(upbeat music) >> Hello, welcome to the Cube's coverage of International Women's Day 2023. I'm John Furrier, host of the CUBE series of profiles around leaders in the tech industry sharing their stories, advice, best practices, what they're doing in their jobs their vision of the future, and more importantly, passing it on and encouraging more and more networking and telling the stories that matter. Our next guest is a great executive leader talking about how to lead in challenging times. Krista Satterthwaite, who is Senior Vice President and GM of Mainstream Compute. Krista great to see you're Cube alumni. We've had you on before talking about compute power. And by the way, congratulations on your BPT and Black Professional Tech Network 2023 Black Tech Exec of the Year Award. >> Thank you very much. Appreciate it. And thanks for having me. >> I knew I liked you the first time we were doing interviews together. You were so smart and so on top of it. Thanks for coming on. >> No problem. >> All kidding aside, let's get into it. You know, one of the things that's coming out on these interviews is leadership is being showcased and there's a network effect happening in the industry and you're starting to see people look and hear stories that they may or may not have heard before or news stories are coming out. So, one of the things that's interesting is that also in the backdrop of post pandemic, there's been a turn in the industry a little bit, there's a little bit of headwind in certain areas, some tailwinds in cloud and other areas. Compute, your area is doing very well. It could be challenging. And as a leader, has the conversation changed? And where are you at right now in the network of folks you're working with? What's the mood? >> Yeah, so actually I, things are much better. Obviously we had a chip shortage last year. Things are much, much better. But I learned a lot when it came to going through challenging times and leadership. And I think when we talk to customers, a lot of 'em are in challenging situations. Sometimes it's budget, sometimes it's attracting and retaining talent and sometimes it's just demands because, it's really exciting that technology is behind everything. But that means the demands on IT are bigger than ever before. So what I find when it comes to challenging times is that there's really three qualities that are game changers when it comes to leading and challenging times. And the first one is positivity. People have to feel like there's a light at the end of the tunnel to make sure that, their attitudes stay up, that they stay working really really hard and they look to the leader for that. The second one is communication. And I read somewhere that communication is leadership. And we had a great example from our CEO Antonio Neri when the pandemic hit and everything shut down. He had an all employee meeting every week for a month and we have tens of thousands of employees. And then even after that month, we had 'em very regularly. But he wanted to make sure that everybody heard from, him his thoughts had all the updates, knew how their peers were doing, how we were helping customers. And I really learned a lot from that in terms of communicating and communicating more during tough times. And then I would say the third one is making sure that they are informed and they feel empowered. So I would say a leader who is able to do that really, really stands out in a challenging time. >> So how do you get yourself together? Obviously you the chip shortage everyone knows in the industry and for the folks not in the tech industry, it was an economic potential disaster, because you don't get the chips you need. You guys make servers and technology, chips power everything. If you miss a shipment, it could cause a lot of backlash. So Cisco had an earnings impact. It has impact to the business. When do you have that code red moment where it's like, okay, we have to kind of put the pause and go into emergency mode. And how do you handle that? >> Well, you know, it is funny 'cause when it, when we have challenges, I come to learn that people can look at challenges and hard work as a burden or a mission and they behave totally different. If they see it as a burden, then they're doing the bare minimum and they're pointing fingers and they're complaining and they're probably not getting a whole lot done. If they see it as a mission, then all of a sudden they're going above and beyond. They're working really hard, they're really partnering. And if it affects customers for HPE, obviously we, HPE is a very customer centric company, so everyone pays attention and tries to pitch in. But when it comes to a mission, I started thinking, what are the real ingredients for a mission? And I think it's important. I think it's, people feel like they can make an impact. And then I think the third one is that the goal is clear, even if the path isn't, 'cause you may have to pivot a lot if it's a challenge. And so when it came to the chip shortage, it was a mission. We wanted to make sure that we could ship to customers as quickly as possible. And it was a mission. Everybody pulled together. I learned how much our team could pull off and pull together through that challenge. >> And the consequences can be quantified in economics. So it's like the burn the boats example, you got to burn the boats, you're stuck. You got to figure out a solution. How does that change the demands on people? Because this is, okay, there's a mission it they're not, it's not normal. What are some of those new demands that arise during those times and how do you manage that? How do you be a leader? >> Yeah, so it's funny, I was reading this statement from James White who used to be the CEO of Jamba Juice. And he was talking about how he got that job. He said, "I think it was one thing I said that really convinced them that I was the right person." And what he said was something like, "I will get more out of people than nine out of 10 leaders on the planet." He said, "Because I will look at their strengths and their capabilities and I will play to their passions." and their capabilities and I will play their passions. and getting the most out people in difficult times, it is all about how much you can get out of people for their own sake and for the company's sake. >> That's great feedback. And to people watching who are early in their careers, leading is getting the best out of your team, attitude. Some of the things you mentioned. What advice would you give folks that are starting to get into the workforce, that are starting to get into that leadership track or might have a trajectory or even might have an innate ability that they know they have and they want to pursue that dream? >> Yeah so. >> What advice would you give them? >> Yeah, what I would say, I say this all the time that, for the first half of my career I was very job conscious, but I wasn't very career conscious. So I'd get in a role and I'd stay in that role for long periods of time and I'd do a good job, but I wasn't really very career conscious. And what I would say is, everybody says how important risk taking is. Well, risk taking can be a little bit of a scary word, right? Or term. And the way I see it is give it a shot and see what happens. You're interested in something, give it a shot and see what happens. It's kind of a less intimidating way of looking at risk because even though I was job conscious, and not career conscious, one thing I did when people asked me to take something on, hey Krista, would you like to take on more responsibility here? The answer was always yes, yes, yes, yes. So I said yes because I said, hey I'll give it a shot and see what happens. And that helped me tremendously because I felt like I am giving it a try. And the more you do that, the the better it is. >> It's great. >> And actually the the less scary it is because you do that, a few times and it goes well. It's like a muscle that builds. >> It's funny, a woman executive was on the program. I said, the word balance comes up a lot. And she stopped and said, "Let's just talk about balance for a second." And then she went contrarian and said, "It's about not being unbalanced. It's about being, taking a chance and being a little bit off balance to put yourself outside your comfort zone to try new things." And then she also came up and followed and said, "If you do that alone, you increase your risk. But if you do it with people, a team that you trust and you're authentic and you're vulnerable and you're communicating, that is the chemistry." And that was a really good point. What's your reaction? 'Cause you were talking about authentic conversations good communications with Antonio. How does someone get, feel, find that team and do you agree with it? And what was your, how would you react to that? >> Yes, I agree with that. And when it comes to being authentic, that's the magic and when someone isn't, if someone's not really being themselves, it's really funny because you can feel it, you can sense it. There's kind of a wall between you and them. And over time people won't be able to put their finger on it, but they'll feel a distance from you. But when you're authentic and you share who you are, what you find is you find things in common with other people. 'Cause you're sharing more of who you are and it's like, oh, I do that too. Oh, I'm interested in that too. And build the bonds between people and the authenticity. And that's what people crave. They want people to be authentic and people can tell when you're authentic and when you're not. >> Is managing and leading through a crisis a born talent or can you learn it? >> Oh, definitely learned. I think that we're born knowing nothing and I once read people are nurtured into greatness and I think that's true. So yeah, definitely learned. >> What are some examples that can come out of a tough time as folks may look at a crisis and be shy away from it? How do they lean into it? What advice would you give folks? How do you handle it? I mean, everyone's got different personality. Okay, they get to a position but stepping through that door. >> Yeah, well, I do this presentation called, "10 things I Wish I Knew Earlier in my Career." And one of those things is about the growth mindset and the growth mindset. There's a book called "Mindset" by Carol Dweck and the growth mindset is all about learning and not always having to know everything, but really the winning is in the learning. And so if you have a growth mindset it makes you feel better about everything because you can't lose. You're winning because you're learning. So when I've learned that, I started looking at things much differently. And when it comes to going through tough times, what I find is you're exercising muscles that you didn't even know you had, which makes you stronger when the crisis is over, obviously. And I also feel like you become a lot a much more creative when you're in challenging times. You're forced to do things that you hadn't had to do before. And it also bonds the team. It's almost like going through bootcamp together. When you go through a challenge together it bonds you for life. >> I mean, you could have bonding, could be trauma bonding or success bonding. People love to be on the success side because that's positive and that's really the key mindset. You're always winning if you have that attitude. And learnings is also positive. So it's not, it's never a failure unless you make it. >> That's right, exactly. As long as you learn from it. And that's the name of the game. So, learning is the goal. >> So I have to ask you, on your job now, you have a really big responsibility HPE compute and big division. What's the current mindset that you have right now in your career, where you're at? What are some of the things on your mind that you think about? We had other, other seniors leaders say, hey, you know I got the software as my brain and the hardware's my body. I like to keep software and hardware working together. What is your current state of your career and how you looking at it, what's next and what's going on in your mind right now? >> Yeah, so for me, I really want to make sure that for my team we're nurturing the next generation of leadership and that we're helping with career development and career growth. And people feel like they can grow their careers here. Luckily at HPE, we have a lot of people stay at HPE a long time, and even people who leave HPE a lot of times they come back because the culture's fantastic. So I just want to make sure I'm contributing to that culture and I'm bringing up the next generation of leaders. >> What's next for you? What are you looking at from a career personal standpoint? >> You know, it's funny, I, I love what I'm doing right now. I'm actually on a joint venture board with H3C, which is HPE Joint Venture Company. And so I'm really enjoying that and exploring more board service opportunities. >> You have a focus of good growth mindset, challenging through, managing through tough times. How do you stay focused on that North star? How do you keep the reinforcement of the mission? How do you nurture the team to greatness? >> Yeah, so I think it's a lot of clarity, providing a lot of clarity about what's important right now. And it goes back to some of the communication that I mentioned earlier, making sure that everybody knows where the North Star is, so everybody's focused on the same thing, because I feel like with the, I always felt like throughout my career I was set up for success if I had the right information, the right guidance and the right goals. And I try to make sure that I do that with my team. >> What are some of the things that you could share as we wrap up here for the folks watching, as the networks increase, as the stories start to unfold more and more on digital like we're doing here, what do you hope people walk away with? What's working, what needs work, and what is some things that people aren't talking about that should be discussed publicly? >> Do you mean from a career standpoint or? >> For career? For growing into tech and into leadership positions. >> Okay. >> Big migration tech is now a wide field. I mean, when I grew up, broke into the eighties, it was computer science, software engineering, and three degrees in engineering, right? >> I see huge swath of AI coming. So many technical careers. There's a lot more women. >> Yeah. And that's what's so exciting about being in a technical career, technical company, is that everything's always changing. There's always opportunity to learn something new. And frankly, you know, every company is in the business of technology right now, because they want to closer to their customers. Typically, they're using technology to do that. Everyone's digitally transforming. And so what I would say is that there's so much opportunity, keep your mind open, explore what interests you and keep learning because it's changing all the time. >> You know I was talking with Sue, former HP, she's on a lot of boards. The balance at the board level still needs a lot of work and the leaderships are getting better, but the board at the seats at the table needs work. Where do you see that transition for you in the future? Is that something on your mind? Maybe a board seat? You mentioned you're on a board with HPE, but maybe sitting on some other boards? Any, any? >> Yes, actually, actually, we actually have a program here at HPE called the Board Ready Now program that I'm a part of. And so HPE is very supportive of me exploring an independent board seat. And so they have some education and programming around that. And I know Sue well, she's awesome. And so yes, I'm looking into those opportunities right now. >> She advises do one no more than two. The day job. >> Yeah, I would only be doing one current job that I have. >> Well, kris, it was great to chat with you about these topics and leadership and challenging times. Great masterclass, great advice. As SVP and GM of mainstream compute for HPE, what's going on in your job these days? What's the most exciting thing happening? Share some of your work situations. >> Sure, so the most exciting thing happening right now is HPE Gen 11, which we just announced and started shipping, brings tremendous performance benefit, has an intuitive operating experience, a trusted security by design, and it's optimized to run workloads so much faster. So if anybody is interested, they should go check it out on hpe.com. >> And of course the CUBE will be at HPE Discover. We'll see you there. Any final wisdom you'd like to share as we wrap up the last minute here? >> Yeah, so I think the last thing I'll say is that when it comes to setting your sights, I think, expecting it, good things to happen usually happens when you believe you deserve it. So what happens is you believe you deserve it, then you expect it and you get it. And so sometimes that's about making sure you raise your thermostat to expect more. And I always talk about you don't have to raise it all up at once. You could do that incrementally and other people can set your thermostat too when they say, hey, you should be, you should get a level this high or that high, but raise your thermostat because what you expect is what you get. >> Krista, thank you so much for contributing to this program. We're going to do it quarterly. We're going to do getting more stories out there, so we'll have you back and if you know anyone with good stories, send them our way. And congratulations on your BPTN Tech Executive of the Year award for 2023. Congratulations, great prize there and great recognition for your hard work. >> Thank you so much, John, I appreciate it. >> Okay, this is the Cube's coverage of National Woodman's Day. I'm John Furrier, stories from the front lines, management ranks, developers, all there, global coverage of international events with theCUBE. Thanks for watching. (soft music)

(bright upbeat music) >> Hello, everyone. Welcome to SiliconANGLE News breaking story here. Amazon Web Services expanding their relationship with Hugging Face, breaking news here on SiliconANGLE. I'm John Furrier, SiliconANGLE reporter, founder, and also co-host of theCUBE. And I have with me, Swami, from Amazon Web Services, vice president of database, analytics, machine learning with AWS. Swami, great to have you on for this breaking news segment on AWS's big news. Thanks for coming on and taking the time. >> Hey, John, pleasure to be here. >> You know- >> Looking forward to it. >> We've had many conversations on theCUBE over the years, we've watched Amazon really move fast into the large data modeling, SageMaker became a very smashing success, obviously you've been on this for a while. Now with ChatGPT OpenAI, a lot of buzz going mainstream, takes it from behind the curtain inside the ropes, if you will, in the industry to a mainstream. And so this is a big moment, I think, in the industry, I want to get your perspective, because your news with Hugging Face, I think is another tell sign that we're about to tip over into a new accelerated growth around making AI now application aware, application centric, more programmable, more API access. What's the big news about, with AWS Hugging Face, you know, what's going on with this announcement? >> Yeah. First of all, they're very excited to announce our expanded collaboration with Hugging Face, because with this partnership, our goal, as you all know, I mean, Hugging Face, I consider them like the GitHub for machine learning. And with this partnership, Hugging Face and AWS, we'll be able to democratize AI for a broad range of developers, not just specific deep AI startups. And now with this, we can accelerate the training, fine tuning and deployment of these large language models, and vision models from Hugging Face in the cloud. And the broader context, when you step back and see what customer problem we are trying to solve with this announcement, essentially if you see these foundational models, are used to now create like a huge number of applications, suggest like tech summarization, question answering, or search image generation, creative, other things. And these are all stuff we are seeing in the likes of these ChatGPT style applications. But there is a broad range of enterprise use cases that we don't even talk about. And it's because these kind of transformative, generative AI capabilities and models are not available to, I mean, millions of developers. And because either training these elements from scratch can be very expensive or time consuming and need deep expertise, or more importantly, they don't need these generic models, they need them to be fine tuned for the specific use cases. And one of the biggest complaints we hear is that these models, when they try to use it for real production use cases, they are incredibly expensive to train and incredibly expensive to run inference on, to use it at a production scale. So, and unlike web search style applications, where the margins can be really huge, here in production use cases and enterprises, you want efficiency at scale. That's where Hugging Face and AWS share our mission. And by integrating with Trainium and Inferentia, we're able to handle the cost efficient training and inference at scale, I'll deep dive on it. And by teaming up on the SageMaker front, now the time it takes to build these models and fine tune them is also coming down. So that's what makes this partnership very unique as well. So I'm very excited. >> I want to get into the time savings and the cost savings as well on the training and inference, it's a huge issue, but before we get into that, just how long have you guys been working with Hugging Face? I know there's a previous relationship, this is an expansion of that relationship, can you comment on what's different about what's happened before and then now? >> Yeah. So, Hugging Face, we have had a great relationship in the past few years as well, where they have actually made their models available to run on AWS, you know, fashion. Even in fact, their Bloom Project was something many of our customers even used. Bloom Project, for context, is their open source project which builds a GPT-3 style model. And now with this expanded collaboration, now Hugging Face selected AWS for that next generation office generative AI model, building on their highly successful Bloom Project as well. And the nice thing is, now, by direct integration with Trainium and Inferentia, where you get cost savings in a really significant way, now, for instance, Trn1 can provide up to 50% cost to train savings, and Inferentia can deliver up to 60% better costs, and four x more higher throughput than (indistinct). Now, these models, especially as they train that next generation generative AI models, it is going to be, not only more accessible to all the developers, who use it in open, so it'll be a lot cheaper as well. And that's what makes this moment really exciting, because we can't democratize AI unless we make it broadly accessible and cost efficient and easy to program and use as well. >> Yeah. >> So very exciting. >> I'll get into the SageMaker and CodeWhisperer angle in a second, but you hit on some good points there. One, accessibility, which is, I call the democratization, which is getting this in the hands of developers, and/or AI to develop, we'll get into that in a second. So, access to coding and Git reasoning is a whole nother wave. But the three things I know you've been working on, I want to put in the buckets here and comment, one, I know you've, over the years, been working on saving time to train, that's a big point, you mentioned some of those stats, also cost, 'cause now cost is an equation on, you know, bundling whether you're uncoupling with hardware and software, that's a big issue. Where do I find the GPUs? Where's the horsepower cost? And then also sustainability. You've mentioned that in the past, is there a sustainability angle here? Can you talk about those three things, time, cost, and sustainability? >> Certainly. So if you look at it from the AWS perspective, we have been supporting customers doing machine learning for the past years. Just for broader context, Amazon has been doing ML the past two decades right from the early days of ML powered recommendation to actually also supporting all kinds of generative AI applications. If you look at even generative AI application within Amazon, Amazon search, when you go search for a product and so forth, we have a team called MFi within Amazon search that helps bring these large language models into creating highly accurate search results. And these are created with models, really large models with tens of billions of parameters, scales to thousands of training jobs every month and trained on large model of hardware. And this is an example of a really good large language foundation model application running at production scale, and also, of course, Alexa, which uses a large generator model as well. And they actually even had a research paper that showed that they are more, and do better in accuracy than other systems like GPT-3 and whatnot. So, and we also touched on things like CodeWhisperer, which uses generative AI to improve developer productivity, but in a responsible manner, because 40% of some of the studies show 40% of this generated code had serious security flaws in it. This is where we didn't just do generative AI, we combined with automated reasoning capabilities, which is a very, very useful technique to identify these issues and couple them so that it produces highly secure code as well. Now, all these learnings taught us few things, and which is what you put in these three buckets. And yeah, like more than 100,000 customers using ML and AI services, including leading startups in the generative AI space, like stability AI, AI21 Labs, or Hugging Face, or even Alexa, for that matter. They care about, I put them in three dimension, one is around cost, which we touched on with Trainium and Inferentia, where we actually, the Trainium, you provide to 50% better cost savings, but the other aspect is, Trainium is a lot more power efficient as well compared to traditional one. And Inferentia is also better in terms of throughput, when it comes to what it is capable of. Like it is able to deliver up to three x higher compute performance and four x higher throughput, compared to it's previous generation, and it is extremely cost efficient and power efficient as well. >> Well. >> Now, the second element that really is important is in a day, developers deeply value the time it takes to build these models, and they don't want to build models from scratch. And this is where SageMaker, which is, even going to Kaggle uses, this is what it is, number one, enterprise ML platform. What it did to traditional machine learning, where tens of thousands of customers use StageMaker today, including the ones I mentioned, is that what used to take like months to build these models have dropped down to now a matter of days, if not less. Now, a generative AI, the cost of building these models, if you look at the landscape, the model parameter size had jumped by more than thousand X in the past three years, thousand x. And that means the training is like a really big distributed systems problem. How do you actually scale these model training? How do you actually ensure that you utilize these efficiently? Because these machines are very expensive, let alone they consume a lot of power. So, this is where SageMaker capability to build, automatically train, tune, and deploy models really concern this, especially with this distributor training infrastructure, and those are some of the reasons why some of the leading generative AI startups are actually leveraging it, because they do not want a giant infrastructure team, which is constantly tuning and fine tuning, and keeping these clusters alive. >> It sounds like a lot like what startups are doing with the cloud early days, no data center, you move to the cloud. So, this is the trend we're seeing, right? You guys are making it easier for developers with Hugging Face, I get that. I love that GitHub for machine learning, large language models are complex and expensive to build, but not anymore, you got Trainium and Inferentia, developers can get faster time to value, but then you got the transformers data sets, token libraries, all that optimized for generator. This is a perfect storm for startups. Jon Turow, a former AWS person, who used to work, I think for you, is now a VC at Madrona Venture, he and I were talking about the generator AI landscape, it's exploding with startups. Every alpha entrepreneur out there is seeing this as the next frontier, that's the 20 mile stairs, next 10 years is going to be huge. What is the big thing that's happened? 'Cause some people were saying, the founder of Yquem said, "Oh, the start ups won't be real, because they don't all have AI experience." John Markoff, former New York Times writer told me that, AI, there's so much work done, this is going to explode, accelerate really fast, because it's almost like it's been waiting for this moment. What's your reaction? >> I actually think there is going to be an explosion of startups, not because they need to be AI startups, but now finally AI is really accessible or going to be accessible, so that they can create remarkable applications, either for enterprises or for disrupting actually how customer service is being done or how creative tools are being built. And I mean, this is going to change in many ways. When we think about generative AI, we always like to think of how it generates like school homework or arts or music or whatnot, but when you look at it on the practical side, generative AI is being actually used across various industries. I'll give an example of like Autodesk. Autodesk is a customer who runs an AWS and SageMaker. They already have an offering that enables generated design, where designers can generate many structural designs for products, whereby you give a specific set of constraints and they actually can generate a structure accordingly. And we see similar kind of trend across various industries, where it can be around creative media editing or various others. I have the strong sense that literally, in the next few years, just like now, conventional machine learning is embedded in every application, every mobile app that we see, it is pervasive, and we don't even think twice about it, same way, like almost all apps are built on cloud. Generative AI is going to be part of every startup, and they are going to create remarkable experiences without needing actually, these deep generative AI scientists. But you won't get that until you actually make these models accessible. And I also don't think one model is going to rule the world, then you want these developers to have access to broad range of models. Just like, go back to the early days of deep learning. Everybody thought it is going to be one framework that will rule the world, and it has been changing, from Caffe to TensorFlow to PyTorch to various other things. And I have a suspicion, we had to enable developers where they are, so. >> You know, Dave Vellante and I have been riffing on this concept called super cloud, and a lot of people have co-opted to be multicloud, but we really were getting at this whole next layer on top of say, AWS. You guys are the most comprehensive cloud, you guys are a super cloud, and even Adam and I are talking about ISVs evolving to ecosystem partners. I mean, your top customers have ecosystems building on top of it. This feels like a whole nother AWS. How are you guys leveraging the history of AWS, which by the way, had the same trajectory, startups came in, they didn't want to provision a data center, the heavy lifting, all the things that have made Amazon successful culturally. And day one thinking is, provide the heavy lifting, undifferentiated heavy lifting, and make it faster for developers to program code. AI's got the same thing. How are you guys taking this to the next level, because now, this is an opportunity for the competition to change the game and take it over? This is, I'm sure, a conversation, you guys have a lot of things going on in AWS that makes you unique. What's the internal and external positioning around how you take it to the next level? >> I mean, so I agree with you that generative AI has a very, very strong potential in terms of what it can enable in terms of next generation application. But this is where Amazon's experience and expertise in putting these foundation models to work internally really has helped us quite a bit. If you look at it, like amazon.com search is like a very, very important application in terms of what is the customer impact on number of customers who use that application openly, and the amount of dollar impact it does for an organization. And we have been doing it silently for a while now. And the same thing is true for like Alexa too, which actually not only uses it for natural language understanding other city, even national leverages is set for creating stories and various other examples. And now, our approach to it from AWS is we actually look at it as in terms of the same three tiers like we did in machine learning, because when you look at generative AI, we genuinely see three sets of customers. One is, like really deep technical expert practitioner startups. These are the startups that are creating the next generation models like the likes of stability AIs or Hugging Face with Bloom or AI21. And they generally want to build their own models, and they want the best price performance of their infrastructure for training and inference. That's where our investments in silicon and hardware and networking innovations, where Trainium and Inferentia really plays a big role. And we can nearly do that, and that is one. The second middle tier is where I do think developers don't want to spend time building their own models, let alone, they actually want the model to be useful to that data. They don't need their models to create like high school homeworks or various other things. What they generally want is, hey, I had this data from my enterprises that I want to fine tune and make it really work only for this, and make it work remarkable, can be for tech summarization, to generate a report, or it can be for better Q&A, and so forth. This is where we are. Our investments in the middle tier with SageMaker, and our partnership with Hugging Face and AI21 and co here are all going to very meaningful. And you'll see us investing, I mean, you already talked about CodeWhisperer, which is an open preview, but we are also partnering with a whole lot of top ISVs, and you'll see more on this front to enable the next wave of generated AI apps too, because this is an area where we do think lot of innovation is yet to be done. It's like day one for us in this space, and we want to enable that huge ecosystem to flourish. >> You know, one of the things Dave Vellante and I were talking about in our first podcast we just did on Friday, we're going to do weekly, is we highlighted the AI ChatGPT example as a horizontal use case, because everyone loves it, people are using it in all their different verticals, and horizontal scalable cloud plays perfectly into it. So I have to ask you, as you look at what AWS is going to bring to the table, a lot's changed over the past 13 years with AWS, a lot more services are available, how should someone rebuild or re-platform and refactor their application of business with AI, with AWS? What are some of the tools that you see and recommend? Is it Serverless, is it SageMaker, CodeWhisperer? What do you think's going to shine brightly within the AWS stack, if you will, or service list, that's going to be part of this? As you mentioned, CodeWhisperer and SageMaker, what else should people be looking at as they start tinkering and getting all these benefits, and scale up their ups? >> You know, if we were a startup, first, I would really work backwards from the customer problem I try to solve, and pick and choose, bar, I don't need to deal with the undifferentiated heavy lifting, so. And that's where the answer is going to change. If you look at it then, the answer is not going to be like a one size fits all, so you need a very strong, I mean, granted on the compute front, if you can actually completely accurate it, so unless, I will always recommend it, instead of running compute for running your ups, because it takes care of all the undifferentiated heavy lifting, but on the data, and that's where we provide a whole variety of databases, right from like relational data, or non-relational, or dynamo, and so forth. And of course, we also have a deep analytical stack, where data directly flows from our relational databases into data lakes and data virus. And you can get value along with partnership with various analytical providers. The area where I do think fundamentally things are changing on what people can do is like, with CodeWhisperer, I was literally trying to actually program a code on sending a message through Twilio, and I was going to pull up to read a documentation, and in my ID, I was actually saying like, let's try sending a message to Twilio, or let's actually update a Route 53 error code. All I had to do was type in just a comment, and it actually started generating the sub-routine. And it is going to be a huge time saver, if I were a developer. And the goal is for us not to actually do it just for AWS developers, and not to just generate the code, but make sure the code is actually highly secure and follows the best practices. So, it's not always about machine learning, it's augmenting with automated reasoning as well. And generative AI is going to be changing, and not just in how people write code, but also how it actually gets built and used as well. You'll see a lot more stuff coming on this front. >> Swami, thank you for your time. I know you're super busy. Thank you for sharing on the news and giving commentary. Again, I think this is a AWS moment and industry moment, heavy lifting, accelerated value, agility. AIOps is going to be probably redefined here. Thanks for sharing your commentary. And we'll see you next time, I'm looking forward to doing more follow up on this. It's going to be a big wave. Thanks. >> Okay. Thanks again, John, always a pleasure. >> Okay. This is SiliconANGLE's breaking news commentary. I'm John Furrier with SiliconANGLE News, as well as host of theCUBE. Swami, who's a leader in AWS, has been on theCUBE multiple times. We've been tracking the growth of how Amazon's journey has just been exploding past five years, in particular, past three. You heard the numbers, great performance, great reviews. This is a watershed moment, I think, for the industry, and it's going to be a lot of fun for the next 10 years. Thanks for watching. (bright music)

