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

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

(bright, upbeat music) >> Hello, brilliant humans and welcome to this special programming on theCUBE featuring Women of the Cloud, brought to you by AWS. My name is Savannah Peterson, and I am very excited to be joined by a brilliant woman both in supply chain as well as digital transformation. Please welcome Jeanette Barlow, VP of Product at Instacart. Jeanette, thank you so much for joining us from Boston today. How you doing? >> Thank you. I'm doing well, thank you. And thank you to the Amazon team for letting me join you. I'm excited to participate in this. I think it's such an important topic to learn all about how as women we're helping shape the future of business, supply chain, consumer experiences. So thank you very much. >> That's fantastic to have you and to be really celebrating women of the cloud properly. To start us off, how long, let's just, let's run with this. How long have you been a woman of the cloud? (Jeanette and Savannah laugh) >> Oh, probably since there, before there was a cloud, actually I have spent my entire career in enterprise technology and I spent nearly 25 years actually with IBM. And, you know, I remember when the internet really took off as far as a highly accessible thing and then the very beginnings of e-commerce where it was really the wild west and it was such a different experience than you get now. And I've been very fortunate throughout that journey to have a variety of roles from sales, marketing, communications. I eventually landed in product management and that's pretty much where I stayed. >> Savannah: At least for now. >> At least for now. >> Sounds like you're very curious. I can tell that you are a very curious person. Since you've been around for what I would consider a, an impressive period of time in an industry, especially when there were not a ton of women to reference or receive mentorship from, what was the initial catalyst or spark or inspiration for you to pursue a career in technology? >> I'll be really honest, getting out of college with college debt, money. (Savannah laughs) The best salary, I'm not going to sugarcoat that but once I landed there, it just was so amazing how technological advance advances were fundamentally changing the way businesses would work or how humans could get things done. And that whole, my whole career trajectory has been very much working at the forefront of new areas whether that be collaboration, software or supply chain which is, obviously we're all well aware, such a deep and important area and even low-code workflow automation before I came to Instacart. >> I love the transparency there. It's a indicator of a great leader and that level of authenticity. Were there any hurdles that you felt you had to overcome in the beginning or was the curiosity enough to power through the initial first few years that are always tough for anyone, no matter their gender or career? >> I think I was a very fortunate person. I do want to say that, sure, there are a lot of long hours and I often felt that I had to be more prepared, maybe than some of my colleagues that were men back, way back in the day. But I had the very good fortune of working for companies throughout my history that really believed in an equitable and respectful workplace. And I had wonderful mentors, both women and men, along the way who really were there to help develop talent. So I never felt that I had sort of a glass ceiling. I definitely felt that I had to to sit there and assert a point of view, at times. >> Savannah: Mm-Hm. >> But, I've seen this whole industry and space change and it's not just gender, but also racial backgrounds educational backgrounds, that neurodiversity I'm now seeing much greater respect for listening to that chorus of voices because we do get better, much better outcomes that way. >> Absolutely. I couldn't agree more and I'm happy to hear that you've been supported along your journey. I think the industry can definitely get a bad rap and there are a lot of people paving the way for us. I want to talk a little bit about supply chain because I don't know about you, but for me I don't think there were as many people talking about the industry and probably what you do, say four years ago, as are now. How did you find your way into supply chain and what is it about helping that be more efficient that excites you? >> Yes. There's nothing like a shortage of toilet paper to get people to. (Savannah laughs) Or to understand what supply chain means. And I, as tough as those times were, especially at the beginning of the pandemic and the uncertainty, it was so exciting for those of us in supply chain because suddenly people got what we did like- >> Savannah: Mm-Hm. >> And they were interested in hearing about it. So I really, I really have, we did enjoy that. I got exposed to that because ultimately I served as the Vice President of Product Management and Strategy for IBM, Sterling Supply Chain which was a very large brand within the IBM portfolio, serving over 10,000 clients worldwide, really focused on their omnichannel order management and their other supply chain processes around order to cash, procure to pay, logistics and things like that. And when you start to learn about the intricacies and that choreography needed across so many players in the value chain, it's an absolutely fascinating puzzle. And- >> Savannah: Yeah. >> Often the further away from the consumer experience you got, the more analog it became. And so the opportunity to start to digitize and transform that was really something that was very, very intriguing. And now here at Instacart, the opportunity to sort of parlay that into one of probably the most complex supply chains that there are, grocery, food just adds another level- >> Yeah. >> Of excitement intrigue to the work. >> I can only imagine there are, I'm just thinking about it right now. I'm not sure there are many supply chains, if any that touch as many lives as food does, as, I mean so is that what brought you, you joined Instacart relatively recently if I'm not mistaken, within the last year. Is that what brought you to them? Was the complexity of that global challenge? >> Absolutely. That was definitely the start of it, was so intriguing to me to see, to, the more I learned about Instacart when they approached me was also they're really changing an industry that's been very static for many, many years, right? And they're fundamentally reshaping that industry. One that's, as you said, is crucial to the everyday lives of pretty much everyone. And I was intrigued by that. But I was also intrigued by the breadth at which they're approaching this, not just the marketplace, but how we are helping retailers through our Instacart platform actually reach their consumers in ways that they like to shop whether it's online or in the store. We are also very, very committed to not just serving from a convenience standpoint, but actually improving access to healthy and nutritious food for as many people as might need that. So it just, core to the complexity of the problem the criticality of it, but also just frankly speaking to the core of who Instacart is as a company, I, it just felt like it was like a culmination of a lot of things to have this opportunity to work here. >> Sounds like a fantastic opportunity. I want to dive a little bit deeper into the technology side there. How is Instacart's technology helping grocers with varying levels of scale and geographical challenges and I'm sure a variety of other things and even a digital skillset. How are you helping them navigate their digital transformation? >> You know, this is probably one of the sectors that lags behind other retail sectors as far as digital transformation. And when the progress that's been made over the last four years is tremendous. And the road ahead is still before us is still a long way to go. I mean Instacart built the world's largest grocery marketplace, if you want to think about that. And so we have more than 10 years of experience in understanding the complexity of that. With, again a supply chain that is very, very complex. So last spring we announced the Instacart platform as a way of really putting a name to a lot of work we were already doing. And it's all about opening up the capability and the technology that we have to help grocers reach their customers directly as well as through our marketplace. So we help grocers like Publix, Wegmans, The Fresh Market just hundreds of grocers build out their own storefronts, their own mobile apps and that we are actually powering for them. We help them create some very unique fulfillment models that might serve customers or be new market opportunities. Certainly we have the traditional full service shop, but we also have virtual convenience that can enable delivery in minutes. And in certain geographies and demographics, that's, you know, really important. We are even going in the store with our connected stores technologies that we announced earlier this year, and that is everything from smart cards to scan and pay to wayfinding that it just, it's a lot of very interesting work we're doing and we're very, very fortunate to be able to partner with some of the best and brightest grocery retailers out there as well as retailers and other verticals as well. But grocery store is sort of our core. >> Yeah, I can only imagine some of the conversations that you have and the user behaviors that you get to learn about as people are on their food journey. You teased a little bit there about what's coming next. What else do you think is in our food future? >> Well, I think, you know, the pandemic pushed the grocery industry to get online to start to digitally transform itself, but we believe it's not an either or. There are virtually no one that's exclusively online and we know more and more there's no one that's exclusively you know, only in the store. We really expect to have that blend and I think as long as we're very, very savvy about understanding the, our retailers' needs as well as their customers' needs on how they can really traverse seamlessly between whether they're online or in store, how they can have an engaging experience that's consistent to the brand of the retailer. >> Savannah: Mm-Hm. >> How they can be rewarded for their loyalty. How they can be encouraged to try new things and just have a much more engaging experience with that grocer because food is a very emotional sort of buy, right? I mean, it's a very sensory rich. And so how- >> Sort of? I think you can go ahead and just make that claim. Just for a lot of people, yeah, yeah. We'll endorse that. >> You're right, yeah, it is. Right, we're passionate about our brand of this or that or we want to touch or smell or do things like that. So there's a tremendous amount of innovation you get online, like personalization and other things that you don't get when you get, you walk into the store, everybody's got the same end cap like I see the same end cap as you see and we might be very different. And then vice versa. I get a very much a sensory experience when I'm in the store, right? That I don't have, how do we blend that? And so there's some really interesting things that we're working on with our retail partners to embrace that omnichannel approach. So we create that flywheel of experience and innovation between the two. So I think you're going to see a lot more focus on an omnichannel experience that traverses between the on and the in, online and the in-store. >> Yeah, I, so I love this because you know, we, there's a continued debate around remote and in-person, working remote and in-person events, but it sounds like hybrid is here to stay when it comes to food and and how we eat, which is very exciting. Last question for you, Jeanette. What would you say to someone, a woman of any age who is looking at this video or maybe dreaming about a career in cloud technology? What's your moment of inspiration? >> You know, I think my best advice is all, you know, stay curious. Just be in love with not even just the technology for technology's sake, but what the technology can unlock as far as an experience and focus on building those experiences. Not only for your direct customer in my case, retailers, grocers, but for their customer. Trying to understand that. And I think if you can connect those dots, you know the cloud is the limit, let's put it that way. (Jeanette and Savannah laugh) >> I'll take it upon that. I love that. Jeanette Barlow, thank you so much for joining us. The team at Instacart is lucky to have you. And thank you to our audience for joining us for this special program on theCUBE featuring Women of the Cloud. My name is Savannah Peterson and I look forward to celebrating more brilliant women like Jeanette with you all soon. (upbeat, happy music)

>> From theCUBE studios in Palo Alto in Boston, bringing you data-driven insights from the cube and ETR, this is Breaking Analysis with Dave Vellante. >> Organizations have considerable room to improve their performance without making expensive changes to their talent, their structure, or their fundamental business model. You don't need a slew of consultants to tell you what to do. You already know. What you need is to immediately ratchet up expectations, energy, urgency, and intensity. You have to fight mediocrity every step of the way. Amp it up and the results will follow. This is the fundamental premise of a hard-hitting new book written by Frank Slootman, CEO of Snowflake, and published earlier this year. It's called "Amp It Up, Leading for Hypergrowth "by Raising Expectations, Increasing Urgency, "and Elevating Intensity." Hello and welcome to this week's Wikibon CUBE Insights, powered by ETR. At Snowflake Summit last month, I was asked to interview Frank on stage about his new book. I've read it several times. And if you haven't read it, you should. Even if you have read it, in this Breaking Analysis, we'll dig deeper into the book and share some clarifying insights and nuances directly from Slootman himself from my one-on-one conversation with him. My first question to Slootman was why do you write this book? Okay, it's kind of a common throwaway question. And how the heck did you find time to do it? It's fairly well-known that a few years ago, Slootman put up a post on LinkedIn with the title Amp It Up. It generated so much buzz and so many requests for Frank's time that he decided that the best way to efficiently scale and share his thoughts on how to create high-performing companies and organizations was to publish a book. Now, he wrote the book during the pandemic. And I joked that they must not have Netflix in Montana where he resides. In a pretty funny moment, he said that writing the book was easier than promoting it. Take a listen. >> Denise, our CMO, you know, she just made sure that this process wasn't going to. It was more work for me to promote this book with all these damn podcasts and other crap, than actually writing the book, you know. And after a while, I was like I'm not doing another podcast. >> Now, the book gives a lot of interesting background information on Slootman's career and what he learned at various companies that he led and participated in. Now, I'm not going to go into most of that today, which is why you should read the book yourself. But Slootman, he's become somewhat of a business hero to many people, myself included. Leaders like Frank, Scott McNealy, Jayshree Ullal, and my old boss, Pat McGovern at IDG, have inspired me over the years. And each has applied his or her own approach to building cultures and companies. Now, when Slootman first took over the reins at Snowflake, I published a Breaking Analysis talking about Snowflake and what we could expect from the company now that Slootman and CFO Mike Scarpelli were back together. In that post, buried toward the end, I referenced the playbook that Frank used at Data Domain and ServiceNow, two companies that I followed quite closely as an analyst, and how it would be applied at Snowflake, that playbook if you will. Frank reached out to me afterwards and said something to the effect of, "I don't use playbooks. "I am a situational leader. "Playbooks, you know, they work in football games. "But in the military, they teach you "situational leadership." Pretty interesting learning moment for me. So I asked Frank on the stage about this. Here's what he said. >> The older you get, the more experience that you have, the more you become a prisoner of your own background because you sort of think in terms of what you know as opposed to, you know, getting outside of what you know and trying to sort of look at things like a five-year-old that has never seen this before. And then how would you, you know, deal with it? And I really try to force myself into I've never seen this before and how do I think about it? Because at least they're very different, you know, interpretations. And be open-minded, just really avoid that rinse and repeat mentality. And you know, I've brought people in from who have worked with me before. Some of them come with me from company to company. And they were falling prey to, you know, rinse and repeat. I would just literally go like that's not what we want. >> So think about that for a moment. I mean, imagine coming in to lead a new company and forcing yourself and your people to forget what they know that works and has worked in the past, put that aside and assess the current situation with an open mind, essentially start over. Now, that doesn't mean you don't apply what has worked in the past. Slootman talked to me about bringing back Scarpelli and the synergistic relationship that they have and how they build cultures and the no BS and hard truth mentality they bring to companies. But he bristles when people ask him, "What type of CEO are you?" He says, "Do we have to put a label on it? "It really depends on the situation." Now, one of the other really hard-hitting parts of the book was the way Frank deals with who to keep and who to let go. He uses the Volkswagen tagline of drivers wanted. He says in his book, in companies there are passengers and there are drivers, and we want drivers. He said, "You have to figure out really quickly "who the drivers are and basically throw the wrong people "off the bus, keep the right people, bring in new people "that fit the culture and put them "in the right seats on the bus." Now, these are not easy decisions to make. But as it pertains to getting rid of people, I'm reminded of the movie "Moneyball." Art Howe, the manager of the Oakland As, he refused to play Scott Hatteberg at first base. So the GM, Billy Bean played by Brad Pitt says to Peter Brand who was played by Jonah Hill, "You have to fire Carlos Pena." Don't learn how to fire people. Billy Bean says, "Just keep it quick. "Tell him he's been traded and that's it." So I asked Frank, "Okay, I get it. "Like the movie, when you have the wrong person "on the bus, you just have to make the decision, "be straightforward, and do it." But I asked him, "What if you're on the fence? "What if you're not completely sure if this person "is a driver or a passenger, if he or she "should be on the bus or not on the bus? "How do you handle that?" Listen to what he said. >> I have a very simple way to break ties. And when there's doubt, there's no doubt, okay? >> When there's doubt, there's no doubt. Slootman's philosophy is you have to be emphatic and have high conviction. You know, back to the baseball analogy, if you're thinking about taking the pitcher out of the game, take 'em out. Confrontation is the single hardest thing in business according to Slootman but you have to be intellectually honest and do what's best for the organization, period. Okay, so wow, that may sound harsh but that's how Slootman approaches it, very Belichickian if you will. But how can you amp it up on a daily basis? What's the approach that Slootman takes? We got into this conversation with a discussion about MBOs, management by objective. Slootman in his