(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)

(gentle music) >> Hello, everyone. Welcome to this Cube Conversation. I'm John Furrier, host of theCUBE here, in our Palo Alto Studios. We're featuring OctoML. I'm with the CEO, Luis Ceze. Chief Executive Officer, Co-founder of OctoML. I'm John Furrier of theCUBE. Thanks for joining us today. Luis, great to see you. Last time we spoke was at "re:MARS" Amazon's event. Kind of a joint event between (indistinct) and Amazon, kind of put a lot together. Great to see you. >> Great to see you again, John. I really have good memories of that interview. You know, that was definitely a great time. Great to chat with you again. >> The world of ML and AI, machine learning and AI is really hot. Everyone's talking about it. It's really great to see that advance. So I'm looking forward to this conversation but before we get started, introduce who you are in OctoML. >> Sure. I'm Luis Ceze, Co-founder and CEO at OctoML. I'm also professor of Computer Science at University of Washington. You know, OctoML grew out of our efforts on the Apache CVM project, which is a compiler in runtime system that enables folks to run machine learning models in a broad set of harder in the Edge and in the Cloud very efficiently. You know, we grew that project and grew that community, definitely saw there was something to pain point there. And then we built OctoML, OctoML is about three and a half years old now. And the mission, the company is to enable customers to deploy models very efficiently in the Cloud. And make them, you know, run. Do it quickly, run fast, and run at a low cost, which is something that's especially timely right now. >> I like to point out also for the folks 'casue they should know that you're also a professor in the Computer Science department at University of Washington. A great program there. This is a really an inflection point with AI machine learning. The computer science industry has been waiting for decades to advance AI with all this new cloud computing, all the hardware and silicon advancements, GPUs. This is the perfect storm. And you know, this the computer science now we we're seeing an acceleration. Can you share your view, and you're obviously a professor in that department but also, an entrepreneur. This is a great time for computer science. Explain why. >> Absolutely, yeah, no. Just like the confluence of you know, advances in what, you know, computers can do as devices to computer information. Plus, you know, advances in AI that enable applications that you know, we thought it was highly futuristic and now it's just right there today. You know, AI that can generate photo realistic images from descriptions, you know, can write text that's pretty good. Can help augment, you know, human creativity in a really meaningful way. So you see the confluence of capabilities and the creativity of humankind into new applications is just extremely exciting, both from a researcher point of view as well as an entrepreneur point of view, right. >> What should people know about these large language models we're seeing with ChatGPT and how Google has got a lot of work going on that air. There's been a lot of work recently. What's different now about these models, and why are they so popular and effective now? What's the difference between now, and say five years ago, that makes it more- >> Oh, yeah. It's a huge inflection on their capabilities, I always say like emergent behavior, right? So as these models got more complex and our ability to train and deploy them, you know, got to this point... You know, they really crossed a threshold into doing things that are truly surprising, right? In terms of generating, you know, exhalation for things generating tax, summarizing tax, expending tax. And you know, exhibiting what to some may look like reasoning. They're not quite reasoning fundamentally. They're generating tax that looks like they're reasoning, but they do it so well, that it feels like was done by a human, right. So I would say that the biggest changes that, you know, now, they can actually do things that are extremely useful for business in people's lives today. And that wasn't the case five years ago. So that's in the model capabilities and that is being paired with huge advances in computing that enabled this to be... Enables this to be, you know, actually see line of sites to be deployed at scale, right. And that's where we come in, by the way, but yeah. >> Yeah, I want to get into that. And also, you know, the fusion of data integrating data sets at scales. Another one we're seeing a lot of happening now. It's not just some, you know, siloed, pre-built data modeling. It's a lot of agility and a lot of new integration capabilities of data. How is that impacting the dynamics? >> Yeah, absolutely. So I'll say that the ability to either take the data that has that exists in training a model to do something useful with it, and more interestingly I would say, using baseline foundational models and with a little bit of data, turn them into something that can do a specialized task really, really well. Created this really fast proliferation of really impactful applications, right? >> If every company now is looking at this trend and I'm seeing a lot... And I think every company will rebuild their business with machine learning. If they're not already doing it. And the folks that aren't will probably be dinosaurs will be out of business. This is a real business transformation moment where machine learning and AI, as it goes mainstream. I think it's just the beginning. This is where you guys come in, and you guys are poised for handling this frenzy to change business with machine learning models. How do you guys help customers as they look at this, you know, transition to get, you know, concept to production with machine learning? >> Great. Great questions, yeah, so I would say that it's fair to say there's a bunch of models out there that can do useful things right off the box, right? So and also, the ability to create models improved quite a bit. So the challenge now shifted to customers, you know. Everyone is looking to incorporating AI into their applications. So what we do for them is to, first of all, how do you do that quickly, without needing highly specialized, difficult to find engineering? And very importantly, how do you do that at cost that's accessible, right? So all of these fantastic models that we just talked about, they use an amount of computing that's just astronomical compared to anything else we've done in the past. It means the costs that come with it, are also very, very high. So it's important to enable customers to, you know, incorporate AI into their applications, to their use cases in a way that they can do, with the people that they have, and the costs that they can afford, such that they can have, you know, the maximum impacting possibly have. And finally, you know, helping them deal with hardware availability, as you know, even though we made a lot of progress in making computing cheaper and cheaper. Even to this day, you know, you can never get enough. And getting an allocation, getting the right hardware to run these incredibly hungry models is hard. And we help customers deal with, you know, harder availability as well. >> Yeah, for the folks watching as a... If you search YouTube, there's an interview we did last year at "re:MARS," I mentioned that earlier, just a great interview. You talked about this hardware independence, this traction. I want to get into that, because if you look at all the foundation models that are out there right now, that are getting traction, you're seeing two trends. You're seeing proprietary and open source. And obviously, open source always wins in my opinion, but, you know, there's this iPhone moment and android moment that one of your investors John Torrey from Madrona, talked about was is iPhone versus Android moment, you know, one's proprietary hardware and they're very specialized high performance and then open source. This is an important distinction and you guys are hardware independent. What's the... Explain what all this means. >> Yeah. Great set of questions. First of all, yeah. So, you know, OpenAI, and of course, they create ChatGPT and they offer an API to run these models that does amazing things. But customers have to be able to go and send their data over to OpenAI, right? So, and run the model there and get the outputs. Now, there's open source models that can do amazing things as well, right? So they typically open source models, so they don't lag behind, you know, these proprietary closed models by more than say, you know, six months or so, let's say. And it means that enabling customers to take the models that they want and deploy under their control is something that's very valuable, because one, you don't have to expose your data to externally. Two, you can customize the model even more to the things that you wanted to do. And then three, you can run on an infrastructure that can be much more cost effective than having to, you know, pay somebody else's, you know, cost and markup, right? So, and where we help them is essentially help customers, enable customers to take machine learning models, say an open source model, and automate the process of putting them into production, optimize them to run with the right performance, and more importantly, give them the independence to run where they need to run, where they can run best, right? >> Yeah, and also, you know, I point out all the time that, you know, there's never any stopping the innovation of hardware silicon. You're seeing cloud computing more coming in there. So, you know, being hardware independent has some advantages. And if you look at OpenAI, for instance, you mentioned ChatGPT, I think this is interesting because I think everyone is scratching their head, going, "Okay, I need to move to this new generation." What's your pro tip and advice for folks who want to move to, or businesses that want to say move to machine learning? How do they get started? What are some of the considerations they need to think about to deploy these models into production? >> Yeah, great though. Great set of questions. First of all, I mean, I'm sure they're very aware of the kind of things that you want to do with AI, right? So you could be interacting with customers, you know, automating, interacting with customers. It could be, you know, finding issues in production lines. It could be, you know... Generating, you know, making it easier to produce content and so on. Like, you know, customers, users would have an idea what they want to do. You know, from that it can actually determine, what kind of machine learning models would solve the problem that would, you know, fits that use case. But then, that's when the hard thing begins, right? So when you find a model, identify the model that can do the thing that you wanted to do, you need to turn that into a thing that you can deploy. So how do you go from machine learning model that does a thing that you need to do, to a container with the right executor, the artifact they can actually go and deploy, right? So we've seen customers doing that on their own, right? So, and it's got a bit of work, and that's why we are excited about the automation that we can offer and then turn that into a turnkey problem, right? So a turnkey process. >> Luis, talk about the use cases. If I don't mind going and double down on the previous answer. You got existing services, and then there's new AI applications, AI for applications. What are the use cases with existing stuff, and the new applications that are being built? >> Yeah, I mean, existing itself is, for example, how do you do very smart search and auto completion, you know, when you are editing documents, for example. Very, very smart search of documents, summarization of tax, expanding bullets into pros in a way that, you know, don't have to spend as much human time. Just some of the existing applications, right? So some of the new ones are like truly AI native ways of producing content. Like there's a company that, you know, we share investors and love what they're doing called runwayyML, for example. It's sort of like an AI first way of editing and creating visual content, right? So you could say you have a video, you could say make this video look like, it's night as opposed to dark, or remove that dog in the corner. You can do that in a way that you couldn't do otherwise. So there's like definitely AI native use cases. And yet not only in life sciences, you know, there's quite a bit of advances on AI-based, you know, therapies and diagnostics processes that are designed using automated processes. And this is something that I feel like, we were just scratching the surface there. There's huge opportunities there, right? >> Talk about the inference and AI and production kind of angle here, because cost is a huge concern when you look at... And there's a hardware and that flexibility there. So I can see how that could help, but is there a cost freight train that can get out of control here if you don't deploy properly? Talk about the scale problem around cost in AI. >> Yeah, absolutely. So, you know, very quickly. One thing that people tend to think about is the cost is. You know, training has really high dollar amounts it tends over index on that. But what you have to think about is that for every model that's actually useful, you're going to train it once, and then run it a large number of times in inference. That means that over the lifetime of a model, the vast majority of the compute cycles and the cost are going to go to inference. And that's what we address, right? So, and to give you some idea, if you're talking about using large language model today, you know, you can say it's going to cost a couple of cents per, you know, 2,000 words output. If you have a million users active, you know, a day, you know, if you're lucky and you have that, you can, this cost can actually balloon very quickly to millions of dollars a month, just in inferencing costs. You know, assuming you know, that you actually have access to the infrastructure to run it, right? So means that if you don't pay attention to these inference costs and that's definitely going to be a surprise. And affects the economics of the product where this is embedded in, right? So this is something that, you know, if there's quite a bit of attention being put on right now on how do you do search with large language models and you don't pay attention to the economics, you know, you can have a surprise. You have to change the business model there. >> Yeah. I think that's important to call out, because you don't want it to be a runaway cost structure where you architected it wrong and then next thing you know, you got to unwind that. I mean, it's more than technical debt, it's actually real debt, it's real money. So, talk about some of the dynamics with the customers. How are they architecting this? How do they get ahead of that problem? What do you guys do specifically to solve that? >> Yeah, I mean, well, we help customers. So, it's first of all, be hyper aware, you know, understanding what's going to be the cost for them deploying the models into production and showing them the possibilities of how you can deploy the model with different cost structure, right? So that's where, you know, the ability to have hardware independence is so important because once you have hardware independence, after you optimize models, obviously, you have a new, you know, dimension of freedom to choose, you know, what is the right throughput per dollar for you. And then where, and what are the options? And once you make that decision, you want to automate the process of putting into production. So the way we help customers is showing very clearly in their use case, you know, how they can deploy their models in a much more cost-effective way. You know, when the cases... There's a case study that we put out recently, showing a 4x reduction in deployment costs, right? So this is by doing a mix optimization and choosing the right hardware. >> How do you address the concern that someone might say, Luis said, "Hey, you know, I don't want to degrade performance and latency, and I don't want the user experience to suffer." What's the answer there? >> Two things. So first of all, all of the manipulations that we do in the model is to turn the model to efficient code without changing the behavior of the models. We wouldn't degrade the experience of the user by having the model be wrong more often. And we don't change that at all. The model behaves the way it was validated for. And then the second thing is, you know, user experience with respect to latency, it's all about a maximum... Like, you could say, I want a model to run at 50 milliseconds or less. If it's much faster than 15 seconds, you're not going to notice the difference. But if it's lower, you're going to notice a difference. So the key here is that, how do you find a set of options to deploy, that you are not overshooting performance in a way that's going to lead to costs that has no additional benefits. And this provides a huge, a very significant margin of choices, set of choices that you can optimize for cost without degrading customer experience, right. End user experience. >> Yeah, and I also point out the large language models like the ChatGPTs of the world, they're coming out with Dave Moth and I were talking on this breaking analysis around, this being like, over 10X more computational intensive on capabilities. So this hardware independence is a huge thing. So, and also supply chain, some people can't get servers by the way, so, or hardware these days. >> Or even more interestingly, right? So they do not grow in trees, John. Like GPUs is not kind of stuff that you plant an orchard until you have a bunch and then you can increase it, but no, these things, you know, take a while. So, and you can't increase it overnight. So being able to live with those cycles that are available to you is not just important for all for cost, but also important for people to scale and serve more users at, you know, at whatever pace that they come, right? >> You know, it's really great to talk to you, and congratulations on OctaML. Looking forward to the startup showcase, we'll be featuring you guys there. But I want to get your personal opinion as someone in the industry and also, someone who's been in the computer science area for your career. You know, computer science has always been great, and there's more people enrolling in computer science, more diversity than ever before, but there's also more computer science related fields. How is this opening up computer science and where's AI going with the computers, with the science? Can you share your vision on, you know, the aperture, or the landscape of CompSci, or CS students, and opportunities. >> Yeah, no, absolutely. I think it's fair to say that computer has been embedded in pretty much every aspect of human life these days. Human life these days, right? So for everything. And AI has been a counterpart, it been an integral component of computer science for a while. And this medicines that happened in the last 10, 15 years in AI has shown, you know, new application has I think re-energized how people see what computers can do. And you, you know, there is this picture in our department that shows computer science at the center called the flower picture, and then all the different paddles like life sciences, social sciences, and then, you know, mechanical engineering, all these other things that, and I feel like it can replace that center with computer science. I put AI there as well, you see AI, you know touching all these applications. AI in healthcare, diagnostics. AI in discovery in the sciences, right? So, but then also AI doing things that, you know, the humans wouldn't have to do anymore. They can do better things with their brains, right? So it's permitting every single aspect of human life from intellectual endeavor to day-to-day work, right? >> Yeah. And I think the ChatGPT and OpenAI has really kind of created a mainstream view that everyone sees value in it. Like you could be in the data center, you could be in bio, you could be in healthcare. I mean, every industry sees value. So this brings up what I can call the horizontally scalable use constance. And so this opens up the conversation, what's going to change from this? Because if you go horizontally scalable, which is a cloud concept as you know, that's going to create a lot of opportunities and some shifting of how you think about architecture around data, for instance. What's your opinion on what this will do to change the inflection of the role of architecting platforms and the role of data specifically? >> Yeah, so good question. There is a lot in there, by the way, I should have added the previous question, that you can use AI to do better AI as well, which is what we do, and other folks are doing as well. And so the point I wanted to make here is that it's pretty clear that you have a cloud focus component with a nudge focused counterparts. Like you have AI models, but both in the Cloud and in the Edge, right? So the ability of being able to run your AI model where it runs best also has a data advantage to it from say, from a privacy point of view. That's inherently could say, "Hey, I want to run something, you know, locally, strictly locally, such that I don't expose the data to an infrastructure." And you know that the data never leaves you, right? Never leaves the device. Now you can imagine things that's already starting to happen, like you do some forms of training and model customization in the model architecture itself and the system architecture, such that you do this as close to the user as possible. And there's something called federated learning that has been around for some time now that's finally happening is, how do you get a data from butcher places, you do, you know, some common learning and then you send a model to the Edges, and they get refined for the final use in a way that you get the advantage of aggregating data but you don't get the disadvantage of privacy issues and so on. >> It's super exciting. >> And some of the considerations, yeah. >> It's super exciting area around data infrastructure, data science, computer science. Luis, congratulations on your success at OctaML. You're in the middle of it. And the best thing about its businesses are looking at this and really reinventing themselves and if a business isn't thinking about restructuring their business around AI, they're probably will be out of business. So this is a great time to be in the field. So thank you for sharing your insights here in theCUBE. >> Great. Thank you very much, John. Always a pleasure talking to you. Always have a lot of fun. And we both speak really fast, I can tell, you know, so. (both laughing) >> I know. We'll not the transcript available, we'll integrate it into our CubeGPT model that we have Luis. >> That's right. >> Great. >> Great. >> Great to talk to you, thank you, John. Thanks, man, bye. >> Hey, this is theCUBE. I'm John Furrier, here in Palo Alto, Cube Conversation. Thanks for watching. (gentle music)

>> Hey everyone. Welcome back to theCUBE's coverage day two of CloudNative Security CON 23. Lisa Martin here in studio in Palo Alto with John Furrier. John, we've had some great conversations. I've had a global event. This was a global event. We had Germany on yesterday. We had the Boston Studio. We had folks on the ground in Seattle. Lot of great conversations, a lot of great momentum at this event. What is your number one takeaway with this inaugural event? >> Well, first of all, our coverage with our CUBE alumni experts coming in remotely this remote event for us, I think this event as an inaugural event stood out because one, it was done very carefully and methodically from the CNCF. I think they didn't want to overplay their hand relative to breaking out from CUBE CON So Kubernetes success and CloudNative development has been such a success and that event and ecosystem is booming, right? So that's the big story is they have the breakout event and the question was, was it a good call? Was it successful? Was it going to, would the dog hunt as they say, in this case, I think the big takeaway is that it was successful by all measures. One, people enthusiastic and confident that this has the ability to stand on its own and still contribute without taking away from the benefits and growth of Kubernetes CUBE CON and CloudNative console. So that was the key. Hallway conversations, the sessions all curated and developed properly to be different and focused for that reason. So I think the big takeaway is that the CNCF did a good job on how they rolled this out. Again, it was very intimate event small reminds me of first CUBE CON in Seattle, kind of let's test it out. Let's see how it goes. Again, clearly it was people successful and they understood why they're doing it. And as we commented out in our earlier segments this is not something new. Amazon Web Services has re:Invent and re:Inforce So a lot of parallels there. I see there. So I think good call. CNCF did the right thing. I think this has legs. And then as Dave pointed out, Dave Vellante, on our last keynote analysis was the business model of the hackers is better than the business model of the industry. They're making more money, it costs less so, you know, they're playing offense and the industry playing defense. That has to change. And as Dave pointed out we have to make the cost of hacking and breaches and cybersecurity higher so that the business model crashes. And I think that's the strategic imperative. So I think the combination of the realities of the market globally and open source has to go faster. It's good to kind of decouple and be highly cohesive in the focus. So to me that's the big takeaway. And then the other one is, is that there's a lot more security problems still unresolved. The emphasis on developers productivity is at risk here, if not solved. You saw supply chain software, again, front and center and then down in the weeds outside of Kubernetes, things like BIND and DNS were brought up. You're seeing the Linux kernel. Really important things got to be paid attention to. So I think very good call, very good focus. >> I would love if for us to be able to, as the months go on talk to some of the practitioners that actually got to attend. There were 72 sessions, that's a lot of content for a small event. Obviously to your point, very well curated. We did hear from some folks yesterday who were just excited to get the community back together in person. To your point, having this dedicated focus on CloudNativesecurity is incredibly important. You talked about, you know, the offense defense, the fact that right now the industry needs to be able to pivot from being on defense to being on offense. This is a challenging thing because it is so lucrative for hackers. But this seems to be from what we've heard in the last couple days, the right community with the right focus to be able to make that pivot. >> Yeah, and I think if you look at the success of Kubernetes, 'cause again we were there at theCUBE first one CUBE CON, the end user stories really drove end user participation. Drove the birth of Kubernetes. Left some of these CloudNative early adopters early pioneers that were using cloud hyperscale really set the table for CloudNative CON. I think you're seeing that here with this CloudNative SecurityCON where I think we're see a lot more