(upbeat music) >> Hello and welcome back to theCUBE. We're here in Palo Alto, California. I'm your host, John Furrier with a special guest here in the studio. As part of our Cloud Native SecurityCon Coverage we had an opportunity to bring in Jon Turow who is the partner at Madrona Venture Partners formerly with AWS and to talk about machine learning, foundational models, and how the future of AI is going to be impacted by some of the innovation around what's going on in the industry. ChatGPT has taken the world by storm. A million downloads, fastest to the million downloads there. Before some were saying it's just a gimmick. Others saying it's a game changer. Jon's here to break it down, and great to have you on. Thanks for coming in. >> Thanks John. Glad to be here. >> Thanks for coming on. So first of all, I'm glad you're here. First of all, because two things. One, you were formerly with AWS, got a lot of experience running projects at AWS. Now a partner at Madrona, a great firm doing great deals, and they had this future at modern application kind of thesis. Now you are putting out some content recently around foundational models. You're deep into computer vision. You were the IoT general manager at AWS among other things, Greengrass. So you know a lot about data. You know a lot about some of this automation, some of the edge stuff. You've been in the middle of all these kind of areas that now seem to be the next wave coming. So I wanted to ask you what your thoughts are of how the machine learning and this new automation wave is coming in, this AI tools are coming out. Is it a platform? Is it going to be smarter? What feeds AI? What's your take on this whole foundational big movement into AI? What's your general reaction to all this? >> So, thanks, Jon, again for having me here. Really excited to talk about these things. AI has been coming for a long time. It's been kind of the next big thing. Always just over the horizon for quite some time. And we've seen really compelling applications in generations before and until now. Amazon and AWS have introduced a lot of them. My firm, Madrona Venture Group has invested in some of those early players as well. But what we're seeing now is something categorically different. That's really exciting and feels like a durable change. And I can try and explain what that is. We have these really large models that are useful in a general way. They can be applied to a lot of different tasks beyond the specific task that the designers envisioned. That makes them more flexible, that makes them more useful for building applications than what we've seen before. And so that, we can talk about the depths of it, but in a nutshell, that's why I think people are really excited. >> And I think one of the things that you wrote about that jumped out at me is that this seems to be this moment where there's been a multiple decades of nerds and computer scientists and programmers and data thinkers around waiting for AI to blossom. And it's like they're scratching that itch. Every year is going to be, and it's like the bottleneck's always been compute power. And we've seen other areas, genome sequencing, all kinds of high computation things where required high forms computing. But now there's no real bottleneck to compute. You got cloud. And so you're starting to see the emergence of a massive acceleration of where AI's been and where it needs to be going. Now, it's almost like it's got a reboot. It's almost a renaissance in the AI community with a whole nother macro environmental things happening. Cloud, younger generation, applications proliferate from mobile to cloud native. It's the perfect storm for this kind of moment to switch over. Am I overreading that? Is that right? >> You're right. And it's been cooking for a cycle or two. And let me try and explain why that is. We have cloud and AWS launch in whatever it was, 2006, and offered more compute to more people than really was possible before. Initially that was about taking existing applications and running them more easily in a bigger scale. But in that period of time what's also become possible is new kinds of computation that really weren't practical or even possible without that vast amount of compute. And so one result that came of that is something called the transformer AI model architecture. And Google came out with that, published a paper in 2017. And what that says is, with a transformer model you can actually train an arbitrarily large amount of data into a model, and see what happens. That's what Google demonstrated in 2017. The what happens is the really exciting part because when you do that, what you start to see, when models exceed a certain size that we had never really seen before all of a sudden they get what we call emerging capabilities of complex reasoning and reasoning outside a domain and reasoning with data. The kinds of things that people describe as spooky when they play with something like ChatGPT. That's the underlying term. We don't as an industry quite know why it happens or how it happens, but we can measure that it does. So cloud enables new kinds of math and science. New kinds of math and science allow new kinds of experimentation. And that experimentation has led to this new generation of models. >> So one of the debates we had on theCUBE at our Supercloud event last month was, what's the barriers to entry for say OpenAI, for instance? Obviously, I weighed in aggressively and said, "The barriers for getting into cloud are high because all the CapEx." And Howie Xu formerly VMware, now at ZScaler, he's an AI machine learning guy. He was like, "Well, you can spend $100 million and replicate it." I saw a quote that set up for 180,000 I can get this other package. What's the barriers to entry? Is ChatGPT or OpenAI, does it have sustainability? Is it easy to get into? What is the market like for AI? I mean, because a lot of entrepreneurs are jumping in. I mean, I just read a story today. San Francisco's got more inbound migration because of the AI action happening, Seattle's booming, Boston with MIT's been working on neural networks for generations. That's what we've found the answer. Get off the neural network, Boston jump on the AI bus. So there's total excitement for this. People are enthusiastic around this area. >> You can think of an iPhone versus Android tension that's happening today. In the iPhone world, there are proprietary models from OpenAI who you might consider as the leader. There's Cohere, there's AI21, there's Anthropic, Google's going to have their own, and a few others. These are proprietary models that developers can build on top of, get started really quickly. They're measured to have the highest accuracy and the highest performance today. That's the proprietary side. On the other side, there is an open source part of the world. These are a proliferation of model architectures that developers and practitioners can take off the shelf and train themselves. Typically found in Hugging face. What people seem to think is that the accuracy and performance of the open source models is something like 18 to 20 months behind the accuracy and performance of the proprietary models. But on the other hand, there's infinite flexibility for teams that are capable enough. So you're going to see teams choose sides based on whether they want speed or flexibility. >> That's interesting. And that brings up a point I was talking to a startup and the debate was, do you abstract away from the hardware and be software-defined or software-led on the AI side and let the hardware side just extremely accelerate on its own, 'cause it's flywheel? So again, back to proprietary, that's with hardware kind of bundled in, bolted on. Is it accelerator or is it bolted on or is it part of it? So to me, I think that the big struggle in understanding this is that which one will end up being right. I mean, is it a beta max versus VHS kind of thing going on? Or iPhone, Android, I mean iPhone makes a lot of sense, but if you're Apple, but is there an Apple moment in the machine learning? >> In proprietary models, here does seem to be a jump ball. That there's going to be a virtuous flywheel that emerges that, for example, all these excitement about ChatGPT. What's really exciting about it is it's really easy to use. The technology isn't so different from what we've seen before even from OpenAI. You mentioned a million users in a short period of time, all providing training data for OpenAI that makes their underlying models, their next generation even better. So it's not unreasonable to guess that there's going to be power laws that emerge on the proprietary side. What I think history has shown is that iPhone, Android, Windows, Linux, there seems to be gravity towards this yin and yang. And my guess, and what other people seem to think is going to be the case is that we're going to continue to see these two poles of AI. >> So let's get into the relationship with data because I've been emerging myself with ChatGPT, fascinated by the ease of use, yes, but also the fidelity of how you query it. And I felt like when I was doing writing SQL back in the eighties and nineties where SQL was emerging. You had to be really a guru at the SQL to get the answers you wanted. It seems like the querying into ChatGPT is a good thing if you know how to talk to it. Labeling whether your input is and it does a great job if you feed it right. If you ask a generic questions like Google. It's like a Google search. It gives you great format, sounds credible, but the facts are kind of wrong. >> That's right. >> That's where general consensus is coming on. So what does that mean? That means people are on one hand saying, "Ah, it's bullshit 'cause it's wrong." But I look at, I'm like, "Wow, that's that's compelling." 'Cause if you feed it the right data, so now we're in the data modeling here, so the role of data's going to be critical. Is there a data operating system emerging? Because if this thing continues to go the way it's going you can almost imagine as you would look at companies to invest in. Who's going to be right on this? What's going to scale? What's sustainable? What could build a durable company? It might not look what like what people think it is. I mean, I remember when Google started everyone thought it was the worst search engine because it wasn't a portal. But it was the best organic search on the planet became successful. So I'm trying to figure out like, okay, how do you read this? How do you read the tea leaves? >> Yeah. There are a few different ways that companies can differentiate themselves. Teams with galactic capabilities to take an open source model and then change the architecture and retrain and go down to the silicon. They can do things that might not have been possible for other teams to do. There's a company that that we're proud to be investors in called RunwayML that provides video accelerated, sorry, AI accelerated video editing capabilities. They were used in everything, everywhere all at once and some others. In order to build RunwayML, they needed a vision of what the future was going to look like and they needed to make deep contributions to the science that was going to enable all that. But not every team has those capabilities, maybe nor should they. So as far as how other teams are going to differentiate there's a couple of things that they can do. One is called prompt engineering where they shape on behalf of their own users exactly how the prompt to get fed to the underlying model. It's not clear whether that's going to be a durable problem or whether like Google, we consumers are going to start to get more intuitive about this. That's one. The second is what's called information retrieval. How can I get information about the world outside, information from a database or a data store or whatever service into these models so they can reason about them. And the third is, this is going to sound funny, but attribution. Just like you would do in a news report or an academic paper. If you can state where your facts are coming from, the downstream consumer or the human being who has to use that information actually is going to be able to make better sense of it and rely better on it. So that's prompt engineering, that's retrieval, and that's attribution. >> So that brings me to my next point I want to dig in on is the foundational model stack that you published. And I'll start by saying that with ChatGPT, if you take out the naysayers who are like throwing cold water on it about being a gimmick or whatever, and then you got the other side, I would call the alpha nerds who are like they can see, "Wow, this is amazing." This is truly NextGen. This isn't yesterday's chatbot nonsense. They're like, they're all over it. It's that everybody's using it right now in every vertical. I heard someone using it for security logs. I heard a data center, hardware vendor using it for pushing out appsec review updates. I mean, I've heard corner cases. We're using it for theCUBE to put our metadata in. So there's a horizontal use case of value. So to me that tells me it's a market there. So when you have horizontal scalability in the use case you're going to have a stack. So you publish this stack and it has an application at the top, applications like Jasper out there. You're seeing ChatGPT. But you go after the bottom, you got silicon, cloud, foundational model operations, the foundational models themselves, tooling, sources, actions. Where'd you get this from? How'd you put this together? Did you just work backwards from the startups or was there a thesis behind this? Could you share your thoughts behind this foundational model stack? >> Sure. Well, I'm a recovering product manager and my job that I think about as a product manager is who is my customer and what problem he wants to solve. And so to put myself in the mindset of an application developer and a founder who is actually my customer as a partner at Madrona, I think about what technology and resources does she need to be really powerful, to be able to take a brilliant idea, and actually bring that to life. And if you spend time with that community, which I do and I've met with hundreds of founders now who are trying to do exactly this, you can see that the stack is emerging. In fact, we first drew it in, not in January 2023, but October 2022. And if you look at the difference between the October '22 and January '23 stacks you're going to see that holes in the stack that we identified in October around tooling and around foundation model ops and the rest are organically starting to get filled because of how much demand from the developers at the top of the stack. >> If you look at the young generation coming out and even some of the analysts, I was just reading an analyst report on who's following the whole data stacks area, Databricks, Snowflake, there's variety of analytics, realtime AI, data's hot. There's a lot of engineers coming out that were either data scientists or I would call data platform engineering folks are becoming very key resources in this area. What's the skillset emerging and what's the mindset of that entrepreneur that sees the opportunity? How does these startups come together? Is there a pattern in the formation? Is there a pattern in the competency or proficiency around the talent behind these ventures? >> Yes. I would say there's two groups. The first is a very distinct pattern, John. For the past 10 years or a little more we've seen a pattern of democratization of ML where more and more people had access to this powerful science and technology. And since about 2017, with the rise of the transformer architecture in these foundation models, that pattern has reversed. All of a sudden what has become broader access is now shrinking to a pretty small group of scientists who can actually train and manipulate the architectures of these models themselves. So that's one. And what that means is the teams who can do that have huge ability to make the future happen in ways that other people don't have access to yet. That's one. The second is there is a broader population of people who by definition has even more collective imagination 'cause there's even more people who sees what should be possible and can use things like the proprietary models, like the OpenAI models that are available off the shelf and try to create something that maybe nobody has seen before. And when they do that, Jasper AI is a great example of that. Jasper AI is a company that creates marketing copy automatically with generative models such as GPT-3. They do that and it's really useful and it's almost fun for a marketer to use that. But there are going to be questions of how they can defend that against someone else who has access to the same technology. It's a different population of founders who has to find other sources of differentiation without being able to go all the way down to the the silicon and the science. >> Yeah, and it's going to be also opportunity recognition is one thing. Building a viable venture product market fit. You got competition. And so when things get crowded you got to have some differentiation. I think that's going to be the key. And that's where I was trying to figure out and I think data with scale I think are big ones. Where's the vulnerability in the stack in terms of gaps? Where's the white space? I shouldn't say vulnerability. I should say where's the opportunity, where's the white space in the stack that you see opportunities for entrepreneurs to attack? >> I would say there's two. At the application level, there is almost infinite opportunity, John, because almost every kind of application is about to be reimagined or disrupted with a new generation that takes advantage of this really powerful new technology. And so if there is a kind of application in almost any vertical, it's hard to rule something out. Almost any vertical that a founder wishes she had created the original app in, well, now it's her time. So that's one. The second is, if you look at the tooling layer that we discussed, tooling is a really powerful way that you can provide more flexibility to app developers to get more differentiation for themselves. And the tooling layer is still forming. This is the interface between the models themselves and the applications. Tools that help bring in data, as you mentioned, connect to external actions, bring context across multiple calls, chain together multiple models. These kinds of things, there's huge opportunity there. >> Well, Jon, I really appreciate you coming in. I had a couple more questions, but I will take a minute to read some of your bios for the audience and we'll get into, I won't embarrass you, but I want to set the context. You said you were recovering product manager, 10 plus years at AWS. Obviously, recovering from AWS, which is a whole nother dimension of recovering. In all seriousness, I talked to Andy Jassy around that time and Dr. Matt Wood and it was about that time when AI was just getting on the radar when they started. So you guys started seeing the wave coming in early on. So I remember at that time as Amazon was starting to grow significantly and even just stock price and overall growth. From a tech perspective, it was pretty clear what was coming, so you were there when this tsunami hit. >> Jon: That's right. >> And you had a front row seat building tech, you were led the product teams for Computer Vision AI, Textract, AI intelligence for document processing, recognition for image and video analysis. You wrote the business product plan for AWS IoT and Greengrass, which we've covered a lot in theCUBE, which extends out to the whole edge thing. So you know a lot about AI/ML, edge computing, IOT, messaging, which I call the law of small numbers that scale become big. This is a big new thing. So as a former AWS leader who's been there and at Madrona, what's your investment thesis as you start to peruse the landscape and talk to entrepreneurs as you got the stack? What's the big picture? What are you looking for? What's the thesis? How do you see this next five years emerging? >> Five years is a really long time given some of this science is only six months out. I'll start with some, no pun intended, some foundational things. And we can talk about some implications of the technology. The basics are the same as they've always been. We want, what I like to call customers with their hair on fire. So they have problems, so urgent they'll buy half a product. The joke is if your hair is on fire you might want a bucket of cold water, but you'll take a tennis racket and you'll beat yourself over the head to put the fire out. You want those customers 'cause they'll meet you more than halfway. And when you find them, you can obsess about them and you can get better every day. So we want customers with their hair on fire. We want founders who have empathy for those customers, understand what is going to be required to serve them really well, and have what I like to call founder-market fit to be able to build the products that those customers are going to need. >> And because that's a good strategy from an emerging, not yet fully baked out requirements definition. >> Jon: That's right. >> Enough where directionally they're leaning in, more than in, they're part of the product development process. >> That's right. And when you're doing early stage development, which is where I personally spend a lot of my time at the seed and A and a little bit beyond that stage often that's going to be what you have to go on because the future is going to be so complex that you can't see the curves beyond it. But if you have customers with their hair on fire and talented founders who have the capability to serve those customers, that's got me interested. >> So if I'm an entrepreneur, I walk in and say, "I have customers that have their hair on fire." What kind of checks do you write? What's the kind of the average you're seeing for seed and series? Probably seed, seed rounds and series As. >> It can depend. I have seen seed rounds of double digit million dollars. I have seen seed rounds much smaller than that. It really depends on what is going to be the right thing for these founders to prove out the hypothesis that they're testing that says, "Look, we have this customer with her hair on fire. We think we can build at least a tennis racket that she can use to start beating herself over the head and put the fire out. And then we're going to have something really interesting that we can scale up from there and we can make the future happen. >> So it sounds like your advice to founders is go out and find some customers, show them a product, don't obsess over full completion, get some sort of vibe on fit and go from there. >> Yeah, and I think by the time founders come to me they may not have a product, they may not have a deck, but if they have a customer with her hair on fire, then I'm really interested. >> Well, I always love the professional services angle on these markets. You go in and you get some business and you understand it. Walk away if you don't like it, but you see the hair on fire, then you go in product mode. >> That's right. >> All Right, Jon, thank you for coming on theCUBE. Really appreciate you stopping by the studio and good luck on your investments. Great to see you. >> You too. >> Thanks for coming on. >> Thank you, Jon. >> CUBE coverage here at Palo Alto. I'm John Furrier, your host. More coverage with CUBE Conversations after this break. (upbeat music)

(upbeat music) >> Welcome back, everyone, to theCUBE's coverage here live on the floor at AWS re:MARS 2022. I'm John Furrier, host for theCUBE. Great event, machine learning, automation, robotics, space, that's MARS. It's part of the re-series of events, re:Invent's the big event at the end of the year, re:Inforce, security, re:MARS, really intersection of the future of space, industrial, automation, which is very heavily DevOps machine learning, of course, machine learning, which is AI. We have Luis Ceze here, who's the CEO co-founder of OctoML. Welcome to theCUBE. >> Thank you very much for having me in the show, John. >> So we've been following you guys. You guys are a growing startup funded by Madrona Venture Capital, one of your backers. You guys are here at the show. This is a, I would say small show relative what it's going to be, but a lot of robotics, a lot of space, a lot of industrial kind of edge, but machine learning is the centerpiece of this trend. You guys are in the middle of it. Tell us your story. >> Absolutely, yeah. So our mission is to make machine learning sustainable and accessible to everyone. So I say sustainable because it means we're going to make it faster and more efficient. You know, use less human effort, and accessible to everyone, accessible to as many developers as possible, and also accessible in any device. So, we started from an open source project that began at University of Washington, where I'm a professor there. And several of the co-founders were PhD students there. We started with this open source project called Apache TVM that had actually contributions and collaborations from Amazon and a bunch of other big tech companies. And that allows you to get a machine learning model and run on any hardware, like run on CPUs, GPUs, various GPUs, accelerators, and so on. It was the kernel of our company and the project's been around for about six years or so. Company is about three years old. And we grew from Apache TVM into a whole platform that essentially supports any model on any hardware cloud and edge. >> So is the thesis that, when it first started, that you want to be agnostic on platform? >> Agnostic on hardware, that's right. >> Hardware, hardware. >> Yeah. >> What was it like back then? What kind of hardware were you talking about back then? Cause a lot's changed, certainly on the silicon side. >> Luis: Absolutely, yeah. >> So take me through the journey, 'cause I could see the progression. I'm connecting the dots here. >> So once upon a time, yeah, no... (both chuckling) >> I walked in the snow with my bare feet. >> You have to be careful because if you wake up the professor in me, then you're going to be here for two hours, you know. >> Fast forward. >> The average version here is that, clearly machine learning has shown to actually solve real interesting, high value problems. And where machine learning runs in the end, it becomes code that runs on different hardware, right? And when we started Apache TVM, which stands for tensor virtual machine, at that time it was just beginning to start using GPUs for machine learning, we already saw that, with a bunch of machine learning models popping up and CPUs and GPU's starting to be used for machine learning, it was clear that it come opportunity to run on everywhere. >> And GPU's were coming fast. >> GPUs were coming and huge diversity of CPUs, of GPU's and accelerators now, and the ecosystem and the system software that maps models to hardware is still very fragmented today. So hardware vendors have their own specific stacks. So Nvidia has its own software stack, and so does Intel, AMD. And honestly, I mean, I hope I'm not being, you know, too controversial here to say that it kind of of looks like the mainframe era. We had tight coupling between hardware and software. You know, if you bought IBM hardware, you had to buy IBM OS and IBM database, IBM applications, it all tightly coupled. And if you want to use IBM software, you had to buy IBM hardware. So that's kind of like what machine learning systems look like today. If you buy a certain big name GPU, you've got to use their software. Even if you use their software, which is pretty good, you have to buy their GPUs, right? So, but you know, we wanted to help peel away the model and the software infrastructure from the hardware to give people choice, ability to run the models where it best suit them. Right? So that includes picking the best instance in the cloud, that's going to give you the right, you know, cost properties, performance properties, or might want to run it on the edge. You might run it on an accelerator. >> What year was that roughly, when you were going this? >> We started that project in 2015, 2016 >> Yeah. So that was pre-conventional wisdom. I think TensorFlow wasn't even around yet. >> Luis: No, it wasn't. >> It was, I'm thinking like 2017 or so. >> Luis: Right. So that was the beginning of, okay, this is opportunity. AWS, I don't think they had released some of the nitro stuff that the Hamilton was working on. So, they were already kind of going that way. It's kind of like converging. >> Luis: Yeah. >> The space was happening, exploding. >> Right. And the way that was dealt with, and to this day, you know, to a large extent as well is by backing machine learning models with a bunch of hardware specific libraries. And we were some of the first ones to say, like, know what, let's take a compilation approach, take a model and compile it to very efficient code for that specific hardware. And what underpins all of that is using machine learning for machine learning code optimization. Right? But it was way back when. We can talk about where we are today. >> No, let's fast forward. >> That's the beginning of the open source project. >> But that was a fundamental belief, worldview there. I mean, you have a world real view that was logical when you compare to the mainframe, but not obvious to the machine learning community. Okay, good call, check. Now let's fast forward, okay. Evolution, we'll go through the speed of the years. More chips are coming, you got GPUs, and seeing what's going on in AWS. Wow! Now it's booming. Now I got unlimited processors, I got silicon on chips, I got, everywhere >> Yeah. And what's interesting is that the ecosystem got even more complex, in fact. Because now you have, there's a cross product between machine learning models, frameworks like TensorFlow, PyTorch, Keras, and like that and so on, and then hardware targets. So how do you navigate that? What we want here, our vision is to say, folks should focus, people should focus on making the machine learning models do what they want to do that solves a value, like solves a problem of high value to them. Right? So another deployment should be completely automatic. Today, it's very, very manual to a large extent. So once you're serious about deploying machine learning model, you got a good understanding where you're going to deploy it, how you're going to deploy it, and then, you know, pick out the right libraries and compilers, and we automated the whole thing in our platform. This is why you see the tagline, the booth is right there, like bringing DevOps agility for machine learning, because our mission is to make that fully transparent. >> Well, I think that, first of all, I use that line here, cause I'm looking at it here on live on camera. People can't see, but it's like, I use it on a couple couple of my interviews because the word agility is very interesting because that's kind of the test on any kind of approach these days. Agility could be, and I talked to the robotics guys, just having their product be more agile. I talked to Pepsi here just before you came on, they had this large scale data environment because they built an architecture, but that fostered agility. So again, this is an architectural concept, it's a systems' view of agility being the output, and removing dependencies, which I think what you guys were trying to do. >> Only part of what we do. Right? So agility means a bunch of things. First, you know-- >> Yeah explain. >> Today it takes a couple months to get a model from, when the model's ready, to production, why not turn that in two hours. Agile, literally, physically agile, in terms of walk off time. Right? And then the other thing is give you flexibility to choose where your model should run. So, in our deployment, between the demo and the platform expansion that we announced yesterday, you know, we give the ability of getting your model and, you know, get it compiled, get it optimized for any instance in the cloud and automatically move it around. Today, that's not the case. You have to pick one instance and that's what you do. And then you might auto scale with that one instance. So we give the agility of actually running and scaling the model the way you want, and the way it gives you the right SLAs. >> Yeah, I think Swami was mentioning that, not specifically that use case for you, but that use case generally, that scale being moving things around, making them faster, not having to do that integration work. >> Scale, and run the models where they need to run. Like some day you want to have a large scale deployment in the cloud. You're going to have models in the edge for various reasons because speed of light is limited. We cannot make lights faster. So, you know, got to have some, that's a physics there you cannot change. There's privacy reasons. You want to keep data locally, not send it around to run the model locally. So anyways, and giving the flexibility. >> Let me jump in real quick. I want to ask this specific question because you made me think of something. So we're just having a data mesh conversation. And one of the comments that's come out of a few of these data as code conversations is data's the product now. So if you can move data to the edge, which everyone's talking about, you know, why move data if you don't have to, but I can move a machine learning algorithm to the edge. Cause it's costly to move data. I can move computer, everyone knows that. But now I can move machine learning to anywhere else and not worry about integrating on the fly. So the model is the code. >> It is the product. >> Yeah. And since you said, the model is the code, okay, now we're talking even more here. So machine learning models today are not treated as code, by the way. So do not have any of the typical properties of code that you can, whenever you write a piece of code, you run a code, you don't know, you don't even think what is a CPU, we don't think where it runs, what kind of CPU it runs, what kind of instance it runs. But with machine learning model, you do. So what we are doing and created this fully transparent automated way of allowing you to treat your machine learning models if you were a regular function that you call and then a function could run anywhere. >> Yeah. >> Right. >> That's why-- >> That's better. >> Bringing DevOps agility-- >> That's better. >> Yeah. And you can use existing-- >> That's better, because I can run it on the Artemis too, in space. >> You could, yeah. >> If they have the hardware. (both laugh) >> And that allows you to run your existing, continue to use your existing DevOps infrastructure and your existing people. >> So I have to ask you, cause since you're a professor, this is like a masterclass on theCube. Thank you for coming on. Professor. (Luis laughing) I'm a hardware guy. I'm building hardware for Boston Dynamics, Spot, the dog, that's the diversity in hardware, it's tends to be purpose driven. I got a spaceship, I'm going to have hardware on there. >> Luis: Right. >> It's generally viewed in the community here, that everyone I talk to and other communities, open source is going to drive all software. That's a check. But the scale and integration is super important. And they're also recognizing that hardware is really about the software. And they even said on stage, here. Hardware is not about the hardware, it's about the software. So if you believe that to be true, then your model checks all the boxes. Are people getting this? >> I think they're starting to. Here is why, right. A lot of companies that were hardware first, that thought about software too late, aren't making it. Right? There's a large number of hardware companies, AI chip companies that aren't making it. Probably some of them that won't make it, unfortunately just because they started thinking about software too late. I'm so glad to see a lot of the early, I hope I'm not just doing our own horn here, but Apache TVM, the infrastructure that we built to map models to different hardware, it's very flexible. So we see a lot of emerging chip companies like SiMa.ai's been doing fantastic work, and they use Apache TVM to map algorithms to their hardware. And there's a bunch of others that are also using Apache TVM. That's because you have, you know, an opening infrastructure that keeps it up to date with all the machine learning frameworks and models and allows you to extend to the chips that you want. So these companies pay attention that early, gives them a much higher fighting chance, I'd say. >> Well, first of all, not only are you backable by the VCs cause you have pedigree, you're a professor, you're smart, and you get good recruiting-- >> Luis: I don't know about the smart part. >> And you get good recruiting for PhDs out of University of Washington, which is not too shabby computer science department. But they want to make money. The VCs want to make money. >> Right. >> So you have to make money. So what's the pitch? What's the business model? >> Yeah. Absolutely. >> Share us what you're thinking there. >> Yeah. The value of using our solution is shorter time to value for your model from months to hours. Second, you shrink operator, op-packs, because you don't need a specialized expensive team. Talk about expensive, expensive engineers who can understand machine learning hardware and software engineering to deploy models. You don't need those teams if you use this automated solution, right? Then you reduce that. And also, in the process of actually getting a model and getting specialized to the hardware, making hardware aware, we're talking about a very significant performance improvement that leads to lower cost of deployment in the cloud. We're talking about very significant reduction in costs in cloud deployment. And also enabling new applications on the edge that weren't possible before. It creates, you know, latent value opportunities. Right? So, that's the high level value pitch. But how do we make money? Well, we charge for access to the platform. Right? >> Usage. Consumption. >> Yeah, and value based. Yeah, so it's consumption and value based. So depends on the scale of the deployment. If you're going to deploy machine learning model at a larger scale, chances are that it produces a lot of value. So then we'll capture some of that value in our pricing scale. >> So, you have direct sales force then to work those deals. >> Exactly. >> Got it. How many customers do you have? Just curious. >> So we started, the SaaS platform just launched now. So we started onboarding customers. We've been building this for a while. We have a bunch of, you know, partners that we can talk about openly, like, you know, revenue generating partners, that's fair to say. We work closely with Qualcomm to enable Snapdragon on TVM and hence our platform. We're close with AMD as well, enabling AMD hardware on the platform. We've been working closely with two hyperscaler cloud providers that-- >> I wonder who they are. >> I don't know who they are, right. >> Both start with the letter A. >> And they're both here, right. What is that? >> They both start with the letter A. >> Oh, that's right. >> I won't give it away. (laughing) >> Don't give it away. >> One has three, one has four. (both laugh) >> I'm guessing, by the way. >> Then we have customers in the, actually, early customers have been using the platform from the beginning in the consumer electronics space, in Japan, you know, self driving car technology, as well. As well as some AI first companies that actually, whose core value, the core business come from AI models. >> So, serious, serious customers. They got deep tech chops. They're integrating, they see this as a strategic part of their architecture. >> That's what I call AI native, exactly. But now there's, we have several enterprise customers in line now, we've been talking to. Of course, because now we launched the platform, now we started onboarding and exploring how we're going to serve it to these customers. But it's pretty clear that our technology can solve a lot of other pain points right now. And we're going to work with them as early customers to go and refine them. >> So, do you sell to the little guys, like us? Will we be customers if we wanted to be? >> You could, absolutely, yeah. >> What we have to do, have machine learning folks on staff? >> So, here's what you're going to have to do. Since you can see the booth, others can't. No, but they can certainly, you can try our demo. >> OctoML. >> And you should look at the transparent AI app that's compiled and optimized with our flow, and deployed and built with our flow. That allows you to get your image and do style transfer. You know, you can get you and a pineapple and see how you look like with a pineapple texture. >> We got a lot of transcript and video data. >> Right. Yeah. Right, exactly. So, you can use that. Then there's a very clear-- >> But I could use it. You're not blocking me from using it. Everyone's, it's pretty much democratized. >> You can try the demo, and then you can request access to the platform. >> But you get a lot of more serious deeper customers. But you can serve anybody, what you're saying. >> Luis: We can serve anybody, yeah. >> All right, so what's the vision going forward? Let me ask this. When did people start getting the epiphany of removing the machine learning from the hardware? Was it recently, a couple years ago? >> Well, on the research side, we helped start that trend a while ago. I don't need to repeat that. But I think the vision that's important here, I want the audience here to take away is that, there's a lot of progress being made in creating machine learning models. So, there's fantastic tools to deal with training data, and creating the models, and so on. And now there's a bunch of models that can solve real problems there. The question is, how do you very easily integrate that into your intelligent applications? Madrona Venture Group has been very vocal and investing heavily in intelligent applications both and user applications as well as enablers. So we say an enable of that because it's so easy to use our flow to get a model integrated into your application. Now, any regular software developer can integrate that. And that's just the beginning, right? Because, you know, now we have CI/CD integration to keep your models up to date, to continue to integrate, and then there's more downstream support for other features that you normally have in regular software development. >> I've been thinking about this for a long, long, time. And I think this whole code, no one thinks about code. Like, I write code, I'm deploying it. I think this idea of machine learning as code independent of other dependencies is really amazing. It's so obvious now that you say it. What's the choices now? Let's just say that, I buy it, I love it, I'm using it. Now what do I got to do if I want to deploy it? Do I have to pick processors? Are there verified platforms that you support? Is there a short list? Is there every piece of hardware? >> We actually can help you. I hope we're not saying we can do everything in the world here, but we can help you with that. So, here's how. When you have them all in the platform you can actually see how this model runs on any instance of any cloud, by the way. So we support all the three major cloud providers. And then you can make decisions. For example, if you care about latency, your model has to run on, at most 50 milliseconds, because you're going to have interactivity. And then, after that, you don't care if it's faster. All you care is that, is it going to run cheap enough. So we can help you navigate. And also going to make it automatic. >> It's like tire kicking in the dealer showroom. >> Right. >> You can test everything out, you can see the simulation. Are they simulations, or are they real tests? >> Oh, no, we run all in real hardware. So, we have, as I said, we support any instances of any of the major clouds. We actually run on the cloud. But we also support a select number of edge devices today, like ARMs and Nvidia Jetsons. And we have the OctoML cloud, which is a bunch of racks with a bunch Raspberry Pis and Nvidia Jetsons, and very soon, a bunch of mobile phones there too that can actually run the real hardware, and validate it, and test it out, so you can see that your model runs performant and economically enough in the cloud. And it can run on the edge devices-- >> You're a machine learning as a service. Would that be an accurate? >> That's part of it, because we're not doing the machine learning model itself. You come with a model and we make it deployable and make it ready to deploy. So, here's why it's important. Let me try. There's a large number of really interesting companies that do API models, as in API as a service. You have an NLP model, you have computer vision models, where you call an API and then point in the cloud. You send an image and you got a description, for example. But it is using a third party. Now, if you want to have your model on your infrastructure but having the same convenience as an API you can use our service. So, today, chances are that, if you have a model that you know that you want to do, there might not be an API for it, we actually automatically create the API for you. >> Okay, so that's why I get the DevOps agility for machine learning is a better description. Cause it's not, you're not providing the service. You're providing the service of deploying it like DevOps infrastructure as code. You're now ML as code. >> It's your model, your API, your infrastructure, but all of the convenience of having it ready to go, fully automatic, hands off. >> Cause I think what's interesting about this is that it brings the craftsmanship back to machine learning. Cause it's a craft. I mean, let's face it. >> Yeah. I want human brains, which are very precious resources, to focus on building those models, that is going to solve business problems. I don't want these very smart human brains figuring out how to scrub this into actually getting run the right way. This should be automatic. That's why we use machine learning, for machine learning to solve that. >> Here's an idea for you. We should write a book called, The Lean Machine Learning. Cause the lean startup was all about DevOps. >> Luis: We call machine leaning. No, that's not it going to work. (laughs) >> Remember when iteration was the big mantra. Oh, yeah, iterate. You know, that was from DevOps. >> Yeah, that's right. >> This code allowed for standing up stuff fast, double down, we all know the history, what it turned out. That was a good value for developers. >> I could really agree. If you don't mind me building on that point. You know, something we see as OctoML, but we also see at Madrona as well. Seeing that there's a trend towards best in breed for each one of the stages of getting a model deployed. From the data aspect of creating the data, and then to the model creation aspect, to the model deployment, and even model monitoring. Right? We develop integrations with all the major pieces of the ecosystem, such that you can integrate, say with model monitoring to go and monitor how a model is doing. Just like you monitor how code is doing in deployment in the cloud. >> It's evolution. I think it's a great step. And again, I love the analogy to the mainstream. I lived during those days. I remember the monolithic propriety, and then, you know, OSI model kind of blew it. But that OSI stack never went full stack, and it only stopped at TCP/IP. So, I think the same thing's going on here. You see some scalability around it to try to uncouple it, free it. >> Absolutely. And sustainability and accessibility to make it run faster and make it run on any deice that you want by any developer. So, that's the tagline. >> Luis Ceze, thanks for coming on. Professor. >> Thank you. >> I didn't know you were a professor. That's great to have you on. It was a masterclass in DevOps agility for machine learning. Thanks for coming on. Appreciate it. >> Thank you very much. Thank you. >> Congratulations, again. All right. OctoML here on theCube. Really important. Uncoupling the machine learning from the hardware specifically. That's only going to make space faster and safer, and more reliable. And that's where the whole theme of re:MARS is. Let's see how they fit in. I'm John for theCube. Thanks for watching. More coverage after this short break. >> Luis: Thank you. (gentle music)