book says he's killed MBOs at every company he's led. And I asked him to explain why. His rationale was that individual MBOs invariably end up in a discussion about relief of the MBO if the person is not hitting his or her targets. And that detracts from the organizational alignment. He said at Snowflake everyone gets paid the same way, from the execs on down. It's a key way he creates focus and energy in an organization, by creating alignment, urgency, and putting more resources into the most important things. This is especially hard, Slootman says, as the organization gets bigger. But if you do approach it this way, everything gets easier. The cadence changes, the tempo accelerates, and it works. Now, and to emphasize that point, he said the following. Play the clip. >> Every meeting that you have, every email, every encounter in the hallway, whatever it is, is an opportunity to amp things up. That's why I use that title. But do you take that opportunity? >> And according to Slootman, if you don't take that opportunity, if you're not in the moment, amping it up, then you're thinking about your golf game or the tennis match that's going on this weekend or being out on your boat. And to the point, this approach is not for everyone. You're either built for it or you're not. But if you can bring people into the organization that can handle this type of dynamic, it creates energy. It becomes fun. Everything moves faster. The conversations are exciting. They're inspiring. And it becomes addictive. Now let's talk about priorities. I said to Frank that for me anyway, his book was an uncomfortable read. And he was somewhat surprised by that. "Really," he said. I said, "Yeah. "I mean, it was an easy read but uncomfortable "because over my career, I've managed thousands of people, "not tens of thousands but thousands, "enough to have to take this stuff very seriously." And I found myself throughout the book, oh, you know, on the one hand saying to myself, "Oh, I got that right, good job, Dave." And then other times, I was thinking to myself, "Oh wow, I probably need to rethink that. "I need to amp it up on that front." And the point is to Frank's leadership philosophy, there's no one correct way to approach all situations. You have to figure it out for yourself. But the one thing in the book that I found the hardest was Slootman challenged the reader. If you had to drop everything and focus on one thing, just one thing, for the rest of the year, what would that one thing be? Think about that for a moment. Were you able to come up with that one thing? What would happen to all the other things on your priority list? Are they all necessary? If so, how would you delegate those? Do you have someone in your organization who can take those off your plate? What would happen if you only focused on that one thing? These are hard questions. But Slootman really forces you to think about them and do that mental exercise. Look at Frank's body language in this screenshot. Imagine going into a management meeting with Frank and being prepared to share all the things you're working on that you're so proud of and all the priorities you have for the coming year. Listen to Frank in this clip and tell me it doesn't really make you think. >> I've been in, you know, on other boards and stuff. And I got a PowerPoint back from the CEO and there's like 15 things. They're our priorities for the year. I'm like you got 15, you got none, right? It's like you just can't decide, you know, what's important. So I'll tell you everything because I just can't figure out. And the thing is it's very hard to just say one thing. But it's really the mental exercise that matters. >> Going through that mental exercise is really important according to Slootman. Let's have a conversation about what really matters at this point in time. Why does it need to happen? And does it take priority over other things? Slootman says you have to pull apart the hairball and drive extraordinary clarity. You could be wrong, he says. And he admits he's been wrong on many things before. He, like everyone, is fearful of being wrong. But if you don't have the conversation according to Slootman, you're already defeated. And one of the most important things Slootman emphasizes in the book is execution. He said that's one of the reasons he wrote "Amp It Up." In our discussion, he referenced Pat Gelsinger, his former boss, who bought Data Domain when he was working for Joe Tucci at EMC. Listen to Frank describe the interaction with Gelsinger. >> Well, one of my prior bosses, you know, Pat Gelsinger, when they acquired Data Domain through EMC, Pat was CEO of Intel. And he quoted Andy Grove as saying, 'cause he was Intel for a long time when he was younger man. And he said no strategy is better than its execution, which if I find one of the most brilliant things. >> Now, before you go changing your strategy, says Slootman, you have to eliminate execution as a potential point of failure. All too often, he says, Silicon Valley wants to change strategy without really understanding whether the execution is right. All too often companies don't consider that maybe the product isn't that great. They will frequently, for example, make a change to sales leadership without questioning whether or not there's a product fit. According to Slootman, you have to drive hardcore intellectual honesty. And as uncomfortable as that may be, it's incredibly important and powerful. Okay, one of the other contrarian points in the book was whether or not to have a customer success department. Slootman says this became really fashionable in Silicon Valley with the SaaS craze. Everyone was following and pattern matching the lead of salesforce.com. He says he's eliminated the customer service department at every company he's led which had a customer success department. Listen to Frank Slootman in his own words talk about the customer success department. >> I view the whole company as a customer success function. Okay, I'm customer success, you know. I said it in my presentation yesterday. We're a customer-first organization. I don't need a department. >> Now, he went on to say that sales owns the commercial relationship with the customer. Engineering owns the technical relationship. And oh, by the way, he always puts support inside of the engineering department because engineering has to back up support. And rather than having a separate department for customer success, he focuses on making sure that the existing departments are functioning properly. Slootman also has always been big on net promoter score, NPS. And Snowflake's is very high at 72. And according to Slootman, it's not just the product. It's the people that drive that type of loyalty. Now, Slootman stresses amping up the big things and even the little things too. He told a story about someone who came into his office to ask his opinion about a tee shirt. And he turned it around on her and said, "Well, what do you think?" And she said, "Well, it's okay." So Frank made the point by flipping the situation. Why are you coming to me with something that's just okay? If we're going to do something, let's do it. Let's do it all out. Let's do it right and get excited about it, not just check the box and get something off your desk. Amp it up, all aspects of our business. Listen to Slootman talk about Steve Jobs and the relevance of demanding excellence and shunning mediocrity. >> He was incredibly intolerant of anything that he didn't think of as great. You know, he was immediately done with it and with the person. You know, I'm not that aggressive, you know, in that way. I'm a little bit nicer, you know, about it. But I still, you know, I don't want to give into expediency and mediocrity. I just don't, I'm just going to fight it, you know, every step of the way. >> Now, that story was about a little thing like some swag. But Slootman talked about some big things too. And one of the major ways Snowflake was making big, sweeping changes to amp up its business was reorganizing its go-to-market around industries like financial services, media, and healthcare. Here's some ETR data that shows Snowflake's net score or spending momentum for key industry segments over time. The red dotted line at 40% is an indicator of highly elevated spending momentum. And you can see for the key areas shown, Snowflake is well above that level. And we cut this data where responses were greater, the response numbers were greater than 15. So not huge ends but large enough to have meaning. Most were in the 20s. Now, it's relatively uncommon to see a company that's having the success of Snowflake make this kind of non-trivial change in the middle of steep S-curve growth. Why did they make this move? Well, I think it's because Snowflake realizes that its data cloud is going to increasingly have industry diversity and unique value by industry, that ecosystems and data marketplaces are forming around industries. So the more industry affinity Snowflake can create, the stronger its moat will be. It also aligns with how the largest and most prominent global system integrators, global SIs, go to market. This is important because as companies are transforming, they are radically changing their data architecture, how they think about data, how they approach data as a competitive advantage, and they're looking at data as specifically a monetization opportunity. So having industry expertise and knowledge and aligning with those customer objectives is going to serve Snowflake and its ecosystems well in my view. Slootman even said he joined the board of Instacart not because he needed another board seat but because he wanted to get out of his comfort zone and expose himself to other industries as a way to learn. So look, we're just barely scratching the surface of Slootman's book and I've pulled some highlights from our conversation. There's so much more that I can share just even from our conversation. And I will as the opportunity arises. But for now, I'll just give you the kind of bumper sticker of "Amp It Up." Raise your standards by taking every opportunity, every interaction, to increase your intensity. Get your people aligned and moving in the same direction. If it's the wrong direction, figure it out and course correct quickly. Prioritize and sharpen your focus on things that will really make a difference. If you do these things and increase the urgency in your organization, you'll naturally pick up the pace and accelerate your company. Do these things and you'll be able to transform, better identify adjacent opportunities and go attack them, and create a lasting and meaningful experience for your employees, customers, and partners. Okay, that's it for today. Thanks for watching. And thank you to Alex Myerson who's on production and he manages the podcast for Breaking Analysis. Kristin Martin and Cheryl Knight help get the word out on social and in our newsletters. And Rob Hove is our EIC over at Silicon Angle who does some wonderful and tremendous editing. Thank you all. Remember, all these episodes are available as podcasts. Wherever you listen, just search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com. And you can email me at david.vellante@siliconangle.com or DM me @dvellante or comment on my LinkedIn posts. And please do check out etr.ai for the best survey data in enterprise tech. This is Dave Vellante for theCUBE Insights, powered by ETR. Thanks for watching. Be well. And we'll see you next time on Breaking Analysis. (upbeat music)

(upbeat music) >> Hello, everyone, welcome to theCUBE's presentation of the AWS Startup Showcase. We're in Season two, Episode three, and this is the topic of MarTech and the Emerging Cloud-Scale Customer Experiences, the ongoing coverage of AWS's ecosystem of large scale growth and new companies and growing companies. I'm your host, John Furrier. We're excited to have Tim Barnes, Global Director, General Manager of Advertiser and Marketing at AWS here doing the keynote cloud-scale customer experience. Tim, thanks for coming on. >> Oh, great to be here and thank you for having me. >> You've seen many cycles of innovation, certainly in the ad tech platform space around data, serving consumers and a lot of big, big scale advertisers over the years as the Web 1.0, 2.0, now 3.0 coming, cloud-scale, roll of data, all big conversations changing the game. We see things like cookies going away. What does this all mean? Silos, walled gardens, a lot of new things are impacting the applications and expectations of consumers, which is also impacting the folks trying to reach the consumers. And this is kind of creating a kind of a current situation, which is challenging, but also an opportunity. Can you share your perspective of what this current situation is, as the emerging MarTech landscape emerges? >> Yeah, sure, John, it's funny in this industry, the only constant has changed and it's an ever-changing industry and never more so than right now. I mean, we're seeing with whether it's the rise of privacy legislation or just breach of security of data or changes in how the top tech providers and browser controllers are changing their process for reaching customers. This is an inflection point in the history of both ad tech and MarTech. You hit the nail on the head with cookie deprecation, with Apple removing IDFA, changes to browsers, et cetera, we're at an interesting point. And by the way, we're also seeing an explosion of content sources and ability to reach customers that's unmatched in the history of advertising. So those two things are somewhat at odds. So whether we see the rise of connected television or digital out of home, you mentioned Web 3.0 and the opportunities that may present in metaverse, et cetera, it's an explosion of opportunity, but how do we continue to connect brands with customers and do so in a privacy compliant way? And that's really the big challenge we're facing. One of the things that I see is the rise of modeling or machine learning as a mechanism to help remove some of these barriers. If you think about the idea of one-to-one targeting, well, that's going to be less and less possible as we progress. So how am I still as a brand advertiser or as a targeted advertiser, how am I going to still reach the right audience with the right message in a world where I don't necessarily know who they are. And modeling is a really key way of achieving that goal and we're seeing that across a number of different angles. >> We've always talked about on the ad tech business for years, it's the behemoth of contextual and behavioral, those dynamics. And if you look at the content side of the business, you have now this new, massive source of new sources, blogging has been around for a long time, you got video, you got newsletters, you got all kinds of people, self-publishing, that's been around for a while, right? So you're seeing all these new sources. Trust is a big factor, but everyone wants to control their data. So this walled garden perpetuation of value, I got to control my data, but machine learning works best when you expose data, so this is kind of a paradox. Can you talk about the current challenge here and how to overcome it because you can't fight fashion, as they say, and we see people kind of going down this road as saying, data's a competitive advantage, but I got to figure out a way to keep it, own it, but also share it for the machine learning. What's your take on that? >> Yeah, I think first and foremost, if I may, I would just start with, it's super important to make that connection with the consumer in the first place. So you hit the nail on the head for advertisers and marketers today, the importance of gaining first party access to your customer and with permission and consent is paramount. And so just how you establish that connection point with trust and with very clear directive on how you're going to use the data has never been more important. So I would start there if I was a brand advertiser or a marketer, trying to figure out how I'm going to better connect with my consumers and get more first party data that I could leverage. So that's just building the scale of first party data to enable you to actually perform some of the types of approaches we'll discuss. The second thing I would say is that increasingly, the challenge exists with the exchange of the data itself. So if I'm a data control, if I own a set of first party data that I have consent with consumers to use, and I'm passing that data over to a third party, and that data is leaked, I'm still responsible for that data. Or if somebody wants to opt out of a communication and that opt out signal doesn't flow to the third party, I'm still liable, or at least from the consumer's perspective, I've provided a poor customer experience. And that's where we see the rise of the next generation, I call it of data clean rooms, the approaches that you're seeing, a number of customers take in terms of how they connect data without actually moving the data between two sources. And we're seeing that as certainly a mechanism by which you can preserve accessibility data, we call that federated data exchange or federated data clean rooms and I think you're seeing that from a number of different parties in the industry. >> That's awesome, I want to get into the data interoperability because we have a lot of startups presenting in this episode around that area, but why I got you here, you mentioned data clean room. Could you define for us, what is a federated data clean room, what is that about? >> Yeah, I would simply describe it as zero data movement in a privacy and secure environment. To be a little bit more explicit and detailed, it really is the idea that if I'm a party A and I want to exchange data with party B, how can I run a query for analytics or other purposes without actually moving data anywhere? Can I run a query that has accessibility to both parties, that has the security and the levels of aggregation that both parties agree to and then run the query and get those results sets back in a way that it actually facilitates business between the two parties. And we're seeing that expand with partners like Snowflake and InfoSum, even within Amazon itself, AWS, we have data sharing capabilities within Redshift and some of our other data-led capabilities. And we're just seeing explosion of demand and need for customers to be able to share data, but do it in a way where they still control the data and don't ever hand it over to a third party for execution. >> So if I understand this correctly, this is kind of an evolution to kind of take away the middleman, if you will, between parties that used to be historically the case, is that right? >> Yeah, I'd say this, the middleman still exists in many cases. If you think about joining two parties' data together, you still have the problem of the match key. How do I make sure that I get the broadest set of data to match up with the broadest set of data on the other side? So we have a number of partners that provide these types of services from LiveRamp, TransUnion, Experian, et cetera. So there's still a place for that so-called middleman in terms of helping to facilitate the transaction, but as a clean room itself, I think that term is becoming outdated in terms of a physical third party location, where you push data for analysis, that's controlled by a third party. >> Yeah, great clarification there. I want to get into this data interoperability because the benefits of AWS and cloud scales we've seen over the past decade and looking forward is, it's an API based economy. So APIs and microservices, cloud native stuff is going to be the key to integration. And so connecting people together is kind of what we're seeing as the trend. People are connecting their data, they're sharing code in open source. So there's an opportunity to connect the ecosystem of companies out there with their data. Can you share your view on this interoperability trend, why it's important and what's the impact to customers who want to go down this either automated or programmatic connection oriented