what we're doing here is in our public survey, the tesis, everyone who cited some spending with Palo Alto, meaning they're a Palo Alto customer, we then cross-reference that with the private tech companies. Who also are they spending with? So what you're seeing here is an overlap. These companies that we have circled are doing the best in Palo Alto's accounts. Now, Palo Alto went and bought Twistlock a few years ago, which this data slide predicted, to be quite honest. And so I don't know if they necessarily are going to go after Snyk. Snyk, sorry. They already have something in that space. What they do need, however, is more on the authentication space. So I'm looking at OneTrust, with a 45% overlap in their overall net sentiment. That is a company that's already existing in their accounts and could be very synergistic to them. BeyondTrust as well, authentication identity. This is something that Palo needs to do to move more down that zero trust path. Now why did I pick Palo first? Because usually they're very inquisitive. They've been a little quiet lately. Secondly, if you look at the backdrop in the markets, the IPO freeze isn't going to last forever. Sooner or later, the IPO markets are going to open up, and some of these private companies are going to tap into public equity. In the meantime, however, cash funding on the private side is drying up. If they need another round, they're not going to get it, and they're certainly not going to get it at the valuations they were getting. So we're seeing valuations maybe come down where they're a touch more attractive, and Palo knows this isn't going to last forever. Cisco knows that, CrowdStrike, Zscaler, all these companies that are trying to make a push to become that vendor that you're consolidating in, around, they have a chance now, they have a window where they need to go make some acquisitions. And that's why I believe leading up to RSA, we're going to see some movement. I think it's going to pretty, a really exciting time in security right now. >> Awesome. Thank you. Great explanation. All right, let's go on the next one. Number four is, it relates to security. Let's stay there. Zero trust moves from hype to reality in 2023. Now again, you might say, "Oh yeah, that's a layup." A lot of these inbounds that we got are very, you know, kind of self-serving, but we always try to put some meat in the bone. So first thing we do is we pull out some commentary from, Eric, your roundtable, your insights roundtable. And we have a CISO from a global hospitality firm says, "For me that's the highest priority." He's talking about zero trust because it's the best ROI, it's the most forward-looking, and it enables a lot of the business transformation activities that we want to do. CISOs tell me that they actually can drive forward transformation projects that have zero trust, and because they can accelerate them, because they don't have to go through the hurdle of, you know, getting, making sure that it's secure. Second comment, zero trust closes that last mile where once you're authenticated, they open up the resource to you in a zero trust way. That's a CISO of a, and a managing director of a cyber risk services enterprise. Your thoughts on this? >> I can be here all day, so I'm going to try to be quick on this one. This is not a fluff piece on this one. There's a couple of other reasons this is happening. One, the board finally gets it. Zero trust at first was just a marketing hype term. Now the board understands it, and that's why CISOs are able to push through it. And what they finally did was redefine what it means. Zero trust simply means moving away from hardware security, moving towards software-defined security, with authentication as its base. The board finally gets that, and now they understand that this is necessary and it's being moved forward. The other reason it's happening now is hybrid work is here to stay. We weren't really sure at first, large companies were still trying to push people back to the office, and it's going to happen. The pendulum will swing back, but hybrid work's not going anywhere. By basically on our own data, we're seeing that 69% of companies expect remote and hybrid to be permanent, with only 30% permanent in office. Zero trust works for a hybrid environment. So all of that is the reason why this is happening right now. And going back to our previous prediction, this is why we're picking Palo, this is why we're picking Zscaler to make these acquisitions. Palo Alto needs to be better on the authentication side, and so does Zscaler. They're both fantastic on zero trust network access, but they need the authentication software defined aspect, and that's why we think this is going to happen. One last thing, in that CISO round table, I also had somebody say, "Listen, Zscaler is incredible. "They're doing incredibly well pervading the enterprise, "but their pricing's getting a little high," and they actually think Palo Alto is well-suited to start taking some of that share, if Palo can make one move. >> Yeah, Palo Alto's consolidation story is very strong. Here's my question and challenge. Do you and me, so I'm always hardcore about, okay, you've got to have evidence. I want to look back at these things a year from now and say, "Did we get it right? Yes or no?" If we got it wrong, we'll tell you we got it wrong. So how are we going to measure this? I'd say a couple things, and you can chime in. One is just the number of vendors talking about it. That's, but the marketing always leads the reality. So the second part of that is we got to get evidence from the buying community. Can you help us with that? >> (laughs) Luckily, that's what I do. I have a data company that asks thousands of IT decision-makers what they're adopting and what they're increasing spend on, as well as what they're decreasing spend on and what they're replacing. So I have snapshots in time over the last 11 years where I can go ahead and compare and contrast whether this adoption is happening or not. So come back to me in 12 months and I'll let you know. >> Now, you know, I will. Okay, let's bring up the next one. Number five, generative AI hits where the Metaverse missed. Of course everybody's talking about ChatGPT, we just wrote last week in a breaking analysis with John Furrier and Sarjeet Joha our take on that. We think 2023 does mark a pivot point as natural language processing really infiltrates enterprise tech just as Amazon turned the data center into an API. We think going forward, you're going to be interacting with technology through natural language, through English commands or other, you know, foreign language commands, and investors are lining up, all the VCs are getting excited about creating something competitive to ChatGPT, according to (indistinct) a hundred million dollars gets you a seat at the table, gets you into the game. (laughing) That's before you have to start doing promotion. But he thinks that's what it takes to actually create a clone or something equivalent. We've seen stuff from, you know, the head of Facebook's, you know, AI saying, "Oh, it's really not that sophisticated, ChatGPT, "it's kind of like IBM Watson, it's great engineering, "but you know, we've got more advanced technology." We know Google's working on some really interesting stuff. But here's the thing. ETR just launched this survey for the February survey. It's in the field now. We circle open AI in this category. They weren't even in the survey, Eric, last quarter. So 52% of the ETR survey respondents indicated a positive sentiment toward open AI. I added up all the sort of different bars, we could double click on that. And then I got this inbound from Scott Stevenson of Deep Graham. He said "AI is recession-proof." I don't know if that's the case, but it's a good quote. So bring this back up and take us through this. Explain this chart for us, if you would. >> First of all, I like Scott's quote better than the Facebook one. I think that's some sour grapes. Meta just spent an insane amount of money on the Metaverse and that's a dud. Microsoft just spent money on open AI and it is hot, undoubtedly hot. We've only been in the field with our current ETS survey for a week. So my caveat is it's preliminary data, but I don't care if it's preliminary data. (laughing) We're getting a sneak peek here at what is the number one net sentiment and mindshare leader in the entire machine-learning AI sector within a week. It's beating Data- >> 600. 600 in. >> It's beating Databricks. And we all know Databricks is a huge established enterprise company, not only in machine-learning AI, but it's in the top 10 in the entire survey. We have over 400 vendors in this survey. It's number eight overall, already. In a week. This is not hype. This is real. And I could go on the NLP stuff for a while. Not only here are we seeing it in open AI and machine-learning and AI, but we're seeing NLP in security. It's huge in email security. It's completely transforming that area. It's one of the reasons I thought Palo might take Abnormal out. They're doing such a great job with NLP in this email side, and also in the data prep tools. NLP is going to take out data prep tools. If we have time, I'll discuss that later. But yeah, this is, to me this is a no-brainer, and we're already seeing it in the data. >> Yeah, John Furrier called, you know, the ChatGPT introduction. He said it reminded him of the Netscape moment, when we all first saw Netscape Navigator and went, "Wow, it really could be transformative." All right, number six, the cloud expands to supercloud as edge computing accelerates and CloudFlare is a big winner in 2023. We've reported obviously on cloud, multi-cloud, supercloud and CloudFlare, basically saying what multi-cloud should have been. We pulled this quote from Atif Kahn, who is the founder and CTO of Alkira, thanks, one of the inbounds, thank you. "In 2023, highly distributed IT environments "will become more the norm "as organizations increasingly deploy hybrid cloud, "multi-cloud and edge settings..." Eric, from one of your round tables, "If my sources from edge computing are coming "from the cloud, that means I have my workloads "running in the cloud. "There is no one better than CloudFlare," That's a senior director of IT architecture at a huge financial firm. And then your analysis shows CloudFlare really growing in pervasion, that sort of market presence in the dataset, dramatically, to near 20%, leading, I think you had told me that they're even ahead of Google Cloud in terms of momentum right now. >> That was probably the biggest shock to me in our January 2023 tesis, which covers the public companies in the cloud computing sector. CloudFlare has now overtaken GCP in overall spending, and I was shocked by that. It's already extremely pervasive in networking, of course, for the edge networking side, and also in security. This is the number one leader in SaaSi, web access firewall, DDoS, bot protection, by your definition of supercloud, which we just did a couple of weeks ago, and I really enjoyed that by the way Dave, I think CloudFlare is the one that fits your definition best, because it's bringing all of these aspects together, and most importantly, it's cloud agnostic. It does not need to rely on Azure or AWS to do this. It has its own cloud. So I just think it's, when we look at your definition of supercloud, CloudFlare is the poster child. >> You know, what's interesting about that too, is a lot of people are poo-pooing CloudFlare, "Ah, it's, you know, really kind of not that sophisticated." "You don't have as many tools," but to your point, you're can have those tools in the cloud, Cloudflare's doing serverless on steroids, trying to keep things really simple, doing a phenomenal job at, you know, various locations around the world. And they're definitely one to watch. Somebody put them on my radar (laughing) a while ago and said, "Dave, you got to do a breaking analysis on CloudFlare." And so I want to thank that person. I can't really name them, 'cause they work inside of a giant hyperscaler. But- (Eric laughing) (Dave chuckling) >> Real quickly, if I can from a competitive perspective too, who else is there? They've already taken share from Akamai, and Fastly is their really only other direct comp, and they're not there. And these guys are in poll position and they're the only game in town right now. I just, I don't see it slowing down. >> I thought one of your comments from your roundtable I was reading, one of the folks said, you know, CloudFlare, if my workloads are in the cloud, they are, you know, dominant, they said not as strong with on-prem. And so Akamai is doing better there. I'm like, "Okay, where would you want to be?" (laughing) >> Yeah, which one of those two would you rather be? >> Right? Anyway, all right, let's move on. Number seven, blockchain continues to look for a home in the enterprise, but devs will slowly begin to adopt in 2023. You know, blockchains have got a lot of buzz, obviously crypto is, you know, the killer app for blockchain. Senior IT architect in financial services from your, one of your insight roundtables said quote, "For enterprises to adopt a new technology, "there have to be proven turnkey solutions. "My experience in talking with my peers are, "blockchain is still an open-source component "where you have to build around it." Now I want to thank Ravi Mayuram, who's the CTO of Couchbase sent in, you know, one of the predictions, he said, "DevOps will adopt blockchain, specifically Ethereum." And he referenced actually in his email to me, Solidity, which is the programming language for Ethereum, "will be in every DevOps pro's playbook, "mirroring the boom in machine-learning. "Newer programming languages like Solidity "will enter the toolkits of devs." His point there, you know, Solidity for those of you don't know, you know, Bitcoin is not programmable. Solidity, you know, came out and that was their whole shtick, and they've been improving that, and so forth. But it, Eric, it's true, it really hasn't found its home despite, you know, the potential for smart contracts. IBM's pushing it, VMware has had announcements, and others, really hasn't found its way in the enterprise yet. >> Yeah, and I got to be honest, I don't think it's going to, either. So when we did our top trends series, this was basically chosen as an anti-prediction, I would guess, that it just continues to not gain hold. And the reason why was that first comment, right? It's very much a niche solution that requires a ton of custom work around it. You can't just plug and play it. And at the end of the day, let's be very real what this technology is, it's a database ledger, and we already have database ledgers in the enterprise. So why is this a priority to move to a different database ledger? It's going to be very niche cases. I like the CTO comment from Couchbase about it being adopted by DevOps. I agree with that, but it has to be a DevOps in a very specific use case, and a very sophisticated use case in financial services, most likely. And that's not across the entire enterprise. So I just think it's still going to struggle to get its foothold for a little bit longer, if ever. >> Great, thanks. Okay, let's move on. Number eight, AWS Databricks, Google Snowflake lead the data charge with Microsoft. Keeping it simple. So let's unpack this a little bit. This is the shared accounts peer position for, I pulled data platforms in for analytics, machine-learning and AI and database. So I could grab all these accounts or these vendors and see how they compare in those three sectors. Analytics, machine-learning and database. Snowflake and Databricks, you know, they're on a crash course, as you and I have talked about. They're battling to be the single source of truth in analytics. They're, there's going to be a big focus. They're already started. It's going to be accelerated in 2023 on open formats. Iceberg, Python, you know, they're all the rage. We heard about Iceberg at Snowflake Summit, last summer or last June. Not a lot of people had heard of it, but of course the Databricks crowd, who knows it well. A lot of other open source tooling. There's a company called DBT Labs, which you're going to talk about in a minute. George Gilbert put them on our radar. We just had Tristan Handy, the CEO of DBT labs, on at supercloud last week. They are a new disruptor in data that's, they're essentially making, they're API-ifying, if you will, KPIs inside the data warehouse and dramatically simplifying that whole data pipeline. So really, you know, the ETL guys should be shaking in their boots with them. Coming back to the slide. Google really remains focused on BigQuery adoption. Customers have complained to me that they would like to use Snowflake with Google's AI tools, but they're being forced to go to BigQuery. I got to ask Google about that. AWS continues to stitch together its bespoke data stores, that's gone down that "Right tool for the right job" path. David Foyer two years ago said, "AWS absolutely is going to have to solve that problem." We saw them start to do it in, at Reinvent, bringing together NoETL between Aurora and Redshift, and really trying to simplify those worlds. There's going to be more of that. And then Microsoft, they're just making it cheap and easy to use their stuff, you know, despite some of the complaints that we hear in the community, you know, about things like Cosmos, but Eric, your take? >> Yeah, my concern here is that Snowflake and Databricks are fighting each other, and it's allowing AWS and Microsoft to kind of catch up against them, and I don't know if that's the right move for either of those two companies individually, Azure and AWS are building out functionality. Are they as good? No they're not. The other thing to remember too is that AWS and Azure get paid anyway, because both Databricks and Snowflake run on top of 'em. So (laughing) they're basically collecting their toll, while these two fight it out with each other, and they build out functionality. I think they need to stop focusing on each other, a little bit, and think about the overall strategy. Now for Databricks, we know they came out first as a machine-learning AI tool. They were known better for that spot, and now they're really trying to play catch-up on that data storage compute spot, and inversely for Snowflake, they were killing it with the compute separation from storage, and now they're trying to get into the MLAI spot. I actually wouldn't be surprised to see them make some sort of acquisition. Frank Slootman has been a little bit quiet, in my opinion there. The other thing to mention is your comment about DBT Labs. If we look at our emerging technology survey, last survey when this came out, DBT labs, number one leader in that data integration space, I'm going to just pull it up real quickly. It looks like they had a 33% overall net sentiment to lead data analytics integration. So they are clearly growing, it's fourth straight survey consecutively that they've grown. The other name we're seeing there a little bit is Cribl, but DBT labs is by far the number one player in this space. >> All right. Okay, cool. Moving on, let's go to number nine. With Automation mixer resurgence in 2023, we're showing again data. The x axis is overlap or presence in the dataset, and the vertical axis is shared net score. Net score is a measure of spending momentum. As always, you've seen UI path and Microsoft Power Automate up until the right, that red line, that 40% line is generally considered elevated. UI path is really separating, creating some distance from Automation Anywhere, they, you know, previous quarters they were much closer. Microsoft Power Automate came on the scene in a big way, they loom large with this "Good enough" approach. I will say this, I, somebody sent me a results of a (indistinct) survey, which showed UiPath actually had more mentions than Power Automate, which was surprising, but I think that's not been the case in the ETR data set. We're definitely seeing a shift from back office to front soft office kind of workloads. Having said that, software testing is emerging as a mainstream use case, we're seeing ML and AI become embedded in end-to-end automations, and low-code is serving the line of business. And so this, we think, is going to increasingly have appeal to organizations in the coming year, who want to automate as much as possible and not necessarily, we've seen a lot of layoffs in tech, and people... You're going to have to fill the gaps with automation. That's a trend that's going to continue. >> Yep, agreed. At first that comment about Microsoft Power Automate having less citations than UiPath, that's shocking to me. I'm looking at my chart right here where Microsoft Power Automate was cited by over 60% of our entire survey takers, and UiPath at around 38%. Now don't get me wrong, 38% pervasion's fantastic, but you know you're not going to beat an entrenched Microsoft. So I don't really know where that comment came from. So UiPath, looking at it alone, it's doing incredibly well. It had a huge rebound in its net score this last survey. It had dropped going through the back half of 2022, but we saw a big spike in the last one. So it's got a net score of over 55%. A lot of people citing adoption and increasing. So that's really what you want to see for a name like this. The problem is that just Microsoft is doing its playbook. At the end of the day, I'm going to do a POC, why am I