(upbeat music) >> Hi, everyone. Welcome to today's session of theCUBE presentation of AWS Startup Showcase, the new breakthrough in DevOps, data analytics, cloud management tools, featuring OpsRamp for the cloud management migration track. I'm John Furrier, your hosts of theCUBE Today, we're joined by Jordan Sheer, vice president of corporate marketing and Michael Fisher, director of product management in OpsRamp. Gentlemen, thank you for joining us today for this topic of challenges of delivering availability for the modern enterprise. >> Thanks, John. >> Yeah, thanks for having us. >> Hey, so first of all, I have to congratulate you guys on the successful launch and growth of your company. You've been in the middle of the action of all this DevOps, microservices, cloud scale, and availability is the hottest topic right now. IT Ops, AI Ops, whoever you want to look at it, IT is automating a way in a lot of value. You guys are in the middle of it. Congratulations on that, and congratulations on being featured. Take a minute to explain what you guys do. What's the strategy? What's the vision? What's the platform. >> Yeah, I'll take that one. So I would just kind of take a step back and we look at the broader landscape of the ecosystem of tools that all sits in. There's a lot of promises and a lot of whats and features and functionality that are being announced. Three pillars of durability and all these tools are really trying to solve a fundamental problem we see in the market and this problem transcends the classic IT ops and it's really front and center, even in this modern DevOps market, this is the problem of availability. And so when we talk about availability, we don't just mean the four nines for an uptime metric, availability to the modern enterprise, is really about an application doing what it needs to do to serve the users in a way that works for the business. And I always like to have a classic example of an e-commerce site, right? So maybe you can get to an e-commerce sites online, but you can't add an item to a cart, right? Well, you can't do something that is a meaningful transaction for the business. And because of that, that experience is not available to you as a user and it's not available to the business because it didn't result in a positive outcome. So the promise of OpsRamp is really around this availability concept and the way we rationalize this as a three pillar formats. And so we think the three pillars of availability are the ability to observe data, this is the first piece of it all. And from a problem perspective, what we're really trying to say is do we have the right data at any given point in time to accurately diagnose, assess, and troubleshoot application behavior? And we see it as a huge problem with a lot of enterprises, because data that can be often siloed, too many tools, many teams, and each one has a slightly different understanding of application health. For example, the DevOps team may have a instance of Prometheus or they may have some other monitoring tool, or the IT team may have their own set, right? But when you have that kind of segmented view of the world, you're not really having the data in a central place to understand availability at the most holistic level, which is really from an end-user to that middleware, to the databases, to underlying microservices, which are really providing the end-user experience. So that observed problem is that first thing OpsRamp tries to solve. Secondly, this is the analyze phase, right? So analyze to us means are we giving the proper intelligence on top of the data to drive meaningful insights to this operator and user? And the promise here is that can we understand that baseline performance and potentially even mitigate future instance from happening? How often do we hear a cloud provider going down or some SaaS provider going down because of some microservice migration issue or some third party application or networking they're relying on? I can think of dozens on my head. So that's kind of the second piece. And then lastly is around this act. This is an area of a lot of investment for ops because we think this is the final pillar for nailing this availability problem. Because again, IT teams are not getting larger, they're getting smaller, right? Everyone's trying to do more with less. And so from a platform perspective, how do we enable teams to focus on the most business critical tasks, which are your cloud migrations, adopting microservices to run your modern applications, innovative projects. These are the things that IT and DevOps teams are tasked with. And maintaining availability is not something people want to do, that should be automated. And so when you think of automation, this is a big piece for us. So again, the key problem is how can we enable these IT or DevOps teams to focus on those business critical things, and automate it with the rest. And so this is the OpsRamp's three pillars of availability. >> John: Talk about the platform, if you don't mind. I know you've got a slide on this. I want to jump into it because this comes up a lot, availability's not just throughout uptime, because you know, uptime, five nine reliability is an old school concept. Now you have different kinds of services that might be up but slow, would cause some problems, as applications and this modern era have all these new sets of services. Can you go through and talked about the platform? >> Yeah, absolutely. So OpsRamp has a very... We address this availability problem pretty holistically, like I mentioned. From a platform perspective, there that two core lines that are comprising a product. One is this hybrid monitoring piece. This is that data layer. And the next one is event management, it's more of the we'll talk about that analysis. And so we treat the monitor as a direct feed into this event management. We're layering that on top, or layering machine learning and AI to augment the insights derived from that first pillar. And so this is where we see a really interesting intersection of data science and monitoring tools. We invest a lot in this area because there's a lot of meaningful problems to solve. In particular alert fatigue, or potentially root cause analysis, things that can take an operator or a developer a long time to do on their own, OpsRamp tries to augment that knowledge of your systems and applications so that you can get to the bottom of things faster and get on with your day. And so it's not just for the major outages, it's not just for the things that are on Twitter or CNN that's for daily things that can just distract you from the ability to do your job, which is to be a core innovator for a business. >> I will really say John, that we are already seeing some couple things here. Number one, we're already actually seeing fundamental transformations in the marketplace. Customers who have seen reduction in alert volumes of up to 95% in some cases, which is as you can imagine, that's completely transformational for these businesses. And number two, I think one of the promises of hybrid of observability working in tandem with event and incident management is the idea of finding unknown unknowns within your organization and being able to act upon them. All too many times nowadays, monitoring tools are there to just surface issues that you may know that you're looking for and then help you find it and then take action on them. But I think the idea of OpsRamp is that we really using that big data platform that Michael talks about is to really surface all the issues that you might not be able to see, identify the root cause, and then take action on those root causes. So in our world, application availability is a much more proactive activity where the IT operations team can actually be proactive about these incidents and then take action on them. >> Yes. Jordan, if you don't mind, I'm following up on that real quick. Talk about the difference uptime versus availability, because something could be up and reliable but not available and its services get flaky. Things may look like they're up and running. Can you just unpack that a little? >> So to me, I mean the really key aspect of availability that I think the old definition of uptime doesn't address is performance. That something can be up, but not performing, but still not really be available. And his e-commerce example, I think is a great one. Let's take, for example, you get on Amazon, right? The Amazon e-commerce experience is always available. And what that means is that at any given moment, when I want to click through the e-commerce experience, it performs. It's available. It's always there and I can buy it at any given time. If there's a latency issue, if the application has a lag, if it takes 30 seconds to really perform an activity on that application, in the alternative definition, that's not available anymore. Even though the application may be up, it's not performing, it's not providing a frictionless end customer experience, and it's not driving the business forward, and therefore it's not available. The definition of availability in OpsRamp is creating a meaningful customer experience that actually drives the business forward. So in that definition, if a service is up but it's latent, but it's not providing excellent customer experience that the business wants to promise to its end-user, it's not available. So that's really how we're redefining this whole notion of availability and we're urging our customers and people in the marketplace to do the same. Ask yourself the hard question, is your application available or is it just up? >> Yeah, and I think that the confluence of the business logic around what the outcome is, and I think this is the classic cliche, "Oh, it's all about outcomes." Here, you're saying that the outcome can be factored into the policy of the tech, meaning this is the experience we want for our users, our customers, and this is what we determined as acceptable and excellent. That's the new metric, so that's the new definition. You can almost flip the script. It feels like it's being flipped around. Is that the right way to think about it? >> Well, yeah, I think that's actually absolutely correct that an application needs to be business aware, especially in the modern day because all of the businesses that we work with, their applications are really the stock and trade of the business. And so if you create an application that is not business aware, that is just there for its own sake or is not performing according to the revenue goals or the targets of the business, then it's no longer available. >> I mean, it could be little things. It could be like an interface on the UI, it could be something really small or a microservice that's not getting to the database in time or some backup or some sort of high availability. Really interesting things could happen with microservices and DevOps, can you guys share some examples of what people might fall into from a trap standpoint or just from a bad architecture? What are some of the things that they might see in their environment that would say that they need help? >> Yeah, I can probably take that one. So there's a lot of, I call them symptoms of a bad availability experience. And I wouldn't even say it's a pure microservice specific thing. I would say it's really any application that's end-user phasing. I see similar pitfalls. One is a networking issue. I see the number one thing usually with these kinds of issues that networking or config changes that can cause environments to go down. And so when we talk to organizations get to the bottom of this is usually a config wasn't thought through thoroughly, or it was a QAed, they didn't have the proper controls in place. I would say that's probably the number one reasons I see applications go unavailable. I think that's some majority of DevOps teams that can empathize with that is someone did something and I didn't know, and it caused some applications servers go down and it causes cascading event of issues. That's like modern paradigm of issues. On old school days, it's a layer zero issue, someone unplugged something. Well, modern times it's someone pushed something I don't have an idea of what we're doing opposing a downstream effect it would have been and therefore my application went unavailable. So that's again, probably the number one pitfall. And again, I think the hardest problem in microservices still around networking, right? Enterprise level networking and connecting that with many data center applications. For example, Kubernetes, which is the provider or the opera orchestrator of any microservice is still getting to the level, many organizations are still getting a level of comfort with trusting production applications to run on it because one is a skill gap. There's not many large organizations have a huge Kubernetes application team, usually they're fairly small agile units. And so with that, there's a skill gaps, right? How do you network in Kubernetes? How do you persist in storage? How to make sure that your application has the proper security built into it, right? Because that these are all legacy problems kind of catching up with the modern environments, because just because you're modernizing, it doesn't mean these old problems go away. It just take a different form. >> Yeah. That's a great point. Modernization. You guys, can you guys talk about this modern application movement in context to how DevOps has risen really into providing value there? Certainly with cloud scale and how companies are dealing with the old legacy model of centralized IT or security teams who slow things down? Because one of the things that we're seeing in this market is speed, faster developer time to market, time to value. Especially if you're an e-commerce site, you're seeing potentially real-time impact. So you have the speed game on the application side that's actually good, being slowed down by lack of automation or just slow response to a policy or a change or an incident. I mean, this seems to be a big discussion. Can you guys share your thoughts on this and your reaction to that? >> I can tell you that one of the places that we are displacing, one of the markets that we are displacing is the legacy ITOM market, because it can't provide the speed that you're talking about, John. I think about a couple of specific examples. I won't necessarily name the providers, but there are several legacy item providers that for example, require an appliance. They require an appliance for you to administer IT operations management services. And that in and of itself is a much slower way of deploying item. Number two, they require this customized proof of value, proof of concept operation, where companies, enterprise organizations need to orchestrate the customization of the item platform for their use. You buy separate management packs that would integrate with different existing applications on your stack. To us, that's too slow. It means you have to make a bunch of decisions upfront about your item practice and then live with those decisions for years to come, especially with software licenses. So by even moving that entire operation to SaaS, which is what the OpsRamp platform has done, has accelerated the ability to drive availability for applications. Number two, and I'd like to pitch this over to Michael, because I think this is really fundamental to how OpsRamp is driving availability, is the use of artificial intelligence. So when we think about being proactive and we think about moving more quickly, it takes machine learning to do a lot of that work to be able to monitor alert streams and alert floods, especially with the smaller scale down IT teams that Michael has mentioned before. You need to harness the power of artificial intelligence to do some of that work. So those are two key ways that I see the platform driving additional speed, especially in a DevOps environment. And I'd love to hear as well from Michael, additional enhancements. >> Michael, if you don't mind, I'll add one thing. First of all, great call out there, Jordan. Yeah. So the legacy slow down, it's like say appliance or whatever that also impacts potentially the headroom on automation. So if you could also talk about the AI machine learning, AI piece, as well as how that impacts automation, because the end of the day automation is going to have to be lock step in with the AI. >> Yeah. And this kind of goes back to that OpsRamp three pillars of availability, right? So that's the what we do, but again, it's all goes back to the availability problem. But we see that observe, analyze, and act as a seamless flow, right? To have it under the same group or the same tent provides tremendous opportunity and value for our DevOps or IT Ops teams that trust the OpsRamp platform because I'm a big believer that garbage in, garbage out. Having the monitoring data in native or having this data native to your tool provides a lot of meaningful value for customers because they have their monitoring data, which is coming from the OpsRamp tool. They have the intelligence, which is being provided by their ops cube machine learning. And they have our process automation and workflow to feed off that directly. And so when I think of this modernization problem, I really think about modern DevOps teams and the problems they face, which is around doing more with less, that's kind of the paradigm of many teams, each one is trying to learn, how do I do security for Kubernetes? How do I observe my security in the Kubernetes' cluster? How do I make sure my CI/CD pipeline is set up in such a way that I don't need to monitor it, or I don't need to give it attention? And so having a really seamless flow from that observe, analyze, act enables those problems to be solved in a much more seamless way that I don't see many legacy providers be able to keep up with. >> Awesome. Jordan, if you don't mind, I'd love to get your definition of what modern availability means. >> Yeah. So, you know, as I've gone through a little bit previously, so modern availability to me is availability uptime. It's also performance, right? Is the app location marks set down by both the application team, but also by the business. And number three is it business aware. So a truly modern available application is being able, is driving an excellent customer experience according to the product roadmap, but it's also doing it in a way that moves the business forward. Right? And if your applications today are not meeting those benchmarks, if they're performing but they're not driving the business forward, if they're not performing, if they're not up, if they don't meet any one of those three core tenants, they're not truly available. And I think that what's most impactful to me about what the platform, what OpsRamp in particular does in today's environment is operating under that modern definition of available is more difficult than ever. It is more difficult because we are living in a hybrid, distributed, multi-cloud world with tons of software vendors that are being sold into these organizations today that are promising similar results. So when you're an IT operator, how do you drive availability in light of that kind of environment? You have reduced budget. You have greater complexity, you have more tools than ever, and yet your software is more impactful to the bottom line than ever before. It's in this environment that we took a hard look at what's going on in the world, and we say these operators need help driving availability. That's the germination of the OpsRamp platform. >> That's a great point. We're going to come into the culture. And the second Emily Freeman's keynote about the revolution in DevOps talks about this, multiple personas and multiple tools that drive specialism, specialties that actually don't help in the modern era. So I'm going to hold that for a second. We'll come to the cultural question in a minute. Michael, if you don't mind to pivot off that definition, what are the metrics? With all those tools out there, all these new things, what are the new metrics for modern availability? It's more than MTTR. >> Yeah. This whole metrics that I think people spend a lot of time on, I think it's actually people thinking in the wrong direction if you ask me. So I've seen a lot of work. People say that the red metrics, that rate error duration or its views, utilization, saturation errors, or it's these other more contrived application metrics. I think they're looking at a piece of the stack, they're not looking at the right things. Even things like mean time to resolve and critical and server response time, mean time to tech, those are all downstream indicators. I like to look at much more proactive signals. So things like app deck score, your application index, or application performance index, these are things that are much more end-user facing or even things like NPS score, right? This has never really been a classic metric for these operations teams, but what a NPS score shows you is are your users happy using your applications? Is your experience giving what they expect it to be? And usually when you ask these two questions, even if you ask the DevOps team do you know what your Atlas score is? And you use NPS score, but what are those, right? Because it's just never been in that conversation. Those have been more maybe on the business side or maybe on the product management side. But I think that as organizations modernize, we see a much more homogenous group forming among these DevOps and product units to answer these kinds of questions. That's something we focus a lot on OpsRamp it's not seeing the silo of DevOps product or Ops. We're each thinking of how do you have a better NPS and how do we drive a better app decks? Because those are our leading indicators of whether or not our applications available. >> So I want to ask you guys both before, again, back to the own cultural question I really want to get into, but from a customer standpoint, they're being bombarded with sales folks, "Hey, buy my tool. I got some monitoring over a year. I got AI ops. I got observability." I mean, there's a zillion venture back companies that just do observability, just monitoring, just AI Ops. As the modern error is here, what's going on in the psychology of the customer because they want to like clear the noise. We saw it in cybersecurity years ago. Right? They buy everything, and next thing you know, they're going to fog of tools. What's the current state of the customer? What do they need right now as to be positioned for the automation, for the edge, all these cool cloud-scale next gen opportunities? >> Yeah. So in my mind, it's basically three things, right? Customers, number one, they want a vision. They want a vision that understands their position in the enterprise organization and what the vision for application development is going to be moving forward. Number two, they don't want to be sold anymore. You're absolutely right. It's harder and harder to make a traditional enterprise sale nowadays. It's because there's a million vendors. They're just like us. They're trying to get people on the phone and it can be tough out there. And number three, they want to be able to validate on their own with their own time. So in light of that, we've introduced a free trial of our cloud monitoring. It's a lightweight version of the OpsRamp platform, but it is a hundred percent free right now. It is available for two weeks with an unlimited number of users and resource count. And you come in and you can get started on your own using preloaded infrastructure from us if you want, or you could bring your own infrastructure. And we can tell you that customers who onboard through the free trial can see insights on their infrastructure within 20 minutes of onboarding. And that experience in and of itself is a differentiator and it allows our customers to buy on their own terms and timelines. >> Sure. And that's a great point. We brought this up last quarter in the showcase, one of the VCs brought up and says he was an old school VC, kind of still in the game, but he was saying in the old days in shelf where you didn't know if it was going to be successful until like downstream, now it's SaaS. If a customer doesn't see the value immediately. It's there. I mean, there's no hiding. You cannot hide from the truth of value here in the modern era. That's a huge impact on how customers now are evaluating and making decisions. >> Absolutely. And you know, I don't think any customer out there wants to read it on the white paper on the state of enterprise IT anymore. We recognize that and so we are hyper-focused on driving value for our customers and prospects as fast as possible, and still providing them the control that they need to make decisions on their own terms. >> Michael, I've got to ask you, since you have the keys to the kingdom on the product management side, what's the priorities on your side for customers, obviously the pressure's there, you guys are doing great, customers try it out for free. They can get, see the value and then double down on it. That's the cloud way. That's what's DevOps all about. You have to prioritize the key things, what's going on with your world. >> Yeah. And I would say of course prod has their own perspective on this. Our number one goal right now is to accelerate that time to value. And so when we look at one who we're targeting, right? So there's DevOps user, this modern application of operator, what are their core concerns in the world? One is, again, that data problem. Are we bringing the right type of data to solve meaningful problems? And two, are we making insights out of that? So from my priority's perspective, we're really driving more focus on this time to value problem and reduced time to there's some key value metrics we have and I'll go to that, but it's all an effort to make sure that when they hit our platform and they use our platform, we're showing them their return on investment as fast as possible. And so, what a return on investment means (indistinct) can slightly vary, but we try to narrow focus on our key target persona and market and focused on them. So right now it definitely is on that modern DevOps team enterprise, looking to provide modern application availability. >> Awesome. Hey guys, for the last two minutes, I'd love to shift now to the culture. So Jordan, you mentioned that appliance, the item example, which is I think indicative of many scenarios in the legacy old world, old guard school, where there's a cultural shift where some people are pissed off, they're going to go and they slowing things down, right? So you see people that are unhappy, the sites having performance of an e-commerce sites, having five second delays or some impact to the business, and the developers are moving fast with DevOps. The DevOps has risen up now where it's driving the agenda. Kind of impacting the old school departments, whether it's security or IT, central groups that are responding in days and weeks to requests, not minutes. This is a huge cultural thing. What's your thoughts on this? >> I absolutely think it's true. I think the reason were options differ slightly on that is we do see the rise of DevOps culture and how it starts to take control and rest the customer experience back from the legacy providers within the organization, but we still see that there's value in having a foot in the old and a foot in the new, and it's why that term hybrid, we talked about hybrid observability is really important to us. It's true, DevOps culture has a lot of great reasons why it's taken over, right? Increases in speed, increases in quality, increases in innovation, all of that. And yet the enterprise is still heavily invested in the old way. And so what they are looking for is a platform to get them from the old way to the new way fast. And that's where we really shine. We say we can enable, we can work with the existing tool set that you have, and we can move you even more in the future of this new definition of availability. And we can get you that DevOps state of play even quicker. And so you don't have to make a heavy lift and you don't have to take a big gamble right now. You can still provide this kind of slow moving migration plan that you need to feel comfortable, and it doesn't force you to throw away a bunch of stuff. >> And if you guys can comment on whole day two operations, that's where the whole ops reliability thing comes in, right? This is kind of where we're at right now, Dev and Ops. Ops really driving the quality and reliability, availability and your definition. This is key, right? This is where we're starting to see the materialization of DevOps. >> It's why we have guys like Michael Fisher who are really driving our agenda forward, right? Because I think he represents the vision of the future that we all want to get to. And the platform that the product team in OpsRamp is building is there, right? But we also want to provide a path for day two, right? There are still some companies are living in day one and they want to get to day two. And so that's where we drive out here. >> And Michael, the platform with the things like containers really helps people get there. They don't have to kill the old to bring in the new, they can coexist. Can you quickly comment your reaction to that? >> Yeah, absolutely. And I talked to a lot of, I won't name any but large scale web companies, and they're actually balancing this today. They have some infrastructure or applications running on bare metal that somebody's got Kubernetes, and there's actually, it's not so much, everything has to go one direction. It actually is what makes the business, right? Even for migrating to the cloud, there has to be a compelling business reason to do so. And I think a lot of companies are realizing that for the application side as well. What runs where and how do we run it? Do we migrate a legacy monolith to a microservice? How fast do we do it? What's the business impact of doing it? These are all critical things that DevOps teams are engaged with on a daily basis as part of the core workflows, so that's my take on that. >> Guys. Great segment. Thanks for coming on and sharing that insight. Congratulates the OpsRamp, doing really extremely well, right in the right position on ramp for operations to be DevOps, whatever you want to call it, you guys are in the center of it with a platform. I think that's what people want, delivering on these availability, automation, AI. Congratulations and thanks for coming on theCUBE for the Showcase Summit. >> Thanks so much. >> Thank you so much, John. >> Okay, theCUBE's coverage of AWS showcase hottest startups in cloud. I'm John Furrier, your host. Thanks for watching. (relaxing music)

(bright music) >> Welcome back to the CUBE presents the AWS Startup Showcase The Next Big Thing in cloud startups with AI security and life science tracks, 15 hottest growing startups are presented. And we had a great opening keynote with luminaries in the industry. And now our closing keynote is to get a deeper dive on cracking the code in the enterprise, how startups are changing the game and helping companies change. And they're also changing the game of open source. We have a great guest, Katie Drucker, Head of Business Development, Madrona Venture Group. Katie, thank you for coming on the CUBE for this special closing keynote. >> Thank you for having me, I appreciate it. >> So one of the topics we talked about with Soma from Madrona on the opening keynote, as well as Ali from Databricks is how startups are seeing success faster. So that's the theme of the Cloud speed, agility, but the game has changed in the enterprise. And I want to really discuss with you how growth changes and growth strategy specifically. They talk, go to market. We hear things like good sales to enterprise sales, organic, freemium, there's all kinds of different approaches, but at the end of the day, the most successful companies, the ones that might not be known that just come out of nowhere. So the economics are changing and the buyers are thinking differently. So let's explore that topic. So take us through your view 'cause you have a lot of experience. But first talk about your role at Madrona, what you do. >> Absolutely all great points. So my role at Madrona, I think I have personally one of the more enviable jobs and that my job is to... I get the privilege of working with all of these fantastic entrepreneurs in our portfolio and doing whatever we can as a firm to harness resources, knowledge, expertise, connections, to accelerate their growth. So my role in setting up business development is taking a look at all of those tools in the tool chest and partnering with the portfolio to make it so. And in our portfolio, we have a wide range of companies, some rely on enterprise sales, some have other go to markets. Some are direct to consumer, a wide range. >> Talk about the growth strategies that you see evolving because what's clear with the pandemic. And as we come out of it is that there are growth plays happening that don't look a little bit differently, more obvious now because of the Cloud scale, we're seeing companies like Databricks, like Snowflake, like other companies that have been built on the cloud or standalone. What are some of the new growth techniques, or I don't want to say growth hacking, that is a pejorative term, but like just a way for companies to quickly describe their value to an enterprise buyer who's moving away from the old RFP days of vendor selection. The game has changed. So take us through how you see secret key and unlocking that new equation of how to present value to an enterprise and how you see enterprises evaluating startups. >> Yes, absolutely. Well, and that's got a question, that's got a few components nestled in what I think are some bigger trends going on. AWS of course brought us the Cloud first. I think now the Cloud is more and more a utility. And so it's incumbent upon thinking about how an enterprise 'cause using the Cloud is going to go up the value stack and partner with its cloud provider and other service providers. I think also with that agility of operations, you have thinning, if you will, the systems of record and a lot of new entrance into this space that are saying things like, how can we harness AIML and other emerging trends to provide more value directly around work streams that were historically locked into those systems of record? And then I think you also have some price plans that are far more flexible around usage based as opposed to just flat subscription or even these big clunky annual or multi-year RFP type stuff. So all of those trends are really designed in ways that favor the emerging startup. And I think if done well, and in partnership with those underlying cloud providers, there can be some amazing benefits that the enterprise realizes an opportunity for those startups to grow. And I think that's what you're seeing. I think there's also this emergence of a buyer that's different than the CIO or the site the CISO. You have things with low code, no code. You've got other buyers in the organization, other line of business executives that are coming to the table, making software purchase decisions. And then you also have empowered developers that are these citizen builders and developer buyers and personas that really matter. So lots of inroads in places for a startup to reach in the enterprise to make a connection and to bring value. That's a great insight. I want to ask that just if you don't mind follow up on that, you mentioned personas. And what we're seeing is the shift happens. There's new roles that are emerging and new things that are being reconfigured or refactored if you will, whether it's human resources or AI, and you mentioned ML playing a role in automation. These are big parts of the new value proposition. How should companies posture to the customer? Because I don't want to say pivot 'cause that means it's not working but mostly extending our iterating around their positioning because as new things have not yet been realized, it might not be operationalized in a company or maybe new things need to be operationalized, it's a new solution for that. Positioning the value is super important and a lot of companies often struggle with that, but also if they get it right, that's the key. What's your feeling on startups in their positioning? So people will dismiss it like, "Oh, that's marketing." But maybe that's important. What's your thoughts on the great positioning question? >> I've been in this industry a long time. And I think there are some things that are just tried and true, and it is not unique to tech, which is, look, you have to tell a story and you have to reach the customer and you have to speak to the customer's need. And what that means is, AWS is a great example. They're famous for the whole concept of working back from the customer and thinking about what that customer's need is. I think any startup that is looking to partner or work alongside of AWS really has to embody that very, very customer centric way of thinking about things, even though, as we just talked about those personas are changing who that customer really is in the enterprise. And then speaking to that value proposition and meeting that customer and creating a dialogue with them that really helps to understand not only what their pain points are, but how you were offering solves those pain points. And sometimes the customer doesn't realize that that is their pain point and that's part of the education and part of the way in which you engage that dialogue. That doesn't change a lot, just generation to generation. I think the modality of how we have that dialogue, the methods in which we choose to convey that change, but that basic discussion is what makes us human. >> What's your... Great, great, great insight. I want to ask you on the value proposition question again, the question I often get, and it's hard to answer is am I competing on value or am I competing on commodity? And depending on where you're in the stack, there could be different things like, for example, land is getting faster, smaller, cheaper, as an example on Amazon. That's driving down to low cost high value, but it shifts up the stack. You start to see in companies this changing the criteria for how to evaluate. So an enterprise might be struggling. And I often hear enterprises say, "I don't know how to pick who I need. I buy tools, I don't buy many platforms." So they're constantly trying to look for that answer key, if you will, what's your thoughts on the changing requirements of an enterprise? And how to do vendor selection. >> Yeah, so obviously I don't think there's a single magic bullet. I always liked just philosophically to think about, I think it's always easier and frankly more exciting as a buyer to want to buy stuff that's going to help me make more revenue and build and grow as opposed to do things that save me money. And just in a binary way, I like to think which side of the fence are you sitting on as a product offering? And the best ways that you can articulate that, what opportunities are you unlocking for your customer? The problems that you're solving, what kind of growth and what impact is that going to lead to, even if you're one or two removed from that? And again, that's not a new concept. And I think that the companies that have that squarely in mind when they think about their go-to market strategy, when they think about the dialogue they're having, when they think about the problems that they're solving, find a much faster path. And I think that also speaks to why we're seeing so many explosion in the line of business, SAS apps that are out there. Again, that thinning of the systems of record, really thinking about what are the scenarios and work streams that we can have happened that are going to help with that revenue growth and unlocking those opportunities. >> What's the common startup challenge that you see when they're trying to do business development? Usually they build the product first, product led value, you hear that a lot. And then they go, "Okay, we're ready to sell, hire a sales guy." That seems to be shifting away because of the go to markets are changing. What are some of the challenges that startups have? What are some that you're seeing? >> Well, and I think the point that you're making about the changes are really almost a result of the trends that we're talking about. The sales organization itself is becoming... These work streams are becoming instrumented. Data is being collected, insights are being derived off of those things. So you see companies like Clary or Highspot or two examples or tutorial that are in our portfolio that are looking at that action and making the art of sales and marketing far more sophisticated overall, which then leads to the different growth hacking and the different insights that are driven. I think the common mistakes that I see across the board, especially with earlier stage startups, look you got to find product market fit. I think that's always... You start with a thesis or a belief and a passion that you're building something that you think the market needs. And it's a lot of dialogue you have to have to make sure that you do find that. I think once you find that another common problem that I see is leading with an explanation of technology. And again, not focusing on the buyer or the... Sorry, the buyer about solving a problem and focusing on that problem as opposed to focusing on how cool your technology is. Those are basic and really, really simple. And then I think setting a set of expectations, especially as it comes to business development and partnering with companies like AWS. The researching that you need to adequately meet the demand that can be turned on. And then I'm sure you heard about from Databricks, from an organization like AWS, you have to be pragmatic. >> Yeah, Databricks gone from zero a software sales a few years ago to over a billion. Now it looks like a Snowflake which came out of nowhere and they had a great product, but built on Amazon, they became the data cloud on top of Amazon. And now they're growing just whole new business models and new business development techniques. Katie, thank you for sharing your insight here. The CUBE's closing keynote. Thanks for coming on. >> Appreciate it, thank you. >> Okay, Katie Drucker, Head of Business Development at Madrona Venture Group. Premier VC in the Seattle area and beyond they're doing a lot of cloud action. And of course they know AWS very well and investing in the ecosystem. So great, great stuff there. Next up is Peter Wagner partner at Wing.VX. Love this URL first of all 'cause of the VC domain extension. But Peter is a long time venture capitalist. I've been following his career. He goes back to the old networking days, back when the internet was being connected during the OSI days, when the TCP IP open systems interconnect was really happening and created so much. Well, Peter, great to see you on the CUBE here and congratulations with success at Wing VC. >> Yeah, thanks, John. It's great to be here. I really appreciate you having me. >> Reason why I wanted to have you come on. First of all, you had a great track record in investing over many decades. You've seen many waves of innovation, startups. You've seen all the stories. You've seen the movie a few times, as I say. But now more than ever, enterprise wise it's probably the hottest I've ever seen. And you've got a confluence of many things on the stack. You were also an early seed investor in Snowflake, well-regarded as a huge success. So you've got your eye on some of these awesome deals. Got a great partner over there has got a network experience as well. What is the big aha moment here for the industry? Because it's not your classic enterprise startups anymore. They have multiple things going on and some of the winners are not even known. They come out of nowhere and they connect to enterprise and get the lucrative positions and can create a moat and value. Like out of nowhere, it's not the old way of like going to the airport and doing an RFP and going through the stringent requirements, and then you're in, you get to win the lucrative contract and you're in. Not anymore, that seems to have changed. What's your take on this 'cause people are trying to crack the code here and sometimes you don't have to be well-known. >> Yeah, well, thank goodness the game has changed 'cause that old thing was (indistinct) So I for one don't miss it. There was some modernization movement in the enterprise and the modern enterprise is built on data powered by AI infrastructure. That's an agile workplace. All three of those things are really transformational. There's big investments being made by enterprises, a lot of receptivity and openness to technology to enable all those agendas, and that translates to good prospects for startups. So I think as far as my career goes, I've never seen a more positive or fertile ground for startups in terms of penetrating enterprise, it doesn't mean it's easy to do, but you have a receptive audience on the other side and that hasn't necessarily always been the case. >> Yeah, I got to ask you, I know that you're a big sailor and your family and Franks Lubens also has a boat and sailing metaphor is always good to have 'cause you got to have a race that's being run and they have tactics. And this game that we're in now, you see the successes, there's investment thesises, and then there's also actually bets. And I want to get your thoughts on this because a lot of enterprises are trying to figure out how to evaluate startups and starts also can make the wrong bet. They could sail to the wrong continent and be in the wrong spot. So how do you pick the winners and how should enterprises understand how to pick winners too? >> Yeah, well, one of the real important things right now that enterprise is facing startups are learning how to do and so learning how to leverage product led growth dynamics in selling to the enterprise. And so product led growth has certainly always been important consumer facing companies. And then there's a few enterprise facing companies, early ones that cracked the code, as you said. And some of these examples are so old, if you think about, like the ones that people will want to talk about them and talk about Classy and want to talk about Twilio and these were of course are iconic companies that showed the way for others. But even before that, folks like Solar Winds, they'd go to market model, clearly product red, bottom stuff. Back then we didn't even have those words to talk about it. And then some of the examples are so enormous if think about them like the one right in front of your face, like AWS. (laughing) Pretty good PLG, (indistinct) but it targeted builders, it targeted developers and flipped over the way you think about enterprise infrastructure, as a result some how every company, even if they're harnessing relatively conventional sales and marketing motion, and you think about product led growth as a way to kick that motion off. And so it's not really an either word even more We might think OPLJ, that means there's no sales keep one company not true, but here's a way to set the table so that you can very efficiently use your sales and marketing resources, only have the most attractive targets and ones that are really (indistinct) >> I love the product led growth. I got to ask you because in the networking days, I remember the term inevitability was used being nested in a solution that they're just going to Cisco off router and a firewall is one you can unplug and replace with another vendor. Cisco you'd have to go through no switching costs were huge. So when you get it to the Cloud, how do you see the competitiveness? Because we were riffing on this with Ali, from Databricks where the lock-in might be value. The more value provider is the lock-in. Is their nestedness? Is their intimate ability as a competitive advantage for some of these starts? How do you look at that? Because startups, they're using open source. They want to have a land position in an enterprise, but how do they create that sustainable competitive advantage going forward? Because again, this is what you do. You bet on ones that you can see that could establish a model whatever we want to call it, but a competitive advantage and ongoing nested position. >> Sometimes it has to do with data, John, and so you mentioned Snowflake a couple of times here, a big part of Snowflake's strategy is what they now call the data cloud. And one of the reasons you go there is not to just be able to process data, to actually get access to it, exchange with the partners. And then that of course is a great reason for the customers to come to the Snowflake platform. And so the more data it gets more customers, it gets more data, the whole thing start spinning in the right direction. That's a really big example, but all of these startups that are using ML in a fundamental way, applying it in a novel way, the data modes are really important. So getting to the right data sources and training on it, and then putting it to work so that you can see that in this process better and doing this earlier on that scale. That's a big part of success. Another company that I work with is a good example that I call (indistinct) which works in sales technology space, really crushing it in terms of building better sales organizations both at performance level, in terms of the intelligence level, and just overall revenue attainment using ML, and using novel data sources, like the previously lost data or phone calls or Zoom calls as you already know. So I think the data advantages are really big. And smart startups are thinking through it early. >> It's interest-- >> And they're planning by the way, not to ramble on too much, but they're betting that PLG strategy. So their land option is designed not just to be an interesting way to gain usage, but it's also a way to gain access to data that then enables the expand in a component. >> That is a huge call-out point there, I was going to ask another question, but I think that is the key I see. It's a new go to market in a way. product led with that kind of approach gets you a beachhead and you get a little position, you get some data that is a cloud model, it means variable, whatever you want to call it variable value proposition, value proof, or whatever, getting that data and reiterating it. So it brings up the whole philosophical question of okay, product led growth, I love that with product led growth of data, I get that. Remember the old platform versus a tool? That's the way buyers used to think. How has that changed? 