way of connecting data. >> Never more important than it has been right now. I mean, if you think about the way we transact it and still too today do to a certain extent through cookie swaps and all sorts of crazy exchanges of data, those are going away at some point in the future; it could be a year from now, it could be later, but they're going away. And I think that that puts a great amount of pressure on the broad ecosystem of customers who transact for marketers, on behalf of marketers, both for advertising and marketing. And so data interoperability to me is how we think about providing that transactional layer between multiple parties so that they can continue to transact in a way that's meaningful and seamless, and frankly at lower cost and at greater scale than we've done in the past with less complexity. And so, we're seeing a number of changes in that regard, whether that's data sharing and data clean rooms or federated clean rooms, as we described earlier, whether that's the rise of next generation identity solutions, for example, the UID 2.0 Consortium, which is an effort to use hashed email addresses and other forms of identifiers to facilitate data exchange for the programmatic ecosystem. These are sort of evolutions based on this notion that the old world is going away, the new world is coming, and part of that is how do we connect data sources in a more seamless and frankly, efficient manner. >> It's almost interesting, it's almost flipped upside down, you had this walled garden mentality, I got to control my data, but now I have data interoperability. So you got to own and collect the data, but also share it. This is going to kind of change the paradigm around my identity platforms, attributions, audience, as audiences move around, and with cookies going away, this is going to require a new abstraction, a new way to do it. So you mentioned some of those standards. Is there a path in this evolution that changes it for the better? What's your view on this? What do you see happening? What's going to come out of this new wave? >> Yeah, my father was always fond of telling me, "The customer, my customers is my customer." And I like to put myself in the shoes of the Marc Pritchards of the world at Procter & Gamble and think, what do they want? And frankly, their requirements for data and for marketing have not changed over the last 20 years. It's, I want to reach the right customer at the right time, with the right message and I want to be able to measure it. In other words, summarizing, I want omnichannel execution with omnichannel measurement, and that's become increasingly difficult as you highlighted with the rise of the walled gardens and increasingly data living in silos. And so I think it's important that we, as an industry start to think about what's in the best interest of the one customer who brings virtually 100% of the dollars to this marketplace, which is the CMO and the CMO office. And how do we think about returning value to them in a way that is meaningful and actually drives its industry forward. And I think that's where the data operability piece becomes really important. How do we think about connecting the omnichannel channels of execution? How do we connect that with partners who run attribution offerings with machine learning or partners who provide augmentation or enrichment data such as third party data providers, or even connecting the buy side with the sell side in a more efficient manner? How do I make that connection between the CMO and the publisher in a more efficient and effective way? And these are all challenges facing us today. And I think at the foundational layer of that is how do we think about first of all, what data does the marketer have, what is the first party data? How do we help them ethically source and collect more of that data with proper consent? And then how do we help them join that data into a variety of data sources in a way that they can gain value from it. And that's where machine learning really comes into play. So whether that's the notion of audience expansion, whether that's looking for some sort of cohort analysis that helps with contextual advertising, whether that's the notion of a more of a modeled approach to attribution versus a one-to-one approach, all of those things I think are in play, as we think about returning value back to that customer of our customer. >> That's interesting, you broke down the customer needs in three areas; CMO office and staff, partners ISV software developers, and then third party services. Kind of all different needs, if you will, kind of tiered, kind of at the center of that's the user, the consumer who have the expectations. So it's interesting, you have the stakeholders, you laid out kind of those three areas as to customers, but the end user, the consumer, they have a preference, they kind of don't want to be locked into one thing. They want to move around, they want to download apps, they want to play on Reddit, they want to be on LinkedIn, they want to be all over the place, they don't want to get locked in. So you have now kind of this high velocity user behavior. How do you see that factoring in, because with cookies going away and kind of the convergence of offline-online, really becoming predominant, how do you know someone's paying attention to what and when attention and reputation. All these things seem complex. How do you make sense of it? >> Yeah, it's a great question. I think that the consumer as you said, finds a creepiness factor with a message that follows them around their various sources of engagement with content. So I think at first and foremost, there's the recognition by the brand that we need to be a little bit more thoughtful about how we interact with our customer and how we build that trust and that relationship with the customer. And that all starts with of course, opt-in process consent management center but it also includes how we communicate with them. What message are we actually putting in front of them? Is it meaningful, is it impactful? Does it drive value for the customer? I think we've seen a lot of studies, I won't recite them that state that most consumers do find value in targeted messaging, but I think they want it done correctly and there in lies the problem. So what does that mean by channel, especially when we lose the ability to look at that consumer interaction across those channels. And I think that's where we have to be a little bit more thoughtful with frankly, kind of going back to the beginning with contextual advertising, with advertising that perhaps has meaning, or has empathy with the consumer, perhaps resonates with the consumer in a different way than just a targeted message. And we're seeing that trend, we're seeing that trend both in television, connected television as those converge, but also as we see about connectivity with gaming and other sort of more nuanced channels. The other thing I would say is, I think there's a movement towards less interruptive advertising as well, which kind of removes a little bit of those barriers for the consumer and the brand to interact. And whether that be dynamic product placement, content optimization, or whether that be sponsorship type opportunities within digital. I think we're seeing an increased movement towards those types of executions, which I think will also provide value to both parties. >> Yeah, I think you nailed it there. I totally agree with you on the contextual targeting, I think that's a huge deal and that's proven over the years of providing benefit. People, they're trying to find what they're looking for, whether it's data to consume or a solution they want to buy. So I think that all kind of ties together. The question is these three stakeholders, the CMO office and staff you mentioned, and the software developers, apps, or walled gardens, and then like ad servers as they come together, have to have standards. And so, I think to me, I'm trying to squint through all the movement and the shifting plates that are going on in the industry and trying to figure out where are the dots connecting? And you've seen many cycles of innovation at the end of the day, it comes down to who can perform best for the end user, as well as the marketers and advertisers, so that balance. What's your view on this shift? It's going to land somewhere, it has to land in the right area, and the market's very efficient. I mean, this ad market's very efficient. >> Yeah, I mean, in some way, so from a standards perspective, I support and we interact extensively with the IB and other industry associations on privacy enhancing technologies and how we think about these next generations of connection points or identifiers to connect with consumers. But I'd say this, with respect to the CMO, and I mentioned the publisher earlier, I think over the last 10 years with the rise of programmatic, certainly we saw the power reside mostly with the CMO who was able to amass a large pool of cookies or purchase a large sort of cohort of customers with cookie based attributes and then execute against that. And so almost a blind fashion to the publisher, the publisher was sort of left to say, "Hey, here's an opportunity, do you want to buy it or not?" With no real reason why the marketer might be buying that customer? And I think that we're seeing a shift backwards towards the publisher and perhaps a healthy balance between the two. And so, I do believe that over time, that we're going to see publishers provide a lot more, what I might almost describe as mini walled gardens. So the ability, great publisher or a set of publishers to create a cohort of customers that can be targeted through programmatic or perhaps through programmatic guaranteed in a way that it's a balance between the two. And frankly thinking about that notion of federated data clean rooms, you can see an approach where publishers are able to share their first party data with a marketer's first party data, without either party feeling like they're giving up something or passing all their value over to the other. And I do believe we're going to see some significant technology changes over the next three to four years. That really rely on that interplay between the marketer and the publisher in a way that it helps both sides achieve their goals, and that is, increasing value back to the publisher in terms of higher CPMs, and of course, better reach and frequency controls for the marketer. >> I think you really brought up a big point there we can maybe follow up on, but I think this idea of publishers getting more control and power and value is an example of the market filling a void and the power log at the long tail, it's kind of a straight line. Then it's got the niche kind of communities, it's growing in the middle there, and I think the middle of the torso of that power law is the publishers because they have all the technology to measure the journeys and the click throughs and all this traffic going on their platform, but they just need to connect to someone else. >> Correct. >> That brings in the interoperability. So, as a publisher ourselves, we see that long tail getting really kind of fat in the middle where new brands are going to emerge, if they have audience. I mean, some podcasts have millions of users and some blogs are attracting massive audience, niche audiences that are growing. >> I would say, just look at the rise of what we might not have considered publishers in the past, but are certainly growing as publishers today. Customers like Instacart or Uber who are creating ad platforms or gaming, which of course has been an ad supported platform for some time, but is growing immensely. Retail as a platform, of course, amazon.com being one of the biggest retail platforms with advertising supported models, but we're seeing that growth across the board for retail customers. And I think that again, there's never been more opportunities to reach customers. We just have to do it the right way, in the way that it's not offensive to customers, not creepy, if you want to call it that, and also maximizes value for both parties and that be both the buy and the sell side. >> Yeah, everyone's a publisher and everyone's a media company. Everyone has their own news network, everyone has their own retail, it's a completely new world. Tim, thanks for coming on and sharing your perspective and insights on this key note, Tim Barnes, Global Director, General Manager of Advertiser and Market at AWS here with the Episode three of Season two of the AWS Startup Showcase. I'm John Furrier, thanks for watching. (upbeat music)

>>Okay, welcome back everyone. To the cubes coverage of AWS summit, 2022 in San Francisco, California. We're here, live on the floor at the Mosconi south events are back. I'm John fur, your host. Remember AWS summit 2022 in New York city is coming this summer. We'll be there with the live cube there as well. Look for us there, but of course, we're back in action with the cloud and AWS. Our next guest Garrett Miller is the general manager of navigation at Mapbox. I mean, it's been a Amazon customer for a long time, Garrett. Thanks for coming on the cube. >>Yeah. Thanks for having us, John. >>So you guys are in the middle of, I love the whole location base slash we developer integration. We've had many conversations on the cube around how engineers and developers are becoming embedded into the application, whether it's from a security standpoint, biometrics, all kinds of stuff, being built into the app and, and location navigation. That's right. Is huge from cars. Everyone knows their car, car map. That's right. GPS satellites, some space it's complicated. It sounds like it sounds easy, but I know it's hard. Yeah. You, you get the keynote going on today. Give us a quick update on Mapbox and we'll then we'll talk about the keynote. >>Yeah. You bet John that's right. So as you were saying, you know, it really is. It's all about location intelligence. And how does that get embedded into the applications? And to the point you made vehicles that are out there on the roads to today. So we target developers. Those are our key customers, and we've got over three and a half million registered on the platform today. They consume the modules that we build with APIs, SDKs, data sets, and more and more applications to accomplish whatever those location needs might be. >>Why we appreciate you coming on. You are featured keynote by presenter here at summit, which means Amazon thinks you're super important to share. I'll say your customer. So you, I know you've been a customer for a long time as a company, but what was your keynote about what was the main theme? The developers were all here. You got the builders. What was your content? What did you present this morning on the keynote? Yeah, >>Well, this morning we really talked a lot about logistics and the, this story that we told was know in the logistics industry, there is a massive movement to shorter and shorter delivery windows. And so the, the, the story that we told is really around a 10 minute delivery. Now, have you ever wondered how you get a 10 minute delivery? You, you place an order on your phone and all of a sudden somebody shows up at your front doorstep. You ever wonder about that? >><laugh> >>Some shows supply >>Chain. Someone's waiting in the wings from my call. >>Yeah. >>Yeah. Well, that's right. >>John's about to order sometime soon. That's right. You ready? That's right. Do all these assets. That's >>Right. They're all ready for you. But there's actually a tremendous amount that actually goes into that. And so it really starts with designing the right distribution system up front. And so we've got tools and, and applications and, and APIs that support that. And really it, every single step of the way, location is a critical aspect to making that delivery happen and getting it to a customer's doorstep in 10 minutes or less. And so how are you understanding the real time road graph that underlies a, a, a driver going from perhaps a dark store, dark kitchen to getting in, excuse me, in front of a customer in 10 minutes with hot food. >>I mean, this is a big point. I was just joking about waiting for me, you know, that, but the point is, is that it's not obvious, but it sounds really hard. I know it's hard because to have that delivery, a lot of things have to happen. It's not just knowing location. >>That's exactly >>Right. So can you just UN pull appeal back the covers on that? What's going on there? >>Yeah. So imagine this is, is, it really starts, as I was saying with designing that distribution system and it's putting in place where you might not expect it, it's actually putting in place a retail store, but these aren't any retail stores, right? These are dark stores. These are dark kitchens that are strategically placed as close as possible, the customer density, so that you can actually shorten that window as much as possible to get you whatever that order might be. But from there, it actually goes quite a bit further when an order actually comes in, you've gotta be able to understand how do I sign an, a driver to get that order to the customer in that, in that very short period of time, more often than not, it's getting it to the driver that can get there the fastest, once you've got the right driver identified, how are you actually then going to enable them to get from point a to B to get that order. >>And then perhaps from B to C to get to your front door, being able to do turn by turn navigation that reflects everything. That's how happening in the real world to be able to get there on a timely way is critical. And then wrapping around that actually the, the, the end customer's experience your experience with how are you placing that order? Yeah. How are you using Mapbox services to do that? How are you being able to track on your application and say, well, you know, great, I expect 10 minutes and they're five minutes away. Are you gonna show up our APIs and SDKs power? That experience, >>I wanna get into the product in a second, but you brought up something I think's important to highlight. One is dark kitchens, dark stores. That's right. Okay. Term people may or may not have heard of, we all have experience in COVID going to our favorite restaurant, which has been kind of downsized because of the COVID and we're waiting for our food. And someone comes in from another delivery ever standing in line next was just pick something up. I mean, they're going through the front door. That's like the, the, the branded store. So, so is it right to say that dark kitchens are essentially replicas of the store to minimize that traffic, but, but also to be efficient for something else that's right. >>It actually even goes further than that. There are many restaurant brands. Now, it only exists as a brand. They don't have a restaurant that you can go to and sit down and have that meal. They actually only operate dark kitchens to, to serve that demand of people wanting to order up, Hey, I want my food. I want it. Now, those brands exist to serve that need. >>All right. So great for the definition, we just define dark kitchens, dark stores, but also brings, I wanna get your reaction to this before we get into the product, cuz this is a trend that's right. This is not like a one off thing. That's right. It's not a pulled forward TA a market that was COVID enabled. This is actually a user experience inflection point. That's >>Right. >>Can you share your vision on what this means? Because there's mobile ordering, there's the dynamics of the kitchens as a supplier of something in stores. So that's content or a product that's being delivered to a consumer via of the web. So now there's gotta be a whole nother reef factoring of the operating environment. Now that's what's happening is that might get that >>Right? No, that's exactly right. And even if you step back, even further and you, you think about the, the journey that the logistics industry has been on, it used to be that two days was that really exciting delivery time. Right. And everybody got it super