going to pay more for UiPath, or even take on another separate bill, when we know everyone's consolidating vendors, if my license already includes Microsoft Power Automate? It might not be perfect, it might not be as good, but what I'm hearing all the time is it's good enough, and I really don't want another invoice. >> Right. So how does UiPath, you know, and Automation Anywhere, how do they compete with that? Well, the way they compete with it is they got to have a better product. They got a product that's 10 times better. You know, they- >> Right. >> they're not going to compete based on where the lowest cost, Microsoft's got that locked up, or where the easiest to, you know, Microsoft basically give it away for free, and that's their playbook. So that's, you know, up to UiPath. UiPath brought on Rob Ensslin, I've interviewed him. Very, very capable individual, is now Co-CEO. So he's kind of bringing that adult supervision in, and really tightening up the go to market. So, you know, we know this company has been a rocket ship, and so getting some control on that and really getting focused like a laser, you know, could be good things ahead there for that company. Okay. >> One of the problems, if I could real quick Dave, is what the use cases are. When we first came out with RPA, everyone was super excited about like, "No, UiPath is going to be great for super powerful "projects, use cases." That's not what RPA is being used for. As you mentioned, it's being used for mundane tasks, so it's not automating complex things, which I think UiPath was built for. So if you were going to get UiPath, and choose that over Microsoft, it's going to be 'cause you're doing it for more powerful use case, where it is better. But the problem is that's not where the enterprise is using it. The enterprise are using this for base rote tasks, and simply, Microsoft Power Automate can do that. >> Yeah, it's interesting. I've had people on theCube that are both Microsoft Power Automate customers and UiPath customers, and I've asked them, "Well you know, "how do you differentiate between the two?" And they've said to me, "Look, our users and personal productivity users, "they like Power Automate, "they can use it themselves, and you know, "it doesn't take a lot of, you know, support on our end." The flip side is you could do that with UiPath, but like you said, there's more of a focus now on end-to-end enterprise automation and building out those capabilities. So it's increasingly a value play, and that's going to be obviously the challenge going forward. Okay, my last one, and then I think you've got some bonus ones. Number 10, hybrid events are the new category. Look it, if I can get a thousand inbounds that are largely self-serving, I can do my own here, 'cause we're in the events business. (Eric chuckling) Here's the prediction though, and this is a trend we're seeing, the number of physical events is going to dramatically increase. That might surprise people, but most of the big giant events are going to get smaller. The exception is AWS with Reinvent, I think Snowflake's going to continue to grow. So there are examples of physical events that are growing, but generally, most of the big ones are getting smaller, and there's going to be many more smaller intimate regional events and road shows. These micro-events, they're going to be stitched together. Digital is becoming a first class citizen, so people really got to get their digital acts together, and brands are prioritizing earned media, and they're beginning to build their own news networks, going direct to their customers. And so that's a trend we see, and I, you know, we're right in the middle of it, Eric, so you know we're going to, you mentioned RSA, I think that's perhaps going to be one of those crazy ones that continues to grow. It's shrunk, and then it, you know, 'cause last year- >> Yeah, it did shrink. >> right, it was the last one before the pandemic, and then they sort of made another run at it last year. It was smaller but it was very vibrant, and I think this year's going to be huge. Global World Congress is another one, we're going to be there end of Feb. That's obviously a big big show, but in general, the brands and the technology vendors, even Oracle is going to scale down. I don't know about Salesforce. We'll see. You had a couple of bonus predictions. Quantum and maybe some others? Bring us home. >> Yeah, sure. I got a few more. I think we touched upon one, but I definitely think the data prep tools are facing extinction, unfortunately, you know, the Talons Informatica is some of those names. The problem there is that the BI tools are kind of including data prep into it already. You know, an example of that is Tableau Prep Builder, and then in addition, Advanced NLP is being worked in as well. ThoughtSpot, Intelius, both often say that as their selling point, Tableau has Ask Data, Click has Insight Bot, so you don't have to really be intelligent on data prep anymore. A regular business user can just self-query, using either the search bar, or even just speaking into what it needs, and these tools are kind of doing the data prep for it. I don't think that's a, you know, an out in left field type of prediction, but it's the time is nigh. The other one I would also state is that I think knowledge graphs are going to break through this year. Neo4j in our survey is growing in pervasion in Mindshare. So more and more people are citing it, AWS Neptune's getting its act together, and we're seeing that spending intentions are growing there. Tiger Graph is also growing in our survey sample. I just think that the time is now for knowledge graphs to break through, and if I had to do one more, I'd say real-time streaming analytics moves from the very, very rich big enterprises to downstream, to more people are actually going to be moving towards real-time streaming, again, because the data prep tools and the data pipelines have gotten easier to use, and I think the ROI on real-time streaming is obviously there. So those are three that didn't make the cut, but I thought deserved an honorable mention. >> Yeah, I'm glad you did. Several weeks ago, we did an analyst prediction roundtable, if you will, a cube session power panel with a number of data analysts and that, you know, streaming, real-time streaming was top of mind. So glad you brought that up. Eric, as always, thank you very much. I appreciate the time you put in beforehand. I know it's been crazy, because you guys are wrapping up, you know, the last quarter survey in- >> Been a nuts three weeks for us. (laughing) >> job. I love the fact that you're doing, you know, the ETS survey now, I think it's quarterly now, right? Is that right? >> Yep. >> Yep. So that's phenomenal. >> Four times a year. I'll be happy to jump on with you when we get that done. I know you were really impressed with that last time. >> It's unbelievable. This is so much data at ETR. Okay. Hey, that's a wrap. Thanks again. >> Take care Dave. Good seeing you. >> All right, many thanks to our team here, Alex Myerson as production, he manages the podcast force. Ken Schiffman as well is a critical component of our East Coast studio. Kristen Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hoof is our editor-in-chief. He's at siliconangle.com. He's just a great editing for us. Thank you all. Remember all these episodes that are available as podcasts, wherever you listen, podcast is doing great. Just search "Breaking analysis podcast." Really appreciate you guys listening. I publish each week on wikibon.com and siliconangle.com, or you can email me directly if you want to get in touch, david.vellante@siliconangle.com. That's how I got all these. I really appreciate it. I went through every single one with a yellow highlighter. It took some time, (laughing) but I appreciate it. You could DM me at dvellante, or comment on our LinkedIn post and please check out etr.ai. Its data is amazing. Best survey data in the enterprise tech business. This is Dave Vellante for theCube Insights, powered by ETR. Thanks for watching, and we'll see you next time on "Breaking Analysis." (upbeat music beginning) (upbeat music ending)

>>Hello everyone. Welcome to the Super Cloud Event preview. I'm John Forry, host of the Cube, and with Dave Valante, host of the popular Super cloud events. This is Super Cloud two preview. I'm joined by industry leader and Cube alumni, Victoria Vigo, vice president of klos Cross Cloud Services at VMware. Vittorio. Great to see you. We're here for the preview of Super Cloud two on January 17th, virtual event, live stage performance, but streamed out to the audience virtually. We're gonna do a preview. Thanks for coming in. >>My pleasure. Always glad to be here. >>It's holiday time. We had the first super cloud on in August prior to VMware, explore North America prior to VMware, explore Europe prior to reinvent. We've been through that, but right now, super Cloud has got momentum. Super Cloud two has got some success. Before we dig into it, let's take a step back and set the table. What is Super Cloud and why is important? Why are people buzzing about it? Why is it a thing? >>Look, we have been in the cloud now for like 10, 15 years and the cloud is going strong and I, I would say that going cloud first was deliberate and strategic in most cases. In some cases the, the developer was going for the path of risk resistance, but in any sizable company, this caused the companies to end up in a multi-cloud world where 85% of the companies out there use two or multiple clouds. And with that comes what we call cloud chaos, because each cloud brings their own management tools, development tools, security. And so that increase the complexity and cost. And so we believe that it's time to usher a new era in cloud computing, which we, you call the super cloud. We call it cross cloud services, which allows our customers to have a single way to build, manage, secure, and access any application across any cloud. Lowering the cost and simplifying the environment. Since >>Dave Ante and I introduced and rift on the concept of Supercloud, as we talked about at reinvent last year, a lot has happened. Supercloud one, it was in August, but prior to that, great momentum in the industry. Great conversation. People are loving it, they're hating it, which means it's got some traction. Berkeley has come on board as with a position paper. They're kind of endorsing it. They call it something different. You call it cross cloud services, whatever it is. It's kind of the same theme we're seeing. And so the industry has recognized something is happening that's different than what Cloud one was or the first generation of cloud. Now we have something different. This Super Cloud two in January. This event has traction with practitioners, customers, big name brands, Sachs, fifth Avenue, Warner, media Financial, mercury Financial, other big names are here. They're leaning in. They're excited. Why the traction in the customer's industry converts over to, to the customer traction. Why is it happening? You, you get a lot of data. >>Well, in, in Super Cloud one, it was a vendor fest, right? But these vendors are smart people that get their vision from where, from the customers. This, this stuff doesn't happen in a vacuum. We all talk to customers and we tend to lean on the early adopters and the early adopters of the cloud are the ones that are telling us, we now are in a place where the complexity is too much. The cost is ballooning. We're going towards slow down potentially in the economy. We need to get better economics out of, of our cloud. And so every single customers I talked to today, or any sizable company as this problem, the developers have gone off, built all these applications, and now the business is coming to the operators and asking, where are my applications? Are they performing? What is the security posture? And how do we do compliance? And so now they're realizing we need to do something about this or it is gonna be unmanageable. >>I wanna go to a clip I pulled out from the, our video data lake and the cube. If we can go to that clip, it's Chuck Whitten Dell at a keynote. He was talking about what he calls multi-cloud by default, not by design. This is a state of the, of the industry. If we're gonna roll that clip, and I wanna get your reaction to that. >>Well, look, customers have woken up with multiple clouds, you know, multiple public clouds. On-premise clouds increasingly as the edge becomes much more a reality for customers clouds at the edge. And so that's what we mean by multi-cloud by default. It's not yet been designed strategically. I think our argument yesterday was it can be, and it should be, it is a very logical place for architecture to land because ultimately customers want the innovation across all of the hyperscale public clouds. They will see workloads and use cases where they wanna maintain an on-premise cloud. On-premise clouds are not going away. I mentioned edge Cloud, so it should be strategic. It's just not today. It doesn't work particularly well today. So when we say multi-cloud, by default we mean that's the state of the world. Today, our goal is to bring multi-cloud by design, as you heard. Yeah, I >>Mean, I, okay, Vittorio, that's, that's the head of Dell Technologies president. He obvious he runs it. Michael Dell's still around, but you know, he's the leader. This is a interesting observation. You know, he's not a customer. We have some customer equips we'll go to as well, but by default it kind of happened not by design. So we're now kind of in a zoom out issue where, okay, I got this environment just landed on me. What, what is the, what's your reaction to that clip of how multi-cloud has become present in, in everyone's on everyone's plate right now to deal with? Yeah, >>I it is, it is multi-cloud by default, I would call it by accident. We, we really got there by accident. I think now it's time to make it a strategic asset because look, we're using multiple cloud for a reason, because all these hyperscaler bring tremendous innovation that we want to leverage. But I strongly believe that in it, especially history repeat itself, right? And so if you look at the history of it, as was always when a new level of obstruction that simplify things, that we got the next level of innovation at the lower cost, you know, from going from c plus plus to Visual basic, going from integrating application at the bits of by layer to SOA and then web services. It's, it's only when we simplify the environment that we can go faster and lower cost. And the multi-cloud is ready for that level of obstruction today. >>You know, you've made some good points. You know, developers went crazy building great apps. Now they got, they gotta roll it out and operationalize it globally. A lot of compliance issues going on. The costs are going up. We got an economic challenge, but also agility with the cloud. So using cloud and or hybrid, you can get better agility. And also moving to the cloud, it's kind of still slow. Okay, so I get that at reinvent this year and at VMware explorer we were observing and we reported that you're seeing a transition to a new kind of ecosystem partner. Ones that aren't just ISVs anymore. You have ISVs, independent software vendors, but you got the emergence of bigger players that just, they got platforms, they have their own ecosystems. So you're seeing ecosystems on top of ecosystems where, you know, MongoDB CEO and the Databricks CEO both told me, we're not an isv, we're a platform built on a cloud. So this new kind of super cloudlike thing is going on. Why should someone pay attention to the super cloud movement? We're on two, we're gonna continue to do these out in the open. Anyone can participate. Why should people pay attention to this? Why should they come to the event? Why is this important? Is this truly an inflection point? And if they do pay attention, what should they pay attention to? >>I would pay attention to two things. If you are customers that are now starting to realize that you have a multi-cloud problem and the costs are getting outta control, look at what the leading vendors are saying, connect the dots with the early adopters and some of the customers that we are gonna have at Super Cloud two, and use those learning to not fall into the same trap. So I, I'll give you an example. I was talking to a Fortune 50 in Europe in my latest trip, and this is an a CIO that is telling me >>We build all these applications and now for compliance reason, the business is coming to me, I don't even know where they are, right? And so what I was telling him, so look, there are other customers that are already there. What did they do? They built a platform engineering team. What is the platform? Engineering team is a, is an operation team that understands how developers build modern applications and lays down the foundation across multiple clouds. So the developers can be developers and do their thing, which is writing code. But now you as a cio, as a, as a, as a governing body, as a security team can have the guardrail. So do you know that these applications are performing at a lower cost and are secure and compliant? >>Patura, you know, it's really encouraging and, and love to get your thoughts on this is one is the general consensus of industry leaders. I talked to like yourself in the round is the old way was soft complexity with more complexity. The cloud demand simplicity, you mentioned abstraction layer. This is our next inflection point. It's gotta be simpler and it's gotta be easy and it's gotta be performant. That's the table stakes of the cloud. What's your thoughts on this next wave of simplicity versus complexity? Because again, abstraction layers take away complexity, they should make it simpler. What's your thoughts? >>Yeah, so I'll give you few examples. One, on the development side and runtime. You, you one would think that Kubernetes will solve all the problems you have Kubernetes everywhere, just look at, but every cloud has a different distribution of Kubernetes, right? So for example, at VMware with tansu, we create a single place that allows you to deploy that any Kubernetes environment. But now you have one place to set your policies. We take care of the differences between this, this system. The second area is management, right? So once you have all everything deployed, how do you get a single object model that tells you where your stuff is and how it's performing, and then apply policies to it as well. So these are two areas and security and so on and so forth. So the idea is that figure out what you can abstract and make common across cloud. Make that simple and put it in one place while always allowing the developers to go underneath and use the differentiated features for innovation. >>Yeah, one of the areas I'm excited, I want to get your thoughts of too is, we haven't talked about this in the past, but it, I'll throw it out there. I think the, the new AI coming out chat, G P T and other things like lens, you see it and see new kinds of AI coming that's gonna be right in the heavy lifting opportunity to make things easier with AI and automation. I think AI will be a big factor in super cloud and, and cross cloud. What's your thoughts? >>Well, the one way to look at AI is, is one of the main, main services that you would want out of a multi-cloud, right? You want eventually, right now Google seems to have an edge, but you know, the competition creates, you know, innovation. So later on you wanna use something from Azure or from or from Oracle or something that, so you want at some point that is gonna be prone every single service in in the cloud is gonna be prone to obstruction and simplification. And I, I'm just excited about to see >>What book, I can't wait for the chat services to write code automatically for us. Well, >>They >>Do, they do. They're doing it now. They do. >>Oh, the other day, somebody, you know that I do this song par this for. So for fun sometimes. And somebody the other day said, ask the AI to write a parody song for multi-cloud. And so I have the lyrics stay >>Tuned. I should do that from my blog post. Hey, write a blog post on this January 17th, Victoria, thanks for coming in, sharing the preview bottom line. Why should people come? Why is it important? What's your final kind of takeaway? Billboard message >>History is repeat itself. It goes to three major inflection points, right? We had the inflection point with the cloud and the people that got left behind, they were not as competitive as the people that got on top o of this wave. The new wave is the super cloud, what we call cross cloud services. So if you are a customer that is experiencing this problem today, tune in to to hear from other customers in, in your same space. If you are behind, tune in to avoid the, the, the, the mistakes and the, the shortfalls of this new wave. And so that you can use multi-cloud to accelerate your business and kick butt in the future. >>All right. Kicking kick your names and kicking butt. Okay, we're here on J January 17th. Super Cloud two. Momentum continues. We'll be super cloud three. There'll be super cloud floor. More and more open conversations. Join the community, join the conversation. It's open. We want more voices. We want more, more industry. We want more customers. It's happening. A lot of momentum. Victoria, thank you for your time. Thank you. Okay. I'm John Farer, host of the Cube. Thanks for watching.