end user stories because of the security, the hairs on fire as we heard from Madrona Ventures, you know, as they as an investor you have a lot of use cases out there where customers are leaning in with getting the rolling up their sleeves, working with open source. This has to be the driver. So I'm expecting to see the next level of SecurityCON to be end user focused. Much more than vendor focused. Where CUBECON was very end user focused and then attracted all the vendors in that grew the industry. I expect the similar pattern here where end user action will be very high at the beginning and that will essentially be the rising tide for the vendors to be then participating. So I expect almost a similar trajectory to CUBECON. >> That's a good path that it needs to all be about all the end users. One of the things I'm curious if what you heard was what are some of the key factors that are going to move CloudNative Security forward? What did you hear the last two days? >> I heard that there's a lot of security problems and no one wants to kind of brag about this but there's a lot of under the hood stuff that needs to get taken care of. So if automation scales, and we heard that from one of the startups we've just interviewed. If automation and scale continues to happen and with the business model of the hackers still booming, security has to be refactored quickly and there's going to be an opportunity structurally to use the cloud for that. So I think it's a good opportunity now to get dedicated focus on fixing things like the DNS stuff old school under the hood, plumbing, networking protocols. You're going to start to see this super cloud-like environment emerge where data's involved, everything's happening and so security has to be re imagined. And I think there's a do over opportunity for the security industry with CloudNative driving that. And I think this is the big thing that I see as an opportunity to, from a story standpoint from a coverage standpoint is that it's a do-over for security. >> One of the things that we heard yesterday is that there's a lot of it, it's a pretty high percentage of organizations that either don't have a SOCK or have a very primitive SOCK. Which kind of surprised me that at this day and age the risks are there. We talked about that today's focus and the keynote was a lot about the software supply chain and what's going on there. What did you hear in terms of the appetite for organizations through the voice of the practitioner to say, you know what guys, we got to get going because there's going to be the hackers are they're here. >> I didn't hear much about that in the coverage 'cause we weren't in the hallways. But from reading the tea leaves and talking to the folks on the ground, I think there's an implied like there's an unlimited money from customers. So it's a very robust from the data infrastructure stack building we cover with the angel investor Kane you're seeing data infrastructure's going to be part of the solution here 'cause data and security go hand in hand. So everyone's got basically checkbook wide open everyone wants to have the answer. And we commented that the co-founder of Palo Alto you had on our coverage yesterday was saying that you know, there's no real platform, there's a lot of tools out there. People will buy anything. So there's still a huge appetite and spend in security but the answer's not going to more tool sprawling. It's going to more platform auto, something that enables automation, fix some of the underlying mechanisms involved and fix it fast. So to me I think it's going to be a robust monetary opportunity because of the demand on the business side. So I don't see that changing at all and I think it's going to accelerate. >> It's a great point in terms of the demand for the business side because as we know as we said yesterday, the next Log4j is out there. It's not a matter of if this happens again it's when, it's the extent, it's how frequent we know that. So organizations all the way up to the board have to be concerned about brand reputation. Nobody wants to be the next big headline in terms of breaches and customer data being given to hackers and hackers making all this money on that. That has to go all the way up to the board and there needs to be alignment between the board and the executives at the organization in terms of how they're going to deal with security, and now. This is not a conversation that can wait. Yeah, I mean I think the five C's we talked about yesterday the culture of companies, the cloud is an enabler, you've got clusters of servers and capabilities, Kubernetes clusters, you've got code and you've got all kinds of, you know, things going on there. Each one has elements that are at risk for hacking, right? So that to me is something that's super important. I think that's why the focus on security's different and important, but it's not going to fork the main event. So that's why I think the spin out was, spinout, or the new event is a good call by the CNCF. >> One of the things today that struck me they're talking a lot about software supply chain and that's been in the headlines for quite a while now. And a stat that was shared this morning during the keynote just blew my brains that there was a 742% increase in the software supply chain attacks occurring over the last three years. It's during Covid times, that is a massive increase. The threat landscape is just growing so amorphously but organizations need to help dial that down because their success and the health of the individuals and the end users is at risk. Well, Covid is an environment where everyone's kind of working at home. So there was some disruption to infrastructure. Also, when you have change like that, there's opportunities for hackers, they'll arbitrage that big time. But I think general the landscape is changing. There's no perimeter anymore. It's CloudNative, this is where it is and people who are moving from old IT to CloudNative, they're at risk. That's why there's tons of ransomware. That's why there's tons of risk. There's just hygiene, from hygiene to architecture and like Nick said from Palo Alto, the co-founder, there's not a lot of architecture in security. So yeah, people have bulked up their security teams but you're going to start to see much more holistic thinking around redoing security. I think that's the opportunity to propel CloudNative, and I think you'll see a lot more coming out of this. >> Did you hear any specific information on some of the CloudNative projects going on that really excite you in terms of these are the right people going after the right challenges to solve in the right direction? >> Well I saw the sessions and what jumped out to me at the sessions was it's a lot of extensions of what we heard at CUBECON and I think what they want to do is take out the big items and break 'em out in security. Kubescape was one we just covered. They want to get more sandbox type stuff into the security side that's very security focused but also plays well with CUBECON. So we'll hear more about how this plays out when we're in Amsterdam coming up in April for CUBECON to hear how that ecosystem, because I think it'll be kind of a relief to kind of decouple security 'cause that gives more focus to the stakeholders in CUBECON. There's a lot of issues going on there and you know service meshes and whatnot. So it's a lot of good stuff happening. >> A lot of good stuff happening. One of the things that'll be great about CUBECON is that we always get the voice of the customer. We get vendors coming on with the voice of the customer talking about and you know in that case how they're using Kubernetes to drive the business forward. But it'll be great to be able to pull in some of the security conversations that spin out of CloudNative Security CON to understand how those end users are embracing the technology. You brought up I think Nir Zuk from Palo Alto Networks, one of the themes there when Dave and I did their Ignite event in December was, of 22, was really consolidation. There are so many tools out there that organizations have to wrap their heads around and they need to be able to have the right enablement content which this event probably delivered to figure out how do we consolidate security tools effectively, efficiently in a way that helps dial down our risk profile because the risks just seem to keep growing. >> Yeah, and I love the technical nature of all that and I think this is going to be the continued focus. Chris Aniszczyk who's the CTO listed like E and BPF we covered with Liz Rice is one of the most three important points of the conference and it's just, it's very nerdy and that's what's needed. I mean it's technical. And again, there's no real standards bodies anymore. The old days developers I think are super important to be the arbiters here. And again, what I love about the CNCF is that they're developer focused and we heard developer first even in security. So you know, this is a sea change and I think, you know, developers' choice will be the standards bodies. >> Lisa: Yeah, yeah. >> They decide the future. >> Yeah. >> And I think having the sandboxing and bringing this out will hopefully accelerate more developer choice and self-service. >> You've been talking about kind of putting the developers in the driver's seat as really being the key decision makers for a while. Did you hear information over the last couple of days that validates that? >> Yeah, absolutely. It's clearly the fact that they did this was one. The other one is, is that engineering teams and dev teams and script teams, they're blending together. It's not just separate silos and the ones that are changing their team dynamics, again, back to the culture are winning. And I think this has to happen. Security has to be embedded everywhere in making it frictionless and to provide kind of the guardrail so developers don't slow down. And I think where security has become a drag or an anchor or a blocker has been just configuration of how the organization's handling it. So I think when people recognize that the developers are in charge and they're should be driving the application development you got to make sure that's secure. And so that's always going to be friction and I think whoever does it, whoever unlocks that for the developer to go faster will win. >> Right. Oh, that's what I'm sure magic to a developer's ear is the ability to go faster and be able to focus on co-development in a secure fashion. What are some of the things that you're excited about for CUBECON. Here we are in February, 2023 and CUBECON is just around the corner in April. What are some of the things that you're excited about based on the groundswell momentum that this first inaugural CloudNative Security CON is generating from a community, a culture perspective? >> I think this year's going to be very interesting 'cause we have an economic challenge globally. There's all kinds of geopolitical things happening. I think there's going to be very entrepreneurial activity this year more than ever. I think you're going to see a lot more innovative projects ideas hitting the table. I think it's going to be a lot more entrepreneurial just because the cycle we're in. And also I think the acceleration of mainstream deployments of out of the CNCF's main event CUBECON will happen. You'll see a lot more successes, scale, more clarity on where the security holes are or aren't. Where the benefits are. I think containers and microservices are continuing to surge. I think the Cloud scale hyperscale as Amazon, Azure, Google will be more aggressive. I think AI will be a big theme this year. I think you can see how data is going to infect some of the innovation thinking. I'm really excited about the data infrastructure because it powers a lot of things in the Cloud. So I think the Amazon Web Services, Azure next level gen clouds will impact what happens in the CloudNative foundation. >> Did you have any conversations yesterday or today with respect to AI and security? Was that a focus of anybody's? Talk to me about that. >> Well, I didn't hear any sessions on AI but we saw some demos on stage. But they're teasing out that this is an augmentation to their mission, right? So I think a lot of people are looking at AI as, again, like I always said there's the naysayers who think it's kind of a gimmick or nothing to see here, and then some are just going to blown away. I think the people who are alpha geeks and the industry connect the dots and understand that AI is going to be an accelerant to a lot of heavy lifting that was either manual, you know, hard to do things that was boring or muck as they say. I think that's going to be where you'll see the AI stories where it's going to accelerate either ways to make security better or make developers more confident and productive. >> Or both. >> Yeah. So definitely AI will be part of it. Yeah, definitely. One of the things too that I'm wondering if, you know, we talk about CloudNative and the goal of it, the importance of it. Do you think that this event, in terms of what we were able to see, obviously being remote the event going on in Seattle, us being here in Palo Alto and Boston and guests on from Seattle and Germany and all over, did you hear the really the validation for why CloudNative Security why CloudNative is important for organizations whether it's a bank or a hospital or a retailer? Is that validation clear and present? >> Yeah, absolutely. I think it was implied. I don't think there was like anyone's trying to debate that. I think this conference was more of it's assumed and they were really trying to push the ability to make security less defensive, more offensive and more accelerated into the solving the problems with the businesses that are out there. So clearly the CloudNative community understands where the security challenges are and where they're emerging. So having a dedicated event will help address that. And they've got great co-chairs too that put it together. So I think that's very positive. >> Yeah. Do you think, is it possible, I mean, like you said several times today so eloquently the industry's on the defense when it comes to security and the hackers are on the offense. Is it really possible to make that switch or obviously get some balances. As technology advances and industry gets to take advantage of that, so do the hackers, is that balance achievable? >> Absolutely. I mean, I think totally achievable. The question's going to be what's the environment going to be like? And I remember as context to understanding whether it's viable or not, is to look at, just go back 13 years ago, I remember in 2010 Amazon was viewed as an unsecure environment. Everyone's saying, "Oh, the cloud is not secure." And I remember interviewing Steve Schmidt at AWS and we discussed specifically how Amazon Cloud was being leveraged by hackers. They made it more complex for the hackers. And he said, "This is just the beginning." It's kind of like barbed wire on a fence. It's yeah, you're not going to climb it so people can get over it. And so since then what's happened is the Cloud has become more secure than on premises for a lot of either you know, personnel reasons, culture reasons, not updating, you know, from patches to just being insecure to be more insecure. So that to me means that the flip the script can be flipped. >> Yeah. And I think with CloudNative they can build in automation and code to solve some of these problems and make it more complex for the hacker. >> Lisa: Yes. >> And increase the cost. >> Yeah, exactly. Make it more complex. Increase the cost. That'll be in interesting journey to follow. So John, here we are early February, 2023 theCUBE starting out strong as always. What year are we in, 12? Year 12? >> 13th year >> 13! What's next for theCUBE? What's coming up that excites you? >> Well, we're going to do a lot more events. We got the theCUBE in studio that I call theCUBE Center as kind of internal code word, but like, this is more about getting the word out that we can cover events remotely as events are starting to change with hybrid, digital is going to be a big part of that. So I think you're going to see a lot more CUBE on location. We're going to do, still do theCUBE and have theCUBE cover events from the studio to get deeper perspective because we can then bring people in remote through our our studio team. We can bring our CUBE alumni in. We have a corpus of content and experts to bring to table. So I think the coverage will be increased. The expertise and data will be flowing through theCUBE and so Cube Center, CUBE CUBE Studio. >> Lisa: Love it. >> Will be a integral part of our coverage. >> I love that. And we have such great conversations with guests in person, but also virtually, digitally as well. We still get the voices of the practitioners and the customers and the vendors and the partner ecosystem really kind of lauded loud and clear through theCUBE megaphone as I would say. >> And of course getting the clips out there, getting the highlights. >> Yeah. >> Getting more stories. No stories too small for theCUBE. We can make it easy to get the best content. >> The best content. John, it's been fun covering CloudNative security CON with you with you. And Dave and our guests, thank you so much for the opportunity and looking forward to the next event. >> John: All right. We'll see you at Amsterdam. >> Yeah, I'll be there. We want to thank you so much for watching TheCUBES's two day coverage of CloudNative Security CON 23. We're live in Palo Alto. You are live wherever you are and we appreciate your time and your view of this event. For John Furrier, Dave Vellante, I'm Lisa Martin. Thanks for watching guys. We'll see you at the next show.

(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)

>>Hi, welcome to this cube conversation here in Palo Alto, California. I'm John fur, host of the cube here at our headquarters on the west coast in Palo Alto, California. Got a great news guest here. Matt McGill, Wayne managing director of Madrona venture group is here with me on the big news and drone raising their record 690 million fund and partnering with their innovative founders. Matt, thanks for coming on and, and talking about the news and congratulations on the dry powder. >>Well, Hey, thanks so much, John. Appreciate you having me on the show. >>Well, great news here. Oley validation. We're in a new market. Everyone's talking about the new normal, we're talking about a recession inflation, but yet we've been reporting that this is kind of the first generation that cloud hyperscale economic scale and technical benefits have kind of hit any kind of economic downturn. If you go back to to 2008, our last downturn, the cloud really hasn't hit that tipping point. Now the innovation, as we've been reporting with our startup showcases and looking at the results from the hyperscalers, this funding news is kind of validation that the tech society intersection is working. You guys just get to the news 430 million in the Madrona fund nine and 200. And I think 60 million acceleration fund three, which means you're gonna go stay with your roots with seed early stage and then have some rocket fuel for kind of the accelerated expansion growth side of it. Not like late stage growth, but like scaling growth. This is kind of the news. Is that right? >>That's right. You know, we, we've had a long time strategy over 25 years here in Seattle of being early, early stage. You know, it's like our friends at Amazon like to say is, well, we're there at day one and we wanna help build companies for the long run for over 25 years. We've been doing that in Seattle. And I think one of the things we've realized, I mean, this is, these funds are the largest funds ever raised by a Seattle based venture capital firm and that's notable in and of itself. But we think that's the reason is because Seattle has continued to innovate in areas like consumer internet software cloud, of course, where the cloud capital of the world and increasingly the applications of machine learning. And so with all that combination, we believe there's a ton more companies to be built here in the Pacific Northwest and in Seattle in particular. And then through our acceleration fund where we're investing in companies anywhere in the country, in fact, anywhere in the world, those are the kinds of companies that want to have the Seattle point of view. They don't understand how to work with Amazon and AWS. They don't understand how to work with Microsoft and we have some unique relationships in those places and we think we can help them succeed in doing that. >>You know, it's notable that you guys in particular have been very close with Jeff Bayo Andy Jesse, and the success of ABUS as well as Microsoft. So, you know, Seattle has become cloud city. Everyone kind of knows that from a cloud perspective, obviously Microsoft's roots have been there for a long, long time. You go back, I mean, August capital, early days, funding Microsoft. You remember those days not to date myself, but you know, Microsoft kind of went up there and kind of established it a Amazon there as well. Now you got Google here, you got Facebook in the valley. You guys are now also coming down. This funding comes on the heels of you appointing a new managing director here in Palo Alto. This is now the migration of Madrona coming into the valley. Is that right? Is that what we're seeing? >>Well, I think what we're trying to do is bring the things that we know uniquely from Seattle and the companies here down to Silicon valley. We've got a terrific partner in Karama Hend, Andrew he's somebody that we have worked together with over the years, co-investing in companies. So we knew him really well. It was a bit opportunistic for us, but what we're hearing over and over again is a lot of these companies based in the valley, based in other parts of the country, they don't know really how to best work with the Microsoft and Amazons are understand the services that they offer. And, you know, we have that capability. We have those relationships. We wanna bring that to bear and helping build great companies. >>What is your expectation on the Silicon valley presence here? You can kind of give a hint here kind of a gateway to Seattle, but you got a lot of developers here. We just reported this morning that MEA just open source pie, torch to the Linux foundation again, and Mary material kind of trend we are seeing open source now has become there's no debate anymore has become the software industry. There's no more issue around that. This is real. I >>Think that's right. I mean, you know, once, you know, Satya became CEO, Microsoft, and they started embracing open source, you know, that was gonna be the last big tech holdout. We think open source is very interesting in terms of what it can produce and create in terms of next generation, innovative innovation. It's great to see companies like Facebook like Uber and others that have had a long track record of open source capabilities. But what we're also seeing is you need to build businesses around that, that a lot of enterprises don't wanna buy just the open source and stitch it together themselves. They want somebody to do it with them. And whether that's the way that, you know, companies like MongoDB have built that out over time or that's, you know, or elastic or, you know, companies like opt ML and our portfolio, or even the big cloud, you know, hyperscalers, you know, they are increasingly embracing open source and building finished services, managed services on top of it. So that's a big wave that we've been investing in for a number of years now and are highly confident gonna >>Continue. You know, I've been a big fan of Pacific Northwest for a while. You know, love going up there and talking to the folks at Microsoft and Amazon and AWS, but there's been a big trend in venture capital where a lot of the, the later stage folks, including private equity have come in, you seen tiger global even tiger global alumni, that the Cubs they call them, you know, they're coming down and playing in the early state and the results haven't been that good. You guys have had a track record in your success. Again, a hundred percent of your institutional investors have honed up with you on this two fund strategy of close to 700 million. What's this formula says, why aren't they winning what's is it, they don't have the ecosystem? Is it they're spraying and praying without a lot of discipline? What's the dynamic between the folks like Madrona, the Neas of the world who kind of come in and Sequoia who kind of do it right, right. Come in. And they get it done in the right way. The early stage. I just say the private equity folks, >>You know, I think that early stage venture is a local business. It is a geographically proximate business when you're helping incredible founders, try to really dial in that early founder market fit. This is before you even get to product market fit. And, and so the, the team building that goes on the talking to potential customers, the ITER iterating on business strategy, this is a roll up your sleeves kind of thing. It's not a financial transaction. And so what you're trying to do is have a presence and an understanding, a prepared mind of one of the big themes and the kinds of founders that with, you know, our encouragement and our help can go build lasting companies. Now, when you get to a, a, a later stage, you know, you get to that growth stage. It is generally more of a financial, you know, kind of engineering sort of proposition. And there's some folks that are great at that. What we do is we support these companies all the way through. We reserve enough capital to be with them at the seed stage, the series B stage the, you know, the crossover round before you go public, all of those sorts of things. And we love partnering with some of these other people, but there's a lot of heavy lifting at the early, early stages of a business. And it's, it's not, I think a model that everybody's architected to do >>Well, you know, trust becomes a big factor in all this. You kind of, when you talk about like that, I hear you speaking. It makes me think of like trusted advisor meets money, not so much telling people what to do. You guys have had a good track record and, and being added value, not values from track. And sometimes that values from track is getting in the way of the entrepreneur by, you know, running the certain meetings, driving board meetings and driving the agenda that you see to see that trend where people try too hard and that a force function, the entrepreneur we're living in a world now where everyone's talking to each other, you got, you know, there's no more glass door it's everyone's on Twitter, right? So you can see some move, someone trying to control the supply chain of talent by term sheet, overvaluing them. >>You guys are, have a different strategy. You guys have a network I've noticed that Madrona has attracted them high end talent coming outta Microsoft outta AWS season, season, senior talent. I won't say, you know, senior citizens, but you know, people have done things scaled up businesses, as well as attract young talent. Can you share with our audience that dynamic of the, the seasoned veterans, the systems thinkers, the ones who have been there done that built software, built teams to the new young entrepreneurs coming in, what's the dynamic, like, how do you guys look at at those networks? How do you nurture them? Could you share your, your strategy on how you're gonna pull all this together, going