excited. Then it became next day. Then it became same day and now it's become 10 minutes. And even some companies are out there offering seven minute deliveries, right. And in order to do that, you've gotta completely retool your business. So you can think the logistics and industry really existing on two continuums, you've got pre-planned deliveries on one hand and on-demand deliveries on the other. And there are two parallel distribution systems and ecosystems and industries really springing up to serve those different types of demand. >>So you've been on Amazon web services customer for how many years, >>Since 2013 in our founding. And you know, actually we're really proud to say that we were born on Amazon and we have scaled on Amazon. >>How are they helping you scale? What are they doing to help you? >>Well, I'd say just about everything. And if you think about their, the, the services that Amazon provides for us, they power every single step of our business along the way. And so I'll give you an example. We can walk through that with some of the tech technology. I, if you think about again, how do you get 10 minutes? You gotta have a pretty precise understanding of what's going on in the real world. And so to do that, it, for us, it all starts with collecting billions and billions of data points from sensors that are out there in the world. We stream that into our technology stack, starting at the very beginning with Amazon ESIS. And so that's just the start. But then that feeds into our entire technology stack that all runs on site on top of AWS, to where we're running our own AI models that we use Amazon SageMaker to make, and then stream it back out to our AP, through our APIs, to our se Ks and applications that sit on the edge again, all leveraging Amazon technology. >>Well, I think this is a great use case and I'll get back into the, the Mapbox a second, but Amazon, you guys are executing what I call the super cloud vision, which is snowflake you guys building on their CapX leverage. You're building a super cloud on your own. It's your app, it's your cloud. >>That's right. That's right. So if you, again, if you think about it, you know, and actually just stepping back for a moment, tell about Mapbox for a second is what, what Mapbox can do is provide the most accurate digital representation of the physical world. Think about the Mapbox technology, delivering the most accurate digital twin of mother earth, right? That's what we do. And the way that we do that is to consume, as I said earlier, vast amounts of data, we've got powerful AI that structures that data, and then really robust and scalable infrastructure that underpins all of that. Now the benefit of working with a company like AWS is that they take care of that third point. Yeah. Which means we get to go focus on the first two, which is how we differentiate and build our >>Business. And that's exactly the model of how you win in the cloud. In my opinion, that's the go big piece, the go and there's tools that fit in white spaces. But that's the, I think that's the right formula. Let's get back to Mac boxer. I know you've got news. You got the, the, uh, Mapbox fleet SDK. You announced, I wanna hold on that we'll get to in a second, let's get back to what you got are providing that example as you have this new operating environment of how delivery and, and supply chain and that's example, I want to know how tech your technology is making all that work. Because I was just talking to someone last night about this web van was web 1.0 and crash never was successful. Instacart is kind of hurting. So maybe they're optimized. You could save them. I mean, cuz the economics gotta work. If you don't have the underlying system built, that might fail. So there'll be probably the third version that gets it. Right. Maybe at Mapbox again, I'm speculating, but I'll let you talk. Yeah. How does Mapbox solve the, that problem? >>You know, it's interesting if you come back to that, that, that analogy we're using with AWS and how do you use AWS to win in the cloud? It's the same story for Mapbox of how do you win in the location industry? And what we do is provide those same tool sets of APIs and SDKs, the thing go power, those logistics companies like an Instacart, who's a great customer of ours to run in their logistics business on top of it again, it's all about how do you provide technology that allows your customers yeah. To focus on what matters from a differentiation perspective as they focus on building their >>Business and you think your economics is gonna enable these people to be successful >>100%. That's >>The goal >>100%. >>All right. So another thing that I wanna bring up is the fleet SDK, what was the, that you announced they can, you just quickly share the news on what this >>Is? Yeah, yeah, absolutely. I appreciate that, John. Yeah. So today on the Eve of earth day, we're very excited to announce Mapbox fleet going into, uh, our beta launch and what Mapbox fleet is, is, uh, a set of tools and application that allows our customers to more efficiently route their vehicles, which means lowering their fuel consumption. And at the same time, more efficiently dispatching those vehicles, which means that you can get more done with fewer assets, essentially. How do you get more packages onto a single vehicle to get those to the customers? Now you may be watching the news and understanding, yeah, there's a tremendous explosion of delivery going. Yeah. And that's fantastic. Right? That's great business for our logistics customers. Good business for us too. What's happening though, is that as those volumes are ballooning, everybody's all of a sudden, really looking at their cost structures and trying to understand how do I manage this? >>Right. I have efficiency targets as a business. Maybe I've been really focused on customer acquisition. Now it's time to flip the model and really understand in the economics of profitable growth. We help with that, with that focus on efficiency, but the double edged sword with growth and, and you know, running a logistics business is that you actually have a tremendous amount of carbon emissions that are associated with that. Yeah. For a car to show up or a truck to show up, to deliver something to your house, their emissions associated with that. So what we find is that we're able to not only drive dollar savings for our customers, but also as part of that, with the efficiency angle, really help to drive down the overall carbon impact in the carbon footprint of what they do. And >>How do you do that? >>Well, it's the efficiency lens, right? So if somebody is driving 20%, fewer miles, they're going to emit 20% fewer. Okay. >>Gotcha. So it's a numbers game across the board with actual measurement. That's exactly right. Built in and say optimization paths, all kinds of navigation. >>That's exactly right. So embedded within Mapbox fleet application are those optimization algorithm. >>So you got a platform that's setting up for the next level delivery system slash new way to connect people to goods and services and other things getting from point a to point B, you got the sustainability check you're in the cloud, born in the cloud cloud scale. I gotta a, I can't go without asking if you're on Amazon, you do all this cool stuff. There's gotta be a machine learning an AI angle. So what is that? Yeah, absolutely. >>Absolutely. AB yeah. You know, <laugh> guilty as charged. >>I would say >>John. Uh, so look, I >>Think, I mean AI and, and sustainability are gonna be, I think filings in my, in the future we be talking about on the cube, if you're not an AI company or, and doing force for good stuff, I think there's gonna be mandatory requirements on those. >>I couldn't three more. I think the ESG agenda is an incredibly important one. One that's core to Mapbox has been since the founding of the company back in 2013. Uh, but if you look at how does AI and ML fit into Mapbox, it does that in a number of different ways. And really when we come back to this idea of Mapbox creating a digital twin of the earth, the way that we do that, it is through ingesting a tremendous amount of sensor data. Right? You can imagine, uh, Mapbox customers on any given week drive, billions of miles, we're capturing all of that telemetry data to understand and make sense of what does that mean for, for, for the world that allows us to push in any given day 700,000 updates to our underly, your location technology stack, and at the same time provide insights as to exactly what's happening. Are there roadside incidents? Are there, are there issues with traffic? So by collecting all of that data, we run incredibly powerful AI models on top of it that allow us to create the, the real world representation of what's happening. That's exactly how >>It works. What, what, as they say in the, um, big data AI world is you guys have a tremendous observation space. You're looking at a lot of surface area data that's exactly right. Across multiple workloads and apps. That's >>Exactly >>Right. You can connect those dots with the right AI. >>That's exactly right. That's exactly right. And I think I, you know, coming back to your point around sustainability, I do think that the AI and ML capabilities that are being delivered are going to be paramount to that. It being such a fundamental aspect to what am, to what Mapbox does as a business allows us to launch these game changing solutions like Mapbox fleet and staying on that, that kind of environmental and sustainable kick for a second. Just last week, we launched our, our EV routing API that powers the next generation of EVs. So AI ML sustainability. If it's not core business today, it's gotta very quickly become core. >>It's really interesting. I really think what we're teasing out here and it's fun to talk about it now because we'll look back at it later 10 years or more and say, wow, this is completely refactored the industry and lives and the planet ultimately. Right. So this is a, a kind of got force for good built into the system natively. That's >>Right. That's >>That's interesting, Garrett, thanks so much for sharing the story. Give you the last word, share with the audience, what you guys are up to, what you're promoting, what you're looking for. Are you hiring, uh, is there a call to action? You wanna share? Give the plug for the company? Yeah, >>Absolutely hiring like crazy come join Mapbox and BU build the future of geolocation and intelligent location services with us. Uh, the, thanks so much for the time, >>John. Thanks for coming on cube coverage here in San Francisco, California Mosconi center back at live events. I'm John for host cube stayed with us as day two wraps down. Remember New York city. This summer will be there as well. Cube coverage of more cloud coverage events are back. Thanks for watching.

[Music] welcome everyone to thecube's presentation of the aws startup showcase data as code this is season two episode two of our ongoing series covering exciting startups in the aws ecosystem to talk about data and analytics i'm your host lisa martin i have a cube alumni here with me socket sarah the ceo and founder of nexla he's here to talk about a future of automated data engineering socket welcome back great to see you lisa thank you for having me pleasure to be here again let's dig into nexla's mission ready to use data in the hands of every user what does that mean that means that you know every organization what what are they trying to do with data they want to make use of data they want to make decisions from data they want to make data a part of their business right the challenge is that every function in an organization today needs to leverage data whether it is finance whether it is hr whether it is marketing sales or product the problem for companies is that for each of these users into each of these teams the data is not ready for them to use as it is there is a lot that goes on before the data can be in their hands and it's in the tools that they like to work with and that's where a lot of data engineering happens today i would say that is by far one of the biggest bottlenecks today for companies in accelerating their business and being you know truly data-driven so talk to me about what makes nexla unique when you're in customer conversations as every company these days in every industry has to be a data company what do you tell them about what differentiates you yeah one of the biggest challenges out there is that the variety of data that companies work with is growing tremendously you know every sas application you use becomes a data source every type of database every type of user event anything can be a source of data now it is a tremendous engineering challenge for companies to make the data usable and the biggest challenge there is people companies just cannot have enough people to write that code to make the data engineering happen and where we come in with a very unique value is how to start thinking about making this whole process much faster much more automated at the end of the day lisa time to value and time to results is by far the number one thing on top of mind for customers time to value is critical we're all thin on patients these days whether we're in our consumerizer our business lives but being able to get access to data to make intelligent decisions whether it's on something that you're going to buy or a product or service you're going to deliver is really critical give me a snapshot of some of the users of nexla yeah the users of nexla are actually across different industries one of the main one of the interesting things is that the data challenges whether you are in financial services whether you are in retail e-commerce whether you are in healthcare they are very similar is basically getting connected to all these data systems and having the data now what people do with the data is very specific to their industry so for example within the e-commerce world or retail world itself you know companies from the likes of bed bath beyond and forever 21 and poshmark which are retailers or e-commerce companies they use nexla today to bring a lot of data in uh so do delivery companies like dodash and instacart and you know so do for example logistics providers like you know narwhal or customer loyalty and customer data companies like yacht pro so across the board for example just in retail we cover a whole bunch of companies got it now let's dig into you're here to talk about the future of automated data engineering talk to me about data engineering what is it let's define it and crack it open yeah um data engineering is i would say by far one of the hottest areas of work today the one of the hardest people to hire if you're looking for one data engineering is basically um all the code you know the process and the people that is basically connecting to their system so just to give a very practical example right for um for somebody in e-commerce let's say a take-off case of door dash right it's extremely important for them to have data as to which stores have what products what is available is this something they can list for people to go and buy is this something that they can therefore deliver right this is data that changes all the time now imagine them getting data from hundreds of different merchants across the board so it is the task of data engineering to then consume that data from all these different places different formats different apis different systems and then somehow unify all the data so that it can be used by the applications that they are building so data engineering in this case becomes taking data from different places and making it useful again back to what i was talking about ready to use data it is a lot of code it's a lot of people not just that it is something that runs every single day so it means it has monitoring it has reliability um it has performance it has every aspect of engineering as we know going into it you mentioned it's a hot topic which it is but it's also really challenging to accomplish how does nexla help enable that yeah data engineering is quite interesting in that one it is difficult to implement you know the the necessary sort of pieces but it is also very repetitive at some level right i mean when you connect to say 10 systems and get data from them you know that's not the end of it you have 10 more and 10 more and 10 more and then at some point you have thousands of such you know data connectivity and data flows happening it's hard to maintain them right as well so the way nexla gets into the whole picture is looking at what can we understand about data what can we observe about the data systems what can be done from that and then start to automate certain pieces of data engineering so that we are helping those teams just accelerate a lot faster and it i would say comes down to more people being able to do these tasks rather than only very very specialized people more people being able to do the tasks more users kind of democratization of data really there can you talk to us in more detail about how naxa is automating data engineering yeah i think um you know i think this is best shared through a visual so let me walk you through that a little bit as to how we automated engineering right so if we think about data engineering three of the most core components are many parts to it but three of the most core components of that are integrating with data systems preparing and transforming data and then monitoring that right so automating data engineering happens in you know three different ways first of all connecting connecting to data is is basically about the gateway to data the ability to read and write data from different systems this is where the data journey starts but it is extremely complex because people have to write code to connect to different systems one part that we have automated is generating these connectors so that you don't have to write code for that also making them bi-directional is extremely valuable because now you can read and write from any system the second part is that the gateway the connector has read the data but how do you represent it to the user so anybody can understand it and that's where the concept of data product comes in so we also look at auto generating data products these become the common language and entity that people can understand and work with and then the third part is taking all this automation and bringing the human in the loop no automation is perfect and therefore bringing the human in the loop means that somebody who is an expert in data who can look at it and understand it can now do things which only data systems experts were able to do before so bringing in that user of data directly into the into the picture is one important part but let's not forget data challenges are very diverse and very complex so the same system also becomes accessible to the engineers who are experts in that and now both of these can work together while an engineer will come through apis and sdk and command interfaces a data user comes in through a nice no code user interface and all of these things coming together are what is accelerating back to that time to value that really everybody cares about so if i'm in marketing and i'm a data user i'm able to have a collaborative workflow with the data engineer yeah yeah for the first time that is actually possible and everybody's focuses on their expertise and their know-how so you know um somebody who for example in financial services really understands portfolio and transactions and different type of asset classes they have the data in front of them the engineers who understand the underlying real-time data feeds and those they are still involved in the loop but now they are not doing that back and forth you know as the user of data i'm not going to the engineer saying hey can you do this for me can you get the data here and that back and forth is not only time taking it's frustrating and the number one hold back right yeah that and that's time that nobody has to waste as we know for many