>>Good afternoon from the Venetian Expo, center, hall, whatever you wanna call it, in Las Vegas. Lisa Martin here. It's day four. I'm not sure what this place is called. Wait, >>What? >>Lisa Martin here with Dave Ante. This is the cube. This is day four of a ton of coverage that we've been delivering to you, which, you know, cause you've been watching since Monday night, Dave, we are almost at the end, we're almost at the show wrap. Excited to bring back, we've been talking about security, a lot about security. Excited to bring back a, an alumni to talk about that. But what's your final thoughts? >>Well, so just in, in, in the context of security, we've had just three in a row talking about cyber, which is like the most important topic. And I, and I love that we're having Palo Alto Networks on Palo Alto Networks is the gold standard in security. Talk to CISOs, they wanna work with them. And, and it was, it's interesting because I've been following them for a little bit now, watch them move to the cloud and a couple of little stumbling points. But I said at the time, they're gonna figure it out and, and come rocking back. And they have, and the company's just performing unbelievably well despite, you know, all the macro headwinds that we love to >>Talk about. So. Right. And we're gonna be unpacking all of that with one of our alumni. As I mentioned, Anker Shaw is with us, the SVP and GM of Palo Alto Networks. Anker, welcome back to the Cub. It's great to see you. It's been a while. >>It's good to be here after a couple years. Yeah, >>Yeah. I think three. >>Yeah, yeah, for sure. Yeah. Yeah. It's a bit of a blur after Covid. >>Everyone's saying that. Yeah. Are you surprised that there are still this many people on the show floor? Cuz I am. >>I am. Yeah. Look, I am not, this is my fourth, last year was probably one third or one fourth of this size. Yeah. But pre covid, this is what dream went looked like. And it's energizing, it's exciting. It's just good to be doing the good old things. So many people and yeah. Amazing technology and innovation. It's been incredible. >>Let's talk about innovation. I know you guys, Palo Alto Networks recently acquired cyber security. Talk to us a little bit about that. How is it gonna compliment Prisma? Give us all the scoop on that. >>Yeah, for sure. Look, some of the recent, the cybersecurity attacks that we have seen are related to supply chain, the colonial pipeline, many, many supply chain. And the reason for that is the modern software supply chain, not the physical supply chain, the one that AWS announced, but this is the software supply chain is really incredibly complicated, complicated developers that are building and shipping code faster than ever before. And the, the site acquisition at the center, the heart of that was securing the entire supply chain. White House came with a new initiative on supply chain security and SBO software bill of material. And we needed a technology, a company, and a set of people who can really deliver to that. And that's why we acquired that for supply chain security, otherwise known as cicd, security, c >>IDC security. Yeah. So how will that complement PRIs McCloud? >>Yeah, so look, if you look at our history lease over the last four years, we have been wanting to, our mission mission has been to build a single code to cloud platform. As you may know, there are over 3000 security vendors in the industry. And we said enough is enough. We need a platform player who can really deliver a unified cohesive platform solution for our customers because they're sick and tired of buying PI point product. So our mission has been to deliver that code to cloud platform supply chain security was a missing piece and we acquired them, it fits right really nicely into our portfolio of products and solution that customers have. And they'll have a single pin of glass with this. >>Yeah. So there's a lot going on. You've got, you've got an adversary that is incredibly capable. Yeah. These days and highly motivated and extremely sophisticated mentioned supply chain. It's caused a shift in, in CSO strategies, talking about the pandemic, of course we know work from home that changed things. You've mentioned public policy. Yeah. And, and so, and as well you have the cloud, cloud, you know, relatively new. I mean, it's not that new, but still. Yeah. But you've got the shared responsibility model and not, not only do you have the shared responsibility model, you have the shared responsibility across clouds and OnPrem. So yes, the cloud helps with security, but that the CISO has to worry about all these other things. The, the app dev team is being asked to shift left, you know, secure and they're not security pros. Yeah. And you know, kind audit is like the last line of defense. So I love this event, I love the cloud, but customers need help in making their lives simpler. Yeah. And the cloud in and of itself, because, you know, shared responsibility doesn't do that. Yeah. That's what Palo Alto and firms like yours come in. >>Absolutely. So look, Jim, this is a unable situation for a lot of the Cisco, simply because there are over 26 million developers, less than 3 million security professional. If you just look at all the announcement the AWS made, I bet you there were like probably over 2000 features. Yeah. I mean, they're shipping faster than ever before. Developers are moving really, really fast and just not enough security people to keep up with the velocity and the innovation. So you are right, while AWS will guarantee securing the infrastructure layer, but everything that is built on top of it, the new machine learning stuff, the new application, the new supply chain applications that are developed, that's the responsibility of the ciso. They stay up at night, they don't know what's going on because developers are bringing new services and new technology. And that's why, you know, we've always taken a platform approach where customers and the systems don't have to worry about it. >>What AWS new service they have, it's covered, it's secured. And that's why the adopters, McCloud and Palo Alto Networks, because regardless what developers bring, security is always there by their side. And so security teams need just a simple one click solution. They don't have to worry about it. They can sleep at night, keep the bad actors away. And, and that's, that's where Palo Alto Networks has been innovating in this area. AWS is one of our biggest partners and you know, we've integrated with, with a lot of their services. We launch about three integrations with their services. And we've been doing this historically for more and >>More. Are you still having conversations with the security folks? Or because security is a board level conversation, are your conversations going up a stack because this is a C-suite problem, this is a board level initiative? >>Absolutely. Look, you know, there was a time about four years ago, like the best we could do is director of security. Now it's just so CEO level conversation, board level conversation to your point, simply because I mean, if, if all your financial stuff is going to public cloud, all your healthcare data, all your supply chain data is going to public cloud, the board is asking very simple question, what are you doing to secure that? And to be honest, the question is simple. The answer's not because all the stuff that we talked about, too many applications, lots and lots of different services, different threat vectors and the bad actors, the bad guys are always a step ahead of the curve. And that's why this has become a board level conversation. They wanna make sure that things are secure from the get go before, you know, the enterprises go too deep into public cloud adoption. >>I mean there, there was shift topics a little bit. There was hope or kinda early this year that that cyber was somewhat insulated from the sort of macro press pressures. Nobody's safe. Even the cloud is sort of, you know, facing those, those headwinds people optimizing costs. But one thing when you talk to customers is, I always like to talk about that, that optiv graph. We've all seen it, right? And it's just this eye test of tools and it's a beautiful taxonomy, but there's just too many tools. So we're seeing a shift from point tools to platforms because obviously a platform play, and that's a way. So what are you seeing in the, in the field with customers trying to optimize their infrastructure costs with regard to consolidating to >>Platforms? Yeah. Look, you rightly pointed out one thing, the cybersecurity industry in general and Palo Alto networks, knock on wood, the stocks doing well. The macro headwinds hasn't impacted the security spend so far, right? Like time will tell, we'll, we'll see how things go. And one of the primary reason is that when you know the economy starts to slow down, the customers again want to invest in platforms. It's simple to deploy, simple to operationalize. They want a security partner of choice that knows that they, it's gonna be by them through the entire journey from code to cloud. And so that's why platform, especially times like these are more important than they've ever been before. You know, customers are investing in the, the, the product I lead at Palo Alto network called Prisma Cloud. It's in the cloud network application protection platform seen app space where once again, customers that investing in platform from quote to cloud and avoiding all the point products for sure. >>Yeah. Yeah. And you've seen it in, in Palo Alto's performance. I mean, not every cyber firm has is, is, >>You know, I know. Ouch. CrowdStrike Yeah. >>Was not. Well you saw that. I mean, and it was, and and you know, the large customers were continuing to spend, it was the small and mid-size businesses Yeah. That were, were were a little bit soft. Yeah. You know, it's a really, it's really, I mean, you see Okta now, you know, after they had some troubles announcing that, you know, their, their, their visibility's a little bit better. So it's, it's very hard to predict right now. And of course if TOMA Brava is buying you, then your stock price has been up and steady. That's, >>Yeah. Look, I think the key is to have a diversified portfolio of products. Four years ago before our CEO cash took over the reins of the company, we were a single product X firewall company. Right. And over time we have added XDR with the first one to introduce that recently launched x Im, you know, to, to make sure we build an NextGen team, cloud security is a completely net new investment, zero trust with access as workers started working remotely and they needed to make sure enterprises needed to make sure that they're accessing the applications securely. So we've added a lot of portfolio products over time. So you have to remain incredibly diversified, stay strong, because there will be stuff like remote work that slowed down. But if you've got other portfolio product like cloud security, while those secular tailwinds continue to grow, I mean, look how fast AWS is growing. 35, 40%, like $80 billion run rate. Crazy at that, that scale. So luckily we've got the portfolio of products to ensure that regardless of what the customer's journey is, macro headwinds are, we've got portfolio of solutions to help our customers. >>Talk a little bit about the AWS partnership. You talked about the run rate and I was reading a few days ago. You're right. It's an 82 billion arr, massive run rate. It's crazy. Well, what are, what is a Palo Alto Networks doing with aws and what's the value in it to help your customers on a secure digital transformation journey? >>Well, absolutely. We have been doing business with aws. We've been one of their security partners of choice for many years now. We have a presence in the marketplace where customers can through one click deploy the, the several Palo Alto Networks security solutions. So that's available. Like I said, we had launch partner to many, many new products and innovation that AWS comes up with. But always the day one partner, Adam was talking about some of those announcements and his keynote security data lake was one of those. And they were like a bunch of others related to compute and others. So we have been a partner for a long time, and look, AWS is an incredibly customer obsessed company. They've got their own security products. But if the customer says like, Hey, like I'd like to pick this from yours, but there's three other things from Palo Alto Networks or S MacCloud or whatever else that may be, they're open to it. And that's the great thing about AWS where it doesn't have to be wall garden open ecosystem, let the customer pick the best. >>And, and that's, I mean, there's, there's examples where AWS is directly competitive. I mean, my favorite example is Redshift and Snowflake. I mean those are directly competitive products, but, but Snowflake is an unbelievably great relationship with aws. They do cyber's, I think different, I mean, yeah, you got guard duty and you got some other stuff there. But generally speaking, the, correct me if I'm wrong, the e the ecosystem has more room to play on AWS than it may on some other clouds. >>A hundred percent. Yeah. Once again, you know, guard duty for examples, we've got a lot of customers who use guard duty and Prisma Cloud and other Palo Alto Networks products. And we also ingest the data from guard duty. So if customers want a single pane of glass, they can use the best of AWS in terms of guard duty threat detection, but leverage other technology suite from, you know, a platform provider like Palo Alto Networks. So you know, that that, you know, look, world is a complicated place. Some like blue, some like red, whatever that may be. But we believe in giving customers that choice, just like AWS customers want that. Not a >>Problem. And at least today they're not like directly, you know, in your space. Yeah. You know, and even if they were, you've got such a much mature stack. Absolutely. And my, my frankly Microsoft's different, right? I mean, you see, I mean even the analysts were saying that some of the CrowdStrike's troubles for, cuz Microsoft's got the good enough, right? So >>Yeah. Endpoint security. Yeah. And >>Yeah, for sure. So >>Do you have a favorite example of a customer where Palo Alto Networks has really helped them come in and, and enable that secure business transformation? Anything come to mind that you think really shines a light on Palo Alto Networks and what it's able to do? >>Yeah, look, we have customers across, and I'm gonna speak to public cloud in general, right? Like Palo Alto has over 60,000 customers. So we've been helping with that business transformation for years now. But because it's reinvented aws, the Prisma cloud product has been helping customers across different industry verticals. Some of the largest credit card processing companies, they can process transactions because we are running security on top of the workloads, the biggest financial services, biggest healthcare customers. They're able to put the patient health records in public cloud because Palo Alto Networks is helping them get there. So we are helping accelerated that digital journey. We've been an enabler. Security is often perceived as a blocker, but we have always treated our role as enabler. How can we get developers and enterprises to move as fast as possible? And like, my favorite thing is that, you know, moving fast and going digital is not a monopoly of just a tech company. Every company is gonna be a tech company Oh absolutely. To public cloud. Yes. And we want to help them get there. Yeah. >>So the other thing too, I mean, I'll just give you some data. I love data. I have a, ETR is our survey partner and I'm looking at Data 395. They do a survey every quarter, 1,250 respondents on this survey. 395 were Palo Alto customers, fortune 500 s and P 500, you know, big global 2000 companies as well. Some small companies. Single digit churn. Yeah. Okay. Yeah. Very, very low replacement >>Rates. Absolutely. >>And still high single digit new adoption. Yeah. Right. So you've got that tailwind going for you. Yeah, >>Right. It's, it's sticky because especially our, our main business firewall, once you deploy the firewall, we are inspecting all the network traffic. It's just so hard to rip and replace. Customers are getting value every second, every minute because we are thwarting attacks from public cloud. And look, we, we, we provide solutions not just product, we just don't leave the product and ask the customers to deploy it. We help them with deployment consumption of the product. And we've been really fortunate with that kind of gross dollar and netten rate for our customers. >>Now, before we wrap, I gotta tease, the cube is gonna be at Palo Alto Ignite. Yeah. In two weeks back here. I think we're at D mgm, right? We >>Were at D MGM December 13th and >>14th. So give us a little, show us a little leg if you would. What could we expect? >>Hey, look, I mean, a lot of exciting new things coming. Obviously I can't talk about it right now. The PR Inc is still not dry yet. But lots of, lots of new innovation across our three main businesses. Network security, public cloud, security, as well as XDR X. Im so stay tuned. You know, you'll, you'll see a lot of new exciting things coming up. >>Looking forward to it. >>We are looking forward to it. Last question on curf. You, if you had a billboard to place in New York Times Square. Yeah. You're gonna take over the the the Times Square Nasdaq. What does the billboard say about why organizations should be working with Palo Alto Networks? Yeah. To really embed security into their dna. Yeah. >>You know when Jim said Palo Alto Networks is the gold standard for security, I thought it was gonna steal it. I think it's pretty good gold standard for security. But I'm gonna go with our mission cyber security partner's choice. We want to be known as that and that's who we are. >>Beautifully said. Walker, thank you so much for joining David in the program. We really appreciate your insights, your time. We look forward to seeing you in a couple weeks back here in Vegas. >>Absolutely. Can't have enough of Vegas. Thank you. Lisa. >>Can't have in Vegas, >>I dunno about that. By this time of the year, I think we can have had enough of Vegas, but we're gonna be able to see you on the cubes coverage, which you could catch up. Palo Alto Networks show Ignite December, I believe 13th and 14th on the cube.net. We want to thank Anker Shaw for joining us. For Dave Ante, this is Lisa Martin. You're watching the Cube, the leader in live enterprise and emerging tech coverage.