forward? >>You know, we, we think a lot about building the innovation ecosystem, like a phrase around here that you hear a lot is the bigger pie theory. How do we build the bigger pie? If we're focusing on building the bigger pie, there'll be plenty of that pie for Madrona Madrona companies. And in that mindset says, okay, how are we gonna invest in the innovation ecosystem? And then actually to use a term, you know, one of our founders who unfortunately passed away this year, Tom Aber, he had just written a book called flywheel. And I think it embodies this mindset that we have of how do you create that flywheel within a community? And of course, interestingly enough, I think Tom both learned and contributed to that. He was on the board of Amazon for almost 20 years in helping build some of the flywheels at Amazon. >>So that's what we carry forward. And we know that there's a lot of value in experiential learning. And so we've been fortunate to have some folks, you know, that have worked at some of those, you know, kind iconic companies, join us and find that they really love this company building journey. We've also got some terrific younger folks that have, you know, some very fresh perspectives and a lot of, a lot of creativity. And they're bringing that together with our team overall. And you know, what we really are trying to do at the end of the day is find incredible founders who wanna build something lasting, insignificant, and provide our kind of our time, our best ideas, our, our perspective. And of course our capital to help them be >>Successful. I love the ecosystem play. I think that's a human capital game too. I like the way you guys are thinking about that. I do wanna get your reaction, cause I know you're close to Amazon and Microsoft, but mainly Jeff Bezos as well. You mentioned your, your partner who passed away was on the board. A lot of great props on and tributes online. I saw that, I know I didn't know him at all. So I really can't comment, but I did notice that Bezos and, and jazz in particular were complimentary. And recently I just saw Bezos comment on Twitter about the, you know, the Lord of the rings movie. They're putting out the series and he says, you gotta have a team. That's kinda like rebels. I'm paraphrasing, cuz these folks never done a movie like this before. So they're, they're getting good props and reviews in this new world order where entrepreneurs gotta do things different. >>What's the one thing that you think entrepreneurs need to do different to make this next startup journey different and successful because the world is different. There's not a lot of press to relate to Andy Jassey even on stage last week in, in, in LA was kind of, he's not really revealing. He's on his talking points, message, the press aren't out there and big numbers anymore. And you got a lot of different go-to market strategies, omnichannel, social different ways to communicate to customers. Yeah. So product market fit is becomes big. So how do you see this new flywheel emerging for those entrepreneurs have to go out there, roll up their sleeves and make it happen. And what kind of resources do you think they need to be successful? What are you guys advocating? >>Well, you know, what's really interesting about that question is I've heard Jeff say many times that when people ask him, what's gotta be different. He, he reminds them to think about what's not gonna change. And he usually starts to then talk about things like price, convenience, and selection. Customer's never gonna want a higher price, less convenience, smaller selection. And so when you build on some of those principles of, what's not gonna change, it's easier for you to understand what could be changing as it relates to the differences. One of the biggest differences, I don't think any of us have fully figured out yet is what does it mean to be productive in a hybrid work mode? We happen to believe that it's still gonna have a kernel of people that are geographically close, that are part of the founding and building in the early stages of a company. >>And, and it's an and equation that they're going to also have people that are distributed around the country, perhaps around the world that are some of the best talent that they attract to their team. The other thing that I think coming back to what remains the same is being hyper focused on a certain customer and a certain problem that you're passionate about solving. And that's really what we look for when we look for this founder market fit. And it can be a lot of different things from the next generation water bottle to a better way to handle deep learning models and get 'em deployed in the cloud. If you've got that passion and you've got some inkling of the skill of how to build a better solution, that's never gonna go away. That's gonna be enduring, but exactly how you do that as a team in a hybrid world, I think that's gonna be different. >>Yeah. One thing that's not changing is that your investor, makeup's not changing a hundred percent of your existing institutional investors have signed back on with you guys and your oversubscribed, lot of demand. What is your flywheel success formula? Why is Tron is so successful? Can you share some feedback from your investors? What are they saying? Why are they re-upping share some inside baseball or anecdotal praise? >>Well, I think it's very kind to you to frame it that way. I mean, you know, it does for investors come back to performance. You know, these are university endowments and foundations that have a responsibility to, to generate great returns. And we understand that and we're very aligned with that. I think to be specific in the last couple years, they appreciated that we were also not holding onto our, our stocks forever, that we actually made some thoughtful decisions to sell some shares of companies like Smartsheet and snowflake and accolade in others, and actually distribute capital back to them when things were looking really, really good. But I think the thing, other thing that's very important here is that we've created a flywheel with our core strategy being Seattle based and then going out from there to try to find the best founders, build great companies with them, roll up our sleeves in a productive way and help them for the long term, which now leads to multiple generations of people, you know, at those companies. And beyond that we wanna be, you know, partner with and back again. And so you create this flywheel by having success with people in doing it in a respectful. And as you said earlier, a trusted way, >>What's the message for the Silicon valley crowd, obviously bay area, Silicon valley, Palo Alto office, and the center of it. Obviously you got them hybrid workforce hybrid venture model developing what's the goals. What's the message for Silicon valley? >>Well, our message for folks in Silicon valley is the same. It's always been, we we're excited to partner with them largely up here again, cause this is still our home base, but there'll be a, you know, select number of opportunities where we'll get a chance to partner together down in Silicon valley. And we think we bring something different with that deep understanding of cloud computing, that deep understanding of applied machine learning. And of course, some of our unique relationships up here that can be additive to what the they've already done. And some of them are just great partners and have built, you know, help build some really incredible companies over >>The years. Matt, I really appreciate you taking the time for this interview, given them big news. I guess the question on everyone's mind, certainly the entrepreneur's mind is how do I get some of that cash you have and put it into work for my opportunity. One what's the investment thesis can take a minute to put the plug in for the firm. What are you looking to invest in? What's the thesis? What kind of entrepreneurs you're looking for? I know fund one is seed fund nine is seed to, to a and B and the second one is beyond B and beyond for growth. What's the pitch. What's the pitch. >>Yeah. Well you can, you can think of us as you know, any stage from pre-seed to series seed. You know, we'll make a new investment in companies in all of those stages. You know, I think that, you know, the, the core pitch, you know, to us is, you know, your passion for the, for the problem that you're trying to, trying to get solved. And we're of course, very excited about that. And you know, at, at, at the end of the day, you know, if you want somebody that has a distinct point of view on the market that is based up here and can roll up their sleeves and work alongside you. We're, we're, we're the ones that are more than happy to do that. Proven track record of doing that for 25 plus years. And there's so much innovation ahead. There's so many opportunities to disrupt to pioneer, and we're excited to be a part of working with great founders to do that. >>Well, great stuff. We'll see you ATS reinvent coming up shortly and your annual get together. You always have your crew down there and, and team engaging with some of the cloud players as well. And looking forward to seeing how the Palo Alto team expands out. And Matt, thanks for coming on the cube. Appreciate your time. >>Thanks very much, John. Appreciate you having me look forward to seeing you at reinvent. >>Okay. Matt, Matt here with Madrona venture group, he's the partner managing partner Madrona group raises 690 million to fund nine and, and, and again, and big funds for accelerated growth fund. Three lot of dry powder. Again, entrepreneurship in technology is scaling. It's not going down. It's continuing to accelerate into this next generation super cloud multi-cloud hybrid cloud world steady state. This is the cubes coverage. I'm John for Silicon angle and host of the cube. Thanks for watching.

(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)

>>Welcome back to the cubes. Continuing coverage of AWS reinvent 2021 live from Las Vegas. I'm Lisa Martin. We have to set two live sets here with the cube two remote sets over 100 guests on the program for three and a half days talking about the next decade and cloud innovation. And I have two alumni back with me. Please. Welcome back, Mandy Dolly, while the CMO of Boomi and ed. Makowski the head of product at Boomi guys. It's so great to see you. Great to see you, Lisa, thank you in person zoom. Incredible. So in the time, since it's been, since I've seen you, booty is a verb. You, I can see your cheeks bursting. Yeah. Just >>Boom, yet go, boom. It go. Boom. Yet, >>Talk to me about what, what that means, because this is something that you discovered through customers during the pandemic. >>Absolutely. And really it's a Testament to the platform that's been built and the experience of 18,000 customers, a hundred thousand community members, anytime there's disparate data. And it needs to be connected in a way that's secure, reliable performance. And it just works that confidence and trust our customers are telling us that they just Boomi it. And so we figured it was a rally cry. And as a marketing team, it was handed to us. We didn't have to push a Boulder up hill. Our customers are, are just booming it. And so our rally cry to the market is take advantage of the experience of those that have come before you and go build what you need to. It works, >>Period. It works well as the chief marketing officer, there's probably nothing better, nothing better than the validating voice of the customer, right? That's the most honest that you're going to get, but having a customer create the verb for you, there's going to be nothing that prepares you for that. Nothing like it, but also how great does that make it when you're having conversations with prospective customers or even partners that there's that confidence and that trust that your 18,000 plus now customer's house right in >>Lummi right. And adding what? Eight a day. Yeah. Every day we're adding eight new customers. >>Thank you customers a day. The Boomi versus what? A hundred thousand strong now. Yes. >>In two years we built that. Is that right? Yes. >>Wow. Oh my goodness. During the >>Pandemic, the momentum is incredible. Yeah. It's >>Incredible. >>Then you're on your growth from a usage perspective. So yeah, we're skyrocketing >>Use the most need like, uh, you know, neck braces from whiplash going so fast. >>Oh, we're ready. >>Good. I know, I know you are. So talk to me about, you know, we've seen such change in the last 22 months, massive acceleration to the cloud digital transformation. We're now seeing every company has to be a data company to survive and actually to be competitive, to be a competitor. But one of the things that used to be okay back in the day was, you know, these, uh, experiences that weren't integrated, like when you went to well, like when I was back in college and I would go in and you would pay for this class and that cause everything was disconnected and we didn't know what we didn't know. Now the integrated experience is table stakes for any organization. And talk to me about when you're talking with customers, where are they like across industries and going, we don't have a choice. We've got to be able to connect these experiences for our customers, for our employees and to be a comparator. >>Okay. Yeah. I mean, it used to be about for us application data integration, that sort of thing. That's where we were born. But particularly through the pandemic, it's become integrated experiences and automation. It's not just about moving data between systems, that sort of thing. It's about connecting with your end users, your employees, your customers, et cetera, like you were saying, and automating and using intelligence to continue automating those things faster. Because if, if you're not moving faster in today's world, you're, you're in peril. So, >>And that was one of the themes that we were actually talking about this morning during our kickoff that you're hearing is every company is a data company. And if they're not, they're not going to be around much longer many. Talk to me when you're talking with customers who have to really reckon with that and go, how do we connect these experiences? Because if we can't do that, then we're not going to be around. >>Yeah. The answer lies in the problems, right? There are real-world problems that need to be solved. We have a customer just north of here, a, a university. And, um, as they were bringing students back to campus, right, you're trying to deliver a connected campus experience. Well, how do you handle contact tracing, right. For COVID-19 that's a real modern day problem. Right? And so there you're able to now connect disparate data sources to go deliver on a way, an automated way to be able to handle that and provide safety to your students. Table-stakes oh, it is right. Digital identity management again in a university set setting critical. Right? So these things are now a part of our fabric of the way we live. The consumerization of tech has hit B2B. It's merging. Yeah. >>And it's good. There's definitely silver linings that have come out of the last 22 months. And I'm sure there will be a few more as we go through Omicron and whatever Greek letter is next in the alphabet, but don't want to hear we are at reinvent so much. There's always so much news at reinvent. Here we are. First 10th, 10th reinvent. You can't believe 10th reinvent. AWS is 15 years old brand new leader. And of course, yesterday ad starts the flood of announcements yesterday, today. Talk to me about what it's like to be part of that powerful AWS ecosystem from a partner perspective and how, how influential is Boomi and its customers and the Boomi verse in the direction that AWS goes in because there's so customer obsessed like you guys are >>Well, it was really exciting for us because we're a customer and a partner of AWS, right? We, we run our infrastructure on AWS. So we get to take advantage of all the new announcements that they make and all the cool stuff they bring to the table. So we're really excited for that. But also as all these things come up and customers want to take advantage of them, if they're creating different data, sets, different data silos or opportunity for automation around the business, we're right there for our customers and partners to go take advantage of that and quickly get these things up and running as they get released by AWS. So it's all very exciting. And we look forward to all these different announcements. >>One of the things also that I felt in the last day and a half, since everything really kicked off yesterday was the customer flywheel. AWS always talks about, we work backwards from the customer forwards. And that is a resounding theme that I'm hearing throughout all of the partners that I've talked about. They have a massive ecosystem. Boomi has a massive ecosystem to working with those partners, but also ensuring that, you know, at the end of the day, we're here to help customers resolve problems, problems that are here today, problems that are going to be here tomorrow. How do you help customers deal with Mandy with, with some of the challenges of today, when they say Mandy help us future-proof or integrations what we're doing going forward, what does that mean to Boomi? Yeah, >>I think for us, the way we approach it is you start with Boomi with a connectivity kind of problem, right? We're able to take disparate data silos and be able to connect and be able to create this backbone of connectivity. Once you have that, you can go build these workflows and these user engagement mechanisms to automate these processes and scale, right? So that's 0.1, we have a company called health bridge financial, right? They're a health tech company, financial services company. They are working towards, they run on AWS. They, they have, uh, a very, um, uh, secure, compliant infrastructure requirement, especially around HIPAA because they're dealing with healthcare, right? And they have needs to be able to integrate quickly and not a big budget to start with. They grew very quickly and Lummi powered their, their AWS ecosystem. So as our workloads grew on RDS, as well as SQS as three, we were able to go in and perform these HIPAA compliant integrations for them. So they could go provide reimbursement on medical spending claims for their end customers. So not only did we give them user engagement and an outstanding customer experience, we were able to help them grow as a business and be able to leverage the AWS ecosystem. That's a win, win, win across the board for all of us. >>That's one plus one equals three, for sure. Yep. One of the things too, that's interesting is, you know, when we see the plethora of AWS services, like I mentioned a minute ago, there's always so many announcements, but there's so much choice for customers, right? When you're talking ed with customers, Boomi customers that are looking for AWS services, tell me about some of those conversations. Can we help guide them along that journey? >>I mean, we help them from an architectural standpoint, as far as what services they should choose from AWS to integrate their different data sources within the AWS ecosystem and maybe to others, um, we've helped our customers going back a little bit to, to the future-proofing over the time we've at our platform, we've connected with our customers over 180,000 different data sources, including AWS and others, that as we continue to grow, our customers never need to upgrade. We're a cloud model, ourselves running an AWS. So they just get to keep taking advantage of that. Their business grows and evolves. And as AWS grows and evolves for them, and they're modernizing their infrastructure bringing in, in AWS, we continue to stay on the forefront with keeping connectivity and automation and integration options. >>And that's a massive advantage for customers in any industry, especially, I know one of the first things I thought of when the pandemic first struck and we saw this, you know, the rise of the pharma companies working on vaccine was Madrona. Madonna's a Boomi customer. If they are talk to me about some of the things that you've helped them facilitate, because there was that obviously that time where everyone's scattered, nobody could get onsite having a cloud native solution. Must've been a huge advantage. Yeah. Well getting us all back here, really >>Exactly. First and foremost, getting more people on board into their business to help go find the race for the cure. And then being able to connect that data right. That they were generating and really find a solution. So we had an integral role to play in that. That's definitely a feather in our cap. We're really proud of that. Um, again, right. It's it's about speed and agility and the way we're architected, we're a low code platform. We're not developer heavy. You can log in and go and start building right away. What, what used to take months now takes weeks. If not days, if you use the Boomi platform, those brittle code integrations no longer need to be a part of your day to day. >>And that probably was a major instrument in the survival of a lot of businesses in the very beginning when it was chaotic, right? And it was pivot, pivot, pivot, pivot, pivot, that, that, you know, one of the things we learned during the pandemic is that there is access to real-time data. Real-time integrations. Isn't a nice to have anymore. It's required. It's fundamental for employee experiences, customer experiences in every industry >>And banking. We've had several banks who were able to stand up and start taking PPP loans. Uh, they used to do this in person. They were able to take them within literally some of our banks within four days had the whole process built into it. >>Wow. And so from a differentiation perspective, how have your customer conversations changed? Obviously go Boomi. It is now is something that you do, you have t-shirts yet, by the way, they're coming. And can I get one? Yes, absolutely. Excellent. But talk to me about how those customer conversations have changed is, is what Boomi enables organizations is this snow at the C-suite the board level going? We've got to make sure that these data sources are connected because they're only gonna keep proliferating. >>Yeah, I think it's coming, right. We're not quite there yet, but as we're starting to get this groundswell at the integration developer level at the enterprise architect level, I think the C-suite especially is realizing the value of the delivery of this integrated experience now, right? These data fueled experiences are the differentiators for new business models. So transformation is something that's required. Obviously you need to modernize. We heard about that in the keynotes here at the conference, but now it's the innovation layer and that's where we're squarely focused is once you're able to connect this data and be able to modernize your systems, how do you go build new business models with innovation? That's where the C-suites leaning in with >>Us. Got it. And that's the opportunity is to really unlock the value of all this data and identify new products, new services, new target markets, and really that innovation kicks the door wide open on a competitor if you're focused on really becoming a data company, I think. Yeah, exactly. Yeah. What are some of the things that, that you're looking forward to as we, as we wrap up 2021 and let's cross our fingers, we're going into a much better 20, 22. What question for both of you and we'll start with you, what's next for Boomi? >>So we just recently laid out our hyper automation vision, right. And what hyper automation is, is adding intelligence, artificial intelligence, and machine learning to your automation to make you go faster and faster and help you with decisions that you may have been making over and over as an example, or any workflows you do as an employee. So there is this convergence of RPA and iPads that's happening in the market. And we're on the forefront of that around robotic process automation. And then bringing that, those types of things into our platform and just helping our customers automate more and more, because that's what they're looking for. That's what go Boomi. It's all about. They've integrated their stuff. We were taking the lead from our customers who are automating things. We had blue force tracking as an example, where in Amsterdam, they have security guards running around and, and, and using, um, wearable devices to track them on cameras. And that's not an application integration use case that's automation. So we're moving there, we're looking with our customers on how we can help them get faster and better and provide things like safety and that use case. So, >>And we're our customers in terms of, of embracing hyper automation. Because when we talk about, we know a lot of, uh, news around AI and, and model last day and a half, but when you think about kind of like, where are most organizations with from a maturation perspective, are they ready for hyper automation? >>I think they're ready for automation. They're learning about hyper automation. I think we're pushing the term further ahead. You know, we're, we're, we're on the forefront of that because industries are thinking, our customers are thinking about automation. They're thinking about AIML, we're introducing them to hyper automation and, and kind of explaining to them, you're doing this already. Think more along these lines, how can you drive your business forward with these? And they're embracing it really well. So >>Is that conversation elevating up to the board level yet? Is that a board level initiative or >>What it is? It's, it's a little more grassroots. I think that's, I was thinking that's where came from because the employees teams are solving problems. They're showcasing these things to their executives and saying, look at the cool stuff we're doing for the business. And the executives are now saying, well with this problem, can we now go boob? Can we Boomi it because they're there, they're starting to understand what we can do. Okay. >>That's awesome. Oh my goodness. Mandy, you've been the chief marketing officer for three over three years now. I can't believe the amount of change that you've seen, not just the last 22 months, but the last three years. What are you excited about as Boomi heads into 2022? I think, >>And new opportunities to get deeper and broader into the market. Our ownership changed as you know this past year. And, um, you know, we have a new leg on growth, if you will, right? And so whole new trajectory ahead of us, bigger brand building more pervasiveness or ease of use around our platform, right? We're available now in a pay as you go model on our website and on a $50 a month model or, uh, um, atmosphere go and then also on marketplace. So we're making the product and the platform more accessible to more people so they can begin on faster, build faster, and go solve these problems. So really democratizing integration is something that I'm very excited about. Democratizing integration, as well as more air cover, just to let people know that this technology exists. So it's really a marketer's dream >>And why they should go buy me it. Right. Exactly. You guys. It was great to have you on the program. Congratulations on the success on, on becoming a verb. That's pretty awesome. I'll look forward to my t-shirt. So I smelled flu and >>You got it. >>All right. For my guests. I'm Lisa Martin. You're watching the cube, the global leader in life tech coverage.

