(upbeat music) >> Hello everyone. Welcome to theCube's presentation of the "AWS Startup Showcase." The topic this episode is AI and machine learning, top startups building foundational model infrastructure. This is season three, episode one of the ongoing series covering exciting startups from the AWS ecosystem. And this time we're talking about AI and machine learning. I'm your host, John Furrier. I'm excited I'm joined today by Robert Nishihara, who's the co-founder and CEO of a hot startup called Anyscale. He's here to talk about Ray, the open source project, Anyscale's infrastructure for foundation as well. Robert, thank you for joining us today. >> Yeah, thanks so much as well. >> I've been following your company since the founding pre pandemic and you guys really had a great vision scaled up and in a perfect position for this big wave that we all see with ChatGPT and OpenAI that's gone mainstream. Finally, AI has broken out through the ropes and now gone mainstream, so I think you guys are really well positioned. I'm looking forward to to talking with you today. But before we get into it, introduce the core mission for Anyscale. Why do you guys exist? What is the North Star for Anyscale? >> Yeah, like you mentioned, there's a tremendous amount of excitement about AI right now. You know, I think a lot of us believe that AI can transform just every different industry. So one of the things that was clear to us when we started this company was that the amount of compute needed to do AI was just exploding. Like to actually succeed with AI, companies like OpenAI or Google or you know, these companies getting a lot of value from AI, were not just running these machine learning models on their laptops or on a single machine. They were scaling these applications across hundreds or thousands or more machines and GPUs and other resources in the Cloud. And so to actually succeed with AI, and this has been one of the biggest trends in computing, maybe the biggest trend in computing in, you know, in recent history, the amount of compute has been exploding. And so to actually succeed with that AI, to actually build these scalable applications and scale the AI applications, there's a tremendous software engineering lift to build the infrastructure to actually run these scalable applications. And that's very hard to do. So one of the reasons many AI projects and initiatives fail is that, or don't make it to production, is the need for this scale, the infrastructure lift, to actually make it happen. So our goal here with Anyscale and Ray, is to make that easy, is to make scalable computing easy. So that as a developer or as a business, if you want to do AI, if you want to get value out of AI, all you need to know is how to program on your laptop. Like, all you need to know is how to program in Python. And if you can do that, then you're good to go. Then you can do what companies like OpenAI or Google do and get value out of machine learning. >> That programming example of how easy it is with Python reminds me of the early days of Cloud, when infrastructure as code was talked about was, it was just code the infrastructure programmable. That's super important. That's what AI people wanted, first program AI. That's the new trend. And I want to understand, if you don't mind explaining, the relationship that Anyscale has to these foundational models and particular the large language models, also called LLMs, was seen with like OpenAI and ChatGPT. Before you get into the relationship that you have with them, can you explain why the hype around foundational models? Why are people going crazy over foundational models? What is it and why is it so important? >> Yeah, so foundational models and foundation models are incredibly important because they enable businesses and developers to get value out of machine learning, to use machine learning off the shelf with these large models that have been trained on tons of data and that are useful out of the box. And then, of course, you know, as a business or as a developer, you can take those foundational models and repurpose them or fine tune them or adapt them to your specific use case and what you want to achieve. But it's much easier to do that than to train them from scratch. And I think there are three, for people to actually use foundation models, there are three main types of workloads or problems that need to be solved. One is training these foundation models in the first place, like actually creating them. The second is fine tuning them and adapting them to your use case. And the third is serving them and actually deploying them. Okay, so Ray and Anyscale are used for all of these three different workloads. Companies like OpenAI or Cohere that train large language models. Or open source versions like GPTJ are done on top of Ray. There are many startups and other businesses that fine tune, that, you know, don't want to train the large underlying foundation models, but that do want to fine tune them, do want to adapt them to their purposes, and build products around them and serve them, those are also using Ray and Anyscale for that fine tuning and that serving. And so the reason that Ray and Anyscale are important here is that, you know, building and using foundation models requires a huge scale. It requires a lot of data. It requires a lot of compute, GPUs, TPUs, other resources. And to actually take advantage of that and actually build these scalable applications, there's a lot of infrastructure that needs to happen under the hood. And so you can either use Ray and Anyscale to take care of that and manage the infrastructure and solve those infrastructure problems. Or you can build the infrastructure and manage the infrastructure yourself, which you can do, but it's going to slow your team down. It's going to, you know, many of the businesses we work with simply don't want to be in the business of managing infrastructure and building infrastructure. They want to focus on product development and move faster. >> I know you got a keynote presentation we're going to go to in a second, but I think you hit on something I think is the real tipping point, doing it yourself, hard to do. These are things where opportunities are and the Cloud did that with data centers. Turned a data center and made it an API. The heavy lifting went away and went to the Cloud so people could be more creative and build their product. In this case, build their creativity. Is that kind of what's the big deal? Is that kind of a big deal happening that you guys are taking the learnings and making that available so people don't have to do that? >> That's exactly right. So today, if you want to succeed with AI, if you want to use AI in your business, infrastructure work is on the critical path for doing that. To do AI, you have to build infrastructure. You have to figure out how to scale your applications. That's going to change. We're going to get to the point, and you know, with Ray and Anyscale, we're going to remove the infrastructure from the critical path so that as a developer or as a business, all you need to focus on is your application logic, what you want the the program to do, what you want your application to do, how you want the AI to actually interface with the rest of your product. Now the way that will happen is that Ray and Anyscale will still, the infrastructure work will still happen. It'll just be under the hood and taken care of by Ray in Anyscale. And so I think something like this is really necessary for AI to reach its potential, for AI to have the impact and the reach that we think it will, you have to make it easier to do. >> And just for clarification to point out, if you don't mind explaining the relationship of Ray and Anyscale real quick just before we get into the presentation. >> So Ray is an open source project. We created it. We were at Berkeley doing machine learning. We started Ray so that, in order to provide an easy, a simple open source tool for building and running scalable applications. And Anyscale is the managed version of Ray, basically we will run Ray for you in the Cloud, provide a lot of tools around the developer experience and managing the infrastructure and providing more performance and superior infrastructure. >> Awesome. I know you got a presentation on Ray and Anyscale and you guys are positioning as the infrastructure for foundational models. So I'll let you take it away and then when you're done presenting, we'll come back, I'll probably grill you with a few questions and then we'll close it out so take it away. >> Robert: Sounds great. So I'll say a little bit about how companies are using Ray and Anyscale for foundation models. The first thing I want to mention is just why we're doing this in the first place. And the underlying observation, the underlying trend here, and this is a plot from OpenAI, is that the amount of compute needed to do machine learning has been exploding. It's been growing at something like 35 times every 18 months. This is absolutely enormous. And other people have written papers measuring this trend and you get different numbers. But the point is, no matter how you slice and dice it, it' a astronomical rate. Now if you compare that to something we're all familiar with, like Moore's Law, which says that, you know, the processor performance doubles every roughly 18 months, you can see that there's just a tremendous gap between the needs, the compute needs of machine learning applications, and what you can do with a single chip, right. So even if Moore's Law were continuing strong and you know, doing what it used to be doing, even if that were the case, there would still be a tremendous gap between what you can do with the chip and what you need in order to do machine learning. And so given this graph, what we've seen, and what has been clear to us since we started this company, is that doing AI requires scaling. There's no way around it. It's not a nice to have, it's really a requirement. And so that led us to start Ray, which is the open source project that we started to make it easy to build these scalable Python applications and scalable machine learning applications. And since we started the project, it's been adopted by a tremendous number of companies. Companies like OpenAI, which use Ray to train their large models like ChatGPT, companies like Uber, which run all of their deep learning and classical machine learning on top of Ray, companies like Shopify or Spotify or Instacart or Lyft or Netflix, ByteDance, which use Ray for their machine learning infrastructure. Companies like Ant Group, which makes Alipay, you know, they use Ray across the board for fraud detection, for online learning, for detecting money laundering, you know, for graph processing, stream processing. Companies like Amazon, you know, run Ray at a tremendous scale and just petabytes of data every single day. And so the project has seen just enormous adoption since, over the past few years. And one of the most exciting use cases is really providing the infrastructure for building training, fine tuning, and serving foundation models. So I'll say a little bit about, you know, here are some examples of companies using Ray for foundation models. Cohere trains large language models. OpenAI also trains large language models. You can think about the workloads required there are things like supervised pre-training, also reinforcement learning from human feedback. So this is not only the regular supervised learning, but actually more complex reinforcement learning workloads that take human input about what response to a particular question, you know is better than a certain other response. And incorporating that into the learning. There's open source versions as well, like GPTJ also built on top of Ray as well as projects like Alpa coming out of UC Berkeley. So these are some of the examples of exciting projects in organizations, training and creating these large language models and serving them using Ray. Okay, so what actually is Ray? Well, there are two layers to Ray. At the lowest level, there's the core Ray system. This is essentially low level primitives for building scalable Python applications. Things like taking a Python function or a Python class and executing them in the cluster setting. So Ray core is extremely flexible and you can build arbitrary scalable applications on top of Ray. So on top of Ray, on top of the core system, what really gives Ray a lot of its power is this ecosystem of scalable libraries. So on top of the core system you have libraries, scalable libraries for ingesting and pre-processing data, for training your models, for fine tuning those models, for hyper parameter tuning, for doing batch processing and batch inference, for doing model serving and deployment, right. And a lot of the Ray users, the reason they like Ray is that they want to run multiple workloads. They want to train and serve their models, right. They want to load their data and feed that into training. And Ray provides common infrastructure for all of these different workloads. So this is a little overview of what Ray, the different components of Ray. So why do people choose to go with Ray? I think there are three main reasons. The first is the unified nature. The fact that it is common infrastructure for scaling arbitrary workloads, from data ingest to pre-processing to training to inference and serving, right. This also includes the fact that it's future proof. AI is incredibly fast moving. And so many people, many companies that have built their own machine learning infrastructure and standardized on particular workflows for doing machine learning have found that their workflows are too rigid to enable new capabilities. If they want to do reinforcement learning, if they want to use graph neural networks, they don't have a way of doing that with their standard tooling. And so Ray, being future proof and being flexible and general gives them that ability. Another reason people choose Ray in Anyscale is the scalability. This is really our bread and butter. This is the reason, the whole point of Ray, you know, making it easy to go from your laptop to running on thousands of GPUs, making it easy to scale your development workloads and run them in production, making it easy to scale, you know, training to scale data ingest, pre-processing and so on. So scalability and performance, you know, are critical for doing machine learning and that is something that Ray provides out of the box. And lastly, Ray is an open ecosystem. You can run it anywhere. You can run it on any Cloud provider. Google, you know, Google Cloud, AWS, Asure. You can run it on your Kubernetes cluster. You can run it on your laptop. It's extremely portable. And not only that, it's framework agnostic. You can use Ray to scale arbitrary Python workloads. You can use it to scale and it integrates with libraries like TensorFlow or PyTorch or JAX or XG Boost or Hugging Face or PyTorch Lightning, right, or Scikit-learn or just your own arbitrary Python code. It's open source. And in addition to integrating with the rest of the machine learning ecosystem and these machine learning frameworks, you can use Ray along with all of the other tooling in the machine learning ecosystem. That's things like weights and biases or ML flow, right. Or you know, different data platforms like Databricks, you know, Delta Lake or Snowflake or tools for model monitoring for feature stores, all of these integrate with Ray. And that's, you know, Ray provides that kind of flexibility so that you can integrate it into the rest of your workflow. And then Anyscale is the scalable compute platform that's built on top, you know, that provides Ray. So Anyscale is a managed Ray service that runs in the Cloud. And what Anyscale does is it offers the best way to run Ray. And if you think about what you get with Anyscale, there are fundamentally two things. One is about moving faster, accelerating the time to market. And you get that by having the managed service so that as a developer you don't have to worry about managing infrastructure, you don't have to worry about configuring infrastructure. You also, it provides, you know, optimized developer workflows. Things like easily moving from development to production, things like having the observability tooling, the debug ability to actually easily diagnose what's going wrong in a distributed application. So things like the dashboards and the other other kinds of tooling for collaboration, for monitoring and so on. And then on top of that, so that's the first bucket, developer productivity, moving faster, faster experimentation and iteration. The second reason that people choose Anyscale is superior infrastructure. So this is things like, you know, cost deficiency, being able to easily take advantage of spot instances, being able to get higher GPU utilization, things like faster cluster startup times and auto scaling. Things like just overall better performance and faster scheduling. And so these are the kinds of things that Anyscale provides on top of Ray. It's the managed infrastructure. It's fast, it's like the developer productivity and velocity as well as performance. So this is what I wanted to share about Ray in Anyscale. >> John: Awesome. >> Provide that context. But John, I'm curious what you think. >> I love it. I love the, so first of all, it's a platform because that's the platform architecture right there. So just to clarify, this is an Anyscale platform, not- >> That's right. >> Tools. So you got tools in the platform. Okay, that's key. Love that managed service. Just curious, you mentioned Python multiple times, is that because of PyTorch and TensorFlow or Python's the most friendly with machine learning or it's because it's very common amongst all developers? >> That's a great question. Python is the language that people are using to do machine learning. So it's the natural starting point. Now, of course, Ray is actually designed in a language agnostic way and there are companies out there that use Ray to build scalable Java applications. But for the most part right now we're focused on Python and being the best way to build these scalable Python and machine learning applications. But, of course, down the road there always is that potential. >> So if you're slinging Python code out there and you're watching that, you're watching this video, get on Anyscale bus quickly. Also, I just, while you were giving the presentation, I couldn't help, since you mentioned OpenAI, which by the way, congratulations 'cause they've had great scale, I've noticed in their rapid growth 'cause they were the fastest company to the number of users than anyone in the history of the computer industry, so major successor, OpenAI and ChatGPT, huge fan. I'm not a skeptic at all. I think it's just the beginning, so congratulations. But I actually typed into ChatGPT, what are the top three benefits of Anyscale and came up with scalability, flexibility, and ease of use. Obviously, scalability is what you guys are called. >> That's pretty good. >> So that's what they came up with. So they nailed it. Did you have an inside prompt training, buy it there? Only kidding. (Robert laughs) >> Yeah, we hard coded that one. >> But that's the kind of thing that came up really, really quickly if I asked it to write a sales document, it probably will, but this is the future interface. This is why people are getting excited about the foundational models and the large language models because it's allowing the interface with the user, the consumer, to be more human, more natural. And this is clearly will be in every application in the future. >> Absolutely. This is how people are going to interface with software, how they're going to interface with products in the future. It's not just something, you know, not just a chat bot that you talk to. This is going to be how you get things done, right. How you use your web browser or how you use, you know, how you use Photoshop or how you use other products. Like you're not going to spend hours learning all the APIs and how to use them. You're going to talk to it and tell it what you want it to do. And of course, you know, if it doesn't understand it, it's going to ask clarifying questions. You're going to have a conversation and then it'll figure it out. >> This is going to be one of those things, we're going to look back at this time Robert and saying, "Yeah, from that company, that was the beginning of that wave." And just like AWS and Cloud Computing, the folks who got in early really were in position when say the pandemic came. So getting in early is a good thing and that's what everyone's talking about is getting in early and playing around, maybe replatforming or even picking one or few apps to refactor with some staff and managed services. So people are definitely jumping in. So I have to ask you the ROI cost question. You mentioned some of those, Moore's Law versus what's going on in the industry. When you look at that kind of scale, the first thing that jumps out at people is, "Okay, I love it. Let's go play around." But what's it going to cost me? Am I going to be tied to certain GPUs? What's the landscape look like from an operational standpoint, from the customer? Are they locked in and the benefit was flexibility, are you flexible to handle any Cloud? What is the customers, what are they looking at? Basically, that's my question. What's the customer looking at? >> Cost is super important here and many of the companies, I mean, companies are spending a huge amount on their Cloud computing, on AWS, and on doing AI, right. And I think a lot of the advantage of Anyscale, what we can provide here is not only better performance, but cost efficiency. Because if we can run something faster and more efficiently, it can also use less resources and you can lower your Cloud spending, right. We've seen companies go from, you know, 20% GPU utilization with their current setup and the current tools they're using to running on Anyscale and getting more like 95, you know, 100% GPU utilization. That's something like a five x improvement right there. So depending on the kind of application you're running, you know, it's a significant cost savings. We've seen companies that have, you know, processing petabytes of data every single day with Ray going from, you know, getting order of magnitude cost savings by switching from what they were previously doing to running their application on Ray. And when you have applications that are spending, you know, potentially $100 million a year and getting a 10 X cost savings is just absolutely enormous. So these are some of the kinds of- >> Data infrastructure is super important. Again, if the customer, if you're a prospect to this and thinking about going in here, just like the Cloud, you got infrastructure, you got the platform, you got SaaS, same kind of thing's going to go on in AI. So I want to get into that, you know, ROI discussion and some of the impact with your customers that are leveraging the platform. But first I hear you got a demo. >> Robert: Yeah, so let me show you, let me give you a quick run through here. So what I have open here is the Anyscale UI. I've started a little Anyscale Workspace. So Workspaces are the Anyscale concept for interactive developments, right. So here, imagine I'm just, you want to have a familiar experience like you're developing on your laptop. And here I have a terminal. It's not on my laptop. It's actually in the cloud running on Anyscale. And I'm just going to kick this off. This is going to train a large language model, so OPT. And it's doing this on 32 GPUs. We've got a cluster here with a bunch of CPU cores, bunch of memory. And as that's running, and by the way, if I wanted to run this on instead of 32 GPUs, 64, 128, this is just a one line change when I launch the Workspace. And what I can do is I can pull up VS code, right. Remember this is the interactive development experience. I can look at the actual code. Here it's using Ray train to train the torch model. We've got the training loop and we're saying that each worker gets access to one GPU and four CPU cores. And, of course, as I make the model larger, this is using deep speed, as I make the model larger, I could increase the number of GPUs that each worker gets access to, right. And how that is distributed across the cluster. And if I wanted to run on CPUs instead of GPUs or a different, you know, accelerator type, again, this is just a one line change. And here we're using Ray train to train the models, just taking my vanilla PyTorch model using Hugging Face and then scaling that across a bunch of GPUs. And, of course, if I want to look at the dashboard, I can go to the Ray dashboard. There are a bunch of different visualizations I can look at. I can look at the GPU utilization. I can look at, you know, the CPU utilization here where I think we're currently loading the model and running that actual application to start the training. And some of the things that are really convenient here about Anyscale, both I can get that interactive development experience with VS code. You know, I can look at the dashboards. I can monitor what's going on. It feels, I have a terminal, it feels like my laptop, but it's actually running on a large cluster. And I can, with however many GPUs or other resources that I want. And so it's really trying to combine the best of having the familiar experience of programming on your laptop, but with the benefits, you know, being able to take advantage of all the resources in the Cloud to scale. And it's like when, you know, you're talking about cost efficiency. One of the biggest reasons that people waste money, one of the silly reasons for wasting money is just forgetting to turn off your GPUs. And what you can do here is, of course, things will auto terminate if they're idle. But imagine you go to sleep, I have this big cluster. You can turn it off, shut off the cluster, come back tomorrow, restart the Workspace, and you know, your big cluster is back up and all of your code changes are still there. All of your local file edits. It's like you just closed your laptop and came back and opened it up again. And so this is the kind of experience we want to provide for our users. So that's what I wanted to share with you. >> Well, I think that whole, couple of things, lines of code change, single line of code change, that's game changing. And then the cost thing, I mean human error is a big deal. People pass out at their computer. They've been coding all night or they just forget about it. I mean, and then it's just like leaving the lights on or your water running in your house. It's just, at the scale that it is, the numbers will add up. That's a huge deal. So I think, you know, compute back in the old days, there's no compute. Okay, it's just compute sitting there idle. But you know, data cranking the models is doing, that's a big point. >> Another thing I want to add there about cost efficiency is that we make it really easy to use, if you're running on Anyscale, to use spot instances and these preemptable instances that can just be significantly cheaper than the on-demand instances. And so when we see our customers go from what they're doing before to using Anyscale and they go from not using these spot instances 'cause they don't have the infrastructure around it, the fault tolerance to handle the preemption and things like that, to being able to just check a box and use spot instances and save a bunch of money. >> You know, this was my whole, my feature article at Reinvent last year when I met with Adam Selipsky, this next gen Cloud is here. I mean, it's not auto scale, it's infrastructure scale. It's agility. It's flexibility. I think this is where the world needs to go. Almost what DevOps did for Cloud and what you were showing me that demo had this whole SRE vibe. And remember Google had site reliability engines to manage all those servers. This is kind of like an SRE vibe for data at scale. I mean, a similar kind of order of magnitude. I mean, I might be a little bit off base there, but how would you explain it? >> It's a nice analogy. I mean, what we are trying to do here is get to the point where developers don't think about infrastructure. Where developers only think about their application logic. And where businesses can do AI, can succeed with AI, and build these scalable applications, but they don't have to build, you know, an infrastructure team. They don't have to develop that expertise. They don't have to invest years in building their internal machine learning infrastructure. They can just focus on the Python code, on their application logic, and run the stuff out of the box. >> Awesome. Well, I appreciate the time. Before we wrap up here, give a plug for the company. I know you got a couple websites. Again, go, Ray's got its own website. You got Anyscale. You got an event coming up. Give a plug for the company looking to hire. Put a plug in for the company. >> Yeah, absolutely. Thank you. So first of all, you know, we think AI is really going to transform every industry and the opportunity is there, right. We can be the infrastructure that enables all of that to happen, that makes it easy for companies to succeed with AI, and get value out of AI. Now we have, if you're interested in learning more about Ray, Ray has been emerging as the standard way to build scalable applications. Our adoption has been exploding. I mentioned companies like OpenAI using Ray to train their models. But really across the board companies like Netflix and Cruise and Instacart and Lyft and Uber, you know, just among tech companies. It's across every industry. You know, gaming companies, agriculture, you know, farming, robotics, drug discovery, you know, FinTech, we see it across the board. And all of these companies can get value out of AI, can really use AI to improve their businesses. So if you're interested in learning more about Ray and Anyscale, we have our Ray Summit coming up in September. This is going to highlight a lot of the most impressive use cases and stories across the industry. And if your business, if you want to use LLMs, you want to train these LLMs, these large language models, you want to fine tune them with your data, you want to deploy them, serve them, and build applications and products around them, give us a call, talk to us. You know, we can really take the infrastructure piece, you know, off the critical path and make that easy for you. So that's what I would say. And, you know, like you mentioned, we're hiring across the board, you know, engineering, product, go-to-market, and it's an exciting time. >> Robert Nishihara, co-founder and CEO of Anyscale, congratulations on a great company you've built and continuing to iterate on and you got growth ahead of you, you got a tailwind. I mean, the AI wave is here. I think OpenAI and ChatGPT, a customer of yours, have really opened up the mainstream visibility into this new generation of applications, user interface, roll of data, large scale, how to make that programmable so we're going to need that infrastructure. So thanks for coming on this season three, episode one of the ongoing series of the hot startups. In this case, this episode is the top startups building foundational model infrastructure for AI and ML. I'm John Furrier, your host. Thanks for watching. (upbeat music)

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

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