(lively music) >> Good afternoon and welcome back to beautiful Las Vegas, Nevada, where we're here live from the show floor, all four days of AWS re:Invent. I'm Savannah Peterson, joined with my co-host Dave Vellante. Dave, how you doing? >> Good. Beautiful and chilly Las Vegas. Can't wait to get back to New England where it's warm. >> Balmy, New England this time of year in December. Wow, Dave, that's a bold statement. I am super excited about the conversation that we're going to be having next. And, you know, I'm not even going to tee it up. I just want to bring Dilip on. Dilip, thank you so much for being here. How you doing? >> Savannah, Dave, thank you so much. >> Hey, Dilip. >> Excited to be here. >> It's joy to have you. So, you have been working at Amazon for about 20 years. >> Almost. Almost. >> Yes. >> Feels like 20, 19 1/2. >> Which is very exciting. You've had a lot of roles. I'm going to touch on some of them, but you just came over to AWS from the physical retail side. Talk to me about that. >> Yup, so I've been to Amazon for 19 1/2 years. Done pricing, supply chain. I was Jeff Bezos technical advisor for a couple years. >> Casual name drop. >> Casual name drop. >> Savannah: But a couple people here for that name before. >> Humble brag, hashtag. And then I, for the last several years, I was leading our physical retail initiatives. We just walk out Amazon One, bringing convenience to physical spaces. And then in August, with like as those things were getting a lot of traction and we were selling to third parties, we felt that it would be better suited in AWS. And, but along with that, there was also another trend that's been brewing, which is, you know, companies have loved building on AWS. They love the infrastructure services, but increasingly, they're also asking us to build applications that are higher up in the stack. Solving key, turnkey business problems. Just walk out Amazon One or examples of that, Amazon Connect. We just recently announced supply chain, so now there's a bevy interesting services all coming together, higher up in the stack for customers. So it's an exciting time. >> It was interesting that you're able to, you know, transfer from that retail. I mean, normally, in historically, if you're within an industry, retail, manufacturing, automotive whatever. You were kind as locked in a little bit. >> Dilip: Siloed a little bit. Yeah, yeah, yeah. >> Because they had their own, your own value chain. And I guess, data has changed that maybe, that you can traverse now. >> Yeah, if you think about the things that we did, even when we were in retail, the tenants was less about the industries and more about how can we bring convenience to physical spaces? The fact that you don't like to wait in line is no more like likely, you know, five years from now than it is today. So, it's a very durable tenant, but it's equally applicable whether you're in a grocery store, a convenience store, a stadium, an airport. So it actually transcends any, and like supply chain, think of supply chain. Supply chain isn't, you know, targeted to any one particular industry. It has broad applicability. So these things are very, you know, horizontally applicable. >> Anything that makes my life easier, I'm down. >> Savannah: We're all here for the easy button. We've been talking about it a bit this week. I'm in. And the retail store, I mean, I'm in San Francisco. I've had the experience of going through. Very interesting and seamless journey, honestly. It's very exciting. So tell us a little bit more about the applications group at AWS. >> Yup. So as I said, you know, we are, the applications group is a combination of several services. You know, we have communication developer services, which is the ability to add simple email service or video and embed video, voice chat using a chime SDK. In a higher up in the stack, we are taking care of things that IT administrators have to deal with where you can provision an entire desktop with the workspaces or provide a femoral access to it. And then as you go up even higher up in the stack, you have productivity applications like AWS Wicker, which we just did GA, you know, last week in AWS Clean Rooms which we announced as a service in preview. And then you have, you know, Connect, which is our cloud contact center, AWS supply chain. Just walk out Amazon One, it just feels like we're getting started. >> Just a couple things going on. >> So, clean rooms. Part of the governance play, part of data sharing. Can you explain, you know, we were talking offline, but I remember back in the disk drive days. We were in a clean room, they'd show you the clean room, you couldn't go near it unless you had a hazmat suit on. So now you're applying that to data. Explain that concept. >> Yeah, so the companies across, you know, financial services or healthcare, advertising, they all want to be able to combine and pull together data`sets with their partners in order to get these collaborative insights. The problem is either the data's fragmented, it's siloed or you have, you know, data governance issues that's preventing them from sharing. And the key requirement is that they want to be able to share this data without exposing any of the underlying data. Clean rooms are always emerged as a solution to that, but the problem with that is that they're hard to maintain. They're expensive. You have to write complex privacy queries. And if you make a mistake, you risk exposing the same data that you've been, you know, studiously trying to protect. >> Trying to protect. >> You know, take advertising as an industry, as an example. You know, advertisers care about, is my ad effective? But it turns out that if you're an advertiser and let's say you're a Nike or some other advertiser and your pop, you know, you place an ad on the website. Well, you want to stop showing the ad to people who have already purchased the product. However, people who purchased the product,- >> Savannah: It happens all the time. >> that purchasing data is not accessible to them easily. But if you could combine those insights, you know, the publishers benefit, advertisers benefits. So AWS Clean Rooms is that service that allows you very easily to be able to collaborate with a group of folks and then be able to gain these collaborative insights. >> And the consumers benefit. I mean, how many times you bought, you search it. >> It happens all the time. >> They know. And like, I just bought that guys, you know? >> Yeah, no, exactly. >> Four weeks. >> And I'm like, you don't need to serve me that, you know? And we understand the marketing backend. And it's just a waste of money and energy and resources. I mean, we're talking about sustainability as well. I don't think supply chain has ever had a hotter moment than it's had the last two and a half years. Tell me more about the announcement. >> Yup, so super excited about this. As you know, as you said, supply chains have always been very critical and very core for companies. The pandemic exacerbated it. So, ours way of sort of thinking about supply chains is to say that, you know, companies have taken, over the years many, like dozens, like millions and millions of dollars of investment in building their own supply chains. But the problem with supply chains is that the reason that they're not as functional as they could be is because of the lack of visibility. Because they're strung together very many disparate systems, that lack of visibility affects agility. And so, our approach in it was to say that, well, if we could have folks use their existing supply chain what can we do to improve the investment on the ROI of what they're getting? By creating a layer on top of it, that provides them that insights, connects all of these disparate data and then provides them insights to say, well, you know, here's where you overstock, here's where you under stock. You know, this is the, you know, the carbon emission impact of being able to transfer something. So like rather without requiring people to re-platform, what's the way that we can add value in it? And then also build upon Amazon's, you know, years of supply chain experience, to be able to build these predictive analytics for customers. >> So, that's a good, I like that you started with the why. >> Yes. >> Right now, what is it? It's an abstraction layer and then you're connecting into different data points. >> Yes, that's correct. >> Injecting ML. >> Feel like you can pick in, like if you think about supply chain, you can have warehouse management systems, order management systems. It could be in disparate things. We use ML to be able to bring all of this disparate data in and create our unified data lake. Once you have that unified data lake, you can then run an insights layer on top of it to be able to say, so that as the data changes, supply chain is not a static thing. Data's constantly changing. As the data's changing, the data lake now reflects the most up-to-date information. You can have alerts and insights set up on it to say that, what are the kinds of things that you're interested in? And then more importantly, supply chain and agility is about communication. In order to be able to make certain things happen, you need to be able to communicate, you need to make sure that everyone's on the same page. And we allow for a lot of the communication and collaboration tools to be built within this platform so that you're not necessarily leaving to go and toggle from one place to the other to solve your problems. >> And in the pie chart of how people spend their time, they're spending a lot less time communicating and being proactive. >> That's correct. >> And getting ahead of the curve. They're spending more time trying to figure out actually what's going on. >> Yes. >> And that's the problem that you're going to solve. >> Well, and it ensures that the customer at the other end of that supply chain experience is going to have their expectations managed in terms of when their good might get there or whatever's going to happen. >> Exactly. >> I feel like that expectation management has been such a big part of it. Okay, I just have to ask because I'm very curious. What was it like advising Jeff? >> Quite possibly the best job that I've ever had. You know, he's a fascinating individual. >> Did he pay you to say that? >> Nope. But I would've, like, I would've done it for like, it's remarkable seeing how he thinks and his approach to problem solving. It is, you know, you could be really tactical and go very deep. You could be extremely strategic. And to be able to sort of move effortlessly between those two is a unique skill. I learned a lot. >> Yeah, absolutely. So what made you want to evolve your career at Amazon after that? 'Cause I see on your LinkedIn, you say, it was the best job you ever had. With curiosity? >> Yeah, so one of the things, so the role is designed for you to be able to transition to something new. >> Savannah: Oh, cool. >> So after I finished that role, we were just getting into our foray with physical stores. And the idea between physical stores is that, you and I as consumers, we all have a lot of choices for physical stores. You know, there's a lot of options, there's a lot of formats. And so the last thing we wanted to do is come up with another me too offering. So, our approach was that what can we do to improve convenience in physical stores? That's what resulted in just walk out to Amazon Go. That's what resulted in Amazon One, which is another in a fast, convenient, contactless way to pay using the power of your palm. And now, what started in Amazon retail is now expanded to several third parties in, you know, stadiums, convention centers, airports. >> Airport, I just had, was in the Houston airport and got to do a humanless checkout. >> Dilip: Exactly. >> And actually in Honolulu a couple weeks ago as well too. Yeah, so we're going to see more and more of this. >> Yes. >> So what Amazon, I think has over a million employees. A lot of those are warehouse employees. But what advice would you give to somebody who's somewhere inside of Amazon, maybe they're on AWS, maybe they're Amazon. What advice would you give somebody inside that's maybe, you know, hey, I've been at this job for five, six years, three, four years, whatever it is. I want to do something else. And there's so much opportunity inside Amazon, right? What would you advise them? >> My single advice, which is actually transferable and I use it for myself is choose something that makes you a little uncomfortable. >> Dave: Get out of your comfort zone. >> It's like, you got to do that. It's like, it's not the easiest thing to hear, but it's also the most satisfying. Because almost every single time that I've done it for myself, it's resulted in like, you don't really know what the answer is. You don't really know exactly where you're going to end up, but the process and the journey through it, if you experience a little bit of discomfort constantly, it makes you non complacent. It makes you sort of not take the job, sort of in a stride. You have to be on it to do it. So that's the advice that I would give anyone. >> Yeah, that's good. So something that's maybe adjacent and maybe not completely foreign to you, but also something that, you know, you got to go dig a little bit and learn. >> You're planning a career change over here, Dave? >> No, I know a lot of people in Amazon are like, hey, I'm trying to figure out what I want to do next. I mean, I love it here. I live by the LPS, you know, but, and there's so much to choose from. >> It is, you know, when I joined in 2003, there were so many things that we were sort of doing today. None of those existed. It's a fascinating company. And the evolution, you could be in 20 different places and the breadth of the kinds of things that, you know, the Amazon experience provides is timeless. It's fascinating. >> And, you know, you look at a company like Amazon, and, you know, it's so amazing. You look at this ecosystem. I've been around- >> Even a show floor. >> I've been around a lot of time. And the show floor says it all. But I've seen a lot of, you know, waves. And each subsequent wave, you know, we always talk about how many companies were in the Fortune 1000 and aren't anymore. And, but the leaders, you know, survive and they thrive. And I think it's fascinating to try to better understand the culture that enables that. You know, you look at a company like Microsoft that was irrelevant and then came back. You know, even IBM was on death store for a while and they come back and so they. And so, but Amazon just feels, you know, at the moment you feel like, "Oh wow, nothing can stop this machine." 'Cause everybody's trying to disrupt Amazon and then, you know, only the paranoid survive, all that stuff. But it's not like, past is not prologue, all right? So that's why I asked these questions. And you just said that a lot of the services today that although the ideas didn't even exist, I mean, walkout. I mean, that's just amazing. >> I think one of the things that Amazon does really well culturally is that they create the single threaded leadership. They give people focus. If you have to get something done, you have to give people focus. You can't distract them with like seven different things and then say that, oh, by the way, your eighth job is to innovate. It just doesn't work that way. It's like it's hard. Like it can be- >> And where were the energy come from that? >> Exactly. And so giving people that single threaded focus is super important. >> Frank Slootman, the CEO of Snowflake, has a great quote. He wrote on his book. He said, "If you got 14 priorities, you got none." And he asks,- >> Well said. >> he challenges people. If you had to give up everything and do only one thing for the next 365 days, what would that be? It's a really hard question to answer. >> I feel like as we're around New Year's resolution times. I mean when we thinking about that, maybe we can all share our one thing. So, Dilip, you've been with the the applications team for five months. What's coming up next? >> Well, as I said, you know, it feels like it's still day one for applications. If you think about the things, the news that we introduced and the several services that we introduced, it has applicability across a variety of horizontal industries. But then we're also feeling that there's considerable vertical applications that can be built for specific things. Like, it could be in advertising, it could be in financial services, it could be in manufacturing. The opportunities are endless. I think the notion of people wanting applications higher up in the stack and a little more turnkey solutions is also, it's not new for us, but it's also new and creative too. You know, AWS has traditionally been doing. >> So again, this relates to what we were sort of talking about before. And maybe, this came from Jazzy or maybe it came from Bezos. But you hear a lot, it's okay to be misunderstood or if we were misunderstood for a long time. So when people hear up the stack, they think, when you think about apps, you know, in the last 10 years it was taking on-prem and bringing it into the cloud. Okay, you saw that with CREM, email, CRM, service management, you know, data warehouses, et cetera. Amazon is thinking about this in a different way. It's like you're looking at the world saying, okay, how can we improve whatever? Workflows, people's lives, doing something that's not been done before? And that seems to be the kind of applications that you guys are thinking about building. >> Yeah. >> And that's unique. It's not just, okay, we're going to take something on-prem put it in the cloud. Been there, done that. That S-curve is sort of flattening now. But there's a new S-curve which is completely new workflows and innovations and processes that we really haven't thought about yet. Or you're thinking about, I presume. >> Yeah. Having said that, I'd also like to sort of remind folks that when you consider the, you know, the entire spend, the portion of workloads that are running in the cloud is a teeny tiny fraction. It's like less than 5%, like 4% or something like that. So it's a very, there's still plenty of things that can sort of move to the cloud. But you're right that there is another trend of where in the stack and the types of applications that you can provide as well. >> Yeah, new innovation that haven't well thought of yet. >> So, Dilip, we have a new tradition here on theCUBE at re:Invent. Where we're looking for your 30 minute Instagram reel, your hot take, biggest key theme, either for you, your team, or just general vibe from the show. >> General vibe from the show. Well, 19 1/2 years at Amazon, this is actually my first re:Invent, believe it or not. This is my, as a AWS employee now, as re:Invent with like launching services. So that's the first. I've been to re:Invent before, but as an attendee rather than as a person who's, you know, a contributing number of the workforce. >> Working actually? >> If you will. >> Actually doing your job. >> And so I'm just amazed at the energy and the breadth. And the, you know, from the partners to the customers to the diversity of people who are coming here from everywhere. I had meetings from people in New Zealand. Like, you know, the UK, like customers are coming at us from like very many different places. And it's fascinating for me to see. It's new for me as well given, you know, some of my past experience. But this is a, it's been a blast. >> People are pumped. >> People are pumped. >> They can't believe the booth traffic. Not only that quality. >> Right. All of our guests have talked about that. >> Like, yeah, you know, we're going to throw half of these leads away, but they're saying no, I'm having like really substantive conversations with business people. This is, I think, my 10th re:Invent. And the first one was mostly developers. And I'm like, what are you talking about? And, you know, so. Now it's a lot more business people, a lot of developers too. >> Yeah. >> It's just. >> The community really makes it. Dilip, thank you so much for joining us today on theCube. >> Thank you for having me. >> You're fantastic. I could ask you a million questions. Be sure and tell Jeff that we said hi. >> Will do. >> Savannah: Next time you guys are hanging out. And thank all of you. >> You want to go into space? >> Yeah. Yes, yes, absolutely. I'm perhaps the most space obsessed on the show. And with that, we will continue our out of this world coverage shortly from fabulous Las Vegas where we are at AWS re:Invent. It is day four with Dave Vellante. I'm Savannah Peterson and you're watching theCUBE, the leader in high tech coverage. (lively music)