>> Hi, I'm Natalie Ehrlich and welcome to the AWS startup showcase presented by The Cube. We have an amazing lineup of great guests who will share their insights on the latest innovations and solutions and leveraging cloud scale data in AI, security and life sciences. And now we're joined by the co-founders and co-CEOs of The Cube, Dave Vellante and John Furrier. Thank you gentlemen for joining me. >> Hey Natalie. >> Hey Natalie. >> How are you doing. Hey John. >> Well, I'd love to get your insights here, let's kick it off and what are you looking forward to. >> Dave, I think one of the things that we've been doing on the cube for 11 years is looking at the signal in the marketplace. I wanted to focus on this because AI is cutting across all industries. So we're seeing that with cybersecurity and life sciences, it's the first time we've had a life sciences track in the showcase, which is amazing because it shows that growth of the cloud scale. So I'm super excited by that. And I think that's going to showcase some new business models and of course the keynotes Ali Ghodsi, who's the CEO Data bricks pushing a billion dollars in revenue, clear validation that startups can go from zero to a billion dollars in revenues. So that should be really interesting. And of course the top venture capitalists coming in to talk about what the enterprise dynamics are all about. And what about you, Dave? >> You know, I thought it was an interesting mix and choice of startups. When you think about, you know, AI security and healthcare, and I've been thinking about that. Healthcare is the perfect industry, it is ripe for disruption. If you think about healthcare, you know, we all complain how expensive it is not transparent. There's a lot of discussion about, you know, can everybody have equal access that certainly with COVID the staff is burned out. There's a real divergence and diversity of the quality of healthcare and you know, it all results in patients not being happy, and I mean, if you had to do an NPS score on the patients and healthcare will be pretty low, John, you know. So when I think about, you know, AI and security in the context of healthcare in cloud, I ask questions like when are machines going to be able to better meet or make better diagnoses than doctors? And that's starting. I mean, it's really in assistance putting into play today. But I think when you think about cheaper and more accurate image analysis, when you think about the overall patient experience and trust and personalized medicine, self-service, you know, remote medicine that we've seen during the COVID pandemic, disease tracking, language translation, I mean, there are so many things where the cloud and data, and then it can help. And then at the end of it, it's all about, okay, how do I authenticate? How do I deal with privacy and personal information and tamper resistance? And that's where the security play comes in. So it's a very interesting mix of startups. I think that I'm really looking forward to hearing from... >> You know Natalie one of the things we talked about, some of these companies, Dave, we've talked a lot of these companies and to me the business model innovations that are coming out of two factors, the pandemic is kind of coming to an end so that accelerated and really showed who had the right stuff in my opinion. So you were either on the wrong side or right side of history when it comes to the pandemic and as we look back, as we come out of it with clear growth in certain companies and certain companies that adopted let's say cloud. And the other one is cloud scale. So the focus of these startup showcases is really to focus on how startups can align with the enterprise buyers and create the new kind of refactoring business models to go from, you know, a re-pivot or refactoring to more value. And the other thing that's interesting is that the business model isn't just for the good guys. If you look at say ransomware, for instance, the business model of hackers is gone completely amazing too. They're kicking it but in terms of revenue, they have their own they're well-funded machines on how to extort cash from companies. So there's a lot of security issues around the business model as well. So to me, the business model innovation with cloud-scale tech, with the pandemic forcing function, you've seen a lot of new kinds of decision-making in enterprises. You seeing how enterprise buyers are changing their decision criteria, and frankly their existing suppliers. So if you're an old guard supplier, you're going to be potentially out because if you didn't deliver during the pandemic, this is the issue that everyone's talking about. And it's kind of not publicized in the press very much, but this is actually happening. >> Well thank you both very much for joining me to kick off our AWS startup showcase. Now we're going to go to our very special guest Ali Ghodsi and John Furrier will seat with him for a fireside chat and Dave and I will see you on the other side. >> Okay, Ali great to see you. Thanks for coming on our AWS startup showcase, our second edition, second batch, season two, whatever we want to call it it's our second version of this new series where we feature, you know, the hottest startups coming out of the AWS ecosystem. And you're one of them, I've been there, but you're not a startup anymore, you're here pushing serious success on the revenue side and company. Congratulations and great to see you. >> Likewise. Thank you so much, good to see you again. >> You know I remember the first time we chatted on The Cube, you weren't really doing much software revenue, you were really talking about the new revolution in data. And you were all in on cloud. And I will say that from day one, you were always adamant that it was cloud cloud scale before anyone was really talking about it. And at that time it was on premises with Hadoop and those kinds of things. You saw that early. I remember that conversation, boy, that bet paid out great. So congratulations. >> Thank you so much. >> So I've got to ask you to jump right in. Enterprises are making decisions differently now and you are an example of that company that has gone from literally zero software sales to pushing a billion dollars as it's being reported. Certainly the success of Data bricks has been written about, but what's not written about is the success of how you guys align with the changing criteria for the enterprise customer. Take us through that and these companies here are aligning the same thing and enterprises want to change. They want to be in the right side of history. What's the success formula? >> Yeah. I mean, basically what we always did was look a few years out, the how can we help these enterprises, future proof, what they're trying to achieve, right? They have, you know, 30 years of legacy software and, you know baggage, and they have compliance and regulations, how do we help them move to the future? So we try to identify those kinds of secular trends that we think are going to maybe you see them a little bit right now, cloud was one of them, but it gets more and more and more. So we identified those and there were sort of three or four of those that we kind of latched onto. And then every year the passes, we're a little bit more right. Cause it's a secular trend in the market. And then eventually, it becomes a force that you can't kind of fight anymore. >> Yeah. And I just want to put a plug for your clubhouse talks with Andreessen Horowitz. You're always on clubhouse talking about, you know, I won't say the killer instinct, but being a CEO in a time where there's so much change going on, you're constantly under pressure. It's a lonely job at the top, I know that, but you've made some good calls. What was some of the key moments that you can point to, where you were like, okay, the wave is coming in now, we'd better get on it. What were some of those key decisions? Cause a lot of these startups want to be in your position, and a lot of buyers want to take advantage of the technology that's coming. They got to figure it out. What was some of those key inflection points for you? >> So if you're just listening to what everybody's saying, you're going to miss those trends. So then you're just going with the stream. So, Juan you mentioned that cloud. Cloud was a thing at the time, we thought it's going to be the thing that takes over everything. Today it's actually multi-cloud. So multi-cloud is a thing, it's more and more people are thinking, wow, I'm paying a lot's to the cloud vendors, do I want to buy more from them or do I want to have some optionality? So that's one. Two, open. They're worried about lock-in, you know, lock-in has happened for many, many decades. So they want open architectures, open source, open standards. So that's the second one that we bet on. The third one, which you know, initially wasn't sort of super obvious was AI and machine learning. Now it's super obvious, everybody's talking about it. But when we started, it was kind of called artificial intelligence referred to robotics, and machine learning wasn't a term that people really knew about. Today, it's sort of, everybody's doing machine learning and AI. So betting on those future trends, those secular trends as we call them super critical. >> And one of the things that I want to get your thoughts on is this idea of re-platforming versus refactoring. You see a lot being talked about in some of these, what does that even mean? It's people trying to figure that out. Re-platforming I get the cloud scale. But as you look at the cloud benefits, what do you say to customers out there and enterprises that are trying to use the benefits of the cloud? Say data for instance, in the middle of how could they be thinking about refactoring? And how can they make a better selection on suppliers? I mean, how do you know it used to be RFP, you deliver these speeds and feeds and you get selected. Now I think there's a little bit different science and methodology behind it. What's your thoughts on this refactoring as a buyer? What do I got to do? >> Well, I mean let's start with you said RFP and so on. Times have changed. Back in the day, you had to kind of sign up for something and then much later you're going to get it. So then you have to go through this arduous process. In the cloud, would pay us to go model elasticity and so on. You can kind of try your way to it. You can try before you buy. And you can use more and more. You can gradually, you don't need to go in all in and you know, say we commit to 50,000,000 and six months later to find out that wow, this stuff has got shelf where it doesn't work. So that's one thing that has changed it's beneficial. But the second thing is, don't just mimic what you had on prem in the cloud. So that's what this refactoring is about. If you had, you know, Hadoop data lake, now you're just going to have an S3 data lake. If you had an on-prem data warehouse now you just going to have a cloud data warehouse. You're just repeating what you did on prem in the cloud, architected for the future. And you know, for us, the most important thing that we say is that this lake house paradigm is a cloud native way of organizing your data. That's different from how you would do things on premises. So think through what's the right way of doing it in the cloud. Don't just try to copy paste what you had on premises in the cloud. >> It's interesting one of the things that we're observing and I'd love to get your reaction to this. Dave a lot** and I have been reporting on it is, two personas in the enterprise are changing their organization. One is I call IT ops or there's an SRE role developing. And the data teams are being dismantled and being kind of sprinkled through into other teams is this notion of data, pipelining being part of workflows, not just the department. Are you seeing organizational shifts in how people are organizing their resources, their human resources to take advantage of say that the data problems that are need to being solved with machine learning and whatnot and cloud-scale? >> Yeah, absolutely. So you're right. SRE became a thing, lots of DevOps people. It was because when the cloud vendors launched their infrastructure as a service to stitch all these things together and get it all working you needed a lot of devOps people. But now things are maturing. So, you know, with vendors like Data bricks and other multi-cloud vendors, you can actually get much higher level services where you don't need to necessarily have lots of lots of DevOps people that are themselves trying to stitch together lots of services to make this work. So that's one trend. But secondly, you're seeing more data teams being sort of completely ubiquitous in these organizations. Before it used to be you have one data team and then we'll have data and AI and we'll be done. ' It's a one and done. But that's not how it works. That's not how Google, Facebook, Twitter did it, they had data throughout the organization. Every BU was empowered. It's sales, it's marketing, it's finance, it's engineering. So how do you embed all those data teams and make them actually run fast? And you know, there's this concept of a data mesh which is super important where you can actually decentralize and enable all these teams to focus on their domains and run super fast. And that's really enabled by this Lake house paradigm in the cloud that we're talking about. Where you're open, you're basing it on open standards. You have flexibility in the data types and how they're going to store their data. So you kind of provide a lot of that flexibility, but at the same time, you have sort of centralized governance for it. So absolutely things are changing in the market. >> Well, you're just the professor, the masterclass right here is amazing. Thanks for sharing that insight. You're always got to go out of date and that's why we have you on here. You're amazing, great resource for the community. Ransomware is a huge problem, it's now the government's focus. We're being attacked and we don't know where it's coming from. This business models around cyber that's expanding rapidly. There's real revenue behind it. There's a data problem. It's not just a security problem. So one of the themes in all of these startup showcases is data is ubiquitous in the value propositions. One of them is ransomware. What's your thoughts on ransomware? Is it a data problem? Does cloud help? Some are saying that cloud's got better security with ransomware, then say on premise. What's your vision of how you see this ransomware problem being addressed besides the government taking over? >> Yeah, that's a great question. Let me start by saying, you know, we're a data company, right? And if you say you're a data company, you might as well just said, we're a privacy company, right? It's like some people say, well, what do you think about privacy? Do you guys even do privacy? We're a data company. So yeah, we're a privacy company as well. Like you can't talk about data without talking about privacy. With every customer, with every enterprise. So that's obviously top of mind for us. I do think that in the cloud, security is much better because, you know, vendors like us, we're investing so much resources into security and making sure that we harden the infrastructure and, you know, by actually having all of this infrastructure, we can monitor it, detect if something is, you know, an attack is happening, and we can immediately sort of stop it. So that's different from when it's on prem, you have kind of like the separated duties where the software vendor, which would have been us, doesn't really see what's happening in the data center. So, you know, there's an IT team that didn't develop the software is responsible for the security. So I think things are much better now. I think we're much better set up, but of course, things like cryptocurrencies and so on are making it easier for people to sort of hide. There decentralized networks. So, you know, the attackers are getting more and more sophisticated as well. So that's definitely something that's super important. It's super top of mind. We're all investing heavily into security and privacy because, you know, that's going to be super critical going forward. >> Yeah, we got to move that red line, and figure that out and get more intelligence. Decentralized trends not going away it's going to be more of that, less of the centralized. But centralized does come into play with data. It's a mix, it's not mutually exclusive. And I'll get your thoughts on this. Architectural question with, you know, 5G and the edge coming. Amazon's got that outpost stringent, the wavelength, you're seeing mobile world Congress coming up in this month. The focus on processing data at the edge is a huge issue. And enterprises are now going to be commercial part of that. So architecture decisions are being made in enterprises right now. And this is a big issue. So you mentioned multi-cloud, so tools versus platforms. Now I'm an enterprise buyer and there's no more RFPs. I got all this new choices for startups and growing companies to choose from that are cloud native. I got all kinds of new challenges and opportunities. How do I build my architecture so I don't foreclose a future opportunity. >> Yeah, as I said, look, you're actually right. Cloud is becoming even more and more something that everybody's adopting, but at the same time, there is this thing that the edge is also more and more important. And the connectivity between those two and making sure that you can really do that efficiently. My ask from enterprises, and I think this is top of mind for all the enterprise architects is, choose open because that way you can avoid locking yourself in. So that's one thing that's really, really important. In the past, you know, all these vendors that locked you in, and then you try to move off of them, they were highly innovative back in the day. In the 80's and the 90's, there were the best companies. You gave them all your data and it was fantastic. But then because you were locked in, they didn't need to innovate anymore. And you know, they focused on margins instead. And then over time, the innovation stopped and now you were kind of locked in. So I think openness is really important. I think preserving optionality with multi-cloud because we see the different clouds have different strengths and weaknesses and it changes over time. All right. Early on AWS was the only game that either showed up with much better security, active directory, and so on. Now Google with AI capabilities, which one's going to win, which one's going to be better. Actually, probably all three are going to be around. So having that optionality that you can pick between the three and then artificial intelligence. I think that's going to be the key to the future. You know, you asked about security earlier. That's how people detect zero day attacks, right? You ask about the edge, same thing there, that's where the predictions are going to happen. So make sure that you invest in AI and artificial intelligence very early on because it's not something you can just bolt on later on and have a little data team somewhere that then now you have AI and it's one and done. >> All right. Great insight. I've got to ask you, the folks may or may not know, but you're a professor at Berkeley as well, done a lot of great work. That's where you kind of came out of when Data bricks was formed. And the Berkeley basically was it invented distributed computing back in the 80's. I remember I was breaking in when Unix was proprietary, when software wasn't open you actually had the deal that under the table to get code. Now it's all open. Isn't the internet now with distributed computing and how interconnects are happening. I mean, the internet didn't break during the pandemic, which proves the benefit of the internet. And that's a positive. But as you start seeing edge, it's essentially distributed computing. So I got to ask you from a computer science standpoint. What do you see as the key learnings or connect the dots for how this distributed model will work? I see hybrids clearly, hybrid cloud is clearly the operating model but if you take it to the next level of distributed computing, what are some of the key things that you look for in the next five years as this starts to be completely interoperable, obviously software is going to drive a lot of it. What's your vision on that? >> Yeah, I mean, you know, so Berkeley, you're right for the gigs, you know, there was a now project 20, 30 years ago that basically is how we do things. There was a project on how you search in the very early on with Inktomi that became how Google and everybody else to search today. So workday was super, super early, sometimes way too early. And that was actually the mistake. Was that they were so early that people said that that stuff doesn't work. And then 20 years later you were invented. So I think 2009, Berkeley published just above the clouds saying the cloud is the future. At that time, most industry leaders said, that's just, you know, that doesn't work. Today, recently they published a research paper called, Sky Computing. So sky computing is what you get above the clouds, right? So we have the cloud as the future, the next level after that is the sky. That's one on top of them. That's what multi-cloud is. So that's a lot of the research at Berkeley, you know, into distributed systems labs is about this. And we're excited about that. Then we're one of the sky computing vendors out there. So I think you're going to see much more innovation happening at the sky level than at the compute level where you needed all those DevOps and SRE people to like, you know, build everything manually themselves. I can just see the memes now coming Ali, sky net, star track. You've got space too, by the way, space is another frontier that is seeing a lot of action going on because now the surface area of data with satellites is huge. So again, I know you guys are doing a lot of business with folks in that vertical where you starting to see real time data acquisition coming from these satellites. What's your take on the whole space as the, not the final frontier, but certainly as a new congested and contested space for, for data? >> Well, I mean, as a data vendor, we see a lot of, you know, alternative data sources coming in and people aren't using machine learning< AI to eat out signal out of the, you know, massive amounts of imagery that's coming out of these satellites. So that's actually a pretty common in FinTech, which is a vertical for us. And also sort of in the public sector, lots of, lots of, lots of satellites, imagery data that's coming. And these are massive volumes. I mean, it's like huge data sets and it's a super, super exciting what they can do. Like, you know, extracting signal from the satellite imagery is, and you know, being able to handle that amount of data, it's a challenge for all the companies that we work with. So we're excited about that too. I mean, definitely that's a trend that's going to continue. >> All right. I'm super excited for you. And thanks for coming on The Cube here for our keynote. I got to ask you a final question. As you think about the future, I see your company has achieved great success in a very short time, and again, you guys done the work, I've been following your company as you know. We've been been breaking that Data bricks story for a long time. I've been excited by it, but now what's changed. You got to start thinking about the next 20 miles stair when you look at, you know, the sky computing, you're thinking about these new architectures. As the CEO, your job is to one, not run out of money which you don't have to worry about that anymore, so hiring. And then, you got to figure out that next 20 miles stair as a company. What's that going on in your mind? Take us through your mindset of what's next. And what do you see out in that landscape? >> Yeah, so what I mentioned around Sky company optionality around multi-cloud, you're going to see a lot of capabilities around that. Like how do you get multi-cloud disaster recovery? How do you leverage the best of all the clouds while at the same time not having to just pick one? So there's a lot of innovation there that, you know, we haven't announced yet, but you're going to see a lot of it over the next many years. Things that you can do when you have the optionality across the different parts. And the second thing that's really exciting for us is bringing AI to the masses. Democratizing data and AI. So how can you actually apply machine learning to machine learning? How can you automate machine learning? Today machine learning is still quite complicated and it's pretty advanced. It's not going to be that way 10 years from now. It's going to be very simple. Everybody's going to have it at their fingertips. So how do we apply machine learning to machine learning? It's called auto ML, automatic, you know, machine learning. So that's an area, and that's not something that can be done with, right? But the goal is to eventually be able to automate a way the whole machine learning engineer and the machine learning data scientist altogether. >> You know it's really fun and talking with you is that, you know, for years we've been talking about this inside the ropes, inside the industry, around the future. Now people starting to get some visibility, the pandemics forced that. You seeing the bad projects being exposed. It's like the tide pulled out and you see all the scabs and bad projects that were justified old guard technologies. If you get it right you're on a good wave. And this is clearly what we're seeing. And you guys example of that. So as enterprises realize this, that they're going to have to look double down on the right projects and probably trash the bad projects, new criteria, how should people be thinking about buying? Because again, we talked about the RFP before. I want to kind of circle back because this is something that people are trying to figure out. You seeing, you know, organic, you come in freemium models as cloud scale becomes the advantage in the lock-in frankly seems to be the value proposition. The more value you provide, the more lock-in you get. Which sounds like that's the way it should be versus proprietary, you know, protocols. The protocol is value. How should enterprises organize their teams? Is it end to end workflows? Is it, and how should they evaluate the criteria for these technologies that they want to buy? >> Yeah, that's a great question. So I, you know, it's very simple, try to future proof your decision-making. Make sure that whatever you're doing is not blocking your in. So whatever decision you're making, what if the world changes in five years, make sure that if you making a mistake now, that's not going to bite you in about five years later. So how do you do that? Well, open source is great. If you're leveraging open-source, you can try it out already. You don't even need to talk to any vendor. Your teams can already download it and try it out and get some value out of it. If you're in the cloud, this pay as you go models, you don't have to do a big RFP and commit big. You can try it, pay the vendor, pay as you go, $10, $15. It doesn't need to be a million dollar contract and slowly grow as you're providing value. And then make sure that you're not just locking yourself in to one cloud or, you know, one particular vendor. As much as possible preserve your optionality because then that's not a one-way door. If it turns out later you want to do something else, you can, you know, pick other things as well. You're not locked in. So that's what I would say. Keep that top of mind that you're not locking yourself into a particular decision that you made today, that you might regret in five years. >> I really appreciate you coming on and sharing your with our community and The Cube. And as always great to see you. I really enjoy your clubhouse talks, and I really appreciate how you give back to the community. And I want to thank you for coming on and taking the time with us today. >> Thanks John, always appreciate talking to you. >> Okay Ali Ghodsi, CEO of Data bricks, a success story that proves the validation of cloud scale, open and create value, values the new lock-in. So Natalie, back to you for continuing coverage. >> That was a terrific interview John, but I'd love to get Dave's insights first. What were your takeaways, Dave? >> Well, if we have more time I'll tell you how Data bricks got to where they are today, but I'll say this, the most important thing to me that Allie said was he conveyed a very clear understanding of what data companies are outright and are getting ready. Talked about four things. There's not one data team, there's many data teams. And he talked about data is decentralized, and data has to have context and that context lives in the business. He said, look, think about it. The way that the data companies would get it right, they get data in teams and sales and marketing and finance and engineering. They all have their own data and data teams. And he referred to that as a data mesh. That's a term that is your mock, the Gany coined and the warehouse of the data lake it's merely a node in that global message. It meshes discoverable, he talked about federated governance, and Data bricks, they're breaking the model of shoving everything into a single repository and trying to make that the so-called single version of the truth. Rather what they're doing, which is right on is putting data in the hands of the business owners. And that's how true data companies do. And the last thing you talked about with sky computing, which I loved, it's that future layer, we talked about multi-cloud a lot that abstracts the underlying complexity of the technical details of the cloud and creates additional value on top. I always say that the cloud players like Amazon have given the gift to the world of 100 billion dollars a year they spend in CapEx. Thank you. Now we're going to innovate on top of it. Yeah. And I think the refactoring... >> Hope by John. >> That was great insight and I totally agree. The refactoring piece too was key, he brought that home. But to me, I think Data bricks that Ali shared there and why he's been open and sharing a lot of his insights and the community. But what he's not saying, cause he's humble and polite is they cracked the code on the enterprise, Dave. And to Dave's points exactly reason why they did it, they saw an opportunity to make it easier, at that time had dupe was the rage, and they just made it easier. They was smart, they made good bets, they had a good formula and they cracked the code with the enterprise. They brought it in and they brought value. And see that's the key to the cloud as Dave pointed out. You get replatform with the cloud, then you refactor. And I think he pointed out the multi-cloud and that really kind of teases out the whole future and landscape, which is essentially distributed computing. And I think, you know, companies are starting to figure that out with hybrid and this on premises and now super edge I call it, with 5G coming. So it's just pretty incredible. >> Yeah. Data bricks, IPO is coming and people should know. I mean, what everybody, they created spark as you know John and everybody thought they were going to do is mimic red hat and sell subscriptions and support. They didn't, they developed a managed service and they embedded AI tools to simplify data science. So to your point, enterprises could buy instead of build, we know this. Enterprises will spend money to make things simpler. They don't have the resources, and so this was what they got right was really embedding that, making a building a managed service, not mimicking the kind of the red hat model, but actually creating a new value layer there. And that's big part of their success. >> If I could just add one thing Natalie to that Dave saying is really right on. And as an enterprise buyer, if we go the other side of the equation, it used to be that you had to be a known company, get PR, you fill out RFPs, you had to meet all the speeds. It's like going to the airport and get a swab test, and get a COVID test and all kinds of mechanisms to like block you and filter you. Most of the biggest success stories that have created the most value for enterprises have been the companies that nobody's understood. And Andy Jazz's famous quote of, you know, being misunderstood is actually a good thing. Data bricks was very misunderstood at the beginning and no one kind of knew who they were but they did it right. And so the enterprise buyers out there, don't be afraid to test the startups because you know the