(bright music) >> Hello everyone and welcome back to AWS re:Invent, here in wonderful Las Vegas, Nevada. We're theCUBE. I am Savannah Peterson. Joined with my co-host, Dave Vellante. Day four, you look great. Your voice has come back somehow. >> Yeah, a little bit. I don't know how. I took last night off. You guys, I know, were out partying all night, but - >> I don't know what you're talking about. (Dave laughing) >> Well, you were celebrating John's birthday. John Furrier's birthday today. >> Yes, happy birthday John! >> He's on his way to England. >> Yeah. >> To attend his nephew's wedding. Awesome family. And so good luck, John. I hope you feel better, he's got a little cold. >> I know, good luck to the newlyweds. I love this. I know we're both really excited for our next guest, so I'm going to bring out, Lena Smart from MongoDB. Thank you so much for being here. >> Thank you for having me. >> How's the show going for you? >> Good. It's been a long week. And I just, not much voice left, so. >> We'll be gentle on you. >> I'll give you what's left of it. >> All right, we'll take that. >> Okay. >> You had a fireside chat, at the show? >> Lena: I did. >> Can you tell us a little bit about that? >> So we were talking about the Rise, The developer is a platform. In this massive theater. I thought it would be like an intimate, you know, fireside chat. I keep believing them when they say to me come and do these talks, it'll be intimate. And you turn up and there's a stage and a theater and it's like, oh my god. But it was really interesting. It was well attended. Got some really good questions at the end as well. Lots of follow up, which was interesting. And it was really just about, you know, how we've brought together this developer platform that's got our integrated services. It's just what developers want, it gives them time to innovate and disrupt, rather than worry about the minutia of management. >> Savannah: Do the cool stuff. >> Exactly. >> Yeah, so you know Lena, it's funny that you're saying that oh wow, the lights came on and it was this big thing. When when we were at re:Inforced, Lena was on stage and it was so funny, Lena, you were self deprecating like making jokes about the audience. >> Savannah: (indistinct) >> It was hilarious. And so, but it was really endearing to the audience and so we were like - >> Lena: It was terrifying. >> You got huge props for that, I'll tell you. >> Absolutely terrifying. Because they told me I wouldn't see anyone. Because we did the rehearsal the day before, and they were like, it's just going to be like - >> Sometimes it just looks like blackness out there. >> Yeah, yeah. It wasn't, they lied. I could see eyeballs. It was terrifying. >> Would you rather know that going in though? Or is it better to be, is ignorance bliss in that moment? >> Ignorance is bliss. >> Yeah, yeah yeah. >> Good call Savannah, right? Yeah, just go. >> The older I get, the more I'm just, I'm on the ignorance is bliss train. I just, I don't need to know anything that's going to hurt my soul. >> Exactly. >> One of the things that you mentioned, and this has actually been a really frequent theme here on the show this week, is you said that this has been a transformative year for developers. >> Lena: Yeah. >> What did you mean by that? >> So I think developers are starting to come to the fore, if you like, the fore. And I'm not in any way being deprecating about developers 'cause I love them. >> Savannah: I think everyone here does. >> I was married to one, I live with one now. It's like, they follow me everywhere. They don't. But, I think they, this is my opinion obviously but I think that we're seeing more and more the value that developers bring to the table. They're not just code geeks anymore. They're not just code monkeys, you know, churning out lines and lines of code. Some of the most interesting discussions I've had this week have been with developers. And that's why I'm so pleased that our developer data platform is going to give these folks back time, so that they can go and innovate. And do super interesting things and do the next big thing. It was interesting, I was talking to Mary, our comms person earlier and she had said that Dave I guess, my boss, was on your show - >> Dave: Yeah, he was over here last night. >> Yeah. And he was saying that two thirds of the companies that had been mentioned so far, within the whole gamut of this conference use MongoDB. And so take that, extrapolate that, of all the developers >> Wow. >> who are there. I know, isn't that awesome? >> That's awesome. Congrats on that, that's like - >> Did I hear that right now? >> I know, I just had that moment. >> I know she just told me, I'm like, really? That's - >> That's so cool. >> 'Cause the first thing I thought of was then, oh my god, how many developers are we reaching then? 'Cause they're the ones. I mean, it's kind of interesting. So my job has kind of grown from, over the years, being the security geek in the back room that nobody talks to, to avoiding me in the lift, to I've got a seat at the table now. We meet with the board. And I think that I can see that that's where the developer mindset is moving towards. It's like, give us the right tools and we'll change your world. >> And let the human capital go back to doing the fun stuff and not just the maintenance stuff. >> And, but then you say that, you can't have everything automated. I get that automation is also the buzzword of the week. And I get that, trust me. Someone has to write the code to do the automation. >> Savannah: Right. >> So, so yeah, definitely give these people back time, so that they can work on ML, AI, choose your buzzword. You know, by giving people things like queriable encryption for example, you're going to free up a whole bunch of head space. They don't have to worry about their data being, you know harvested from memory or harvested while at rest or in motion. And it's like, okay, I don't have to worry about that now, let me go do something fun. >> How about the role of the developer as it relates to SecOps, right? They're being asked to do a lot. You and I talked about this at re:Inforce. You seem to have a pretty good handle on it. Like a lot of companies I think are struggling with it. I mean, the other thing you said said to me is you don't have a lack of talent at Mongo, right? 'Cause you're Mongo. But a lot of companies do. But a lot of the developers, you know we were just talking about this earlier with Capgemini, the developer metrics or the application development team's metrics might not be aligned with the CSO's metrics. How, what are you seeing there? What, how do you deal with it within Mongo? What do you advise your customers? >> So in terms of internal, I work very closely with our development group. So I work with Tara Hernandez, who's our new VP of developer productivity. And she and her team are very much interested in making developers more productive. That's her job. And so we get together because sometimes security can definitely be seen as a blocker. You know, funnily enough, I actually had a Slack that I had to respond to three seconds before I come on here. And it was like, help, we need some help getting this application through procurement, because blah, blah, blah. And it's weird the kind of change, the shift in mindset. Whereas before they might have gone to procurement or HR or someone to ask for this. Now they're coming to the CSO. 'Cause they know if I say yes, it'll go through. >> Talk about social engineering. >> Exactly. >> You were talking about - >> But turn it around though. If I say no, you know, I don't like to say no. I prefer to be the CSO that says yes, but. And so that's what we've done. We've definitely got that culture of ask, we'll tell you the risks, and then you can go away and be innovative and do what you need to do. And we basically do the same with our customers. Here's what you can do. Our application is secure out of the box. Here's how we can help you make it even more, you know, streamlined or bespoke to what you need. >> So mobile was a big inflection point, you know, I dunno, it seems like forever ago. >> 2007. >> 2007. Yeah, iPhone came out in 2007. >> You remember your first iPhone? >> Dave: Yeah. >> Yeah? Same. >> Yeah. It was pretty awesome, actually. >> Yeah, I do too. >> Yeah, I was on the train to Boston going up to see some friends at MIT on the consortium that I worked with. And I had, it was the wee one, 'member? But you thought it was massive. >> Oh, it felt - >> It felt big. And I remember I was sitting on the train to Boston it was like the Estella and there was these people, these two women sitting beside me. And they were all like glam, like you and unlike me. >> Dave: That's awesome. >> And they, you could see them like nudging each other. And I'm being like, I'm just sitting like this. >> You're chilling. >> Like please look at my phone, come on just look at it. Ask me about it. And eventually I'm like - >> You're baiting them. >> nonchalantly laid it on the table. And you know, I'm like, and they're like, is that an iPhone? And I'm like, yeah, you want to see it? >> I thought you'd never ask. >> I know. And I really played with it. And I showed them all the cool stuff, and they're like, oh we're going to buy iPhones. And so I should have probably worked for Apple, but I didn't. >> I was going to say, where was your referral kickback on that? Especially - >> It was a little like Tesla, right? When you first, we first saw Tesla, it was Ray Wong, you know, Ray? From Pasadena? >> It really was a moment and going from the Blackberry keyboard to that - >> He's like want to see my car? And I'm like oh yeah sure, what's the big deal? >> Yeah, then you see it and you're like, ooh. >> Yeah, that really was such a pivotal moment. >> Anyway, so we lost a track, 2007. >> Yeah, what were we talking about? 2007 mobile. >> Mobile. >> Key inflection point, is where you got us here. Thank you. >> I gotchu Dave, I gotchu. >> Bring us back here. My mind needs help right now. Day four. Okay, so - >> We're all getting here on day four, we're - >> I'm socially engineering you to end this, so I can go to bed and die quietly. That's what me and Mary are, we're counting down the minutes. >> Holy. >> That's so sick. >> You're breaking my heart right now. I love it. I'm with you, sis, I'm with you. >> So I dunno where I was, really where I was going with this, but, okay, there's - >> 2007. Three things happened. >> Another inflection point. Okay yeah, tell us what happened. But no, tell us that, but then - >> AWS, clones, 2006. >> Well 2006, 2007. Right, okay. >> 2007, the iPhone, the world blew up. So you've already got this platform ready to take all this data. >> Dave: Right. >> You've got this little slab of gorgeousness called the iPhone, ready to give you all that data. And then MongoDB pops up, it's like, woo-hoo. But what we could offer was, I mean back then was awesome, but it was, we knew that we would have to iterate and grow and grow and grow. So that was kind of the three things that came together in 2007. >> Yeah, and then Cloud came in big time, and now you've got this platform. So what's the next inflection point do you think? >> Oh... >> Good question, Dave. >> Don't even ask me that. >> I mean, is it Edge? Is it IOT? Is there another disruptor out there? >> I think it's going to be artificial intelligence. >> Dave: Is it AI? >> I mean I don't know enough about it to talk about it, to any level, so don't ask me any questions about it. >> This is like one of those ignorance is bliss moments. It feels right. >> Yeah. >> Well, does it scare you, from a security perspective? Or? >> Great question, Dave. >> Yeah, it scares me more from a humanity standpoint. Like - >> More than social scared you? 'Cause social was so benign when it started. >> Oh it was - >> You're like, oh - I remember, >> It was like a yearbook. I was on the Estella and we were - >> Shout out to Amtrak there. >> I was with, we were starting basically a wikibond, it was an open source. >> Yeah, yeah. >> Kind of, you know, technology community. And we saw these and we were like enamored of Facebook. And there were these two young kids on the train, and we were at 'em, we were picking the brain. Do you like Facebook? "I love Facebook." They're like "oh, Facebook's unbelievable." Now, kids today, "I hate Facebook," right? So, but social at the beginning it was kind of, like I say, benign and now everybody's like - >> Savannah: We didn't know what we were getting into. >> Right. >> I know. >> Exactly. >> Can you imagine if you could have seen into the future 20 years ago? Well first of all, we'd have all bought Facebook and Apple stock. >> Savannah: Right. >> And Tesla stock. But apart from, but yeah apart from that. >> Okay, so what about Quantum? Does that scare you at all? >> I think the only thing that scares me about Quantum is we have all this security in place today. And I'm not an expert in Quantum, but we have all this security in place that's securing what we have today. And my worry is, in 10 years, is it still going to be secure? 'Cause we're still going to be using that data in some way, shape, or form. And my question is to the quantum geniuses out there, what do we do in 10 years like to retrofit the stuff? >> Dave: Like a Y2K moment? >> Kind of. Although I think Y2K is coming in 2038, isn't it? When the Linux date flips. I'll be off the grid by then, I'll be living in Scotland. >> Somebody else's problem. >> Somebody else's problem. I'll be with the sheep in Glasgow, in Scotland. >> Y2K was a boondoggle for tech, right? >> What a farce. I mean, that whole - >> I worked in the power industry in Y2K. That was a nightmare. >> Dave: Oh I bet. >> Savannah: Oh my God. >> Yeah, 'cause we just assumed that the world was going to stop and there been no power, and we had nuclear power plants. And it's like holy moly. Yeah. >> More than moly. >> I was going to say, you did a good job holding that other word in. >> I think I was going to, in case my mom hears this. >> I grew up near Diablo Canyon in, in California. So you were, I mean we were legitimately worried that that exactly was going to happen. And what about the waste? And yeah it was chaos. We've covered a lot. >> Well, what does worry you? Like, it is culture? Is it - >> Why are you trying to freak her out? >> No, no, because it's a CSO, trying to get inside the CSO's head. >> You don't think I have enough to worry about? You want to keep piling on? >> Well if it's not Quantum, you know? Maybe it's spiders or like - >> Oh but I like spiders, well spiders are okay. I don't like bridges, that's my biggest fear. Bridges. >> Seriously? >> And I had to drive over the Tappan Zee bridge, which is one of the longest, for 17 years, every day, twice. The last time I drove over it, I was crying my heart out, and happy as anything. >> Stay out of Oakland. >> I've never driven over it since. Stay out of where? >> Stay out of Oakland. >> I'm staying out of anywhere that's got lots of water. 'Cause it'll have bridges. >> Savannah: Well it's good we're here in the desert. >> Exactly. So what scares me? Bridges, there you go. >> Yeah, right. What? >> Well wait a minute. So if I'm bridging technology, is that the scary stuff? >> Oh God, that was not - >> Was it really bad? >> It was really bad. >> Wow. Wow, the puns. >> There's a lot of seems in those bridges. >> It is lit on theCUBE A floor, we are all struggling. I'm curious because I've seen, your team is all over the place here on the show, of course. Your booth has been packed the whole time. >> Lena: Yes. >> The fingerprint. Talk to me about your shirt. >> So, this was designed by my team in house. It is the most wanted swag in the company, because only my security people wear it. So, we make it like, yeah, you could maybe have one, if this turns out well. >> I feel like we're on the right track. >> Dave: If it turns out well. >> Yeah, I just love it. It's so, it's just brilliant. I mean, it's the leaf, it's a fingerprint. It's just brilliant. >> That's why I wanted to call it out. You know, you see a lot of shirts, a lot of swag shirts. Some are really unfortunately sad, or not funny, >> They are. >> or they're just trying too hard. Now there's like, with this one, I thought oh I bet that's clever. >> Lena: It is very cool. Yes, I love it. >> I saw a good one yesterday. >> Yeah? >> We fix shit, 'member? >> Oh yeah, yeah. >> That was pretty good. >> I like when they're >> That's a pretty good one. >> just straightforward, like that, yeah yeah. >> But the only thing with this is when you're say in front of a green screen, you look as though you've got no tummy. >> A portal through your body. >> And so, when we did our first - >> That's a really good point, actually. >> Yeah, it's like the black hole to nothingless. And I'm like wow, that's my soul. >> I was just going to say, I don't want to see my soul like that. I don't want to know. >> But we had to do like, it was just when the pandemic first started, so we had to do our big presentation live announcement from home. And so they shipped us all this camera equipment for home and thank God my partner knows how that works, so he set it all up. And then he had me test with a green screen, and he's like, you have no tummy. I'm like, what the hell are you talking about? He's like, come and see. It's like this, I dunno what it was. So I had to actually go upstairs and felt tip with a magic marker and make it black. >> Wow. >> So that was why I did for two hours on a Friday, yeah. >> Couldn't think of another alternative, huh? >> Well no, 'cause I'm myopic when it comes to marketing and I knew I had to keep the tshirt on, and I just did that. >> Yeah. >> In hindsight, yes I could have worn an "I Fix Shit" tshirt, but I don't think my husband would've been very happy. I secure shit? >> There you go, yeah. >> There you go. >> Over to you, Savannah. >> I was going to say, I got acquainted, I don't know if I can say this, but I'm going to say it 'cause we're here right now. I got acquainted with theCUBE, wearing a shirt that said "Unfuck Kubernetes," 'cause it was a marketing campaign that I was running for one of my clients at Kim Con last year. >> That's so good. >> Yeah, so - >> Oh my God. I'll give you one of these if you get me one of those. >> I can, we can do a swapskee. We can absolutely. >> We need a few edits on this film, on the file. >> Lena: Okay, this is nothing - >> We're fallin' off the wheel. Okay, on that note, I'm going to bring us to our challenge that we discussed, before we got started on this really diverse discussion that we have had in the last 15 minutes. We've covered everything from felt tip markers to nuclear power plants. >> To the darkness of my soul. >> To the darkness of all of our souls. >> All of our souls, yes. >> Which is perhaps a little too accurate, especially at this stage in the conference. You've obviously seen a lot Lena, and you've been rockin' it, I know John was in your suite up here, at at at the Venetian. What's your 30 second hot take? Most important story, coming out of the show or for you all at Mongo this year? >> Genuinely, it was when I learned that two-thirds of the customers that had been mentioned, here, are MongoDB customers. And that just exploded in my head. 'Cause now I'm thinking of all the numbers and the metrics and how we can use that. And I just think it's amazing, so. >> Yeah, congratulations on that. That's awesome. >> Yeah, I thought it was amazing. >> And it makes sense actually, 'cause Mongo so easy to use. We were talking about Tengen. >> We knew you when, I feel that's our like, we - >> Yeah, but it's true. And so, Mongo was just really easy to use. And people are like, ah, it doesn't scale. It's like, turns out it actually does scale. >> Lena: Turns out, it scales pretty well. >> Well Lena, without question, this is my favorite conversation of the show so far. >> Thank you. >> Thank you so much for joining us. >> Thank you very much for having me. >> Dave: Great to see you. >> It's always a pleasure. >> Dave: Thanks Lena. >> Thank you. >> And thank you all, tuning in live, for tolerating wherever we take these conversations. >> Dave: Whatever that was. >> I bet you weren't ready for this one, folks. We're at AWS re:Invent in Las Vegas, Nevada. With Dave Vellante, I'm Savannah Peterson. You're washing theCUBE, the leader for high tech coverage.

>>Well, hello everybody. John Walls here and continuing our coverage here at AWS Reinvent 22 on the queue. We continue our segments here in the Global Startup program, which of course is sponsored by AWS Startup Showcase, and with us to talk about any scale as the co-founder and CEO of the company, Robert and n, you are Robert. Good to see you. Thanks for joining us. >>Yeah, great. And thank you. >>You bet. Yeah. Glad to have you aboard here. So let's talk about Annie Scale, first off, for those at home and might not be familiar with what you do. Yeah. Because you've only been around for a short period of time, you're telling me >>Company's about >>Three years now. Three >>Years old, >>Yeah. Yeah. So tell us all about it. Yeah, >>Absolutely. So one of the biggest things happening in computing right now is the proliferation of ai. AI is just spreading throughout every industry has the potential to transform every industry. But the thing about doing AI is that it's incredibly computationally intensive. So if you wanna do do ai, you're not, you're probably not just doing it on your laptop, you're doing it across many machines, many gpu, many compute resources, and that's incredibly hard to do. It requires a lot of software engineering expertise, a lot of infrastructure expertise, a lot of cloud computing expertise to build the software infrastructure and distributed systems to really scale AI across all of the, across the cloud. And to do it in a way where you're really getting value out of ai. And so that is the, the problem statement that AI has tremendous potential. It's incredibly hard to do because of the, the scale required. >>And what we are building at any scale is really trying to make that easy. So trying to get to the point where, as a developer, if you know how to program on your laptop, then if you know how to program saying Python on your laptop, then that's enough, right? Then you can do ai, you can get value out of it, you can scale it, you can build the kinds of, you know, incredibly powerful applica AI applications that companies like Google and, and Facebook and others can build. But you don't have to learn about all of the distributed systems and infrastructure. It just, you know, we'll handle that for you. So that's, if we're successful, you know, that's what we're trying to achieve here. >>Yeah. What, what makes AI so hard to work with? I mean, you talk about the complexity. Yeah. A lot of moving parts. I mean, literally moving parts, but, but what is it in, in your mind that, that gets people's eyes spinning a little bit when they, they look at great potential. Yeah. But also they look at the downside of maybe having to work your way through Pike mere of sorts. >>So, so the potential is definitely there, but it's important to remember that a lot of AI initiatives fail. Like a lot of initiative AI initiatives, something like 80 or 90% don't make it out of, you know, the research or prototyping phase and inter production. Hmm. So, some of the things that are hard about AI and the reasons that AI initiatives can fail, one is the scale required, you know, moving. It's one thing to develop something on your laptop, it's another thing to run it across thousands of machines. So that's scale, right? Another is the transition from development and prototyping to production. Those are very different, have very different requirements. Absolutely. A lot of times it's different teams within a company. They have different tech stacks, different software they're using. You know, we hear companies say that when they move from develop, you know, once they prototype and develop a model, it could take six to 12 weeks to get that model in production. >>And that often involves rewriting a lot of code and handing it off to another team. So the transition from development to production is, is a big challenge. So the scale, the development to production handoff. And then lastly, a big challenge is around flexibility. So AI's a fast moving field, you see new developments, new algorithms, new models coming out all the time. And a lot of teams we work with, you know, they've, they've built infrastructure. They're using products out there to do ai, but they've found that it's sort of locking them into rigid workflows or specific tools, and they don't have the flexibility to adopt new algorithms or new strategies or approaches as they're being developed as they come out. And so they, but their developers want the flexibility to use the latest tools, the latest strategies. And so those are some of the main problems we see. It's really like, how do you scale scalability? How do you move easily from development and production and back? And how do you remain flexible? How do you adapt and, and use the best tools that are coming out? And so those are, yeah, just those are and often reasons that people start to use Ray, which is our open source project in any scale, which is our, our product. So tell >>Me about Ray, right? Yeah. Opensource project. I think you said you worked on it >>At Berkeley. That's right. Yeah. So before this company, I did a PhD in machine learning at Berkeley. And one of the challenges that we were running into ourselves, we were trying to do machine learning. We actually weren't infrastructure or distributed systems people, but we found ourselves in order to do machine learning, we found ourselves building all sorts of tools, ad hoc tools and systems to scale the machine learning, to be able to run it in a reasonable amount of time and to be able to leverage the compute that we needed. And it wasn't just us people all across, you know, machine learning researchers, machine learning practitioners were building their own tooling and infrastructure. And that was one of the things that we felt was really holding back progress. And so that's how we slowly and kind of gradually got into saying, Hey, we could build better tools here. >>We could build, we could try to make this easier to do so that all of these people don't have to build their own infrastructure. They can focus on the actual machine learning applications that they're trying to build. And so we started, Ray started this open source project for basically scaling Python applications and scaling machine learning applications. And, well, initially we were running around Berkeley trying to get all of our friends to try it out and, and adopt it and, you know, and give us feedback. And if it didn't work, we would debug it right away. And that slow, you know, that gradually turned into more companies starting to adopt it, bigger teams starting to adopt it, external contributors starting to, to contribute back to the open source project and make it better. And, you know, before you know it, we were hosting meetups, giving to talks, running tutorials, and the project was just taking off. And so that's a big part of what we continue to develop today at any scale, is like really fostering this open source community, growing the open source user base, making sure Ray is just the best way to scale Python applications and, and machine learning applications. >>So, so this was a graduate school project That's right. You say on, on your way to getting your doctorate and now you commercializing now, right? Yeah. I mean, so you're being able to offer it, first off, what a journey that was, right? I mean, who would've thought Absolutely. I guess you probably did think that at some point, but >>No, you know, when we started, when we were working on Ray, we actually didn't anticipate becoming a company, or we at least just weren't looking that far ahead. We were really excited about solving this problem of making distributed computing easy, you know, getting to the point where developers just don't have to learn about infrastructure and distributed systems, but get all the benefits. And of course, it wasn't until, you know, later on as we were graduating from Berkeley and we wanted to continue really taking this project further and, and really solving this problem that it, we realized it made sense to start a company. >>So help me out, like, like what, what, and I might have missed this, so I apologize if I did, but in terms of, of Ray's that building block and essential for your, your ML or AI work down the road, you know, what, what is it doing for me or what, what will it allow me to do in either one of those realms that I, I can't do now? >>Yeah. And so, so like why use Ray versus not using Ray? Yeah, I think the, the answer is that you, you know, if you're doing ai, you need to scale. It's becoming, if you don't find that to be the case today, you probably will tomorrow, you know, or the day after that. And so it's really increasingly, it's a requirement. It's not an option. And so if you're scaling, if you're trying to build these scalable applications you are building, you're either going to use Ray or, or something like Ray or you're going to build the infrastructure yourself and building the infrastructure yourself, that's a long journey. >>So why take that on, right? >>And many of the companies we work with don't want to be in the business of building and managing infrastructure. No. Because, you know, if they, they want their their best engineers to build their product, right? To, to get their product to market faster. >>I want, I want you to do that for me. >>Right? Exactly. And so, you know, we can really accelerate what these teams can do and, you know, and if we can make the infrastructure something they just don't have to think about, that's, that's why you would choose to use Ray. >>Okay. You know, between a and I and ml are, are they different animals in terms of what you're trying to get done or what Ray can do? >>Yeah, and actually I should say like, it's not just, you know, teams that are new teams that are starting out, that are using Ray, many companies that have built, already built their own infrastructure will then switch to using Ray. And to give you a few examples, like Uber runs all their deep learning on Ray, okay. And, you know, open ai, which is really at the frontier of training large models and, and you know, pushing the boundaries of, of ai, they train their largest models using Ray. You know, companies like Shopify rebuilt their entire machine learning platform using Ray, >>But they started somewhere else. >>They had, this is all, you know, like, it's not like the v1, you know, of their, of their machine learning infrastructure. This is like, they did it a different way before, this is like the second version or the third iteration of of, of how they're doing it. And they realize often it's because, you know, I mean in the case of, of Uber, just to give you one example, they built a system called hova for scaling deep learning on a bunch of GPUs. Right Now, as you scale deep learning on GPUs for them, the bottleneck shifted away from, you know, as you scale GPU's training, the bottleneck shifted away from training and to the data ingest and pre-processing. And they wanted to scale data ingest and pre-processing on CPUs. So now Hova, it's a deep learning framework. It doesn't do the data ingest and pre-processing on CPUs, but you can, if you run Hova on top of Ray, you can scale training on GPUs. >>And then Ray has another library called Ray Data you can, that lets you scale the ingest and pre-processing on CPUs. You can pipeline them together. And that allowed them to train larger models on more data before, just to take one example, ETA prediction, if you get in an Uber, it tells you what time you're supposed to arrive. Sure. That uses a deep learning model called d eta. And before they were able to train on about two weeks worth of data. Now, you know, using Ray and for scaling the data, ingestive pre-processing and training, they can train on much more data. You know, you can get more accurate ETA predictions. So that's just one example of the kind of benefit they were able to get. Right. Also, because it's running on top of, of Ray and Ray has this ecosystem of libraries, you know, they can also use Ray's hyper parameter tuning library to do hyper parameter tuning for their deep learning models. >>They can also use it for inference and you know, because these are all built on top of Ray, they inherit the like, elasticity and fault tolerance of running on top of Ray. So really it simplifies things on the infrastructure side cuz there's just, if you have Ray as common infrastructure for your machine learning workloads, there's just one system to, to kind of manage and operate. And if you are, it simplifies things for the end users like the developers because from their perspective, they're just writing a Python application. They don't have to learn how to use three different distributed systems and stitch them together and all of this. >>So aws, before I let you go, how do they come into play here for you? I mean, are you part of the showcase, a startup showcase? So obviously a major partner and major figure in the offering that you're presenting >>People? Yeah, well you can run. So any scale is a managed ray service. Like any scale is just the best way to run Ray and deploy Ray. And we run on top of aws. So many of our customers are, you know, using Ray through any scale on aws. And so we work very closely together and, and you know, we have, we have joint customers and basically, and you know, a lot of the value that any scale is adding on top of Ray is around the production story. So basically, you know, things like high availability, things like failure handling, retry alerting, persistence, reproducibility, these are a lot of the value, the values of, you know, the value that our platform adds on top of the open source project. A lot of stuff as well around collaboration, you know, imagine you are, you, something goes wrong with your application, your production job, you want to debug it, you can just share the URL with your, your coworker. They can click a button, reproduce the exact same thing, look at the same logs, you know, and, and, and figure out what's going on. And also a lot around, one thing that's, that's important for a lot of our customers is efficiency around cost. And so we >>Support every customer. >>Exactly. A lot of people are spending a lot of money on, on aws. Yeah. Right? And so any scale supports running out of the box on cheaper like spot instances, these preempt instances, which, you know, just reduce costs by quite a bit. And so things like that. >>Well, the company is any scale and you're on the show floor, right? So if you're having a chance to watch this during reinvent, go down and check 'em out. Robert Ashihara joining us here, the co-founder and ceo and Robert, thanks for being with us. Yeah. Here on the cube. Really enjoyed it. Me too. Thanks so much. Boy, three years graduate program and boom, here you are, you know, with off to the enterprise you go. Very nicely done. All right, we're gonna continue our coverage here on the Cube with more here from Las Vegas. We're the Venetian, we're AWS Reinvent 22 and you're watching the Cube, the leader in high tech coverage.