reasons talk to me about when you look into your crystal ball which i'm sure you have one what is the future of of data engineering from nexus perspective you talked about the automation what's the future hold i think the future of data engineering becomes that we up level this at a point where um companies don't have to be slowed down for it um i think a lot of tooling is already happening the way to think about this is that here in 2022 if we think that our data challenges are you know like x they will be a thousand x in five years right i mean this complexity is just increasing very rapidly so we think that this becomes one of those fundamental layers you know and you know as i was saying maybe the last time this is like the road you know you don't feel it you just move on it you do your job you build your products deliver your services as a company this just works for you um and that's where i think the future is and that's where i think the future should be we all need to work towards that we're not there yet not there yet a lot of a lot of potential a lot of opportunity and a lot of momentum speaking of momentum i want to talk about data mesh that is a topic of a lot of excitement a lot of discussion let's unpack that yeah i think uh you know the idea that data should be democratized that people should get access to the data and it's all coming back to that sort of basic concept of scale companies can scale only when more people can do the relevant jobs without depending on each other right so the idea of data democratization has been there for a long time but you know recently in the last couple of years the concept of data mesh was introduced by zamak digani and thoughtworks and that has really caught the attention of people and the imagination of leadership as well the idea that data should be available as a product you know that democratization can happen what is the entity of the democratization that's data presented as a product that people can use and collaborate is extremely powerful um i think a lot of companies are gravitating towards that and that's why it's exciting this is promising a future that is you know possible so second speaking of data products we talked a little bit about this last time but can you really help us understand see smell touch feel what a data product is and give us that context yeah absolutely i think uh best to orient ourselves with the general thinking of how we consider something as a product right a product is something that we find ready to use for example this table that i'm using right now made out of raw materials wood metal screws somebody designed it somebody produced it and i'm using it right now when we think about data products we think about data as the raw material so for example a spreadsheet an api a database query those are the raw raw materials what is a data product is something that further enriches and enhances that entity to be much more usable ready to use right um let me illustrate that with a little bit of a visual actually and that might help okay um the idea of the data product and this is how a data product looks like in next lab for a user to write as you see the concept of a data product is something that first of all it's a logical entity this simply means that it's not a new copy of data just like containers or logical compute units you know these data products are logical entities but they represent data in the same consistent fashion regardless of where the data comes from what format it is in they provide the user the idea of what the structure of data is what the sample data looks like what the characteristics of data are it allows people to have some documentation around it what does the data mean what do these attributes you know mean and how to interpret them how to validate that data something that users often know in an industry how is my data looking like well this value can never be negative because it's a price for example right um then the ability to take these data products that you know we automate by generating as i was mentioning earlier automatically creating these data products taking these data products to create new data products now that's something that's very unique about data you could take data off about an order for a from a company and say well the order data has an order id and a user id but i need to look up shipping address so i can combine user and order data to get that information in one place so you know creating new data products giving people access hey i've designed a data product i think you'll find it useful you can go use that as it is you don't have to go from scratch so all of those things together make a data product something that people can find ready to use again and this is this is also usable by the again that example where i'm in marketing uh or i'm in sales this is available to me as a general user as a general user in the tool of your choice so you can say oh no i am most familiar with using data in a spreadsheet i would like it there or i prefer my data in a tableau or a looker to visualize it and you can have it there so these data products give multiple interfaces for the end user to make use of it got it i like it you're meeting the user where they are with relevant data that helps them understand so much more contextually i'm curious when you're in customer conversations customers that come to you saying saka we need to build the data mesh how is nexl relevant they're how what is your conversation like yeah when people want to build a data mesh they're really looking for how their organization will scale into the future uh there are multiple components to building a data mesh there's a tooling part of it the technology portion there are people and processes right i mean unless you train people in certain processes and say hey when you build a data product you know make sure you have taken care of privacy or compliance to certain rules or who do you give access to is something you have to follow some rules about so we provide the technology component of it and then the people and process is something that companies you know then as they adopt and do that right so the concept of data product becomes core to building the data mesh having governance on it uh having all this be self-serve it's an essential part of that so that's where we come into the picture as a as a technology component to the whole story and working to deliver on that mission to getting data in the hands of every user you mentioned i want to dig into in the last few minutes here that we have uh the target audience you mentioned a few by name big names customers that nexla has you i heard retail i heard e-commerce i think i heard logistics but talk to me about the target customer for nexla any verticals in particular or any company's sizes in particular as well yeah you know the one of the top three banks in the country is a big user of nexla as part of their data stack uh we actually sit as part of their enterprise-wide ai platform providing data to their data scientists um we're not allowed to share their name unfortunately but um you know there are multiple other companies in asset management area for example they work with a lot of data in markets portfolio and so on um the leading medical devices companies using nexla data scientists there are using data coming in real time or streaming for medical devices to train and um and combine that with other data to do sort of clinical trial related research that they do um we have you know the companies for example linkedin is an excellent customer linkedin is by far the largest social network um their marketing team leverages nexla to bring data from different type of systems together as well um you know so are companies in education space like nerdy is a public company that uses nexla for you know student enrollment education data as they collaborate with school districts for example um you know there are companies across the board in marketing live brand you know for example uses nexla so we are um we are you know from who uses nexla is today mostly mid to large to very large enterprises today leverage nexla as a very critical component and often mission critical data for which they leverage us do you see that changing anytime soon as every company these days has to be a data company we expect that as consumers whether it's my grocery store um or my local coffee shop that they've got to use data to deliver me that personalized experience do you see the target audience kind of shifting down to more into mid-market smb space for next level oh yeah absolutely look we started the journey of the company with the thinking that the most complex data challenges exist in the large enterprise and if we can make it no code self-serve easy to use for them we can bring the same high-end technology to everybody and this is exactly why we recently launched in the amazon marketplace so anybody can go there get access to nexla and start to use it and you will see more and more of that happen where we will be bringing even some free versions of our product available so you're absolutely right every company needs to leverage data and i think people are getting much better at it you know especially in the last couple of years i've seen that teams have become much more sophisticated yes even if you are a coffee shop and you're running campaigns you know getting people yelp reviews and so on this data that you can use and understand better your demographic your customer and run your business better so one day yes we will absolutely be in the hands of every single person here a lot more opportunity to delight a lot more consumers and customers socket thank you so much for joining me on the program during the startup showcase you did a great job of helping us understand the future of automated data engineering we appreciate your insights thank you so much lisa it's a pleasure talking to you likewise for soccer sarah i'm lisa martin you're watching thecube's coverage of the aws startup showcase season two episode two stick around more great content coming up from the cube the leader in hybrid tech event coverage [Music]

(soft techno music) >> Good evening. Welcome back to the Kube. Live in Los Angeles. We are at KubeCon Cloud Native Con 2021. Lisa Martin with Dave Nicholson, rounding out our day. We're going to introduce you to a new company, a new company that's new to us. I should say, log DNA. Peter Choi joins us the VP of product. Peter, welcome to the program. >> Thanks for having me. >> (Lisa) Talk to us about log DNA. Who are you guys? What do you do? >> So, you know, log DNA is a log medicine platform. Traditionally, we've been focused on, you know, offering log analysis, log management capabilities to dev ops teams. So your classic kind of troubleshooting, debugging, getting into your systems. More recently, maybe in like the last year or so we've been focused on a lot of control functionality around log medicine. So what I mean by that is a lot of people typically think of kind of the analysis or the dashboards, but with the pandemic, we noticed that you see this kind of surge of data because all of the services are being used, but you also see a downward pressure on cost, right? Because all of a sudden you don't want to be spending two X on those digital experiences. So we've been focused really on kind of tamping down kind of controls on the volume of log data coming in and making sure that they have a higher kind of signal and noise ratio. And then, you know, I'll talk about it a little bit, but we've really been honing in on how can we take those capabilities and kind of form them more in a pipeline. So log management, dev ops, you know, focusing on log data, but moving forward really focused on that flow of data. >> (Dave) So, when you talk about the flow of data and logs that are being read, make this a little more real, bring it up, bring it up just to level in terms of data, from what? >> Yeah. >> What kind of logs? What things are generating logs? What's the relevant information that's being. Kept track of? >> Yeah, I mean, so from our perspective, we're actually agnostic to data source. So we have an assist log integration. We have kind of basic API's. We have, you know, agents for any sort of operating system. Funny enough people actually use those agents to install, log DNA on robots, right? And so we have a customer they're, you know, one of the largest E-commerce platforms on, in the, in the world and they have a warehouse robots division and they installed the agent on every single one of those robots. They're, you know, they're running like arm 64 processors and they will send the log data directly to us. Right? So to us, it's no different. A robot is no different from a server is no different from an application is no different from a router. We take in all that data. Traditionally though, to answer your question, I guess, in the simplest way, mostly applications, servers, firewalls, all the traditional stuff you'd expect kind of going into a log platform. >> So you mentioned a big name customer. I've got a guess as to who that is. I won't, I won't say, but talk to us about the observability pipeline. What is that? What are the benefits in it for customers? >> (Peter) Sure. So, like if we zoom out again, you know, you think about logs traditionally. I think a lot of folks say, okay, we'll ingest the logs. We'll analyze them. What we noticed is that there's a lot of value in the step before that. So I think in the earlier days it was really novel to say, Hey, we're going to get logs and we're going to put it into a system. We're going to analyze it. We're going to centralize. Right. And that had its merits. But I think over time it got a little chaotic. And so you saw a lot of the vendors over the last three years consolidating and doing more of a single pane of glass, all the pillars of observability and whatnot. But then the downside of that is you're seeing a lot of the teams that are using that then saying being constrained by single vendor for all the ways that you can access that data. So we decided that the control point being on the analysis side on, on the very far right side was constricting. So we said, okay, let's move the control point up into a pipeline where the logs are coming to a single point of ingress. And then what we'll do is we will offer views, but also allow you to stream into other systems. So we'll allow you to stream into like a SIM or a data warehouse or something, something like that. Right? So, and you know, we're still trying to like nail down the messaging. I'm sure our marketing person's going to roast me after this. But the simplest way to think of observability pipeline is it's the step before the analysis part, that kind of ingest processes and routes the data. >> (Dave) Yeah. This is the Kube, by the way, neither one of us is a weather reporter. (laughing) So, so the technical stuff is good with us. >> Yes. It is. What are, and talk to us about some of the key features and capabilities and maybe anything that's newly announced are going to be announced. >> Yeah. For sure. So what we recently announced early access on is our streaming capabilities. So it's something that we built in conjunction with IBM and with a couple of, you know, large major institutions that we were working with on the IBM cloud. And basically we realized as we were ingesting a log data, some of those consumers wanted to access subsets of that data and other systems such as Q radar or, you know, a security product. So we ended up taking, we filtered down a subset of that data and we stream it out into those systems. And so we're taking those capabilities and then bringing it into our direct product, you know, whatever you access via logging.com. That is what's essentially going to be the seed for the kind of observability pipeline moving forward. So when you start thinking about it, all of this stuff that I mentioned, where we say, we're focusing on control, like allowing you to exclude logs, allowing you to transform logs, you take those processing capabilities, you take the streaming capabilities, you put them together and all of a sudden that's the pipeline, right? So that's the biggest focus for us now. And then we also have supporting features such as, you know, control API's. We have index rate alerting so that you can get notified if you see aberrations in the amount of flow of data. We have things like variable retention. So when a certain subset of logs come in, if you want it store it for seven days or 30 days, you can go ahead and do that because we know that a large block of logs is going to have many different use cases and many different associated values, right? >> So let's pretend for a moment that a user, somebody who has spent their money on log DNA is putting together a Yelp review and they've given you five stars. >> Yup. >> What do they say about log DNA? Why did they give you that five star rating? >> Yeah. Absolutely. I think, you know, the most common one and it's funny it's Yelp because we actually religiously mine, our G2 crowd reviews. (all laughing) And so the thing that we hear most often is, it's ease of use, right? A lot of these tools. I mean, I'm sure, you know, you're talking to founders and product leaders every day with developers. Like the, the bar, the baseline is so low, you know, a lot of, a lot of organizations where like, we'll give them the, you know, their coders, they'll figure it out. We'll just give them docs and they'll figure it out. But we, we went a little bit extra in terms of like, how can we smooth that experience so that when you go to your computer and you type in QTPL, blah, blah, blah, two lines, and all of a sudden all your logs are shipping from your cluster to log DNA. So that's the constant theme for us in all of our views is, Hey, I showed up, I signed up and within 30 minutes I had everything going that I needed to get. >> (Lisa) So fast time to value. >> Yes. >> Which is critical these days. >> Absolutely. >> Talk to me. So here we are at, at KubeCon, the CNCF community is huge. I think I, the number I saw yesterday was 138,000 contributors. Lots of activity, because we're in person, which is great. We can have those hallway networking conversations that we haven't been able to have in a year and a half. What are some of the things that you guys have heard at the booth in terms of being able to engage with the community again? >> You know, the thing that we've heard most often is just like having a finger on the pulse. It's so hard to do that because you know, when we're all at our computers, we just go from zoom to zoom. And so it, it like, unless it punches you in the face, you're not aware of it. Right. But when you come here, you look around, you go, you can start to identify trends, you hear the conversations in the hallway, you see the sessions. It's just that, that sense of, it's almost like a Phantom limb that, that sense of community and being kind of connected. I think that's the thing that we've heard most often that people are excited. And, you know, I think a lot of us are just kind of treating this like a dry run. Like we're kind of easing our way back in. And so it, you know, it felt good to be back. >> Well, they've done a great job here, right? I mean, you have to show your proof of vaccination. They're doing temperature checks, or you can show your clear health pass. So they're making it. We were talking to the executive director of CNCF earlier today and you're making it, it's not rocket science. We have enough data to know that this can be done carefully and safely. >> (David) Don't forget the wristbands. >> That's right. And, and did you see the wristbands? >> (Peter) Oh yeah. >> Yeah, yeah that's great. >> Yep, it is great. >> I was, I was on the fence by the way. I was like, I was a green or yellow, depending on the person. >> (both) Yeah. >> Yeah. But giving, giving everybody the opportunity to socialize again and to have those, those conversations that you just can't have by zoom, because you have somebody