next Data bricks is out there. And I think that's where I see the psychology changing from the old IT buyers, Dave. It's like, okay, let's let's test this company. And there's plenty of ways to do that. He illuminated those premium, small pilots, you don't need to go on these big things. So I think that is going to be a shift in how companies going to evaluate startups. >> Yeah. Think about it this way. Why should the large banks and insurance companies and big manufacturers and pharma companies, governments, why should they burn resources managing containers and figuring out data science tools if they can just tap into solutions like Data bricks which is an AI platform in the cloud and let the experts manage all that stuff. Think about how much money in time that saves enterprises. >> Yeah, I mean, we've got 15 companies here we're showcasing this batch and this season if you call it. That episode we are going to call it? They're awesome. Right? And the next 15 will be the same. And these companies could be the next billion dollar revenue generator because the cloud enables that day. I think that's the exciting part. >> Well thank you both so much for these insights. Really appreciate it. AWS startup showcase highlights the innovation that helps startups succeed. And no one knows that better than our very next guest, Jeff Barr. Welcome to the show and I will send this interview now to Dave and John and see you just in the bit. >> Okay, hey Jeff, great to see you. Thanks for coming on again. >> Great to be back. >> So this is a regular community segment with Jeff Barr who's a legend in the industry. Everyone knows your name. Everyone knows that. Congratulations on your recent blog posts we have reading. Tons of news, I want to get your update because 5G has been all over the news, mobile world congress is right around the corner. I know Bill Vass was a keynote out there, virtual keynote. There's a lot of Amazon discussion around the edge with wavelength. Specifically, this is the outpost piece. And I know there is news I want to get to, but the top of mind is there's massive Amazon expansion and the cloud is going to the edge, it's here. What's up with wavelength. Take us through the, I call it the power edge, the super edge. >> Well, I'm really excited about this mostly because it gives a lot more choice and flexibility and options to our customers. This idea that with wavelength we announced quite some time ago, at least quite some time ago if we think in cloud years. We announced that we would be working with 5G providers all over the world to basically put AWS in the telecom providers data centers or telecom centers, so that as their customers build apps, that those apps would take advantage of the low latency, the high bandwidth, the reliability of 5G, be able to get to some compute and storage services that are incredibly close geographically and latency wise to the compute and storage that is just going to give customers this new power and say, well, what are the cool things we can build? >> Do you see any correlation between wavelength and some of the early Amazon services? Because to me, my gut feels like there's so much headroom there. I mean, I was just riffing on the notion of low latency packets. I mean, just think about the applications, gaming and VR, and metaverse kind of cool stuff like that where having the edge be that how much power there. It just feels like a new, it feels like a new AWS. I mean, what's your take? You've seen the evolutions and the growth of a lot of the key services. Like EC2 and SA3. >> So welcome to my life. And so to me, the way I always think about this is it's like when I go to a home improvement store and I wander through the aisles and I often wonder through with no particular thing that I actually need, but I just go there and say, wow, they've got this and they've got this, they've got this other interesting thing. And I just let my creativity run wild. And instead of trying to solve a problem, I'm saying, well, if I had these different parts, well, what could I actually build with them? And I really think that this breadth of different services and locations and options and communication technologies. I suspect a lot of our customers and customers to be and are in this the same mode where they're saying, I've got all this awesomeness at my fingertips, what might I be able to do with it? >> He reminds me when Fry's was around in Palo Alto, that store is no longer here but it used to be back in the day when it was good. It was you go in and just kind of spend hours and then next thing you know, you built a compute. Like what, I didn't come in here, whether it gets some cables. Now I got a motherboard. >> I clearly remember Fry's and before that there was the weird stuff warehouse was another really cool place to hang out if you remember that. >> Yeah I do. >> I wonder if I could jump in and you guys talking about the edge and Jeff I wanted to ask you about something that is, I think people are starting to really understand and appreciate what you did with the entrepreneur acquisition, what you do with nitro and graviton, and really driving costs down, driving performance up. I mean, there's like a compute Renaissance. And I wonder if you could talk about the importance of that at the edge, because it's got to be low power, it has to be low cost. You got to be doing processing at the edge. What's your take on how that's evolving? >> Certainly so you're totally right that we started working with and then ultimately acquired Annapurna labs in Israel a couple of years ago. I've worked directly with those folks and it's really awesome to see what they've been able to do. Just really saying, let's look at all of these different aspects of building the cloud that were once effectively kind of somewhat software intensive and say, where does it make sense to actually design build fabricate, deploy custom Silicon? So from putting up the system to doing all kinds of additional kinds of security checks, to running local IO devices, running the NBME as fast as possible to support the EBS. Each of those things has been a contributing factor to not just the power of the hardware itself, but what I'm seeing and have seen for the last probably two or three years at this point is the pace of innovation on instance types just continues to get faster and faster. And it's not just cranking out new instance types because we can, it's because our awesomely diverse base of customers keeps coming to us and saying, well, we're happy with what we have so far, but here's this really interesting new use case. And we needed a different ratio of memory to CPU, or we need more cores based on the amount of memory, or we needed a lot of IO bandwidth. And having that nitro as the base lets us really, I don't want to say plug and play, cause I haven't actually built this myself, but it seems like they can actually put the different elements together, very very quickly and then come up with new instance types that just our customers say, yeah, that's exactly what I asked for and be able to just do this entire range of from like micro and nano sized all the way up to incredibly large with incredible just to me like, when we talk about terabytes of memory that are just like actually just RAM memory. It's like, that's just an inconceivably large number by the standards of where I started out in my career. So it's all putting this power in customer hands. >> You used the term plug and play, but it does give you that nitro gives you that optionality. And then other thing that to me is really exciting is the way in which ISVs are writing to whatever's underneath. So you're making that, you know, transparent to the users so I can choose as a customer, the best price performance for my workload and that that's just going to grow that ISV portfolio. >> I think it's really important to be accurate and detailed and as thorough as possible as we launch each one of these new instance types with like what kind of processor is in there and what clock speed does it run at? What kind of, you know, how much memory do we have? What are the, just the ins and outs, and is it Intel or arm or AMD based? It's such an interesting to me contrast. I can still remember back in the very very early days of back, you know, going back almost 15 years at this point and effectively everybody said, well, not everybody. A few people looked and said, yeah, we kind of get the value here. Some people said, this just sounds like a bunch of generic hardware, just kind of generic hardware in Iraq. And even back then it was something that we were very careful with to design and optimize for use cases. But this idea that is generic is so, so, so incredibly inaccurate that I think people are now getting this. And it's okay. It's fine too, not just for the cloud, but for very specific kinds of workloads and use cases. >> And you guys have announced obviously the performance improvements on a lamb** does getting faster, you got the per billing, second billings on windows and SQL server on ECE too**. So I mean, obviously everyone kind of gets that, that's been your DNA, keep making it faster, cheaper, better, easier to use. But the other area I want to get your thoughts on because this is also more on the footprint side, is that the regions and local regions. So you've got more region news, take us through the update on the expansion on the footprint of AWS because you know, a startup can come in and these 15 companies that are here, they're global with AWS, right? So this is a major benefit for customers around the world. And you know, Ali from Data bricks mentioned privacy. Everyone's a privacy company now. So the huge issue, take us through the news on the region. >> Sure, so the two most recent regions that we announced are in the UAE and in Israel. And we generally like to pre-announce these anywhere from six months to two years at a time because we do know that the customers want to start making longer term plans to where they can start thinking about where they can do their computing, where they can store their data. I think at this point we now have seven regions under construction. And, again it's all about customer trice. Sometimes it's because they have very specific reasons where for based on local laws, based on national laws, that they must compute and restore within a particular geographic area. Other times I say, well, a lot of our customers are in this part of the world. Why don't we pick a region that is as close to that part of the world as possible. And one really important thing that I always like to remind our customers of in my audience is, anything that you choose to put in a region, stays in that region unless you very explicitly take an action that says I'd like to replicate it somewhere else. So if someone says, I want to store data in the US, or I want to store it in Frankfurt, or I want to store it in Sao Paulo, or I want to store it in Tokyo or Osaka. They get to make that very specific choice. We give them a lot of tools to help copy and replicate and do cross region operations of various sorts. But at the heart, the customer gets to choose those locations. And that in the early days I think there was this weird sense that you would, you'd put things in the cloud that would just mysteriously just kind of propagate all over the world. That's never been true, and we're very very clear on that. And I just always like to reinforce that point. >> That's great stuff, Jeff. Great to have you on again as a regular update here, just for the folks watching and don't know Jeff he'd been blogging and sharing. He'd been the one man media band for Amazon it's early days. Now he's got departments, he's got peoples on doing videos. It's an immediate franchise in and of itself, but without your rough days we wouldn't have gotten all the great news we subscribe to. We watch all the blog posts. It's essentially the flow coming out of AWS which is just a tsunami of a new announcements. Always great to read, must read. Jeff, thanks for coming on, really appreciate it. That's great. >> Thank you John, great to catch up as always. >> Jeff Barr with AWS again, and follow his stuff. He's got a great audience and community. They talk back, they collaborate and they're highly engaged. So check out Jeff's blog and his social presence. All right, Natalie, back to you for more coverage. >> Terrific. Well, did you guys know that Jeff took a three week AWS road trip across 15 cities in America to meet with cloud computing enthusiasts? 5,500 miles he drove, really incredible I didn't realize that. Let's unpack that interview though. What stood out to you John? >> I think Jeff, Barr's an example of what I call direct to audience a business model. He's been doing it from the beginning and I've been following his career. I remember back in the day when Amazon was started, he was always building stuff. He's a builder, he's classic. And he's been there from the beginning. At the beginning he was just the blog and it became a huge audience. It's now morphed into, he was power blogging so hard. He has now support and he still does it now. It's basically the conduit for information coming out of Amazon. I think Jeff has single-handedly made Amazon so successful at the community developer level, and that's the startup action happened and that got them going. And I think he deserves a lot of the success for AWS. >> And Dave, how about you? What is your reaction? >> Well I think you know, and everybody knows about the cloud and back stop X** and agility, and you know, eliminating the undifferentiated, heavy lifting and all that stuff. And one of the things that's often overlooked which is why I'm excited to be part of this program is the innovation. And the innovation comes from startups, and startups start in the cloud. And so I think that that's part of the flywheel effect. You just don't see a lot of startups these days saying, okay, I'm going to do something that's outside of the cloud. There are some, but for the most part, you know, if you saw in software, you're starting in the cloud, it's so capital efficient. I think that's one thing, I've throughout my career. I've been obsessed with every part of the stack from whether it's, you know, close to the business process with the applications. And right now I'm really obsessed with the plumbing, which is why I was excited to talk about, you know, the Annapurna acquisition. Amazon bought and a part of the $350 million, it's reported, you know, maybe a little bit more, but that isn't an amazing acquisition. And the reason why that's so important is because Amazon is continuing to drive costs down, drive performance up. And in my opinion, leaving a lot of the traditional players in their dust, especially when it comes to the power and cooling. You have often overlooked things. And the other piece of the interview was that Amazon is actually getting ISVs to write to these new platforms so that you don't have to worry about there's the software run on this chip or that chip, or x86 or arm or whatever it is. It runs. And so I can choose the best price performance. And that's where people don't, they misunderstand, you always say it John, just said that people are misunderstood. I think they misunderstand, they confused, you know, the price of the cloud with the cost of the cloud. They ignore all the labor costs that are associated with that. And so, you know, there's a lot of discussion now about the cloud tax. I just think the pace is accelerating. The gap is not closing, it's widening. >> If you look at the one question I asked them about wavelength and I had a follow up there when I said, you know, we riff on it and you see, he lit up like he beam was beaming because he said something interesting. It's not that there's a problem to solve at this opportunity. And he conveyed it to like I said, walking through Fry's. But like, you go into a store and he's a builder. So he sees opportunity. And this comes back down to the Martine Casada paradox posts he wrote about do you optimize for CapEx or future revenue? And I think the tell sign is at the wavelength edge piece is going to be so creative and that's going to open up massive opportunities. I think that's the place to watch. That's the place I'm watching. And I think startups going to come out of the woodwork because that's where the action will be. And that's just Amazon at the edge, I mean, that's just cloud at the edge. I think that is going to be very effective. And his that's a little TeleSign, he kind of revealed a little bit there, a lot there with that comment. >> Well that's a to be continued conversation. >> Indeed, I would love to introduce our next guest. We actually have Soma on the line. He's the managing director at Madrona venture group. Thank you Soma very much for coming for our keynote program. >> Thank you Natalie and I'm great to be here and will have the opportunity to spend some time with you all. >> Well, you have a long to nerd history in the enterprise. How would you define the modern enterprise also known as cloud scale? >> Yeah, so I would say I have, first of all, like, you know, we've all heard this now for the last, you know, say 10 years or so. Like, software is eating the world. Okay. Put it another way, we think about like, hey, every enterprise is a software company first and foremost. Okay. And companies that truly internalize that, that truly think about that, and truly act that way are going to start up, continue running well and things that don't internalize that, and don't do that are going to be left behind sooner than later. Right. And the last few years you start off thing and not take it to the next level and talk about like, not every enterprise is not going through a digital transformation. Okay. So when you sort of think about the world from that lens. Okay. Modern enterprise has to think about like, and I am first and foremost, a technology company. I may be in the business of making a car art, you know, manufacturing paper, or like you know, manufacturing some healthcare products or what have you got out there. But technology and software is what is going to give me a unique, differentiated advantage that's going to let me do what I need to do for my customers in the best possible way [Indistinct]. So that sort of level of focus, level of execution, has to be there in a modern enterprise. The other thing is like not every modern enterprise needs to think about regular. I'm competing for talent, not anymore with my peers in my industry. I'm competing for technology talent and software talent with the top five technology companies in the world. Whether it is Amazon or Facebook or Microsoft or Google, or what have you cannot think, right? So you really have to have that mindset, and then everything flows from that. >> So I got to ask you on the enterprise side again, you've seen many ways of innovation. You've got, you know, been in the industry for many, many years. The old way was enterprises want the best proven product and the startups want that lucrative contract. Right? Yeah. And get that beach in. And it used to be, and we addressed this in our earlier keynote with Ali and how it's changing, the buyers are changing because the cloud has enabled this new kind of execution. I call it agile, call it what you want. Developers are driving modern applications, so enterprises are still, there's no, the playbooks evolving. Right? So we see that with the pandemic, people had needs, urgent needs, and they tried new stuff and it worked. The parachute opened as they say. So how do you look at this as you look at stars, you're investing in and you're coaching them. What's the playbook? What's the secret sauce of how to crack the enterprise code today. And if you're an enterprise buyer, what do I need to do? I want to be more agile. Is there a clear path? Is there's a TSA to let stuff go through faster? I mean, what is the modern playbook for buying and being a supplier? >> That's a fantastic question, John, because I think that sort of playbook is changing, even as we speak here currently. A couple of key things to understand first of all is like, you know, decision-making inside an enterprise is getting more and more de-centralized. Particularly decisions around what technology to use and what solutions to use to be able to do what people need to do. That decision making is no longer sort of, you know, all done like the CEO's office or the CTO's office kind of thing. Developers are more and more like you rightly said, like sort of the central of the workflow and the decision making process. So it'll be who both the enterprises, as well as the startups to really understand that. So what does it mean now from a startup perspective, from a startup perspective, it means like, right. In addition to thinking about like hey, not do I go create an enterprise sales post, do I sell to the enterprise like what I might have done in the past? Is that the best way of moving forward, or should I be thinking about a product led growth go to market initiative? You know, build a product that is easy to use, that made self serve really works, you know, get the developers to start using to see the value to fall in love with the product and then you think about like hey, how do I go translate that into a contract with enterprise. Right? And more and more what I call particularly, you know, startups and technology companies that are focused on the developer audience are thinking about like, you know, how do I have a bottom up go to market motion? And sometime I may sort of, you know, overlap that with the top down enterprise sales motion that we know that has been going on for many, many years or decades kind of thing. But really this product led growth bottom up a go to market motion is something that we are seeing on the rise. I would say they're going to have more than half the startup that we come across today, have that in some way shape or form. And so the enterprise also needs to understand this, the CIO or the CTO needs to know that like hey, I'm not decision-making is getting de-centralized. I need to empower my engineers and my engineering managers and my engineering leaders to be able to make the right decision and trust them. I'm going to give them some guard rails so that I don't find myself in a soup, you know, sometime down the road. But once I give them the guard rails, I'm going to enable people to make the decisions. People who are closer to the problem, to make the right decision. >> Well Soma, what are some of the ways that startups can accelerate their enterprise penetration? >> I think that's another good question. First of all, you need to think about like, Hey, what are enterprises wanting to rec? Okay. If you start off take like two steps back and think about what the enterprise is really think about it going. I'm a software company, but I'm really manufacturing paper. What do I do? Right? The core thing that most enterprises care about is like, hey, how do I better engage with my customers? How do I better serve my customers? And how do I do it in the most optimal way? At the end of the day that's what like most enterprises really care about. So startups need to understand, what are the problems that the enterprise is trying to solve? What kind of tools and platform technologies and infrastructure support, and, you know, everything else that they need to be able to do what they need to do and what only they can do in the most optimal way. Right? So to the extent you are providing either a tool or platform or some technology that is going to enable your enterprise to make progress on what they want to do, you're going to get more traction within the enterprise. In other words, stop thinking about technology, and start thinking about the customer problem that they want to solve. And the more you anchor your company, and more you anchor your conversation with the customer around that, the more the enterprise is going to get excited about wanting to work with you. >> So I got to ask you on the enterprise and developer equation because CSOs and CXOs, depending who you talk to have that same answer. Oh yeah. In the 90's and 2000's, we kind of didn't, we throttled down, we were using the legacy developer tools and cloud came and then we had to rebuild and we didn't really know what to do. So you seeing a shift, and this is kind of been going on for at least the past five to eight years, a lot more developers being hired yet. I mean, at FinTech is clearly a vertical, they always had developers and everyone had developers, but there's a fast ramp up of developers now and the role of open source has changed. Just looking at the participation. They're not just consuming open source, open source is part of the business model for mainstream enterprises. How is this, first of all, do you agree? And if so, how has this changed the course of an enterprise human resource selection? How they're organized? What's your vision on that? >> Yeah. So as I mentioned earlier, John, in my mind the first thing is, and this sort of, you know, like you said financial services has always been sort of hiring people [Indistinct]. And this is like five-year old story. So bear with me I'll tell you the firewall story and then come to I was trying to, the cloud CIO or the Goldman Sachs. Okay. And this is five years ago when people were still like, hey, is this cloud thing real and now is cloud going to take over the world? You know, am I really ready to put my data in the cloud? So there are a lot of questions and conversations can affect. The CIO of Goldman Sachs told me two things that I remember to this day. One is, hey, we've got a internal edict. That we made a decision that in the next five years, everything in Goldman Sachs is going to be on the public law. And I literally jumped out of the chair and I said like now are you going to get there? And then he laughed and said like now it really doesn't matter whether we get there or not. We want to set the tone, set the direction for the organization that hey, public cloud is here. Public cloud is there. And we need to like, you know, move as fast as we realistically can and think about all the financial regulations and security and privacy. And all these things that we care about deeply. But given all of that, the world is going towards public load and we better be on the leading edge as opposed to the lagging edge. And the second thing he said, like we're talking about like hey, how are you hiring, you know, engineers at Goldman Sachs Canada? And he said like in hey, I sort of, my team goes out to the top 20 schools in the US. And the people we really compete with are, and he was saying this, Hey, we don't compete with JP Morgan or Morgan Stanley, or pick any of your favorite financial institutions. We really think about like, hey, we want to get the best talent into Goldman Sachs out of these schools. And we really compete head to head with Google. We compete head to head with Microsoft. We compete head to head with Facebook. And we know that the caliber of people that we want to get is no different than what these companies want. If you want to continue being a successful, leading it, you know, financial services player. That sort of tells you what's going on. You also talked a little bit about like hey, open source is here to stay. What does that really mean kind of thing. In my mind like now, you can tell me that I can have from given my pedigree at Microsoft, I can tell you that we were the first embraces of open source in this world. So I'll say that right off the bat. But having said that we did in our turn around and said like, hey, this open source is real, this open source is going to be great. How can we embrace and how can we participate? And you fast forward to today, like in a Microsoft is probably as good as open source as probably any other large company I would say. Right? Including like the work that the company has done in terms of acquiring GitHub and letting it stay true to its original promise of open source and community can I think, right? I think Microsoft has come a long way kind of thing. But the thing that like in all these enterprises need to think about is you want your developers to have access to the latest and greatest tools. To the latest and greatest that the software can provide. And you really don't want your engineers to be reinventing the wheel all the time. So there is something available in the open source world. Go ahead, please set up, think about whether that makes sense for you to use it. And likewise, if you think that is something you can contribute to the open source work, go ahead and do that. So it's really a two way somebody Arctic relationship that enterprises need to have, and they need to enable their developers to want to have that symbiotic relationship. >> Soma, fantastic insights. Thank you so much for joining our keynote program. >> Thank you Natalie and thank you John. It was always fun to chat with you guys. Thank you. >> Thank you. >> John we would love to get your quick insight on that. >> Well I think first of all, he's a prolific investor the great from Madrona venture partners, which is well known in the tech circles. They're in Seattle, which is in the hub of I call cloud city. You've got Amazon and Microsoft there. He'd been at Microsoft and he knows the developer ecosystem. And reason why I like his perspective is that he understands the value of having developers as a core competency in Microsoft. That's their DNA. You look at Microsoft, their number one thing from day one besides software was developers. That was their army, the thousand centurions that one won everything for them. That has shifted. And he brought up open source, and .net and how they've embraced Linux, but something that tele before he became CEO, we interviewed him in the cube at an Xcel partners event at Stanford. He was open before he was CEO. He was talking about opening up. They opened up a lot of their open source infrastructure projects to the open compute foundation early. So they had already had that going and at that price, since that time, the stock price of Microsoft has skyrocketed because as Ali said, open always wins. And I think that is what you see here, and as an investor now he's picking in startups and investing in them. He's got to read the tea leaves. He's got to be in the right side of history. So he brings a great perspective because he sees the old way and he understands the new way. That is the key for success we've seen in the enterprise and with the startups. The people who get the future, and can create the value are going to win. >> Yeah, really excellent point. And just really quickly. What do you think were some of our greatest hits on this hour of programming? >> Well first of all I'm really impressed that Ali took the time to come join us because I know he's super busy. I think they're at a $28 billion valuation now they're pushing a billion dollars in revenue, gap revenue. And again, just a few short years ago, they had zero software revenue. So of these 15 companies we're showcasing today, you know, there's a next Data bricks in there. They're all going to be successful. They already are successful. And they're all on this rocket ship trajectory. Ali is smart, he's also got the advantage of being part of that Berkeley community which they're early on a lot of things now. Being early means you're wrong a lot, but you're also right, and you're right big. So Berkeley and Stanford obviously big areas here in the bay area as research. He is smart, He's got a great team and he's really open. So having him share his best practices, I thought that was a great highlight. Of course, Jeff Barr highlighting some of the insights that he brings and honestly having a perspective of a VC. And we're going to have Peter Wagner from wing VC who's a classic enterprise investors, super smart. So he'll add some insight. Of course, one of the community session, whenever our influencers coming on, it's our beat coming on at the end, as well as Katie Drucker. Another Madrona person is going to talk about growth hacking, growth strategies, but yeah, sights Raleigh coming on. >> Terrific, well thank you so much for those insights and thank you to everyone who is watching the first hour of our live coverage of the AWS startup showcase for myself, Natalie Ehrlich, John, for your and Dave Vellante we want to thank you very much for watching and do stay tuned for more amazing content, as well as a special live segment that John Furrier is going to be hosting. It takes place at 12:30 PM Pacific time, and it's called cracking the code, lessons learned on how enterprise buyers evaluate new startups. Don't go anywhere.