>> Male: TheCUBE presents, UIPATH, Forward 5 brought to you by, UIPATH. >> Okay the party has started here at forward 5 UIPATH big customer event if you're watching the cube. We're wrapping up day one with the co-CE0 segment. Daniel Dines is here. He's the founder and Co-CEO of UIPATH and Rob Enslin, is co-CEO. Gents, great to see you. Thanks for spending some time with us. I know you're super busy. >> Thanks Dave. >> So I've been looking forward to this. Daniel you know I've followed the company for a long time. The really interesting path you took, to get to where you are today. How did you guys meet? And why did you decide to hire Rob? >> Male: (laughs) >> Rob: Well let me start. I uh, I was looking for a partner. Actually, in our work to your stand here, we are talking about how, how you feel in this job. You feel so alone. Because you are the center of all pressure points. And having a partner, having someone that has your back, it's kind of awesome. So I was looking for a partner. And our current friend, Carl Escenbach, he introduced us to each other, and we instantly clicked. And this is the type of job where it's uh either work well or it doesn't. It cannot be anything in the middle. >> Right, okay with Carl, we know Carl well. Awesome operator. Knows the business super well. So Rob, what attracted you to UIPATH? You had a great situation at google. You guys were growing like crazy. Why did you decide to come here? What did you see that attracted you? >> Yeah you know when I, when I went to google, I went to google because I really believed that data and AI was necessary for companies. And business is to be competitive in the future. And we did some great stuff at google cloud in the 3 years. But I knew UIPATH from a couple of years ago when they were mainly a RPA space. And I just felt that there was a place in time when automation was going expand. And as I sat down with Carl a couple of times, spoke to carl. And then I sat down with Daniel, I knew that there was something special with UIPATH, that could be a generational opportunity. Not any for myself but for the company in the future. And then I, you know I got to know Daniel. And at this stage of my career I was like, I'm pretty fussy about what I want to do and what I want and where I want to go. First of all, I want to go to a company that had great product, had a great culture, and I wanted to work with somebody that we could shake the future together and you know, Daniel and I just hit it off from the very first time we met. He got to meet my family, my dogs and we did the whole, we did the whole courting thing before we actually decided this was going to be a good thing for both of us. >> Dave: That's good. >> Rob: Yeah. >> Dave: You got to meet the family. That's very good. >> We just had, John Furrier and I just had, Mohit Aron and Sanjay Poonen into out studio. Cause Mohit, you know, formal google. Long time. And they decided to kind of split duties. Mohit's going into product, he didn't keep his CEO title. He walked. How are you guys splitting you time? What are each of you going to, responsible for? >> Daniel: Well its, its kind of similar. On a day by day operation I, I rely heavily on Rob. We do it together. Strategic decisions about the company's destiny. I'm doing mostly the product these days. Which is a big relief for me. And I think we also split a bit of customers visit. Which is great. I still enjoy meeting customers. I need, customers are food for my cause. >> Dave: (laughs) yeah and your awesome product visionary. You've been there since day one. Now Rob, you said in the key note today that you've seen around about a hundred customers. You've transverse the world. What did you learn from them that informed you? That gave you confidence that the the move to the internet platform, even though you had already started that. >> Male: Yeah. >> But you're really doubling down on that >> Rob: You know when I... >> from a stand point. >> Rob: You know Dave, when you think about it, like I was, I was so impressed that Daniel had the vision to create a platform 3 years ago. >> Dave: Yeah. >> All right. And as we went around the world. As I went around the world, and it was one of the very first things I've seen. I've got to understand how customers see UIPATH, from their advantage point. What are they looking for from us? Why is this company, why doe customers like this company so much? And as I went around the world. I went to Asia a couple, I went to Asia, Australia, Singapore, Japan. I was in Europe twice. We did the trip together. We went to visit customers. And it was very much the same thing. Helps us expand automation faster. And we are so surprise, at the break of your platform. We never knew that. And so it kind of just had, for me, it was conviction. It's like, this walls is the right decision you've made. There's so much opportunity there. And that's, you know that's kind of what I've learned through the last four five months. >> Dave: Now as you know Daniel, I've written a lot about your company. One of the things I've said is that, that start ups, if I can call you that back pre-IPO, typically don't have as much international exposure as UIPATH had. I mean you sort of, you sort of started as an international company and became more US centric. You said, in the, in the key note today, you're talking to Ray Wong about people may don't understand that challenges of FX. Point being, when you convert international dollars into US dollars there are less of them cause the dollars stronger. But still, I've always felt like that international footprint is an advantage. Rob you came from SAP, you know, again European based company. I don't, (stutters), do you regret that? Now? I mean I know it's technical, I'm sure you don't, but talk about that sort of international exposure? Why that's a long term benefit. >> Well, you, first of all, you expand faster. I think we expanded faster than our competition because our global footprint was larger. And we had the courage. Go in Japan, for instance. Everybody told me, it's impossible to make for such a small starter. It's impossible to make a business in Japan. But we didn't believe it. We're just crazy and we went there, and be built a very sizable business in Japan. Fifty-five percent of our revenue, even today, it's outside U.S. Now of course that has a down side. When uh, When the local currencies, you know, are losing the value compared to the dollars, we're impacted. As we go to... to investors, until now, so we are seeing like a (indistinct) in terms of ARI. It's huge. Only because (indistinct) and losing the business in Russia. But it still, it's the strength of our company. Things will come back. And then, you know, the growth engine will re-accelerate again. >> Dave: Yeah but when the dollars weakens that'll be in your favor. Rob I want to pick up on something you said today in your keynote. You went back and started, you know the cycles of ERP and you know, internet, et cetera. I kind of have a love hate with ERP. I have to be honest. >> Male: (laughing) >> But it, but but (chuckles) but if I go back to that. Late eighties nineties, you wouldn't have be able to pick SAP as the winner. And then SAP emerged. You know, very clearly. But the more interesting thing, is that the customers who are implementing ERP well. The practitioners did better than their peers, and dominated their industries. And their stocks went up. Their evaluations went up. Different worlds obviously but, do you see the same thing happening with RPA and automation? What gives you confidence that that's the case? >> I absolutely do see the same thing happening with automation and RPA being a part of, in being a part of that. The reason, the reason I believe that is speed is so critical. (stutters) And if you think about how hard it is for a CIO or a c level executive to consume the technology coming at them, plus all the changes in the world being thrown at them. It's compiling and compiling and compiling. We have an incredible solution, that can help companies. And there comes certain times, the love outcomes to the business. Like no one else gets. And when I see that, I view that as just like the beginning of what's going to happen in the future so, in many ways, and I've said this to many of my friends, it feels like 1992, 1993 to me. And it's interesting because no one really understood then why SAP would be great in 1992 and 93. And they got a couple of things right. They got the eco system right. Their new partners were important. And the knew they needed to drive business outcome for companies, in which they did. And so I feel like we are in a very similar place. Very different technology obviously. And the speed of change now is so dramatic, compared to what it was. And there's very few technology that can provide that level of speed and accomodation to their customers. >> All right, let's talk about priorities. You guys got a lot of work to do and you've, you've laid it out to the financial community. You've got to have profitable growth, because of FX, it part, you've lowered your forecast. But I think there's some conservative in their as well. Um, but you got to do that balance. You've given some guidance on gross margins. Cloud maybe brings that down a little bit. RnD I saw wide range. Thirteen to seventeen percent. I hope you keep spending on RnD. Big fan of that. You know stock buybacks and, RnD if in your position are going to be better. And the product priorities, continue to build that out. But question, let's start with the product. So you've got an on-prem stack and you've got a cloud stack that's emerging, how do you balance those out? How do you do the integration? You've done a great job with the integration. Does it, are you concerned about your ability to continue to work at that speed with two code bases? I wonder if you could address that? >> Daniel: We've become a cloud first company. We deliver all of our products first in the cloud. We've deliver on the two week (indistinct) in the cloud. So that helps us integrate quite fast. I think we made a very good business decision to build our cloud team in Seattle. In Bellevue to be specific. And we have access to great talent that knows how to build serious cloud service. Which is hard to find dollar. And uh, so, and also we, we have, we benef- one of our only benefits was, we have the really good architecture. We have an architecture that work easily on-prem and on the cloud. And even today, our work flow foundation, our local designers, were easy to modernize. So right now we are launching studio weapon. But behind the scene, it's the same workflow engine. Our customers don't have to rewrite anything. It just works. And it does the same to take our own brand product and brand it in the multicloud. So, it's, there is no friction at all. Actually cloud is just helping us accelerate. But we benefit then again of a really solid architectural foundation. >> Daniel: Architecture matters. We've seen that in this industry. We got the B52s rocking out in the background, I love it, but I've got so many questions for you guys. I want to talk about the go to market. Because Rob, it's obviously a strength of yours. You've come in. You've communicated to the street, that you're reshaping the sales floors. Are they lowering the ratios of sales? People, the customers at the high end, mid range as well, using digital. I mean the numbers are one to ten now. At the top. One to maybe fifty at the mid range. Where are you in terms of that journey? You've got to find people, you got to train them, how do you get the productivity out of those guys? Take us through your thinking there? >> Rob: Yeah firstly, I think we have enough resources. Having resources is not an issue. Um, we have an incredible vehicle to acquire customers inside the company. Our digital sales motion, it's probably the best I've seen. And so we have the ability to acquire customers really fast. And we get the first workload in really fast. The challenge is we need to, we need to be able to drive a (indistinct) model and we graduate customs when we acquire them into the direct sales floors. And then direct sales floors, we're not going to go one to thirty, we're talking one to ten for the direct sales floor. And even the high up in the pyramid, we want to have an even denser model than that. And the whole purpose is to drive the time to consumption much quicker, much faster. So we know exactly if we acquire a customer, will they spend? Do they have a (indistinct) spend? On what level do they have a (indistinct) spend? And therefore when we capture them, we can immediately surround them, and put the right resources so we can grow faster. We think this will have a significant impact on the organization. We'll start to implement certain pieces in the next quarter. Um, things like packaging solutions. Putting them in, enabling the sales organization. And buy the beginning of next year, we'll be ready to actually go full board, globally. We already put some pieces in place when I joined. Chris Weber, my chief business officer, did a great job doing some of those pieces. So we're on the journey already. >> Dave: Yeah and even before you guys were public and you weren't publishing your NRR numbers. Our ETR survey partner, we, we always thought you had very low churn. And I think you broke out just yesterday. The, the NRR for overseas vs U.S, U.S I think was 140 plus percent. >> Male: Yeah >> Very very strong. A little, a little less overseas but the churn is still very low. >> Male: Yep. >> Okay so that's super positive. Customer affinity, I was wanted to code these events. I listen to the key notes very carefully, and then interview customers on the cube, and I try to identify, is there alignment there? And I see very strong alignment, I have to say, and strong customer affinity. So that's in your favor. I have, Daniel, I got another question for you on product. What is Symantec automation? What the heck is that? Can you explain that? I don't understand >> Dave, have you seen the demo in my (indistinct)? >> Dave: You know, I had to leave and do interviews, so I, uh, I missed it. >> I think, I think that demo answer complete your question. So in the s-, you know there saying that great, you can not distinguish great technology by magic. I think technology should be simple. And we, we show today, one of the simplest demo that you can imagine. But it's so, such a complex technology behind the scene, that you also can not imagine. So what was demo? We show how one business user, without any technical skills, can build any type of document. Can be a passport, can be an invoice, can be a legal (indistinct), and just go, "I want to copy data from here, and I want to paste data there". Can be a spreadsheet, can be another obligation, and like a human user, without understanding, without having prior knowledge about data, document layout, about screens, screens layouts, nothing, we analyze real time. Document. We discover, we discover the meaning of the information. We analyze the screen. We understand the screen but we understand the meaning of the screen. And we understand how the information in one side relate to the other side. And we just connects the dots and we copy the information and we paste it. A job that you'll do as a human user, maybe three minutes, is done in ten seconds. This is powerful. >> Yeah that is powerful. Thank you for that. I mean, and you take the date, whether it's transaction data or unstructured data and and and bring meaning out of it. That's powerful. Last question and I'll let you guys go. Rob, you got traders, and you've got long term investors. All right traders going to be defensive, today. I get that. Make the case for UIPATH, for long term investors. >> Rob: I think we're going to be a multi-gern- multi-billion company and we're going to be a generational company of our time. And we will define enterprise automation. And it's going to be a long term game and we feel like really strong that we'll be the lead in that game. >> Dave: Guys, thanks so much for coming to the cube. Great show. Always fun at UiPath Forward. Really appreciate your time. Thank you. >> Thanks dave. >> Appreciate it as well. >> Okay wrap it up, day one, we're here tomorrow, first thing, Dave Vellante and Dave Nicholson. Thanks for watching, forward 5, Uipath big customer event, we'll see you tomorrow. (music)

>>The Cube Presents UI Path Forward five. Brought to you by UI Path, >>Everybody. We're back in Las Vegas. The cube's coverage we're day one at UI Path forward. Five. Pascal Borne is here. He's an expert and bestselling author in the topic of AI and automation and the book Intelligent Automation. Welcome to the world of Hyper Automation, the first book on the topic. And of course, Ray Wong is back on the cube. He's the founder, chairman and principal analyst, Constellation Reese, also bestselling author of Everybody Wants To Rule the World. Guys, thanks so much for coming on The Cubes. Always a pleasure. Ray Pascal, First time on the Cube, I believe. >>Yes, thank you. Thanks for the invitation. Thank you. >>So what is artificial about artificial intelligence, >>For sure, not people. >>So, okay, so you guys are both speaking at the conference, Ray today. I think you're interviewing the co CEOs. What do you make of that? What's, what are you gonna, what are you gonna probe with these guys? Like, how they're gonna divide their divide and conquer, and why do you think the, the company Danielle in particular, decided to bring in Rob Sland? >>Well, you know what I mean, Like, you know, these companies are now at a different stage of growth, right? There's that early battle between RPA vendors. Now we're actually talking something different, right? We're talking about where does automation go? How do we get the decisioning? What's the next best action? That's gonna be the next step. And to take where UI path is today to somewhere else, You really want someone with that enterprise cred and experience the sales motions, the packages, the partnership capabilities, and who else better than Roblin? He, that's, he's done, he can do that in his sleep, but now he's gotta do that in a new space, taking whole category to another level. Now, Daniel on the other hand, right, I mean, he's the visionary founder. He put this thing from nothing to where he is today, right? I mean, at that point you want your founder thinking about the next set of ideas, right? So you get this interesting dynamic that we've seen for a while with co CEOs, those that are doing the operations, getting the stuff out the door, and then letting the founders get a chance to go back and rethink, take a look at the perspective, and hopefully get a chance to build the next idea or take the next idea back into the organization. >>Right? Very well said. Pascal, why did you write your book on intelligent automation and, and hyper automation, and what's changed since you've written that book? >>So, I, I wrote this book, An Intelligent Automation, two years ago. At that time, it was really a new topic. It was really about the key, the, the key, the key content of the, of the book is really about combining different technologies to automate the most complex end to end business processes in companies. And when I say capabilities, it's, we, we hear a lot about up here, especially here, robotic process automation. But up here alone, if you just trying to transform a company with only up here, you just fall short. Okay? A lot of those processes need more than execution. They need language, they need the capacity to view, to see, they need the capacity to understand and to, and to create insights. So by combining process automation with ai, natural language processing, computer vision, you give this capability to create impact by automating end to end processes in companies. >>I, I like the test, what I hear in the keynote with independent experts like yourself. So we're hearing that that intelligent automation or automation is a fundamental component of digital transformation. Is it? Or is it more sort of a back office sort of hidden in inside plumbing Ray? What do you think? >>Well, you start by understanding what's going on in the process phase. And that's where you see discover become very important in that keynote, right? And that's where process mining's playing a role. Then you gotta automate stuff. But when you get to operations, that's really where the change is going to happen, right? We actually think that, you know, when you're doing the digital transformation pieces, right? Analytics, automation and AI are coming together to create a concept we call decision velocity. You and I make a quick decision, boom, how long does it take to get out? Management committee could free forever, right? A week, two months, never. But if you're thinking about competing with the automation, right? These decisions are actually being done a hundred times per second by machine, even a thousand times per second. That asymmetry is really what people are facing at the moment. >>And the companies that are gonna be able to do that and start automating decisions are gonna be operating at another level. Back to what Pascal's book talking about, right? And there are four questions everyone has to ask you, like, when do you fully intelligently automate? And that happens right in the background when you augment the machine with a human. So we can find why did you make an exception? Why did you break a roll? Why didn't you follow this protocol so we can get it down to a higher level confidence? When do you augment the human with the machine so we can give you the information so you can act quickly. And the last one is, when do you wanna insert a human in the process? That's gonna be the biggest question. Order to cash, incident or resolution, Hire to retire, procure to pay. It doesn't matter. When do you want to put a human in the process? When do you want a man in the middle, person in the middle? And more importantly, when do you want insert friction? >>So Pascal, you wrote your book in the middle of the, the pandemic. Yes. And, and so, you know, pre pandemic digital transformation was kind of a buzzword. A lot of people gave it lip service, eh, not on my watch, I don't have to worry about that. But then it became sort of, you're not a digital business, you're out of business. So, so what have you seen as the catalyst for adoption of automation? Was it the, the pandemic? Was it sort of good runway before that? What's changed? You know, pre isolation, post isolation economy. >>You, you make me think about a joke. Who, who did your best digital transformation over the last years? The ceo, C H R O, the Covid. >>It's a big record ball, right? Yeah. >>Right. And that's exactly true. You know, before pandemic digital transformation was a competitive advantage. >>Companies that went into it had an opportunity to get a bit better than their, their competitors during the pandemic. Things have changed completely. Companies that were not digitalized and automated could not survive. And we've seen so many companies just burning out and, and, and those companies that have been able to capitalize on intelligent automation, digital transformations during the pandemic have been able not only to survive, but to, to thrive, to really create their place on the market. So that's, that has been a catalyst, definitely a catalyst for that. That explains the success of the book, basically. Yeah. >>Okay. Okay. >>So you're familiar with the concept of Stew the food, right? So Stew by definition is something that's delicious to eat. Stew isn't simply taking one of every ingredient from the pantry and throwing it in the pot and stirring it around. When we start talking about intelligent automation, artificial intelligence, augmented intelligence, it starts getting a bit overwhelming. My spy sense goes off and I start thinking, this sounds like mush. It doesn't sound like Stew. So I wanna hear from each of you, what is the methodical process that, that people need to go through when they're going through digital trans transmission, digital transformation, so that you get delicious stew instead of a mush that's just confused everything in your business. So you, Ray, you want, you want to, you wanna answer that first? >>Yeah. You know, I mean, we've been talking about digital transformation since 2010, right? And part of it was really getting the business model, right? What are you trying to achieve? Is that a new type of offering? Are you changing the way you monetize something? Are you taking existing process and applying it to a new set of technologies? And what do you wanna accomplish, right? Once you start there, then it becomes a whole lot of operational stuff. And it's more than st right? I mean, it, it could be like, well, I can't use those words there. But the point being is it could be a complete like, operational exercise. It could be a complete revenue exercise, it could be a regulatory exercise, it could be something about where you want to take growth into the next level. And each one of those processes, some of it is automation, right? There's a big component of it today. But most of it is really rethinking about what you want things to do, right? How do you actually make things to be successful, right? Do I reorganize a process? Do I insert a place to do monetization? Where do I put engagement in place? How do I collect data along the way so I can build better feedback loop? What can I do to build the business graph so that I have that knowledge for the future so I can go forward doing that so I can be successful. >>The Pascal should, should, should the directive be first ia, then ai? Or are these, are these things going to happen in parallel naturally? What's your position on that? Is it first, >>So it, so, >>So AI is part of IA because that's, it's, it's part of the big umbrella. And very often I got the question. So how do you differentiate AI in, I a, I like to say that AI is only the brain. So think of ai cuz I'm consider, I consider AI as machine learning, Okay? Think of AI in a, like a brain near jar that only can think, create, insight, learn, but doesn't do anything, doesn't have any arms, doesn't have any eyes, doesn't not have any mouth and ears can't talk, can't understand with ia, you, you give those capabilities to ai. You, you basically, you create a cap, the capability, technological capability that is able to do more than just thinking, learning and, and create insight, but also acting, speaking, understanding the environment, viewing it, interacting with it. So basically performing these, those end to end processes that are performed currently by people in companies. >>Yeah, we're gonna get to a point where we get to what we call a dynamic scenario generation. You're talking to me, you get excited, well, I changed the story because something else shows up, or you're talking to me and you're really upset. We're gonna have to actually ch, you know, address that issue right away. Well, we want the ability to have that sense and respond capability so that the next best action is served. So your data, your process, the journey, all the analytics on the top end, that's all gonna be served up and changed along the way. As we go from 2D journeys to 3D scenarios in the metaverse, if we think about what happens from a decentralized world to decentralized, and we think about what's happening from web two to web three, we're gonna make those types of shifts so that things are moving along. Everything's a choose your end venture journey. >>So I hope I remember this correctly from your book. You talked about disruption scenarios within industries and within companies. And I go back to the early days of, of our industry and East coast Prime, Wang, dg, they're all gone. And then, but, but you look at companies like Microsoft, you know, they were, they were able to, you know, get through that novel. Yeah. Ibm, you know, I call it survived. Intel is now going through their, you know, their challenge. So, so maybe it's inevitable, but how do you see the future in terms of disruption with an industry, Forget our industry for a second, all industry across, whether it's healthcare, financial services, manufacturing, automobiles, et cetera. How do you see the disruption scenario? I'm pretty sure you talked about this in your book, it's been a while since I read it, but I wonder if you could talk about that disruption scenario and, and the role that automation is going to play, either as the disruptor or as the protector of the incumbents. >>Let's take healthcare and auto as an example. Healthcare is a great example. If we think about what's going on, not enough nurses, massive shortage, right? What are we doing at the moment? We're setting five foot nine robots to do non-patient care. We're trying to capture enough information off, you know, patient analytics like this watch is gonna capture vitals from a going forward. We're doing a lot what we can do in the ambient level so that information and data is automatically captured and decisions are being rendered against that. Maybe you're gonna change your diet along the way, maybe you're gonna walk an extra 10 minutes. All those things are gonna be provided in that level of automation. Take the car business. It's not about selling cars. Tesla's a great example. We talk about this all the time. What Tesla's doing, they're basically gonna be an insurance company with all the data they have. They have better data than the insurance companies. They can do better underwriting, they've got better mapping information and insights they can actually suggest next best action do collision avoidance, right? Those are all the things that are actually happening today. And automation plays a big role, not just in the collection of that, that information insight, but also in the ability to make recommendations, to do predictions and to help you prevent things from going wrong. >>So, you know, it's interesting. It's like you talk about Tesla as the, the disrupting the insurance companies. It's almost like the over the top vendors have all the data relative to the telcos and mopped them up for lunch. Pascal, I wanna ask you, you know, the topic of future of work kind of was a bromide before, but, but now I feel like, you know, post pandemic, it, it actually has substance. How do you see the future of work? Can you even summarize what it's gonna look like? It's, it's, Or are we here? >>It's, yeah, it's, and definitely it's, it's more and more important topic currently. And you, you all heard about the great resignation and how employee experience is more and more important for companies according to have a business review. The companies that take care of their employee experience are four times more profitable that those that don't. So it's a, it's a, it's an issue for CEOs and, and shareholders. Now, how do we get there? How, how do we, how do we improve the, the quality of the employee experience, understanding the people, getting information from them, educating them. I'm talking about educating them on those new technologies and how they can benefit from those empowering them. And, and I think we've talked a lot about this, about the democratization local type of, of technologies that democratize the access to those technologies. Everyone can be empowered today to change their work, improve their work, and finally, incentivization. I think it's a very important point where companies that, yeah, I >>Give that. What's gonna be the key message of your talk tomorrow. Give us the bumper sticker, >>If you will. Oh, I'm gonna talk, It's a little bit different. I'm gonna talk for the IT community in this, in the context of the IT summit. And I'm gonna talk about the future of intelligent automation. So basically how new technologies will impact beyond what we see today, The future of work. >>Well, I always love having you on the cube, so articulate and, and and crisp. What's, what's exciting you these days, you know, in your world, I know you're traveling around a lot, but what's, what's hot? >>Yeah, I think one of the coolest thing that's going on right now is the fact that we're trying to figure out do we go to work or do we not go to work? Back to your other point, I mean, I don't know, work, work is, I mean, for me, work has been everywhere, right? And we're starting to figure out what that means. I think the second thing though is this notion around mission and purpose. And everyone's trying to figure out what does that mean for themselves? And that's really, I don't know if it's a great, great resignation. We call it great refactoring, right? Where you work, when you work, how we work, why you work, that's changing. But more importantly, the business models are changing. The monetization models are changing macro dynamics that are happening. Us versus China, G seven versus bricks, right? War on the dollar. All these things are happening around us at this moment and, and I think it's gonna really reshape us the way that we came out of the seventies into the eighties. >>Guys, always a pleasure having folks like yourself on, Thank you, Pascal. Been great to see you again. All right, Dave Nicholson, Dave Ante, keep it right there. Forward five from Las Vegas. You're watching the cue.