'Cause now almost, this conversation throws out the whole platform thing, but isn't like a platform. >> It looks like it's all. (laughs) you can if it is a platform, though to do that you can reveal that later, but you're looking for adoption, so if it's down stock product, you're looking for adoption by like developers or DevOps people or SOEs, and they're trying to solve a problem, and they want rapid gratification. So they don't want to have an architectural boomimg, placed in front of them. And if it's up stock product and application, then it's a user or the business or whatever that is, is adopting the application. And again, they're trying to solve a very specific problem. You need instant and immediate obvious time and value. And now you have a ticket to the dance and build on that and maybe a platform strategy can gradually take shape. But you know who's not in this conversation is the CIO, it's like, "I'm always the last to know." >> That's the CISO though. And they got him there on the firing lines. CISOs are buying tools like it's nobody's business. They need everything. They'll buy anything or you go meet with sand, they'll buy it. >> And you make it sound so easy. (laughing) We do a lot of security investment if only (indistinct) (laughing) >> I'm a little bit over the top, but CISOs are under a lot of pressure. I would talk to the CISO at Capital One and he was saying that he's on Amazon, now he's going to another cloud, not as a hedge, but he doesn't want to focus development teams. So he's making human resource decisions as well. Again, back to what IT used to be back in the old days where you made a vendor decision, you built around it. So again, clouds play that way. I see that happening. But the question is that I think you nailed this whole idea of cross hairs on the target persona, because you got to know who you are and then go to the market. So if you know you're a problem solving and the lower in the stack, do it and get a beachhead. That's a strategy, you can do that. You can't try to be the platform and then solve a problem at the same time. So you got to be careful. Is that what you were getting at? >> Well, I think you just understand what you're trying to achieve in that line of notion. And how those dynamics work and you just can't drag it out. And they could make it too difficult. Another company I work with is a very strategic cloud data platform. It's a (indistinct) on systems. We're not trying to foist that vision though (laughs) or not adopters today. We're solving some thorny problems with them in the short term, rapid time to value operational needs in scale. And then yeah, once they found success with (indistinct) there's would be an opportunity to be increasing the platform, and an obstacle for those customers. But we're not talking about that. >> Well, Peter, I appreciate you taking the time and coming out of a board meeting, I know that you're super busy and I really appreciate you making time for us. I know you've got an impressive partner in (indistinct) who's a former Sequoia, but Redback Networks part of that company over the years, you guys are doing extremely well, even a unique investment thesis. I'd like you to put the plug in for the firm. I think you guys have a good approach. I like what you guys are doing. You're humble, you don't brag a lot, but you make a lot of great investments. So could you take them in to explain what your investment thesis is and then how that relates to how an enterprise is making their investment thesis? >> Yeah, yeah, for sure. Well, the concept that I described earlier that the modern enterprise movement as a workplace built on data powered by AI. That's what we're trying to work with founders to enable. And also we're investing in companies that build the products and services that enable that modern enterprise to exist. And we do it from very early stages, but with a longterm outlook. So we'll be leading series and series, rounds of investment but staying deeply involved, both operationally financially throughout the whole life cycle of the company. And then we've done that a bunch of times, our goal is always the big independent public company and they don't always make it but enough for them to have it all be worthwhile. An interesting special case of this, and by the way, I think it intersects with some of startup showcase here is in the life sciences. And I know you were highlighting a lot of healthcare websites and deals, and that's a vertical where to disrupt tremendous impact of data both new data availability and new ways to put it to use. I know several of my partners are very focused on that. They call it bio-X data. It's a transformation all on its own. >> That's awesome. And I think that the reason why we're focusing on these verticals is if you have a cloud horizontal scale view and vertically specialized with machine learning, every vertical is impacted by data. It's so interesting that I think, first start, I was probably best time to be a cloud startup right now. I really am bullish on it. So I appreciate you taking the time Peter to come in again from your board meeting, popping out. Thanks for-- (indistinct) Go back in and approve those stock options for all the employees. Yeah, thanks for coming on. Appreciate it. >> All right, thank you John, it's a pleasure. >> Okay, Peter Wagner, Premier VC, very humble Wing.VC is a great firm. Really respect them. They do a lot of great investing investments, Snowflake, and we have Dave Vellante back who knows a lot about Snowflake's been covering like a blanket and Sarbjeet Johal. Cloud Influencer friend of the CUBE. Cloud commentator and cloud experience built clouds, runs clouds now invests. So V. Dave, thanks for coming back on. You heard Peter Wagner at Wing VC. These guys have their roots in networking, which networking back in the day was, V. Dave. You remember the internet Cisco days, remember Cisco, Wellfleet routers. I think Peter invested in Arrow Point, remember Arrow Point, that was about in the 495 belt where you were. >> Lynch's company. >> That was Chris Lynch's company. I think, was he a sales guy there? (indistinct) >> That was his first big hit I think. >> All right, well guys, let's wrap this up. We've got a great program here. Sarbjeet, thank you for coming on. >> No worries. Glad to be here todays. >> Hey, Sarbjeet. >> First of all, really appreciate the Twitter activity lately on the commentary, the observability piece on Jeremy Burton's launch, Dave was phenomenal, but Peter was talking about this dynamic and I think ties this cracking the code thing together, which is there's a product led strategy that feels like a platform, but it's also a tool. In other words, it's not mutually exclusive, the old methods thrown out the window. Land in an account, know what problem you're solving. If you're below the stack, nail it, get data and go from there. If you're a process improvement up the stack, you have to much more of a platform longer-term sale, more business oriented, different motions, different mechanics. What do you think about that? What's your reaction? >> Yeah, I was thinking about this when I was listening to some of the startups pitching, if you will, or talking about what they bring to the table in this cloud scale or cloud era, if you will. And there are tools, there are applications and then they're big monolithic platforms, if you will. And then they're part of the ecosystem. So I think the companies need to know where they play. A startup cannot be platform from the get-go I believe. Now many aspire to be, but they have to start with tooling. I believe in, especially in B2B side of things, and then go into the applications, one way is to go into the application area, if you will, like a very precise use cases for certain verticals and stuff like that. And other parties that are going into the platform, which is like horizontal play, if you will, in technology. So I think they have to understand their age, like how old they are, how new they are, how small they are, because when their size matter when you are procuring as a big business, procuring your technology vendors size matters and the economic viability matters and their proximity to other windows matter as well. So I think we'll jump into that in other discussions later, but I think that's key, as you said. >> I would agree with that. I would phrase it in my mind, somewhat differently from Sarbjeet which is you have product led growth, and that's your early phase and you get product market fit, you get product led growth, and then you expand and there are many, many examples of this, and that's when you... As part of your team expansion strategy, you're going to get into the platform discussion. There's so many examples of that. You take a look at Ali Ghodsi today with what's happening at Databricks, Snowflake is another good example. They've started with product led growth. And then now they're like, "Okay, we've got to expand the team." Okta is another example that just acquired zero. That's about building out the platform, versus more of a point product. And there's just many, many examples of that, but you cannot to your point, very hard to start with a platform. Arm did it, but that was like a one in a million chance. >> It's just harder, especially if it's new and it's not operationalized yet. So one of the things Dave that we've observed the Cloud is some of the best known successes where nobody's not known at all, database we've been covering from the beginning 'cause we were close to that movement when they came out of Berkeley. But they still were misunderstood and they just started generating revenue in only last year. So again, only a few years ago, zero software revenue, now they're approaching a billion dollars. So it's not easy to make these vendor selections anymore. And if you're new and you don't have someone to operate it or your there's no department and the departments changing, that's another problem. These are all like enterprisey problems. What's your thoughts on that, Dave? >> Well, I think there's a big discussion right now when you've been talking all day about how should enterprise think about startups and think about most of these startups they're software companies and software is very capital efficient business. At the same time, these companies are raising hundreds of millions, sometimes over a billion dollars before they go to IPO. Why is that? A lot of it's going to promotion. I look at it as... And there's a big discussion going on but well, maybe sales can be more efficient and more direct and so forth. I really think it comes down to the golden rule. Two things really mattered in the early days in the startup it's sales and engineering. And writers should probably say engineering and sales and start with engineering. And then you got to figure out your go to market. Everything else is peripheral to those two and you don't get those two things right, you struggle. And I think that's what some of these successful startups are proving. >> Sarbjeet, what's your take on that point? >> Could you repeat the point again? Sorry, I lost-- >> As cloud scale comes in this whole idea of competing, the roles are changing. So look at IOT, look at the Edge, for instance, you got all kinds of new use cases that no one actually knows is a problem to solve. It's just pure opportunity. So there's no one's operational I could have a product, but it don't know we can buy it yet. It's a problem. >> Yeah, I think the solutions have to be point solutions and the startups need to focus on the practitioners, number one, not the big buyers, not the IT, if you will, but the line of business, even within that sphere, like just focus on the practitioners who are going to use that technology. I talked to, I think it wasn't Fiddler, no, it was CoreLogics. I think that story was great today earlier in how they kind of struggle in the beginning, they were trying to do a big bang approach as a startup, but then they almost stumbled. And then they found their mojo, if you will. They went to Don the market, actually, that's a very classic theory of disruption, like what we study from Harvard School of Business that you go down the market, go to the non-consumers, because if you're trying to compete head to head with big guys. Because most of the big guys have lot of feature and functionality, especially at the platform level. And if you're trying to innovate in that space, you have to go to the practitioners and solve their core problems and then learn and expand kind of thing. So I think you have to focus on practitioners a lot more than the traditional oracle buyers. >> Sarbjeet, we had a great thread last night in Twitter, on observability that you started. And there's a couple of examples there. Chaos searches and relatively small company right now, they just raised them though. And they're part of this star showcase. And they could've said, "Hey, we're going to go after Splunk." But they chose not to. They said, "Okay, let's kind of disrupt the elk stack and simplify that." Another example is a company observed, you've mentioned Jeremy Burton's company, John. They're focused really on SAS companies. They're not going after initially these complicated enterprise deals because they got to get it right or else they'll get churn, and churn is that silent killer of software companies. >> The interesting other company that was on the showcase was Tetra Science. I don't know if you noticed that one in the life science track, and again, Peter Wagner pointed out the life science. That's an under recognized in the press vertical that's exploding. Certainly during the pandemic you saw it, Tetra science is an R&D cloud, Dave, R&D data cloud. So pharmaceuticals, they need to do their research. So the pandemic has brought to life, this now notion of tapping into data resources, not just data lakes, but like real deal. >> Yeah, you and Natalie and I were talking about that this morning and that's one of the opportunities for R&D and you have all these different data sources and yeah, it's not just about the data lake. It's about the ecosystem that you're building around them. And I see, it's really interesting to juxtapose what Databricks is doing and what Snowflake is doing. They've got different strategies, but they play a part there. You can see how ecosystems can build that system. It's not one company is going to solve all these problems. It's going to really have to be connections across these various companies. And that's what the Cloud enables and ecosystems have all this data flowing that can really drive new insights. >> And I want to call your attention to a tweet Sarbjeet you wrote about Splunk's earnings and they're data companies as well. They got Teresa Carlson there now AWS as the president, working with Doug, that should change the game a little bit more. But there was a thread of the neath there. Andy Thry says to replies to Dave you or Sarbjeet, you, if you're on AWS, they're a fine solution. The world doesn't just revolve around AWS, smiley face. Well, a lot of it does actually. So (laughing) nice point, Andy. But he brings up this thing and Ali brought it up too, Hybrid now is a new operating system for what now Edge does. So we got Mobile World Congress happening this month in person. This whole Telco 5G brings up a whole nother piece of the Cloud puzzle. Jeff Barr pointed out in his keynote, Dave. Guys, I want to get your reaction. The Edge now is... I'm calling it the super Edge because it's not just Edge as we know it before. You're going to have these pops, these points of presence that are going to have wavelength as your spectrum or whatever they have. I think that's the solution for Azure. So you're going to have all this new cloud power for low latency applications. Self-driving delivery VR, AR, gaming, Telemetry data from Teslas, you name it, it's happening. This is huge, what's your thoughts? Sarbjeet, we'll start with you. >> Yeah, I think Edge is like bound to happen. And for many reasons, the volume of data is increasing. Our use cases are also expanding if you will, with the democratization of computer analysis. Specialization of computer, actually Dave wrote extensively about how Intel and other chip players are gearing up for that future if you will. Most of the inference in the AI world will happen in the field close to the workloads if you will, that can be mobility, the self-driving car that can be AR, VR. It can be healthcare. It can be gaming, you name it. Those are the few use cases, which are in the forefront and what alarm or use cases will come into the play I believe. I've said this many times, Edge, I think it will be dominated by the hyperscalers, mainly because they're building their Metro data centers now. And with a very low latency in the Metro areas where the population is, we're serving the people still, not the machines yet, or the empty areas where there is no population. So wherever the population is, all these big players are putting their data centers there. And I think they will dominate the Edge. And I know some Edge lovers. (indistinct) >> Edge huggers. >> Edge huggers, yeah. They don't like the hyperscalers story, but I think that's the way were' going. Why would we go backwards? >> I think you're right, first of all, I agree with the hyperscale dying you look at the top three clouds right now. They're all in the Edge, Hardcore it's a huge competitive battleground, Dave. And I think the missing piece, that's going to be uncovered at Mobile Congress. Maybe they'll miss it this year, but it's the developer traction, whoever wins the developer market or wins the loyalty, winning over the market or having adoption. The applications will drive the Edge. >> And I would add the fourth cloud is Alibaba. Alibaba is actually bigger than Google and they're crushing it as well. But I would say this, first of all, it's popular to say, "Oh not everything's going to move into the Cloud, John, Dave, Sarbjeet." But the fact is that AWS they're trend setter. They are crushing it in terms of features. And you'd look at what they're doing in the plumbing with Annapurna. Everybody's following suit. So you can't just ignore that, number one. Second thing is what is the Edge? Well, the edge is... Where's the logical place to process the data? That's what the Edge is. And I think to your point, both Sarbjeet and John, the Edge is going to be won by developers. It's going to be one by programmability and it's going to be low cost and really super efficient. And most of the data is going to stay at the Edge. And so who is in the best position to actually create that? Is it going to be somebody who was taking an x86 box and throw it over the fence and give it a fancy name with the Edge in it and saying, "Here's our Edge box." No, that's not what's going to win the Edge. And so I think first of all it's huge, it's wide open. And I think where's the innovation coming from? I agree with you it's the hyperscalers. >> I think the developers as John said, developers are the kingmakers. They build the solutions. And in that context, I always talk about the skills gravity, a lot of people are educated in certain technologies and they will keep using those technologies. Their proximity to that technology is huge and they don't want to learn something new. So as humans we just tend to go what we know how to use it. So from that front, I usually talk with consumption economics of cloud and Edge. It has to focus on the practitioners. And in this case, practitioners are developers because you're just cooking up those solutions right now. We're not serving that in huge quantity right now, but-- >> Well, let's unpack that Sarbjeet, let's unpack that 'cause I think you're right on the money on that. The consumption of the tech and also the consumption of the application, the end use and end user. And I think the reason why hyperscalers will continue to dominate besides the fact that they have all the resource and they're going to bring that to the Edge, is that the developers are going to be driving the applications at the Edge. So if you're low latency Edge, that's going to open up new applications, not just the obvious ones I did mention, gaming, VR, AR, metaverse and other things that are obvious. There's going to be non-obvious things that are going to be huge that are going to come out from the developers. But the Cloud native aspect of the hyperscalers, to me is where the scales are tipping, let me explain. IT was built to build a supply resource to the businesses who were writing business applications. Mostly driven by IBM in the mainframe in the old days, Dave, and then IT became IT. Telcos have been OT closed, "This is our thing, that's it." Now they have to open up. And the Cloud native technologies is the fastest way to value. And I think that paths, Sarbjeet is going to be defined by this new developer and this new super Edge concept. So I think it's going to be wide open. I don't know what to say. I can't guess, but it's going to be creative. >> Let me ask you a question. You said years ago, data's new development kit, does low code and no code to Sarbjeet's point, change the equation? In other words, putting data in the hands of those OT professionals, those practitioners who have the context. Does low-code and no-code enable, more of those protocols? I know it's a bromide, but the citizen developer, and what impact does that have? And who's in the best position? >> Well, I think that anything that reduces friction to getting stuff out there that can be automated, will increase the value. And then the question is, that's not even a debate. That's just fact that's going to be like rent, massive rise. Then the issue comes down to who has the best asset? The software asset that's eating the world or the tower and the physical infrastructure. So if the physical infrastructure aka the Telcos, can't generate value fast enough, in my opinion, the private equity will come in and take it over, and then refactor that business model to take advantage of the over the top software model. That to me is the big stare down competition between the Telco world and this new cloud native, whichever one yields in valley is going to blink first, if you say. And I think the Cloud native wins this one hands down because the assets are valuable, but only if they enable the new model. If the old model tries to hang on to the old hog, the old model as the Edge hugger, as Sarbjeet says, they'll just going to slowly milk that cow dry. So it's like, it's over. So to me, they have to move. And I think this Mobile World Congress day, we will see, we will be looking for that. >> Yeah, I think that in the Mobile World Congress context, I think Telcos should partner with the hyperscalers very closely like everybody else has. And they have to cave in. (laughs) I usually say that to them, like the people came in IBM tried to fight and they cave in. Other second tier vendors tried to fight the big cloud vendors like top three or four. And then they cave in. okay, we will serve our stuff through your cloud. And that's where all the buyers are congregating. They're going to buy stuff along with the skills gravity, the feature proximity. I've got another term I'll turn a coin. It matters a lot when you're doing one thing and you want to do another thing when you're doing all this transactional stuff and regular stuff, and now you want to do data science, where do you go? You go next to it, wherever you have been. Your skills are in that same bucket. And then also you don't have to write a new contract with a new vendor, you just go there. So in order to serve, this is a lesson for startups as well. You need to prepare yourself for being in the Cloud marketplaces. You cannot go alone independently to fight. >> Cloud marketplace is going to replace procurement, for sure, we know that. And this brings up the point, Dave, we talked about years ago, remember on the CUBE. We said, there's going to be Tier two clouds. I used that word in quotes cause nothing... What does it even mean Tier two. And we were talking about like Amazon, versus Microsoft and Google. We set at the time and Alibaba but they're in China, put that aside for a second, but the big three. They're going to win it all. And they're all going to be successful to a relative terms, but whoever can enable that second tier. And it ended up happening, Snowflake is that example. As is Databricks as is others. So Google and Microsoft as fast as they can replicate the success of AWS by enabling someone to build their business on their cloud in a way that allows the customer to refactor their business will win. They will win most of the lion's share my opinion. So I think that applies to the Edge as well. So whoever can come in and say... Whichever cloud says, "I'm going to enable the next Snowflake, the next enterprise solution." I think takes it. >> Well, I think that it comes back... Every conversation coming back to the data. And if you think about the prevailing way in which we treated data with the exceptions of the two data driven companies in their quotes is as we've shoved all the data into some single repository and tried to come up with a single version of the truth and it's adjudicated by a centralized team, with hyper specialized roles. And then guess what? The line of business, there's no context for the business in that data architecture or data Corpus, if you will. And then the time it takes to go from idea for a data product or data service commoditization is way too long. And that's changing. And the winners are going to be the ones who are able to exploit this notion of leaving data where it is, the point about data gravity or courting a new term. I liked that, I think you said skills gravity. And then enabling the business lines to have access to their own data teams. That's exactly what Ali Ghodsi, he was saying this morning. And really having the ability to create their own data products without having to go bow down to an ivory tower. That is an emerging model. All right, well guys, I really appreciate the wrap up here, Dave and Sarbjeet. I'd love to get your final thoughts. I'll just start by saying that one of the highlights for me was the luminary guests size of 15 great companies, the luminary guests we had from our community on our keynotes today, but Ali Ghodsi said, "Don't listen to what everyone's saying in the press." That was his position. He says, "You got to figure out where the puck's going." He didn't say that, but I'm saying, I'm paraphrasing what he said. And I love how he brought up Sky Cloud. I call it Sky net. That's an interesting philosophy. And then he also brought up that machine learning auto ML has got to be table stakes. So I think to me, that's the highlight walk away. And the second one is this idea that the enterprises have to have a new way to procure and not just the consumption, but some vendor selection. I think it's going to be very interesting as value can be proved with data. So maybe the procurement process becomes, here's a beachhead, here's a little bit of data. Let me see what it can do. >> I would say... Again, I said it was this morning, that the big four have given... Last year they spent a hundred billion dollars more on CapEx. To me, that's a gift. In so many companies, especially focusing on trying to hang onto the legacy business. They're saying, "Well not everything's going to move to the Cloud." Whatever, the narrative should change to, "Hey, thank you for that gift. We're now going to build value on top of the Cloud." Ali Ghodsi laid that out, how Databricks is doing it. And it's clearly what Snowflake's new with the data cloud. It basically a layer that abstracts all that underlying complexity and add value on top. Eventually going out to the Edge. That's a value added model that's enabled by the hyperscalers. And that to me, if I have to evaluate where I'm going to place my bets as a CIO or IT practitioner, I'm going to look at who are the ones that are actually embracing that investment that's been made and adding value on top in a way that can drive my data-driven, my digital business or whatever buzzword you want to throw on. >> Yeah, I think we were talking about the startups in today's sessions. I think for startups, my advice is to be as close as you can be to hyperscalers and anybody who awards them, they will cave in at the end of the day, because that's where the whole span of gravity is. That's what the innovation gravity is, everybody's gravitating towards that. And I would say quite a few times in the last couple of years that the rate of innovation happening in a non-cloud companies, when I talk about non-cloud means are not public companies. I think it's like diminishing, if you will, as compared to in cloud, there's a lot of innovation. The Cloud companies are not paying by power people anymore. They have all sophisticated platforms and leverage those, and also leverage the marketplaces and leverage their buyers. And the key will be how you highlight yourself in that cloud market place if you will. It's like in a grocery store where your product is placed and you have to market around it, and you have to have a good story telling team in place as well after you do the product market fit. I think that's a key. I think just being close to the Cloud providers, that's the way to go for startups. >> Real, real quick. Each of you talk about what it takes to crack the code for the enterprise in the modern era now. Dave, we'll start with you. What's it take? (indistinct) >> You got to have it be solving a problem that is 10X better at one 10th a cost of anybody else, if you're a small company, that rule number one. Number two is you obviously got to get product market fit. You got to then figure out. And I think, and again, you're in your early phases, you have to be almost processed builders, figure out... Your KPIs should all be built around retention. How do I define customer success? How do I keep customers and how do I make them loyal so that I know that my cost of acquisition is going to be at least one-third or lower than my lifetime value of that customer? So you've got to nail that. And then once you nail that, you've got to codify that process in the next phase, which really probably gets into your platform discussion. And that's really where you can start to standardize and scale and figure out your go to market and the relationship between marketing spend and sales productivity. And then when you get that, then you got to move on to figure out your Mot. Your Mot might just be a brand. It might be some secret sauce, but more often than not though, it's going to be the relationship that you build. And I think you've got to think about those phases and in today's world, you got to move really fast. Sarbjeet, real quick. What's the secret to crack the code? >> I think the secret to crack the code is partnership and alliances. As a small company selling to the bigger enterprises, the vendors size will be one of the big objections. Even if they don't say it, it's on the back of their mind, "What if these guys disappear tomorrow what would we do if we pick this technology?" And another thing is like, if you're building on the left side, which is the developer side, not on the right side, which is the operations or production side, if you will, you have to understand the sales cycles are longer on the right side and left side is easier to get to, but that's why we see a lot more startups. And on the left side of your DevOps space, if you will, because it's easier to sell to practitioners and market to them and then show the value correctly. And also understand that on the left side, the developers are very know how hungry, on the right side people are very cost-conscious. So understanding the traits of these different personas, if you will buyers, it will, I think set you apart. And as Dave said, you have to solve a problem, focus on practitioners first, because you're small. You have to solve political problems very well. And then you can expand. >> Well, guys, I really appreciate the time. Dave, we're going to do more of these, Sarbjeet we're going to do more of these. We're going to add more community to it. We're going to add our community rooms next time. We're going to do these quarterly and try to do them as more frequently, we learned a lot and we still got a lot more to learn. There's a lot more contribution out in the community that we're going to tap into. Certainly the CUBE Club as we call it, Dave. We're going to build this actively around Cloud. This is another 20 years. The Edge brings us more life with Cloud, it's really exciting. And again, enterprise is no longer an enterprise, it's just the world now. So great companies here, the next Databricks, the next IPO. The next big thing is in this list, Dave. >> Hey, John, we'll see you in Barcelona. Looking forward to that. Sarbjeet, I know in a second half, we're going to run into each other. So (indistinct) thank you John. >> Trouble has started. Great talking to you guys today and have fun in Barcelona and keep us informed. >> Thanks for coming. I want to thank Natalie Erlich who's in Rome right now. She's probably well past her bedtime, but she kicked it off and emceeing and hosting with Dave and I for this AW startup showcase. This is batch two episode two day. What do we call this? It's like a release so that the next 15 startups are coming. So we'll figure it out. (laughs) Thanks for watching everyone. Thanks. (bright music)