you've seen someone and it jogs your memory and also the control of do I want to shake someone's hand or do I not. They've done a great job. And I think hopefully this is a good test in the water for others, other organizations to learn. This can be done safely because of the community. You can't replicate that on video. >> (Peter) Absolutely. And I'll tell you this one for us, this is our, this is our event. This is the event for us every single year. We, we it's the only event we care about at the end of the day. So. >> What are some of the things that you've seen in the last year, in terms of where, we were talking a lot about the, the adoption of Kubernetes, kind of, where is it in its maturation state, but we've seen so much acceleration and digital transformation in the last 18 months for every industry businesses rapidly pivoting multiple times to try to, to survive one and then figure out a new way to thrive in this, this new I'll call it the new. Now I'll borrow that from a friend at Citrix, the new now, not the new normal, the new now, what are some of the things that you've seen in the last year and a half from, from your customer base in terms of what have they been coming to you saying help? >> (Peter) You know, I think going back to the earlier point about time to value, that's the thing that a lot. So a lot of our customers are, you know, very big Kubernetes, you know, they're, they're big consumers of Kubernetes. I would say, you know, for me, when I do the, we do our, our QBRs with our top customers, I would say 80% of them are huge Kubernetes shops. Right. And the biggest bottleneck for them actually is onboarding new engineers because a lot of the, and you know, we have a customer, we have better mortgage. We have, IBM, we have Rappi is a customer of ours. They're like Latin American version of Instacart. They double their engineering base and you, you know, like 18 in 18 months. And so that's, you know, I think it was maybe from 1500 to 3000 developers or so, so their thing is like, we need to get people on board as soon as possible. We need to get them in these tools, getting access to, to, to their longs, to whatever they need. And so that's been the biggest thing that we've heard over and over again is A, how can we hire? And then B when we hire them, how do we onboard them as quickly as possible, so that they're ramped up and they're adding value. >> How do you help with that onboarding, making it faster, seamless so that they can get value faster? >> So for us, you know, we really lean in on our, our customer success teams. So they do, you know, they do trainings, they do best practices. Basically. We kind of think of ourselves given how much Kubernetes contradiction we have, we think of ourselves as cross pollinators. So a lot of the times we'll go into those decks and we'll try to learn just as much as we're trying to try to teach. And then we'll go and repeat that process through every single set of our customers. So a lot of the patterns that we'll see are, well, you know, what kinds of tools are you using for orchestration? What kind of tools are you using for deployment? How are you thinking about X, Y, and Z? And then, you know, even our own SRE teams will kind of get into the mix and, you know, provide tips and feedback. >> (Lisa) Customer centricity is key. We've heard that a lot today. We hear that from a lot of companies. It's one thing to hear it. It's another thing to see it. And it sounds like the Yelp review that you would have given, or, or what you're hearing through G2 crowd. I mean, that voice of the customer is valid. That's, that's the only validation. I think that really matters because analysts are paid. >> Yeah. >> But hearing that validation through the voice of the customer consistently lets you know, we're going in the right direction here. >> Absolutely. >> I think it's, it's interesting that ease of use comes up. You wonder if those are only anonymous reviews, you don't necessarily associate open source community with cutting edge, you know, we're the people on the pirate ship. >> (Peter) Yeah. And so when, when, when people start to finally admit, you know, some ease of use would be nice. I think that's an indication of maturity at a certain point. It's saying, okay, not everyone is going to come in and sit behind a keyboard and program things in machine language. Every time we want to do some simple tasks, let's automate, let's get some ease of use into this. >> And I'll tell you in the early days it drove me and our, our CEO talker. It drove us nuts that people would say easiest to be like, that's so shallow. It doesn't mean anything. Well, you know, all of that. However, but to your point, if we don't meet the use case, if we don't have the power behind it, the ease of use is abstracting away. It's like an iceberg, right. It's abstracting away a lot. So we can't even have the ease of use conversation unless we're able to meet the use case. So, so what we've been doing is digging into that more, be like, okay, ease of use, but what were you trying to do? What, what is it that we enabled? Because ease of use, if it's a very shallow set of use cases is not as valid as ease of use for petabytes of data for an organization like IBM. Right? >> That's a great, I'm glad that you dug into that because ease of use is one of those things that you'll see it in marketing materials, but to your point, you want to know what does this actually mean? What are we delivering? >> Right. >> And now, you know what you're delivering with Peter, thank you for sharing with us about logged in and what you guys are doing, how you're helping your community of customers and hearing the voice of the customer through G2 and others. Good work. >> Thank you. And by the way, I'll be remiss if I, if I don't say this, if you're interested in learning more about some of the stuff that we're working on, just go to logging in dot com. We've got, I think we've got a banner for the early access programs that I mentioned earlier. So, you know, at the end of the day, to your point about customer centricity, everything we prioritize is based on our customers, what they need, what they tell us about. And so, you know, whatever engagement that we get from the people at the show and prospects, like that's how we drive a roadmap. >> (Lisa) Yup. That's why we're all here. Log dna.com. Peter, thank you for joining Dave and me today. We appreciate it. >> Thanks for having me. >> Our pleasure for Dave Nicholson. I'm Lisa Martin signing off from Los Angeles today. The Kubes coverage of KubeCon clouding of con 21 continues tomorrow. We'll see then. (soft techno music)

(upbeat music) >> Narrator: From theCUBE Studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. >> Welcome to theCUBE's coverage of PagerDuty Summit, I'm your host from theCUBE Natalie Erlich. Now we're joined by Sean Scott, the Chief Product Officer at PagerDuty, thank you very much for joining the program. >> Glad to be here, thank you for having me. >> Terrific. Well, you've been with PagerDuty for about six months, how's it going? >> It's going great. So, I joined PagerDuty because I saw the entire world was shifting to digital first and PagerDuty is key infrastructure for many of the world's largest companies, in fact over 60% of the Fortune 100 are customers. And more importantly, I see a much broader future our platform will play in digital operations for these companies going forward, and I'm excited to be part of that. >> Terrific. Well, you have really robust experience, over 20 years in the Valley leading product, marketing, and engineering teams. What prompted the move? I mean, you explained a bit, but just really curious why you made that? >> Sure, so yeah I had a long career at Amazon where I was responsible for much of the shopping experience, I ran the homepage, product page, checkout, a lot of the underlying tools and tech that supports that worldwide across all devices. And then more recently I built and launched the Scout autonomous delivery robot from the ground up, so. But after 15 years, and I was starting to look for a change and I started talking to Jen, our CEO, and the more we talked, the more excited I became about the platform and what it can be going forward for our customers. You know, the fact that we are already integrated with so many customers around the world and playing such a critical role as part of their infrastructure, and yet, I think we're just getting started, and we can help out companies in so many more use cases across our organizations and really eliminate a lot of time and waste from their processes. Well, this is your first PagerDuty Summit, I would love it if you could share perhaps some insight what you're planning to announce this week? >> Yeah, sure. So, we have a few things that we're announcing. One is, we announced last year, probably the biggest news last September was our acquisition of Rundeck; and so as part of that we're announcing our first integration of PagerDuty and Rundeck in the form of Runbook actions. So this is a, you could think of it as kind of quick, kind of micro-automations or short automations to give responders much more insights into what's actually happening with an incident. So maybe it's running a MIM command or a script on a server, we can actually run that directly from the PagerDuty interface so you don't have to SSH into a box for example, which is all just takes time and effort, and so when you're trying to remediate an issue of maybe a site being down or a service being down, it all happens right there. And even your frontline responders can now do those remediations as well, and those automation actions, to again, before they need to escalate to the next tier or bring in other devs to help troubleshoot. So that's pretty exciting. We're also announcing Service Craft, which is a new way to model your services and to show your services, and really understand your dependency graph. So if you think about one of the biggest challenges often when you're trying to remediate an issue is understanding is it me, or is it one of my dependent services? And so now we actually have new visualizations to really show the responders exactly what's happening and you can quickly see is it you, or is it maybe some dependency, maybe multiple teams are having the same issue that because one of the core services that everybody leverages is down and you can quickly see that. So that's pretty exciting as well. We have change correlation and incident outliers. So change correlation, you know, most incidents occur because of changes that were made by us people, and so being able to spotlight things like here's a change that was recently made, or here's a change based on our machine learning algorithms that we detected that could be a culprit here. So providing much richer insights, to again, reduce that mean time to resolution. So this whole team, our Event Intelligence team, that's our whole purpose in life is really just to reduce that mean time to resolution for our customers. Imagine waking up, you know, tomorrow, and your mean time to resolution just magically goes down because of our software updates, and that's how that team focuses on. And then the last one in this group is internet outliers, which is all about telling you if an incident, is this rare, or is this a frequent incident? And just giving you a little more insights into what you're seeing, which will again, help the responders. We have some other announcements coming up, but I'll save that for Summit. >> Perfect. Well, you know, I'd love it if you could share some insight on the competitive landscape, and how PagerDuty is, how you see its product that they're offering different from the others? >> Sure. So, we go head-to-head with a lot of competitors, and we, we have the, you know, being in the fortunate position that we do have a few competitors coming after us and some big names as well. But, you know, when we go head-to-head with these companies, we generally win. And we see we're constantly getting put in bake-offs with these other competitors. We had one customer I was talking to a few weeks back and they paired us against the incumbent, and out of the box, we saw a 50% improvement in mean time to acknowledge, so this is how quickly we can pull in the responder. And then in addition, I thought was more interesting, is we saw a 50% improvement in the mean time to resolution over the incumbent. And so while we do have competitors coming at us, I'm really happy with the way our product performs and our customers are too. So after these bake-offs, it's usually pretty clear who's staying and who's going. >> Yeah, so, when you were helping develop this program this week, what were some of the key areas that you really wanted to highlight? >> Yeah, so one of the big areas is really talking about our vision, and what is our go forward plan. Because I think while we're really known for incident response, I think, you know, some of the exciting things you'll hear about at Summit are kind of where we're going in terms of four pillars to our vision. One is flexibility. Flexible workflows, and enabling flexibility. So, if you think about all the things that our product is doing beyond DevOps. So for example, you know, we had a customer telling us about they had put PagerDuty in front of everything they're doing, so their whole building is IP enabled, and so they had a contractor drill through a water main, and it was instantly able to shut off the water. So they, you know, within 30 seconds, PagerDuty had notified the right responders of building maintenance, and within a minute and a half the water was shut off, and they made the comment that PagerDuty just paid for itself with this one incident. We see IOT device management, we see even organ transplant delivery using our product, and so we want to continue to fuel that with our flexibility. Second pillar is connect to everyone. We see that we have a lot of people connected, but we just launched fairly recently a customer service offering, so now we can get customer service not only informed what's going on, but also connecting to the dev teams, and engineering teams, and the service owners, to really give them more insights into the blast radius and what they may be seeing. The next one is connect everything. So we have over 550 out of the box integrations, and so that makes it seamless to connect to apps like Datadog. But then also we work where our customers work, so we can actually do work in Slack or MS Teams and take action right in those tools. And the last one is automate away to toil. So we want to automate what can be automated, and this goes back to the Rundeck acquisition that I mentioned, and getting that more deeply integrated with the stack, and with processes across an organization. And we're seeing that when our customers really take advantage of that platform they can really automate away to toil, and automate a lot of redundant work, and work that is just busy work that keeps people from doing their day jobs, so to speak. >> Yeah, well, obviously we had a really unusual last year with the pandemic. How do you think that it changed up business for you? Did it inspire you to move in a new direction? What do you see next in the near future? >> For sure. So, I saw that, and it's probably the reason why I came to PagerDuty, because I saw the transformation industries are making to digital first. Right? And so there was a lot of teams, a lot of companies struggled, but then a lot of companies also, florists, you'd take companies like Instacart, and DoorDash, and Zoom, you know, had a terrific year. And so, you know, PagerDuty, even with the pandemic, and companies that were struggling, we still grew pretty rapidly last year, and that's, I think it's pretty exciting, and it really speaks to that migration to digital where digital is now becoming, you know, table stakes, and just part of what you have to do as a business as opposed to it used to be a goal that oh, we need to do more on digital platform, and now it's like, you have to, you know, focus on your digital platform if you want to simply stay relevant today. And so I think that's really important for PagerDuty because that's where we really help companies thrive. >> Sean, that's really interesting. To close out this interview, do you have any last thoughts? >> No, I think that covers it, I think we're, you know, really excited to grow with our customers and we're seeing great traction in the market, and look forward to a bright future, and our platform really helping customers solve new problems that they might've not even considered us for yet. >> Terrific. Well, thank you very much for your insights. Sean Scott, the Chief Product Officer at PagerDuty. And that wraps up our coverage today for the PagerDuty Summit. I'm your host Natalie Erlich for theCUBE. Thank you for watching. (upbeat music)

(bright music) >> Narrator: From theCube studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is theCube conversation. >> Welcome to theCube's coverage of PagerDuty Summit. I'm your host from the cube Natalie Ehrlich. Now we're joined by Sean Scott the Chief Product Officer of PagerDuty. Thank you very much for joining the program. >> Glad to be here, thank you for having me. >> Terrific, while you've been with PagerDuty for about six months, how is it going? >> Well and great. So I joined PagerDuty because I saw the entire world was shifting to digital first and PagerDuty is key infrastructure for many of the world's largest companies. In fact, over 60% of the Fortune 100 are customers. And more importantly, I see a much broader future our platform will play in digital operations for these companies going forward. I'm excited to be a part of that. >> Terrific. Well, you have really robust experience over 20 years in the value leading product, marketing and engineering teams. What prompted the move? I mean, you explained it but just really curious why you made that. >> So, yeah, I had a long career at Amazon where I was responsible for much of the shopping experience. I ran the homepage, product page, checkout a lot of the underlying tools and tech that supports that worldwide across all devices. And then more recently I built and launched the scout autonomous delivery robot from the ground up. So, but after 15 years and I was starting to look for a change and I started talking to Jen, our CEO and the more we talked the more excited I became about the platform and what it can be going forward for our customers. You know, the fact that we are already integrated with so many customers around the world and playing such a critical role as part of their infrastructure. And yet I think we're just getting started and we can help out companies and so many more use cases across our organizations and really eliminate a lot of time in a waste from their processes. >> Well, this is your first PagerDuty summit. Do tell us, what do you think is the vision for this year's program? >> Yeah, so we'll be launching a lot of new products that I'm excited to talk about and I'll be sharing some of the vision about what I've been thinking about and what I've been working on for my time that I've been here so far. And that starts with our vision, which is really how do we enable more flexibility across our platform. I mentioned our customers are using us for a lot of unique ways beyond DevOps. Things like IOT device management. You know, I heard one yesterday of, you know really doing building management. So the building was having a water leak and instantly it was hooked up to PagerDuty already beforehand. And so within 30 seconds they had alerted and within a minute and a half, they had the water shut off of the building. So way beyond the DevOps use case to even organ transplant delivery, if you can believe that our platform is being used on. So it's pretty exciting to think about all our product already does, but we want to continue to accelerate that. And so building much more flexibility into our product to really capture more of that value and more of the