>>Welcome back to super cloud 22, our inaugural event. It's a pilot event here in the cube studios we're live and streaming virtually until we do it in person. Maybe next year. I'm John fury, host of the cube with Dave Lon two great guests, distinguished engineers managers, CTOs investors. Mariana Tessel is a CTO of Intuit ins Ray founder of vertex ventures. Both have a lot of DNA. Founder allow cloud here with mark Andre and Ben Horowitz, a variety of other great ventures you've done. And now you're an investor. Yep. Maria, you've been a seasoned CTO, VP of engineering, VMware Docker Intuit. Now thanks for joining us. >>Absolutely. >>So super cloud is a, is a thing. And apparently it's got a lot of momentum and you guys got stats over there at, at Intuit in, so you're investing and we were challenged on super cloud. Our initial thesis was you build on the clouds, get all that leverage like snowflake, you get a good differentiation and then you compete and then move to other clouds. Now it's becoming a thing where I can do this. Every enterprise could possibly do it. So I want to get your guys thoughts on what you think of super cloud concept and where are the holes in it, what needs to be defined. And so we'll start with you. You've done a lot of cloud things in your day. What >>Do you think? Yeah, it's the whole cloud journey started with a desire to consolidate and desire to actually provide uniformity and, and standards driven ways of doing things. And I think Amazon was a leader there. They helped kind of teach everybody else. You know, when I was in loud cloud, we were trying to do it with proprietary stacks just wouldn't work. But once everyone standardized upon Unix and you know, the chip sets no longer became as relevant. They did a lot of good things there, but what's happened since then is now you've got competing standards at the API layer at the interface layer no longer at the chip set layer, no longer at the operating system layer. Right? So the evolution of the, the, the battles are still there. When you talk about multicloud and super cloud, though, like one of the big things you have to keep in mind is latency is not free. Latency is very expensive and it's getting even more expensive now with, with multi-cloud. So you have to really understand where the separations of boundaries are between your data, your compute, and, and the network is just there as a facilitator to help binding compute and data. Right? And I think there's a lot of bets being made across different vendors like CloudFlare Akamai, as well as Amazon Google Microsoft in terms of how they think we should take computing either to the edge, from the core or back and forth. >>These, this is structural change. I mean, this is structural, >>It's desired by incumbents, but it's not something that I'm seeing from the consumption. I'd love to hear, hear from our end's per perspective, from a consumption point of view, like how much edge computing really matters. Right. >>Mario. >>So I think there's like, there's kind of a, a story of like two, like it's kind of, you can cut it for both edges. No, no pun intended on one end. It is really simplifying to actually go into like a single cloud and standardize on it and just have everything there. But I think what over time companies find is that they end up in multiple clouds, whether like, you know, through acquisitions or through like needing to use a service in another cloud. So you do find yourself in a situation where you have multi multi-cloud and you have to kind of work through it and understand how to make it all like work and latency is an issue, but also for many, many workloads, you can work around it and you can make it work where you have workloads that actually span multiple vendors and clouds. You know, again, having said that, I would say the world is such, that is still a simplifying assumption. When if you go to a single cloud, it's much easier to just go and, and bet on that >>Easier in terms of everything's integrated, IAS works with SAS, they solve a lot of problems. >>Correct. And you can do like for your developers, you can actually provide an environment that's super homogenous, simple. You can use services easily up and down the stack. And, you know, we, we actually made that deliberate decision. When we started migrating to the cloud at the beginning, it was like, oh, let's do like hybrid we'll, you know, make it, so it work anywhere. It was so complicated. It was not worth it. >>When was the, when did you give up, what was the moment? Was there a flash point where you said, oh, this is terrible. This is >>Dead. Yeah. When, when we started to try to make it interoperable and you just see what it requires to do that and the complexity of the architecture that it just became not worth it for the gains you have. >>So speaking obviously as a SAS provider, right. So it just doesn't, it didn't make business case sense for you guys to do that. So it was super cloud. Then an infrastructure thing we just heard from Ben wa deja VI that they're not, they're going beyond instantiating their, their data cloud. They're actually running, you know, their own little snow grid. They called it. And, and then when I asked him, well, what about latency? He said, well, we copied data over, you know, so, okay. That's you have to do, but that's a singular experience with the same governance or the same security. Just wasn't worth it for you guys is what I'm hearing. >>Correct. But again, like for some workload or for some services that we want to use, we are gonna go there and we are gonna then figure out what is the work around the latency issue, whether it's like copy or, you know, redundancy. >>Well, the question I have Dave on snowflake is maybe the question for you and in the panel is snowflake a tan expansion opportunity, or is there a technical reason to go to other clouds? >>I think they wanted to leverage the hyperscale infrastructure globally. And they said that they're out there, it's a free gift. We're gonna go take it. I, I think it started with we're on AWS. Do you think? And then we're on Azure and then we're on Google. And then they said, why don't we just connect all these and make it a singular experience? And yeah, I guess it's a TA expansion as a differentiator and it's, it adds value. Right. If I can share data across that global network, >>We have customers on Azure now, >>Right? Yeah. Yeah. Of course. >>You guys don't need to go CP. What do you think about that? >>Well, I think Snowflake's in a good position cuz they work mostly with analytical workloads and you have capacity. That's always gonna increase like no one subtracts, their analytical workload like ever, right. So there was just compounded growth is like 50% or 80% for, you know, many enterprises despite their best intentions, not to collect more data, they just can't stop doing it. So it's different than if you're like an Oracle or a transactional database where you don't have those, you know, like kind of infinite growth paths. So Snowflake's gonna continue to expand footprint their customers. They don't mind as long as you, they can figure out the, the lowest cost on denominator for, for that. >>Yeah. So it makes sense to be in all the clouds >>For them, for, for them, for sure. Yeah. >>But, but, but Oracle just announced with Microsoft what I would call super cloud, a, a cross cloud database service running on OCI and Azure with very low latency and a database that looks like a, the singular experience. Yeah. With, with a PAs layers >>That lost me after OCI that's >>Okay. You know, but that's the, that's the, the BS answer for all U VCs. The do nobody develops on Oracle? Well, it's a 240 billion market cap company. Show me who you all want be. >>We're gonna talk about SRDF and em C next, you >>All want Oracle. So there we go. You throw that into, you all want Oracle to buy your companies, your funding, you know, cause, cause we all wanna be like Oracle with that kinda cash flow. But, but anyway, >>Here's, here's one thing that I'm noticing that is gonna be really practical. I think for companies that do run SA is because like, you know, you have all these solutions, whether it's like analytics or like monitoring or logging or whatever. And each one of them is very data hungry and all of them have like SAS solutions that end up copy the data, moving data to their cloud, and then they might charge you by the size of your data. It does become kind of overwhelming for companies to use that many tools and basically maybe have that data kind of charge for it, multiple places because you use it for different purposes or just in general, if you have a lot of data, you know, that that is becoming an issue. So that's something that I've noticed in our, in our own kind of, you know, a world, but it's just something that I think companies need to think about how they solve because eventually a lot of companies will say, I cannot have all these solutions, so there's no way I'm gonna be willing to have so many copies of the data and actually pay for that. >>So many times, just something to think about. >>But one of the criticisms of the super cloud concept is that it's just SAS. If I'm running workload on prem and I, and I've got, you know, a connection to the cloud, which you probably do, that's, that's SAS, what's, what's the big deal and that's not anything new or different. So I'd love to get your thoughts on that. But Goldman Sachs, for instance, just announced the service last reinvent with AWS, connecting their tools, their data, and their software from on-prem to AWS, they're offering it as a service. I'm like, Hmm. Kind of looking like Supercloud, but maybe it's just SAS. >>It could be. And like, what I'm talking about is not so much like, you know, like what you wanna connect your data. But the idea is like a lot of the providers of different services, like in the past and, and like higher layer, they're actually COPI the data. They need the data in their cloud or their solution. And it just becomes complicated and expensive is, is kind of like my point. So yes, connecting it like for you to have the data in one place and then be able to connect to it. I think that is a valid, if, if that's kinda what you think about as a super cloud, that is a valid need, I think that companies will >>Have where developers actually want access to tools that might exist. >>Also the key is developers, right? Yeah. Developers decide all decisions, not database on administrators, not, you know, a hundred percent security engineers, not admins. So what's really interesting is where are the developers going next? If you look at the current winners in the current ecosystem, companies like MongoDB, I mean, they capture the minds of yeah. The JavaScript, you know, no JS developers absolutely very early on. And I started catch base and I could tell you like the difference was that capture motion was so important. So developers are basically used to this game-like experience now where they want to see tools that are free, whether it's open source or not, they actually don't care. They just want, and they want it SAS. They want it SAS delivered on demand. Right. And pay as you go. And so there's a lot of these different frameworks coming out next generation, no code, low code, whether it's Java, JavaScript, rust, you know, whatever, you know, go Lang. And there's a lot of people fighting religious wars about how to develop the next kind of modern pattern design pattern. Okay. And that's where a lot of excitement is how we look at like investment opportunities. Like where are those big bets who are, you know, frustrated developers, who are they frustrated, what's wrong with their current environment? You know, do they really enjoy using Kubernetes or trying to use Kubernetes? Yeah. Right. Like developers have a very different view than operator, >>But you mentioned couch base. I mean, I look at couch base what they're doing with Capellas as a form of Supercloud. I mean, I think that's an excellent, they're bringing that out to the edge. We're gonna hear later on from someone from couch base. That's gonna talk about that now. It's kind of a lightweight, you know, sort of, it's gonna be a, a synchronization, but it's the beginning >>A cool new venture deal that I'm not in, but was like duck DB. I'm like, what's duck DB like, well, it's an Emory database that has like this like remote store thing. I'm like, okay, that sounds interesting. Like let's call Mike Olson cuz that sounds like sleepy cat redone red distributed world. But like it's, it's like there's a lot of people refactoring design patterns that we're all grew up with since the popup days of, you know, typical round. Right? >>Yeah. That's the refactory I think that's the big pattern. So I have to ask you guys, what are you guys investing in? We've got a couple minutes left to chat about that. What are you investing at into it from a, from a, a CTO engineering perspective and what are you investing in that feels super cloud like to you? >>Well, the, the thing that like I'm focused on is to make sure that we have absolutely best in the world development environment for our engineers, where it's modern, it's easy to use and it incorporates as many things as we can into that environment. So the engineers don't have to think about it. Like one big example would be security and how we incorporated that into development environment. So again, the engineers don't have to bother with trying to think through how they secure their workloads and every step of the way their other things that we incorporated, whether it's like rollbacks or monitoring or, you know, like baly enough other things. But I think that's really an investment that has panned off for us. We actually started investing in development environment several years ago. We started measure our development velocity and we, it actually went up by six X justly investing. So >>User experience, developer experience and productivity pretty much right. >>Yeah. AB absolutely. Yeah. That's like a big investment area for us that, you know, cloud cloud >>Sounds like super cloudlike factor and I'm assuming it's you're on AWS. >>We are mostly on AWS. Yes. >>And so what are you investing in that from a VC money doling out standpoint? That feels super cloudlike >>So very similar to what we just touched on a lot of developer tool experiences. We have a company that we've invested in called ops level that the service catalogs it's, it's helping, you know, understand your, where your services live and how they could be accessed and, and you know, enterprise kind of that come with that. And then we have a company called Lugo that helps you do serverless debugging container debugging, cuz it turns out debugging distributed, you know, applications is a real problem right now just you can only do so much by log tracing, right? We have a company haven't announced yet that's in the web assembly space. So we're looking at modernizing the next generation past stack and throwing everything out the window, including Java and all of the, you know, current prebuilt components because turns out 90% of enterprise workloads are actually not used. They're they're just policy code. You compiled with they're sitting there as vulnerabilities that no one's actually accessing, but you still have to compile with all of it. So we have a lot of bloatware happening in the enterprise. So we're thinking about how do you skinny that up with the next generation paths that's enterprise capable with security context and frameworks >>Super pass. >>Well, yeah, super pass. That's a kind of good way to, well, is >>It, is it a consistent developer experience across clouds? >>It is. And, and, and, and web assembly is a very raw standard if you can call it that. I mean it's, but it's supported by every modern browser, every major platform, vendor cloud, and Adobe and others, and are using it for their uses. And it's not just about your edge browser compute. It's really, you can take the same framework and compile it down to server side as well as client site, just like JavaScript was a client side tool before it became node. Right. Right. So we're looking at that as a very interesting opportunity. It's very nascent. Yeah. >>Great patterns. Yeah. Well, thanks so much for spending the time outta your busy day. Ariana. Thanks for your commentary. Appreciate your coming on the cubes first in IGUR super cloud event, pilot. Thanks for, for sharing. Thanks for having, thanks for having us. Okay. More coverage here. Super cloud 2022. I'm Jeff David Alane stay with us. We got our cloud ARA panel coming up next.