>> Announcer: From the Cube's studios in Palo Alto in Boston, bringing you data-driven insights from The Cube and ETR. This is "Breaking Analysis" with Dave Vellante. >> Exactly one week after Pat Gelsinger's announcement of his plans to reinvent Intel. Arm announced version nine of its architecture and laid out its vision for the next decade. We believe this vision is extremely strong as it combines an end-to-end capability from Edge to Cloud, to the data center, to the home and everything in between. Arms aspirations are ambitious and powerful. Leveraging its business model, ecosystem and software compatibility with previous generations. Hello every one and welcome to this week's Wikibon Cube Insights powered by ETR. And this breaking analysis will explain why we think this announcement is so important and what it means for Intel and the broader technology landscape. We'll also share with you some feedback that we received from the Cube Community on last week's episode and a little inside baseball on how Intel, IBM, Samsung, TSMC and the U.S. government might be thinking about the shifting landscape of semiconductor technology. Now, there were two notable announcements this week that were directly related to Intel's announcement of March 23rd. The Armv9 news and TSMC's plans to invest a $100 billion in chip manufacturing and development over the next three years. That is a big number. It appears to tramp Intel's plan $20 billion investment to launch two new fabs in the U.S. starting in 2024. You may remember back in 2019, Samsung pledged to invest a $116 billion to diversify its production beyond memory trip, memory chips. Why are all these companies getting so aggressive? And won't this cause a glut in chips? Well, first, China looms large and aims to dominate its local markets, which in turn is going to confer advantages globally. The second, there's a huge chip shortage right now. And the belief is that it's going to continue through the decade and possibly beyond. We are seeing a new inflection point in the demand as we discussed last week. Stemming from digital, IOT, cloud, autos in new use cases in the home as so well presented by Sarjeet Johal in our community. As to the glut, these manufacturers believe that demand will outstrip supply indefinitely. And I understand that a lack of manufacturing capacity is actually more deadly than an oversupply. Look, if there's a glut, manufacturers can cut production and take the financial hit. Whereas capacity constraints mean you can miss entire cycles of growth and really miss out on the demand and the cost reductions. So, all these manufacturers are going for it. Now let's talk about Arm, its approach and the announcements that it made this week. Now last week, we talked about how Pat Gelsinger his vision of a system on package was an attempt to leapfrog system on chip SOC, while Arm is taking a similar system approach. But in our view, it's even broader than the vision laid out by Pat at Intel. Arm is targeting a wide variety of use cases that are shown here. Arm's fundamental philosophy is that the future will require highly specialized chips and Intel as you recall from Pat's announcement, would agree. But Arm historically takes an ecosystem approach that is different from Intel's model. Arm is all about enabling the production of specialized chips to really fit a specific application. For example, think about the amount of AI going on iPhones. They move if I remember from fingerprint to face recognition. This requires specialized neural processing units, NPUs that are designed by Apple for that particular use case. Arm is facilitating the creation of these specialized chips to be designed and produced by the ecosystem. Intel on the other hand has historically taken a one size fits all approach. Built around the x86. The Intel's design has always been about improving the processor. For example, in terms of speed, density, adding vector processing to accommodate AI, et cetera. And Intel does all the design and the manufacturing in any specialization for the ecosystem is done by Intel. Much of the value, that's added from the ecosystem is frankly been bending metal or adding displays or other features at the margin. But, the advantage is that the x86 architecture is well understood. It's consistent, reliable, and let's face it. Most enterprise software runs on x86. So, but very, very different models historically, which we heard from Gelsinger last week they're going to change with a new trusted foundry strategy. Now let's go through an example that might help explain the power of Arm's model. Let's say, your AWS and you're doing graviton. Designing graviton and graviton2. Or Apple, designing the M1 chip, or Tesla designing its own chip, or any other company in in any one of these use cases that are shown here. Tesla is a really good example. In order to optimize for video processing, Tesla needed to add specialized code firmware in the NPU for it's specific use case within autos. It was happy to take off the shelf CPU or GPU or whatever, and leverage Arm's standards there. And then it added its own value in the NPU. So the advantage of this model is Tesla could go from tape out in less or, or, or or in less than a year versus get the tape out in less than a year versus what would normally take many years. Arm is, think of Arm is like customize a Lego blocks that enable unique value add by the ecosystem with a much faster time to market. So like I say, the Tesla goes from logical tape out if you will, to Samsung and then says, okay run this against your manufacturing process. And it should all work as advertised by Arm. Tesla, interestingly, just as an aside chose the 14 nanometer process to keep its costs down. It didn't need the latest and greatest density. Okay, so you can see big difference in philosophies historically between Arm and Intel. And you can see Intel vectoring toward the Arm model based on what Gelsinger said last week for its foundry business. Essentially it has to. Now, Arm announced a new Arm architecture, Armv9. v9 is backwards compatible with previous generations. Perhaps Arm learned from Intel's failed, Itanium effort for those remember that word. Had no backward compatibility and it really floundered. As well, Arm adds some additional capabilities. And today we're going to focus on the two areas that have highlighted, machine learning piece and security. I'll take note of the call out, 300 billion chips. That's Arm's vision. That's a lot. And we've said, before, Arm's way for volumes are 10X those of x86. Volume, we sound like a broken record. Volume equals cost reduction. We'll come back to that a little bit later. Now let's have a word on AI and machine learning. Arm is betting on AI and ML. Big as are many others. And this chart really shows why, it's a graphic that shows ETR data and spending momentum and pervasiveness in the dataset across all the different sectors that ETR tracks within its taxonomy. Note that ML/AI gets the top spot on the vertical axis, which represents net score. That's a measure of spending momentum or spending velocity. The horizontal axis is market share presence in the dataset. And we give this sector four stars to signify it's consistent lead in the data. So pretty reasonable bet by Arm. But the other area that we're going to talk about is security. And its vision day, Arm talked about confidential compute architecture and these things called realms. Note in the left-hand side, showing data traveling all over the different use cases and around the world and the call-out from the CISO below, it's a large public airline CISO that spoke at an ETR Venn round table. And this individual noted that the shifting end points increase the threat vectors. We all know that. Arm said something that really resonated. Specifically, they said today, there's far too much trust on the OS and the hypervisor that are running these applications. And their broad access to data is a weakness. Arm's concept of realms as shown in the right-hand side, underscores the company strategy to remove the assumption that privileged software. Like the hypervisor needs to be able to see the data. So by creating realms, in a virtualized multi-tenant environment, data can be more protected from memory leaks which of course is a major opportunity for hackers that they exploit. So it's a nice concept in a way for the system to isolate attendance data from other users. Okay, we want, we want to share some feedback that we got last week from the community on our analysis of Intel. A tech exec from city pointed out that, Intel really didn't miss a mobile, as we said, it really missed smartphones. In fact, whell, this is a kind of a minor distinction, it's important to recognize we think. Because Intel facilitated WIFI with Centrino, under the direction of Paul Alini. Who by the way, was not an engineer. I think he was the first non-engineer to be the CEO of Intel. He was a marketing person by background. Ironically, Intel's work in wifi connectivity enabled, actually enabled the smartphone revolution. And maybe that makes the smartphone missed by Intel all that more egregious, I don't know. Now the other piece of feedback we received related to our IBM scenario and our three-way joint venture prediction bringing together Intel, IBM, and Samsung in a triumvirate where Intel brings the foundry and it's process manufacturing. IBM brings its dis-aggregated memory technology and Samsung brings its its volume and its knowledge of of volume down the learning curve. Let's start with IBM. Remember we said that IBM with power 10 has the best technology in terms of this notion of dis-aggregating compute from memory and sharing memory in a pool across different processor types. So a few things in this regard, IBM when it restructured its micro electronics business under Ginni Rometty, catalyzed the partnership with global foundries and you know, this picture in the upper right it shows the global foundries facility outside of Albany, New York in Malta. And the partnership included AMD and Samsung. But we believe that global foundries is backed away from some of its contractual commitments with IBM causing a bit of a rift between the companies and leaving a hole in your original strategy. And evidently AMD hasn't really leaned in to move the needle in any way and so the New York foundry, is it a bit of a state of limbo with respect to its original vision. Now, well, Arvind Krishna was the face of the Intel announcement. It clearly has deep knowledge of IBM semiconductor strategy. Dario Gill, we think is a key player in the mix. He's the senior vice president director of IBM research. And it is in a position to affect some knowledge sharing and maybe even knowledge transfer with Intel possibly as it relates to disaggregated architecture. His questions remain as to how open IBM will be. And how protected it will be with its IP. It's got, as we said, last week, it's got to have an incentive to do so. Now why would IBM do that? Well, it wants to compete more effectively with VMware who has done a great job leveraging x86 and that's the biggest competitor in threat to open shift. So Arvind needs Intel chips to really execute on IBM's cloud strategy. Because almost all of IBM's customers are running apps on x86. So IBM's cloud and hybrid cloud. Strategy really need to leverage that Intel partnership. Now Intel for its part has great FinFET technology. FinFET is a tactic goes beyond CMOs. You all mainframes might remember when IBM burned the boat on ECL, Emitter-coupled Logic. And then moved to CMOs for its mainframes. Well, this is the next gen beyond, and it could give Intel a leg up on AMD's chiplet intellectual properties. Especially as it relates to latency. And there could be some benefits there for IBM. So maybe there's a quid pro quo going on. Now, where it really gets interesting is New York Senator, Chuck Schumer, is keen on building up an alternative to Silicon Valley in New York now it is Silicon Alley. So it's possible that Intel, who by the way has really good process technology. This is an aside, it really allowed TSMC to run the table with the whole seven nanometers versus 10 minute nanometer narrative. TSMC was at seven nanometer. Intel was at 10 nanometer. And really, we've said in the past that Intel's 10 nanometer tech is pretty close to TSMC seven. So Intel's ahead in that regard, even though in terms of, you know, the intervener thickness density, it's it's not, you know. These are sort of games that the semiconductor companies play, but you know it's possible that Intel with the U.S. government and IBM and Samsung could make a play for that New York foundry as part of Intel's trusted foundry strategy and kind of reshuffle that deck in Albany. Sounds like a "Game of Thrones," doesn't it? By the way, TSMC has been so consumed servicing Apple for five nanometer and eventually four nanometer that it's dropped the ball on some of its other's customers, namely Nvidia. And remember, a long-term competitiveness and cost reductions, they all come down to volume. And we think that Intel can't get to volume without an Arm strategy. Okay, so maybe the JV, the Joint Venture that we talked about, maybe we're out on a limb there and that's a stretch. And perhaps Samsung's not willing to play ball, given it's made huge investments in fabs and infrastructure and other resources, locally, but we think it's still viable scenario because we think Samsung definitely would covet a presence in the United States. No good to do that directly but maybe a partnership makes more sense in terms of gaining ground on TSMC. But anyway, let's say Intel can become a trusted foundry with the help of IBM and the U.S. government. Maybe then it could compete on volume. Well, how would that work? Well, let's say Nvidia, let's say they're not too happy with TSMC. Maybe with entertain Intel as a second source. Would that do it? In and of itself, no. But what about AWS and Google and Facebook? Maybe this is a way to placate the U.S. government and call off the antitrust dogs. Hey, we'll give Intel Foundry our business to secure America's semiconductor leadership and future and pay U.S. government. Why don't you chill out, back off a little bit. Microsoft even though, you know, it's not getting as much scrutiny from the U.S. government, it's anti trustee is maybe perhaps are behind it, who knows. But I think Microsoft would be happy to play ball as well. Now, would this give Intel a competitive volume posture? Yes, we think it would, for sure. If it can gain the trust of these companies and the volume we think would be there. But as we've said, currently, this is a very, very long shot because of the, the, the new strategy, the distance the difference in the Foundry business all those challenges that we laid out last week, it's going to take years to play out. But the dots are starting to connect in this scenario and the stakes are exceedingly high hence the importance of the U.S. government. Okay, that's it for now. Thanks to the community for your comments and insights. And thanks again to David Floyer whose analysis around Arm and semiconductors. And this work that he's done for the past decade is of tremendous help. Remember I publish each week on wikibon.com and siliconangle.com. And these episodes are all available as podcasts, just search for braking analysis podcast and you can always connect on Twitter. You can hit the chat right here or this live event or email me at david.vellante@siliconangle.com. Look, I always appreciate the comments on LinkedIn and Clubhouse. You can follow me so you're notified when we start a room and riff on these topics as well as others. And don't forget to check out etr.plus where all the survey data. This is Dave Vellante for the Cube Insights powered by ETR. Be well, and we'll see you next time. (cheerful music) (cheerful music)

>> From the Cube's studios in Palo Alto in Boston, bringing you data-driven insights from The Cube and ETR. This is "Breaking Analysis" with Dave Vellante. >> Exactly one week after Pat Gelsinger's announcement of his plans to reinvent Intel. Arm announced version nine of its architecture and laid out its vision for the next decade. We believe this vision is extremely strong as it combines an end-to-end capability from Edge to Cloud, to the data center, to the home and everything in between. Arms aspirations are ambitious and powerful. Leveraging its business model, ecosystem and software compatibility with previous generations. Hello every one and welcome to this week's Wikibon Cube Insights powered by ETR. And this breaking analysis will explain why we think this announcement is so important and what it means for Intel and the broader technology landscape. We'll also share with you some feedback that we received from the Cube Community on last week's episode and a little inside baseball on how Intel, IBM, Samsung, TSMC and the U.S. government might be thinking about the shifting landscape of semiconductor technology. Now, there were two notable announcements this week that were directly related to Intel's announcement of March 23rd. The Armv9 news and TSMC's plans to invest a $100 billion in chip manufacturing and development over the next three years. That is a big number. It appears to tramp Intel's plan $20 billion investment to launch two new fabs in the U.S. starting in 2024. You may remember back in 2019, Samsung pledged to invest a $116 billion to diversify its production beyond memory trip, memory chips. Why are all these companies getting so aggressive? And won't this cause a glut in chips? Well, first, China looms large and aims to dominate its local markets, which in turn is going to confer advantages globally. The second, there's a huge chip shortage right now. And the belief is that it's going to continue through the decade and possibly beyond. We are seeing a new inflection point in the demand as we discussed last week. Stemming from digital, IOT, cloud, autos in new use cases in the home as so well presented by Sarjeet Johal in our community. As to the glut, these manufacturers believe that demand will outstrip supply indefinitely. And I understand that a lack of manufacturing capacity is actually more deadly than an oversupply. Look, if there's a glut, manufacturers can cut production and take the financial hit. Whereas capacity constraints mean you can miss entire cycles of growth and really miss out on the demand and the cost reductions. So, all these manufacturers are going for it. Now let's talk about Arm, its approach and the announcements that it made this week. Now last week, we talked about how Pat Gelsinger his vision of a system on package was an attempt to leapfrog system on chip SOC, while Arm is taking a similar system approach. But in our view, it's even broader than the vision laid out by Pat at Intel. Arm is targeting a wide variety of use cases that are shown here. Arm's fundamental philosophy is that the future will require highly specialized chips and Intel as you recall from Pat's announcement, would agree. But Arm historically takes an ecosystem approach that is different from Intel's model. Arm is all about enabling the production of specialized chips to really fit a specific application. For example, think about the amount of AI going on iPhones. They move if I remember from fingerprint to face recognition. This requires specialized neural processing units, NPUs that are designed by Apple for that particular use case. Arm is facilitating the creation of these specialized chips to be designed and produced by the ecosystem. Intel on the other hand has historically taken a one size fits all approach. Built around the x86. The Intel's design has always been about improving the processor. For example, in terms of speed, density, adding vector processing to accommodate AI, et cetera. And Intel does all the design and the manufacturing in any specialization for the ecosystem is done by Intel. Much of the value, that's added from the ecosystem is frankly been bending metal or adding displays or other features at the margin. But, the advantage is that the x86 architecture is well understood. It's consistent, reliable, and let's face it. Most enterprise software runs on x86. So, but very, very different models historically, which we heard from Gelsinger last week they're going to change with a new trusted foundry strategy. Now let's go through an example that might help explain the power of Arm's model. Let's say, your AWS and you're doing graviton. Designing graviton and graviton2. Or Apple, designing the M1 chip, or Tesla designing its own chip, or any other company in in any one of these use cases that are shown here. Tesla is a really good example. In order to optimize for video processing, Tesla needed to add specialized code firmware in the NPU for it's specific use case within autos. It was happy to take off the shelf CPU or GPU or whatever, and leverage Arm's standards there. And then it added its own value in the NPU. So the advantage of this model is Tesla could go from tape out in less or, or, or or in less than a year versus get the tape out in less than a year versus what would normally take many years. Arm is, think of Arm is like customize a Lego blocks that enable unique value add by the ecosystem with a much faster time to market. So like I say, the Tesla goes from logical tape out if you will, to Samsung and then says, okay run this against your manufacturing process. And it should all work as advertised by Arm. Tesla, interestingly, just as an aside chose the 14 nanometer process to keep its costs down. It didn't need the latest and greatest density. Okay, so you can see big difference in philosophies historically between Arm and Intel. And you can see Intel vectoring toward the Arm model based on what Gelsinger said last week for its foundry business. Essentially it has to. Now, Arm announced a new Arm architecture, Armv9. v9 is backwards compatible with previous generations. Perhaps Arm learned from Intel's failed, Itanium effort for those remember that word. Had no backward compatibility and it really floundered. As well, Arm adds some additional capabilities. And today we're going to focus on the two areas that have highlighted, machine learning piece and security. I'll take note of the call out, 300 billion chips. That's Arm's vision. That's a lot. And we've said, before, Arm's way for volumes are 10X those of x86. Volume, we sound like a broken record. Volume equals cost reduction. We'll come back to that a little bit later. Now let's have a word on AI and machine learning. Arm is betting on AI and ML. Big as are many others. And this chart really shows why, it's a graphic that shows ETR data and spending momentum and pervasiveness in the dataset across all the different sectors that ETR tracks within its taxonomy. Note that ML/AI gets the top spot on the vertical axis, which represents net score. That's a measure of spending momentum or spending velocity. The horizontal axis is market share presence in the dataset. And we give this sector four stars to signify it's consistent lead in the data. So pretty reasonable bet by Arm. But the other area that we're going to talk about is security. And its vision day, Arm talked about confidential compute architecture and these things called realms. Note in the left-hand side, showing data traveling all over the different use cases and around the world and the call-out from the CISO below, it's a large public airline CISO that spoke at an ETR Venn round table. And this individual noted that the shifting end points increase the threat vectors. We all know that. Arm said something that really resonated. Specifically, they said today, there's far too much trust on the OS and the hypervisor that are running these applications. And their broad access to data is a weakness. Arm's concept of realms as shown in the right-hand side, underscores the company strategy to remove the assumption that privileged software. Like the hypervisor needs to be able to see the data. So by creating realms, in a virtualized multi-tenant environment, data can be more protected from memory leaks which of course is a major opportunity for hackers that they exploit. So it's a nice concept in a way for the system to isolate attendance data from other users. Okay, we want, we want to share some feedback that we got last week from the community on our analysis of Intel. A tech exec from city pointed out that, Intel really didn't miss a mobile, as we said, it really missed smartphones. In fact, whell, this is a kind of a minor distinction, it's important to recognize we think. Because Intel facilitated WIFI with Centrino, under the direction of Paul Alini. Who by the way, was not an engineer. I think he was the first non-engineer to be the CEO of Intel. He was a marketing person by background. Ironically, Intel's work in wifi connectivity enabled, actually enabled the smartphone revolution. And maybe that makes the smartphone missed by Intel all that more egregious, I don't know. Now the other piece of feedback we received related to our IBM scenario and our three-way joint venture prediction bringing together Intel, IBM, and Samsung in a triumvirate where Intel brings the foundry and it's process manufacturing. IBM brings its dis-aggregated memory technology and Samsung brings its its volume and its knowledge of of volume down the learning curve. Let's start with IBM. Remember we said that IBM with power 10 has the best technology in terms of this notion of dis-aggregating compute from memory and sharing memory in a pool across different processor types. So a few things in this regard, IBM when it restructured its micro electronics business under Ginni Rometty, catalyzed the partnership with global foundries and you know, this picture in the upper right it shows the global foundries facility outside of Albany, New York in Malta. And the partnership included AMD and Samsung. But we believe that global foundries is backed away from some of its contractual commitments with IBM causing a bit of a rift between the companies and leaving a hole in your original strategy. And evidently AMD hasn't really leaned in to move the needle in any way and so the New York foundry, is it a bit of a state of limbo with respect to its original vision. Now, well, Arvind Krishna was the face of the Intel announcement. It clearly has deep knowledge of IBM semiconductor strategy. Dario Gill, we think is a key player in the mix. He's the senior vice president director of IBM research. And it is in a position to affect some knowledge sharing and maybe even knowledge transfer with Intel possibly as it relates to disaggregated architecture. His questions remain as to how open IBM will be. And how protected it will be with its IP. It's got, as we said, last week, it's got to have an incentive to do so. Now why would IBM do that? Well, it wants to compete more effectively with VMware who has done a great job leveraging x86 and that's the biggest competitor in threat to open shift. So Arvind needs Intel chips to really execute on IBM's cloud strategy. Because almost all of IBM's customers are running apps on x86. So IBM's cloud and hybrid cloud. Strategy really need to leverage that Intel partnership. Now Intel for its part has great FinFET technology. FinFET is a tactic goes beyond CMOs. You all mainframes might remember when IBM burned the boat on ECL, Emitter-coupled Logic. And then moved to CMOs for its mainframes. Well, this is the next gen beyond, and it could give Intel a leg up on AMD's chiplet intellectual properties. Especially as it relates to latency. And there could be some benefits there for IBM. So maybe there's a quid pro quo going on. Now, where it really gets interesting is New York Senator, Chuck Schumer, is keen on building up an alternative to Silicon Valley in New York now it is Silicon Alley. So it's possible that Intel, who by the way has really good process technology. This is an aside, it really allowed TSMC to run the table with the whole seven nanometers versus 10 minute nanometer narrative. TSMC was at seven nanometer. Intel was at 10 nanometer. And really, we've said in the past that Intel's 10 nanometer tech is pretty close to TSMC seven. So Intel's ahead in that regard, even though in terms of, you know, the intervener thickness density, it's it's not, you know. These are sort of games that the semiconductor companies play, but you know it's possible that Intel with the U.S. government and IBM and Samsung could make a play for that New York foundry as part of Intel's trusted foundry strategy and kind of reshuffle that deck in Albany. Sounds like a "Game of Thrones," doesn't it? By the way, TSMC has been so consumed servicing Apple for five nanometer and eventually four nanometer that it's dropped the ball on some of its other's customers, namely Nvidia. And remember, a long-term competitiveness and cost reductions, they all come down to volume. And we think that Intel can't get to volume without an Arm strategy. Okay, so maybe the JV, the Joint Venture that we talked about, maybe we're out on a limb there and that's a stretch. And perhaps Samsung's not willing to play ball, given it's made huge investments in fabs and infrastructure and other resources, locally, but we think it's still viable scenario because we think Samsung definitely would covet a presence in the United States. No good to do that directly but maybe a partnership makes more sense in terms of gaining ground on TSMC. But anyway, let's say Intel can become a trusted foundry with the help of IBM and the U.S. government. Maybe then it could compete on volume. Well, how would that work? Well, let's say Nvidia, let's say they're not too happy with TSMC. Maybe with entertain Intel as a second source. Would that do it? In and of itself, no. But what about AWS and Google and Facebook? Maybe this is a way to placate the U.S. government and call off the antitrust dogs. Hey, we'll give Intel Foundry our business to secure America's semiconductor leadership and future and pay U.S. government. Why don't you chill out, back off a little bit. Microsoft even though, you know, it's not getting as much scrutiny from the U.S. government, it's anti trustee is maybe perhaps are behind it, who knows. But I think Microsoft would be happy to play ball as well. Now, would this give Intel a competitive volume posture? Yes, we think it would, for sure. If it can gain the trust of these companies and the volume we think would be there. But as we've said, currently, this is a very, very long shot because of the, the, the new strategy, the distance the difference in the Foundry business all those challenges that we laid out last week, it's going to take years to play out. But the dots are starting to connect in this scenario and the stakes are exceedingly high hence the importance of the U.S. government. Okay, that's it for now. Thanks to the community for your comments and insights. And thanks again to David Floyer whose analysis around Arm and semiconductors. And this work that he's done for the past decade is of tremendous help. Remember I publish each week on wikibon.com and siliconangle.com. And these episodes are all available as podcasts, just search for braking analysis podcast and you can always connect on Twitter. You can hit the chat right here or this live event or email me at david.vellante@siliconangle.com. Look, I always appreciate the comments on LinkedIn and Clubhouse. You can follow me so you're notified when we start a room and riff on these topics as well as others. And don't forget to check out etr.plus where all the survey data. This is Dave Vellante for the Cube Insights powered by ETR. Be well, and we'll see you next time. (cheerful music) (cheerful music)

>>from around the globe. It's the Cube presenting Cuban cloud brought to you by silicon angle. Welcome to the cubes event. Virtual event. Cuban Cloud. I'm John for your host. We're here talking to all the thought leaders getting all the stories around Cloud What's going on this year and next today, Tomorrow and the future. We gotta featured startup here. Jonah Clipper, who is the CEO and founder of Stack Hawks. Developing security software for developers to have them put security baked in from the beginning. Johnny, thanks for coming on and being featured. Start up here is part of our Cuban cloud. Thanks for joining. >>Thanks so much for having me, John. >>So one of our themes this year is obviously Cloud natives gone mainstream. The pandemic has shown that. You know, a lot of things have to be modern. Modern applications, the emerald all they talked about modern applications. Infrastructure is code. Reinvent, um is here. They're talking about the next gen enterprise. Their public cloud. Now you've got hybrid cloud. Now you've got multi cloud. But for developers, you just wanna be building security baked in and they don't care where the infrastructure is. So this is the big trend. Like to get your thoughts on that. But before we jump in, tell us about Stack Hawk What you guys do your founded in 2019. Tell us about your company and what Your mission is >>Awesome. Yeah, our mission is to put application security in the hands of software developers so that they can find and fix upset books before they deployed a production. And we do that through a dynamic application scanning capability. Uh, that's deployable via docker, so engineers can run it locally. They can run it in C I C. D. On every single PR or merge and find bugs in the process of delivering software rather than after it's been production. >>So everyone's talking about shift left, shift left for >>security. What does >>that mean? Uh, these days. And what if some of the hurdles that people are struggling with because all I hear is shift left shift left from, like I mean, what does What does that actually mean? Now, Can you take us through your >>view? Yes, and we use the phrase a lot, and I and I know it can feel a little confusing or overused. Probably. Um, When I think of shift left, I think of that Mobius that we all look at all of the time, Um, and how we deliver and, like, plan, write code, deliver software and then manage it. Monitor it right like that entire Dev ops workflow. And today, when we think about where security lives, it either is a blocker to deploying production. Or most commonly, it lives long after code has been deployed to production. And there's a security team constantly playing catch up, trying to ensure that the development team whose job is to deliver value to their customers quickly, right, deploy as fast as we can, as many great customer facing features, um there, then, looking at it months after software has been deployed and then hurrying and trying to assess where the bugs are. And, um, trying to get that information back to software developers so that they can fix those issues. Shifting left to me means software engineers are finding those bugs as their writing code or in the CIA CD pipeline long before code has been deployed to production. >>And so you guys attack that problem right there so they don't have to ship the code and then come back and fix it again. Or where we forgot what the hell is going on. That point in time some Q 18 gets it. Is that the kind of problem that that's out there? Is that the main pain point? >>Yeah, absolutely. I mean a lot of the way software, specifically software like ours and dynamic applications scanning works is a security team or a pen tester. Maybe, is assessing applications for security vulnerability these, um, veteran prod that's normally where these tools are run and they throw them back over the wall, you know, interrupting sprints and interrupting the developer workflow. So there's a ton of context switching, which is super expensive, and it's very disruptive to the business to not know about those issues before they're in prod. And they're also higher risk issues because they're in fraud s. So you have to be able to see a >>wrong flywheel. Basically, it's like you have a penetration test is okay. I want to do ship this app. Pen test comes back, okay? We gotta fix the bug, interrupts the cycle. They're not coding there in fire drill mode. And then it's a chaotic death spiral at that point, >>right? Or nothing gets done. God, how did >>you What was the vision? How did you get here? What? How did you start? The company's woke up one morning. Seven started a security company. And how did what was the journey? What got you here? >>Sure. Thanks. I've been building software for software engineers since 2010. So the first startup I worked for was very much about making it easy for software engineers to deploy and manage applications super efficiently on any cloud provider. And we did programmatic updates to those applications and could even move them from cloud to cloud. And so that was sort of cutting my teeth and technology and really understanding the developer experience. Then I was a VP of product at a company called Victor Ops. We were purchased by spunk in 2018. But that product was really about empowering software engineers to manage their own code in production. So instead of having a network operations center right who sat in front of screens and was waiting for something to go wrong and would then just end up dialing there, you know, just this middle man trying to dial to find the person who wrote the software so that they can fix it. We made that way more efficient and could just route issues to software engineers. And so that was a very dev ops focused company in terms of, um, improving meantime to know and meantime to resolve by putting up time in the hands of software engineers where it didn't used to live there before it lived in a more traditional operations type of role. But we deploy software way too quickly and way too frequently to production to assume that another human can just sit there and know how to fix it, because the problems aren't repeatable, right? So So I've been living in the space for a long time, and I would go to conferences and people would say, Well, I love for, you know, we have these digital transformation initiatives and I'm in the security team and I don't feel like I'm part of this. I don't know. I don't know how to insert myself in this process. And so I started doing a lot of research about, um, how we can shift this left. And I was actually doing some research about penetration testing at the time, Um, and found just a ton of opportunity, a ton of problems, right that exist with security and how we do it today. So I really think of this company as a Dev Ops first Company, and it just so happens to be that we're taking security, and we're making it, um, just part of the the application testing framework, right? We're testing for security bugs, just like we would test for any other kind of bucks. >>That's an awesome vision of other great great history there. And thanks for sharing that. I think one of the things that I think this ties into that we have been reporting aggressively on is the movement to Dev Stack Up, Dev, Ops Dev SEC Ops. And you know, just doing an interview with the guy who stood up space force and big space conversation and were essentially riffing on the idea that they have to get modern. It's government, but they got to do more commercial. They're using open source. But the key thing was everything. Software defined. And so, as you move into suffer defined, then they say we want security baked in from the beginning and This is the big kind of like sea level conversation. Bake it in from the beginning, but it's not that easy. And this is where I think it's interesting where you start to think, uh, Dev ops for security because security is broken. So this is a huge trend. It sounds easy to say it baked security in whether it's an i o T edge or multi cloud. There's >>a lot >>of work there. What should people understand when they hear that kind of platitude of? I just baked security and it's really easy. It's not. It's not trivial. What's your thoughts on >>that? It isn't trivial. And in my opinion, there aren't a lot of tools on the market that actually make that very easy. You know, there are some you've had sneak on this program and they're doing an excellent job, really speaking to the developer and being part of that modern software delivery workflow. Um, but because a lot of tools were built to run in production, it makes it really difficult to bake them in from the beginning. And so, you know, I think there are several goals here. One is you make the tooling work so that it works for the software engineer and their workflow. And and there's some different values that we have to consider when its foreign engineer versus when it's for a security person, right? Limit the noise, make it as easy as possible. Um, make sure that we only show the most critical things that are worth an engineer. Stopping what they're doing in terms of building business value and going back and fixing that bugs and then create a way to discuss in triage other issues later outside of the development. Workflow. So you really have to have a lot of empathy and understanding for how software is built and how software engineers behave, I think, in order to get this right. So it's not easy. Um, but we're here and other tools air here. Thio support companies in doing that. >>What's the competitive strategy for you guys going forward? Because there's a big sea change. Now I see an inflection point. Obviously, Cove it highlights. It's not the main reason, but Cloud native has proven it's now gone mainstream kubernetes. You're seeing the big movement there. You're seeing scale be a huge issue. Software defined operations are now being discussed. So I think it's It's a simple moment for this kind of solution. How are you guys going to compete? What's what's the winning strategy? How are you guys gonna compete to win? >>Yeah, so there's two pieces to that one is getting the technology right and making sure that it is a product that developers love. And we put a ton of effort into that because when a software engineer says, Hey, I'd love to use the security product, right? CSOs around the world are going to be like, Yes, please. Did a software engineer just ask me, You have the security product. Thank you, Right. We're here to make it so easy for them and get the tech right. And then the other piece, in terms of being competitive, is the business model. There were something like, I don't You would know better than me, but I think the data point I last saw was like 1300 venture backed security companies since 2012 focused on selling to see SOS and Fortune 2000 companies. It is a mess. It's so noisy, nobody can figure out what anybody actually does. What we have done is said no, we're going to take a modern business model approach to security. So you know, it's a SAS platform that makes it super easy for a software engineer or anybody on the team to try and buy the software. So 14 day trial. You don't have to talk to anybody if you don't want Thio Awesome support to make sure that people can get on boarded and with our on boarding flow, we've seen that our customers go from signing up to first successful scan of their platform or whatever app they chose to scan in a knave ridge of about 10 minutes. The fastest is eight, right? So it's about delivering value to our customers really quickly. And there aren't many companies insecurity on the market today. That do that? >>You know, you mentioned pen test earlier. I I hear that word. Nice shit. And, like, pen test penetration test, as it's called, um, Sock reports. I mean, these are things that are kind of like I got to do that again. I know these people are doing things that are gonna be automated, but one of the things that cloud native has proven as be killer app is integrations because when you build a modern app, it has to integrate with someone else. So there you need these kind of pen tests. You gotta have this kind of code review. And as code, um, is part of, say, a purpose built device where it's an I o T. Edge updates have toe happen. So you need mawr automation. You need more scale around both updating software to, ah, purpose built device or for integration. What's your thoughts in reaction to that? Because this is a riel software challenge from a customer standpoint, because there are too many tools out there and every see so that I talk to says, I just want to get rid of half the tools consolidate down around my clouds that I'm working through my environment and b'more developer oriented, not just purchasing stuff. So you have all this going on? What's your reaction to that? You got the you know, the integration and you've got the software updates on purpose built devices. >>Yeah, I mean, we I make a joke a little bit. That security land is like, you know, acronyms. Dio there are so many types of security that you could choose to implement. And they all have a home and different use cases that are certainly valuable toe organizations. Um, what we like to focus on and what we think is interesting and dynamic application scanning is because it's been hard toe automate dynamic application for especially for modern applications. I think a lot of companies have ignored theon pertuan ity Thio really invest in this capability and what's cool about dynamic. And you were mentioning pen testing. Is that because it's actively attacking your app? It when you get a successful test, it's like a It's like a successful negative test. It's that the test executed, which means that bug is present in your code. And so there's a lot less false positives than in other types of scanning or assessment technologies. Not to say there isn't a home for them. There's a lot of we could we could spend a whole hour kind of breaking down all the different types of bugs that the different tools confined. Um, but we think that if you want to get started developer first, you know there's a lot of great technologies. Pick a couple or one right pick stack hawk pick, sneak and just get started and put it in your developer workflow. So integrations are super important. Um, we have integrations with every C I C. D provider, making it easy to scan your code on every merge or release. And then we also have workflow integrations for software engineers associated with where they want to be doing work and how they want to be interrupted or told about an issue. So, you know, we're very early to market, but right out of the gate, we made sure that we had a slack integration so that scans are running. Or as we're finding new things, it's populating in a specific slack channel for those engineers who work on that part of the app and you're a integration right. If we find issues, we can quickly make tickets and route them and make sure that the right people are working on those issues. Eso That's how I think about sort of the integration piece and just getting started. It's like you can't tackle the whole like every accurate, um, at once like pick something that helps you get started and then continue to build out your program, as you have success. >>A lot of these tools can they get in the hands of developers, and then you kind of win their trust by having functionality. Uh, certainly a winning strategy we've seen. You know, Splunk, you mentioned where you worked for Data Dog and very other tools out there just get started easily. If it's good, it will be used. So I love that strategy. Question. I wanna ask you mentioned Dr earlier. Um, they got a real popular environment, but that speaks to the open source area. How do you see the role of open source playing with you guys? Is that gonna be part of your community outreach? Does the feed into the product? Could you share your vision on how stack hawks engaging and playing an open source? >>Yeah, absolutely. Um So when we started this company, my co founders and I, we sat down and said here, What are the problems? Okay, the world doesn't need a better scanner, right? If you walk the floor of, ah, security, uh, conference. It's like our tool finds a