(upbeat music) >> Good morning everybody. Welcome back to Las Vegas. This is day four of theCUBE's wall-to-wall coverage of our Super Bowl, aka AWS re:Invent 2022. I'm here with my co-host, Paul Gillin. My name is Dave Vellante. Sanjay Poonen is in the house, CEO and president of Cohesity. He's sitting in as our guest market watcher, market analyst, you know, deep expertise, new to the job at Cohesity. He was kind enough to sit in, and help us break down what's happening at re:Invent. But Paul, first thing, this morning we heard from Werner Vogels. He was basically given a masterclass on system design. It reminded me of mainframes years ago. When we used to, you know, bury through those IBM blue books and red books. You remember those Sanjay? That's how we- learned back then. >> Oh God, I remember those, Yeah. >> But it made me think, wow, now you know IBM's more of a systems design, nobody talks about IBM anymore. Everybody talks about Amazon. So you wonder, 20 years from now, you know what it's going to be. But >> Well- >> Werner's amazing. >> He pulled out a 24 year old document. >> Yup. >> That he had written early in Amazon's evolution about synchronous design or about essentially distributed architectures that turned out to be prophetic. >> His big thing was nature is asynchronous. So systems are asynchronous. Synchronous is an illusion. It's an abstraction. It's kind of interesting. But, you know- >> Yeah, I mean I've had synonyms for things. Timeless architecture. Werner's an absolute legend. I mean, when you think about folks who've had, you know, impact on technology, you think of people like Jony Ive in design. >> Dave: Yeah. >> You got to think about people like Werner in architecture and just the fact that Andy and the team have been able to keep him engaged that long... I pay attention to his keynote. Peter DeSantis has obviously been very, very influential. And then of course, you know, Adam did a good job, you know, watching from, you know, having watched since I was at the first AWS re:Invent conference, at time was President SAP and there was only a thousand people at this event, okay? Andy had me on stage. I think I was one of the first guest of any tech company in 2011. And to see now this become like, it's a mecca. It's a mother of all IT events, and watch sort of even the transition from Andy to Adam is very special. I got to catch some of Ruba's keynote. So while there's some new people in the mix here, this has become a force of nature. And the last time I was here was 2019, before Covid, watched the last two ones online. But it feels like, I don't know 'about what you guys think, it feels like it's back to 2019 levels. >> I was here in 2019. I feel like this was bigger than 2019 but some people have said that it's about the same. >> I think it was 60,000 versus 50,000. >> Yes. So close. >> It was a little bigger in 2019. But it feels like it's more active. >> And then last year, Sanjay, you weren't here but it was 25,000, which was amazing 'cause it was right in that little space between Omicron, before Omicron hit. But you know, let me ask you a question and this is really more of a question about Amazon's maturity and I know you've been following them since early days. But the way I get the question, number one question I get from people is how is Amazon AWS going to be different under Adam than it was under Andy? What do you think? >> I mean, Adam's not new because he was here before. In some senses he knows the Amazon culture from prior, when he was running sales and marketing prior. But then he took the time off and came back. I mean, this will always be, I think, somewhat Andy's baby, right? Because he was the... I, you know, sent him a text, "You should be really proud of what you accomplished", but you know, I think he also, I asked him when I saw him a few weeks ago "Are you going to come to re:Invent?" And he says, "No, I want to leave this to be Adam's show." And Adam's going to have a slightly different view. His keynotes are probably half the time. It's a little bit more vision. There was a lot more customer stories at the beginning of it. Taking you back to the inspirational pieces of it. I think you're going to see them probably pulling up the stack and not just focused in infrastructure. Many of their platform services are evolved. Many of their, even application services. I'm surprised when I talk to customers. Like Amazon Connect, their sort of call center type technologies, an app layer. It's getting a lot. I mean, I've talked to a couple of Fortune 500 companies that are moving off Ayer to Connect. I mean, it's happening and I did not know that. So it's, you know, I think as they move up the stack, the platform's gotten more... The data centric stack has gotten, and you know, in the area we're working with Cohesity, security, data protection, they're an investor in our company. So this is an important, you know, both... I think tech player and a partner for many companies like us. >> I wonder the, you know, the marketplace... there's been a big push on the marketplace by all the cloud companies last couple of years. Do you see that disrupting the way softwares, enterprise software is sold? >> Oh, for sure. I mean, you have to be a ostrich with your head in the sand to not see this wave happening. I mean, what's it? $150 billion worth of revenue. Even though the growth rates dipped a little bit the last quarter or so, it's still aggregatively between Amazon and Azure and Google, you know, 30% growth. And I think we're still in the second or third inning off a grand 1 trillion or 2 trillion of IT, shifting not all of it to the cloud, but significantly faster. So if you add up all of the big things of the on-premise world, they're, you know, they got to a certain size, their growth is stable, but stalling. These guys are growing significantly faster. And then if you add on top of them, platform companies the data companies, Snowflake, MongoDB, Databricks, you know, Datadog, and then apps companies on top of that. I think the move to the Cloud is inevitable. In SaaS companies, I don't know why you would ever implement a CRM solution on-prem. It's all gone to the Cloud. >> Oh, it is. >> That happened 15 years ago. I mean, begin within three, five years of the advent of Salesforce. And the same thing in HR. Why would you deploy a HR solution now? You've got Workday, you've got, you know, others that are so some of those apps markets are are just never coming back to an on-prem capability. >> Sanjay, I want to ask you, you built a reputation for being able to, you know, forecast accurately, hit your plan, you know, you hit your numbers, you're awesome operator. Even though you have a, you know, technology degree, which you know, that's a two-tool star, multi-tool star. But I call it the slingshot economy. This is like, I mean I've seen probably more downturns than anybody in here, you know, given... Well maybe, maybe- >> Maybe me. >> You and I both. I've never seen anything like this, where where visibility is so unpredictable. The economy is sling-shotting. It's like, oh, hurry up, go Covid, go, go go build, build, build supply, then pull back. And now going forward, now pulling back. Slootman said, you know, on the call, "Hey the guide, is the guide." He said, "we put it out there, We do our best to hit it." But you had CrowdStrike had issues you know, mid-market, ServiceNow. I saw McDermott on the other day on the, on the TV. I just want to pay, you know, buy from the guy. He's so (indistinct) >> But mixed, mixed results, Salesforce, you know, Octa now pre-announcing, hey, they're going to be, or announcing, you know, better visibility, forward guide. Elastic kind of got hit really hard. HPE and Dell actually doing really well in the enterprise. >> Yep. >> 'Course Dell getting killed in the client. But so what are you seeing out there? How, as an executive, do you deal with such poor visibility? >> I think, listen, what the last two or three years have taught us is, you know, with the supply chain crisis, with the surge that people thought you may need of, you know, spending potentially in the pandemic, you have to start off with your tech platform being 10 x better than everybody else. And differentiate, differentiate. 'Cause in a crowded market, but even in a market that's getting tougher, if you're not differentiating constantly through technology innovation, you're going to get left behind. So you named a few places, they're all technology innovators, but even if some of them are having challenges, and then I think you're constantly asking yourselves, how do you move from being a point product to a platform with more and more services where you're getting, you know, many of them moving really fast. In the case of Roe, I like him a lot. He's probably one of the most savvy operators, also that I respect. He calls these speedboats, and you know, his core platform started off with the firewall network security. But he's built now a very credible cloud security, cloud AI security business. And I think that's how you need to be thinking as a tech executive. I mean, if you got core, your core beachhead 10 x better than everybody else. And as you move to adjacencies in these new platforms, have you got now speedboats that are getting to a point where they are competitive advantage? Then as you think of the go-to-market perspective, it really depends on where you are as a company. For a company like our size, we need partners a lot more. Because if we're going to, you know, stand on the shoulders of giants like Isaac Newton said, "I see clearly because I stand on the shoulders giants." I need to really go and cultivate Amazon so they become our lead partner in cloud. And then appropriately Microsoft and Google where I need to. And security. Part of what we announced last week was, last month, yeah, last couple of weeks ago, was the data security alliance with the biggest security players. What was I trying to do with that? First time ever done in my industry was get Palo Alto, CrowdStrike, Wallace, Tenable, CyberArk, Splunk, all to build an alliance with me so I could stand on their shoulders with them helping me. If you're a bigger company, you're constantly asking yourself "how do you make sure you're getting your, like Amazon, their top hundred customers spending more with that?" So I think the the playbook evolves, and I'm watching some of these best companies through this time navigate through this. And I think leadership is going to be tested in enormously interesting ways. >> I'll say. I mean, Snowflake is really interesting because they... 67% growth, which is, I mean, that's best in class for a company that's $2 billion. And, but their guide was still, you know, pretty aggressive. You know, so it's like, do you, you know, when it when it's good times you go, "hey, we can we can guide conservatively and know we can beat it." But when you're not certain, you can't dial down too far 'cause your investors start to bail on you. It's a really tricky- >> But Dave, I think listen, at the end of the day, I mean every CEO should not be worried about the short term up and down in the stock price. You're building a long-term multi-billion dollar company. In the case of Frank, he has, I think I shot to a $10 billion, you know, analytics data warehousing data management company on the back of that platform, because he's eyeing the market that, not just Teradata occupies today, but now Oracle occupies or other databases, right? So his tam as it grows bigger, you're going to have some of these things, but that market's big. I think same with Palo Alto. I mean Datadog's another company, 75% growth. >> Yeah. >> At 20% margins, like almost rule of 95. >> Amazing. >> When they're going after, not just the observability market, they're eating up the sim market, security analytics, the APM market. So I think, you know, that's, you look at these case studies of companies who are going from point product to platforms and are steadily able to grow into new tams. You know, to me that's very inspiring. >> I get it. >> Sanjay: That's what I seek to do at our com. >> I get that it's a marathon, but you know, when you're at VMware, weren't you looking at the stock price every day just out of curiosity? I mean listen, you weren't micromanaging it. >> You do, but at the end of the day, and you certainly look at the days of earnings and so on so forth. >> Yeah. >> Because you want to create shareholder value. >> Yeah. >> I'm not saying that you should not but I think in obsession with that, you know, in a short term, >> Going to kill ya. >> Makes you, you know, sort of myopically focused on what may not be the right thing in the long term. Now in the long arc of time, if you're not creating shareholder value... Look at what happened to Steve Bomber. You needed Satya to come in to change things and he's created a lot of value. >> Dave: Yeah, big time. >> But I think in the short term, my comments were really on the quarter to quarter, but over a four a 12 quarter, if companies are growing and creating profitable growth, they're going to get the valuation they deserve. >> Dave: Yeah. >> Do you the... I want to ask you about something Arvind Krishna said in the previous IBM earnings call, that IT is deflationary and therefore it is resistant to the macroeconomic headwinds. So IT spending should actually thrive in a deflation, in a adverse economic climate. Do you think that's true? >> Not all forms of IT. I pay very close attention to surveys from, whether it's the industry analysts or the Morgan Stanleys, or Goldman Sachs. The financial analysts. And I think there's a gluc in certain sectors that will get pulled back. Traditional view is when the economies are growing people spend on the top line, front office stuff, sales, marketing. If you go and look at just the cloud 100 companies, which are the hottest private companies, and maybe with the public market companies, there's way too many companies focused on sales and marketing. Way too many. I think during a downsizing and recession, that's going to probably shrink some, because they were all built for the 2009 to 2021 era, where it was all about the top line. Okay, maybe there's now a proposition for companies who are focused on cost optimization, supply chain visibility. Security's been intangible, that I think is going to continue to an investment. So I tell, listen, if you are a tech investor or if you're an operator, pay attention to CIO priorities. And right now, in our business at Cohesity, part of the reason we've embraced things like ransomware protection, there is a big focus on security. And you know, by intelligently being a management and a security company around data, I do believe we'll continue to be extremely relevant to CIO budgets. There's a ransomware, 20 ransomware attempts every second. So things of that kind make you relevant in a bank. You have to stay relevant to a buying pattern or else you lose momentum. >> But I think what's happening now is actually IT spending's pretty good. I mean, I track this stuff pretty closely. It's just that expectations were so high and now you're seeing earnings estimates come down and so, okay, and then you, yeah, you've got the, you know the inflationary factors and your discounted cash flows but the market's actually pretty good. >> Yeah. >> You know, relative to other downturns that if this is not a... We're not actually not in a downturn. >> Yeah. >> Not yet anyway. It may be. >> There's a valuation there. >> You have to prepare. >> Not sales. >> Yeah, that's right. >> When I was on CNBC, I said "listen, it's a little bit like that story of Joseph. Seven years of feast, seven years of famine." You have to prepare for potentially your worst. And if it's not the worst, you're in good shape. So will it be a recession 2023? Maybe. You know, high interest rates, inflation, war in Russia, Ukraine, maybe things do get bad. But if you belt tightening, if you're focused in operational excellence, if it's not a recession, you're pleasantly surprised. If it is one, you're prepared for it. >> All right. I'm going to put you in the spot and ask you for predictions. Expert analysis on the World Cup. What do you think? Give us the breakdown. (group laughs) >> As my... I wish India was in the World Cup, but you can't get enough Indians at all to play soccer well enough, but we're not, >> You play cricket, though. >> I'm a US man first. I would love to see one of Brazil, or Argentina. And as a Messi person, I don't know if you'll get that, but it would be really special for Messi to lead, to end his career like Maradonna winning a World Cup. I don't know if that'll happen. I'm probably going to go one of the Latin American countries, if the US doesn't make it far enough. But first loyalty to the US team, and then after one of the Latin American countries. >> And you think one of the Latin American countries is best bet to win or? >> I don't know. It's hard to tell. They're all... What happens now at this stage >> So close, right? >> is anybody could win. >> Yeah. You just have lots of shots of gold. I'm a big soccer fan. It could, I mean, I don't know if the US is favored to win, but if they get far enough, you get to the finals, anybody could win. >> I think they get Netherlands next, right? >> That's tough. >> Really tough. >> But... The European teams are good too, but I would like to see US go far enough, and then I'd like to see Latin America with team one of Argentina, or Brazil. That's my prediction. >> I know you're a big Cricket fan. Are you able to follow Cricket the way you like? >> At god unearthly times the night because they're in Australia, right? >> Oh yeah. >> Yeah. >> I watched the T-20 World Cup, select games of it. Yeah, you know, I'm not rapidly following every single game but the World Cup games, I catch you. >> Yeah, it's good. >> It's good. I mean, I love every sport. American football, soccer. >> That's great. >> You get into basketball now, I mean, I hope the Warriors come back strong. Hey, how about the Warriors Celtics? What do we think? We do it again? >> Well- >> This year. >> I'll tell you what- >> As a Boston Celtics- >> I would love that. I actually still, I have to pay off some folks from Palo Alto office with some bets still. We are seeing unprecedented NBA performance this year. >> Yeah. >> It's amazing. You look at the stats, it's like nothing. I know it's early. Like nothing we've ever seen before. So it's exciting. >> Well, always a pleasure talking to you guys. >> Great to have you on. >> Thanks for having me. >> Thank you. Love the expert analysis. >> Sanjay Poonen. Dave Vellante. Keep it right there. re:Invent 2022, day four. We're winding up in Las Vegas. We'll be right back. You're watching theCUBE, the leader in enterprise and emerging tech coverage. (lighthearted soft music)