(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)

>>from around the globe. It's the >>Cube with digital >>coverage of AWS reinvent 2020 sponsored by Intel, >>AWS and >>our community partners. >>Welcome back to the cubes ongoing coverage of this year's AWS reinvent. You know, normally we'd be in the middle of the San Sands Convention Center. We have two sets and 50,000 of our closest friends. We'd be deking out on cloud. Seems like a long time ago, but the show must go on. And it does. Joe Duffy is here. He's the co founder and CEO of Gloomy, and Justin Fits you is the vice president engineering for Cloud Engineering for snowflake. Welcome, gentlemen. Good to see you. >>It's good to be here, >>Joe. I love what you guys are doing. You know, leading your customers to the cloud and really attacking that I t labor problem that we've dealt with for years and years by playing a role in transforming what I would say is I t ops into cloud ups with programmable infra infrastructure practices. So take >>a >>moment to tell us. Why did you and your co founder start the company how you got it off the ground? People are always interested in how you got it funded. You got a couple of Seattle VCs, Madrona and Tola involved. Any a just got involved. So congrats on that. What's the story of your company? >>Yeah. So my background and my co founder Eric's background. You know, we spent multiple decades at Microsoft just really obsessing over developer platforms and productivity and trying to make you know developers lives as as as as productive as possible. You know, help them harness the power of software >>toe create, >>you know, innovative new applications and really spent time on technologies like Visual Studio and Ahmed. And and, you know, it really struck us that the cloud is changing everything about how we develop software. And yet from our perspective, coming from developer landed had almost changed nothing. You know, most of our customers were still, you know, developing software like they did 15 years ago, where it was a typical enter your application, they'd kind of write the code and then go to their I t team and say, Hey, we need to run this somewhere. Can you provisioned a few virtual machines? Can you prevision You know, maybe a database or two and and And so And then we went and talked Thio, you know, infrastructure teams and found out Hey, you know, folks were really toiling away with tools that air a pale in comparison when it comes to the productivity that we we were accustomed Thio on the developer side. And then frequently we heard from leaders that there were silos between the organizations. They couldn't build things quickly enough. They couldn't move quickly enough in cloud Native and the new public cloud capabilities just really were pushed pushing on that, really, you know. But the most innovative companies we kept hearing were the ones who figured this out, who really figured out how to move faster in the cloud. Companies like Snowflake really are leveraging the cloud toe transform entire businesses. You look at uber lyft Airbnb, these companies that really harnessed the cloud toe not just from a technical productivity standpoint, but really transform the business. Eh? So that was the opportunity that we saw Kalemie was Let's take a step back. We call this cloud engineering. Let's imagine a world where every developers, a cloud developer and infrastructure teams are enabling that new way of building. >>Great. So you mentioned cloud engineering. Now, Justin, you've done a bit a bit of cloud engineering yourself in your day. You know, the Cube has been following Snowflake very closely since it launched really mid last decade. And we've we've covered your novel, architectural approach and your cloud only mantra. Talk about that. And have there been any changes in how you're thinking about cloud adoption and how that's as that's increased and you've seen new use cases emerged. >>Yeah, so I think, you know, obviously Snowflake was was built on the foundation of cloud first, and in fact, cloud Onley are only platform and only infrastructure is is based on the cloud. But, you know, for us, it was absolutely key on. How do you develop a platform and a product that's completely elastic? Lee, scalable on drily, really allows for kind of the paper use and paper consumption model. We didn't really it would be very difficult for us to offer this and Thio offer a product in this way. On def, you start to think about kind of from a cloud engineering perspective. Um, we don't have the typical network engineers. A typical data center engineers that you that you might have seen previously. Instead, we're shifting our model in our what we do include engineering away from kind of an operations model or even devotes model towards the software engineering model. E. I think that's the That's the big shift to cloud engineering is that we're looking to hire and we're building a team of software engineers to build systems and platforms and and tooling Thio have the system self managed as much as possible, and it changes to our infrastructure that we look at any changes in our platform are all through, commits and and deployed via pipelines, as opposed to having Operator's log on and make these changes. And so that's the shift that I think we're seeing. And that's to kind of match the overall stuff like Model of Cloud, first and on and where the product is like just going. >>Like you said in cloud only, Justin, you use Pollux me in your own engineering and also in your product externally. Is that correct? And how so? >>Yeah, we actually use it in, specifically and, um, in our platform, in order to kind of deployed to manage and, uh, just operate a kind of our overall cloud infrastructure. We specifically use it more focused on the good days and and continue ization side of things. But that use cases kind of rapidly expanding across the organization. >>So I'm curious of what do you guys we're seeing in the market place? Joe, you know, thinking about cloud broadly, What's the impact that you're seeing on businesses? Who are the big players that you see out there? Maybe you could talk about some of the differentiation that you've noticed. >>Yeah, I think this notion of plot engineering, you know, even 3.5 years ago when we got started was in its infancy. You know, we definitely saw that. Hey, you know, the world is moving and shifting left, you know, it's just was saying and really, people are looking for new ways to empower developers, but that empowerment has to come with guard rails, right? And so what we're seeing is oftentimes, teams are now modernizing their entire platform infrastructure platform, and they're looking to technologies like kubernetes to do that. But increasingly, you know, aws, Azure gp. You know, when we started, um, there weren't any great managed kubernetes clusters. And now today, fast forward. You know Onley 3.5 years and and many of our customers are using flew me to help them get up and running with the chaos in AWS, for example, you look at a lot of folks transforming on Prem as well again many times, adopting kubernetes is sort of a if they intend to stay on Prem. You know, Thio, at least modernize their approach to application infrastructure delivery. That's where Pollux me really can help. It could be a bridge. Thio hate from on Prem to the public cloud. There's certainly a lot of folks doing great work in the space, you know, I think VM Ware has really kind of emerged as sort of vanguard thought leader in this in this space, especially with, you know, hep dio and now kind of pivotal joining the story. We see other, you know, great companies like hash in court, for we're doing good work in this space. Um, certainly we integrate with a lot of their technologies on you. Combine those with the public cloud providers. There's also a lot of just smaller startups in the space which you know, strikes in my heart. I love I love supporting the startup ecosystem. You know, whether that's for cell or net lif I or server list. You know, really trying to help developers harness more of the cloud. I think that's an emerging trend that we're gonna see accelerating in the coming years. >>Yeah. Thank you. You've mentioned a number of interesting emerging tools companies in the ecosystem. I mean, Justin talked about kubernetes. Are there other tooling that you're using that that might be, you know, some of your customers might like toe to know about. >>Yeah, I think so. So one thing I wanted to actually follow up with what Joe said here is is around kind of the multi cloud nature of what we do is is the tools, like gloomy are critical for us to be able to abstract away specific cloud provider AP ice and such and so given Snowflake operates on all three major public clouds and offers a seamless experience amongst all three of them. We have to have something that abstracts some of that complexity and some of those technical details away. Andi, that's why I kind of blew me, made sense in in this case and has helped us kind of achieved that cloud neutrality piece. Um, in terms of other tools that that you're thinking that we're talking about, I think Bellamy is doing a great job kind of on some of these on some of the kind of that interaction and infrastructure and sensation. But we're looking for tooling to kind of look for the overall workflow automation piece on orchestration. So what sits on top of say, you're using intervals using terra form? You may be using Polonia's well, but what kind of orchestrates all these pieces together? Onda, How do you kind of build workflow automation? And I think there's a lot of companies and technology providers that air starting up in this area to kind of stitch all these pieces together so that you kind of have a seamless kind of work flow across across your infrastructure. >>Got it. So, Joe, I'm kind of curious you talked a little bit about your background at Microsoft, and you're even a TMC where you're helping, you know, people manage Luns. It was a sort of skill set that is not in high demand today. Early. Shouldn't be people really need to transform? I've said that a lot in the queue, but But, you know, maybe talk a little bit about the experiences that you've had in the past that informed the direction that Pollux me is taking and where you see it going specifically. I mean, I've been talking a lot about the next decade of cloud is not gonna be the same as the last decade of the cloud. How did you How do you see it? >>Yeah, I think I recognize a clear trend, you know, in with cloud computing. Uh, you know, back I can't remember 13 years ago, maybe 15 years ago, When, when When the Azure project started. You know Dave Cutler, who actually founded the anti project at Microsoft, Actually, was was one of the first engineers that started Azure. And he called it a cloud operating system. And, you know, I think that vision of hey, the cloud is the new operating system is something that we're still just chipping away at. And that was that was a clear trend, you know, having seen these transformations in the past, you know the shift from, you know, dos to windows from windows to mobile Thio, client server thio now the cloud every step of the way. We always transform the way we build applications. And I think where we're at now is horse, really in the midst of a transition that I think we'll look back. You never know when it's happening right? But you can always look back in hindsight and see that it did happen. And I think the trend that we're going through now with service meshes and just, you know, micro services and service list is really we're building distributed applications. These clouds made of applications, they're distributed applications. And that was the trend that I, I recognized, also recognizes another trend, which is, you know, we spent 30 years building great tools. You know, I d s test frameworks sharing and reuse package managers. We figured out static analysis and how to fix security problems in this in in programming languages that we've got today. Let's not go rebuild all that. Let's leverage that, and and so that's what Eric and I said they want, you know, Let's stand on the shoulders of giants. Let's leverage all this good work that has come before us. Let's just apply that to the infrastructure domain and really try toe smooth things out. Give us a new sort of level playing field to build on. From here is we go forward and I'm excited that Parliament gives us that foundation that we can now build on top of >>Great and Justin, of course, were covered. Aws reinvent you guys. It was kind of your your first platform. It's your largest, the largest component of your business. And I have been saying, Ah lot that, you know the early days of cloud was about infrastructure last 32 throw in some database. But really, there's a new workload that's emerging. And you guys are at the heart of that where people are putting governed data giving access to that data, making it secure, uh, sharing that data across an ecosystem so that new workload is really driving new innovation. I wonder how you see that what you see the next half a decade or decades looking like in terms of innovation? >>Yeah, I think I think it za valid point, which is, um, it's less about infrastructure and more about the services that you're providing with that infrastructure. And what what value are you able to add and So I think that's it, Snowflake. The thing that we're really focused on, which is abstract away, all these tunes and all these knobs and such, and the how much remember you have on a specific and a piece of infrastructure or describes or anything like that. So what's the business value? And how can we present that business value in a uniform way, regardless of kind of the underlying service provider on baby to a different class of business users, someone who wants a low data and just two analysts against that they really don't want to understand what's happening underneath. And I think that's that's where this club engineering piece comes in. Um, and what my team is doing is really focused on How do we abstract away that kind of lower level infrastructure and scalability pieces and allow the application developers to develop this application that is providing business value in a transparent and seamless way and in elastic way such that we can scale up and down we can. We have the ability, obviously, to replicate both within regions and clouds, but also across different clouds. So from a business resiliency and and up time point of view. That's that's something that's been really important. Um, and I think also how do we security is? Becoming is obviously a huge, huge importance, given the classifications type of day that people are putting within our platform. So how are we able Thio ensure that there is a pipeline where developers have reviews and commits of any kind of changes going into the system and their arm's length away, and could be fully audited for various clients and regular regulatory needs? And that's something that kind of this suffer engineering cloud engineering concept has really helped develop and allowed us Thio obviously be successful with various different types of industries. >>Joe, we're almost out of time. I wonder if you could bring us home. I mean, some of the things Justin was talking about I mean, I definitely see a lot of potential disruption coming from the world of developers. Uh, he was talking. He was talking about consumption models different than many of the SAS pricing models. And how do you How do you see it? Developers air kind of the really the new source of innovation. Your final thoughts. >>Yeah. I think we're democratizing access to the cloud for everybody. I think you know it's not just about developers, but it's It's really all engineers of all backgrounds, its developers, its infrastructure engineers, its operations engineers, its security engineers. You know, Justin's mentioning compliance and security. These air really critical elements of how we deliver software into the cloud. So I think you know what you're going to see is you're gonna see a lot of new, compelling experiences built thanks to cloud capabilities. You know, the fact that you've got a I and M l and all these infinitely scalable data services like snowflake and, you know, just an arm's length away that you can use as building blocks in your applications. You know, application developers love that. You know, if we can just empower them to run fast, they will run fast, and we'll build great applications. And infrastructure teams and security engineers will be central to enabling that that new future. I think you also see that you know infrastructure and cloud services will become accessible to an entirely new audience. You know, kids graduating from college, they understand Java script. They understand python now they can really just harness the cloud to build amazing new experiences. So I think we're still, you know, still early days on the transition to the cloud. I know where many years on the journey, but we've got many, many years, you know, in our future. And it's very exciting. >>Well, thank you, guys, Joe and Justin. I really appreciate it. Congratulations on your respective success. I know is Joe said you got a lot more work to do, but I really appreciate you coming on the Cube. >>Awesome. Thank you. You're >>welcome. All right, so we're here covering reinvent 2020. The virtual edition. Keep it right there for more great content. Were unpacking the cloud and looking to the future. You're watching the cube?