million things and someone else is. My tool finds a million and five things. Right, And that's how they're competing on value. It's really about making it easy to use and put in the pipeline. So we decided not to roll. Our own scanner were based on an open source capability called Zap the Set Attack Proxy. Uh, it is the most the world's most downloaded application scanner. And, uh, actually we just hired the founder of Zap to join the Stack Hawk team, and we're really excited to continue to invest in the open source community. There is a ton of opportunity to grow and sort of galvanize that community. And then the work that we do with our customers and the feedback that we get about the bugs we find if there, ah, false positive or this one's commonly risk accepted, we can go back to the community, which were already doing and saying, Hey, ditch this rule, Nobody likes it or we need to improve this test. Um, so it's a really nice relationship that we have, and we are looking forward to continuing to grow that >>great stuff. You guys are hot. Start of love. The software on security angle again def sec. Cox is gonna be It's gonna be really popular. Can you talk about some of the customer success is What's the What's the feedback from customers? Can you share some of the use cases that you guys are participating in where you're winning? You mentioned developers love it and try It can just give us a couple of use cases and examples. >>Yeah. Ah, few things. Um ah, lot of our customers are already selling on the notion. Like before we even went to G A right. They told all of their customers that they scan for security bugs with every single release. So in really critical, uh, industry is like fintech, right. It's really important that their customers trust that they're taking security seriously, which everybody says they dio. But they show it to their customers by saying here, every single deploy I can show you if there were any new security bugs released with that deploy. So that's really awesome. Other things We've heard our, uh, people being able to deploy really quickly thio the Salesforce marketplace, right? Like if they have toe have a scan to prove that that they can sell on Salesforce, they do that really rapidly. Eso all of that's going really well with our customers. >>How would I wanna How would I be a customer if I was interested in, um, using Stack Hawks say we have some software we wanna stand up, and, uh, it's super grade. And so Amazon Microsoft Marketplace Stairs Force They'll have requirements or say I want to do a deal with an integration they don't want. They want to make sure there's no nothing wrong with the code. This seems to be a common use case. How doe I if I was a customer, get involved or just download software? Um, what's the What's the procurement? What's the consumption side of it looked like, >>Yeah, you just go to Stockholm dot com and you create an account. If you'd like to get started that way so you can have a 14 day free trial. We have extremely extensive documentation, so it's really easy to get set up that way. You should have some familiarity. Or grab a software engineer who has familiarity with a couple of things. So one is how to use Docker, right? So Docker is, ah, deployment mechanism for the scanner. We do that so you can run it anywhere that you would like to, and we don't have to do things like pierce firewalls or other protective measures that you've instrumented on your production environment. You just run it, um, wherever you like in your system. So locally, C I c d So docker is an important thing to understand the way we configure our scanner is through a, um, a file. So if you are getting a scan today, either your security team is doing it or you have a pen tester doing it. Um, the whole like getting ready for that engagement takes a lot of time because the people who are running the tests don't know how the software was built. So the way we think about this is, just ask them. So you just fill out a Yamil file with parameters that tell the scanner what to dio tell it how to authenticate and not log out. Um, feed us an A p. I speak if you want, so weaken super efficiently, scan your app and you can be up and running really quickly, and then that's it. You can work with our team at any time if you need help, and then we have a really efficient procurement process >>in my experience some of the pen tests of firms out there, is it? It's like the house keeping seal of approval. You get it once and then you gotta go back again. Software change, new things come in. And it's like, Wait a minute, what's the new pen test? And then you to write a check or engaged to have enough meeting? I mean, this is the problem. I mean, too many meetings. Do you >>guys solve that problem? Do >>you solve that problem? >>We solve a piece of that problem. So I think you know, part of how I talk about our company is this idea that we live in a world where we deploy software every single day. Yet it seems reasonable that once a year or twice a year, we go get a pen test where human runs readily available, open source software on our product and gives us a like, quite literal. Pdf of issues on. It's like this is so intellectually dishonest, like we deploy all of the time. So here's the thing. Pen tests are important and everybody should do them. But that should not be the introduction to these issues that are also easy to automate and find in your system. So the way we think about how we work with pen testers is, um, run, stack hawk or zapped right in an automated fashion on your system, and then give that, give the configuration and give the most recent results to your pen tester and say, Go find the hard stuff. You shouldn't be cutting checks for $30,000 to a pen tester or something that you could easily meet in your flare up. Klein. You could write the checks for finding finding the hard stuff that's much more difficult to automate. >>I totally agree. Final question. Business model Once I get in, is it a service software and services? A monthly fee? How do you guys make money? >>Yep, it is software as a service, it is. A monthly fee were early to market. So I'm not going to pretend that we have perfectly cracked the pricing. Um, but the way that we think about this is this is a team product for software engineers and for, you know, informed constituents, right? You want a product person in the product. You want a security person in the product? Um, and we also want to incent you to scan your APS And the most modern fashion, which is scanning the smallest amount of http that lives in your app, like in a micro services architecture because it makes a lot easier, is easy to isolate the problems where they live and to fix those issues really quickly. So we bundle team and for a UPS and then we scale within, uh, companies as they add more team. So pen users. 10 APS is 3 99 a month. And as you add software engineers and more applications, we scale within your company that way. >>Awesome. So if you're successful, you pay more, but doesn't matter. You already succeeded, and that's the benefit of by As you go Great stuff. Final question. One more thing. Your vision of the future. What are the biggest challenges you see in the next 24 months? Plus beyond, um, that you're trying to attack? That's a preferred future that you see evolving. What's the vision? >>Yeah, you've touched on this a couple of times in this interview with uh being remote, and the way that we need to build software already has been modernizing, and I feel like every company has a digital transformation initiative, but it has toe happen faster. And along with that, we have to figure out how Thio protect and secure these Moderna Gail. The most important thing that we do the hearts and minds of our support engineers and make it really easy for them to use security capabilities and then continue to growth in the organization. And that's not an easy thing tied off. It's easy change, a different way of being security. But I think we have to get their, uh, in order to prepare the security, uh, in these rapidly deployed and developed applications that our customers expect. >>Awesome. Jodi Clippers, CEO and founder of Stack Hawk. Thank you for coming on. I really appreciate it. Thanks for spending the time featured Startup is part of our Cuban cloud. I'm Sean for your host with silicon angle to Cube. Thanks for watching

>> From around the globe, it's theCUBE with digital coverage of AWS re:Invent 2020. Special coverage sponsored by AWS Worldwide Public Sector. >> Hello, welcome back to theCUBES coverage, of AWS re:Invent 2020 virtual. This is theCUBE virtual, I'm John Furrier, your host. We're not there in person this year because of the pandemic, but we're doing the remote. This is special coverage of the public sector, we got two great guests, Manpreet Mattu, who was the Worldwide Public Sector of Startups and Venture Capital team with AWS, and Michael Jackson who's the leader, general manager of Public Health and Venture Capital and Startups. Gentlemen, thanks for joining me. Thanks for coming up. >> Okay, it's my pleasure, thanks for having. >> I loved love welcome to theCUBE. I just want to say that Amazon never forgets the startups, that's where are they were born and bred it's been a startup. It's always day one as the expression goes, but truly even with the success, not just in the enterprise and starts within public sector, it's still a startup agility mindset, just want to call that out and say congratulations. Okay, let's get into it. Tell us about your roles and your backgrounds and why you're here. >> So, I believe so, I'm the head of AWS Public Sector, VC and Startups team, and our mission really is to help our public sector customers, adopt innovation that is built by the startups. I've been with AWS for about two and a half years. And prior to that, I was in a similar role with Booz Allen, helping our public sector customers, adopt innovation data as well. >> Michael. >> Yeah, so I am the general manager of Public Health, for on the Venture Capital and Startups team. My career here at AWS began just over four years ago. I was brought on to the state and local government team, initially building the public health practice from inception, and I also built and led our U S elections business. And I'm really excited now to transition into this global role, to lead our public health VC and startups practice, and really democratize access to innovation for our startups in the healthcare space. >> Well, great journey. You guys are converging, the VC and startup teams are coming together. A lot of macro trends certainly are tailwinds for you guys. Obviously, the pandemic is forcing, more accelerated modern applications in public sector, and we've been covering more and more success stories, of the change happening quickly. As access to capital continues to be great, and agility with the cloud, how has that impacted your teams and your approach? Can you guys share how that's changed this year? Because there's more pressure now to be digital, there's more opportunities, there's more still capital flowing, how has it impacted your roles? >> Now, so at the very high level, Amazon invests in companies because, we want those companies to be successful. And AWS itself makes a substantial investment, in agility, the startup customers success. We have things like service credits and things like, business nurturing programs that we have built over the course of the last seven, eight years. For example, over the past, you had a loan, Amazon has provided more than a billion dollars in credits, through AWS Activate program, to help startups grow and scale their businesses. And not only that a total of more than three and a half billion dollars in credit to more than 140,000 startups, over the last seven years, all through the course of the Activate program. From more so, on the healthcare side, I would want, certainly MJ to also, speak through or speak to, the challenges that the health system has faced in the COVID times, and how AWS is helping the provider, healthcare providers and the startups, really achieve success, and help the patient populations on that note. >> Michael, weighing on this new programs, you guys are launching in the impact healthcare, I see where we're seeing the frontline workers, I mean, it's everyone seeing it on TV and the newspaper, and it's impacting friends and family, give us the update. >> Absolutely, so we're here today to launch a new program. We call it the Healthcare Acceleration program. And basically, there are two halves to the program, with an undercurrent or a recurring undercurrent, I should say. Just really quickly before I touch on that though, I'd be remissed if I didn't make note of the fact that, you're right capital is still flowing, and it's a really big deal particularly, as healthcare and public health becomes such a priority, but one of the strategic imperatives of our team's role, similar to the way we democratize access to innovation for startups, we also find it really important to democratize access, to resources for founders, underrepresented founders, so, that everyone can have a level playing field, and equal access to those resources and funding, and things of that nature. Getting back to some of the healthcare priorities, in particular, I don't have to tell you about, this pandemic where on the third, and possibly the deadliest wave losing over 1000 Americans per day. And so, not only are we interested in helping our customers, our enterprise customers inject innovation from startups so that they can address clinical aspects, of the pandemic and beyond, but there are underlying rippling societal implications as well. Things that have been exacerbated by the pandemic. Things like mental health, behavioral health, including substance use abuse, clinical clinician burnout, things like social determinants of health, which lead to disproportionately impacted demographics. So, there's a whole lot to unpack and I'm sure we will, but at the highest level, that's what we're looking to help, our enterprise customers address, with the help of our innovative high potential startups. >> I mean, strategic focus, just go a little bit further on how important this is, because, programs are needed, there is burnout, okay. >> Yeah. >> You have mental health, physical health, everything in between. What are you guys launching? What's new? What can people take away right now from AWS, and what startups and when, 'cause a lot of people are changing their focus. I was seeing people leave their jobs, to have to get on this new mission. They're seeing the pain, there's a lot of entrepreneurial energy, happening right now here. Go further, please. >> So, you touched right on it. So, there are two sides. I mentioned there are two halves, and an underlying current, right? So, the two halves are the supply and the demand. The supply side is what we refer to as the startups, vetted high potential, high growth startups, in the health tech space, that we can help to accelerate their go to market, right? We can pair them with mentorship, credits, we call it the 4Cs. There's capital, mapping them potentially to investors, who are interested into accelerating their growth. There's code, technical support, whether it's cloud formation templates, or technical expertise, connections such as other startups, incubators, accelerators, etcetera, and finally mapping them to customers. So, that's, what's in it for the startups. And then on the other side, the enterprise side, again, there are so many enterprises from payers to providers and others who are looking to accelerate their efforts, to digitally transform their enterprise. And so, by partnering with AWS, and the Healthcare Acceleration program, they can trust that there are AWS powered startups, that are vetted and prepared, to inject that sense of urgency, that sense of innovation. And the underlying current, the dots that are being connected is, workforce modernization or economic development, because in many cases, you're right, people are losing their jobs, people are looking at ways that they can, modernize the workforce is locally leverage local talent. And so, entrepreneurship is a great way, to stimulate the local economy, and help older workers or workers who are looking to transition into a more relevant occupations, to do just that. So, this is an all encompassing program. >> Let's get into this health accelerator from AWS. This is something that is on the table, AWS Health Accelerator, who are the stakeholders, and what are the benefits of this program? >> Well, I mean, before we actually, go to the accelerator for me, I think there's this focus on the healthcare, as an industry, as a vertical, is very important to talk about. The industry is experiencing transformation. It is experiencing disruption and the COVID-19 pandemic, has only accelerated that. If you made, it has sort of magnified some of the stressors, which were already there in the system. If you combine that with the sort of the undercurrent that MJ mentioned from a technological perspective, the delivery of healthcare globally is going digital. So, you see technology is like artificial intelligence, machine learning, big data, augmented reality, IoT based variables. All of these technologies are coming together, to enable applications, such as remote diagnostics, patient monitoring, predictive prescriptive healthcare. And we truly feel that this presents a tremendous opportunity to improve the patient experience, and more importantly, the patient outcomes, using these technologies, and these newly enabled applications through those technologies. And as an example, in the U S alone, there are 22 key healthcare AI use cases, that are projected to grow by, or to approximately around $22 billion by 2025. So, in AWS, we are collaborating with the wide spectrum of healthcare providers, with public health organizations, with government agencies, all around the globe to support their effort, to cope with the rippling effects of the COVID-19. And arguably, many of them are visible to us today, but I would argue that many many are not even yet, have been begun to understand by us and by our customers. So, that is the reason why we want to put some emphasis, on healthcare from a public sector standpoint. >> Yeah, that's a great call-out Manpreet, I want to just highlight that, maybe get an additional commentary because, the old days it was just the institution, the hospital and then you're done. And then it was okay, hospital plus the caregiver, the doctors, and the workers, and now the patient. So, holistically, you're calling out the big picture, the patient care, right. Their families, their environment, the caregivers, and the institution, and now the supply chain, all of it integrated together. That's where the action is. And that's where the data comes in, that's where cloud scale can come in. Is that right? Am I getting that right there? >> Yeah, that's absolutely. I'm sorry Manpreet. >> Welcome MJ, go on. >> I was going to say you're absolutely right. In fact, we like to look at it almost like a bullseye, right? So, at the center of the bullseye, like you said, usually, the first stakeholder that comes to mind, is the provider or the coordinator of care. Outside of there, you have the payer, outside of there, you have researchers. And in any even further outside still are your regulators, your healthcare agencies at the local state, and federal levels, including military health. So, it's a rippling effect of customers on that side, as well as you asked about stakeholders on the startup side, there's also a bullseye of influence. Starting with the founder herself, the founder, and her executive team, moving out from there to the startup, as an organization outside from there, we've got incubators and accelerators that are in place, to help accelerate that growth as well. And then farther out you've got investors, VCs, and investors, and so on both sides, supply and demand we're looking to tap into, and accelerate the growth, and make connections between the two. >> Yeah, (indistinct) but when I, in back in real life, when we used to go to games, you walk into the stadium, you buy your ticket with your phone, you go to your seat, concessions guys, deliver things there for you, the fan experience, the players are there. I mean, why can't we have that in healthcare? I was just everything is happening, right. Go for good, yeah. And I think that's the Nirvana, hopefully soon. >> We're working on it. >> Good stuff. I know, I just love the vision, I think is so relevant and super important. Now, let's get into this health accelerator. What's this all about? Let's get into that. >> So, the health accelerator will be, a multi-week on-demand program. Where we're going to map high potential vetted startups, to a number of resources, right. I mentioned before that there will be mentorship, there will be technical experts who will be able to, take these startups who have established some presence, but we want to accelerate their ability to go deeper specifically into public health, throughout that ecosystem that I just described, right? Starting with providers and coordinators, payers, researchers, regulators. We want to give them a way to go deep into this, heavily regulated industry, so that they can not only have access to the innovation that many startups would not otherwise, like Manpreet mentioned machine learning AI, but they also have access to the resources, to ensure their success. >> What kind of problems are you guys trying to solve with this? I mean, is there a specific vetting process, is there a criteria? Is there a bar to all over share some specifics? >> Yeah, absolutely. So, for the past few years, a lot of the major change challenges, for our public health customers have been the same, but they require a new approach. And I like to call our approach the HIGH FIVE. So, some of those challenges that have been traditionally, lingering for the past few years, equal social determinants of health. Social determinants, when we talk about that, we not only refer to the nonclinical contributors to a person's overall wellness. So, you think about issues like food deserts or recidivism homelessness, all of that transportation to access to care, right, all of that contributes. But then there's also disparities and health outcomes. When you think about socioeconomic differences, rural health, ethnic and racial minorities, so, that all factors into social determinants of health. Then there's also aging. Now, these are the strategic pillars that we like to focus on, or that we are focusing on. When I mentioned aging every day in the U S, 10,000 people celebrate their 65th birthday. Many of those individuals are suffering from comorbidities, from hypertension, diabetes, cancer, and now the lingering impact of COVID-19. And so, as these aging individuals continue to live longer, the goal is to improve the quality of their life as well. And so, many of them look to technology to age independently at home, etcetera. So, that's our second strategic pillar. The third, is mental and behavioral health. So, when I talk about mental health, I mean, everything from mild depression, all the way through suicide prevention, and especially these days with COVID-19, we see a lot of clinicians suffering from burnout. And so, it's important, that we take care of the frontline workers, those healthcare providers, and even outside of COVID-19, you think about the ways that the patient population, has continued to expand, and the growth within the provider market has not, or the pool of providers has not nearly expanded at the same rate. We've got people living longer, we've got more people than ever insured. And so, we need to leverage technology to help a stagnant, number of providers to treat a growing pool of patients, without sacrificing the quality of care. And then finally, we've got environmental health. From air quality to water purity. It's important to understand the correlation between, the environment and the health care of our population. So, those are the pillars. I know I mentioned the HIGH FIVE, the fifth is not specific to healthcare. I touched on it a little bit earlier, but the fifth is, it is democratizing access to innovation, to resources, specifically for founders from underrepresented communities. >> And that's great insight, Michael great, great Schaeffer pointed that out. Manpreet take us on the final word here. Venture Capital, Startups, AWS, what's the current state share with us, the current worldview from your perspective. >> Oh, so, bringing home this point that MJ mentioned, the strategic plan of focus areas. And if you, look at all those strategic areas there's, you can really sort of put those into two buckets. One is the patient side of the bucket, and then there's the provider side of the bucket, or the caretaker side of the bucket. And if the patient side, what we want to do is work with startups that are, really working across a broad spectrum of use cases, but to solve those two key challenges of the, one on the patient's side and other on the provider side. Then the end goal of providing patient experience, and patient outcomes. For the patient side, it's the patient experience, patient engagement, patient outcomes. So, the startups looking on those sides, on those use cases of criteria. And then we have the provider side where, we want to ensure that the providers have the right set of technologies, the right set of solutions, right set of innovation, to help them where healthcare operations. You have all seen in COVID times, how the provider systems are getting overwhelmed. And that's where the healthcare operations comes into play. Clinical decision support. Now, many patients cannot get to the hospitals. So, how do we provide through our startup partners for startup customers, those solutions where remote diagnostics, remote imaging or remote health delivery could be provided. Things like predictive and prescriptive health solutions. How can we work with our startups to provide, those sort of solutions to the providers, to again, at the end, the better the outcome of the patients, right? So, that's what we were looking at. And that's what this program is all about. Working with public sector provider side of the house and the customers understanding, and helping them understand the need as well, and then bringing the right set of startup solutions, and help solve those challenges that they are facing, and the patients are facing as well. MJ, I'm sure you want to close it out, with some thoughts too. >> Okay. >> Absolutely, I would just close it with this, our goal, like Manpreet said, is to match the high potential startups, with the, the enterprises who are desiring those solutions, and success for us, we'll have three traits. It will be valuable, meaning that there will be a true alignment between what our startups offer and what the market needs. It will be measurable, so that we can quantify the improvement and outcomes. And finally, it will be sustainable. So, beyond COVID-19 beyond the opioid crisis, beyond any situation or condition, we look to bring solutions to market through our startups, that are going to truly sustain a transformative approach to modernizing public health enterprises. >> Great job again, and important work and DevOps, impacting healthcare in all kinds of ways. And it's super important work. I'm glad you guys are doing it, and it's going to develop out beautifully, and if I can give you a high five, Michael, I'll give you a high five off in-person, but remotely, >> Virtual. >> Get virtual high five great program. We're going to spread the word, good work. >> Thank you. >> Thanks for doing it, I appreciate it. >> Thank you very much for your time. >> Okay, it's theCUBE coverage virtual, we are theCUBE virtual bringing all the coverage, super important work being done in public sector, cloud enabling it, great people important, and of course, happening at re:Invent. Thanks for watching. (upbeat music)

>> Narrator: From theCUBE's Studios in Palo Alto and Boston connecting with thought leaders all around the world, this is theCUBE conversation. >> Welcome to this CUBE conversation with Commvault, I'm Lisa Martin, looking forward to having a spirited conversation with my two guests, please welcome Janet Giesen, the VP of Operations and Programs for Metallic, A Commvault Venture. Janet, welcome to theCUBE. >> Yeah, it's happy to be here. >> And joining us from EMEA is Mark Jow, the EMEA VP of Technical Sales at Commvault. Hey Mark, good afternoon to you. >> Good afternoon Lisa, it's great to be here with you. >> So just about a year or so ago, theCUBE had the pleasure of being at Commvault GO 2019 and where Metallic was launched, so happy birthday to Metallic. Some evolution and some recent news. Janet, walk us through what you guys have accomplished recently. >> Absolutely, so last year we launched with three product offerings to Metallic, Office 365 Backup, Endpoint Backup and Backup of Core data like VMs and files. In that year since we started with US only, we're now in Canada and Australia, as well as now in our first set of countries in EMEA which Mark will talk about it a little bit and we've greatly expanded our product offerings. One of the things we did, we just launched the discovery, which is a big deal for folks especially looking for compliance applications and their data protection. So we've had a real journey here and just this quarter, as you see we are doubling our product offerings to Metallic and tripling our country availability. So we're doing a lot and we're a leader in the data protection as a service space. >> A lot accomplished in just a 12 month time period, give me a little bit of a preview Janet, why was metallic launched last year for North America, US expanded to Canada and then I see it was announced... It was launched in Australia, New Zealand in the late summer 2020. I know that the cloud market... Their cloud adoption is quite high but give us a little bit of an overview of the actual go to market sequence from a regional perspective. >> Absolutely, and I'll want Mark to really take this one as well. We started in US only in our initial launch, that's where our first launch event was. That's where a lot of our pilot customers were, and then we expanded to Canada, Australia now EMEA, and this is very thoughtful. You have one chance to really launch in a geography. And we wanted it to take all the steps, whether it was compliance, trademarking, cloud storage availability. We leveraged our partnership with Microsoft and Azure for these launches. And really making sure we had everything lined up to best serve our customers. Mark what would you say about this strategy as well? >> Yeah, I think certainly, I mean the strategy is the right one, it's the right one for following reasons. If you look back to 12 months ago, I think in Colorado, I had a GO user event when we launched Metallic, I was fortunate enough to be hosting a number of EMEA partners and customers, and they were clamoring for the product, they're excited by it, they wanted it. We were (indistinct) some cases pressured to think about releasing it earlier. But all those customers wanted a product that was reversed, secure and coping with specific EMEA requirements that they have for the product in particular GDPR and supporting levels of compliance and data privacy that EMEA has rigorous standards for. And I think if you look at Commvault as a company, you know we take our customer's data extremely seriously. We've got one channels to get this right as Janet said, and I expect our customers absolutely expect and deserve right first time. And so when we launch a product like Metallic with the diversity of workloads, the rigorous high performance and secure environments, we want to make sure it's tested properly, it's compliant in all the jurisdictions. And even in Europe, we think about Europe, it's not one given country, even the EU have different countries with different legal and tax nuances. We want to make sure that when our customers get Metallic, 'cause our customers thankfully first launch in EMEA now can. That purchasing, that user experience is seamless sales and frictionless, and the product stands the promises that we make to those customers. So fully behind half phased release for Metallic as are some of our initial early adopter customers in the geographies that we've launched in already. >> So let's talk about some of the massive changes that we've all experienced since last year, Mark I would stick with you, talk to us about some of the changes that you seen from EMEA customers with respect to data protection and data security 'cause we've seen a lot of things going on globally, ransomware on the rise, every 11 seconds there's a ransomware attack. What are some of the recent challenges that you're hearing from customers that you believe Metallic EMEA is going to resolve? >> Yeah, I mean certainly even before the current COVID crisis, we were seeing a huge increase in uptake of customers wanting to use SaaS applications and to protect SaaS workloads. And the growth thing adoption of Office 365 clearly has driven the need for compelling SaaS based solutions for that market. You overlay on that, the situation that COVID has created for us all. Which in reality is denying our customers with its two most valuable important assets, access to premises and access to staff. And increasingly the staff it does have access to a storing, protecting, generating and creating data, not in the data center, not in the cloud but on laptops. So really for us it's a perfect opportunity and we're seeing an increase in demand from our customers wanting rapid solutions to protecting and managing data, to have low footprint in terms of skills and staff and to reduce the need for them to buy physical infrastructure and to expand an already at capacity set premises. And in many cases they can't even get access to, so it's very much a perfect storm for the solution that Metallic provides. >> Yeah Janet, following onto that and just in terms of when Mark mentioned, you know especially when this first happened, not being able to get access to the premises, this massive pivot to work from home and suddenly millions of endpoints scattered globally. Talk to us about some of the things that you saw here in North America in terms of customer demands changing. >> Oh that's a great question, we absolutely saw changes. I mean I go back to what Satya Nadella said, the CEO of Microsoft. He even said in April and may that what we are seeing is two years of digital transformation happening in a two month period. And that's absolutely what we're seeing, so the interest in fact as Mark mentioned, and then interest in protecting endpoints, your laptops and your desktop, as you have an increasingly remote and distributed workforce has completely changed. I mean when we spoke to you last year ago, we had endpoint backup more for completeness to round out our portfolio. We didn't expect it to be a lead offering and take off the way it has. But now with the changes everyone's seeing and with what IT teams need to do with what security teams and cloud architects need to do, we're absolutely seeing that need for endpoint protection grow. >> Yeah, and just to add to that Lisa is the endpoint potentially is also seeing a change and a shift in the types of markets that are looking to Metallic as a solution, recall that we originally targeted Metallic and SMB mid market, market where people were looking for simple, predictable, low cost but yet still scalable infrastructure. The massive drive to protect endpoints and to maintain compliance and control of data there, is actually driving large enterprise customers to Commvault and Metallic as a solution for protecting not hundreds of endpoints, not thousands, not tens of thousands but hundreds of thousands of endpoints for some of the customers that we're not talking to. >> And that's probably going to be something that we see becomes permanent. You know we're seeing so many leaders, Satya Nadella you mentioned Janet, we've heard other ones, Antonio Neri from HPE saying you know I expect at least 50% of the workforce to stay remote. So this is... Was a big need, it was a big boom and a good amount of this is probably not going to change. How is Metallic positioned to help your customers not just survive this time but be able to thrive and become the winners of tomorrow? >> I think one real advantage of Metallic is the two technologies that it's built on top of, one is Metallic part of Commvault, so what we can do is evolve with the needs of our customers, take all that IP, all those patents decide what workloads are going to help our customers through this times and release those as new offerings delivered as PaaS, it allows us to be agile and to pivot as needed. And that's what you see as I said we're doubling our product offering, we're taking that feedback in real time and that's something we'll be announcing very soon, next month. In addition to that, we're also build on top of Microsoft Azure. So we're leveraging certainly their enterprise scalability, the trust and security that they have because we're really something that flexes from the one terabyte dataset to the 10,000 terabyte as you're looking to scale and protect your infrastructure. So we are poised to take on that agility, that time like these demand. >> Do you think, oh go ahead Mark. >> I think just to add to that as well is if you look at our existing customers that have been traditionally using on-prem Commvault complete software or they bought on a perpetual or subscription basis. A number of those have been looking for Metallic to protect some specific workloads, like endpoint for example, but the way we've done this is, the Metallic solution on the on-prem solution are manageable from a single Commvault interface, a command central interface. So it's not a temporary decision to move to SaaS and then that customer then has to move it back in order to control and manage it in an on-prem environment. They get the best of both worlds from two solutions fit for the purpose they are intended from a company that has a 20 year reputation in designing, building and selling scalable, secure data protection infrastructure. >> Reasonable question in terms of the management console. So for example Mark, the situation that you're talking about customers that may have been using Commvault on-prem for a long time now have had in the last year and now in EMEA the opportunity to leverage SaaS data protection for Office Microsoft 365 for example, endpoints. Talk to me a little bit about the management of that, if a customer, legacy Commvault customer has been using on-prem and now they add Metallic for SaaS, data protection for say Microsoft 365, is that managed by a single console? >> Exactly, it's managed by a command center console. So they can see, manage, control report, all of data that exists within the Metallic SaaS based solution, and that sits within that on-prem or their hybrid cloud environment, giving them that, that total flexibility. And with the recent announcement, the launch earlier in October of MCSS on Microsoft, sorry at Metallic Cloud Storage Solution, that also helps their customers that aren't yet looking to move to metallic, to make the step, to put some of their on-prem data rapidly and easily into cloud as a target, as a metallic cloud storage service. And that's a future stepping stone to a full metallic software as a service solution, should they so choose for a 365 or endpoint? So we're giving customers the ability to move from self-manage to fully managed with a SaaS solution in the middle. >> And for that target market perspective, Mark, some of the things that we've seen globally that are new targets, you mentioned ransomware on the rise, healthcare organizations, schools and governments, are there any specific industries that are going to be leading edge for Metallic in EMEA. >> What we've seen from the initial market data and the market uptake by segment from the America's names that launched is interest from every sector, but a particular interest from the sectors where technology is a key differentiator, particularly finance, banking, insurance, and the telco sector, the tech sector and the retail sector. Interestingly enough, we're also seeing in the government and public services sector from our recent Azure launch and some of the demand and interest in EMEA is validating this, customers in public sector organizations, central and local government who traditionally have been fixated on the CapEx buying model and on-prem solutions, moving and starting to look increasingly at SaaS to get solutions up running, protected and secured rapidly in the cloud. And so we're seeing an encouraging up-taking public sector organizations, which are using SaaS as a way to move from CapEx to OPEX models which is particularly reassuring. >> And Janet question for you if we look at data protection as a service, the fastest growing market segment rather in data protection market, what are some of the things that knowing Metallic's first year in the evolution, the changes that the world has seen, but also this demand for data protection as a service, what are some of the things that we can expect in Metallic's second year? >> Yeah so, first you're absolutely right. Data protection as a service is becoming increasingly popular. You know these are cloud based solutions, also known as backup as a service. And I think what we're finding as we talk to customers is everyone has a cloud based initiative, whether they're starting it or they're well on their way. So having a data protection as a service solution like Metallic can either be your first move into the cloud starting with your backup targets and leveraging MCSS as Mark explained as one way to do that, or it can just be another point in a customer's hybrid story. How they're starting to leverage data protection as a service, SaaS delivery. And there's this whole notion now of SaaS for SaaS. Now you need SaaS backup for your SaaS application to follow how the data moves, and that's what we're doing for Office 365. In the second year, we're certainly aiming to continue increasing our workload, supported the products that... And continuing our geo-expansion as we are right now with the EMEA, this is certainly critical as we continue. We'll also be looking to engage local partners, we work with resellers and distributors today, and we're also going to continue expanding our offerings in Azure marketplace. We went live in Azure marketplace last quarter and we're seeing transactions come through there and we want to continue building out our marketplace model as well. >> Last question Janet, you mentioned SaaS for SaaS and there's been a lot of talk about that recently with customers in every segment. And there was this sort of this a shared responsibility model that Microsoft has in Salesforce right in box. But it's been interesting and a lot of customers I've spoken with in the last few months in salesforce ended support for the data recovery service I think in end of July going, wait we thought it was in the cloud, we have to back it up. So is that another direction in terms of Metallics future of being able to protect more types of SaaS workloads besides Microsoft 365? >> Well that's certainly the idea and starting with Office 365, is how do we compliment what Microsoft already offers. Office 365 Salesforce, all of these tools, they are workflow tools, they're integral in organizations or they're just holding critical data. So how do we compliment that through data backup and protection that give them the controls they need. Whether it's policy customization, smart configurations to help them through this and now E discovery on top to be able to search and manage compliance needs. So we really want to be that kind of extra security blanket for all of these SaaS applications and that's really what we're aiming to do over time but Office 365 is our focus right now. >> Yeah, I think just pick out Lisa on Janet's point about the two points of scale for us about scaling out and launching in new markets and bringing new workloads into the Metallic portfolio. You know one of the things that we understand is we clearly we've seen significant demand for Office 365 and endpoint ussually as for Metallic. But let's also not lose sight of the fact that a number of organizations are coming to us to protect their VMs and their file server environments so being initially in small environments. And they're starting to ask us specifically about our plans to incorporate additional enterprise type on-prem workloads in a Metallic environment. And the fact that we've built 20 years of expertise in IOP in that space, we've been probably the quickest to launch the most innovative and wide this range of workloads in our on-prem and subscription based software makes it far easier for us to pivot and to extend over time rapidly, the workloads that Metallic supports for customers wanting to move traditionally on-prem workloads. That I'll just say 365 endpoint but VMs and other database workloads into the cloud. And that's a unique differentiator for where Metallic can take our customers, not just geographically but in terms of the diversity of workloads that we'll be able to cover. >> Great point Mark, absolutely. >> Well thank you both for explaining the evolution of Metallic, A Commvault Venture in its first year, giving us an insight into some of the recent new announcements and a peek into what's to come. Janet, Mark, we appreciate your time. >> Yeah, thank you. >> That's being a pleasure, thank you. >> For my guests, I'm Lisa Martin, you're watching theCUBE conversation. (upbeat music)