>> Hello friends and welcome back to Fabulous Las Vegas, Nevada. We are here at AWS re:Invent in the heat of day three. Very exciting time. My name is Savannah Peterson, joined with John Furrier here on theCUBE. John, what's your, what's your big hot take from the day? Just from today. >> So right now the velocity of content is continuing to flow on theCUBE. Thank you, everyone, for watching. The security conversations. Also, the cost tuning of the cloud kind of vibe is going on. You're hearing that with the looming recession, but if you look at the show it's the bulk of the keynote time spent talking is on data and security together. So Security, Security Lake, Amazon, they continue to talk about security. This next segment's going to be awesome. We have a multi-, eight-time CUBE alumni coming back and great conversation about security. I'm looking forward to this. >> Alumni VIP, I know, it's so great. Actually, both of these guests have been on theCUBE before so please welcome Dan and Haiyan. Thank you both for being here from F5. How's the show going? You're both smiling and we're midway through day three. Good? >> It's so exciting to be here with you all and it's a great show. >> Awesome. Dan, you having a good time too? >> It's wearing me out. I'm having a great time. (laughter) >> It's okay to be honest. It's okay to be honest. It's wearing out our vocal cords for sure up here, but it is definitely a great time. Haiyan, can you tell me a little bit about F5 just in case the audience isn't familiar? >> Sure, so F5 we specialize in application delivery and security. So our mission is to deliver secure and optimize any applications, any APIs, anywhere. >> I can imagine you have a few customers in the house. >> Absolutely. >> Yeah, that's awesome. So in terms of a problem that, well an annoyance that we've all had, bots. We all want the anti-bots. You have a unique solution to this. How are you helping AWS customers with bots? Let's send it to you. >> Well we, we collect client side signals from all devices. We might study how it does floating point math or how it renders emojis. We analyze those signals and we can make a real time determination if the traffic is from a bot or not. And if it's from a bot, we could take mitigating action. And if it's not, we just forward it on to origin. So client side signals are really important. And then the second aspect of bot protection I think is understanding that bot's retool. They become more sophisticated. >> Savannah: They learn. >> They learn. >> They unfortunately learn as well. >> Exactly, yeah. So you have to have a second stage what we call retrospective analysis where you're looking over all the historical transactions, looking for anything that may have been missed by a realtime defense and then updating that stage one that real time defense to deal with the newly discovered threat. >> Let's take a step back for a second. I want to just set the table in the context for the bot conversation. Bots, automation, that's, people know like spam bots but Amazon has seen the bot networks develop. Can you scope the magnitude and the size of the problem of bots? What is the problem? And give a size of what this magnitude of this is. >> Sure, one thing that's important to realize is not all bots are bad. Okay? Some bots are good and you want to identify the automation from those bots and allow listed so you don't interfere with what they're doing. >> I can imagine that's actually tricky. >> It is, it is. Absolutely. Yeah. >> Savannah: Nuanced. >> Yeah, but the bad bots, these are the ones that are attempting credential stuffing attacks, right? They're trying username password pairs against login forms. And because of consumer habits to reuse usernames and passwords, they end up taking over a lot of accounts. But those are the bookends. There are all sorts of types of bots in between those two bookends. Some are just nuisance, like limited time offer bots. You saw some of this in the news recently with Ticketmaster. >> That's a spicy story. >> Yeah, it really is. And it's the bots that is causing that problem. They use automation to buy all these concert tickets or sneakers or you know, any limited time offer project. And then they resell those on the secondary market. And we've done analysis on some of these groups and they're making millions of dollars. It isn't something they're making like 1200 bucks on. >> I know Amazon doesn't like to talk about this but the cloud for its double edged sword that it is for all the greatness of the agility spinning up resources bots have been taking advantage of that same capability to hide, change, morph. You've seen the matrix when the bots attacked the ship. They come out of nowhere. But Amazon actually has seen the bot problem for a long time, has been working on it. Talk about that kind of evolution of how this problem's being solved. What's Amazon doing about, how do you guys help out? >> Yeah, well we have this CloudFront connector that allows all Amazon CloudFront customers to be able to leverage this technology very, very quickly. So what historically was available only to like, you know the Fortune 500 at most of the global 2000 is now available to all AWS customers who are using CloudFront just by really you can explain how do they turn it on in CloudFront? >> Yeah. So I mean CloudFront technologies like that is so essential to delivering the digital experience. So what we do is we do a integration natively. And so if your CloudFront customers and you can just use our bot defense solution by turning on, you know, that traffic. So go through our API inspection, go through our bot inspection and you can benefit from all the other efficiencies that we acquired through serving the highest and the top institutions in the world. >> So just to get this clarification, this is a super important point. You said it's native to the service. I don't have to bolt it on? Is it part of the customer experience? >> Yeah, we basically built the integration. So if you're already a CloudFront customer and you have the ability to turn on our bot solutions without having to do the integration yourself. >> Flick a switch and it's on. >> Haiyan: Totally. >> Pretty much. >> Haiyan: Yeah. >> That's how I want to get rid of all the spam in my life. We've talked a lot about the easy button. I would also like the anti-spam button if we're >> Haiyan: 100% >> Well we were talking before you came on camera that there's a potentially a solution you can sit charge. There are techniques. >> Yeah. Yeah. We were talking about the spam emails and I thought they just charge, you know 10th of a penny for every sent email. It wouldn't affect me very much. >> What's the, are people on that? You guys are on this but I mean this is never going to stop. We're going to see the underbelly of the web, the dark web continue to do it. People are harvesting past with the dark web using bots that go in test challenge credentials. I mean, it's just happening. It's never going to stop. What's, is it going to be that cat and mouse game? Are we going to see solutions? What's the, when are we going to get some >> Well it's certainly not a cat and mouse game for F5 customers because we win that battle every time. But for enterprises who are still battling the bots as a DIY project, then yes, it's just going to be a cat and mouse. They're continuing to block by IP, you know, by rate limiting. >> Right, which is so early 2000's. >> Exactly. >> If we're being honest. >> Exactly. And the attackers, by the way, the attackers are now coming from hundreds of thousands or even millions of IP addresses and some IPs are using one time. >> Yeah, I mean it seems like such an easy problem to circumnavigate. And still be able to get in. >> What are I, I, let's stick here for a second. What are some of the other trends that you're seeing in how people are defending if they're not using you or just in general? >> Yeah, maybe I'll add to to that. You know, when we think about the bot problem we also sort of zoom out and say, Hey, bot is only one part of the problem when you think about the entire digital experience the customer experiencing, right? So at F5 we actually took a more holistic sort of way to say, well it's about protecting the apps and applications and the APIs that's powering all of those. And we're thinking not only the applications APIs we're thinking the infrastructure that those API workloads are running. So one of the things we're sharing since we acquired Threat Stack, we have been busy doing integrations with our distributed cloud services and we're excited. In a couple weeks you will hear announcement of the integrated solution for our application infrastructure protection. So that's just another thing. >> On that Threat Stack, does that help with that data story too? Because it's a compliance aspect as well. >> Yeah, it helps with the telemetries, collecting more telemetries, the data story but is also think about applications and APIs. You can only be as secure as the infrastructure you're running on it, right? So the infrastructure protection is a key part of application security. And the other dimension is not only we can help with the credentials, staffing and, and things but it's actually thinking about the customer's top line. Because at the end of the day when all this inventory are being siphoned out the customer won't be happy. So how do we make sure their loyal customers have the right experience so that can improve their top line and not just sort of preventing the bots. So there's a lot of mission that we're on. >> Yeah, that surprise and delight in addition to that protection. >> 100% >> If I could talk about the evolution of an engagement with F5. We first go online, deploy the client side signals I described and take care of all the bad bots. Okay. Mitigate them. Allow list all the good bots, now you're just left with human traffic. We have other client side signals that'll identify the bad humans among the good humans and you could deal with them. And then we have additional client side signals that allow us to do silent continuous authentication of your good customers extending their sessions so they don't have to endure the friction of logging in over and over and over. >> Explain that last one again because I think that was, that's, I didn't catch that. >> Yeah. So right now we require a customer to enter in their username and password before we believe it's them. But we had a customer who a lot of their customers were struggling to log in. So we did analysis and we realized that our client side signals, you know of all those that are struggling to log in, we're confident like 40% of 'em are known good customers based on some of these signals. Like they're doing floating point math the way they always have. They're rendering emojis the way they always have all these clients that signals are the same. So why force that customer to log in again? >> Oh yeah. And that's such a frustrating user experience. >> So true. >> I actually had that thought earlier today. How many time, how much of my life am I going to spend typing my email address? Just that in itself. Then I could crawl back under the covers but >> With the biometric Mac, I forget my passwords. >> Or how about solving CAPTCHA's? How fun is that? >> How many pictures have a bus? >> I got one wrong the other day because I had to pick all the street signs. I got it wrong and I called a Russian human click farm and figured out why was I getting it wrong? And they said >> I love that you went down this rabbit hole deeply. >> You know why that's not a street sign. That's a road sign, they told me. >> That's the secret backdoor. >> Oh well yeah. >> Talk about your background because you have fascinating background coming from law enforcement and you're in this kind of role. >> He could probably tell us about our background. >> They expunge those records. I'm only kidding. >> 25, 30 years in working in local, state and federal law enforcement and intelligence among those an FBI agent and a CIA cyber operations officer. And most people are drawn to that because it's interesting >> Three letter agencies can get an eyebrow raise. >> But I'll be honest, my early, early in my career I was a beat cop and that changed my life. That really did, that taught me the importance of an education, taught me the criminal mindset. So yeah, people are drawn to the FBI and CIA background, but I really value the >> So you had a good observation eye for kind of what, how this all builds out. >> It all kind of adds up, you know, constantly fighting the bad guys, whether they're humans, bots, a security threat from a foreign nation. >> Well learning their mindset and learning what motivates them, what their objectives are. It is really important. >> Reading the signals >> You don't mind slipping into the mind of a criminal. It's a union rule. >> Right? It actually is. >> You got to put your foot and your hands in and walk through their shoes as they say. >> That's right. >> The bot networks though, I want to get into, is not it sounds like it's off the cup but they're highly organized networks. >> Dan: They are. >> Talk about the aspect of the franchises or these bots behind them, how they're financed, how they use the money that they make or ransomware, how they collect, what's the enterprise look like? >> Unfortunately, a lot of the nodes on a botnet are now just innocent victim computers using their home computers. They can subscribe to a service and agree to let their their CPU be used while they're not using it in exchange for a free VPN service, say. So now bad actors not, aren't just coming from you know, you know, rogue cloud providers who accept Bitcoin as payment, they're actually coming from residential IPs, which is making it even more difficult for the security teams to identify. It's one thing when it's coming from- >> It's spooky. I'm just sitting here kind of creeped out too. It's these unknown hosts, right? It's like being a carrier. >> You have good traffic coming from it during the day. >> Right, it appears normal. >> And then malicious traffic coming from it. >> Nefarious. >> My last question is your relationship with Amazon. I'll see security center piece of this re:Invent. It's always been day zero as they say but really it's the security data lake. A lot of gaps are being filled in the products. You kind of see that kind of filling out. Talk about the relationship with F5 and AWS. How you guys are working together, what's the status? >> We've been long-term partners and the latest release the connector for CloudFront is just one of the joint work that we did together and try to, I think, to Dan's point, how do we make those technology that was built for the very sophisticated big institutions to be available for all the CloudFront customers? So that's really what's exciting. And we also leverage a lot of the technology. You talked about the data and our entire solution are very data driven, as you know, is automation. If you don't use data, you don't use analytics, you don't use AI, it's hard to really sort of win that war. So a lot of our stuff, it's very data driven >> And the benefit to customers is what? Access? >> The customer's access, the customer's top line. We talked about, you know, like how we're really bringing better experiences at the end of the day. F5's mission is try to bring a better digital world to life. >> And it's also collaborative. We've had a lot of different stories here on on the set about companies collaborating. You're obviously collaborating and I also love that we're increasing access, not just narrowing this focus for the larger companies at scale already, but making sure that these companies starting out, a lot of the founders probably milling around on the floor right now can prevent this and ensure that user experience for their customers. throughout the course of their product development. I think it's awesome. So we have a new tradition here on theCUBE at re:Invent, and since you're alumni, I feel like you're maybe going to be a little bit better at this than some of the rookies. Not that rookies can't be great, but you're veterans. So I feel strong about this. We are looking for your 30-second Instagram reel hot take. Think of it like your sizzle of thought leadership from the show this year. So eventually eight more visits from now we can compile them into a great little highlight reel of all of your sound bites over the evolution of time. Who wants to give us their hot take first? >> Dan? >> Yeah, sure. >> Savannah: You've been elected, I mean you are an agent. A former special agent >> I guess I want everybody to know the bot problem is much worse than they think it is. We go in line and we see 98, 99% of all login traffic is from malicious bots. And so it is not a DIY project. >> 98 to 99%? That means only 1% of traffic is actually legitimate? >> That's right. >> Holy moly. >> I just want to make sure that everybody heard you say that. >> That's right. And it's very common. Didn't happen once or twice. It's happened a lot of times. And when it's not 99 it's 60 or it's 58, it's high. >> And that's costing a lot too. >> Yes, it is. And it's not just in fraud, but think about charges that >> Savannah: I think of cloud service providers >> Cost associated with transactions, you know, fraud tools >> Savannah: All of it. >> Yes. Sims, all those things. There's a lot of costs associated with that much automation. So the client side signals and multi-stage defense is what you need to deal with it. It's not a DIY project. >> Bots are not DIY. How would you like to add to that? >> It's so hard to add to that but I would say cybersecurity is a team sport and is a very data driven solution and we really need to sort of team up together and share intelligence, share, you know, all the things we know so we can be better at this. It's not a DIY project. We need to work together. >> Fantastic, Dan, Haiyan, so great to have you both back on theCUBE. We look forward to seeing you again for our next segment and I hope that the two of you have really beautiful rest of your show. Thank you all for tuning into a fantastic afternoon of coverage here from AWS re:Invent. We are live from Las Vegas, Nevada and don't worry we have more programming coming up for you later today with John Furrier. I'm Savannah Peterson. This is theCUBE, the leader in high tech coverage.