>>Good afternoon, everyone. And welcome back to the VMware Explorer. 2022 live from San Francisco. Lisa Martin, here with Dave. Valante good to be sitting next to you, sir. >>Yeah. Yeah. The big set >>And we're very excited to be welcoming buck. One of our esteemed alumni Sanja poin joins us, the CEO and president of cohesive. Nice to see >>You. Thank you, Lisa. Thank you, Dave. It's great to meet with you all the time and the new sort of setting here, but first >>Time, first time we've been in west, is that right? We've been in north. We've been in south. We've been in Las Vegas, right. But west, >>I mean, it's also good to be back with live shows with absolutely, you know, after sort of the two or three or hiatus. And it was a hard time for the whole world, but I'm kind of driving a little bit of adrenaline just being here with people. So >>You've also got some adrenaline, sorry, Dave. Yeah, you're good because you are new in the role at cohesive. You wrote a great blog that you are identified. The four reasons I came to cohesive. Tell the audience, just give 'em a little bit of a teaser about that. >>Yeah, I think you should all read it. You can Google and, and Google find that article. I talked about the people Mohi is a fantastic founder. You know, he was the, you know, the architect of the Google file system. And you know, one of the senior Google executives was on my board. Bill Corrin said one of the smartest engineers. He was the true father of hyperconverge infrastructure. A lot of the code of Nutanix. He wrote, I consider him really the father of that technology, which brought computer storage. And when he took that same idea of bringing compute to secondary storage, which is really what made the scale out architect unique. And we were at your super cloud event talking about that, Dave. Yeah. Right. So it's a people I really got to respect his smarts, his integrity and the genius, what he is done. I think the customer base, I called a couple of customers. One of them, a fortune 100 customer. I, I can't tell you who it was, but a very important customer. I've known him. He said, I haven't seen tech like this since VMware, 20 years ago, Amazon 10 years ago and now Ko. So that's special league. We're winning very much in the enterprise and that type of segment, the partners, you know, we have HPE, Cisco as investors. Amazon's an investors. So, you know, and then finally the opportunity, I think this whole area of data management and data security now with threats, like ransomware big opportunity. >>Okay. So when you were number two at VMware, you would come on and say, we'd love all our partners and of course, okay. So you know, a little bit about how to work with, with VMware. So, so when you now think about the partnership between cohesive and VMware, what are the things that you're gonna stress to your constituents on the VMware side to convince them that Hey, partnering with cohesive is gonna gonna drive more value for customers, you know, put your thumb on the scale a little bit. You know, you gotta, you gotta unfair advantage somewhat, but you should use it. So what's the narrative gonna be like? >>Yeah, I think listen with VMware and Amazon, that probably their top two partners, Dave, you know, like one of the first calls I made was to Raghu and he knew about this decision before. That's the level of trust I have in him. I even called Michael Dell, you know, before I made the decision, there's a little bit of overlap with Dell, but it's really small compared to the overlap, the potential with Dell hardware that we could compliment. And then I called four CEOs. I was, as I was making this decision, Andy Jassey at Amazon, he was formerly AWS CEO sat Nadela at Microsoft Thomas cor at Google and Arvin Christian, IBM to say, I'm thinking about this making decision. They are many of the mentors and friends to me. So I believe in an ecosystem. And you know, even Chuck Robbins, who the CEO of Cisco is an investor, I texted him and said, Hey, finally, we can be friends. >>It was harder to us to be friends with Cisco, given the overlap of NSX. So I have a big tent towards everybody in our ecosystem with VMware. I think the simple answer is there's no overlap okay. With, with the kind of the primary storage capabilities with VSAN. And by the same thing with Nutanix, we will be friends and, and extend that to be the best data protection solution. But given also what we could do with security, I think this is gonna go a lot further. And then it's all about meet the field. We have common partners. I think, you know, sort of the narrative I talked about in that blog is just like snowflake was replacing Terada and ServiceNow replace remedy and CrowdStrike, replacing Symantec, we're replacing legacy vendors. We are viewed as the modern solution cloud optimized for private and public cloud. We can help you and make VMware and vs a and VCF very relevant to that part of the data management and data security continuum, which I think could end VMware. And by the way, the same thing into the public cloud. So most of the places where we're being successful is clearly withs, but increasingly there's this discussion also about playing into the cloud. So I think both with VMware and Amazon, and of course the other partners in the hyperscaler service, storage, networking place and security, we have some big plans. >>How, how much do you see this? How do you see this multi-cloud narrative that we're hearing here from, from VMware evolving? How much of an opportunity is it? How are customers, you know, we heard about cloud chaos yesterday at the keynote, are customers, do they, do they admit that there's cloud chaos? Some probably do some probably don't how much of an opportunity is that for cohesive, >>It's tremendous opportunity. And I think that's why you need a Switzerland type player in this space to be successful. And you know, and you can't explicitly rule out the fact that the big guys get into this space, but I think it's, if you're gonna back up office 365 or what they call now, Microsoft 365 into AWS or Google workspace into Azure or Salesforce into one of those clouds, you need a Switzerland player. It's gonna be hard. And in many cases, if you're gonna back up data or you protect that data into AWS banks need a second copy of that either on premise or Azure. So it's very hard, even if they have their own native data protection for them to be dual cloud. So I think a multi-cloud story and the fact that there's at least three big vendors of cloud in, in the us, you know, one in China, if include Alibaba creates a Switzerland opportunity for us, that could be fairly big. >>And I think, you know, what we have to do is make sure while we'll be optimized, our preferred cloud is AWS. Our control plane runs there. We can't take an all in AWS stack with the control plane and the data planes at AWS to Walmart. So what I've explained to both Microsoft and AWS is that data plane will need to be multi-cloud. So I can go to an, a Walmart and say, I can back up your data into Azure if you choose to, but the control plane's still gonna be an AWS, same thing with Google. Maybe they have another account. That's very Google centric. So that's how we're gonna believe the, the control plane will be in AWS. We'll optimize it there, but the data plane will be multicloud. >>Yeah. And that's what Mo had explained at Supercloud. You know, and I talked to him, he really helped me hone in on the deployment models. Yes. Where, where, where the cohesive deployment model is instantiating that technology stack into each cloud region and each cloud, which gives you latency advantages and other advantages >>And single code based same platform. >>And then bringing it, tying it together with a unified, you know, interface. That was he, he was, he was key. In fact, I, I wrote about it recently and, and gave him and the other 29 >>Quite a bit in that session, he went deep with you. I >>Mean, with Mohi, when you get a guy who developed a Google file system, you know, who can technically say, okay, this is technically correct or no, Dave, your way off be. So I that's why I had to >>Go. I, I thought you did a great job in that interview because you probed him pretty deep. And I'm glad we could do that together with him next time. Well, maybe do that together here too, but it was really helpful. He's the, he's the, he's the key reason I'm here. >>So you say data management is ripe for disrupt disruption. Talk about that. You talked about this Switzerland effect. That sounds to me like a massive differentiator for cohesive. Why is data management right for disruption and why is cohesive the right partner to do it? >>Yeah, I think, listen, everyone in this sort of data protection backup from years ago have been saying the S Switzerland argument 18 years ago, I was a at Veras an executive there. We used the Switzerland argument, but what's changed is the cloud. And what's changed as a threat vector in security. That's, what's changed. And in that the proposition of a, a Switzerland player has just become more magnified because you didn't have a sales force or Workday service now then, but now you do, you didn't have multi-cloud. You had hardware vendors, you know, Dell, HPE sun at the time. IBM, it's now Lenovo. So that heterogeneity of, of on-premise service, storage, networking, HyperCloud, and, and the apps world has gotten more and more diverse. And I think you really need scale out architectures. Every one of the legacy players were not built with scale out architectures. >>If you take that fundamental notion of bringing compute to storage, you could almost paralyze. Imagine you could paralyze backup recovery and bring so much scale and speed that, and that's what Mo invented. So he took that idea of how he had invented and built Nutanix and applied that to secondary storage. So now everything gets faster and cheaper at scale. And that's a disruptive technology ally. What snowflake did to ator? I mean, the advantage of snowflake is when you took that same concept data, warehousing is not a new concept it's existed from since Ralph Kimball and bill Inman and the people who are fathers of data warehousing, they took that to Webscale. And in that came a disruptive force toter data, right on snowflake. And then of course now data bricks and big query, similar things. So we're doing the same thing. We just have to showcase the customers, which we do. And when large customers see that they're replacing the legacy solutions, I have a lot of respect for legacy solutions, but at some point in time of a solution was invented in 1995 or 2000, 2005. It's right. For change. >>So you use snowflake as an example, Frank SL doesn't like when I say playbook, cuz I says, Dave, I'm a situational CEO, no playbook, but there are patterns here. And one of the things he did is to your point go after, you know, Terra data with a better data warehouse, simplify scale, et cetera. And now he's, he's a constructing a Tam expansion strategy, same way he did at ServiceNow. And I see you guys following a similar pattern. Okay. You get your foot in the door. Let's face it. I mean, a lot of this started with, you know, just straight back. Okay, great. Now it's extending into data management now extending to multi-cloud that's like concentric circles in a Tam expansion strategy. How, how do you, as, as a CEO, that's part of your job is Tam expansion. >>So yeah, I think the way to think about the Tam is, I mean, people say it's 20, 30 billion, but let me tell you how you can piece it apart in size, Dave and Lisa number one, I estimate there's probably about 10 to 20 exabytes of data managed by these legacy players of on-prem stores that they back up to. Okay. So you add them all up in the market shares that they respectively are. And by the way, at the peak, the biggest of these companies got to 2 billion and then shrunk. That was Verto when I was there in 2004, 2 billion, every one of them is small and they stopped growing. You look at the IDC charts. Many of them are shrinking. We are the fastest growing in the last two years, but I estimate there's about 20 exabytes of data that collectively among the legacy players, that's either gonna stay on prem or move to the cloud. Okay. So the opportunity as they replace one of those legacy tools with us is first off to manage that 20 X by cheaper, faster with the Webscale glass offer the cloud guys, we could tip that into the cloud. Okay. >>But you can't stop there. >>Okay. No, we are not doing just backup recovery. We have a platform that can do files. We can do test dev analytics and now security. Okay. That data is potentially at a risk, not so much in the past, but for ransomware, right? How do we classify that? How do we govern that data? How do we run potential? You know, the same way you did antivirus some kind of XDR algorithms on the data to potentially not just catch the recovery process, which is after fact, but maybe the predictive act of before to know, Hey, there's somebody loitering around this data. So if I'm basically managing in the exabytes of data and I can proactively tell you what, this is, one CIO described this very simply to me a few weeks ago that I, and she said, I have 3000 applications, okay. I wanna be prepared for a black Swan event, except it's not a nine 11 planes getting the, the buildings. >>It is an extortion event. And I want to know when that happens, which of my 3000 apps I recover within one hour within one day within one week, no later than one month. Okay. And I don't wanna pay the bad guys at penny. That's what we do. So that's security discussions. We didn't have that discussion in 2004 when I was at another company, because we were talking about flood floods and earthquakes as a disaster recovery. Now you have a lot more security opportunity to be able to describe that. And that's a boardroom discussion. She needs to have that >>Digital risk. O O okay, go ahead please. I >>Was just gonna say, ransomware attack happens every what? One, every 11, 9, 11 seconds. >>And the dollar amount are going up, you know, dollar are going up. Yep. >>And, and when you pay the ransom, you don't always get your data back. So you that's not. >>And listen, there's always an ethical component. Should you do it or not do it? If you, if you don't do it and you're threatened, they may have left an Easter egg there. Listen, I, I feel very fortunate that I've been doing a lot in security, right? I mean, I built the business at, at, at VMware. We got it to over a billion I'm on the board of sneak. I've been doing security and then at SAP ran. So I know a lot about security. So what we do in security and the ecosystem that supports us in security, we will have a very carefully crafted stay tuned. Next three weeks months, you'll see us really rolling out a very kind of disciplined aspect, but we're not gonna pivot this company and become a cyber security company. Some others in our space have done that. I think that's not who we are. We are a data management and a data security company. We're not just a pure security company. We're doing both. And we do it well, intelligently, thoughtfully security is gonna be built into our platform, not voted on. Okay. And there'll be certain security things that we do organically. There's gonna be a lot that we do through partnerships, this >>Security market that's coming to you. You don't have to go claim that you're now a security vendor, right? The market very naturally saying, wow, a comprehensive security strategy has to incorporate a data protection strategy and a recovery, you know, and the things that we've talking about Mount ransomware, I want to ask you, you I've been around a long time, longer than you actually Sanjay. So, but you you've, you've seen a lot. You look, >>Thank you. That's all good. Oh, >>Shucks. So the market, I've never seen a market like this, right? I okay. After the.com crash, we said, and I know you can't talk about IPO. That's not what I'm talking about, but everything was bad after that. Right. 2008, 2000, everything was bad. I've never seen a market. That's half full, half empty, you know, snowflake beats and raises the stock, goes through the roof. Dev if it, if the area announced today, Mongo, DB, beat and Ray, that things getting crushed and, and after market never seen anything like this. It's so fed, driven and, and hard to protect. And, and of course, I know it's a marathon, you know, it's not a sprint, but have you ever seen anything like this? >>Listen, I walk worked through 18 quarters as COO of VMware. You've seen where I've seen public quarters there and you know, was very fortunate. Thanks to the team. I don't think I missed my numbers in 18 quarters except maybe once close. But we, it was, it's tough. Being a public company of the company is tough. I did that also at SAP. So the journey from 10 to 20 billion at SAP, the journey from six to 12 at VMware, that I was able to be fortunate. It's humbling because you, you really, you know, we used to have this, we do the earnings call and then we kind of ask ourselves, what, what do you think the stock price was gonna be a day and a half later? And we'd all take bets as to where this, I think you just basically, as a, as a sea level executive, you try to build a culture of beaten, raise, beaten, raise, beaten, raise, and you wanna set expectations in a way that you're not setting them up for failure. >>And you know, it's you, there's, Dave's a wonderful CEO as is Frank Salman. So it's hard for me to dissect. And sometimes the market are fickle on some small piece of it. But I think also the, when I, I encourage people say, take the long term view. When you take the long term view, you're not bothered about the ups and downs. If you're building a great company over the length of time, now it will be very clear over the arc of many, many quarters that you're business is trouble. If you're starting to see a decay in growth. And like, for example, when you start to see a growth, start to decay significantly by five, 10 percentage points, okay, there's something macro going on at this company. And that's what you won't avoid. But these, you know, ups and downs, my view is like, if you've got both Mongo D and snowflake are fantastic companies, they're CEOs of people I respect. They've actually kind of an, a, you know, advisor to us as a company, you knows moat very well. So we respect him, respect Frank, and you, there have been other quarters where Frank's, you know, the Snowflake's had a down result after that. So you build a long term and they are on the right side of history, snowflake, and both of them in terms of being a modern cloud relevant in the case of MongoDB, open source, two data technology, that's, you know, winning, I, I, we would like to be like them one day >>As, as the new CEO of cohesive, what are you most ask? What are you most anxious about and what are you most excited about? >>I think, listen, you know, you know, everything starts with the employee. You, I always believe I wrote my first memo to all employees. There was an article in Harvard business review called service profit chains that had a seminal impact on my leadership, which is when they studied companies who had been consistently profitable over a long period of time. They found that not just did those companies serve their customers well, but behind happy engaged customers were happy, engaged employees. So I always believe you start with the employee and you ensure that they're engaged, not just recruiting new employees. You know, I put on a tweet today, we're hiring reps and engineers. That's okay. But retaining. So I wanna start with ensuring that everybody, sometimes we have to make some unfortunate decisions with employees. We've, we've got a part company with, but if we can keep the best and brightest retained first, then of course, you know, recruiting machine, I'm trying to recruit the best and brightest to this company, people all over the place. >>I want to get them here. It's been, so I mean, heartwarming to come Tom world and just see people from all walks, kind of giving me hugs. I feel incredibly blessed. And then, you know, after employees, it's customers and partners, I feel like the tech is in really good hands. I don't have to worry about that. Cuz Mo it's in charge. He's got this thing. I can go to bed knowing that he's gonna keep innovating the future. Maybe in some of the companies I've worried about the tech innovation piece, but most doing a great job there. I can kind of leave that in his cap of hands, but employees, customers, partners, that's kind of what I'm focused on. None of them are for me, like a keep up at night, but there are are opportunities, right? And sometimes there's somebody you're trying to salvage to make sure or somebody you're trying to convince to join. >>But you know, customers, I love pursuing customers. I love the win. I hate to lose. So fortune 1000 global, 2000 companies, small companies, big companies, I wanna win every one of them. And it's not, it's not like, I mean, I know all these CEOs in my competitors. I texted him the day I joined and said, listen, I'll compete, honorably, whatever have you, but it's like Kobe and LeBron Kobe's passed away now. So maybe it's Steph Curry. LeBron, whoever your favorite athlete is you put your best on the court and you win. And that's how I am. That's nothing I've known no other gear than to put my best on the court and win, but do it honorably. It should not be the one that you're doing it. Unethically. You're doing it personally. You're not calling people's names. You're competing honorably. And when you win the team celebrates, it's not a victory for me. It's a victory for the team. >>I always think I'm glad that you brought up the employee experience and we're almost out of time, but I always think the employee experience and the customer experience are inextricably linked. This employees have to be empowered. They have to have the data that they need to do their job so that they can deliver to the customer. You can't do one without the other. >>That's so true. I mean, I, it's my belief. And I've talked also on this show and others about servant leadership. You know, one of my favorite poems is Brenda Naor. I went to bed in life. I dreamt that life was joy. I woke up and realized life was service. I acted in service was joy. So when you have a leadership model, which is it's about, I mean, there's lots of layers between me and the individual contributor, but I really care about that sales rep and the engineer. That's the leaf level of the organization. What can I get obstacle outta their way? I love skipping levels of going right. That sales rep let's go and crack this deal. You know? So you have that mindset. Yeah. I mean, you, you empower, you invert the pyramid and you realize the power is at the leaf level of an organization. >>So that's what I'm trying to do. It's a little easier to do it with 2000 people than I dunno, either 20, 20, 2000 people or 35,000 reported me at VMware. And I mean a similar number at SAP, which was even bigger, but you can shape this. Now we are, we're not a startup anymore. We're a midsize company. We'll see. Maybe along the way, there's an IP on the path. We'll wait for that. When it comes, it's a milestone. It's not the destination. So we do that and we are, we, I told people we are gonna build this green company. Cohesive is gonna be a great company like VMware one day, like Amazon. And there's always a day of early beginnings, but we have to work harder. This is kind of like the, you know, eight year old version of your kid, as opposed to the 18 year old version of the kid. And you gotta work a little harder. So I love it. Yeah. >>Good luck. Awesome. Thank you. Best of luck. Congratulations. On the role, it sounds like there's a tremendous amount of adrenaline, a momentum carrying you forward Sanjay. We always appreciate having you. Thank >>You for having in your show. >>Thank you. Our pleasure, Lisa. Thank you for Sanja poin and Dave ante. I'm Lisa Martin. You're watching the cube live from VMware Explorer, 2022, stick around our next guest. Join us momentarily.

>>Good afternoon, everyone. And welcome back to the Cube's day two coverage of VMware Explorer, 2022 live from San Francisco. Lisa Martin, here with Dave. Valante good to be sitting next to you, sir. >>Yeah, the big >>Set and we're very excited to be welcoming back. One of our esteemed alumni Sanja poin joins us, the CEO and president of cohesive. Nice to see >>You. Thank you, Lisa. Thank you, Dave. It's great to meet with you all the time and the new sort of setting here, but >>First time we've been in west, is that right? We've been in north. We've been in south. We've been in Las Vegas, right. But west >>Nice. Well, I mean, it's also good to be back with live shows with absolutely, you know, after sort of the two or three or high. And it was a hard time for the whole world, but I'm kind of driving a little bit of adrenaline just being here with people. So >>You've also got some adrenaline, sorry, Dave. Yeah, you're good because you are new in the role at cohesive. You wrote a great blog that you are identified. The four reasons I came to cohesive. Tell the audience, just give 'em a little bit of a teaser about that. >>Yeah, I think you should all read it. You can Google and, and Google find that article. I talked about the people Mohi is a fantastic founder. You know, he was the, you know, the architect of the Google file system. And you know, one of the senior Google executives who was on my board, bill Corrin said one of the smartest engineers. He was the true father of hyperconverge infrastructure. A lot of the code of Nutanix. He wrote, I consider him really the father of that technology, which brought computer storage. And when he took that same idea of bringing compute to secondary storage, which is really what made the scale out architect unique. And we were at your super cloud event talking about that, Dave. Yeah. Right. So it's a people I really got to respect his smarts, his integrity and the genius, what he is done. >>I think the customer base, I called a couple of customers. One of them, a fortune 100 customer. I, I can't tell you who it was, but a very important customer. I've known him. He said, I haven't seen tech like this since VMware, 20 years ago, Amazon 10 years ago. And now COER so that's special league. We're winning very much in the enterprise and that type of segment, the partners, you know, we have HPE, Cisco as investors, Amazon's an investors. So, you know, and then finally the opportunity, I think this whole area of data management and data security now with threats, like ransomware big opportunity. >>Sure. Okay. So when you were number two at VMware, you would come on and say, we'd love all our partners and of course, okay. So you know, a little bit about how to work with, with VMware. So, so when you now think about the partnership between cohesive and VMware, what are the things that you're gonna stress to your constituents on the VMware side to convince them that Hey, partnering with cohesive is gonna gonna drive more value for customers, you know, put your thumb on the scale a little bit. You know, you gotta, you gotta unfair advantage somewhat, but you should use it. So what's the narrative gonna be like? >>Yeah. I think listen with VMware and Amazon, that probably their top two partners, Dave, you know, like one of the first calls I made was to Raghu and he knew about this decision before. That's the level of trust I have in him. I even called Michael Dell, you know, before I made the decision, there's a little bit of an overlap with Dell, but it's really small compared to the overlap, the potential with Dell hardware that we could compliment. And then I called four CEOs. I was, as I was making this decision, Andy Jassy at Amazon, he was formerly AWS CEO sat Nadela at Microsoft Thomas cor at Google and Arvin Christian at IBM to say, I'm thinking about this making decision. They are many of the mentors and friends to me. So I believe in an ecosystem. And you know, even Chuck Robbins, who the CEO of Cisco is an investor, I texted him and said, Hey, finally, we can be friends. >>It was harder to us to be friends with Cisco, given the overlap of NEX. So I have a big tent towards everybody in our ecosystem with VMware. I think the simple answer is there's no overlap okay. With, with the kind of the primary storage capabilities with VSAN. And by the same thing with Nutanix, we will be friends and, and extend that to be the best data protection solution. But given also what we could do with security, I think this is gonna go a lot further. And then it's all about meet in the field. We have common partners. I think, you know, sort of the narrative I talked about in that blog is just like snowflake was replacing Terada and ServiceNow replace remedy and CrowdStrike, replacing Symantec, we're replacing legacy vendors. We are viewed as the modern solution cloud optimized for private and public cloud. We can help you and make VMware and VSAN and VCF very relevant to that part of the data management and data security continuum, which I think could enhance VMware. And by the way, the same thing into the public cloud. So most of the places where we're being successful is clearly withs, but increasingly there's this discussion also about playing into the cloud. So I think both with VMware and Amazon, and of course the other partners in the hyperscaler service, storage, networking place and security, we have some big plans. >>How, how much do you see this? How do you see this multi-cloud narrative that we're hearing here from, from VMware evolving? How much of an opportunity is it? How are customers, you know, we heard about cloud chaos yesterday at the keynote, are customers, do they, do they admit that there's cloud chaos? Some probably do some probably don't how much of an opportunity is that for cohesive, >>It's tremendous opportunity. And I think that's why you need a Switzerland type player in this space to be successful. And you know, and you can't explicitly rule out the fact that the big guys get into this space, but I think it's, if you're gonna back up office 365 or what they call now, Microsoft 365 into AWS or Google workspace into Azure or Salesforce into one of those clouds, you need a Switzerland player it's gonna be out. And in many cases, if you're gonna back up data or you protect that data into AWS banks need a second copy of that either on premise or Azure. So it's very hard, even if they have their own native data protection for them to be dual cloud. So I think a multi-cloud story and the fact that there's at least three big vendors of cloud in, in the us, you know, one in China, if include Alibaba creates a Switzerland opportunity for us, that could be fairly big. >>And I think, you know, what we have to do is make sure while we'll be optimized, our preferred cloud is AWS. Our control plane runs there. We can't take an all in AWS stack with the control plane and the data planes at AWS to Walmart. So what I've explained to both Microsoft and AWS is that data plane will need to be multicloud. So I can go to an a Walmart and say, I can back up your data into Azure if you choose to, but the control, plane's still gonna be an AWS, same thing with Google. Maybe they have another account. That's very Google centric. So that's how we're gonna play the, the control plane will be in AWS. We'll optimize it there, but the data plane will be multi-cloud. >>Yeah. And that's what Mo had explained at Supercloud. You know, and I talked to, he really helped me hone in on the deployment models. Yes. Where, where, where the cohesive deployment model is instantiating that technology stack into each cloud region and each cloud, which gives you latency advantages and other advantages >>And single code based same platform, >>And then bringing it, tying it together with a unified, you know, interface. That was he, he was, he was key. In fact, I, I wrote about it recently and, and gave him and the other 20, >>Quite a bit in that session. Yeah. So he went deep with you. I >>Mean, with Mohi, when you get a guy who developed a Google file system, you know, who can technically say, okay, this is technically correct or no, Dave, your way off be so I that's why I had to >>Go. I, I thought you did a great job in that interview because you probed him pretty deep and I'm glad we could do that together with him next time. Well, maybe do that together here too, but it was really helpful. He's the, he's the, he's the key reason I'm here. >>So you say data management is ripe for disrupt disruption. Talk about that. You talked about this Switzerland effect. That sounds to me like a massive differentiator for cohesive. Why is data management right. For disruption and why is cohesive the right partner to do it? >>Yeah, I think, listen, everyone in this sort of data protection backup from years ago have been saying the S Switzerland argument 18 years ago, I was a at Veras an executive there. We used the Switzerland argument, but what's changed is the cloud. And what's changed as a threat vector in security. That's, what's changed. And in that the proposition of a, a Switzerland player has just become more magnified because you didn't have a sales force or Workday service now then, but now you do, you didn't have multi-cloud. You had hardware vendors, you know, Dell, HPE sun at the time. IBM, it's now Lenovo. So that heterogeneity of, of on-premise service, storage, networking, HyperCloud, and, and the apps world has gotten more and more diverse. And I think you really need scale out architectures. Every one of the legacy players were not built with scale out architectures. >>If you take that fundamental notion of bringing compute to storage, you could almost paralyze. Imagine you could paralyze backup recovery and bring so much scale and speed that, and that's what Mo invented. So he took that idea of how he had invented and built Nutanix and applied that to secondary storage. So now everything gets faster and cheaper at scale. And that's a disruptive technology ally. What snowflake did to ator? I mean, the advantage of snowflake is when you took that same concept data, warehousing is not a new concept it's existed from since Ralph Kimble and bill Inman and the people who are fathers of data warehousing, they took that to Webscale. And in that came a disruptive force toter data, right? And snowflake. And then of course now data bricks and big query, similar things. So we're doing the same thing. We just have to showcase the customers, which we do. And when large customers see that they're replacing the legacy solutions, I have a lot of respect for legacy solutions, but at some point in time of a solution was invented in 1995 or 2000, 2005. It's right. For change. >>So you use snowflake as an example, Frank sluman doesn't like when I say playbook, cuz I says, Dave, I'm a situational. See you no playbook, but there are patterns here. And one of the things he did is to your point go after, you know, Terra data with a better data warehouse, simplify scale, et cetera. And now he's, he's a constructing a Tam expansion strategy, same way he did at ServiceNow. And I, you guys following a similar pattern. Okay. You get your foot in the door. Let's face it. I mean, a lot of this started with, you know, just straight back. Okay, great. Now it's extending into data management now extending to multi-cloud that's like concentric circles in a Tam expansion strategy. How, how do as, as a CEO, that's part of your job is Tam expansion. >>So yeah, I think the way to think about the Tam is, I mean, people say it's 20, 30 billion, but let me tell you how you can piece it apart in size, Dave and Lisa number one, I estimate there's probably about 10 to 20 exabytes of data managed by these legacy players of on-prem stores that they back up to. Okay. So you add them all up in the market shares that they respectively are. And by the way, at the peak, the biggest of these companies got to 2 billion and then shrunk. That was Verto when I was there in 2004, 2 billion, every one of them is small and they stopped growing. You look at the IDC charts. Many of them are shrinking. We are the fastest growing in the last two years, but I estimate there's about 20 exabytes of data that collectively among the legacy players, that's either gonna stay on prem or move to the cloud. Okay. So the opportunity as they replace one of those legacy tools with us is first off to manage that 20 X bike cheaper, faster with the Webscale, a glass or for the cloud guys, we could tip that into the cloud. Okay. >>But you can't stop there. >>Okay. No, we are not doing just back recovery. Right. We have a platform that can do files. We can do test dev analytics and now security. Okay. That data is potentially at a risk, not so much in the past, but for ransomware, right? How do we classify that? How do we govern that data? How do we run potential? You know, the same way you did antivirus some kind of XDR algorithms on the data to potentially not just catch the recovery process, which is after fact, but maybe the predictive act of before to know, Hey, there's somebody loitering around this data. So if I'm basically managing in the exabytes of data and I can proactively tell you what, this is, one CIO described this very simply to me a few weeks ago that I, and she said, I have 3000 applications, okay. I wanna be prepared for a black Swan event, except it's not a nine 11 planes hitting the, the buildings. >>It is an extortion event. And I want to know when that happens, which of my 3000 apps I recover within one hour within one day within one week, no lay than one month. Okay. And I don't wanna pay the bad guys of penny. That's what we do. So that's security discussions. We didn't have that discussion in 2004 when I was at another company, because we were talking about flood floods and earthquakes as a disaster recovery. Now you have a lot more security opportunity to be able to describe that. And that's a boardroom discussion. She needs to have that >>Digital risk. O O okay, go ahead please. I >>Was just gonna say, ransomware attack happens every what? One, every 11, 9, 11 seconds. >>And the dollar amount are going up, you know, dollar of what? >>Yep. And, and when you pay the ransom, you don't always get your data back. So you that's >>Not. And listen, there's always an ethical component. Should you do it or not do it? If you, if you don't do it and you're threatened, they may have left an Easter egg there. Listen, I, I feel very fortunate that I've been doing a lot in security, right? I mean, I built the business at, at, at VMware. We got it to over a billion I'm on the board of sneak. I've been doing security and then at SAP ran. So I know a lot about security. So what we do in security and the ecosystem that supports us in security, we will have a very carefully crafted stay tuned. Next three weeks months, you'll see us really rolling out a very kind of disciplined aspect, but we're not gonna pivot this company and become a cyber security company. Some others in our space have done that. I think that's not who we are. We are a data management and a data security company. We're not just a pure security company. We're doing both. And we do it well, intelligently, thoughtfully security is gonna be built into our platform, not bolted on, okay. And there'll be certain security things that we do organically. There's gonna be a lot that we do through partnerships, >>This security market that's coming to you. You don't have to go claim that you're now a security vendor, right? The market very naturally saying, wow, a comprehensive security strategy has to incorporate a data protection strategy and a recovery, you know, and the things we've talking about, Mount ransomware, I want to ask you, you know, I've been around a long time, longer than you actually Sanjay. So, but you you've, you've seen a lot. You look incredibly, >>Thank you. That's all good. Oh, >>Shocks. So the market, I've never seen a market like this, right? I okay. After the.com crash, we said, and I know you can't talk about IPO. That's not what I'm talking about, but everything was bad after that. Right. 2008, 2000, everything was bad. I've never seen a market. That's half full, half empty, you know, snowflake beats and raises the stock, goes through the roof. Dev if it, the area announced today, Mongo, DB, beat and Ray, that things getting crushed. And, and after market never seen anything like this. It's so fed, driven and, and hard to protect. And, and of course, I know it's a marathon, you know, it's not a sprint, but have you ever seen anything like this? >>Listen, I walk worked through 18 quarters as COO of VMware. You seen, I've seen public quarters there and you know, was very fortunate. Thanks to the team. I don't think I missed my numbers in 18 quarters except maybe once close. But we, it was, it's tough. Being a public company. Officer of the company is tough. I did that also at SAP. So the journey from 10 to 20 billion at SAP, the journey from six to 12 at VMware, that I was able to be fortunate. It's humbling because you, you really, you know, we used to have this, we do the earnings call and then we kind of ask ourselves, what, what do you think the stock price was gonna be a day and a half later? And we'd all take bets as to wear this. I think you just basically, as a, as a sea level executive, you try to build a culture of beaten, raise, beaten, raise, beaten, raise, and you wanna set expectations in a way that you're not setting them up for failure. >>And you know, it's you, there's, Dave's a wonderful CEO as is Frank movement. So it's hard for me to dissect. And sometimes the market are fickle on some small piece of it. But I think also the, when I, I encourage people say, take the long term view. When you take the long term view, you're not bothered about the ups and downs. If you're building a great company over the length of time, now it will be very clear over the arc of many, many quarters that you're business is trouble. If you're starting to see a decay in growth. And like, for example, when you start to see a growth, start to decay significantly by five, 10 percentage points, okay, there's something macro going on at this company. And that's what you won't avoid. But these, you know, ups and downs, my view is like, if you've got both Mongo, DIA and snowflake are fantastic companies, they're CEOs of people I respect. They've actually a kind of an, a, you know, advisor to us as a company, you knows mot very well. So we respect him, respect Frank, and you, there have been other quarters where Frank's, you know, the snowflakes had a down result after that. So you build a long term and they are on the right side of history, snowflake, and both of them in terms of being a modern cloud relevant in the case of MongoDB open source to data technology, that's, you know, winning, I, we would like to be like them one day >>As, as the new CEO of cohesive, what are you most, what are you most anxious about? And what are you most excited about? >>I think, listen, you know, you know, everything starts with the employee. You, I always believe I wrote my first memo to all employees. There was an article in Harvard business review called service profit chains that had a seminal impact on my leadership, which is when they studied companies who had been consistently profitable over a long period of time. They found that not just did those companies serve their customers well, but behind happy engaged customers were happy, engaged employees. So I always believe you start with the employee and you ensure that they're engaged, not just recruiting new employees. You know, I put on a tweet today, we're hiring reps and engineers. That's okay. But retaining. So I wanna start with ensuring that everybody, sometimes we have to make some unfortunate decisions with employees. We've, we've got a part company with, but if we can keep the best and brightest retained first, then of course, you know, recruiting machine, I'm trying to recruit the best and brightest to this company, people all over the place. >>I want to get them here. It's been, so I mean, heartwarming to come to world and just see people from all walks, kind of giving me hugs. I feel incredibly blessed. And then, you know, after employees, it's customers and partners, I feel like the tech is in really good hands. I don't have to worry about that. Cuz Mo it's in charge. He's got this thing. I can go to bed knowing that he's gonna keep innovating the future. Maybe in some of the companies, I would worried about the tech innovation piece, but most doing a great job there. I can kind of leave that in his cap of hands, but employees, customers, partners, that's kind of what I'm focused on. None of them are for me, like a keep up at night, but they're are opportunities, right? And sometimes there's somebody you're trying to salvage to make sure or somebody you're trying to convince to join. >>But you know, customers, I love pursuing customers. I love the win. I hate to lose. So fortune 1000 global, 2000 companies, small companies, big companies, I wanna win every one of 'em and it's not, it's not like, I mean, I know all these CEOs in my competitors. I texted him the day I joined and said, listen, I'll compete, honorably, whatever have you, but it's like Kobe and LeBron Kobe's passed away now. So maybe it's step Curry. LeBron, whoever your favorite athlete is you put your best on the court and you win. And that's how I am. That's nothing I've known no other gear than to put my best on the court and win, but do it honorably. It should not be the one that you're doing it. Unethically. You're doing it personally. You're not calling people's names. You're competing honorably. And when you win the team celebrates, it's not a victory for me, it's a victory for the team. >>I always think I'm glad that you brought out the employee experience and we're almost out of time, but I always think the employee experience and the customer experience are inextricably linked. This employees have to be empowered. They have to have the data that they need to do their job so that they can deliver to the customer. You can't do one without the other. >>That's so true. I mean, I, it's my belief. And I've talked also on this show and others about servant leadership. You know, one of my favorite poems is Brenda NA Tago. I went to bed in life. I dreamt that life was joy. I woke up and realized life was service. I acted in service was joy. So when you have a leadership model, which is it's about, I mean, there's lots of layers between me and the individual contributor, but I really care about that sales rep and the engineer. That's the leaf level of the organization. What can I get obstacle outta their way? I love skipping levels and going write that sales rep let's go and crack this deal. You know? So you have that mindset. Yeah. I mean, you, you empower, you invert the pyramid and you realize the power is at the leaf level of an organization. >>So that's what I'm trying to do. It's a little easier to do it with 2000 people than I dunno, either 20, 20, 2000 people or 35,000 reported me at VMware. And I mean a similar number at SAP, which was even bigger, but you can shape this. Now we are, we're not a startup anymore. We're a mid-size company. We'll see. Maybe along the way, there's an IP on the path. We'll wait for that. When it comes, it's a milestone. It's not the destination. So we do that and we are, we, I told people we are gonna build this green company. Cohesive is gonna be a great company like VMware one day, like Amazon. And there's always a day of early beginnings, but we have to work harder. This is kind of like the, you know, eight year old version of your kid, as opposed to the 18 year old version of the kid. And you gotta work a little harder. So I love it. Yeah. >>Good luck. Awesome. Thank you too. Best of luck. Congratulations on the role, it sounds like there's a tremendous amount of adrenaline, a momentum carrying you forward Sanja. We always appreciate having thank >>You for having in your show. >>Thank you. Our pleasure, Lisa. Thank you for Sanjay poin and Dave ante. I'm Lisa Martin. You're watching the cube live from VMware Explorer, 2022, stick around our next guest. Join us momentarily.