>> Narrator: From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world. This is theCUBE conversation. >> Hey, welcome back everybody. Jeff Frick here with theCUBE, we're in our Palo Alto studios, the COVID crisis continues. Luckily we've got the ability to interview guests from remote and so we're excited to have this next guest. There's a lot of activity going on around equality and gender diversity, Black Lives Matter, and it feels like it really does feel like there's kind of a step function in moving this along. And there's a lot of groups out there that are trying to take a very active role, and one of the things they're trying to do is help women get on more corporate board seats, more representation, and we're really excited to have our next guest. Who's really taking a slightly different approach, a new approach to this, and we're happy to be joined by Rita Scroggin. She is the founder of FirstBoard.io, and she's also the Practice Director, Executive Group at Triad Group. So Rita, great to see you. >> Thank you very much, Jeff, for having me, I'm super excited to be here and to share the story about FirstBoard.io, what we're doing and how hopefully that will change the world just a little bit. >> That's great. Well, the way that this came about is I was on LinkedIn, I'm on LinkedIn all the time, and all of a sudden this big picture hit my feed and a ton of familiar faces. I think that's what it is four by eight. And I see Abby Kearns, Dao Jensen, Eve Maler, Wendy Perilli, Jocelyn is in there Syamla in there. And I thought, wow, I know a bunch of these women, and I'm always happy to promote the women in theCUBE alumni. And I reached out and I think it was Wendy said, "Hey, this is... She said, "I'm a founding member of this thing called FirstBoard.io. And I (indistinct) and she said, we got to talk to Rita. So it was great to meet you. And this is a new organization. I think you said you started at the very beginning of this year. >> Yeah. >> Why? Let's get kind of to the origin story. >> Yeah. >> What gave you the idea? Why did you think that this was something that needed to be done? And what caused you to actually take the leap of faith and start FirstBoard? >> Yeah, very good question. So in the fall of 2019, I did an event in partnership with K&L Gates and it was about how to get on board, and it wasn't gender specific, but I invited a lot of women from my network, and through K&L Gates, there was a speaker on the panel, Cheryl Bolton, who is now a supporter of FirstBoard.io. And we spoke after the panel discussion, so I was the moderator, and she said, "Do you place people or women specifically, "on private company boards? I said, I do now let's have a conversation about that. So we talked some more and we kind of felt like there's really a need for companies to diversify their boards, particularly private tech companies. And so then I thought about more about the idea. I reached out to a few women in my network and I said, hey, I have this idea. I'm thinking about starting an initiative around this topic, would you be interested in being part of it? And a lot of the women who I reached out to said, I'd love the idea, I would love to get involved. So that was really the origin, then we met, we had a little sort of social get together in, I think it was early December in Palo Alto. And then we said, let's launch officially in January, which we did. So in January we had our first and only in-person meeting, the idea initially was that we would meet every quarter in person. So it would be very localized to Silicon Valley and then COVID happened and everything changed. And we are now meeting via Zoom every six to eight weeks. We have members who are in different locations, most of our members are on Silicon Valley, but we also have a member in New York, in Seattle, in Dallas, and I might forget a location, but we're a little bit more distributed right now. And so that is where we are today. >> So you've done it a little bit different. You've got this group of women, there's 32 women in that picture, the founding members. And so you're taking almost like a cohort approach, a group approach. Why that approach? What did you see that wanted you to go that way, versus doing individual searches for individual companies, looking for individual kind of board members. Why the group approach? What type of dynamic does that introduce? How do the women leverage one another inside of this structure? >> Yeah, that's a good question. That's really the idea. The idea is that we work together collaboratively and that we leverage each other's networks. We raise each other's platform. I might know 10 or 15 or whatever, decision makers let's say VCs, CEOs, but the next member might know an equal number or more or less. So what I was thinking is if we leverage each other's network, we exponentially grow our network and we exponentially grow our visibility. So our focus right now is to really raise the profile of FirstBoard.io and the profile of each member of the group. So it it's fundamentally different, 'cause we're working together, kind of almost like a company that can accelerate where if we have a success, it's everybody's success. Because it raises the profile of everybody else. >> Right. >> So that's the idea, which is different than a networking organization, where you are an unknown member. And we're trying to make this in a different way. >> Right, right. And is the goal, within all the women that have joined, the founding members for all of them to get on a board, I mean, is that all of them are >> That's the goal. qualified people to be on a proper board. >> Yeah, that is the goal, that's the idea, we may not accomplish that in the first round because this is a problem that's been going on for a long time, but we're getting close to our first board placement. So that's I think initial great success. And we're working on a number of initiatives right now to raise the profile. We're doing a video interview with all our supporters. We are creating a campaign, how to reach out to CEOs and VCs. So we're working on a number of things right now behind the background to really target our audience, and our audience is specific to the tech world. So we're focusing really on private tech companies and we're focusing on our decision makers within those organizations. So whether it's the investor, the private equity, growth equity, or venture capital community, or the CEO or other board members for that matter, who may be aware that there's an opening and we're trying to tap into those as well. >> Right, right. So you've mentioned Silicon Valley, VCs and private equity a couple of times. So is the focus more in kind of that ecosystem that we're familiar with here in Silicon Valley with more private, kind of private and growth opportunities, or are you also just fully looking for large, regular public companies as well? >> We wouldn't turn down a public company opportunity, but none of our members have been on a board so far. And I think it's probably more realistic that, the first board position might be at a private tech company where the operating experience is particularly valuable. So that's our primary focus in terms of reaching out of the old But if a public company would come our way and say, we absolutely would love to talk to some of your members, of course we wouldn't turn that down. >> Jeff: Right. >> But actively we are going after private tech companies, and they can be located anywhere, so it's not specific this to Silicon Valley, of course a lot of tech companies are clustered there or here, but it could also be company in New York, or Boston, or wherever, but the focus is really on tech versus a broader focus of any kind of company. >> Right, right. So when you're working with these women who've never been on a board, what do you find is kind of the biggest gap that they need to fill, whether that's a real gap or perceived gap in their either skillsets or experience or whatever, to kind of make the jump and get into one of these board seats. Is it in any particular skill, any particular kind of point of view, what are the types of things that you do as a group to help them be better received, I guess, for the opportunities? >> Yeah. What we don't do is we don't really a training program or prep here. There are other organizations who do that, I think we do a very, very good job. Some of our members are part of other organizations as well. So what we're thinking more is the company oftentimes has, in a certain growth stage, has a gap in some form. And in looking at board opportunities, I think it's important to identify where's that gap, maybe it's go to market, or maybe it is a certain technical expertise, and match them up with the experience of our founding members. So we don't have a program to prepare women, we're more focused on... Okay, we're assuming you're prepared, that might be various degrees, and we're just trying to match kind of the operating expertise to the gap on a fully independent board member at any given company. >> Right, right I think we talked before we turned on the cameras, the three things you said you focus on really is, is operational expertise, skill experience, as well as domain expertise. >> Yeah. >> And so you're really trying to kind of map against a gap that the company has against a skillset that one of the members has. >> Yeah. So far I've sort of facilitated three different board opportunities and two of them, what they had in common, that the company was looking for somebody who really had domain expertise with the audience they were looking at, and who understood the buyer, and who had deep expertise in what to market strategies, developing them. So that's one example, right. And the other company, the third one was looking for somebody who had connections in the space who really understood that particular domain. And so it all depends, and I think it also depends on what stage the company's in. And I think the further along the company is, the more it's about governance and regulations. And earlier on, it's really filling a certain gap on the leadership team. >> Right. >> In the private equity world is also very interesting to us because oftentimes there's a timeline and there are certain growth objectives the company wants to reach. And that's a great opportunity, I think, for FirstBoard to bring in a founding member with that particular operating expertise. >> Right, right. So I'm curious, that's a great segue into kind of the customer side, if you will, the people that are looking for board members. Have you seen over the last several months or years, I'll open it up, kind of a shift in terms of people a, just kind of accepting that there are going to be more women and people of color on the board, but also more of kind of an active search and a more kind of progressive goal to make sure that they do increase the diversity on their boards, whether that be for women or people of color or whatever, just to bring more diversity. Have you seen a shift in your customer base, in terms of they're really focused on prioritization on that? >> Well, I think it's certainly on people's mind and I think now more so than ever with the recent changes and sort of uprising of Black Lives Matter, but I wouldn't say that has really transferred over into real meaningful diversity on boards. I think we still have a long, long way to go, and there's an organization, Him For Her, and I think it was the Calyx Management Institute, they did a study last year and they found that privately, heavily funded companies, 60% of those don't have a single woman on the board. And I think women in general held about 7% of board seats at these companies. So I think there's still a long way to go, but I think it's very important that in the future, a larger proportion of the population is reflected in the boards. Right? So whether it's women, women of color, people of color, so everybody should be part of the leadership team on the board level and on the leadership level. And I think that has become certainly more of a topic, I think, especially for large companies. And I think startups are now recognizing that it's important for them too, especially if they want to be perceived as a company, which has fair and equal values. >> Right. Right. So given that kind of landscape, if you will, what are kind of the expectations that you have with this founding member group? And I presume there'll be other groups in the future once these people all find a great board seat and are doing their thing, kind of, is it a really tough road ahead? Do you see that it's really not that tough on maybe in the macro level, but on the micro level there are some real opportunities, how are you as a group of 32 founding members trying to take this Hill, if you will, against pretty tough odds actually. >> But I think we're going to take it one step at a time. We already did a press release, we have a website, we have some visibility on LinkedIn and we already have been able to curate three different board conversations. So I think step by step, I think we will become more visible. I think we will be more known. We will have more opportunities to introduce founding members, this current cohort and future cohorts. And through that, I think we will make progress. So I'm very optimistic that we can make a difference, that we can get more women on boards. And once the founding members have joined a board, the plan is to launch a group where basically we create a peer group, which will then mentor and support the next cohort. And we also have an amazing group of supporters and partners already. We have Steve Singh from Madrona Ventures. We have Rohini from NGP Capital, and we're always looking for more partners and supporters. I'm not going list everybody right now, but I'm very proud about that we have partners and supporters who bought into the mission and who are helping us accomplish the mission. So I feel very optimistic that we will be able to move the needle. >> Jeff: Yeah. >> It might be at slower pace, but it was still the making a difference. >> Right. Right. Well, the hundredth anniversary of women getting the vote is coming up here in a couple of weeks. Right. And that took a long time to get done, So this stuff it does not happen easily. It does not happen overnight. But I would think certainly too with the increasing number of women in VC roles, as partners, and are also getting on board seats that hopefully that the things are starting to fall in the right direction. And hopefully with each progressive placement is a little bit easier than the one before. So Rita it's great to meet you, everyone I talked to you about you is so excited about the work that you're doing and what you're doing with FirstBoard. >> Thank you. >> I want to give you kind of the last word before we sign off, how should people learn more? How can people support the cause? How should people get involved, so that they can move the needle. >> That's great. Thank you. Get in touch with us on, if you go to the website FirstBoard.io, there is a way to partner with us, there's a link to partner with us, there's a link if you are interested in joining the future cohort. Please contact me and I will respond. And we would love to talk to companies, who are thinking about diversifying their board, we would love to talk to VCs for whom this is important. So please get in touch, and we'll figure out how to change the world together. >> Right And, oh by the way, most studies show you get better business outcomes, right. With diversity of opinion, diversity of points of view. So it's not only the right thing to do, it's also very good business. >> And I think the next decade we are ready for change. I think the society, I think is ready for change. And I think how companies run and are operated, I think people are ready for a change too. So I think the timing is really, really right. And I think we can make it happen. >> Great. Well, Rita, thank you again for taking a few minutes >> Thank you >> and telling your story and joining us on theCUBE. >> Thank you very much. It was pressure of Jeff and I look forward to talk again. >> Yeah. Maybe in person after we get through all this COVID madness. >> Maybe in person, yeah. >> All right. Well, thanks again, Rita. >> Rita: Thank you very much. >> All right She's Rita, I'm Jeff. You're watching theCUBE. Thanks for watching. We'll see you next time. (soft music)

>> 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)

>> Narrator: Live from Las Vegas, it's theCube covering AWS re:Invent 2017 presented by AWS, Intel, and our ecosystem of partners. (upbeat music) >> Okay, welcome back everyone. We're here live in Las Vegas. This is theCube's exclusive coverage of Amazon Web Services re:Invent 2017, our fifth year covering AWS re:Invent. I'm John Furrier the founder of SiliconANGLE Media with my co-host Stu Miniman. I'm so excited 45,000 people and boy, I remember when it was just a small, little, fast-growing company. We're here with Teresa Carlson who's been here with us the whole way. She's a Senior Vice President of Public Sector. Teresa, welcome to theCube, good to see you. >> I'm always glad to be here on theCube. >> So, you've been running public sector, you've been really, I've gotta say, I gave a tweet, not to sound that I'm fawning over you right now, but you've really grown the business in a significant way. As Andy was saying, a meaningful way. Take us through, because it's almost mind-blowing. We have already had a few guests on theCube. I went to your breakfast. You are changing the game, but not without scar tissue. You've done a lot of hard work to get there, so, one, congratulations, but give us a state-of-the-union right now for public sector, because you're winning, you're doing great, but it wasn't easy. >> No, it's not been easy, but it's been a lot of fun. I mean, it's been a lot of fun, in fact, as you said, this is our sixth year of doing re:Invent and yesterday or two days ago, we had a public sector breakfast and it was so full, we got shut down by the fire marshal. So that is when you know you've got customers and partners showing up, because they want to be there. We have grown significantly and that has been through the work of both working with customers and partners on security, compliance, policy, acquisition vehicles, to just make sure that we have the right balance of everything needed to really drive and grow the business in the right way. As I've talked about we didn't leave any stone unturned. We had to really go through all the hard processes to do this right and I think it really has paid off because you never want to take short-cuts. You wanna make sure you're doing the right thing in order for customers to have better technology, for us to help drive good government, good education. >> I gotta say, one of the big trends we're seeing here on siliconANGLE, theCube, and Wikibon is the public-private Partnerships are accelerating. You're seeing public sector help on security to the private sector, private sector helping government move faster and so you're seeing a balance and an equilibrium coming together, but also old guard companies sometimes have a federal division or a separate DNA culture. You guys don't, you have one culture at Amazon, but the striking thing for me, is that you're now enabling companies to get into public sector that couldn't before. So I wanted to ask you specifically, is it like that now, we're you're starting to see new people come in with solutions because you guys have done that heavy lifting where before they'd have to wait in line, get certified, are we seeing new solutions, are you enabling that, is that actually happening? >> It absolutely is happening and we never forget our roots of start-ups here at AWS, because they are really a huge reason why we exist and for public sector, I saw a change in my previous life I never had venture capitalists or private equity firms come and say we want our companies in government. We are creating new education tech companies, which was really not even heard of. >> It's a growth strategy for them. >> It's a huge growth strategy, so venture capitalist and private equity like Andreessen Horowitz, Madrona, C5, Bridgewater, we see tons and they come to us saying, we have this portfolio, can you help us talk to them about how they get into government? As a result of that, we do sales and marketing, we work with them on FedRAMP I-E slash security compliance. We ensure that they understand the elements and components of how they work in government and by the way, government loves that we are bringing in innovative new technologies. We can also do that through the marketplace, the AWS marketplace, which allows them to move faster, be more agile, and start getting that business. >> Teresa, I'm wondering if you could share a little more. You talk about innovation, we've been lovin' for years, I love when I talk about regional governments, education, you get non-profits under your umbrella, where it used to be, I didn't have the budget, I can't move fast. Now, we're seeing some great innovation from the private side as well as well as some of the public-private interactions. >> Definitely, in fact, I was in California about a month ago where we announced an innovation center with California Polytechnic University, CalPoly and the president there, Jeff Armstrong, it is amazing, they literally had been looking at what AWS was doing and they took the pillars that they'd been seeing us talk about for public sector and they created an innovation center to work on these opportunities and challenges and just as in public safety, health, agri, sex trafficking and child exploitation, through seeing what Thorn was doing in the International Center for Missing and Exploited Children. >> How is this leveling the playing field? Because everyone, citizens at least in the United States, I'm sure it's happening in other markets as well, they want the government to move faster. And you guys are like the freight train that's out of control speed-wise, just more and more services. How does the government keep up? Because I would imagine that if I am a government official or I'm the public sector, oh my God, I can't handle Amazon. I can't ride that beast, it's too strong. I mean do they say that, is that the wrong vibe, or are they more hey I want you to do, is it more your flywheel, do they have to get involved? What's the relation, what's the sentiment of the government? >> Well, they wanna move fast. In fact, in the U.S. government, the White House does have an entire initiative now on modernization. You're seeing countries like the U.K government go cloud native. You saw the country of Bahrain which is going all-in in the cloud and they've already established new policies and a cloud-first policy of moving. But I would tell you, if you look at groups like the intelligence community in the U.S. government, we just announced our secret region and that allows them to have top-secret capabilities, secret, unclassified in our GovCloud, so they have capabilities across the entire spectrum of workloads and what they've always said to us and our other customers is can we build cloud tools, can we build a cloud? Yes, but can we innovate at the rate and speed you're innovating? No, because we provide them innovation ahead of their demand. >> Yeah, Teresa, I remember when GovCloud launched and it was, like, wow, this is like AWS isn't just like a monolithic service around the globe and everything. It seems like secret region goes along that line. How does the dynamic between AWS as a whole and what you're doing in your organization, how do you work through that and kinda balance, I want services around the globe, yet meet the needs of your clients. >> On the GovCloud region, that was our first entre into doing something unique for government. That region has grown 185% every year since 2011 and we just announced a second region on the east coast for GovCloud, U.S. GovCloud. The interaction with our services team is amazing. Charlie Bell who runs all of our services, we have a tight relationship, we talk to our government customers in these regions, understand their priorities, then we roll them out and it's really that simple. They get the exact same thing in their classified regions as we give our other customers, it's just their network. >> Well, you got the date set, I'm looking at my picture here I took, June 20th and 21st, save the date, AWS Public Sector Summit, #AWSPSSummit as it's called on Twitter hashtag. Every year, you started out in a little conference room, in a ballroom, bigger hotel, now the convention center. Massive growth. >> And theCube was there this year, which I was happy. >> That makes it legitimate, and theCube's there, we'll be there this year, >> Good, yes. >> But of course, this is the growth. V.C.s, private equity, this is a growth market, this is not a unique, siloed market anymore. You guys have leveled the silos within Amazon, I mean you never had silos, but you are now agile to come to the government. What's next for you? You've done a great job, you're now cruising altitude, what's your growth strategy for Public Sector Summit, how are you going to take it to the next level? >> Well, even though we have grown a lot, thank you to our customers and partners, we really are just scratching the surface. It is day one still for us. Our customers are really just still getting going on a lot of mission critical workloads. They're moving in things they really hadn't thought about. They're starting to do things like higher more developers in government, so they can take advantage of the tools used, a lot you saw yesterday. But additionally, what we're seeing is we are spending a lot of time going into countries around the world, helping countries set a strategy for digital transformation. New jobs growth, new companies, economic development, how do they train and educate for a cloud-based workforce, we call it and that's really fun to go in and tell governments, look you really have to prepare your country for a digital transformation and again if you look at groups like Bahrain, what the U.K have done, they are doing that and they are making a massive transformation around this. >> Final question for you, what are you most proud of looking back since you joined AWS seven years ago. I think it was seven years ago you started? >> Yep, seven years ago this month. >> Congratulations, so what are you most proud of and then two, what do you think about the most as you execute day-to-day in growing the business? >> Well, I would say the fact that I have had an amazing brand to work with out of the gate Amazon was such a great brand, and the fact that, again, based on think big, Andy Jassy's leadership, really he and I having a conversation together saying, we can change the world and make it a better place and you've heard me say a lot in my openings, we have two themes that we talk about in public sector, which is paving the way for disruptive innovation and making the world a better place. And if I look back, it's really the things that we're helping to do this that we are driving new policies, companies are seeing results, agencies, and we are making the world a better place. I would say that's humbling and amazing and we're just getting going. >> As a chief of public sector, you're like, you've seen it grow and you're running it every day and you have a great team, do you ever have a pinch me moment once in a while? Kind of say, wow, what have you done? >> Well, I think the pinch me moments are when I hear the customers and partners tell me how fast they're moving and the results they have. We always have a goal of really working with our mission partners and we've hired now more than 17,000 veterans at Amazon and growing. It's things like that that we can do to really help that transformation and not just talk the talk, but walk the walk as a company. I would say for where we wanna go and what I sort of worry about our growth, I guess I worry and stay up a little late at night to make sure that we keep our hiring bar high, that we really maintain our focus on customer obsession, >> John: Security. >> Security is always on my mind. >> Do you sleep at all it must keep you up late a lot. >> No, I don't really, no. But the last thing I would say is just really thinking through ensuring that we're continually pushing hard, that we have a little bit of sharp elbows, going in we're trying to change policy, we don't give up on the things that really matter for doing this massive transformation, for countries, for state and local agencies, for feds, for educational institutions around cloud transformation. >> I really respect your results and I love your hard-charging style. It's fantastic, your success obviously speaks for itself. We'll see you at the Summit in June. This is theCube, Teresa Carlson The Chief of the Public Sector business, she's the Vice President of Public Sector. I'm John Furrier, Stu Miniman. More live coverage here at AWS re:Invent after this short break. (upbeat music)