(soft electronic music) >> Good afternoon guys and gals. Welcome back to theCube's Live coverage of AWS re:Invent 2022. We've been in Sin City since Monday night, giving you a load of content. I'm sure you've been watching the whole time, so you already know. Lisa Martin here with John Furrier. John, we love having these conversations at AWS re:Invent. So many different topics of conversation. We also love talking to AWS's partner ecosystem. There's so much emphasis on it, so much growth and innovation. >> Yeah, and the thing is we got two great leaders from a very popular company that's doing very well. Security, security's a big part of the story. Data and security. Taking up all the keynote time, you're hearing a lot of it. This company's a company we've been following from the beginning. Doing really good stuff in open source, cloud native, security, shifting-left. Snyk's just a great company. With the CTO and the head of the product organization, these guys have the keys to the kingdom in security. We're going to have a great conversation. >> Yeah, we are. Both from Snyk, Manoj Nair joins us, rejoins us, for your, I believe, 11th visit. Chief Product Officer of Snyk. Adi Sharabani, Chief Technology Officer. Welcome guys. Great to have you. >> Yeah, thank you. >> Great to be back. >> So what's going on at Snyk? I know we get to talk to you often, but Manoj, give us the lowdown on what are some of the things that are new since we last connected with Snyk. >> A lot of innovation going on. We just had a major launch last month and you know when we talked to our customers three big themes are happening in parallel. One is the shift to going from traditional development to, really, DevOps, but we need to make that DevSecOps and Snyk was ahead of, that was the genesis of Snyk, but we're still, you know, maybe 15, 20% of organizations have realized that. So that one big theme. Supply chain security, top of mind for everyone. And then really, cloud and, you know, how do you really take advantage of cloud. Cloud is code. So our innovation map to those three big themes, we have done a lot in terms of that shift-left. And Adi will talk about, kind of, some of our original, like, you know, thinking behind that. But we flipped the security paradigm on its head. Was to make sure developers loved what they were, you know, experiencing with Snyk. And oh, by the way, they're fixing security issues. The second one, supply chain. So you know, SBOMs and everyone hears about this and executive orders, what do you do? Who does what with that? So we launched a few things in terms of simplifying that. You can go to our website and, you know, just upload your SBOM. It'll tell you using the best security intelligence data. In fact, the same data is used by AWS inside their products, inside Inspector. So we use that data from Snyk's intelligence to light up and tell you what vulnerabilities do your third party code have. Even things that you might not be scanning. And then the last one is really code to cloud. Cloud is code. So we have brought the ability to monitor your cloud environments all the way into your platform and the security engineering teams, rather than later on and after the fact. Those are some of the big ones that we're working on. >> Lisa: Lots going on. >> Yeah. >> Lisa: Wow. >> Lots going on there. I mean, SBOMs, Software Bill of Materials. I mean, who would've thought in the developer community, going back a decade, that we'd be talking about bill of materials, open source becomes so popular. You guys are cloud native. Developer productivity's a hot trend. Not much going on here, talking about developer productivity. Maybe Werner, keynote tomorrow will talk about it. Software supply chain, huge security risk. You guys are in the front lines. I want to understand, if you can share, why is Snyk successful? Everyone is hearing about you guys. Your business is doing great. What's the secret sauce of your success? Why are you guys so successful? >> I think that, you know, I've been doing application security for more than two decades now and in the past we always saw the potential associated with transferring, shifting-left in a sense, before the term, right? Taking those security solutions out of the hands of the security people and putting it in the hands of developers. It's speeds up the process. It's very, very clear to anyone. The problem was that we always looked at it the wrong way. We did shift-left, and shift-left is not enough because in my terminology shift-left, meaning let's take those security solution put it earlier in the cycle, but that's not enough because the developer is not speaking those terms. The developer is not a security persona. The security persona is thinking in terms of risk. What are the risks that a specific issue creates? The developer is thinking in terms of the application. What would be the impact on application of a change I would might make into it. And so the root cause of Snyk success, in my opinion, is the fact that from the get-go we scratch that, we build a solution for the developer that is based on how the workflows of the developer, whether it's the ID, whether it's the change management, the pull request. Whether it's integration with the Gits and so on. And whether it's with integration with the cloud and the interaction with the cloud providers. And doing that properly, addressing the developers how they want to context, to get, with the context they want to get as part of the issues, with the workflows they want to get. That's kind of the secret sauce, in a sense. And very easy maybe to say, but very, very hard to implement properly. >> This is huge. I want to unpack that. I want to just, great call out, great description. This is huge. This is a, we're seeing the past three years in particular, maybe three with the pandemic. Okay, maybe go a couple years earlier, then. The developers' behavior is driving the change. And you know, if you look at the past three DockerCons we've covered, we've been powering that site, been following that community very closely since the beginning, as well. It just seems in the past three to four years that the developers choices at scale, not what they're buying or who's pushing tools to them, has been one big trend. >> Yeah. >> They're setting the pace. >> Developer is the king. >> If it's self-service, we've seen self-service. Whether it's freemium to paid, that works. This is the new equation. Developer, developer choice is critical. So self-service they want. And two, the language barrier or jargon between or mindsets between security and developers. Okay, so DevOps brings IT into the workflow. Check. DevSecOps brings in there. You guys crack the code on that, is that what you're saying? >> Yes, and it's both the product, like how do you use the solution, as well as the go to market. How do you consume the solution? And you alluded to that with the PLG motion, that I think Synk has done the superb job at and that really helped our businesses. >> Okay, so Manoj, product, you got the keys to the kingdom, you got the product roadmap. I could imagine, and what I'd love to get your reaction too Adi, if you don't mind. If you do that, what you've done, the consequence of that is now security teams and the data teams can build guardrails. We're reporting a lot of that in the queue. We're hearing that we can provide guardrails. So the velocity of the developer seems to be increasing. Do you see that? Is that a consequence? >> That's something that we actually measure in the product. Right, so Snyk's focus is not finding issues, it's fixing issues. So one of the things we have been able to heuristically look at our thousands of customers and say, they're fixing issues 27 days faster than they were prior to Snyk. So, you know, I'm a Formula one fan. Guardrails, you say. I say there's a speed circuit. Developers love speed. We give them the speed. We give the security teams the ability to sit on those towers and, you know, put the right policies and guardrails in place to make sure that it's not speed without safety. >> And then I'm sure you guys are in the luxury box now, partying while the developers are (Lisa laughing) no more friction, no more fighting, right? >> The culture is changing. I had a discussion with a Fortune 50 CISO a month ago, and they told me, "Adi, it's the first time in my life where the development teams are coming to me, asking me, hey I want you to buy us this security solution." And for, that was mind blowing for him, right? Because it really changes the discussion with the security teams and the development teams >> Before Lisa jumps in, well how long, okay, let me ask you that question on that point. When did that tipping point change, culturally? Was it just the past few years? Has there, has DevOps kind of brought that in, can you? >> Yeah, I think it's a journey that happened together with Snyk's, kind of, growth. So if three years ago it was the very early adopters that were starting to consume that. So companies that are very, you know, modern in the way they developed and so on. And we saw it in our business. In the early days, most of our business came from the high tech industry. And now it's like everywhere. You have manufacturing, you have banks, you have like every segment whatsoever. >> Talk about that cultural shift. That's really challenging for organizations to achieve. Are you seeing, so that, that CISO was quite surprised that the developer came and said, this is what I want. Are you seeing more of that cultural changes? Is that becoming pervasive? >> Yeah, so I think that the root cause of that is that, you mentioned the growth, like the increased speed of velocity in applications. We have 30 million developers in the world today. 30 millions. By the end of the decade it's going to be 45 millions and all of them are using open source, third party code. Look at what's going on here in the event, right? This accelerates the speed for which they develop. So with that, what happened in the digital transformation world, the organizations are facing that huge growth, exponential growth in the amount of technology and products that are being built by their teams. But the way they manage that before, from a security perspective, just doesn't scale. And it breaks and it breaks and it breaks. This is why you need a different approach. A solution that is based on the developers, who are the ones that created the problems and the ones that will be responsible of fixing the issues. This is why we are kind of centering ourselves around them. >> And the world has changed, right? What is cloud? It's code, it's not infrastructure. Old infrastructure, hosted infrastructure. So if cloud is code and cloud native applications are all code and they're being deployed with Terraform packages and cloud formations, that's code. Why take an old school approach of scanning it outside-in. I talked to CISO today who said, I feel bad that, you know, our policy makes it such that a terraform change takes six months. What did I do? I made cloud look like infrastructure. >> Yeah, it's too slow. >> So that, you know, so both sides, you know, CISOs want something that the business, you know, accepts and adopts and it's, culture changes happen because the power is with the developers because all of this is code, and we enabled that whole seamless journey, all the way from code to cloud. So it's kind, you know, I think that this is a part of it. It's by direction, it's a bridge and both sides are meeting in the middle here. >> It's a bridge. I'm curious, how are you facilitating that bridge? You, we talk about the developers being the kings and queens and really so influential in business decisions these days. And you're talking about the developers now embracing Snyk. But you're also talking to CISOs. Is your customer conversation level changing as a result of security folks understanding why it needs to shift-left. >> We had a breakfast meeting with customers, prospects and everyone, I think this morning. It was interesting, we were remarking. There are CTOs, VPs of engineering, CISOs, VPs of AppSec. And it was such a rich conversation on both sides, right? So just the joy of facilitating that conversation and dialogue. CISOs, and so the levels are changing. It started for us in CTOs and VPs of engineering and now it's both because, you know, one of the things Adi talks about is, like, that security has to become development aware. And that's starting to be like the reality. Me getting another solution, with maybe a better acronym than the old acronym, but it's still outside-in, it's scan based. I light up up the Christmas tree, who is going to fix it? And with the speed of cloud, now I got throw in more lights. Those lights are no longer valid. >> The automation. >> The automation without prioritization and actual empowerment is useless. >> All right, I know we got a couple minutes left, but I want to get into that point about automation because inside-out, you've made me think about this. I want to get your thought Adi, if you don't mind. The integration challenges now are much more part of the ecosystem, more joint engineering. You mentioned these meetings are not just salesperson and customer buyer, it's teams are talking to each other. There's a lot of that going on. How do you guys look at that? Because now the worst things that I hear and when I talk to customers is, I hate the word PenTest and AppSec review. It slows things down. People want to go faster. So how do you guys look at that? What's Snyk doing around making the AppSec review process, integration across companies, work better? >> So I'll give you an example from the cloud and then I will relate to the AppSec. And this relates to what you mentioned before. We had a discussion yesterday with a CISO that said, we are scanning the cloud, we are opening the lights, we see this issue. Now what do I do? Who needs to fix this? So they have this long process of finding the actual team that is required to fix it. Now they get to the team and they say, why didn't you tell me about it when I developed it? The same goes for AppSec, right? The audit is a very late stage of the game. You want to make sure that the testing, that the policies, everything is under the same structure, the same policies. So when you do the same thing, it's part of the first time of code that you create, it's part of the change management, it's part of the build, it's part of the deployment and it's part of the audit. And you have everything together being done under the same platform. And this is, kind of, one of the strengths that we bring to the table. The discussion changes because now you have an aligned strategy, rather than kind of blocks that we have, kind of, mashed up together. >> So the new workflow, it's a new workflow, basically, in the mindset of the customer. They got to get their arms around that thing. If we don't design it in, the wheels could come off the bus at the 11th hour. >> Adi: Yeah. >> And everything slows down. >> I had a discussion with Amazon today, actually, that they had an internal discussion and they said, like, some of the teams were like, why have you blocked my app from being released? And they said, have you ever scanned your app? Have you ever looked at your, like, and, and they're like, if you haven't, then you're not really onboard with the platform and it just breaks. This is what happens. >> Great conversation. I know we don't, I wish we had more time. We'll do a follow up on theCube for sure. Should we get into the new twist? >> I've got one final question for you guys. We're making some Instagram reels, so think about your elevator pitch in 30 seconds. And I want to ask you about Snyk's evolution. Manoj, I want to start with you. What is that elevator pitch about Snyk's evolution to the end user customer? >> Empower developers, help them go faster, more productive and do it in a way that security is really built in, not bolted on. And that's really, you know, from a, the evolution and the power that we are giving is make the organization more productive because security is just happening as a part of making the developer more productive. >> Awesome. And Adi, question for you, how, your elevator pitch on how Snyk is really an enabler for CISOs these days? >> Yeah, so I always ask the CISO first of all, are you excited about the way your environment looks like today? Do you need to have a cultural change? Because if you need to have a cultural change, if you want to get those two teams working closely together, we are here to enable that. And it goes from the product, it goes from our education pieces that we can talk about in another section, and it works around the language that we build to allow and enable that discussion. >> Awesome. Guys, that was a double mic drop for both of you. >> Manoj: Thank you. >> Adi: Thank you, Lisa. >> Thank you so much for joining John and me, talking about what's happening with Snyk, what you're enabling customers to do and how, really, you're enabling cultural change. That's hard to do. That's awesome stuff guys. And congratulations on your 11th and your first Cube. >> Second, second, >> Second. >> Adi: I will be here more, but (laughs) >> You got it, you got it. You have to come back because we have too much to talk about. >> Adi: Exactly. (laughs) >> Thanks guys, we appreciate it. >> If we can without Manoj, so I can catch up. (Manoj laughs) >> Okay. We'll work on that. >> Bring you in the studio. (everyone laughing) >> Exactly. >> Eight straight interviews. (John and Lisa laughing) >> We hope you've enjoyed this conversation. We want to thank our guests. For John Furrier, I'm Lisa Martin. You're watching theCUBE, the leader in emerging and enterprise tech coverage. (soft electronic music)

(upbeat music) >> Good morning and welcome back to fabulous Las Vegas, Nevada. We're here at AWS re:Invent with wall-to-wall coverage all day long on theCUBE. My name is Savannah Peterson and I am joined this morning by the beautiful Lisa Martin. Lisa, good morning. >> Good morning. Good. >> How you feeling day three? >> Day three is we are going to be shot out of a cannon today. The amount of content coming at you from theCUBE today- >> Get ready, you all. >> Us two gals, is a lot. We're going to have some great conversations. >> And we're starting with a really great one with a Cube Alumni to the max. You've been on the show multiple times. >> John: Yeah. >> Very excited to welcome John, the CMO of Couchbase. Welcome. How you doing this morning? >> Thanks. I'm doing great. Great to be here with you. >> How do you feel about the show so far? What's your pulse? >> The show has been great. I say the energy is great. The traffic at our booth, the conversations that we're having, both with prospective customers and even just partners, right? They're all here. The ecosystem is here >> And everyone's finally back in person and it feels so good. >> John: It does. >> So, we're going to dig in a little bit but just in case the audience isn't familiar, tell us about Couchbase. >> Sure. Couchbase is a publicly traded database company. We have a cloud database platform called Capella which is hosted on AWS and GCP. It is used for building mission-critical applications. So, we have great customers, we're building apps that really matter and are using to drive their business. So, we're very excited about that. 30% of the Fortune 100 are Couchbase customers. >> Nice. Talk a little bit about the AWS relationship. >> Mm-hm. Yeah, so we have a great AWS relationship. In fact, yesterday we announced a deepening of that relationship, a strategic collaboration agreement. We're very excited. It's a multi-year agreement. It's focused on go-to market, from a sales and marketing standpoint. We're going to target, you know, various verticals and, you know, really generate joint business between the two of us. So, it's a deepening of a already strong relationship and we're really excited about that. >> Savannah: Yeah. Go ahead. >> What are some of the industry verticals that you're going to be tackling together? >> Well, gaming for one, right? Manufacturing, the workloads that Couchbase is good for are these mission-critical workloads are ones that are really suited for us to be used with AWS. So, we've done some work with them already in those areas and I'm sure we'll be digging in even deeper. >> That's exciting. Speaking of digging in deeper, tell us a little bit more about Capella. >> Capella. It's a cloud databases services I mentioned. We launched it last October and we are super excited by the uptake, the interest that we're seeing. We have a free 30 day trial, so, you know, people can come and try it and get their hands dirty just getting experience with the product and then, you know, become a customer after that. And we're seeing very strong interest from our existing customer base as well. So, we're really excited about how things are going. >> Talk about Capella and the latest release and how it's really enabling Couchbase to invest deeper into the developer experience. >> Yeah, so, at the end of October, we announced a revamp of our user interface, our user experience for Capella really focused on developers. And what we've done is make it so that it's familiar to developers, right? It's a GitHub-like experience. So, developer comes in, they're very familiar, of course, with GitHub, they are familiar with how the Couchbase Capella interface will work. And so that's something that, you know, we've really invested, in fact, we've invested in developers quite a bit. We announced a Couchbase community hub and a Couchbase ambassadors program, both focused on developers and getting out there and building our community. >> A community is a big topic that we've been talking about at all the conferences this year. We're all back in person, in community. How often are you communicating with your community to get feedback on what that experience should be like? >> Yeah, I mean, we actually have a Discord server, so we're in constant communication. (Savannah laughing) >> Savannah: Yes. (John laughing) 24/7. (laughing) >> Basically, you know, we have staff who's dedicated to making sure that the users on there are getting their answers and giving us feedback on the experience. The ambassadors are somebody who have a really strong relationship, who get early insight and give us feedback before we even release a product. So, it gives us a chance to really test-drive it with core developers and get the insight we need before we get it in the market. >> Yeah. It matters so much. You can build it, but they won't come if it's not fantastic. >> John: Exactly. >> Lisa: Right. >> Let's shift a little bit and talk about customers. How, and price, how do you guys compare? >> Customers and? >> And price, your price performance? >> Price, oh. So, customers, we also announced this week a joint customer Arthrex with AWS. Arthrex is a orthopedics medical devices company and they use our Edge capabilities along with running Couchbase on AWS. So, you think of the kinds of surgeries that orthopedic surgeons do, it's scopes and they are often inside. So, what it does is it collects the data, the video data and all of that on a medical devices and then brings it back to a centralized app for the doctors to use sort of in post when they're actually doing further medical recommendations. >> Savannah: It's so cool. >> So, it's cool, the thing about it is it can work whether it's online or offline, it's one of the reasons that Arthrex selected us because the fact that it can, you know, often sometimes there's not connectivity in the operating room, I'd say deep inside of a hospital. So, these devices work regardless and then when they get connectivity, it sinks back to that centralized service. So, it's one of the main reasons that they selected us. >> That's outstanding. You know, one of the things that John Furrier, you know, John, well, you guys go way back. >> John: Way back. >> He had a sit down with Adam Selensky, oh, about 10 days or so ago. He gets an exclusive with the CEO of AWS every pre re:Invent. And one of the things that