>>Hey everyone. It's the cube live at VMware Explorer, 2022. We're at Mascone center and lovely, beautiful San Francisco. Dave Volante is with me, Lisa Martin. Beautiful weather here today. >>It is beautiful. I couldn't have missed this one because you know, the orange and the pure and VA right. Are history together. I had a, I had a switch sets. You >>Did. You were gonna have FOMO without a guest. Who's back. One of our longtime alumni V Stewart, VP of global technology alliances partners at pure storage one. It's great to have you back on the program, seeing you in 3d >>It's. It's so great to be here and we get a guest interviewer. So this >>Is >>Fantastic. Fly by. Fantastic. >>So talk to us, what's going on at pure. It's been a while since we had a chance to talk, >>Right. Well, well, besides the fact that it's great to see in person and to be back at a conference and see all of our customers, partners and prospects, you know, pure storage has just been on a tear just for your audience. Many, those who don't follow pure, right? We finished our last year with our Q4 being 41% year over year growth. And in the year, just under 2.2 billion, and then we come outta the gates this year, close our Q1 at 50% year over year, quarter quarterly growth. Have you ever seen a storage company or an infrastructure partner at 2 billion grow at that rate? >>Well, the thing was, was striking was that the acceleration of growth, because, you know, I mean, COVID, there were supply chain issues and you know, you saw that. And then, and we've seen this before at cloud companies, we see actually AWS as accelerated growth. So this is my premise here is you guys are actually becoming a cloud-like company building on top of, of infrastructure going from on-prem to cloud. But we're gonna talk about that. >>This is very much that super cloud premise. Well, >>It is. And, and, but I think it's it's one of the characteristics is you can actually, it, you know, we used to see companies, they go, they'd come out of escape velocity, and then they'd they'd growth would slow. I used to be at IDC. We'd see it. We'd see it. Okay. Down then it'd be single digits. You guys are seeing the opposite. >>It's it's not just our bookings. And by the way, I would be remiss if I didn't remind your audience that our second quarter earnings call is tomorrow. So we'll see how this philosophy and momentum keeps going. See, right. But besides the growth, right? All the external metrics around our business are increasing as well. So our net promoter score increased right at 85.2. We are the gold standard, not just in storage in infrastructure period. Like there's no one close to us, >>85. I mean, that's like, that's a, like apple, >>It's higher than apple than apple. It's apple higher than Tesla. It's higher than AWS shopping. And if you look in like our review of our products, flash rate is the leader in the gardener magic quadrant for, for storage array. It's been there for eight years. Port works is the leader in the GIGO OME radar for native Kubernetes storage three years in a row. Like just, it's great to be at a company that's hitting on all cylinders. You know, particularly at a time that's just got so much change going on in our >>Industry. Yeah. Tremendous amount of change. Talk about the, the VMware partnership from a momentum of velocity perspective what's going on there. And some of the things that you're accelerating. >>Absolutely. So VMware is, is the, the oldest or the longest tenured technology partner that we've had. I'm about to start my 10th year at pure storage. It feels like it was yesterday. When I joined, they were a, an Alliance partner before I joined. And so not to make that about me, but that's just like we built some of the key aspects around our first product, the flash array with VMware workloads in mind. And so we are a, a co-development partner. We've worked with them on a number of projects over years of, of late things that are top of mind is like the evolution of vials, the NV support for NVMe over fabric storage, more recently SRM support for automating Dr. With Viv a deployments, you know, and, and, and then our work around VMware ex extends to not just with VMware, they're really the catalyst for a lot of three way partnerships. So partnerships into our investments in data protection partners. Well, you gotta support V ADP for backing up the VMware space, our partnership within Nvidia, well, you gotta support NVA. I, so they can accelerate bringing those technologies into the enterprise. And so it's it, it's not just a, a, a, you know, unilateral partnership. It's a bidirectional piece because for a lot of customers, VMware's kind of like a touchpoint for managing the infrastructure. >>So how is that changing? Because you you've mentioned, you know, all the, the, the previous days, it was like, okay, let's get, make storage work. Let's do the integration. Let's do the hard work. It was kind of a race for the engineering teams to get there. All the storage companies would compete. And it was actually really good for the industry. Yeah, yeah. Right. Because it, it went from, you know, really complex, to much, much simpler. And now with the port works acquisition, it brings you closer to the whole DevOps scene. And you're seeing now VMware it's with its multi-cloud initiatives, really focusing on, you know, the applications and that, and that layer. So how does that dynamic evolve in terms of the partnership and, and where the focus is? >>So there's always in the last decade or so, right. There's always been some amount of overlap or competing with your partnerships, right. Something in their portfolios they're expanding maybe, or you expand you encroach on them. I think, I think two parts to how I would want to answer your question. The retrospective look V VMware is our number one ISV from a, a partner that we, we turn transactions with. The booking's growth that I shared with you, you could almost say is a direct reflection of how we're growing within that, that VMware marketplace. We are bringing a platform that I think customers feel services their workloads well today and gives them the flexibility of what might come in their cloud tomorrow. So you look at programs like our evergreen one subscription model, where you can deploy a consumption based subscription model. So very cloud-like only pay for what you use on-prem and turn that dial as you need to dial it into a, a cloud or, or multiple clouds. >>That's just one example. Looking forward, look, port works is probably the platform that VMware should have bought because when you look at today's story, right, when kit Culbert shared a, a cross cloud services, right, it was, it was the modern version of what VMware used to say, which was, here's a software defined data center. We're gonna standardize all your dissimilar hardware, another saying software defined management to standardize all your dissimilar clouds. We do that for Kubernetes. We talk about accelerating customers' adoption of Kubernetes by, by allowing developers, just to turn on an enable features, be its security, backup high availability, but we don't do it mono in a, you know, in a, in a homogeneous environment, we allow customers to do it heterogeneously so I can deploy VMware Tansu and connect it to Amazon EKS. I can switch one of those over to red head OpenShift, non disruptively, if I need to. >>Right? So as customers are going on this journey, particularly the enterprise customers, and they're not sure where they're going, we're giving them a platform that standardizes where they want to go. On-prem in the cloud and anywhere in between. And what's really interesting is our latest feature within the port works portfolio is called port works data services, and allows customers to deploy databases on demand. Like, install it, download the binaries. You have a cus there, you got a database, you got a database. You want Cassandra, you want Mongo, right? Yeah. You know, and, and for a lot of enterprise customers, who've kind of not, not know where to don't know where to start with port works. We found that to be a great place where they're like, I have this need side of my infrastructure. You can help me reduce cost time. Right. And deliver databases to teams. And that's how they kick off their Tansu journey. For example. >>It's interesting. So port works was the enabler you mentioned maybe VMware should above. Of course they had to get the value out of, out of pivotal. >>Understood. >>So, okay. Okay. So that, so how subsequent to the port works acquisition, how has it changed the way that you guys think about storage and how your customers are actually deploying and managing storage? >>Sure. So you touched base earlier on what was really great about the cloud and VMware was this evolution of simplifying storage technologies, usually operational functions, right? Making things simpler, more API driven, right. So they could be automated. I think what we're seeing customers do to today is first off, there's a tremendous rise in everyone wanting to do every customer, not every customer, a large portion of the customer bases, wanting to acquire technology on as OPEX. And it, I think it's really driven by like eliminate technical debt. I sign a short term agreement, our short, our shortest commitment's nine months. If we don't deliver around what we say, you walk away from us in nine months. Like you, you couldn't do that historically. Furthermore, I think customers are looking for the flexibility for our subscriptions, you know, more from between on-prem and cloud, as I shared earlier, is, is been a, a, a big driver in that space. >>And, and lastly, I would, would probably touch on our environmental and sustainability efforts. You saw this morning, Ragu in the keynote touch on what was it? Zero carbon consumption initiative, or ZCI my apologies to the veer folks. If I missed VO, you know, we've had, we've had sustainability into our products since day one. I don't know if you saw our inaugural ESG report that came out about 60 days ago, but the bottom line is, is, is our portfolio reduces the, the power directly consumed by storage race by up to 80%. And another aspect to look at is that 97% of all of the products that we sold in the last six years are still in the market today. They're not being put into, you know, into, to recycle bins and whatnot, pure storage's goal by the end of this decade is to further drive the efficiency of our platforms by another 66%. And so, you know, it's an ambitious goal, but we believe it's >>Important. Yeah. I was at HQ earlier this month, so I actually did see it. So, >>Yeah. And where is sustainability from a differentiation perspective, but also from a customer requirements perspective, I'm talking to a lot of customers that are putting that requirement when they're doing RFPs and whatnot on the vendors. >>I think we would like to all, and this is a free form VO comment here. So my apologies, but I think we'd all like to, to believe that we can reduce the energy consumption in the planet through these efforts. And in some ways maybe we can, what I fear in the technology space that I think we've all and, and many of your viewers have seen is there's always more tomorrow, right? There's more apps, more vendors, more offerings, more, more, more data to store. And so I think it's really just an imperative is you've gotta continue to be able to provide more services or store more data in this in yesterday's footprint tomorrow. A and part of the way they get to is through a sustainability effort, whether it's in chip design, you know, storage technologies, et cetera. And, and unfortunately it's, it's, it's something that organizations need to adopt today. And, and we've had a number of wins where customers have said, I thought I had to evacuate this data center. Your technology comes in and now it buys me more years of time in this in infrastructure. And so it can be very strategic to a lot of vendors who think their only option is like data center evacuation. >>So I don't want to, I, I don't wanna set you up, but I do want to have the super cloud conversation. And so let's go, and you, can you, you been around a long time, your, your technical, or you're more technical than I am, so we can at least sort of try to figure it out together when I first saw you guys. I think Lisa, so you and I were at, was it, when did you announce a block storage for AWS? The, was that 2019 >>Cloud block store? I believe block four years >>Ago. Okay. So 20 18, 20 18, 20 18. Okay. So we were there at, at accelerate at accelerate and I said, oh, that's interesting. So basically if I, if I go back there, it was, it was a hybrid model. You, you connecting your on-prem, you were, you were using, I think, priority E C two, you know, infrastructure to get high performance and connecting the two. And it was a singular experience yeah. Between on-prem and AWS in a pure customer saw pure. Right. Okay. So that was the first time I started to think about Supercloud. I mean, I think thought about it in different forms years ago, but that was the first actual instantiation. So my, my I'm interested in how that's evolved, how it's evolving, how it's going across clouds. Can you talk just conceptually about how that architecture is, is morphing? >>Sure. I just to set the expectations appropriately, right? We've got, we've got a lot of engineering work that that's going on right now. There's a bunch of stuff that I would love to share with you that I feel is right around the corner. And so hopefully we'll get across the line where we're at today, where we're at today. So the connective DNA of, of flash array, OnPrem cloud block store in the cloud, we can set up for, for, you know, what we call active. Dr. So, so again, customers are looking at these arrays is a, is a, is a pair that allows workloads to be put into the, put into the cloud or, or transferred between the cloud. That's kind of like your basic building, you know, blocking tackling 1 0 1. Like what do I do for Dr. Example, right? Or, or gimme an easy button to, to evacuate a data center where we've seen a, a lot of growth is around cloud block store and cloud block store really was released as like a software version of our hardware, Ray on-prem and it's been, and, and it hasn't been making the news, but it's been continually evolving. >>And so today the way you would look at cloud block store is, is really bringing enterprise data services to like EBS for, for AWS customers or to like, you know, is Azure premium disc for Azure users. And what do I mean by enterprise data services? It's, it's the, the, the way that large scale applications are managed, on-prem not just their performance and their avail availability considerations. How do I stage the, the development team, the sandbox team before they patch? You know, what's my cyber protection, not just data protection, how, how am I protected from a cyber hack? We bring all those capabilities to those storage platforms. And the, the best result is because of our data reduction technologies, which was critical in reducing the cost of flash 10 years ago, reduces the cost of the cloud by 50% or more and pays for the, for pays more than pays for our software of cloud block store to enable these enterprise data services, to give all these rapid capabilities like instant database, clones, instant, you know, recovery from cyber tech, things of that nature. >>Do customers. We heard today that cloud chaos are, are customers saying so, okay, you can run an Azure, you can run an AWS fine. Are customers saying, Hey, we want to connect those islands. Are you hearing that from customers or is it still sort of still too early? >>I think it's still too early. It doesn't mean we don't have customers who are very much in let's buy, let me buy some software that will monitor the price of my cloud. And I might move stuff around, but there's also a cost to moving, right? The, the egress charges can add up, particularly if you're at scale. So I don't know how much I seen. And even through the cloud days, how much I saw the, the notion of workloads moving, like kind of in the early days, like VMO, we thought there might be like a, is there gonna be a fall of the moon computing, you know, surge here, like, you know, have your workload run where power costs are lower. We didn't really see that coming to fruition. So I think there is a, is a desire for customers to have standardization because they gain the benefits of that from an operational perspective. Right. Whether they put that in motion to move workloads back and forth. I think >>So let's say, let's say to be determined, let let's say they let's say they don't move them because your point you knows too expensive, but, but, but, but you just, I think touched on it is they do want some kind of standard in terms of the workflow. Yep. You you're saying you're, you're starting to see demand >>Standard operating practices. Okay. >>Yeah. SOPs. And if they're, if they're big into pure, why wouldn't they want that? If assuming they have, you know, multiple clouds, which a lot of customers do. >>I, I, I I'll assure with you one thing that the going back to like basic primitives and I touched it touched on it a minute ago with data reduction. You have customers look at their, their storage bills in the cloud and say, we're gonna reduce that by half or more. You have a conversation >>Because they can bring your stack yeah. Into the cloud. And it's got more maturity than what you'd find from a cloud company, cloud >>Vendor. Yeah. Just data. Reduction's not part of block storage today in the cloud. So we've got an advantage there that we, we bring to bear. Yeah. >>So here we are at, at VMware Explorer, the first one of this name, and I love the theme, the center of the multi-cloud universe. Doesn't that sound like a Marvel movie. I feel like there should be superheroes walking around here. At some point >>We got Mr. Fantastic. Right here. We do >>Gone for, I dunno it >>Is. But a lot of, a lot of news this morning in the keynote, you were in the keynote, what are some of the things that you're hearing from VMware and what excites you about this continued evolution of the partnership with pure >>Yeah. Great point. So I, I think I touched on the, the two things that really caught my attention. Obviously, you know, we've got a lot of investment in V realize it was now kind of rebranded as ay, that, you know, I think we're really eager to see if we can help drive that consumption a bit higher, cuz we believe that plays into our favor as a vendor. We've we've we have over a hundred templates for the area platform right now to, you know, automation templates, whether it's, you know, levels set your platform, you know, automatically move workloads, deploy on demand. Like just so, so again, I think the focus there is very exciting for us, obviously when they've got a new release, like vSphere eight, that's gonna drive a lot of channel behaviors. So we've gotta get our, you know, we're a hundred percent channel company. And so we've gotta go get our channel ready because with about half of the updates of vSphere is, is hardware refresh. And so, you know, we've gotta be, be prepared for that. So, you know, some of the excitements about just being how to find more points in the market to do more business together. >>All right. Exciting cover the grounds. Right. I mean, so, okay. You guys announce earnings tomorrow, so we can't obviously quiet period, but of course you're not gonna divulge that anyway. So we'll be looking for that. What other catalysts are out there that we should be paying attention to? You know, we got, we got reinvent coming up in yep. In November, you guys are obviously gonna be there in, in a big way. Accelerate was back this year. How was accelerate >>Accelerate in was in Los Angeles this year? Mm. We had great weather. It was a phenomenal venue, great event, great partner event to kick it off. We happened to, to share the facility with the president and a bunch of international delegates. So that did make for a little bit of some logistic securities. >>It was like the summit of the Americas. I, I believe I'm recalling that correctly, but it was fantastic. Right. You, you get, you get to bring the customers out. You get to put a bunch of the engineers on display for the products that we're building. You know, one of the high, you know, two of the highlights there were, we, we announced our new flash blade S so, you know, higher, more performant, more scalable version of our, our scale and object and file platform with that. We also announced the, the next generation of our a I R I, which is our AI ready, AI ready infrastructure within video. So think of it like converged infrastructure for AI workloads. We're seeing tremendous growth in that unstructured space. And so, you know, we obviously pure was funded around block storage, a lot around virtual machines. The data growth is in unstructured, right? >>We're just seeing, we're seeing, you know, just tons of machine learning, you know, opportunities, a lot of opportunities, whether we're looking at health, life sciences, genome sequencing, medical imaging, we're seeing a lot of, of velocity in the federal space. You know, things, I can't talk about a lot of velocity in the automotive space. And so just, you know, from a completeness of platform, you know, flat flash blade is, is really addressing a need really kind of changing the market from NAS as like tier two storage or object is tier three to like both as a tier one performance candidate. And now you see applications that are supporting running on top of object, right? All your analytics platforms are on an object today, Absolut. So it's a, it's a whole new world. >>Awesome. And Pierce also what I see on the website, a tech Fest going on, you guys are gonna be in Seoul, Mexico city in Singapore in the next week alone. So customers get the chance to be able to in person talk with those execs once again. >>Yeah. We've been doing the accelerate tech tech fests, sorry about that around the globe. And if one of those align with your schedule, or you can free your schedule to join us, I would encourage you. The whole list of events dates are on pure storage.com. >>I'm looking at it right now. Vaon thank you so much for joining Dave and me. I got to sit between two dapper dudes, great conversation about what's going on at pure pure with VMware better together and the, and the CATA, the cat catalysis that's going on on both sides. I think that's an actual word I should. Now I have a degree biology for Vaughn Stewart and Dave Valante I'm Lisa Martin. You're watching the cube live from VMware Explorer, 22. We'll be right back with our next guest. So keep it here.