>> Announcer: Live from Manhattan, it's the CUBE. Covering AWS Summit, New York City, 2017. Brought to you by Amazon web services. >> And we are live here at AWS Summit here at the Javits Center, New York City, we're midtown, Manhattan, a lot of activity going on outside, you can imagine all the buzz inside as well. Somewhere between 6, 7, 8,000 attendees, kind of tough to tell right now, but everybody's jammed inside here on the show floor and they've been here all day and they're going to stay for a while I think too. As I said, a lot of buzz going on, and good buzz too. Along with Stu Miniman, I'm John Walls and we're now joined by Erica Windisch who is the Co-founder and the CTO of IOpipe. Erica, thanks for being with us here on the CUBE. >> Thank you, thank you for having me. >> You have had a big day. >> Yes we have, yeah. >> It's always fun to talk about money but you did have a fairly significant announcement this morning to make. Tell us about that. >> Yeah, so this morning we announced funding, $2.5 million from several investors including NEA, Madrona, and Underscore. >> So, yeah, you don't often get to high-five everybody for a day like that. I mean that kind of validation, obviously is something that not you just take to the bank, you take it to the marketplace too. >> Yeah, absolutely. And we actually started, our first check was from Techstars so we joined Techstars here in New York City and did that last year for their summer program and it was really great and that was the first foundation that we really had, and then having that further validation from major VCs like NEA and Madrona, Underscore, you know that really was really validating for us as well as just the fact that we're building, we're hiring and we're building and having what I think is an increasingly awesome product. >> Sure, well tell us about IOpipe, for folks at home who are watching might not be familiar with your space, what you do and how you do it. >> Yeah, so we provide tools for software developers to build and manage their applications on Amazon Lambda. So, basically, it's all serverless, we're actually built on serverless as well, we monitor with IOpipe, we dogfood everything. And we are providing deeper insights into those application workloads as well as correlating that information in more useful ways. Deeper knowledge of what exactly is happening in the run times, so we're able to see the data we ingest tells us information on the processes and the containers and the virtual machines that are running your Lambda workload, so we can see things like memory leaks and we can see file descriptor leaks and displaced utilization leaks, things like that that Amazon doesn't collect or at least doesn't give you that information. So, we're looking at ways we can provide more value to users of Lambda and also extending it with plugins so we have a plugin for tracing where you can time aspects of your application as well as profiler, so you can enable a profiling plugin and you get a full flame graph. So you can see, these are all the functions and this one ran and this one ran and the stack looks like this and so you can see the full flame graph of what happened and when and full timing information. This kind of insight that nothing else really gives you. >> Yeah, Erica, every time we have a new technology we go through this kind of diffusion of innovation that goes through. Remember back, I go back thinking about when virtualization came, people, what is it, how do I use it? We saw that in containers and each wave seems to be going faster and faster so there's still plenty of people I talked to that were like, "serverless what?" You know, some new as a service, I mean I thought I knew it with SAS and everything else like that. You're digging into these environments further. Can you give us, what are some of the kind of key use cases you're seeing, what are the challenges that customers are having? What works, what doesn't work, help us unpack that some? >> So, I think there's a number of challenges that users run into today. One is the fact that it is new so some of the tools are still evolving. Operations tools, development tools are still evolving. Just this week, Amazon announced SAM local so you can do editing and debugging locally on your machine or your laptop. That wasn't available before, right? So these tools, we're very much still in a learning phase for some of the tools, but some of the things like what we're doing with IOpipe, in some ways is more traditional because we're bringing in some of the basic monitoring tools and capabilities that you would expect from other platforms. But the other side, also innovating because we're bridging that development and operations into a single tool so it's not development and operations, it's, not even just different tools for those two things, but single tools for those. So I think that's part of the solution, part of the problem, in terms of workloads, I think there's a lot of ETLs, streaming applications, very infrequent things like chron jobs, web applications, you can take flask applications or express applications and just port them directly over to Lambda with almost a lift and shift for those, right? So there's a lot of power for bringing on the web 'cause you pay per the request. You don't scale your application and build your application for the number of servers that you need to handle the requests, it scales it per request and you pay per request and that's what's powerful in both scale of operations and team and like financially, but also, yeah, I lost train of thought there, but it all scales that way, right? Like just according to the request. >> Yeah, bring us into a typical customer, I know there are no typical customers, everyone's a little bit different, but you've got the developers, you've got the operators, finance has always had, you know, there's challenges with cloud in general but serverless at least promises that it's going to be less expensive. What are those dynamics from an organizational standpoint that you see inside? >> In terms of cost? >> Not just cost, but do the developers make something and the operators are like, wait, you know, there's challenges there? Or who drives this initiative in general? Does finance come and say, has finance heard about this and said hey, I heard I could save 60-70% on my cloud if you just re-architect this on Lambda. Or is it the developers coming through and saying, oh, wow, this is great, and can do it, or are operators, who's driving the initiatives and what are some of those dynamics? >> So I see a combination of these things. Some organizations, and I don't want to say names 'cause I don't want to like, you know, they did this and that's how it is. But I get the impression that certain organizations they have a top-down approach where they're going like, everything is going to be serverless and the cost really matters. So you're going to build serverless unless you can't, right? Serverless by default, anything else as an exception. Then there's organizations where developers are really pushing for it because it simplifies their requirements, right? It's a self-service aspect, right, even if they can spit out VMs, even if they have self-service VMs, they won't have to spit out VMs, they don't have to build docker images, they don't have to look at how the operating system is configured. They write code and they deploy code. There's no other steps, right? They're not like, oh, what version of Python is on here and how do I install all the libraries and how do I, right, like with serverless you just write the code and you ship the code. Which is really, really nice. So, in a way it's like having a golden image that you can't change, and you just know you're always going to build for in every application and every organization is going to the same golden image. Which simplifies a lot of things. >> Stu and I were talking about serverless, the whole concept, because it's really not truly serverless it's just different server, or it's a different flavor of it basically. So, first off, what gave birth to that and then where do you think, with serverless computering, serverless application, so on and so forth, where's that going? >> Yeah. >> What's going to be the real value at the end of the day of that? >> So, first of all the term "serverless," I look at it as, yes there are servers, serverless is servers are not my concern as a developer, right, I am not worrying about what the server looks like or operating the servers necessarily. I care about building my application which is why we're looking at building tools that are bridging development and operations so that operations is part of your development. But I see, the direction of serverless, really interesting in a few ways. One is that it's going to be available for more use cases. So right now there's certain use cases that make sense and one of the challenges is figuring out which use cases it doesn't work for. Eventually, you're not going to have that question, potentially, right? So maybe we get to a point where you don't have to ask, the challenge isn't, is serverless good for this use case? Maybe it's good for all use cases eventually down the road, maybe. Another thing is... >> If I could just follow up on that. Some of the announcements today like AWS Glue has serverless in the background there. Seems very promising, things like machine learning, artificial intelligence, serverless, IOT where you know, I need to balance the surface area of attack there but with serverless it won't be active as much and there will be links that are a little bit more dynamic. So, lots of those new use cases seem to be built really well for serverless. What are some of the cases today that you just say, hey, don't even go serverless there. >> Oh don't go serverless, where to do that? Well, so, Lambda has an execution time window which can be limiting for some things that you might want to do. So, like, Lambda in particular may not be the best case for all video encoding tasks. Some video encoding tasks if you can time limit it can be fine. But it's not good for all video encoded tasks because it's a batch process, potentially. Serverless processes that can let's say paralyze that and say, we're going to run Lambda but we're going to say split this up into segments, for instance, you can do that, or if you do it as a stream, right? Like you pipe a video and blocks into Kinesis, right, you can make that work. But it becomes a challenge to those kinds of use cases. >> Yeah, there was the example I think in the keynote was, this high process that would have taken five years, we can do 155 seconds. >> Right, but you have to paralyze it, right? >> Stu: Exactly. >> And if you can't paralyze a task and you can't do it within five or ten minutes, you can't use Lambda for it today. But it also depends on how you define serverless, right, because if serverless is Lambda, that's one thing. But if serverless is these other SAS products as well potentially, like AWS Transcode service, well is that serverless? If it is, then there you go. There's a solution potentially for you. So there's very blurry lines sometimes around what is serverless, and we're looking at IOpipe around serverless functions. I feel the same way around cloud in general was that there's cloud compute and it kind of evolved over time and the cloud is everything like all these things are in a cloud. But originally when we're talking cloud, five years ago, ten years ago, it was all compute. That's what we were talking about. So these terms change over time, so it's hard to say what serverless will be in five years or ten years because it'll mean something different. >> Or next week, for that matter. >> Yeah. >> Erica, last question I have. $2.5 million, what's that going to drive, what should we expect to see from your company and give us any final thoughts on what you'd like to see for the maturation of the serverless technology field? >> Yeah, so we've been hiring and building out a team, we're working on improving the user experience of the product, we are adding additional plugins and enhancements to the service. We feel that we have a really good base with our 1.0 announcement, 'cause we're not just the 2.5 million, we also announced our 1.0. And the 1.0 has a really good base of functionality and we're looking at adding additional plugins and additional features that can extend the service. So we're looking at doing that with that money. And with serverless in general, I think this is really compelling, what we're going to see in the next year, because we're going to see more large enterprises and more enterprise adoption, I think. I mean I was involved early in cloud. I was involved early in docker. And this point of serverless is very much at the early days of these technologies, and I definitely see a rocket ship taking off, and I think in the next year it's going to be really interesting to kind of see it starting to orbit a little bit. >> Well, new product, new funding, and a new day for IOpipe. >> Yes. >> So congratulations on a good day and thank you for being with us here on the CUBE. >> Thank you very much. >> You bet, we'll continue here at the Javits Center we're in midtown Manhattan continuing our coverage of the AWS Summit, here on the CUBE. (futuristic music)

(uplifting music) >> Narrator: Partners. Now, here are your hosts, John Furrier and Stu Miniman. >> US Amazon Web Services re:Invent 2016 their annual conference. 32,000 people, record setting number. I'm John Furrier, Stu Miniman co-host in theCUBE for three days of wall-to-wall coverage. Day two, day one of the conference our next guest is Kiran Bhageshpur, who's the CEO and co-founder of Igneous Systems. He was a hot startup in the, I don't want to say storage area, kind of disrupting storage in a new way. Kiran great to see you, thanks for coming on theCUBE. >> Thanks a lot, glad to be here, John. >> So, you're living the dream the cloud dream, it's not a nightmare for you because you're one of the progressive new ways. I want to get your thoughts on Andy Jassy's Keynote because he really lays out the new mindset of the cloud. Your startup that you founded with your team is doing something kind of, I won't say contrarian, some might say contrarian, but contrarians usually become the big winners, like Amazon was a contrarian now they're obviously the winning. So, take a minute to explain what you guys are doing. You're funded by Madrona Ventures and NEA, New Enterprise Associates, great backers, smart. Your track record at Isilon, you know the business. Take a minute to describe what you guys are doing. >> Great, yes I will. So, Igneous Systems was founded to really deliver cloud services to the enterprise data center for data-centric workloads. So what to we mean by that? With cloud services, just like with Amazon, customers don't buy hardware, license software. They do not monitor or manage your infrastructure. They consume it across API and they pay for it by the drip rather than the drink. Similarly, the same case with us but we make that all available within a customer's data center itself. And we focus on sort of data-centric, data heavy workloads. I don't know whether you saw James Hamilton's-- >> Yeah. >> Speech yesterday, but he also talked about the same thing that Mary Meeker talked about earlier this year which is an overwhelming amount of data generated today is machine generated and machine consumed and that's growing really rapidly. And our view is the same techniques that have made Amazon so powerful and so valuable are needed out at the edge or on-premise, close to where users and machines are generating and using the data. So that's kind of what we do. Very much the cloud model taken out to the enterprise data center. So, think of it as a hybrid. >> Kiran, let's talk about storage and where it lives because I think something that many people miss is that cloud typically starts with very compute heavy types of applications and we know that data is tough to move. I mean, Amazon rolled out a truck to show how they move 100 petabyes. And not just to show it, this is a new product they had 'cause customers do want to be able to migrate data and that's really tough and takes a lot of time. You mentioned IoT at the edge, they announced kind of query services on your data up in S3, so what are you hearing from customers? You know, kind of large data from your previous jobs. Where's the data living, where's data being created, where does data need to be worked on and how does that play into what you're doing? >> That's a great question Stu. What we find with customers, especially the one's with large and growing data sets is there is still a challenge of not just how to go store it but how to go process that on the fly. On a camera today or a next generation microscope could produce tens of terabytes of data per hour and that is not stuff that you can move across the internet to the cloud. And so the ask and the call from customers is to be able to go ingest that, curate that, process that locally and the cloud still has a very compelling role to play as a distribution mechanism and for a sharing mechanism of that data. I found it pretty wild that a big part of Andy Jassy's Keynote was for the first time they talked about hybrid and acknowledged the fact that it is the cloud and cloud-like techniques out in the enterprise data center. So, I look at that as hugely validating what we have been talking about which is bringing cloud native paradigms into the enterprise data center. >> Let's talk about that operational model because what you're highlighting and what Jassy pointed out is an operational model now for IT. >> Kiran: Yep. >> How are you guys creating value for customers? And be specific, is it, 'cause the on-prem is not going away, we've talked about this before and certainly VMware sees the cloud but also on-prem too. What is the value for customers? Because now this operational model of on the cloud is there, one way-- >> Yes. >> But how do I get cloud inside my data center? >> The way we do that is, very similar to the cloud operating model, right? So, we sell customers essentially an annual subscription service and that service is delivered using appliances that are purpose-built. Think of it as, like snowball, if you will, that goes into the customers data centers fully managed by our software running in our cloud. So, for a customer point of view, it happens to live within their data center, but they are consuming it pretty much the same way that they would consume a cloud service. That's the value, it's the same tool chains, the same programming paradigms that they are used to with, say, a native OS. But within their data centers at lower latencies addressing the same things that Andy Jassy brought up, which is you need a truck to go move large amounts of data. >> Well, I want to also bring up James Hamilton's presentation. You mentioned that yesterday one of the key points he made was that scaling up for these peak loads like they have on the Friday's, their Prime Friday spikes, they do instantly and elastic is a big deal we know that. His point though was they would have to provision on bare metal or in the data center months in advance to even rationalize what that peak could be which still is an unknown number. So, the scale point and provisioning is a huge headache for customers, so that's why that's relevant. How do you guys answer that claim when you say, "Hey, I need stuff to be done fast, "I don't have time to provision"? How do you guys, do you address that at all? How do you talk to that specific point? >> We take care of the provisioning and the additional expansion and shrinking of capacity within the customer's data center, because just like Amazon monitors their infrastructure users in the data center, we do that for our infrastructure within the customer's data center, and therefore we can react to go scale up or scale down. But then there's another point to the whole thing, which is the interesting thing is the elasticity is much more important for compute as opposed to data. Data just linearly grows, you never throw that stuff away. The things that you captured, the processing is highly elastic and you might want to do some additional processing and burst out and so on. So, that's another aspect of hybrid we see with our customers which is, I want my work flow here, I want to be able to burst out to the public cloud for that peak capacity that I don't want to have infrastructure locally for. >> So Kiran, sorry. So James Hamilton's presentation talks a lot about, just that hyper scale. They claim they've got the most scale and therefore nobody else should do anything because oversimplifying a little bit, but we've got the best price, we've got the whole stack, give you all the solutions. You talk to enterprises. Scale means different things for different applications for what I need to get done, what I have. What does that really mean to you? How does that hybrid piece fit in to the whole scale discussion? >> So, a lot of what we do is really ride on the coattails of the Amazon and the Google and the Microsoft because everyone has access to the same raw components, hard drives and CPUs and so on and so forth. And then the question is how do you go assemble those in a form factor that is appropriate for that particular use case? If you're going to go build a data center that's one level of scale, but if you look at a vast majority of applications and enterprises, their scales are much smaller. So, we literally look at taking a rack of infrastructure which might have, say, 40 servers and a couple of switches in sheet metal and shrinking that to a 4U form factor which has got 60 of our nano servers which has got switches and has got sheet metal. So, it's shrinking the whole thing down. The economy's of scale are still quite compelling because we use the exact same raw materials from the same suppliers to the cloud guys, right? And the real difference in cost is how things are put together and how they are operationalized. In which case, we are much more like Amazon than not. >> The other thing that's really interesting to watch, if you look at Amazon's storage move, is storage is in a silo, they've now got all these services that I can start doing this. How does the enterprise look at that? How does the solution like yours enable us to be able to use our data more? >> I absolutely think there is a palpable need for and desire for those sorts of new paradigms in the enterprise data center too because what you can do with not just storage but with lambda and with a bunch of other advanced services on top of that, what that really does is allows enterprises and customers to just focus on what is differentiated to them. This is the whole low-code, no-code moment, if you will, right, movement, and that's a compelling trend. It is something that we've actively embraced. We've got our architecture enables that on day one and that's kind of the way you're going to go build applications now onwards. >> So will we see lambda functions calling things on your end? >> Stay tuned. I think my, yeah, stay tuned. >> That's a smile, that's a yes. (laughs) Talk about the drivers in your business, 'cause you guys are new, you're a startup. For the folks watching you're making some bets, big bets obviously funded by some pretty big venture capitalists out there. What is your big bet? Is it true private cloud is going to emerge on-premise? Is the bet that cloud adoption with scalable compute and storage is going to be unmanaged or manageless or serverless, what's the big bet? >> So our bet is the cloud is going to win and I mean the cloud paradigm, which means consuming infrastructure by the drip rather than the drink across APIs. Flexibility, agility is going to win. One answer which is very compelling is the public cloud today. We believe that similar patterns will exist on the on-premise world and we believe we are very well positioned to supply that thing. And the infrastructure which shrinks would be very traditional infrastructure and software technology stacks which has really existed in the enterprise data center for the last 20 years. That will shrink and everything will look similar as in highly flexible, highly scalable, very easy to go put things together and you're going to have very similar patterns in both the public cloud and within your data center. >> Our Wikibon research team is looking at the practitioner side of the market. One of the things they're observing is, among a lot of things, is that you're seeing AWS teams come together. We're seeing Accenture was on earlier talking about the same dynamic. That's the pattern that we're seeing is these teams are coming together, some handful of people, the pizza box teams-- >> Yep. >> As Jeff Bezos calls it, growing into fully functional bigger teams. So, depending upon that progression, what's your advice to practitioners? And how do you add value into this momentum of as they scratch their head go, "Okay, we're going to go to the cloud"? So they know that's the mandate. How do you help them and why should they look at your solution and where do you fit into that? >> So one of the things customers and partners tell us is we are a great on-ramp to the cloud if you will. Everybody wants to embrace the new programming patterns, new programming paradigms and many people have taken that big leap and done the full shift in one step. You've heard Finra, you've heard Capital One all of these guys talk, but not everyone is that far out there. So what we sort of become for these folks is a stepping stone. We are on-premise. It allows them to get used to it. They start using the same patterns that can scale there. There can decide what workflows remain local and why and what go there, and that's our view. We very much live in they hybrid world to burst out to the world, bring it back as appropriate. >> Kiran thanks so much for coming on theCUBE, we really appreciate it, we're getting the break but I do want to ask one personal question. You're back in the entrepreneurial zeal again, you've got the startup, you have some capital but you're not loaded with cash, a good amount to achieve what you need to do. What's it like for you right now? I mean, what do you believe in? What's your guiding principles and what's it like to get back on the entrepreneurial treadmill again? >> You know, it's actually quite exhilarating and liberating to be back in a startup environment because it forces you to focus on what is important what is urgent and important at all points in time, and a guiding principle for us is less is more. Let's be driven by customers and do what is required there and then slowly extend that out. And actually, being a startup and not having infinite money to throw like, large legacy players would frees you from trying to do too many things and focus on only what is important and that's really key to success. >> And how are you making the decisions as an executive like, product-wise? Is it more agile, are you guys doubling down? >> Very, very agile, we can move very quickly. Since we are delivering a service, we are continuously updating infrastructure just like Amazon does within their data center so we can turn around very, very quickly. So I'm very impressed the fact that the Amazon rolls out 1,000 new features this year, but I can see how that is possible at scale and that's what we're doing. >> At Isilon you were very successful scaling up that generation of web scale, we saw that with Facebook and the Apples of the world. What's different now than then? Just in the short years between the web scalers dominating to now full Multi-Cloud, Hybrid Cloud cloud. In your mind, what's different about the landscape out there? Share your thoughts. >> I think there's a couple of things. One of them is Isilon was incredible, was a very useful infrastructure, was something that was easy to deploy, but it was still that something you built, you managed, you owned, if you will. The big transition is away from that, from build to consume and not worry about that infrastructure at all. And that is not something that you can retrofit into an existing architecture, you have to start from scratch to go do that. So, that's the biggest number one. Two, second one is just the scale is bigger. You heard Andy Jassy talk about the exobyte moving problem and he commented on the fact that exobytes are not all that rare and he's true because you go back 10 years ago, maybe four companies had an exobyte problem. It's now a lot more than that. And so the scale is two or three orders of magnitude larger than when Isilon was growing up. >> Scales at table stakes and consumption of infrastructure, that's a dev-ops ethos gone mainstream. >> Yes. >> Thanks so much for sharing. We're live here in Las Vegas for Amazon re:Invent. I'm John Furrier, Stu Miniman, we're back with more live coverage, three days of wall-to-wall coverage. theCUBE will be right back. (upbeat electronic music) (relaxing guitar music)