(bright upbeat music) >> Announcer: From theCUBE studios in Palo Alto in Boston, connecting with thought leaders around the globe, these are Cloud Native Insights. >> Hi, I'm Stu Miniman, the host of Cloud Native Insights. We talk about cloud native, we're talking about how customers can take advantage of the innovation and agility that's out there in the clouds, one of the undercurrents, not so hidden if you've been watching the program so far. We've talked a bit about serverless, say something that's helping remove the friction, allowed developers to take advantage of technology and definitely move really fast. So I'm really happy to welcome to the program, for coming from Fauna. First of all, I have the CTO and Co-founder, who's Evan Weaver. And also joining him is the new CEO Eric Berg. They said, both from Fauna, talking serverless, talking data as an API and talking the modern database. So first of all, thank you both for joining us. >> Thanks for having us Stu. >> Hi, good to be here. >> All right, so Evan, we're going to start with you. I love talking to founders always. If you could take us back a little bit, Fauna as a project first before it was a company, you of course were an early employee at Twitter. So if you could just bring us back a little bit, what created the Fauna project and bring us through a brief history if you would. >> So I was employee 15 and Twitter, I joined in 2008. And I had a database background, I was sort of a performance analyst and worked on Ruby on Rails sites at CNET networks with the team that went on to found GitHub actually. Now I went to Twitter 'cause I wanted Twitter the product to stay alive. And for no greater ambition than that. And I ended up running the back end engineering team there and building out all the distributed storage for the core business objects, tweets, timelines, the social graph, image storage, the cache, that kind of thing. And this was early in the cloud era. API's were new and weird. You couldn't get Amazon EC2 off the shelf easily. We were racking hardware and code ancient center. And there were no databases or platforms for data of any kind. They really let us the Twitter engineering team focus on building the product. And we did a lot of open source work there. Some of which has influenced Fauna, originally, Twitter's open source was hosted on the Fauna GitHub account, which predated Twitter like you mentioned. And I was there for four years build out the team, basically scaled the site, especially scaled the Twitter.com API. And we just never found a platform which was suitable for what we were trying to accomplish. Like a lot of what Twitter did was itself a platform. We had developers all over the world using the Twitter API to interact with tweets. And we're frustrated that we basically had to become specialists in data systems because there wasn't a data API, we can just build the product on. And ultimately, then data API that we wished we had, is now Fauna. >> Well, it's a story we've loved hearing. And it's fascinating one, is that the marketplace wasn't doing what we needed. Often open source is a piece of that, how do we scale that out? How do we build that? Realized that the problem that you have is what others have. And hey, maybe there's a company. So could you give us that transition, Fauna as a product, as a company, where was it understood that, hey, there's a lot of other people that can take advantage from some of the same tools that you needed before. >> I mean, we saw it in the developers working with the Twitter platform. We weren't like, your traditional database experiences, either manage cloud or on-prem, you have to administrate the machine, and you're responsible for its security and its availability and its location and backups and all that kind of thing. People building against Twitter's API weren't doing that. They're just using the web interface that we provided to them. It was our responsibility as a platform provider. We saw lots of successful companies being built on the API, but obviously, it was limited specifically to interacting with tweets. And we also saw peers from Twitter who went on to found companies, other people we knew in the startup scene, struggling to just get something out the door, because they had to do all this undifferentiated heavy lifting, which didn't contribute to their product at all, if they did succeed and they struggled with scalability problems and security problems and that kind of thing. And I think it's been a drag on the market overall, we're essentially, in cloud services. We're more or less built for the enterprise for mature and mid market and enterprise companies that already had resources to put behind these things, then wasn't sort of the cloud equivalent of the web, where individuals, people with fewer resources, people starting new projects, people doing more speculative work, which is what we originally and Jack was doing at Twitter, it just get going and build dynamic web applications. So I think the move to cloud kind of left this gap, which ultimately was starting to be filled with serverless, in particular, that we sort of backtracked from the productivity of the '90s with the lamp era, you can do everything on a single machine, nobody bothered you, you didn't have to pay anyone, just RPM install and you're good to go. To this Kubernetes, containers, cloud, multi site, multi region world where it's just too hard to get a basic product out the door and now serverless is sort of brought that around full circle, we see people building those products again, because the tools have probably matured. >> Well, Evan, I really appreciate you helping set the table. I think you've clearly articulated some of the big challenges we're seeing in the industry right now. Eric, I want to bring you into the conversation. So you relatively recently brought in as CEO, came from Okta a company that is also doing quite well. So give us if you could really the business opportunity here, serverless is not exactly the most mature market, there's a lot of interest excitement, we've been tracking it for years and see some good growth. But what brought you in and what do you see is that big opportunity. >> Yeah, absolutely, so the first thing I'll comment on is what, when I was looking for my next opportunity, what was really important is to, I think you can build some of the most interesting businesses and companies when there are significant technological shifts happening. Okta, which you mentioned, took advantage of the fact of SaaS application, really being adopted by enterprise, which back in 2009, wasn't an exactly a known thing. And similarly, when I look at Fauna, the move that Evan talked about, which is really the maturation of serverless. And therefore, that as an underpinning for a new type of applications is really just starting to take hold. And so then there creates opportunities that for a variety of different people in that stack that to build interesting businesses and obviously, the databases is an incredibly important part of that. And the other thing I've mentioned is that, a lot of people don't know this but there's a very good chunk of Okta's business, which is what they call their customer identity business, which is basically, servicing of identity is a set of API's that people can integrate into their applications. And you see a lot of enterprises using this as a part of their digital transformation effort. And so I was very familiar with that model and how prevalent, how much investment, how much aid was out there for customers, as every company becoming a software company and needing to rethink their business and build applications. And so you put those two trends together and you just see that serverless is going to be able to meet the needs of a lot of those companies. And as Evan mentioned, databases in general and traditionally have come with a lot of complexity from an operational perspective. And so when you look at the technology and some of the problems that Fauna has solved, in terms of really removing all of that operational burden when it comes to starting with and scaling a database, not only locally but globally. It's sort of a new, no brainer, everybody would love to have a database that scales, that is reliable and secure that they don't have to manage. >> Yeah, Eric, one follow up question for you. I think back a few years ago, you talked to companies and it's like, okay, database is the center of my business. It's a big expense. I have a team that works on it. There have been dealt so much change in the database market than most customers I talked to, is I have lots of solutions out there. I'm using Mongo, I've got Snowflake, Amazon has flavors of things I'm looking at. Snowflake just filed for their IPO, so we see the growth in the space. So maybe if you could just obviously serverless is a differentiation. There's a couple of solutions out there, like from Amazon or whether Aurora serverless solution but how does Fauna look to differentiate. Could you give us a little bit of kind of compared to the market out there? >> Sure, yeah, so at the high level, just to clarify, at the super high level for databases, there tends to be two types operational databases and then data warehouse which Snowflake is an example of a data warehouse. And as you probably already know, the former CEO of Snowflake is actually a chairman of Fauna. So Bob Muglia. So we have a lot of good insight into that business. But Fauna is very much on the operational database side. So the other half of that market, if you will, so really focused on being the core operational store for your application. And I think Evan mentioned it a little bit, there's been a lot of the transformation that's happened if we rewind all the way back to the early '90s, when it was Oracle, and Microsoft SQL Server were kind of the big players there. And then as those architectures basically hit limits, when it came to applications moving to the web, you had this whole rise in a lot of different no SQL solutions, but those solutions sort of gave up on some of the promises of a relational database in order to achieve some of the ability to scale in the performance required at the web. But we required then a little bit more sophistication, intelligence, in order to be able to basically create logic in your application that could make up for the fact that those databases didn't actually deliver on the promises of traditional relational databases. And so, enter Fauna and it's really sort of a combination of those two things, which is providing the trust, the security and reliability of a traditional relational database, but offering it as serverless, as we talked about, at the scale that you need it for a web application. And so it's a very interesting combination of those capabilities that we think, as Evan was talking about, allows people who don't have large DevOps teams or very sophisticated developers who can code around some of the limitations of these other databases, to really be able to use a database for what they're looking for. What I write to it is what I'm going to read from it and that we maintain that commitment and make that super easy. >> Yeah, it's important to know that the part of the reason that operational database, the database for mission critical business data has remained a cost center is because the conventional wisdom was that something like Fauna was impossible to build. People said, you literally cannot in information science create a global API for data which is transactional and consistent and suitable for relying on, for mission critical, user login, banking payments, user generated content, social graphs, internal IT data, anything that's irreplaceable. People said, there can be no general service that can do this ubiquitously a global internet scale, you have to do it specifically. So it's sort of like, we had no power company. Instead, you could call up Amazon, they drive a truck with a generator to your house and hook you up. And you're like, right on, I didn't have to like, install the generator myself. But like, it's not a good experience. It's still a pain in the neck, it's still specific to the location you're at. It's not getting utility computing from the cloud the way, it's been a dream for many decades that we get all our services through brokers and API's and the web and it's finally real with serverless. I want to emphasize that the Fauna it technology is new and novel. And based on and inspired by our experience at Twitter and also academic research with some of our advisors like Dr. Daniel Abadi. It's one of the things that attracted us, Snowflake chairman to our company that we'd solve groundbreaking problems in information science in the cloud, just the way Snowflakes had. >> Yeah, well and Evan, yeah please go on Eric. >> Yeah, I'm just going to have one thing to that, which is, in addition, I think when you think about Fauna and you mentioned MongoDB, I think they're one of a great examples of database companies over the last decade, who's been able to build a standalone business. And if you look at it from a business model perspective, the thing that was really successful for them is they didn't go into try to necessarily like, rip and replace in big database migrations, they started evolving with a new class of developers and new applications that were being developed and then rode that obviously into sort of a land and expand model into enterprises over time. And so when you think about Fauna from your business value proposition is harnessing the technological innovation that Evan talked about. And then combining this with a product that bottoms up developer first business motion that kind of rides this technological shift into you creating a presence in the database market over time. >> Well, Evan, I just want to go back to that, it's impossible comment that you made, a lot of people they learn about a technology and they feel that that's the way the technology works. Serverless is obviously often misunderstood from the name itself, too. We had a conversation with Andy Jassy, the CEO of AWS a couple years ago, and he said, "If I could rebuild AWS from the ground up today, "it would be using all serverless," that doesn't mean only lambda, but they're rebuilding a lot of their pieces underneath it. So I've looked at the container world and we're only starting the last year or so, talking about people using databases with Kubernetes and containers, so what is it that allows you to be able to have as you said, there's the consistency. So we're talking about acid there, not worry about things like cold starts, which are thing lots of people are concerned about when it comes to serverless and help us understand a little bit that what you do and the underlying technologies that you leverage. >> Yeah, databases are always the last to evolve because they're the riskiest to change and the hardest to build. And basically, through the cloud era, we've done this lift and shift of existing on premises solutions, especially with databases into cloud machines, but it's still the metaphor of the physical computer, which is the overriding unit of granularity mental concept, everything like you mentioned, containers, like we had machines then we had Vms, now we have containers, it's still a computer. And the database goes in that one computer, in one spot and it sits there and you got to talk to it. Wherever that is in the world, no matter how far away it is from you. And people said, well, the relational database is great. You can use locks within a single machine to make sure that you're not conflicting your data when you update it, you going to have transactionality, you can have serialize ability. What do you do, if you want to make that experience highly available at global scale? We went through a series of evolutions as an industry. From initially that the on-prem RDBMS to things like Google's percolator scheme, which essentially scales that up to data center scale and puts different parts of the traditional database on different physical machines on low latency links, but otherwise doesn't change the consistency properties, then to things like Google Spanner, which rely on synchronized atomic clocks to guarantee consistency. Well, not everyone has synchronized atomic clocks just lying around. And they're also, their issues with noisy neighbors and tenancy and things because you have to make sure that you can always read the clock in a consistent amount of time, not just have the time accurate in the first place. And Fauna is based on and inspired and evolved from an algorithm called Calvin, which came out of a buddy's lab at Yale. And what Calvin does is invert the traditional database relationship and say, instead of doing a bunch of work on the disk and then figuring out which transactions won by seeing what time it is, we will create a global pre determined order of transactions which is arbitrary by journaling them and replicating them. And then we will use that to essentially derive the time from the transactions which have already been committed to disk. And then once we know the order, we can say which one's won and didn't win and which happened before, happen after and present the appearance of consistency to all possible observers. And when this paper came out, it came out about a decade ago now I think, it was very opaque. There's a lot of kind of hand waving exercises left to the reader. Some scary statements about how wasn't suitable for things that in particular SQL requires. We met, my co-founder and I met as Fauna chief architect, he worked on my team at Twitter, at one of the database groups. We were building Fauna we were doing our market discovery or prototyping and we knew we needed to be a global API. We knew we needed low latency, high performance at global scale. We looked at Spanner and Spanner couldn't do it. But we found that this paper proposed a way that could and we can see based on our experience at Twitter that you could overcome all these obstacles which had made the paper overall being neglected by industry and it took us quite a while to implement it at industrial quality and scale, to qualify it with analysts and others, prove to the world that it was real. And Eric mentioned Mongo, we did a lot of work with Cassandra as well at Twitter, we're early in the Cassandra community. Like I wrote, the first tutorial for Cassandra where data stacks was founded. These vendors were telling people that you could not have transactionality and scale at the same time, and it was literally impossible. Then we had this incrementalism like things with Spanner. And it wasn't till Fauna that anyone had proved to the world that that just wasn't true. There was more marketing around their failure to solve the information science problem, than something fundamental. >> Eric, I'm wondering if you're able to share just order of magnitude, how many customers you have out there from a partnership standpoint, we'd like to understand a little bit how you work or fit into the public cloud ecosystems out there. I noticed that Alphabets General Venture Fund was one of the contributors to the last raise. And obviously, there's some underlying Google technology there. So if you could just customers and ecosystem. >> Yeah, so as I mentioned, we've had a very aggressive product lead developer go to market. And so we have 10s of thousands of people now on the service, using Fauna at different levels. And now we're focused on, how do we continue to build that momentum, again, going back to the model of focus on a developer lead model, really what we're focused on there is taking everything that Evan just talked about, which is real and very differentiated in terms of the real core tech in the back end and then combining that with a developer experience that makes it extremely easy to use and really, we think that's the magic in terms of what Fauna is bringing, so we got 10s of thousands of users and we got more signing up every day, coming to the service, we have an aggressive free plan there and then they can migrate up to higher paying plans as they consume over time. And the ecosystem, we're aggressively playing in the broader serverless ecosystem. So what we're looking at is as Evan mentioned, sometimes the databases is the last thing to change, it's also not necessarily the first thing that a developer starts from when they think about building their application or their website. And so we're plugging into the larger serverless ecosystem where people are making their choices about potentially their compute platform or maybe a development platform like I know you've talked to the folks over at JAMstack, sorry at Netlify and Purcell, who are big in the JAMstack community and providing really great workflows for new web and application developers on these platforms. And then at the compute layer, obviously, our Amazon, Google, Microsoft all have a serverless compute solution. CloudFlare is doing some really interesting things out at the edge. And so there's a variety of people up and down that stack, if you will, when people are thinking about this new generation of applications that we're plugging into to make sure that the Fauna is that the default database of choice. >> Wonderful, last question, Evan if I could, I love what I got somebody with your background. Talk about just so many different technologies maturing, give us a little bit as to some of the challenges you see serverless ecosystem, what's being attacked, what do we still need to work on? >> I mean, serverless is in the same place that Lamp was in the in the early '90s. We have the old conservatives ecosystem with the JAMstack players that Eric mentioned. We have closed proprietary ecosystems like the AWS stack or the Google Firebase stack. As to your point, Google has also invested in us so they're placing their bets widely. But it's seeing the same kind of criticism. That Lamp, the Linux, Apache, MySQL, PHP, Perl, it's not mature, it's a toy, no one will ever use this for real business. We can't switch from like DV2 or mumps to MySQL, like no one is doing that. The movement and the momentum in serverless is real. And the challenge now is for all the vendors in collaboration with the community of developers to mature the tools as those the products and applications being built on the new more productive stack also mature, so we have to keep ahead of our audience and make sure we start delivering and this is partly why Eric is here. Those those mid market and ultimately enterprise requirements so that business is built on top of Fauna today, can grow like Twitter did from small to giant. >> Yeah, I'd add on to that, this is reminiscent for me, back in 2009 at Okta, we were one of the early ISVs that built on in relied 100% on AWS. At that time there was still, it was very commonplace for people racking and stacking their own boxes and using Colo and we used to have conversations about I wonder how long it's going to be before we exceed the cost of this AWS thing and we go and run our own data centers. And that would be laughable to even consider today, right, no one would ever even think about that. And I think serverless is in a similar situation where the consumption model is very attractive to get started, some people sitting there, is it going to be too expensive as I scale. And as Evan mentioned, when we think about if you fast forward to kind of what the innovation that we can anticipate both technologically and economically it's just going to be the default model that people are going to wonder why they used to spend all these time managing these machines, if they don't have to. >> Evan and Eric, thank you so much, is great to hear the progress that you've made and big supporters, the serverless ecosystem, so excited to watch the progress there. Thanks so much. >> Thanks Stu. >> Thanks for having us Stu. >> All right and I'm Stu Miniman. Stay tuned. Every week we are putting out the Cloud Native Insights. Appreciate. Thank you for watching. (bright upbeat music)

>>from the Cube >>Studios in Palo Alto and Boston connecting with thought leaders all around the world. This is a cube conversation. >>I am stupid, man. And welcome to a cube conversation. Really? Please welcome back to the program. One of our Cube alumni, Sony, Ge and Donnie. She is a co founder and also the business off of pensando. Tony, thanks so much for joining us. >>I thank you for having me here. >>All right. So, Sonny, we've had you on the program a few times. You know, those that have watched the program or followed your career? You've had a story career. You know, I've worked with you as a partner back through some of the spinning disk. You're one of the mpls group. And now, of course, Pensando we helped launch towards the end of 2019. I just want to take a step back and, you know, understand, You know, how did you find yourself in the startup world? >>You know, I got involved with startup ventures as part of the Mpls team. This is going back now. Gosh, 20 years ago, in calendar year 2000 my first venture was with Andy ammo. It was a very unique situation that Mario look up on myself or part of a set up on a startup venture. But all four of us, the Mpls group, did not have any equity in it. Look, and I basically what asked to operate within the with that venture to ensure its ultimate success from a product execution on the go to market perspective? Ah, lot of those elements did not exist from a go to market perspective in Cisco at that time, and it was basically a ground up effort for look and me to not have any financial association with the outcome off the Andy, um, a venture, but at the same time, take on the responsibility from the execution perspective and building up the whole go to market. >>Yeah, so, you know, talking about that these startups, you've been apart of two things. First of all, you were part of and, ya know, you ova in CNI. So did you need to learn Italian to be part of these projects? But more importantly, how did how did you work on that? You know, product customer fit, understanding what the build and, you know, you talk about right How do you make some things that festival? It is super challenging. >>Yeah, well, first and foremost, I think I've been fortunate in that the group that we're all part off it is definitely Italian Indian. And some folks, like from Indiana, for example, like Randy Pond, who is part of this venture with us at Pensando. If I if I would go back and take a look at the simple formula, I mean Mario look, and from, ah, they're veterans in this industry. And they typically focused on the conceiving off the idea and the brought up, uh, and starting with a clean slate approach. Of course, I participate from a market validation development, competitive landscape on a business on all related aspects, bringing the product to market on how that maps into customers and partners what we have consistently focused on market disruption. Particularly for the last two decades, the biggest focus has been on what are the market transitions occurring both from a business and a technology perspective on that is ultimately what creates the opportunity to emerge on and drive these concepts into reality and what yourself, in a market leadership position, is to capture the transition at the right time. >>Yeah, I think back. You know, some of your previous ventures and understand, you know, some of the waves of technologies coming together sometimes the maturity of a technology or being able to take advantage of something new to talk specifically about. Pensando what are you know, those waves of change and the technology coming together that makes the opportunity that you're in today? >>Well, I mean, if you go back and you take a look at really what has been exciting about this pensando opportunity has been to look at the unique ability that have been coming upon us. You know, with this market transition where the cloud is moving to the edge, what is ultimately driving this movement to the edge has been the application. Uh, the applications is is you know, whether it's driven by technology trends like five G, for example. Ah, and and the fact that bulk off what the customer's data is being driven is going to be at the edge. That when when you look at the cloud moving to the edge and evolving that with the transitions occurring, ah, this will require deep innovation. Deep innovation in the areas of distributed network processing security, like encryption, full observe ability while you have turned on encryption, traffic engineering and doing it at very low, predictable agency at the speeds of 100 gig and above all, doing it on a small footprint. We were really the only guys and gals who could do this. And we have done it, >>Yeah, so certainly some really big challenges that they laid out there bring >>us inside >>a little bit. You know, customers. You know, I think about, you know, when I've been watching edge computing for the last three or four years. Uh, you know, it's still relatively early days for customers, but there's a lot of technical challenges there, So help us understand how much you know it was you had technology that could help solve something and how much it is driven by some of the customers that you've been talking to over the years >>Now. One of the key things that we learned and this was going back to the early days of Cisco is that everything we were doing, we had the customer at the center off and at the heart off what innovation we were building from an engineering perspective. You want to build things that can have the most impact in the marketplace and within your customer base. So, uh, one of the early times we went back, who do getting our customers involved in the innovations we were bringing to bear. I still have recollections off a blueprint that we had iterated upon, uh, and sitting in a room, whether it was with the likes of Josh Matthew at Goldman Sachs all whether it was with some of our early cloud customers like the Oracle Cloud, to better understand with these innovation and these blueprints, what were their burning problems? What were they used, cases that we could really go and tackle? So it is one thing to think about market destructions. It's another to bring it to life and having customers engaged with you during the early phases. Off as you are incubating, something is a very important item because it helps you focus your biggest energy on the areas so that you can put your arms around what problems are worth solving. And how can you bring that to life with with customers? Use case. And this is something we have done time and over again. So this is a constant refinement off what we have been doing now for now, to over two decades. As I said, >>Yeah, it's, you know, fascinating here. And when you've got the chief business officer idle, Sony, You know, one of the biggest changes, obviously, is if I look back in the spin ins, you kind of understood how to go to market was what was involved the, you know, the Cisco execution machine that the sales process that they had in plug in a product, that they would help. All right, what you're doing now, you've got some, you know, feel, William partnerships. You have relationship with customers, help us understand a little bit. You know the update on the go to market, how you have. I have a solution that fits for not only the end users, but through multiple different, uh, you know, go to market partners. >>So I think it's, you know, it's very important that as a startup we stay very close to our customers and apart, not just men. We are thinking about what the innovation is and how can it solve their problems. But I think in a world where the way we want to go solve for what? The customer where we want the customer, where our customers want us to be our partnerships is a core part of it. I mean, if you look at from the early days we secured successfully funding from our customers and our strategic partners and it is these customers and strategic partners that are shaping the roadmap on are shaping the routes to market on. What we're doing is we're successfully not only delivering the product, so these strategic customers and partners, but we're also then replicating it across the verticals. If you think about in the enterprise space, our focus has been the focus on regulated market markets where security is essential. Real time, observe ability that can increase your security posture is a very important element. So taking the blueprints that we're taking into global financial services customers, the healthcare industry, the the education market on the federal market, then those are the industries that really care about, and I in regulated markets where we can take the blueprint that we have already built on an amplify across those customers. So there again includes alignment and a partnership with HP. We're working very closely that, while recognizing that we will be doing strategic elements only with partners like HP, we're also on boarding and getting certifications done with Dell because most enterprises have at least dual source vendors from a server, so that that is one aspect. The other aspect is working in a high touch model with the cloud customers and having the opportunity to deliver to them Ah, and onboard them from a production worthy perspective while taking that same blueprint and applying it to other cloud customers and other service provider edge providers that can take advantage of the similar capability. >>Yeah, um, I'm curious. Sony, you know, obviously, the cloud is a space that has been going through a lot of change and accelerating. You know, I'd say much faster than traditional networking did. So you know, curious what you see what you're hearing from customers when they talk about you know, their needs for your solution, what they're doing with multi cloud environment. What is that? That landscape you. And I guess we would love to hear a little bit about how you would compare and contrast yourself. The other solutions out there the one that comes to mind, of course, is you know, eight of us what they're doing with the Annapurna chip in there nitro offering as part of their out. >>You know, as I mentioned earlier, I think the cloud is pushing to the edge. There's a high demand for a lot of packet processing needs with these New Age applications. Customers want to build on and give the you know, we want to be in a position to provide through the democratization and open availability off our products to multiple cloud providers, our technology and as they are experiencing tremendous growth, they're seeking to build cloud with more capacity, with greater degree off security and services functionality. And the ability to process a lot of data at the edge is with millions of simultaneous connections happening at a very small footprint. And that's where we come in. The value that we are essentially providing who not only the existing cloud strategic partners but additional cloud customers we're taking into production this year is that we are enabling them to leapfrog the nitro technology on multiple, whether it is the ability to ah have predictably low latency on and consistently low jitter in the nanoseconds. That is the eight times superior than what a nitro can do today, or the ability to pack their toe process up to nine times more backend processes in the millions of on the ability to do it in a power footprint, which is almost 1/3 that of what you would need on AWS nitro, where they need five times more nitro elements than then we can with a single device, Um, or whether it is the ability now to handle not just power and latency, but millions off flows that can run simultaneously on maintaining the state of all of those and the power of the end, the ability to run multiple services. Uh, with security turned on at the same time are all elements that really differentiate us on. This technology is now readily available to all of us. >>All right, so I understand some of the technical issue items that you're stating there. What I'm curious about is when I look at out both, most customers don't really think about the night. It's that Amazon's providing an extension of their solution into my environment, and they manage everything and so you know, you can't talk about multi cloud environment without talking about Amazon is every customer almost everything right? More than one cloud in one of them is almost always Amazon, though. How does your solution fit into that whole discussion? And then? >>So I think that, you know, one of the things that becomes very important is that if I put my customer enterprise customer hat on, I want to be an enabling my private cloud the private cloud that I build. You have the ability to not just have the option to the port and Amazon cloud, but I typically already and minimal child and barn. So while Outpost and Nitro Nitrogen really enabling, are supposed to deliver those services on our customer's premises, it's only allowing that customer to be locked into one way off dealing with one public cloud company. But if I had to go and think about as I build out my hybrid cloud strategy as an enterprise customer, I want to have the same building blocks on the same policy models that are consistent with all the with the entire dress off cloud vendors that I'm dealing with. The bulk of our customers are essentially telling us I don't want to be locked into a single public cloud company from a hybrid strategy. I want to have the ability to drive a public, private cloud architect that is cloud like from a policy delivery perspective. But at the same time, I want to have the flexibility off deploying a multi cloud and BART, and what we would provide them is the consistency off that same policy model that you would only find in a public cloud with the freedom to not have to buy themselves or lock themselves up into a single public cloud costs. >>So your team, you said, over two decades of experience, there have been some global impacts that have happened during that you got together in 2000. 2001 was right there in front of you that the 2008 you know, down in there, though you're in 2020 obviously the global endemic, as you know, broad financial ripples. How's this impacting Dondo? How's that impacting your discussions with your partners and your customers? >>Well, you know, honestly, I would say that we, like everyone else, have been affected by the pandemic, and we pray that everyone recovers soon with minimum lost to themselves and their families. And this is something very personal. This is here. I feel very passionate about hoping that everybody comes through with this on and their families are all OK. That's all the most important thing in my mind now for us, from a pandemic perspective. What this has done is it has made us more resolute to continue to execute remotely to the best of our ability to meet our customers. Expectations. The advice that I would give to other startups is Keep your head down. Focus on the 80 20 rule, execute on 20% of the things that need to be done, that we'll have 80% of the impact to your business, including undeterred product execution. Stay close to your customers and your partners. Spend your cash judiciously. You know, be very careful on where you're spending your money to make it last. As long as you can ride this pandemic out and double down on being close to your partners and customers. Fortify your sales plans. Meet your customers where they are not where you thought they were, but where they really are and partner with them on this journey and partner with your supply chain. You're going to need that. So this is your time to really be a partner to them, as opposed to see how can you change them? No, no. The really partner with your supply chain Because you're gonna need that. >>Yeah, that's a very sound advice there, Sony. While we're talking advice that, you know, you're very successful career, I'm wondering what advice you would give the other women look at pursuing careers. In fact, specifically, if you know they wanted, you know, start a startup, be a founder, whether that in Silicon Valley or outside, what advice would >>you know? My advice would be to have an undeterred focus. Focus is extremely important. Look, I used to always remind me, Sony, when you're focused on two things, you're d focus. So focus on data. Focus, be driven. Believe in the vision that you have set out for yourself and your team on and keep your eye on the customer. I think in customers successful on your success. That's the message I would give. I would give that same message. My female and the male colleagues. >>Alright, well, we know that you and your team. Sony are very focused, so I'll give you the final word. Gives a little look forward if we go forward. You know, 18 to 24 months. What should we be expecting to see from PENSANDO and your solution? >>Well, in the next 18 to 24 months, we would like to meet and hopefully also exceed our customer's expectation in terms of product execution and the ramp off course. Profitability will be a very important aspect that we're going to keep a very close eye. I think it's too early to be thinking of an ideal, and our focus remains to be on customer success. We have been in the market for a little over. I was a little less than six months. Ah, with the product, September 2019 October 2019 is really when we launched the company on and, uh, the customer always is at the center of everything we do. So that's where we're gonna be focusing on product execution and ramp ramp off product, ramp off estimates. >>Well, so needy. And Dani, it's a pleasure to catch up with you. Thank you so much in the state. >>Thank you. You too. >>Alright. Be sure to check out the cube dot net for all the interviews, you can go see the launch videos that did at go back office in New York City from 2019. If you go to the cube dot net and many more interviews from Sony and her team, I'm stew Minimum. And thank you for watching you. Yeah, yeah, yeah, yeah, yeah.