>>From the cube studios in Palo Alto, in Boston, bringing you data driven insights from the cube and ETR. This is breaking analysis with Dave ante. >>When Frank sluman took service, now public many people undervalued the company, positioning it as just a better help desk tool. You know, it turns out that the firm actually had a massive Tam expansion opportunity in it. SM customer service, HR, logistics, security marketing, and service management. Generally now stock price followed over the years, the stellar execution under Slootman and CFO, Mike scar Kelly's leadership. Now, when they took the reins at snowflake expectations were already set that they'd repeat the feet, but this time, if anything, the company was overvalued out of the gate, the thing is people didn't really better understand the market opportunity this time around, other than that, it was a bet on Salman's track record of execution and on data, pretty good bets, but folks really didn't appreciate that snowflake. Wasn't just a better data warehouse that it was building what they call a data cloud, and we've turned a data super cloud. >>Hello and welcome to this. Week's Wikibon cube insights powered by ETR in this breaking analysis, we'll do four things. First. We're gonna review the recent narrative and concerns about snowflake and its value. Second, we're gonna share survey data from ETR that will confirm precisely what the company's CFO has been telling anyone who will listen. And third, we're gonna share our view of what snowflake is building IE, trying to become the defacto standard data platform, and four convey our expectations for the upcoming snowflake summit. Next week at Caesar's palace in Las Vegas, Snowflake's most recent quarterly results they've been well covered and well documented. It basically hit its targets, which for snowflake investors was bad news wall street piled on expressing concerns about Snowflake's consumption, pricing model, slowing growth rates, lack of profitability and valuation. Given the, given the current macro market conditions, the stock dropped below its IPO offering price, which you couldn't touch on day one, by the way, as the stock opened well above that and, and certainly closed well above that price of one 20 and folks express concerns about some pretty massive insider selling throughout 2021 and early 2022, all this caused the stock price to drop quite substantially. >>And today it's down around 63% or more year to date, but the only real substantive change in the company's business is that some of its largest consumer facing companies, while still growing dialed back, their consumption this past quarter, the tone of the call was I wouldn't say contentious the earnings call, but Scarelli, I think was getting somewhat annoyed with the implication from some analyst questions that something is fundamentally wrong with Snowflake's business. So let's unpack this a bit first. I wanna talk about the consumption pricing on the earnings call. One of the analysts asked if snowflake would consider more of a subscription based model so that they could better weather such fluctuations and demand before the analyst could even finish the question, CFO Scarelli emphatically interrupted and said, no, <laugh> the analyst might as well have asked, Hey Mike, have you ever considered changing your pricing model and screwing your customers the same way most legacy SaaS companies lock their customers in? >>So you could squeeze more revenue out of them and make my forecasting life a little bit easier. <laugh> consumption pricing is one of the things that makes a company like snowflake so attractive because customers is especially large customers facing fluctuating demand can dial and their end demand can dial down usage for certain workloads that are maybe not yet revenue producing or critical. Now let's jump to insider trading. There were a lot of insider selling going on last year and into 2022 now, I mean a lot sloop and Scarelli Christine Kleinman. Mike SP several board members. They sold stock worth, you know, many, many hundreds of millions of dollars or, or more at prices in the two hundreds and three hundreds and even four hundreds. You remember the company at one point was valued at a hundred billion dollars, surpassing the value of service now, which is this stupid at this point in the company's tenure and the insider's cost basis was very often in the single digit. >>So on the one hand, I can't blame them. You know what a gift the market gave them last year. Now also famed investor, Peter Linsey famously said, insiders sell for many reasons, but they only buy for one. But I have to say there wasn't a lot of insider buying of the stock when it was in the three hundreds and above. And so yeah, this pattern is something to watch our insiders buying. Now, I'm not sure we'll keep watching snowflake. It's pretty generous with stock based compensation and insiders still own plenty of stock. So, you know, maybe not, but we'll see in future disclosures, but the bottom line is Snowflake's business. Hasn't dramatically changed with the exception of these large consumer facing companies. Now, another analyst pointed out that companies like snap, he pointed to company snap, Peloton, Netflix, and face Facebook have been cutting back. >>And Scarelli said, and what was a bit of a surprise to me? Well, I'm not gonna name the customers, but it's not the ones you mentioned. So I, I thought I would've, you know, if I were the analyst I would've follow up with, how about Walmart target visa, Amex, Expedia price line, or Uber? Any of those Mike? I, I doubt he would've answered me anything. Anyway, the one thing that Scarelli did do is update Snowflake's fiscal year 2029 outlook to emphasize the long term opportunity that the company sees. This chart shows a financial snapshot of Snowflake's current business using a combination of quarterly and full year numbers in a model of what the business will look like. According to Scarelli in Dave ante with a little bit of judgment in 2029. So this is essentially based on the company's framework. Snowflake this year will surpass 2 billion in revenues and targeting 10 billion by 2029. >>Its current growth rate is 84% and its target is 30% in the out years, which is pretty impressive. Gross margins are gonna tick up a bit, but remember Snowflake's cost a good sold they're dominated by its cloud cost. So it's got a governor. There has to pay AWS Azure and Google for its infrastructure. But high seventies is a, is a good target. It's not like the historical Microsoft, you know, 80, 90% gross margin. Not that Microsoft is there anymore, but, but snowflake, you know, was gonna be limited by how far it can, how much it can push gross margin because of that factor. It's got a tiny operating margin today and it's targeting 20% in 2029. So that would be 2 billion. And you would certainly expect it's operating leverage in the out years to enable much, much, much lower SGNA than the current 54%. I'm guessing R and D's gonna stay healthy, you know, coming in at 15% or so. >>But the real interesting number to watch is free cash flow, 16% this year for the full fiscal year growing to 25% by 2029. So 2.5 billion in free cash flow in the out years, which I believe is up from previous Scarelli forecast in that 10, you know, out year view 2029 view and expect the net revenue retention, the NRR, it's gonna moderate. It's gonna come down, but it's still gonna be well over a hundred percent. We pegged it at 130% based on some of Mike's guidance. Now today, snowflake and every other stock is well off this morning. The company had a 40 billion value would drop well below that midday, but let's stick with the 40 billion on this, this sad Friday on the stock market, we'll go to 40 billion and who knows what the stock is gonna be valued in 2029? No idea, but let's say between 40 and 200 billion and look, it could get even ugly in the market as interest rates rise. >>And if inflation stays high, you know, until we get a Paul Voker like action, which is gonna be painful from the fed share, you know, let's hope we don't have a repeat of the long drawn out 1970s stagflation, but that is a concern among investors. We're gonna try to keep it positive here and we'll do a little sensitivity analysis of snowflake based on Scarelli and Ante's 2029 projections. What we've done here is we've calculated in this chart. Today's current valuation at about 40 billion and run a CAGR through 2029 with our estimates of valuation at that time. So if it stays at 40 billion valuation, can you imagine snowflake grow into a 10 billion company with no increase in valuation by the end, by by 2029 fiscal 2029, that would be a major bummer and investors would get a, a 0% return at 50 billion, 4% Kager 60 billion, 7%. >>Kegar now 7% market return is historically not bad relative to say the S and P 500, but with that kind of revenue and profitability growth projected by snowflake combined with inflation, that would again be a, a kind of a buzzkill for investors. The picture at 75 billion valuation, isn't much brighter, but it picks up at, at a hundred billion, even with inflation that should outperform the market. And as you get to 200 billion, which would track by the way, revenue growth, you get a 30% plus return, which would be pretty good. Could snowflake beat these projections. Absolutely. Could the market perform at the optimistic end of the spectrum? Sure. It could. It could outperform these levels. Could it not perform at these levels? You bet, but hopefully this gives a little context and framework to what Scarelli was talking about and his framework, not with notwithstanding the market's unpredictability you're you're on your own. >>There. I can't help snowflake looks like it's going to continue either way in amazing run compared to other software companies historically, and whether that's reflected in the stock price. Again, I, I, I can't predict, okay. Let's look at some ETR survey data, which aligns really well with what snowflake is telling the street. This chart shows the breakdown of Snowflake's net score and net score. Remember is ETS proprietary methodology that measures the percent of customers in their survey that are adding the platform new. That's the lime green at 19% existing snowflake customers that are ex spending 6% or more on the platform relative to last year. That's the forest green that's 55%. That's a big number flat spend. That's the gray at 21% decreasing spending. That's the pinkish at 5% and churning that's the red only 1% or, or moving off the platform, tiny, tiny churn, subtract the red from the greens and you get a net score that, that, that nets out to 68%. >>That's an, a very impressive net score by ETR standards. But it's down from the highs of the seventies and mid eighties, where high seventies and mid eighties, where snowflake has been since January of 2019 note that this survey of 1500 or so organizations includes 155 snowflake customers. What was really interesting is when we cut the data by industry sector, two of Snowflake's most important verticals, our finance and healthcare, both of those sectors are holding a net score in the ETR survey at its historic range. 83%. Hasn't really moved off that, you know, 80% plus number really encouraging, but retail consumer showed a dramatic decline. This past survey from 73% in the previous quarter down to 54%, 54% in just three months time. So this data aligns almost perfectly with what CFO Scarelli has been telling the street. So I give a lot of credibility to that narrative. >>Now here's a time series chart for the net score and the provision in the data set, meaning how penetrated snowflake is in the survey. Again, net score measures, spending velocity and a specific platform and provision measures the presence in the data set. You can see the steep downward trend in net score this past quarter. Now for context note, the red dotted line on the vertical axis at 40%, that's a bit of a magic number. Anything above that is best in class in our view, snowflake still a well, well above that line, but the April survey as we reported on May 7th in quite a bit of detail shows a meaningful break in the snowflake trend as shown by ETRS call out on the bottom line. You can see a steady rise in the survey, which is a proxy for Snowflake's overall market penetration. So steadily moving up and up. >>Here's a bit of a different view on that data bringing in some of Snowflake's peers and other data platforms. This XY graph shows net score on the vertical axis and provision on the horizontal with the red dotted line. At 40%, you can see from the ETR callouts again, that snowflake while declining in net score still holds the highest net score in the survey. So of course the highest data platforms while the spending velocity on AWS and Microsoft, uh, data platforms, outperforms that have, uh, sorry, while they're spending velocity on snowflake outperforms, that of AWS and, and Microsoft data platforms, those two are still well above the 40% line with a stronger market presence in the category. That's impressive because of their size. And you can see Google cloud and Mongo DB right around the 40% line. Now we reported on Mongo last week and discussed the commentary on consumption models. >>And we referenced Ray Lenchos what we thought was, was quite thoughtful research, uh, that rewarded Mongo DB for its forecasting transparency and, and accuracy and, and less likelihood of facing consumption headwinds. And, and I'll reiterate what I said last week, that snowflake, while seeing demand fluctuations this past quarter from those large customers is, is not like a data lake where you're just gonna shove data in and figure it out later, no schema on, right. Just throw it into the pond. That's gonna be more discretionary and you can turn that stuff off. More likely. Now you, you bring data into the snowflake data cloud with the intent of driving insights, which leads to actions, which leads to value creation. And as snowflake adds capabilities and expands its platform features and innovations and its ecosystem more and more data products are gonna be developed in the snowflake data cloud and by data products. >>We mean products and services that are conceived by business users. And that can be directly monetized, not just via analytics, but through governed data sharing and direct monetization. Here's a picture of that opportunity as we see it, this is our spin on our snowflake total available market chart that we've published many, many times. The key point here goes back to our opening statements. The snowflake data cloud is evolving well beyond just being a simpler and easier to use and more elastic cloud database snowflake is building what we often refer to as a super cloud. That is an abstraction layer that companies that, that comprises rich features and leverages the underlying primitives and APIs of the cloud providers, but hides all that complexity and adds new value beyond that infrastructure that value is seen in the left example in terms of compressed cycle time, snowflake often uses the example of pharmaceutical companies compressing time to discover a drug by years. >>Great example, there are many others this, and, and then through organic development and ecosystem expansion, snowflake will accelerate feature delivery. Snowflake's data cloud vision is not about vertically integrating all the functionality into its platform. Rather it's about creating a platform and delivering secure governed and facile and powerful analytics and data sharing capabilities to its customers, partners in a broad ecosystem so they can create additional value. On top of that ecosystem is how snowflake fills the gaps in its platform by building the best cloud data platform in the world, in terms of collaboration, security, governance, developer, friendliness, machine intelligence, etcetera, snowflake believes and plans to create a defacto standard. In our view in data platforms, get your data into the data cloud and all these native capabilities will be available to you. Now, is that a walled garden? Some might say it is. It's an interesting question and <laugh>, it's a moving target. >>It's definitely proprietary in the sense that snowflake is building something that is highly differentiatable and is building a moat around it. But the more open snowflake can make its platform. The more open source it uses, the more developer friendly and the great greater likelihood people will gravitate toward snowflake. Now, my new friend Tani, she's the creator of the data mesh concept. She might bristle at this narrative in favor, a more open source version of what snowflake is trying to build, but practically speaking, I think she'd recognize that we're a long ways off from that. And I also think that the benefits of a platform that despite requiring data to be inside of the data cloud can distribute data globally, enable facile governed, and computational data sharing, and to a large degree be a self-service platform for data, product builders. So this is how we see snow, the snowflake data cloud vision evolving question is edge part of that vision on the right hand side. >>Well, again, we think that is going to be a future challenge where the ecosystem is gonna have to come to play to fill those gaps. If snowflake can tap the edge, it'll bring even more clarity as to how it can expand into what we believe is a massive 200 billion Tam. Okay, let's close on next. Week's snowflake summit in Las Vegas. The cube is very excited to be there. I'll be hosting with Lisa Martin and we'll have Frank son as well as Christian Kleinman and several other snowflake experts. Analysts are gonna be there, uh, customers. And we're gonna have a number of ecosystem partners on as well. Here's what we'll be looking for. At least some of the things, evidence that our view of Snowflake's data cloud is actually taking shape and evolving in the way that we showed on the previous chart, where we also wanna figure out where snowflake is with it. >>Streamlet acquisition. Remember streamlet is a data science play and an expansion into data, bricks, territory, data, bricks, and snowflake have been going at it for a while. Streamlet brings an open source Python library and machine learning and kind of developer friendly data science environment. We also expect to hear some discussion, hopefully a lot of discussion about developers. Snowflake has a dedicated developer conference in November. So we expect to hear more about that and how it's gonna be leveraging further leveraging snow park, which it has previously announced, including a public preview of programming for unstructured data and data monetization along the lines of what we suggested earlier that is building data products that have the bells and whistles of native snowflake and can be directly monetized by Snowflake's customers. Snowflake's already announced a new workload this past week in security, and we'll be watching for others. >>And finally, what's happening in the all important ecosystem. One of the things we noted when we covered service now, cause we use service now as, as an example because Frank Lupin and Mike Scarelli and others, you know, DNA were there and they're improving on that service. Now in his post IPO, early adult years had a very slow pace. In our view was often one of our criticism of ecosystem development, you know, ServiceNow. They had some niche SI uh, like cloud Sherpa, and eventually the big guys came in and, and, and began to really lean in. And you had some other innovators kind of circling the mothership, some smaller companies, but generally we see sluman emphasizing the ecosystem growth much, much more than with this previous company. And that is a fundamental requirement in our view of any cloud or modern cloud company now to paraphrase the crazy man, Steve bomber developers, developers, developers, cause he screamed it and ranted and ran around the stage and was sweating <laugh> ecosystem ecosystem ecosystem equals optionality for developers and that's what they want. >>And that's how we see the current and future state of snowflake. Thanks today. If you're in Vegas next week, please stop by and say hello with the cube. Thanks to my colleagues, Stephanie Chan, who sometimes helps research breaking analysis topics. Alex, my is, and OS Myerson is on production. And today Andrew Frick, Sarah hiney, Steven Conti Anderson hill Chuck all and the entire team in Palo Alto, including Christian. Sorry, didn't mean to forget you Christian writer, of course, Kristin Martin and Cheryl Knight, they helped get the word out. And Rob ho is our E IIC over at Silicon angle. Remember, all these episodes are available as podcast, wherever you listen to search breaking analysis podcast, I publish each week on wikibon.com and Silicon angle.com. You can email me directly anytime David dot Valante Silicon angle.com. If you got something interesting, I'll respond. If not, I won't or DM me@deteorcommentonmylinkedinpostsandpleasedocheckoutetr.ai for the best survey data in the enterprise tech business. This is Dave Valante for the insights powered by ETR. Thanks for watching. And we'll see you next week. I hope if not, we'll see you next time on breaking analysis.