work that's happening across the organization, connect to everyone. >> That's really incredible. We'd love it if you could share perhaps some insight what you're planning to announce this week. >> Yeah, sure. So we have a few things that we're announcing. One is we announced last year by the biggest news last September was our acquisition of Rundeck. And so as part of that, we're announcing our first integration of PagerDuty in Rundeck in the form of Runbook action. So this is a, you can think of it as kind of quick kind of micro automations or short automations to give responders much more insights into what's actually happening with an incident. So maybe it's say running a MIM command or a script on a server, we can actually run that directly from the PagerDuty interface. So you don't have to SSH into a box, for example what does all just takes time and effort. And so when you're trying to remediate that issue of maybe a site being down or a service being down, it all happens right there. And even your frontline responders can now do those remediations as well and those automation actions, to again before they need to escalate to the next tier or bring in other devs to help troubleshoot. So that's pretty exciting. We're also announcing service graft which is a new way to model your services and show your services and really understand your dependency graph. So if you think about one of the biggest challenges often when you're trying to remediate issues is understanding, is that me, or is that one of my dependent services? And so now we actually have new visualizations to really show that our responders exactly what's happening and you can quickly see, is it you or is it maybe some dependency maybe multiple teams are having the same issue that because one of the core services that everybody leverages is down and you can quickly see that. So that's pretty exciting as well. We have change correlation and internet outliers. So change correlation, you know, most incidents occur because of changes that were made by us people. And so being able to spotlight things like here's a change that was recently made, or here's a change based on our machine learning algorithms that we detected that could be a culprit here. So providing a much richer insights to again reduce that meantime to resolution. So this whole team, our intelligence team that's our whole purpose in life is really just to reduce that meantime to resolution for our customers. Imagine waking up, you know, tomorrow and your meantime to resolution just magically goes down because of our software updates and that's how that team focuses on. And then the last one in this group is internet outliers which is all about telling you have an incident, is this rare or is this a frequent incident? And just giving you a little more insights into what you're seeing which will again help the responders. We have some other announcements coming up, but I'll save that for something. >> Terrific. Well, you know, I'd love it if you could share some insight on the competitive landscape and how PagerDuty is, how you see its product offering different from the others. >> Sure. So we go head to head with a lot of our competitors and we have the, you know, being in the fortunate position that we do have a few competitors coming after us and some big names as well. But you know, when we go head to head with these companies we generally win and we see we're constantly getting put in bake-offs with these other competitors. We have one customer, I was talking to a few weeks back and they paired us against the incumbent and out of the box, we saw 50% improvement in meantime to acknowledge. So this is how quickly we can pull the responder. And then in addition, I thought was more interesting as we saw a 50% improvement in the meantime to resolution over the incumbent. And so while we do have competitors coming at us I'm really happy with the way our product performs and our customers are too. So after these bake-offs, it's usually pretty clear who's staying and who's going. >> Yeah. So when you were helping develop this program this week what were some of the key areas that you really wanted to highlight? >> So one of the big areas is really talking about our vision and what is our go forward plan, because I think while we're really known for incident response, I think some of the exciting things you'll hear about at the summit are kind of where we're going in terms of four pillars to our vision. One is flexibility. Flexible workflows and enabling flexibility. So if you think about all the things that our product is doing beyond DevOps. So for example, you know we had a customer telling us about they had put PagerDuty in front of everything they're doing. So their whole building is IP enabled. And so they had a contractor drill through a watermain and it was instantly able to shut off the water. So they, you know, within 30 seconds they had the PagerDuty had notified the right responders of building maintenance and within a minute and a half the water was shut off and they made the comment that PagerDuty just paid for itself with this one incident. We see IOT device management. We see even organ transplant delivery using our product. And so we will continue to fuel that with our flexibility. Second pillar is connect to everyone. We see that we have a lot of people connected, but we just launched fairly recently a customer service offering. So now we can get customer service not only informed what's going on, but also connecting to the dev teams and the engineering teams and the service owners to really give them more insights into the blast radius and what they may be seeing. The next one is connect everything. So we have over 550 out of the box integrations. So that makes it seamless to connect to apps like Datadog. But then also we work where our customers work. So we can actually do work in Slack or MS Teams and take action right in those tools. And the last one is automated way to toil. So we want to automate what can be automated. And this goes back to the one deck acquisition that I mentioned and getting that more deeply integrated with the stack and with processes across an organization. And we're seeing that when our customer has really taken advantage of that platform they can really automate a way to toil and automate a lot of redundant work and work that is just busy work and that keeps people from doing their day jobs, so to speak. >> Yeah, well obviously we had a really unusual last year with the pandemic. How do you think that it changed a business for you? Did it inspire you to move in a new direction? What do you see next in the near future? >> For sure. So I saw that, I mean, it's probably the reason why I came to PagerDuty because I saw the transformation industries are making a digital first, right. And so there was a lot of teams a lot of companies struggled, but then a lot of companies also flourished you'd take, you know companies like Instacart and DoorDash and Zoom, you know had a terrific year. And so, you know, PagerDuty even with the pandemic and companies that were struggling, we still grew pretty rapidly last year. And that's, I think it's pretty exciting. And it really speaks to that migration to digital where digital is now becoming table stakes and just part of what you have to do as a business as opposed to it used to be a goal that we need to do more on digital platform. And now it's like, you have to, you know focus on a digital platform if you want to simply stay relevant today. And so I think that's really important for PagerDuty because that's where we really help companies thrive. >> Sean, that's really interesting. To close out this interview, do you have any last thoughts? >> No, I think that covers it. I think we're really excited to grow with our customers and we're seeing great traction in the market and look forward to a bright future in our platform. Really helping customers solve new problems that they might've not even considered us for yet. >> Terrific, well, thank you very much for your insights. Sean Scott the Chief Product Officer at PagerDuty. And that wraps up our coverage today for the PagerDuty Summit. I'm your host, Natalie Erlich for theCube. Thank you for watching. (bright music)

(upbeat pop music) >> Well, hi everyone, John Walls here on "theCUBE" as we continue our CUBE conversations as part of the "AWS Startup Showcase". And we welcome in today Krishna Gade who is the founder and the CEO of Fiddler AI. and Krishna, good to see you today. Thanks for joining us here on the "theCUBE". >> Hey John, thanks so much for inviting us and I'm glad to be here, and looking forward to our conversation. >> Yeah me two, and first off, I want to say congratulations as I look at your company's, this tremendous roster, this list of awards that just keep coming your way. Most recently recognized by "Forbes" as one of the Top 50 AI Companies To Watch here in 2021. I know Gartner called you one of their Cool Companies not too long ago. World Economic Forum also giving you a shout out. So whatever it is you're doing, you're doing it very well, but it's got to feel good I would think, some validation to get all this kind of recognition. >> Absolutely, I know we've been very fortunate to get all the recognition. You know, part of it is also because of the space we are playing in, right? A lot of companies are, you know, operationalizing AI and therefore, you know, this whole point of, you know, explainability monitoring and governance of AI is like forefront and it's in the news for various different reasons. So there's a lot of, you know, good sort of talk that is going on in the press around how one should bear responsible AI. And we are very fortunate to be, you know, in the space and pioneering, you know, some of the technologies here. >> Right. And talking about machine learning monitoring, obviously, in the AI space, and you mentioned explainability. So let's just talk about that concept broadly first off and explain to our viewers what you mean by explainability in this particular context. >> Yeah, that's a good question. So if you think about an AI system, one of the main differences between it and a traditional software system is that it's a black box in the sense that you cannot open it up and read it's code like a traditional software system. The reason is, you know, the AI systems that are built using data and training models which are represented in this non-human readable format. And you cannot really understand how a model is actually making a prediction at any given point of time. So therefore what happens is when you are deploying these AI systems at scale for a variety of use cases, let's say credit underwriting or, you know, screening resumes, or clinical diagnosis which are extremely, you know, important for general human beings. There is a need to understand how the AI system is working. You know, why did it approve a positive person's loan or reject someone's loan? Or why did it reject someone's, you know, resume from, you know, a job screening pipeline? How is it working overall? Right? And so this is where explainability becomes important because you need to understand the AI system, you need a way to probe it, to interrogate it, to understand how the system is making predictions, how is it being influenced by various inputs you're supplying to the system. And so this gamut of technologies or the algorithms that have come across in the last, you know, few years have really matured to a point where, you know, products like Fiddler are developing them and productizing them for the general enterprise to you know, put it in their machine learning and AI workflows. >> So you're talking about context basically, right? I mean, trying to give everybody an idea. This is, you know, kind of where this inputs coming, this is where the problem is, this is where the bottleneck might be, whatever it is, and and doing that in real time. Very efficient operation here. Well, let's talk about the ML world right now and in terms of how it relates to artificial intelligence and this interaction you know, that we're seeing and the, I guess, the problem that you are trying to fix, if you will, in terms of machine learning monitoring. So let's just deal with that first off. When you look at somebody's architecture and somebody set up, what do you see? What are you looking for? And what kind of problems are you trying to solve for your clients? >> Yeah. So just following up what I said. The two main problems with operationalizing AI is one is the black box nature of AI, which I already talked about. The other problem is that the AI system is fundamentally a stochastic system or a probabilistic system. By that, I mean that its performance, you know, its predictions can change over time based on the data it is receiving. So it's not a deterministic system like traditional software systems where you expect the same output all the time, right? So when you have a system that is stochastic in nature where its performance can vary based on the data it is receiving, then you are in a situation where you have uncertainty, right? You know, you let's say you have an AI system that is deployed for serving a credit underwriting model or a fraud, you know, detection use case. And you see that, okay, sometimes accuracy is up, sometimes accuracy is down. You know, when do you want, when do you trust your predictions, when you're not. How do you know if the model is actually performing in the same manner that you trained it? All of these issues open up the need for continuous monitoring of these AI systems, because without which you may have AI systems making bad predictions for your users, hurting your business metrics, potentially making biased decisions that can put your company into a compliance or a brand reputation risk scenario. To avoid all of these things you can actually monitor these AI systems continuously so that you know exactly if they're performing the way you expect them to be. Do you to retrain them right now, right? Or do you need to shut them down because they are actually not predicting the way that you expect them to be? So this is actually very important. And so that's what Fiddler tries to solve for our customers by helping them operationalize AI with full visibility and explainability, right? So you can essentially install Fiddler in your workflow to continuously monitor your AI systems and analyze and explain them when you have questions about how they're working. >> I mean, you talked about governance earlier a little bit, you know, compliance, obviously a great critical issue, big concern, fraud detection. Security, just in general here, as we know, I mean, we keep almost every day it seems like we're hearing about some kinds of security intrusion. So, in terms of identifying vulnerabilities or in terms of identifying anomalies, whatever it might be, what kind of work are you doing in that space to give your client base the kind of comfort and the peace of mind that everybody's searching for these days? >> Right, I mean, if you step back a little bit, John, we are truly living in the age of algorithms, right? So everything that we interact with on a day-to-day basis, the movies we watch, or when we request an Uber driver, or when we go to a financial institution and request for a loan application or a mortgage, there are algorithms behind the scenes that are processing our requests and delivering the experiences that we have. Now, increasingly these algorithms are becoming AI based algorithms. And when you have these AI based algorithms, they're trained on this data that's available, that an institution may collect from their users, or they may buy from other third parties. And when you develop these AI systems based on this data, if this data is not equally distributed amongst all different ethnicity backgrounds, people coming from different cultures, different religions, different races, different genders, you may actually build systems that can make very different decisions for different individuals based on like this bias that could creep into them. And so this actually needs, this means that at the end of the day, you can actually create a dystopian world where, you know, some people get like really great decisions from your systems, where some people are left out, right? So therefore, you know, this aspect of governing your AI systems so that you're validating what you're building upfront. You're validating the data that you're using to train the systems. You're continuously monitoring the systems there so that they're actually producing the right outcomes for your users. And then you can actually explain if some customer asks you or some regulator or a third party asks you how your system is working. It's very very important. This is an emerging area in industry, certain sectors already have this, for example, financial services. It's in companies like banks, where it is mandated to have model governance, so that every model that they are deploying needs to be validated and needs to be monitored. And we are seeing the emergence of generally AI governance creeping into other sectors as well. And so this is like a broader topic that covers explainability, covers monitoring, covers detecting bias in your AI systems and ensuring that you're building safe and responsible AI for your customers and your organization. >> Yeah, I find the bias point really interesting, actually, because I hadn't really thought about these prejudices or subjectivities, you know, it might bring to our work with us in terms of what we look at, what we ignore, what we process, how we don't. But it's a really interesting point you just raised. So thank you for that. And then there's also the kind of issue with data drift too a little bit, right? It's like, where did it go (laughing)? >> Right. >> What are we doing here? What happened to it? So maybe if you could talk about that a little bit in terms of all this data that's coming in and corralling it, right? Making sure that it stays organized and stays in a way that you can analyze and process it, and then glean insight from. >> Yeah, data drift is one of the main reasons why AI systems deteriorate in performance. So for example, let's say I'm trying to build a recommendation system that predicts the items that you want to buy when you go to an E-commerce website. Now, if I have used data pre-COVID, then the user behavior was very different, right? That kind of items people were probably buying before you know, February, 2020 was like probably much different than the kind of items that people were buying after it. So what happens is when you train your AI systems on datasets that are older but then that data has changed ever since because of an event like COVID-19 has happened, or some other seasonality has kicked in, then your AI systems are seeing different distribution data. For example, you may see that suddenly, you know, people who were shopping, let's say, in March or April last year, people were shopping for all kinds of, you know, toilet paper and all kinds of things to stock up, you know, to be ready for lockdown, right? And maybe they were not buying similar amounts in there previously. So therefore, if you have an inventory management system based on AI or an E-commerce recommendation system based on AI, you know, they would see data drift, because the buying patterns are different. The amount of stuff that people are buying in terms of toilet paper has completely shifted. And so their model is actually, may not be predicting as accurately as it would, right? So therefore identifying this data drift and alerting your AI engineer so that they can be prepared for this is very important. Otherwise, what you would see is if you're an E-commerce company, this has actually happened, you know? Instacart, a grocery delivery company and another company www.etsy.com, they blogged about it where they have seen their models go down in accuracy from 90% to 65% when this data shift happened, you know, especially during COVID-19. And so you need the