>> Narrator: From around the globe it's theCUBE! With digital coverage of UiPath Live, the release show. Brought to you by UiPath. >> Hi everybody this is Dave Valenti, welcome back to our RPA Drill Down. Ted Kummert is here he is Executive Vice President for Products and Engineering at UiPath. Ted, thanks for coming on, great to see you. >> Dave, it's great to be here, thanks so much. >> Dave your background is pretty interesting, you started as a Silicon Valley Engineer, they pulled you out, you did a huge stint at Microsoft. You got experience in SAS, you've got VC chops with Madrona. And at Microsoft you saw it all, the NT, the CE Space, Workflow, even MSN you did stuff with MSN, and then the all important data. So I'm interested in what attracted you to UiPath? >> Yeah Dave, I feel super fortunate to have worked in the industry in this span of time, it's been an amazing journey, and I had a great run at Microsoft it was fantastic. You mentioned one experience in the middle there, when I first went to the server business, the enterprise business, I owned our Integration and Workflow products, and I would say that's the first I encountered this idea. Often in the software industry there are ideas that have been around for a long time, and what we're doing is refining how we're delivering them. And we had ideas we talked about in terms of Business Process Management, Business Activity Monitoring, Workflow. The ways to efficiently able somebody to express the business process in a piece of software. Bring systems together, make everybody productive, bring humans into it. These were the ideas we talked about. Now in reality there were some real gaps. Because what happened in the technology was pretty different from what the actual business process was. And so lets fast forward then, I met Madrona Venture Group, Seattle based Venture Capital Firm. We actually made a decision to participate in one of UiPath's fundraising rounds. And that's the first I really came encountered with the company and had to have more than an intellectual understanding of RPA. 'Cause when I first saw it, I said "oh, I think that's desktop automation" I didn't look very close, maybe that's going to run out of runway, whatever. And then I got more acquainted with it and figured out "Oh, there's a much bigger idea here". And the power is that by really considering the process and the implementation from the humans work in, then you have an opportunity really to automate the real work. Not that what we were doing before wasn't significant, this is just that much more powerful. And that's when I got really excited. And then the companies statistics and growth and everything else just speaks for itself, in terms of an opportunity to work, I believe, in one of the most significant platforms going in the enterprise today, and work at one of the fastest growing companies around. It was like almost an automatic decision to decide to come to the company. >> Well you know, you bring up a good point you think about software historically through our industry, a lot of it was 'okay here's this software, now figure out how to map your processes to make it all work' and today the processes, especially you think about this pandemic, the processes are unknown. And so the software really has to be adaptable. So I'm wondering, and essentially we're talking about a fundamental shift in the way we work. And is there really a fundamental shift going on in how we write software and how would you describe that? >> Well there certainly are, and in a way that's the job of what we do when we build platforms for the enterprises, is try and give our customers a new way to get work done, that's more efficient and helps them build more powerful applications. And that's exactly what RPA does, the efficiency, it's not that this is the only way in software to express a lot of this, it just happens to be the quickest. You know in most ways. Especially as you start thinking about initiatives like our StudioX product to what we talk about as enabling citizen developers. It's an expression that allows customers to just do what they could have done otherwise much more quickly and efficient. And the value on that is always high, certainly in an unknown era like this, it's even more valuable, there are specific processes we've been helping automate in the healthcare, in financial services, with things like SBA Loan Processing, that we weren't thinking about six months ago, or they weren't thinking about six months ago. We're all thinking about how we're reinventing the way we work as individuals and corporations because of what's going on with the coronavirus crisis, having a platform like this that gives you agility and mapping the real work to what your computer state and applications all know how to do, is even more valuable in a climate like that. >> What attracted us originally to UiPath, we knew Bobby Patrick CMO, he said "Dave, go download a copy, go build some automations and go try it with some other companies". So that really struck us as wow, this is actually quite simple. Yet at the same time, and so you've of course been automating all these simple tasks, but now you've got real aspiration, you're glomming on to this term of Hyperautomation, you've made some acquisitions, you've got a vision, that really has taken you beyond 'paving the cow path' I sometimes say, of all these existing processes. It's really trying to discover new processes and opportunities for automation, which you would think after 50 or whatever years we've been in this industry, we'd have attacked a lot of it, but wow, seems like we have a long way to go. Again, especially what we're learning through this pandemic. Your thoughts on that? >> Yeah, I'd say Hyperautomation. It's actually a Gartner term, it's not our term. But there is a bigger idea here, built around the core automation platform. So let's talk for a second just what's not about the core platform and then what Hyperautomation really means around that. And I think of that as the bookends of how do I discover and plan, how do I improve my ability to do more automations, and find the real opportunities that I have. And how do I measure and optimize? And that's a lot of what we delivered in 20.4 as a new capability. So let's talk about discover and plan. One aspect of that is the wisdom of the crowd. We have a product we call Automation Hub that is all about that. Enabling people who have ideas, they're the ones doing the work, they have the observation into what efficiencies can be. Enabling them to either with our Ask Capture Utility capture that and document that, or just directly document that. And then, people across the company can then collaborate eventually moving on building the best ideas out of that. So there's capturing the crowd, and then there's a more scientific way of capturing actually what the opportunities are. So we've got two products we introduced. One is process mining, and process mining is about going outside in from the, let's call it the larger processes, more end to end processes in the enterprise. Things like order-to-cash and procure-to-pay, helping you understand by watching the events, and doing the analytics around that, where your bottle necks, where are you opportunities. And then task mining said "let's watch an individual, or group of individuals, what their tasks are, let's watch the log of events there, let's apply some machine learning processing to that, and say here's the repetitive things we've found." And really helping you then scientifically discover what your opportunities are. And these ideas have been along for a long time, process mining is not new. But the connection to an automation platform, we think is a new and powerful idea, and something we plan to invest a lot in going forward. So that's the first bookend. And then the second bookend is really about attaching rich analytics, so how do I measure it, so there's operationally how are my robots doing? And then there's everything down to return on investment. How do I understand how they are performing, verses what I would have spent if I was continuing to do them the old way. >> Yeah that's big 'cause (laughing) the hero reports for the executives to say "hey, this is actually working" but at the same time you've got to take a systems view. You don't want to just optimize one part of the system at the detriment to others. So you talk about process mining, which is kind of discovering the backend systems, ERP and the like, where the task mining it sounds like it's more the collaboration and front end. So that whole system thinking, really applies, doesn't it? >> Yeah. Very much so. Another part of what we talked about then, in the system is, how do we capture the ideas and how do we enable more people to build these automations? And that really gets down to, we talk about it in our company level vision, is a robot for every person. Every person should have a digital assistant. It can help you with things you do less frequently, it can help you with things you do all the time to do your job. And how do we help you create those? We've released a new tool we call StudioX. So for our RPA Developers we have Studio, and StudioX is really trying to enable a citizen developer. It's not unlike the art that we saw in Business Intelligence there was the era where analytics and reporting were the domain of experts, and they produced formalized reports that people could consume. But the people that had the questions would have to work with them and couldn't do the work themselves. And then along comes ClickView and Tableau and Power BI enabling the self services model, and all of a sudden people could do that work themselves, and that enabled powerful things. We think the same arch happens here, and StudioX is really our way of enabling that, citizen developer with the ideas to get some automation work done on their own. >> Got a lot in this announcement, things like document understanding, bring your own AI with AI fabric, how are you able to launch so many products, and have them fit together, you've made some acquisitions. Can you talk about the architecture that enables you to do that? >> Yeah, it's clearly in terms of ambition, and I've been there for 10 weeks, but in terms of ambition you don't have to have been there when they started the release after Forward III in October to know that this is the most ambitious thing that this company has ever done from a release perspective. Just in terms of the surface area we're delivering across now as an organization, is substantive. We talk about 1,000 feature improvements, 100's of discreet features, new products, as well as now our automation cloud has become generally available as well. So we've had muscle building over this past time to become world class at offering SAS, in addition to on-premises. And then we've got this big surface area, and architecture is a key component of how you can do this. How do you deliver efficiently the same software on-premises and in the cloud? Well you do that by having the right architecture and making the right bets. And certainly you look forward, how are companies doing this today? It's really all about Cloud-Native Platform. But it's about an architecture such that we can do that efficiently. So there is a lot about just your technical strategy. And then it's just about a ton of discipline and customer focus. It keeps you focused on the right things. StudioX was a great example of we were led by customers through a lot of what we actually delivered, a couple of the major features in it, certainly the out of box templates, the studio governance features, came out of customer suggestions. I think we had about 100 that we have sitting in the backlog, a lot of which we've already done, and really being disciplined and really focused on what customers are telling. So make sure you have the right technical strategy and architecture, really follow your customers, and really stay disciplined and focused on what matters most as you execute on the release. >> What can we learn from previous examples, I think about for instance SQL Server, you obviously have some knowledge in it, it started out pretty simple workloads and then at the time we all said "wow, it's a lot more powerful to come from below that it is, if a Db2, or an Oracle sort of goes down market", Microsoft proved that, obviously built in the robustness necessary, is there a similar metaphor here with regard to things like governance and security, just in terms of where UiPath started and where you see it going? >> Well I think the similarities have more to do with we have an idea of a bigger platform that we're now delivering against. In the database market, that was, we started, SQL Server started out as more of just a transactional database product, and ultimately grew to all of the workloads in the data platform, including transaction for transactional apps, data warehousing and as well as business intelligence. I see the same analogy here of thinking more broadly of the needs, and what the ability of an integrated platform, what it can do to enable great things for customers, I think that's a very consistent thing. And I think another consistent thing is know who you are. SQL Server knew exactly who it had to be when it entered the database market. That it was going to set a new benchmark on simplicity, TCO, and that was going to be the way it differentiated. In this case, we're out ahead of the market, we have a vision that's broader than a lot of the market is today. I think we see a lot of people coming in to this space, but we see them building to where we were, and we're out ahead. So we are operating from a leadership position, and I'm not going to tell you one's easier that the other, and both you have to execute with great urgency. But we're really executing out ahead, so we've got to keep thinking about, and there's no one's tail lights to follow, we have to be the ones really blazing the trail on what all of this means. >> I want to ask you about this incorporation of existing systems. Some markets they take off, it's kind of a one shot deal, and the market just embeds. I think you guys have bigger aspirations than that, I look at it like a service now, misunderstood early on, built the platform and now really is fundamental part of a lot of enterprises. I also look at things like EDW, which again, you have some experience in. In my view it failed to live up to a lot of it's promises even though it delivered a lot of value. You look at some of the big data initiatives, you know EDW still plugs in, it's the system of record, okay that's fine. How do you see RPA evolving? Are we going to incorporate, do we have to embrace existing business process systems? Or is this largely a do-over in your opinion? >> Well I think it's certainly about a new way of building automation, and it's starting to incorporate and include the other ways, for instance in the current release we added support for long running workflow, it was about human workflow based scenarios, now the human is collaborating with the robot, and we built those capabilities. So I do see us combining some of the old and new way. I think one of the most significant things here, is also that impact that AI and ML based technologies and skills can have on the power of the automations that we deliver. We've certainly got a surface area that, I think about our AI and ML strategy in two parts, that we are building first class first party skills, that we're including in the platform, and then we're building a platform for third parties and customers to bring their what their data science teams have delivered, so those can also be a part of our ecosystem, and part of automations. And so things like document understanding, how do I easily extract data from more structured, semi-structured and completely unstructured documents, accurately? And include those in my automations. Computer vision which gives us an ability to automate at a UI level across other types of systems than say a Windows and a browser base application. And task mining is built on a very robust, multi layer ML system, and the innovation opportunity that I think just consider there, you know continue there. You think it's a macro level if there's aspects of machine learning that are about captured human knowledge, well what exactly is an automation that captured where you're capturing a lot of human knowledge. The impact of ML and AI are going to be significant going out into the future. >> Yeah, I want to ask you about them, and I think a lot of people are just afraid of AI, as a separate thing and they have to figure out how to operationalize it. And I think companies like UiPath are really in a position to embed UI into applications AI into applications everywhere, so that maybe those folks that haven't climbed on the digital bandwagon, who are now with this pandemic are realizing "wow, we better accelerate this" they can actually tap machine intelligence through your products and others as well. Your thoughts on that sort of narrative? >> Yeah, I agree with that point of view, it's AI and ML is still maturing discipline across the industry. And you have to build new muscle, and you build new muscle and data science, and it forces you to think about data and how you manage your data in a different way. And that's a journey we've been on as a company to not only build our first party skills, but also to build the platform. It's what's given us the knowledge that to help us figure out, well what do we need to include here so our customers can bring their skills, actually to our platform, and I do think this is a place where we're going to see the real impact of AI and ML in a broader way. Based on the kind of apps it is and the kind of skills we can bring to bear. >> Okay last question, you're ten weeks in, when you're 50, 100, 200 weeks in, what should we be watching, what do you want to have accomplished? >> Well we're listening, we're obviously listening closely to our customers, right now we're still having a great week, 'cause there's nothing like shipping new software. So right now we're actually thinking deeply about where we're headed next. We see there's lots of opportunities and robot for every person, and that initiative, and so we're launched a bunch of important new capabilities there, and we're going to keep working with the market to understand how we can, how we can add additional capability there. We've just got the GA of our automation cloud, I think you should expect more and more services in our automation cloud going forward. I think this area we talked about, in terms of AI and ML and those technologies, I think you should expect more investment and innovation there from us and the community, helping our customers, and I think you will also see us then, as we talked about this convergence of the ways we bring together systems through integrate and build business process, I think we'll see a convergence into the platform of more of those methods. I look ahead to the next releases, and want to see us making some very significant releases that are advancing all of those things, and continuing our leadership in what we talk about now as the Hyperautomation platform. >> Well Ted, lot of innovation opportunities and of course everybody's hopping on the automation bandwagon. Everybody's going to want a piece of your RPA hide, and you're in the lead, we're really excited for you, we're excited to have you on theCUBE, so thanks very much for all your time and your insight. Really appreciate it. >> Yeah, thanks Dave, great to spend this time with you. >> All right thank you for watching everybody, this is Dave Velanti for theCUBE, and our RPA Drill Down Series, keep it right there we'll be right back, right after this short break. (calming instrumental music)

>> From the Silicon Angle Media Office in Boston Massachusetts, it's "The Cube." (groovy techno music) Now, here's your host, Dave Vellante. >> Hello, everyone. The rise of open source is really powering the digital economy. And in a world where every company is essentially under pressure to become a software firm, open source software really becomes the linchpin of digital services for both incumbents and, of course, digital natives. Here's the challenge, is when developers tap and apply open source, they're often bringing in hundreds, or even thousands of lines of code that reside in open sourced packages and libraries. And these code bases, they have dependencies, and essentially hidden traps. Now typically, security vulnerabilities in code, they're attacked after the software's developed. Or maybe thrown over the fence to the sec-ops team and SNYK is a company that set out to solve this problem within the application development life cycle, not after the fact as a built-on. Now, with us to talk about this mega-trend is Peter McKay, a friend of The Cube and CEO of SNYK. Peter, great to see you again. >> Good to see you, dude. >> So I got to start with the name. SNYK, what does it mean? >> SNYK, So Now You Know. You know, people it's sneakers sneak. And they tend to use the snick. So it's SNYK or snick. But it is SNYK and it stands for So Now You Know. Kind of a security, so now you know a lot more about your applications than you ever did before. So it's kind of a fitting name. >> So you heard my narrative upfront. Maybe you can add a little color to that and provide some additional background. >> Yeah, I mean, it's a, you know, when you think of the larger trends that are going on in the market, you know, every company is going through this digital transformation. You know, and every CEO, it's the number one priority. We've got to change our business from, you know, financial services, healthcare, insurance company, whatever, are all switching to digital, you know, more of a software company. And with that, more software equals more software risk and cybersecurity continues to be, you know, a major. I think 72% of CEOs worry about cybersecurity as a top issue in protecting companies' data. And so for us, we've been in the software in the security space for the four and a half years. I've been in the security space since, you know, Watchfire 20 years ago. And right now, with more and more, as you said, open source and containers, the challenge of being able to address the cybersecurity issues that have never been more challenging. And so especially when you add the gap between the need for security professionals and what they have. I think it's four million open positions for security people. So you know, with all this added risk, more and more open source, more and more digitization, it's created this opportunity in the market where you're traditional approaches to addressing security don't work today, you know? Like you said, throwing it over the fence and having someone in security, you know, check and make sure and finding all these vulnerabilities, and throw it back to developers to fix is very slow and something at this point is not driving to success. >> So talk a little bit more about what attracted you to SNYK early. I mean, you've been with the company, you're at least involved in the company for a couple years now. What were the trends that you saw, and what was it about SNYK that, you know, led you to become an investor and ultimately, CEO? >> Yeah, so four years involved in the business. So you know, I've always loved the security space. I've been in it for a number, almost 20 years. So I enjoy the space. You know, I've watched it. The founder, Guy Podjarny, one of the founders of SNYK, has been a friend of mine for 16 years from back in the Watchfire days. So we've always stayed connected. I've always worked well together with him. And so when you started, and I was on the board, the first board member of the company, so I could see what was going on, and it was this, you know, changing, kind of the right place at the right time in terms of developer first security. Really taking all the things that are going on in the security space that impacts a developer or can be addressed by the developer, and embedding it into the software into that developer community, in a way that developers use, the tools that they use. So it's a developer-first mindset with security expertise built-in. And so when you look at the market, the number of open source container evolution, you know, it's a huge market opportunity. Then you look at the business momentum, just took off over the past, you know, four years. That it was something that I was getting more and more involved in. And then when Guy asked me to join as the CEO, it was like, "Sure, what took you so long?" (Dave laughing) >> We had Guy on at Node JS Summit. I want to say it was a couple years ago now. And what he was describing is when you package, take the example of Node. When you package code in Node, you bring in all these dependencies, kind of what I was talking about there, but the challenge that he sort of described was really making it seamless as part of the development workflow. It seems like that's unique to SNYK. Maybe you could talk about-- >> Yeah, it is. And you know, we've built it from the ground up. You know, it's very difficult. If it was a security tool for security people, and then say, "Oh, let's adapt it for the developer," that is almost impossible. Why I think we've been so successful from the 400,000 developers in the community using Freemium to paid, was we built it from the ground up for developer, embedded into the application-development life cycle. Into their process, the look and feel, easy for them to use, easy for them to try it, and then we focused on just developer adoption. A great experience, developers will continue to use it and expand with it. And most of our opportunities that we've been successful at, the customers, we have over 400 customers. That had been this try, you know, start it with the community. They used the Freemium, they tried it for their new application, then they tried it for all their new, and then they go back and replace the old. So it was kind of this Freemium, land and expand has been a great way for developers to try it, use it. Does it work, yes, buy more. And that's the way we work. >> We're really happy, Peter, that you came on because you've got some news today that you're choosing to share with us in our Cube community. So it's around financing, bring us up to date. What's the news? >> Yeah so you know, I'd say four months ago, five months ago, we raised a $70 million round from great investors. And that was really led by one of our existing investors, who kind of knew us the best and it was you know, Excel Venture, and then Excel Growth came in and led the $70 million round. And part of that was a few new investors that came in and Stripes, which is you know a very large growth equity investor were part of that $70 million round said you know, preempted it and said, "Look it, we know you don't need the money, but we want to," you know, "We want to preempt. We believe your customer momentum," here we did, you know, five or six really large deals. You know, one, 700, seven million, 7.4 million, one's 3.5 million. So we started getting these bigger deals and we doubled since the $70 million round. And so we said, "Okay, we want to make money not the issue." So they led the next round, which is $150 million round, at a valuation of over a billion. That really allows us now to, with the number of other really top tier, (mumbles) and Tiger and Trend and others, who have been part of watching the space and understand the market. And are really helping us grow this business internationally. So it's an exciting time. So you know, again, we weren't looking to raise. This was something that kind of came to us and you know, when people are that excited about it like we are and they know us the best because they've been part of our board of directors since their round, it allows us to do the things that we want to do faster. >> So $150 million raise this round, brings you up to the 250, is that correct? >> Yes, 250. >> And obviously, an up-round. So congratulations, that's great. >> Yeah, you know, I think a big part of that is you know, we're not, I mean, we've always been very fiscally responsible. I mean, yes we have the money and most of it's still in the bank. We're growing at the pace that we think is right for us and right for the market. You know, we continue to invest product, product, product, is making sure we continue our product-led organization. You know, from that bottoms up, which is something we continue to do. This allows us to accelerate that more aggressively, but also the community, which is a big part of what makes that, you know, when you have a bottoms up, you need to have that community. And we've grown that and we're going to continue to invest aggressively and build in that community. And lastly, go to market. Not only invest, invest aggressively in the North America, but also Europe and APJ, which, you know, a lot of the things we've learned from my Veeam experience, you know how to grow fast, go big or go home. You know, are things that we're going to do but we're going to do it in the right way. >> So the Golden Rule is product and sales, right? >> Yes, you're either building it or selling it. >> Right, that's kind of where you're going to put your money. You know, you talk a lot about people, companies will do IPOs to get seen, but companies today, I mean, even software companies, which is a capital-efficient industry, they raise a lot of dough and they put it towards promotion to compete. What are your thoughts on that? >> You know, we've had, the model is very straightforward. It's bottoms up, you know? Developers, you know, there's 28 million developers in the world, you know? What we want is every one of those 28 million to be using our product. Whether it's free or paid, I want SNYK used in every application-development life cycle. If you're one developer, or you're a sales force with standardized on 12,000 developers, we want them using SNYK. So for us, it's get it in the hands. And that, you know, it's not like-- developers aren't going to look at Super Bowl ads, they're not going to be looking. It's you know, it's finding the ways, like the conference. We bought the DevSecCon, you know, the conference for developer security. Another way to promote kind of our, you know, security for developers and grow that developer community. That's not to say that there isn't a security part. Because, you know, what we do is help security organizations with visibility and finding a much more scalable way that gets them out of the, you know, the slows-down, the speed bump to the moving apps more aggressively into production. And so this is very much about helping security people. A lot of times the budgets do come from security or dev-ops. But it's because of our focus on the developer and the success of fixing, finding, fixing, and auto-remediating that developer environment is what makes us special. >> And it's sounds like a key to your success is you're not asking developer to context switch into a new environment, right? It's part of their existing workflow. >> It has to be, right? Don't change how they do their job, right? I mean, their job is to develop incredible applications that are better than the competitors, get them to market faster than they can, than they've ever been able to do before and faster than the competitor, but do it securely. Our goal is to do the third, but not sacrifice on one and two, right? Help you drive it, help you get your applications to market, help you beat your competition, but do it in a secure fashion. So don't slow them down. >> Well, the other thing I like about you guys is the emphasis is on fixing. It's not just alerting people that there's a problem. I mean, for instance, a company like Red Hat, is that they're going to put a lot of fixes in. But you, of course, have to go implement them. What you're doing is saying, "Hey, we're going to do that for you. Push the button and then we'll do it," right? So that, to me, that's important because it enables automation, it enables scale. >> Exactly, and I think this has been one of the challenges for kind of more of the traditional legacy, is they find a whole bunch of vulnerabilities, right? And we feel as though just that alone, we're the best in the world at. Finding vulnerabilities in applications in open source container. And so the other part of it is, okay, you find all them, but prioritizing what it is that I should fix first? And that's become really big issue because the vulnerabilities, as you can imagine, continue to grow. But focusing on hey, fix this top 10%, then the next, and to the extent you can, auto-fix. Auto-remediate those problems, that's ultimately, we're measured by how many vulnerabilities do we fix, right? I mean, finding them, that's one thing. But fixing them is how we judge a successful customer. And now it's possible. Before, it was like, "Oh, okay, you're just going to show me more things." No, when you talk about Google and Salesforce and Intuit, and all of our customers, they're actually getting far better. They're seeing what they have in terms of their exposure, and they're fixing the problems. And that's ultimately what we're focused on. >> So some of those big whales that you just mentioned, it seems to me that the value proposition for those guys, Peter, is the quality of the code that they can develop and obviously, the time that it takes to do that. But if you think about it more of a traditional enterprise, which I'm sure is part of your (mumbles), they'll tell you, the (mumbles) will tell you our biggest problem is we don't have enough people with the skills. Does this help? >> It absolutely-- >> And how so? >> Yeah, I mean, there's a massive gap in security expertise. And the current approach, the tools, are, you know, like you said at the very beginning, it's I'm doing too late in the process. I need to do it upstream. So you've got to leverage the 28 million developers that are developing the applications. It's the only way to solve the problem of, you know, this application security challenge. We call it Cloud Dative Application Security, which all these applications usually are new apps that they're moving into the Cloud. And so to really fix it, to solve the problem, you got to embed it, make it really easy for developers to leverage SNYK in their whole, we call it, you know, it's that concept of shift left, you know? Our view is that it needs to be embedded within the development process. And that's how you fix the problem. >> And talk about the business model again. You said it's Freemium model, you just talked about a big seven figure deals that you're doing and that starts with a Freemium, and then what? I upgrade to a subscription and then it's a land and expand? Describe that. >> Yeah we call it, it's you know, it's the community. Let's get every developer in a community. 28 million, we want to get into our community. From there, you know, leverage our Freemium, use it. You know, we encourage you to use it. Everybody to use our Freemium. And it's full functionality. It's not restricted in anyway. You can use it. And there's a subset of those that are ready to say, "Look it, I want to use the paid version," which allows me to get more visibility across more developers. So as you get larger organization, you want to leverage the power of kind of a bigger, managing multiple developers, like a lot of, in different teams. And so that kind of gets that shift to that paid. Then it goes into that Freemium, land, expand, we call it explode. Sales force, kind of explode. And then renew. That's been our model. Get in the door, get them using Freemium, we have a great experience, go to paid. And that's usually for an application, then it goes to 10 applications, and then 300 developers and then the way we price is by developer. So the more developers who use, the better your developer adoption, the bigger the ultimate opportunity is for us. >> There's a subscription service right? >> All subscription. >> Okay and then you guys have experts that are identifying vulnerabilities, right? You put them into a database, presumably, and then you sort of operationalize that into your software and your service. >> Yeah, we have 15 people in our security team that do nothing everyday but looking for the next vulnerability. That's our vulnerability database, in a large case, is a lot of our big companies start with the database. Because you think of like Netflix and you think of Facebook, all of these companies have large security organizations that are looking for issues, looking for vulnerabilities. And they're saying, "Well okay, if I can get that feed from you, why do I have my own?" And so a lot of companies start just with the database feed and say, "Look, I'll get rid of mine, and use yours." And then eventually, we'll use this scanning and we'll evolve down the process. But there's no doubt in the market people who use our solution or other solution will say our known the database of known vulnerabilities, is far better than anybody else in the market. >> And who do you sell to, again? Who are the constituencies? Is it sec-ops, is it, you know, software engineering? Is it developers, dev-ops? >> Users are always developers. In some cases dev-ops, or dev-sec. Apps-sec, you're starting to see kind of the world, the developer security becoming bigger. You know, as you get larger, you're definitely security becomes a bigger part of the journey and some of the budget comes from the security teams. Or the risk or dev-ops. But I think if we were to, you know, with the user and some of the influencers from developers, dev-ops, and security are kind of the key people in the equation. >> Is your, you have a lot of experience in the enterprise. How do you see your go to market in this world different, given that it's really a developer constituency that you're targeting? I mean, normally, you'd go out, hire a bunch of expensive sales guys, go to market, is that the model or is it a little different here because of the target? >> Yeah, you know, to be honest, a lot of the momentum that we've had at this point has been inbound. Like most of the opportunities that come in, come to us from the community, from this ground up. And so we have a very large inside sales team that just kind of follows up on the inbound interest. And that's still, you know, 65, 70% of the opportunities that come to us both here and Europe and APJ, are coming from the community inbound. Okay, I'm using 10 licenses of SNYK, you know, I want to get the enterprise version of it. And so that's been how we've grown. Very much of a very cost-effective inside sales. Now, when you get to the Googles and Salesforces and Nordstroms of the world, and they have already 500 licenses us, either paid or free, then we usually have more of a, you know, senior sales person that will be involved in those deals. >> To sort of mine those accounts. But it's really all about driving the efficiency of that inbound, and then at some point driving more inbound and sort of getting that flywheel effect. >> Developer adoption, developer adoption. That's the number one driver for everybody in our company. We have a customer success team, developer adoption. You know, just make the developer successful and good things happen to all the other parts of the organization. >> Okay, so that's a key performance indicator. What are the, let's wrap kind of the milestones and the things that you want to accomplish in the next, let's call it 12 months, 18 months? What should we be watching? >> Yeah, so I mean it continues to be the community, right? The community, recruiting more developers around the globe. We're expanding, you know, APJ's becoming a bigger part. And a lot of it is through just our efforts and just building out this community. We now have 20 people, their sole job is to build out, is to continue to build our developer community. Which is, you know, content, you know, information, how to learn, you know, webinars, all these things that are very separate and apart from the commercial side of the business and the community side of the business. So community adoption is a critical measurement for us, you know, yeah, you look at Freemium adoption. And then, you know, new customers. How are we adding new customers and retaining our existing customers? And you know, we have a 95% retention rate. So it's very sticky because you're getting the data feed, is a daily data feed. So it's like, you know, it's not one that you're going to hook on and then stop at any time soon. So you know, those are the measurements. You look at your community, you look at your Freemium, you look at your customer growth, your retention rates, those are all the things that we measure our business by. >> And your big pockets of brain power here, obviously in Boston, kind of CEO's prerogative, you got a big presence in London, right? And also in Israel, is that correct? >> Yeah, I would say we have four hubs and then we have a lot of remote employees. So, you know, Tel Aviv, where a lot of our security expertise is, in London, a lot of engineering. So between London and Tel Aviv is kind of the security teams, the developers are all in the community is kind of there. You know, Boston, is kind of more go to market side of things, and then we have Ottawa, which is kind of where Watchfire started, so a lot of good security experience there. And then, you know, we've, like a lot of modern companies, we hired the best people wherever we can find them. You know, we have some in Sydney, we've got some all around the world. Especially security, where finding really good security talent is a challenge. And so we're always looking for the best and brightest wherever they are. >> Well, Peter, congratulations on the raise, the new role, really, thank you for coming in and sharing with The Cube community. Really appreciate it. >> Well, it's great to be here. Always enjoy the conversations, especially the Patriots, Red Sox, kind of banter back and forth. It's always good. >> Well, how do you feel about that? >> Which one? >> Well, the Patriots, you know, sort of strange that they're not deep into the playoffs, I mean, for us. But how about the Red Sox now? Is it a team of shame? All my friends who were sort of jealous of Boston sports are saying you should be embarrassed, what are your thoughts? >> It's all about Houston, you know? Alex Cora, was one of the assistant coaches at Houston where all the issues are, I'm not sure those issues apply to Boston, but we'll see, TBD. TBD, I am optimistic as usual. I'm a Boston fan making sure that there isn't any spillover from the Houston world. >> Well we just got our Sox tickets, so you know, hopefully, they'll recover quickly, you know, from this. >> They will, they got to get a coach first. >> Yeah, they got to get a coach first. >> We need something to distract us from the Patriots. >> So you're not ready to attach an asterisk yet to 2018? >> No, no. No, no, no. >> All right, I like the optimism. Maybe you made the right call on Tom Brady. >> Did I? >> Yeah a couple years ago. >> Still since we talked what, two in one. And they won one. >> So they were in two, won one, and he threw for what, 600 yards in the first one so you can't, it wasn't his fault. >> And they'll sign him again, he'll be back. >> Is that your prediction? I hope so. >> I do, I do. >> All right, Peter. Always a pleasure, man. >> Great to see you. >> Thank you so much, and thank you for watching everybody, we'll see you next time. (groovy techno music)