(upbeat music) >> We're back. This is Dave Vellante of theCUBE, and we're here with Ray Wang, who just wrote a book reminiscent of the famous Tears for Fears song, Everybody Wants to Rule the World: Surviving and Thriving in a World of Digital Giants. Ray, great to see again, man. >> What's going on, man, how are you? >> Oh great, thanks for coming on. You know, it was crazy, been crazy, but it's good to see you face-to-face. >> Ray: This is, we're in the flesh, it's live, we're having conversations, and the information that we're getting is cut right. >> Dave: Yeah, so why did you write this book and how did you find the time? >> Hey, we're in the middle of pandemic. No, I wrote the book because what was happening was digital transformation efforts, they're starting to pop up, but companies weren't always succeeding. And something was happening with digital giants that was very different. They were winning in the marketplace. And never in the form of, if you think about extreme capitalism, if we think about capitalism in general, never in the history of capitalism have we seen growth of large companies. They get large, they fall apart, they don't have anything to build, they can't scale. Their organizations are in shambles. But what happened? If you look at 2017, the combined market cap of the FAANGs and Microsoft was 2 trillion. Today, it is almost 10.2 trillion. It's quintupled. That's never happened. And there's something behind that business model that they put into place that others have copied, from the Airbnbs to the Robloxes to what's going to happen with like a Starlink, and of course, the Robinhoods and you know, Robinhoods and Coinbases of the world. >> And the fundamental premise is all around data, right? Putting data at the core, if you don't do that, you're going to fly blind. >> It is and the secret behind that is the long-term platforms called data-driven digital networks. These platforms take the ability, large memberships, our large devices, they look at that effect. Then they look at figuring out how to actually win on data supremacy. And then of course, they monetize off that data. And that's really the secret behind that is you've got to build that capability and what they do really well is they dis-intermediate customer account control. They take the relationships, aggregate them together. So food delivery app companies are great example of that. You know, small businesses are out there that hundreds and thousands of customers. Today, what happens? Well, they've been aggregated. Millions of customers together into food delivery app. >> Well, I think, you know, this is really interesting what you're saying, because if you think about how we deal with Netflix, we don't call the Netflix sales department or the marketing department of the service, just one interface, the Netflix. So they've been able to put data at their core. Can incumbents do that? How can they do that? >> Incumbents can definitely do that. And it's really about figuring out how to automate that capture. What you really want to do is you start in the cloud, you bring the data together, and you start putting the three A's, analytics, automation, and AI are what you have to be able to put into place. And when you do do that, you now have the ability to go out and figure out how to create that flywheel effect inside those data-driven digital networks. These DDDNS are important. So in Netflix, what are they capturing? They're looking at sentiment, they're looking at context. Like why did you interact with, you know, one title versus another? Did you watch Ted Lasso? Did you switch out of Apple TV to Netflix? Well, I want to know why, right? Did you actually jump into another category? You switched into genres. After 10:00 p.m., what are you watching? Maybe something very different than what you're watching at 2:00 p.m.. How many members are in the home, right? All these questions are being answered and that's the business graph behind all this. >> How much of this is kind of related to the way organizations or companies are organized? In other words, you think about, historically, they would maybe put the process at the core or the, in a bottling plant, the manufacturing facility at the core and the data's all dispersed. Everybody talks about silos. So will AI be the answer to that? Will some new database, Snowflake? Is that the answer? What's the answer to sort of bringing that data together and how do you deal with the organizational inertia? >> Well, the trick to it is really to have a single plane to be able to access that data. I don't care where the data sits, whether it's on premise, whether it's in the cloud, whether it's in the edge, it makes no difference. That's really what you want to be able to do is bring that information together. But the glue is the context. What time was it? What's the weather outside? What location are you in? What's your heart rate? Are you smiling, right? All of those factors come into play. And what we're trying to do is take a user, right? So it could be a customer, a supplier, a partner, or an employee. And how do they interact with an order doc, an invoice, an incident, and then apply the context. And what we're doing is mining that context and information. Now, the more, back to your other point on self service and automation, the more you can actually collect those data points, the more you can capture that context, the more you're able to get to refine that information. >> Context, that's interesting, because if you think about our operational systems, we've contextualized most of them, whether it's sales, marketing, logistics, but we haven't really contextualized our data systems, our data architecture. It's generally run by a technical group. They don't necessarily have the line of business context. You see what HPE is doing today is trying to be inclusive of data on prem. I mentioned Snowflake, they're saying no way. Frank Slootman says we're not going on prem. So that's kind of interesting. So how do you see sort of context evolving with the actually the business line? Not only who has the context actually can, I hate to use the word, but I'm going to, own the data. >> You have to have a data to decisions pathway. That data decisions pathway is you start with all types of data, structured, unstructured, semi-structured, you align it to a business process as an issue, issue to resolution, order to cash, procure to pay, hire to retire. You bring that together, and then you start mining and figuring out what patterns exist. Once you have the patterns, you can then figure out the next best action. And when you get the next best action, you can compete on decisions. And that becomes a very important part. That decision piece, that's going to be automated. And when we think about that, you and I make a decision one per second, how long does it get out of management committee? Could be a week, two weeks, a quarter, a year. It takes forever to get anything out of management committee. But these new systems, if you think about machines, can make decisions a hundred times per second, a thousand times per second. And that's what we're competing against. That asymmetry is the decision velocity. How quickly you can make decisions will be a competitive weapon. >> Is there a dissonance between the fact that you just mentioned, speed, compressing, that sort of time to decision, and the flip side of that coin, quality, security, governance. How do you see squaring that circle? >> Well, that's really why we're going to have to make that, that's the automated, that's the AI piece. Just like we have all types of data, we got to spew up automated ontologies, we got to spit them up, we got to be using, we've got to put them back into play, and then we got to be able to take back into action. And so you want enterprise class capabilities. That's your data quality. That's your security. That's the data governance. That's the ability to actually take that data and understand time series, and actually make sure that the integrity of that data is there. >> What do you think about this sort of notion that increasingly, people are going to be building data products and services that can be monetized? And that's kind of goes back to context, the business lines kind of being responsible for their own data, not having to get permission to add another data source. Do you see that trend? Do you see that decentralization trend? Two-part question. And where do you see HPE fitting into that? >> I see, one, that that trend is definitely going to exist. I'll give you an example. I can actually destroy the top two television manufacturers in the world in less than five years. I could take them out of the business and I'll show you how to do it. So I'm going to make you an offer. $15 per month for the next five years. I'm going to give you a 72 inch, is it 74? 75 inch, 75 inch smart TV, 4k, big TV, right? And it comes with a warranty. And if anything breaks, I'm going to return it to you in 48 hours or less with a brand new one. I don't want your personal information. I'm only going to monitor performance data. I want to know the operations. I want to know which supplier lied to me, which components are working, what features you use. I don't need to know your personal viewing habits, okay? Would you take that deal? >> TV is a service, sure, of course I would. >> 15 bucks and I'm going to make you a better deal. For $25 a month, you get to make an upgrade anytime during that five-year period. What would happen to the two largest TV manufacturers if I did that? >> Yeah, they'd be disrupted. Now, you obviously have a pile of VC money that you're going to do that. Will you ever make money at that model? >> Well, here's why I'll get there and I'll explain. What's going to happen is I lock them out of the market for four to five years. I'm going to take 50 to 60% of the market. Yes, I got to raise $10 billion to figure out how to do that. But that's not really what happens at the end. I become a data company because I have warranty data. I'm going to buy a company that does, you know, insurance like in Asurion. I'm going to get break/fix data from like a Best Buy or a company like that. I'm going to get at safety data from an underwriter's lab. It's a competition for data. And suddenly, I know those habits better than anyone else. I'm going to go do other things more than the TV. I'm not done with the TV. I'm going to do your entire kitchen. For $100 a month, I'll do a mid range. For like $500 a month, I'm going to take your dish washer, your washer, your dryer, your refrigerator, your range. And I'll do like Miele, Gaggenau, right? If you want to go down Viking, Wolf, I'll do it for $450 a month for the next 10 years. By year five, I have better insurance information than the insurance companies from warranty. And I can even make that deal portable. You see where we're going? >> Yeah so each of those are, I see them as data products. So you've got your TV service products, you've got your kitchen products, you've got your maintenance, you know, data products. All those can be monetized. >> And I went from TV manufacturer to underwriter overnight. I'm competing on data, on insurance, and underwriting. And more importantly, here's the green initiative. Here's why someone would give me $10 billion to do it. I now control 50% of all power consumption in North America because I'm also going to do HVAC units, right? And I can actually engineer the green capabilities in there to actually do better power purchase consumption, better monitoring, and of course, smart capabilities in those, in those appliances. And that's how you actually build a model like that. And that's how you can win on a data model. Now, where does HPE fit into that? Their job is to bring that data together at the edge. They bring that together in the middle. Then they have the ability to manage that on a remote basis and actually deliver those services in the cloud so that someone else can consume it. >> All right, so if you, you're hitting on something that some people have have talked about, but it's, I don't think it's widely sort of discussed. And that is, historically, if you're in an industry, you're in that industry's vertical stack, the sales, the marketing, the manufacturing, the R&D. You become an expert in insurance or financial services or whatever, you know, automobile manufacturing or radio and television, et cetera. Obviously, you're seeing the big internet giants, those 10 trillion, you know, some of the market caps, they're using data to traverse industries. We've never seen this before. Amazon in content, you're seeing Apple in finance, others going into the healthcare. So they're technology companies that are able to traverse industries. Never seen this before, and it's because of data. >> And it's the collapsing value chains. Their data value chains are collapsing. Comms, media, entertainment, tech, same business. Whether you sell me a live stream TV, a book, a video game, or some enterprise software, it's the same data value stream on multi-sided networks. And once you understand that, you can see retail, right? Distribution, manufacturing collapsed in the same kind of way. >> So Silicon Valley broadly defined, if I can include, you know, Microsoft and Amazon in there, they seem to have a dual disruption agenda, right? One is on the technology front, disrupting, you know, the traditional enterprise business. The other is they're disrupting industries. How do you see that playing out? >> Well the problem is, they're never going to be able to get into new industries going forward because of the monopoly power that people believe they have, and that's what's going on, but they're going to invest in creating joint venture startups in other industries, as they power the tools to enable other industries to jump and leap frog from where they are. So healthcare, for example, we're going to have AI in monitoring in ways that we never seen before. You can see devices enter healthcare, but you see joint venture partnerships between a big hyperscaler and some of the healthcare providers. >> So HPE transforming into a cloud company as a service, do you see them getting into insurance as you just described in your little digital example? >> No, but I see them powering the folks that are in insurance, right? >> They're not going to compete with their customers maybe the way that Amazon did. >> No, that's actually why you would go to them as opposed to a hyperscale that might compete with you, right? So is Google going to get into the insurance business? Probably not. Would Amazon? Maybe. Is Tesla in the business? Yeah, they're definitely in insurance. >> Yeah, big time, right. So, okay. So tell me more about your book. How's it being received? What's the reaction? What's your next book? >> So the book is doing well. We're really excited. We did a 20 city book tour. We had chances to meet everybody across the board. Clients we couldn't see in a while, partners we didn't see in a while. And that was fun. The reaction is, if you read the book carefully, there are $3 trillion market cap opportunities, $1000 billion unicorns that can be built right there. >> Is, do you have a copy for me that's signed? (audience laughing) >> Ray: Sorry (coughs) I'm choking on my makeup. I can get one actually, do you want one? >> Dave: I do, I want, I want one. >> Can someone bring my book bag? I actually have one, I can sign it right here. >> Dave: Yeah, you know what? If we have a book, I'd love to hold it. >> Ray: Do you have any here as well? >> So it's obviously you know, Everybody Wants to Rule the World: Surviving and Thriving in a world of Digital Giants, available, you know, wherever you buy books. >> Yeah, so, oh, are we still going? >> Dave: Yeah, yeah, we're going. >> Okay. >> Dave: What's the next book? >> Next book? Well, it's about disrupting those digital giants and it's going to happen in the metaverse economy. If we think about where the metaverse is, not just the hardware platforms, not just the engines, not just what's going on with the platforms around defy decentralization and the content producers, we see those as four different parts today. What we're going to actually see is a whole comp, it's a confluence of events that's going to happen where we actually bring in the metaverse economy and the stuff that Neal Stephenson was writing about ages ago in Snow Crash is going to come out real. >> So, okay. So you're laying out a scenario that the big guys, the disruptors, could get disrupted. It sounds like crypto is possibly a force in that disruption. >> Ray: Decentralized currencies, crypto plays a role, but it's the value exchange mechanisms in an Algorand, in an Ether, right, in a Cardano, that actually enables that to happen because the value exchange in the smart contracts power that capability, and what we're actually seeing is the reinvention of the internet. So you think, see things like SIOM pop-up, which actually is creating the new set of the internet standards, and when those things come together, what we're actually going to move from is the seller is completely transparent, the buyer's completely anonymous and it's in a trust framework that actually allows you to do that. >> Well, you think about those protocols, the internet protocols that were invented whenever, 30 years ago, maybe more, TCP/IP, wow. I mean, okay. And they've been co-opted by the internet giants. It's the crypto guys, some of the guys you've mentioned that are actually innovating and putting, putting down new innovation really and have been well-funded to do so. >> I mean, I'll give you another example of how this could happen. About four years ago, five years ago, I wanted to buy Air Canada's mileage program, $400 million, 10 million users, 40 bucks a user. What do I want them in a mileage program? Well think about it. It's funded, a penny per mile. It's redeemed at 1.6 cents a mile. It's 2 cents if you buy magazines, 2 1/2 cents if you want, you know, electronics, jewelry, or sporting equipment. You don't lose money on these. CFOs hate them, they're just like (groans) liability on the books, but they mortgage the crap out of them in the middle of an ish problem and banks pay millions of dollars a year pour those mileage points. But I don't want it for the 10 million flyers in Canada. What I really want is the access to 762 million people in Star Alliance. What would happen if I turned that airline mileage program into cryptocurrency? One, I would be the world's largest cryptocurrency on day one. What would happen on day two? I'd be the world's largest ad network. Cookie apocalypse, go away. We don't need that anymore. And more importantly, on day three, what would I do? My ESG here? 2.2 billion people are unbanked in the world. All you need is a mobile device and a connection, now you have a currency without any government regulation around, you know, crayon banking, intermediaries, a whole bunch of people like taking cuts, loansharking, that all goes away. You suddenly have people that are now banked and you've unbanked, you've banked the unbanked. And that creates a whole very different environment. >> Not a lot of people thinking about how the big giants get disintermediated. Get the book, look into it, big ideas. Ray Wang, great to see you, man. >> Ray: Hey man, thanks a lot. >> Hey, thank you. All right and thank you for watching. Keep it right there for more great content from HPE's big GreenLake announcements. Be right back. (bright music)

>>Hello and welcome back to the cubes coverage of dr khan 2021. I'm john for your host of the cube. Great guests here cube alumni Stephen Chin, vice president of developer relations for jay frog Stephen, great to see you again this remote this time this last time was in person. Our last physical event. We had you in the queue but great to see you. Thanks for coming in remotely. >>No, no, I'm very glad to be here. And also it was, it was awesome to be in person at our s a conference when we last talked and the last year has been super exciting with a whole bunch of crazy things like the I. P. O. And doing virtual events. So we've, we're transitioning to the new normal. We're looking forward to things getting to be hybrid. >>Great success with jay frog. We've been documenting the history of this company, very developer focused the successful I. P. O. And just the continuation that you guys have transitioned beautifully to virtual because you know, developer company, it runs virtual, but also you guys have been all about simplicity for developers and and we've been talking for many, many years with you guys on this. This is the theme that dr khan again, this is a developer conference, not so much an operator conference, but more of a deva deV developer focused. You guys have been there from the beginning, um nationally reported on it. But talk about jay Frog and the Doctor partnership and why is this event so important for you? >>Yeah. So I think um like like you said, jay Frog has and always is a developer focused company. So we we build tools and things which which focus on developer use cases, how you get your code to production and streamlining the entire devoPS pipeline. And one of the things which which we believe very strongly in and I think we're very aligned with with doctor on this is having secure clean upstream dependencies for your Docker images for other package and language dependencies and um you know, with the announcement of dr khan and dr Hubbs model changing, we wanted to make sure that we have the best integration with doctor and also the best support for our customers on with Docker hub. So one of the things we did strategically is um, we um combined our platforms so um you can get the best in class developer tools for managing images from Docker. Um everyone uses their um desktop tools for for building and managing your containers and then you can push them right to the best container registry for managing Docker Images, which is the jay frog platform. And just like Docker has free tools available for developers to use. We have a free tier which integrates nicely what their offerings and one of the things which we collaborate with them on is for anybody using our free tier in the cloud. Um there's there's no limits on the Docker images. You can pull no rate limiting, no throttling. So it just makes a clean seamless developer experience to to manage your cloud native projects and applications. >>What's the role of the container registry in cloud NATO? You brought that up? But can you just expand on that point? >>Yeah. So I think when you when you're doing deployments to production, you want to make sure both that you have the best security so that you're making sure that you're scanning and checking for vulnerabilities in your application and also that you have a complete um traceability. Basically you need a database in a log of everything you're pushing out to production. So what container registries allow you to do is um they keep all of the um releases all of the Docker images which are pushing out. You can go back and roll back to a previous version. You can see exactly what's included in those Docker images. And we jay frog, we have a product called X ray which does deep scanning of container images. So it'll go into the Docker Image, it'll go into any packages installed, it'll go into application libraries and it does kind of this onion peel apart of your entire document image to figure out exactly what you're using. Are there any vulnerabilities? And the funny thing about about Docker Images is um because of the number of libraries and packages and installed things which you haven't given Docker Image. If you just take your released Docker Image and let it sit on the shelf for a month, you have thousands of vulnerabilities, just just buy it um, by accruing from different reported zero day vulnerabilities over time. So it's extremely important that you, you know what those are, you can evaluate the risk to your organization and then mitigated as quickly as possible. If there is anything which could impact your customers, >>you bring up a great point right there and that is ultimately a developer thing that's been, that's generational, you know what generation you come from and that's always the problem getting the patches in the old days, getting a new code updated now when you have cloud native, that's more important than ever. And I also want to get your thoughts on this because you guys have been early on shift left two years ago, shift left was not it was not a new thing for you guys ever. So you got shift left building security at the point of coding, but you're bringing up a whole another thing which is okay automation. How do you make it? So the developments nothing stop what they're doing and then get back and say, okay, what's out there and my containers. So so how do you simplify that role? Because that's where the partnership, I think really people are looking to you guys and Dakar on is how do you make my life easier? Bottom line, what's it, what's it, what's it about? >>Yeah. So I I think when you when you're looking at trying to manage um large applications which are deployed to big kubernetes clusters and and how you have kind of this, this um all this infrastructure behind it. One of the one of the challenges is how do you know what you have that in production? Um So what, how do you know exactly what's released and what dependencies are out there and how easily can you trace those back? Um And one of the things which we're gonna be talking about at um swamp up next week is managing the overall devops lifecycle from code all the way through to production. Um And we we have a great platform for doing package management for doing vulnerability scanning, for doing um ci cd but you you need a bunch of other tools too. So you need um integrations like docker so you can get trusted packages into your system. You need integrations with observe ability tools like data, dog, elastic and you need it some tools for doing incident management like Patriot duty. And what we've, what we've built out um is we built out an ecosystem of partner integrations which with the J frog platform at the center lets you manage your entire and and life cycle of um devops infrastructure. And this this addresses security. It addresses the need to do quick patches and fixes and production and it kind of stitches together all the tools which all of the successful companies are using to manage their fast moving continuous release cycle, um and puts all that information together with seamless integration with even developer tools which um which folks are using on a day to day basis, like slack jeer A and M. S. Teams. >>So the bottom line then for the developer is you take the best of breed stuff and put it, make it all work together easily. That right? >>Yeah. I mean it's like it's seamless from you. You've got an incidents, you click a button, it sticks Ajira ticket in for you to resolve. Um you can tie that with the code, commits what you're doing and then directly to the security vulnerability which is reported by X ray. So it stitches all these different tools and technologies together for a for a seamless developer experience. And I think the great relationship we have with Docker um offers developers again, this this best in class container management um and trusted images combined with the world's best container registry. >>Awesome. Well let's get into that container issue products. I think that's the fascinating and super important thing that you guys solve a big problem for. So I gotta ask you, what are the security risks of using unverified and outdated Docker containers? Could you share your thoughts on what people should pay attention to because if they got unverified and outdated Docker containers, you mentioned vulnerabilities. What are those specific risks to them? >>Yeah, so I there's there's a lot of um different instances where you can see in the news or even some of the new government mandates coming out that um if you're not taking the right measures to secure your production applications and to patch critical vulnerabilities and libraries you're using, um you end up with um supply chain vulnerability risks like what happened to solar winds and what's been fueling the recent government mandates. So I think there's a there's a whole class of of different vulnerabilities which um bad actors can exploit. It can actually go quite deep with um folks um exploiting application software. Neither your your company or in other people's systems with with the move to cloud native, we also have heavily interconnected systems with a lot of different attack points from the container to the application level to the operating system level. So there's multiple different attack vectors for people to get into your software. And the best defense is an organization against security. Vulnerabilities is to know about them quickly and to mitigate them and fix them in production as quickly as possible. And this requires having a fast continuous deployment strategy for how you can update your code quickly, very quick identification of vulnerabilities with tools like X ray and other security scanning tools, um and just just good um integration with tools developers are using because at the end of the day it's the developers who both are picking the libraries and dependencies which are gonna be pushed into production and also they're the ones who have to react and and fix it when there's a uh production incident, >>you know, machine learning and automation. And it's always, I love that tech because it's always kind of cool because it's it's devops in action, but you know, it's it's not like a silver bullet, your machine, your machine learning is only as good as your your data and the code is written on staying with automation. You're not automating the right things or or wrong things. It's all it's all subjective based on what you're doing and you know Beauty's in the eye of the beholder when you do things like that. So I wanna hear your thoughts on on automation because that's really been a big part of the story here, both on simplicity and making the load lighter for developers. So when you have to go out and look at modifying code updates and looking at say um unverified containers or one that gets a little bit of a hair on it with with with more updates that are needed as we say, what do you what's the role of automation? How do you guys view that and how do you talk to the developers out there when posturing for a strategy on and a playbook for automation? >>Yeah, I think you're you're touching on one of the most critical parts of of any good devops um platform is from end to end. Everything should be automated with the right quality gates inserted at different points so that if there's a um test failure, if you have a build failure, if you have a security vulnerability, the the automatic um points in there will be triggered so that your release process will be stopped um that you have automated rollbacks in production um so that you can make sure that their issues which affect your customers, you can quickly roll back and once you get into production um having the right tools for observe ability so that you can actually sift through what is a essentially a big data problem. So with large systems you get so much data coming back from your application, from the production systems, from all these different sources that even an easy way to sift through and identify what are the messages coming back telling you that there's a problem that there's a real issue that you need to address versus what's just background noise about different different processes or different application alerts, which really don't affect the security of the functionality of your applications. So I think this this end to end automation gives you the visibility and the single pane of glass to to know how to manage and diagnose your devops infrastructure. >>You know, steve you bring up a great point. I love this conversation because it always highlights to me why I love uh Coop Con and Cloud Native con part of the C N C F and dr khan, because to me it's like a microcosm of two worlds that are living together. Right? You got I think Coop khan has proven its more operated but not like operator operator, developer operators. And you got dr khan almost pure software development, but now becoming operators. So you've got that almost those two worlds are fusing together where they are running together. You have operating concerns like well the Parachute open, will it work? And how do I roll back these roll back? These are like operating questions that now developers got to think about. So I think we're seeing this kind of confluence of true devops next level where you can't you can be just a developer and have a little bit of opposite you and not be a problem. Right? Or or get down under the under the hood and be an operator whenever you want. So they're seeing a flex. What's your thoughts on this is just more about my observation kind of real time here? >>Yeah, so um I think it's an interesting, obviously observation on the industry and I think you know, I've been doing DEVOPS for for a long time now and um I started as a developer who needed to push to production, needed to have the ability to to manage releases and packages and be able to automate everything. Um and this naturally leads you on a path of doing more operations, being able to manage your production, being able to have fewer incidents and issues. Um I think DEVOPS has evolved to become a very complicated um set of tools and problems which it solves and even kubernetes as an example. Um It's not easy to set up like setting up a kubernetes cluster and managing, it is a full time job now that said, I think what you're seeing now is more and more companies are shifting back to developers as a focus because teams and developers are the kingmakers ends with the rise of cloud computing, you don't need a full operations team, you don't need a huge infrastructure stack, you can you can easily get set up in the cloud on on amazon google or as your and start deploying today to production from from a small team straight from code to production. And I think as we evolve and as we get better tools, simpler ways of managing your deployments of managing your packages, this makes it possible for um development teams to do that entire site lifecycle from code through to production with good quality checks with um good security and also with the ability to manage simple production incidents all by themselves. So I think that's that's coming where devoPS is shifting back to development teams. >>It's great to have your leadership and your experience. All right there. That's a great call out, great observation, nice gym there. I think that's right on. I think to get your thoughts if you don't mind going next level because you're, you're nailing what I see is the successful companies having these teams that could be and and workflows and have a mix of a team. I was talking about Dana Lawson who was the VP of engineering get up and she and I were riffing on this idea that you don't have to have a monolithic team because you've got you no longer have a monolithic environment. So you have this microservices and now you can have these, I'm gonna call micro teams, but you're starting to see an SRE on the team, that's the developer. Right? So this idea of having an SRE department maybe for big companies, that could be cool if you're hyper scalar, but these development teams are having certain formations. What's your observation to your customer base in terms of how your customers are organizing? Because I think you nailed the success form of how teams are executing because it's so much more agile, you get the reliability, you need to have security baked in, you want end to end visibility because you got services starting and stopping. How are teams? How are you seeing developers? What's the state of the art in your mind for formation? >>Yeah, so I think um we we work with a lot of the biggest companies who were really at the bleeding edge of innovation and devoPS and continuous delivery. And when you look at those teams, they have, they have very, very small teams, um supporting thousands of developers teams um building and deploying applications. So um when you think of of SRE and deVOPS focus there is actually a very small number of those folks who typically support humongous organizations and I think what we're hearing from them is their increasingly getting requirements from the teams who want to be self service, right? They want to be able to take their applications, have simple platforms to deploy it themselves to manage things. Um They don't they don't want to go through heavy way processes, they wanted to be automated and lightweight and I think this is this is putting pressure on deVOPS teams to to evolve and to adopt more platforms and services which allow developers to to do things themselves. And I think over time um this doesn't this doesn't get rid of the need for for devops and for SRE roles and organizations but it it changes because now they become the enablers of success and good development teams. It's it's kind of like um like how I. T. Organizations they support you with automated rollouts with all these tools rather than in person as much as they can do with automation. Um That helps the entire organization. I think devops is becoming the same thing where they're now simplifying and automating how developers can be self service and organizations. >>And I think it's a great evolution to because that makes total sense because it is kind of like what the I. T. Used to do in the old days but its the scale is different, the services are different, the deVOPS tools are different and so they really are enabling not just the cost center there really driving value. Um and this brings up the whole next threat. I'd love to get your thoughts because you guys are, have been doing this for developers for a while. Tools versus platform because you know, this whole platform where we're a platform were control plane, there's still a need for tooling for developers. How do we thread the needle between? What's, what's good for a tool? What's good for a platform? >>Yeah, So I I think that um, you know, there's always a lot of focus and it's, it's easier if you can take an end to end platform, which solves a bunch of different use cases together. But um, I I think a lot of folks, um, when you're looking at what you need and how you want to apply, um, devops practices to your organization, you ideally you want to be able to use best in breed tools to be able to solve exactly what your use cases. And this is one of the reasons why as a company with jay frog, we we try to be as open as possible to integrations with the entire vendor ecosystem. So um, it doesn't matter what ci cd tool you're using, you could be using Jenkins circle, ci spinnaker checked on, it doesn't matter what observe ability platform you're using in production, it doesn't matter what um tools you're using for collaboration. We, we support that whole ecosystem and we make it possible for you to select the the best of breed tools and technologies that you need to be successful as an organization. And I think the risk is if, if you, if you kind of accept vendor lock in on a single platform or or a single cloud platform even um then you're, you're not getting the best in breed tools and technologies which you need to stay ahead of the curve and devops is a very, very fast moving um, um, discipline along with all the cloud native technologies which you use for application development and for production. So if you're, if you're not staying at the bleeding edge and kind of pushing things forward, then you're then you're behind and if you're behind, you're not be able to keep up with the releases, the deployments, you need to be secure. So I think what you see is the leading organizations are pushing the envelope on on security, on deployment and they're they're using the best tools in the industry to make that happen. >>Stephen great to have you on the cube. I want to just get your thoughts on jay frog and the doctor partnership to wrap this up. Could you take them in to explain what's the most important thing that developers should pay attention to when it comes to security for Docker images? >>Yeah. So I think when you're when you're developer and you're looking at your your security strategy, um you want tools that help you that come to you and that help you. So you want things which are going to give you alerts in your I. D. With things which are going to trigger your in your Ci cd and your build process. And we should make it easy for you to identify mitigate and release um things which will help you do that. So we we provide a lot of those tools with jay frog and our doctor partnership. And I think if you if you look at our push towards helping developers to become more productive, build better applications and more secure applications, this is something the entire industry needs for us to address. What's increasingly a risk to software development, which is a higher profile vulnerabilities, which are affecting the entire industry. >>Great stuff. Big fan of jay frog watching you guys be so successful, you know, making things easy for developers is uh, and simpler and reducing the steps it takes to do things as a, I say, is the classic magic formula for any company, Make it easier, reduce the steps it takes to do something and make it simple. Um, good success formula. Great stuff. Great to have you on um for a minute or two, take a minute to plug what's going on in jay frog and share what's the latest increase with the company, what you guys are doing? Obviously public company. Great place to work, getting awards for that. Give the update on jay frog, put a plug in. >>Yeah. And also dr Frog, I've been having a lot of fun working at J frog, it's very, very fast growing. We have a lot of awesome announcements at swamp up. Um like the partnerships were doing um secure release bundles for deployments and just just a range of advances. I think the number of new features and innovation we put into the product in the past six months since I. P. O. Is astounding. So we're really trying to push the edge on devops um and we're also gonna be announcing and talking about stuff that dr khan as well and continue to invest in the cloud native and the devops ecosystem with our support of the continuous delivery foundation and the C. N C F, which I'm also heavily involved in. So it's it's exciting time to be in the devoPS industry and I think you can see that we're really helping software developers to improve their art to become better, better at release. Again, managing production applications >>and the ecosystem is just flourishing. It's only the beginning and again Making bring the craft back in Agile, which is a super big theme this year. Stephen. Great, great to see you. Thanks for dropping those gems and insights here on the Cube here at Dr. 2021 virtual. Thanks for coming on. >>Yeah. Thank you john. >>Okay. Dr. 2020 coverage virtual. I'm John for your host of the Cube. Thanks for watching. Mhm. Mhm. Yeah.