Adam said is that the role or the title, data analyst, is going to go away, in that every role will have responsibilities of analyzing data. And I always think of that in terms of operations, marketing, finance, sales, but you just brought up physicians as data analysts in their jobs, right? Probably not, we're thinking about it in that way. >> Yeah. >> But it's so interesting how data is really being democratized. >> John: Yeah. >> And how Couchbase is an enabler of that in an operating room. >> John: Yeah, yeah, yeah. >> That's amazing. >> It's a great story. There's many others and I think, you know, we have embedded operational analytics in Couchbase Capella, and, you know, in our offerings in general. So, what that does is allows us to do real-time, highly personalized applications based on that analytics that are coming in real-time from the data from the applications. And so that's something that's actually driving a highly interactive user experience, one that's very personalized and customized. And that's one of the things that our customers really like about what we do. >> It's fascinating. I never thought about it from a medical device perspective. >> Lisa: No, no. >> John: No. >> My gosh is if doctors don't have enough cognitive burden load. >> John: I know. >> You know, right? Like, they don't need to be a data analyst. I would much rather they were just good at the surgery part. That's a piece of the puzzle I need them to do. Yeah, for sure. That's a fascinating customer example. Can you share any other joint AWS examples with us? >> Joint AW- I mean, there's many in the gaming area where, because Couchbase is memory-first architecture, we deliver very, very interactive user experiences and we're used a lot for session management, user ID management in the gaming space, specifically with AWS. It's an area we've done some joint work already and had a lot of success, you know, with small and large gaming companies. >> Yeah. It looks like you also, according to my notes here, we've got things in travel and hospitality as well. >> Yes. Also Carnival Cruises is a great example. We enable their on-ship, on-board experience, highly customized, everybody wears a device called a medallion, and as they move around the ship, it knows where they are and it's able to provide customized services. You walk up to a bar, you have your favorite drink, it can be hit the bar when you land there. >> I'll take that. >> How about that? (laugh) >> That's outstanding. >> Isn't that great? >> Can we carry that onto the AWS show floor? >> Exactly. >> Or Starbucks order? >> Yeah, yeah. Yes, please. Yes, please. Well, another thing that's so interesting these days, is that every company has to be a data company. Say they have to be a software company. They have to be a data company. You just gave some great examples. Hospitality, gaming, healthcare, where that data democratization has to happen. >> John: Yeah. >> Businesses has to transform. But one of the things that Adam also told John is that CIOs, CEOs are coming to him not wanting to talk about technology but about transformation. >> Yeah. >> Huge topic. >> And that's a journey where every customer is at different levels. >> Yeah. >> How is Couchbase helping businesses transform and where are your customer conversations these days? >> Yeah, yeah, yeah. So, I mean, the transformation of the business is a major topic of conversation. So, we completely agree with that. How Couchbase helps is, you know, in our database, one of the things we have is the SQL engine. And so as people are looking to move and modernize their infrastructure, if they're moving off of, or from like a technology that's principally based on SQL but doesn't give all the flexibility of a JSON database or document database like we do, we actually enable them to get more easily onto our platform so that they can start that transformation. And then it's a, you know, it's a journey of how they want to transform their business and it's really focused on how do they better serve their customers and clients, whether it's internal or external? >> It really matters. I mean, and that ease of use as well as the transformation journey. It takes a long time for people to adapt. So, every piece of that puzzle, every Lego being quicker or easier, more intuitive, like you said, with the user experience, we can tell you're very thoughtful. How does this improve the total cost of ownership for your customers? >> That's one of the things that we announced along with that developer changes, was a new storage engine underneath Couchbase Capella. And it's 10 X more dense storage. And what that means is fewer servers. So, fewer servers is a much better cost of ownership story. That plus just the performance of the platform itself, we find, you know, against competition, we can do things on say six nodes that take 18 nodes for others. >> Lisa: Oh wow. >> And we have a great consolidation story as well because we have, it's a multi-modal database, meaning that it has SQL engine, document database, full tech search, eventing and analytics, all these pieces on one common data layer. So, you can actually consolidate off of other technologies onto one, onto Couchbase, and that actually saves you money. So, that's a great story for us. >> There's got to be a sustainability element to that as well? >> Yeah, I mean it's, obviously, if you're using less, using fewer servers, there's a kind of power consumption aspect of it as well. Absolutely. >> Are you finding that a lot of customers and companies we talk to these days have in their RFPs, they must only work with vendors who have an actual ESG program? Are you finding more customers coming to you saying, how can you help us dial down our carbon emissions? >> John: Yeah. >> Savannah: Great question. >> We've got a sustainability program that we've got to meet, we've got commitments to our customers. >> John: Yeah. >> Is that something that's really now kind of a hard and fast requirement? >> We're hearing it, we're definitely hearing it. I wouldn't say it's, you know, massively pervasive but I would say it's a growing component of, as you said, RFPs. And it's something that we feel like we have a great story for. And so, you know, it's something that helps when we get into those conversations, we can clearly articulate how we can provide that value and how we meet some of those needs that they have. >> Yeah, that's awesome. So, we have a bit of a challenge, new to the show at re:Invent. >> John: Mm-hm. >> Where we are prompting you to give us your 30 second Instagram Reel sizzle highlight. Don't worry, I'm not actually timing you, but your thought leadership hot-take on the most important theme or takeaway from this year's show. >> From the conference here. I would say that, and I think this was talked about a little bit by AWS as well, but the convergence of analytics and operational data, you know, through the applications is one that we're certainly seeing as well. It's the reason we have analytics in our database. But as I walk around and look at it, I see that very much as a common theme as well, in terms of what other vendors are saying and just the conversations we're having. So for me, that's one of the things I think would be a takeaway from this show. >> Yeah. Embedded analytics, real-time, everybody wants to know what's going on, in context. >> Yeah. That's right. >> Right now, not last week, not what we're processing from last month. >> Exactly. >> I mean, right? (cross-talking) >> So, I can react and take advantage or take an action if I have to. >> Exactly. And then deliver that personalized experience that we all expect these days. >> Oh, yes. >> I'll take that medallion- >> It's about the medallion. I was like, okay. >> You up with that, John? >> We'll get right on it. >> Lisa: All right. (laughs) >> About this. So, what's next for Couchbase? >> John: Well- >> I know you got the partnership, you've got all this exciting momentum. >> So, we're excited heading into next year. We're going to continue to innovate on Capella, right? Continue to deliver more value, lean into our developer community that we have. We're investing heavily, not just from a product standpoint but from a company standpoint in terms of, you know, our community meetups and some of those things. We have a big community-focused event coming up in March called Connect, Couchbase Connect. So, that's something that we'll, you know, continue to drive. That'll be a major theme for us next year. Cloud and developers and, you know, continuing to enable that ecosystem. >> Lisa: Excellent. >> I just had a Microsoft moment where I saw you saying, "Cloud developers," on stage. (Lisa and Savannah laughing) >> I'm not going Steve Ballmer on you. (all laughing) >> Pardon. I was trying to get someone to sing yesterday. I was hoping you were my Ballmer dance. Oh, man. Well, this has been a really great way to start the day. John, thank you so much for being on the show with us, seriously. And it's great that you keep coming back. I'm glad we haven't scared you off. (John laughing) >> Never. >> Savannah: We will have you anytime. >> Thank you. >> And thank you all for tuning in for yet another fantastic day of all day live coverage here from AWS re:Invent. We are in Sin City, having a fabulous time with Lisa Martin. I'm Savannah Peterson. This is theCUBE and we are the leader in high-tech technology coverage. (upbeat music) (upbeat music fades)

>>Well, hello everybody at John Wallace here on the Cube, and glad to have you along here for day two of our coverage here at AWS Reinvent 22. We're up in the global startup program, which is part of AWS's Startup Showcase, and I've got Kevin Farley with me. He is the director of Strategic Alliances with Maria Day db. And Kevin, good to see you this morning. Good to see you, John. Thanks for joining us. Thank >>You. >>Appreciate it. Yeah. First off, tell us about Maria db. Sure. Obviously data's your thing. Yep. But to share that with some folks at home who might not be familiar with your offering. >>Yeah. So Maria DB's been around as a corporate entity for 10 plus years, and we have a massive customer base. You know, there's a billion downloads from Docker Hub, 75% of the Fortune 500. We have an enormous sea of really happy users. But what we realize is that all of these users are really thinking about what do we, what does it mean to transform it? What does cloud modernization mean? And how do we build a strategy on something we really love to drive it into the cloud and take it to the future. So what we launched about two years ago, two and a half years ago, is Skye. It's our database as a service. It leverages all the best elements, what we provide on the enterprise platform. It marries to the AWS cloud, and it really provides the best of both worlds for our >>Customers. So in your thought then, what, what problem is that solving? >>I think what you see in the overall database market is that many people have been using what we would call legacy technology. There's been lots of sort of stratification and mixes of different database solutions. All of them come with some promise, and all of 'em come with a lot of compromise. So I think what the market is really looking for is something that can take what they know and love, can bring it to the cloud and can survive the port drive the performance and scale. That completely changes the landscape, especially as you think about what modern data needs look like, right? What people did 10 years ago with the exponential scale of data no longer works. And what they need is something that not only can really deliver against their core business values and their core business deliverables, but gets 'em to the future. How do we drive something new? How do we innovate? How do we change the game? And I think what we built with AWS really delivers what we call cloud scale. It's taking something that is the best technology, and I as a V can build, marrying it to, you know, Kubernetes layer, marrying it to global availability, thinking about having true global high availability across all of your environments and really delivering that to customers through an integrated partnership. >>Could we see this coming? I mean, because you know data, right? I mean, yeah, we, we, everybody talked about the tsunami of growth, you know, >>Back 10 >>Sure. 11 years ago. But, but maybe the headlights didn't go far enough or, or, but, but you could see that there was going to be crunch time. >>There's no doubt. And I think that this has been a, there's, there's been these sort of pocket solutions, right? So if you think at the entire no sequel world, right? People said, oh, I need scale, I can get it, but what do I have to give up asset compliance? So I have to change the way I think about what data is and how I, I can govern it. So there's been these things that deliver on half the promise, but there's never been something that comes together and really drives what we deliver through CIQ is something called expand. So distributed SQL really tied to the SQL Query language, having that asset data. So having everything you need without the compromise built on the cloud allows you to scale out and allows you to think about, I can actually do exponential layers of, of data, data modeling, data querying, complete read, write, driving that forward. And I think it gives us a whole nother dynamic that we can deliver on in a way that hasn't been before. And I think that's kind of the holy grail of what people are looking for is how am I building modern applications and how do I have a database in the cloud that's really gonna support >>It? You know, you talk about distributed, you know, sequel and, and I mean, there's a little mystery behind it, isn't there? Or at least maybe not mystery. There's a little, I guess, confusion or, or just misunderstanding. I mean, I, how, nail that down a little bit. I >>Would say the best way to say it, honestly, this is the great thing, is it people believe it's too good to be true. And I think what we see over and over >>Again, you know, what they say about that. >>But this is the great part is, you know, you know, we've just had two taste studies recently with aws, with HIT labs and Certified power, both on expand, both proof in the pudding. They did the POCs, they're like, oh my God, this works. If you watch the keynote yesterday, you know, Adam had a slide that was, you know, as big as the entire room and it highlighted Samsung and they said, you know, we're doing 80,000 requests per second. So the, you know, the story there is that AWS is able as, as an entity with their scale and their breadth to handle that kind of workload. But guess what that is? That's MariaDB expand underneath there driving all of that utilization. So it's already there, it's already married, it's already in the cloud, and now we're taking it to a completely different level with a fully managed database solution. Right? >>How impressive is that? Right? I mean, you would think that somebody out there who, I mean that that volume, that kind of capacity is, is mind blowing. >>I mean, to your kind of previous point, it's like one of those things, do I see what's coming and it's here, right? You know, it's, is it actually ever gonna be possible? And now we're showing that it really is on a daily basis for some of the biggest brands in the world. We're also seeing companies moving off not only transitioning from, you know, MariaDB or myse, but all of the big licensed, you know, conversions as well. So you think about Oracle DBS Bank is one of our biggest customers, one of the largest Oracle conversions in the world onto MariaDB. And now thinking about what is the promise of connecting that to the cloud? How do you take things that you're currently doing, OnPrem delivering a hybrid model that also then starts to say, Hey, here's my path to cloud modernization. Skye gives me that bridge. And then you take it one layer farther and you think about multi-cloud, right? That's one of the things that's critical that ISVs can really only deliver in a meaningful way, is how can we have a solution for a customer that we can take to any availability zone. We can have performance, proximity, cost, proximity. We're always able to have that total data dexterity across any environment we need and we can build on that for the future. >>So if, if we're talking about cloud database and there's so many good things going forward here. You're talking about easy use and scalability and all that. But as with ever have you talked about this, there's some push and there's some pull. Yeah. So, so what's the, what's the other side that's still, you know, you that you think has to be >>Addressed? And I think that's a great question. So there's, we see that there's poll, right? We've seen these deals, this pipeline growth, this, there's great adoption. But what I think we're still not at the point of massive hockey stick adoption is that customers still don't fully understand the capabilities distributed SQL and the power they can actually deliver. So the more we drive case studies, the more we drive POCs, the more we prove the model, I think you're gonna see just a massive adoption scale. And I also think customers are tired of doing lots of different things in lots of different pockets. So neither one of the key elements of Sky SQL is we can do both transactional and analytical data out of the same database driven by the same proxy. So what, instead of having DBAs and developers try to figure out, okay, I'm gonna pull from this database here. >>Yeah. That there, it's, it's this big spaghetti wire concept that is super expensive and super time intensive. So the ability to write modern applications and pull data from both pockets and really be able to have that as a seamless entity and deliver that to customers is massive. I mean, another part of the keynote yesterday was a new deliverable, like kind of no etl. Adam talked about Aurora and Redshift and the massive complexity of what used to exist for getting data back and forth. You also have to pay for two different databases. It's super expensive. So I think the idea that you can take the real focus of AWS and US is customer value. How do you deliver that next thing that changes the game? Always utilizes AWS delivers on that promise, but then takes a net new technology that really starts to think about how do we bring things together? How do we make it more simple? How do we make it more powerful? And how do we deliver more customer value as we go forward? >>But you know, if, if I'm, I'm still an on-prim guy, just pretend I'm not saying I am. Just pretend I just for the sake of the discussion here, it's like I just can't let it go. Yeah. Right. I, I still, you know, there's control, there's the known versus the unknown. The uncertain. Yeah. So twist my arm just a little bit more and get me over the hum. >>Well, first of all, you don't have to, right? And there's gonna be some industries and some verticals that will always have elements of their business that will be OnPrem. Guess what? We make the best based in the world. It can be MariaDB, but there's those that then say, these, these elements of our business are gonna be far more effective moving to the cloud. So we give you Skye, there's a natural symbiotic bridge between everything we do and how we deliver it. Where you can be hybrid and it's great. You can adopt the cloud as your business needs grow. And you can have multi-cloud. This is that, that idea that you can, can have your cake and eat it too, right? You can literally have all these elements of your business met without these big pressure to say, you gotta throw that away. You gotta move to this. It's really, how do you kind of gracefully adopt the cloud in a way that makes sense for your business? Where are you trying to drive your business? Is it time to value, right? Is it governance? Is it is there's different elements of what matters the most to individual businesses. You know, we wanna address those and we can address >>Those. So you're saying you don't have to dive >>In, you don't have to dive >>In. You, you can, you can go ankle deep, knee deep, whatever you wanna >>Do. Absolutely. And you know, some of the largest MariaDB users still have massive, massive on-prem implementations. And that's okay. But there's elements that are starting to fall behind. There's cost savings, there's things that they need to do in the cloud that they can't do. OnPrem. And that's where expand Skye really says, okay, here is your platform. Grow as you want to, migrate as you want to. And we're there every step along the way. We, we also provide a whole Sky DBA team. Some guys just say, I wanna get outta the database world at all. This is, this is expensive, it's costly and it's difficult to be an expert. So you can bring in our DBA team and they'll man and run, they'll, they'll run your entire environment. They'll optimize it, you know, they'll troubleshoot it, they'll bug fix, they'll do everything for you. So you can just say, I just wanna focus on building phenomenal applications for my customers. And the database game as we knew it is not something that I know I want to invest in anymore. Right. I wanna make that transition >>That makes that really, yeah. You know, I mean really attractive to a lot of people because you are, you talk about a lot of headache there. Yeah. So let's talk about AWS before Sure. I let you go just about that relationship. Okay. You've talked about the platform that it provides you and, and obviously the benefits, but just talk about how you've worked with AWS over the years Yep. And, and how you see that relationship allowing you to expand your services, no pun intended. >>For sure. So, I mean, I would start with the way we even contemplated architecture. You know, we worked with the satisfactory team. We made sure that the things that we built were optimized in their environment. You know, I think it was a lot of collaboration on how does this combined entity really make the most value for our customers? How does it make the most sense for our developers as we build it out? Then we work in the, in the global startup team. So the strategic element of who we are, not all startups are created equal, right? We have, right, we have 75% of the Fortune 100, we've got over a billion downloads. So, you know, we come in with promise. And the reason this partnership is so valuable and the reason there's so much investment going forward is cuz what really, what do the cloud guys care about? >>The very, very most, they want all of these mission critical, big workloads that are on prem to land in their cloud. What do we have a massive, massive TAM sitting out there, these customers that could go to aws. So we both see, like if we can deliver incredible value to that customer base, these big workloads will end up in aws. They'll use other AWS services. And as we scale and grow, you know, we have that platform that's already built for it. So I think that when you go back to like the tenants, the core principles of aws, the one that always stands out, the one that we always kind of lean back on is, are we delivering customer value? Is this the best thing for the customer? Because we do have some competition just like many other, other partners do, right? So there is Aurora and there is rds and there is times when that's a great service for a customer. But when people are really thinking about where do I need my database to go? Where do I really need to be set for the future growth? Where am I gonna get the kind of ROI I need going forward? That's where you can go, Hey, sky sql, expand distributed sql. This is the best game in town. It's built on aws and collectively, you know, we're gonna present that to a customer. I'm >>Sold. Done. >>I love it. Right? >>Maria db, check 'em out, they're on the show floor. Great traffic. I know at at the, at the booth. They're here at AWS Reinvent. So check 'em out. Maria db. Thanks >>Kevin. Hey, thanks John. Appreciate your >>Time. Appreciate Great. That was great. Right back with more, you're watching the cube, the leader in high tech coverage.