ability to continuously monitor for drift so that when you can catch these things earlier, and then, you know, save your business from losing, you know, in terms of business metrics like such as number of sales that you may be making, number of bad recommendations that your systems are making to your users. >> So we've talked a lot about these various components of monitoring of which, you know, all of which you do extremely well. And I was reading earlier, just a little bit about the company, and we talked about accountability. We've already talked about that. We talked about fraud detection, we talked about reliability. There was also a point about ethical considerations, you know, and so I was interested in that, hearing from you about that in terms of why that's a pillar of your service or what exactly that was pointed toward in terms of monitoring, and what you can do. >> Right. So, I guess I'll just go back to like a famous quote from Marc Andreessen. He mentioned, you know, a few years ago that software is eating the world, right? Now, what's happening is AI is eating software. All the software that we are consuming is becoming AI based software, because basically at the end of the day some intelligence is being baked into the software to make it, you know, predict more interesting things for you to make those decisions. Instead of rule-based decisions, make it more AI based decisions. And so therefore it is very important that when we are building the software, we need to use ethical practices. You know, we need to know how, where you're collecting the data from. It can be very dangerous if you don't do it and you can land into trouble. And we have seen these incidents many times, right? For example, in 2019, when Apple and Goldman Sachs came up with a credit card, a lot of customers complained about gender bias with respect to the credit card limits that the algorithm was setting. You know, in the same household, the husband and wife were getting 10 times in terms of a difference between the credit limit between a male and a female, right? Even though they probably had similar salary ranges, similar FICO scores, right? So if you do not actually make sure that, you know, you're collecting data from the right sources that your datasets are not outbalanced. If your models, if your algorithms are tested for bias you know, before hand, before you deploy them and then you're continuously monitoring them, these are all ethical practices. These are all the responsible ways of building your AI. You can actually, you know, land into trouble. Your customers will complain about it. You know, you would lose your brand reputation. And at the end of the day you'll be essentially, and instead of actually adding value to the customers, you may be actually hurting them, right? And so this is actually why it's so important, and it's become more important when the more stakes, the higher the stakes are, right? You know, for example, when it's being used for criminal justice scenarios or when it's being used for clinical diagnosis scenarios. Being able to ensure that the system is making unbiased decisions is very, very important. >> Well, before I let you go, too, I like you to touch base on your AWS relationship about, you know, what was the Genesis of that. And currently what it is that you're working on together to provide this great value to your customers. >> Absolutely. So the follow-up to this ethical AI is like Amazon as a company is interested in pursuing, you know, the responsible AI but, you know, they have a lot of AI products. So they are looking for, you know, fostering a community and ecosystem of AI technologies. And in that hypothesis they actually invested in Fiddler last year in terms of enabling us to develop this explainable AI and ethical AI technology. And so we are working with Alexa Fund and also like AWS ecosystem in terms of partnering with how effectively Fiddler can be delivered to other AWS customers through, like, through their marketplace and other sort of areas that we can distribute the software. So it's a great partnership. We are very, very excited about the opportunity to work with Alexa Fund as well as the AWS ecosystem. It increases another opportunity for us to enable a lot more customers than we than we can otherwise. So this is a great win-win situation for both Amazon and Fiddler. >> Well, it sure is. And congratulations on that and developing that partnership. I know it's working well for your clients and it's working well for Fiddler AI obviously by the number of recognitions that have been coming your way. So Krishna, we wish you continued success and thanks for the time here today on "theCUBE". >> Yep. Thank you so much, John. It was a pleasure talking to you today. >> I enjoyed it. Thank you. John Walls here wrapping up our conversation with Fiddler AI's Krishna Gade, talking today about machine learning monitoring on the "AWS Startup Showcase". (upbeat pop music)

>> My name is Dave Vellante. And with me are two world-class technologists, visionaries and entrepreneurs. Benoit Dageville, he co-founded Snowflake and he's now the President of the Product Division, and Florian Douetteau is the Co-founder and CEO of Dataiku. Gentlemen, welcome to the cube to first timers, love it. >> Yup, great to be here. >> Now Florian you and Benoit, you have a number of customers in common, and I've said many times on theCUBE, that the first era of cloud was really about infrastructure, making it more agile, taking out costs. And the next generation of innovation, is really coming from the application of machine intelligence to data with the cloud, is really the scale platform. So is that premise relevant to you, do you buy that? And why do you think Snowflake, and Dataiku make a good match for customers? >> I think that because it's our values that aligned, when it gets all about actually today, and knowing complexity of our customers, so you close the gap. Where we need to commoditize the access to data, the access to technology, it's not only about data. Data is important, but it's also about the impacts of data. How can you make the best out of data as fast as possible, as easily as possible, within an organization. And another value is about just the openness of the platform, building a future together. Having a platform that is not just about the platform, but also for the ecosystem of partners around it, bringing the level of accessibility, and flexibility you need for the 10 years of that. >> Yeah, so that's key, that it's not just data. It's turning data into insights. Now Benoit, you came out of the world of very powerful, but highly complex databases. And we know we all know that you and the Snowflake team, you get very high marks for really radically simplifying customers' lives. But can you talk specifically about the types of challenges that your customers are using Snowflake to solve? >> Yeah, so the challenge before snowflake, I would say, was really to put all the data in one place, and run all the computes, all the workloads that you wanted to run against that data. And of course existing legacy platforms were not able to support that level of concurrency, many workload, we talk about machine learning, data science, data engineering, data warehouse, big data workloads, all running in one place didn't make sense at all. And therefore be what customers did this to create silos, silos of data everywhere, with different system, having a subset of the data. And of course now, you cannot analyze this data in one place. So Snowflake, we really solved that problem by creating a single architecture where you can put all the data into cloud. So it's a really cloud native. We really thought about how solve that problem, how to create, leverage cloud, and the elasticity of cloud to really put all the data in one place. But at the same time, not run all workload at the same place. So each workload that runs in Snowflake, at its dedicated compute resources to run. And that makes it agile, right? Florian talked about data scientist having to run analysis, so they need a lot of compute resources, but only for a few hours. And with Snowflake, they can run these new workload, add this workload to the system, get the compute resources that they need to run this workload. And then when it's over, they can shut down their system, it will automatically shut down. Therefore they would not pay for the resources that they don't use. So it's a very agile system, where you can do this analysis when you need, and you have all the power to run all these workload at the same time. >> Well, it's profound what you guys built. I mean to me, I mean of course everybody's trying to copy it now, it was like, I remember that bringing the notion of bringing compute to the data, in the Hadoop days. And I think that, as I say, everybody is sort of following your suit now or trying to. Florian, I got to say the first data scientist I ever interviewed on theCUBE, it was the amazing Hillary Mason, right after she started at Bitly, and she made data sciences sounds so compelling, but data science is a hard. So same question for you, what do you see as the biggest challenges for customers that they're facing with data science? >> The biggest challenge from my perspective, is that once you solve the issue of the data silo, with Snowflake, you don't want to bring another silo, which will be a silo of skills. And essentially, thanks to the talent gap, between the talent available to the markets, or are released to actually find recruits, train data scientists, and what needs to be done. And so you need actually to simplify the access to technologies such as, every organization can make it, whatever the talent, by bridging that gap. And to get there, there's a need of actually backing up the silos. Having a collaborative approach, where technologies and business work together, and actually all puts up their ends into those data projects together. >> It makes sense, Florain let's stay with you for a minute, if I can. Your observation space, it's pretty, pretty global. And so you have a unique perspective on how can companies around the world might be using data, and data science. Are you seeing any trends, maybe differences between regions, or maybe within different industries? What are you seeing? >> Yeah, definitely I do see trends that are not geographic, that much, but much more in terms of maturity of certain industries and certain sectors. Which are, that certain industries invested a lot, in terms of data, data access, ability to store data. As well as experience, and know region level of maturity, where they can invest more, and get to the next steps. And it's really relying on the ability of certain leaders, certain organizations, actually, to have built these long-term data strategy, a few years ago when no stats reaping of the benefits. >> A decade ago, Florian, Hal Varian famously said that the sexy job in the next 10 years will be statisticians. And then everybody sort of changed that to data scientist. And then everybody, all the statisticians became data scientists, and they got a raise. But data science requires more than just statistics acumen. What skills do you see as critical for the next generation of data science? >> Yeah, it's a great question because I think the first generation of data scientists, became data scientists because they could have done some Python quickly, and be flexible. And I think that the skills of the next generation of data scientists will definitely be different. It will be, first of all, being able to speak the language of the business, meaning how you translates data insight, predictive modeling, all of this into actionable insights of business impact. And it would be about how you collaborate with the rest of the business. It's not just how fast you can build something, how fast you can do a notebook in Python, or do predictive models of some sorts. It's about how you actually build this bridge with the business, and obviously those things are important, but we also must be cognizant of the fact that technology will evolve in the future. There will be new tools, new technologies, and they will still need to keep this level of flexibility to understand quickly what are the next tools they need to use a new languages, or whatever to get there. >> As you look back on 2020, what are you thinking? What are you telling people as we head into next year? >> Yeah, I think it's very interesting, right? This crises has told us that the world really can change from one day to the next. And this has dramatic and perform the aspects. For example companies all of a sudden, show their revenue line dropping, and they had to do less with data. And some other companies was the reverse, right? All of a sudden, they were online like Instacart, for example, and their business completely changed from one day to the other. So this agility of adjusting the resources that you have to do the task, and need that can change, using solution like Snowflake really helps that. Then we saw both in our customers. Some customers from one day to the next, were growing like big time, because they benefited from COVID, and their business benefited. But others had to drop. And what is nice with cloud, it allows you to adjust compute resources to your business needs, and really address it in house. The other aspect is understanding what happening, right? You need to analyze. We saw all our customers basically, wanted to understand what is the going to be the impact on my business? How can I adapt? How can I adjust? And for that, they needed to analyze data. And of course, a lot of data which are not necessarily data about their business, but also they are from the outside. For example, COVID data, where is the States, what is the impact, geographic impact on COVID, the time. And access to this data is critical. So this is the premise of the data cloud, right? Having one single place, where you can put all the data of the world. So our customer obviously then, started to consume the COVID data from that our data marketplace. And we had delete already thousand customers looking at this data, analyzing these data, and to make good decisions. So this agility and this, adapting from one hour to the next is really critical. And that goes with data, with cloud, with interesting resources, and that doesn't exist on premise. So indeed I think the lesson learned is we are living in a world, which is changing all the time, and we have to understand it. We have to adjust, and that's why cloud some ways is great. >> Excellent thank you. In theCUBE we like to talk about disruption, of course, who doesn't? And also, I mean, you look at AI, and the impact that it's beginning to have, and kind of pre-COVID. You look at some of the industries that were getting disrupted by, everyone talks about digital transformation. And you had on the one end of the spectrum, industries like publishing, which are highly disrupted, or taxis. And you can say, okay, well that's Bits versus Adam, the old Negroponte thing. But then the flip side of, you say look at financial services that hadn't been dramatically disrupted, certainly healthcare, which is ripe for disruption, defense. So there a number of industries that really hadn't leaned into digital transformation, if it ain't broke, don't fix it. Not on my watch. There was this complacency. And then of course COVID broke everything. So Florian I wonder if you could comment, what industry or industries do you think are going to be most impacted by data science, and what I call machine intelligence, or AI, in the coming years and decade? >> Honestly, I think it's all of them, or at least most of them, because for some industries, the impact is very visible, because we have talking about brand new products, drones, flying cars, or whatever that are very visible for us. But for others, we are talking about a part from changes in the way you operate as an organization. Even if financial industry itself doesn't seem to be so impacted, when you look at it from the consumer side, or the outside insights in Germany, it's probably impacted just because the way you use data (mumbles) for flexibility you need. Is there kind of the cost gain you can get by leveraging the latest technologies, is just the numbers. And so it's will actually comes from the industry that also. And overall, I think that 2020, is a year where, from the perspective of AI and analytics, we understood this idea of maturity and resilience, maturity meaning that when you've got to crisis you actually need data and AI more than before, you need to actually call the people from data in the room to take better decisions, and look for one and a backlog. And I think that's a very important learning from 2020, that will tell things about 2021. And the resilience, it's like, data analytics today is a function transforming every industries, and is so important that it's something that needs to work. So the infrastructure needs to work, the infrastructure needs to be super resilient, so probably not on prem or not fully on prem, at some point. And the kind of resilience where you need to be able to blend for literally anything, like no hypothesis in terms of BLOs, can be taken for granted. And that's something that is new, and which is just signaling that we are just getting to a next step for data analytics. >> I wonder Benoir if you have anything to add to that. I mean, I often wonder, when are machines going to be able to make better diagnoses than doctors, some people say already. Will the financial services, traditional banks lose control of payment systems? What's going to happen to big retail stores? I mean, maybe bring us home with maybe some of your finals thoughts. >> Yeah, I would say I don't see that as a negative, right? The human being will always be involved very closely, but then the machine, and the data can really help, see correlation in the data that would be impossible for human being alone to discover. So I think it's going to be a compliment not a replacement. And everything that has made us faster, doesn't mean that we have less work to do. It means that we can do more. And we have so much to do, that I will not be worried about the effect of being more efficient, and bare at our work. And indeed, I fundamentally think that data, processing of images, and doing AI on these images, and discovering patterns, and potentially flagging disease way earlier than it was possible. It is going to have a huge impact in health care. And as Florian was saying, every industry is going to be impacted by that technology. So, yeah, I'm very optimistic. >> Great, guys, I wish we had more time. I've got to leave it there, but so thanks so much for coming on theCUBE. It was really a pleasure having you.

>> Hello everyone, welcome back to theCUBE'S wall to wall coverage of the Snowflake Data Cloud Summit. My name is Dave Vellante and with me are two world-class technologists, visionaries, and entrepreneurs. Benoit Dageville is the, he co-founded Snowflake. And he's now the president of the Product division and Florian Douetteau is the co-founder and CEO of Dataiku. Gentlemen, welcome to theCUBE, two first timers, love it. >> Great time to be here. >> Now Florian, you and Benoit, you have a number of customers in common. And I've said many times on theCUBE that, the first era of cloud was really about infrastructure, making it more agile taking out costs. And the next generation of innovation is really coming from the application of machine intelligence to data with the cloud, is really the scale platform. So is that premise relevant to you, do you buy that? And why do you think Snowflake and Dataiku make a good match for customers? >> I think that because it's our values that align. When it gets all about actually today, and knowing complexity per customer, so you close the gap or we need to commoditize the access to data, the access to technology, it's not only about data, data is important, but it's also about the impacts of data. How can you make the best out of data as fast as possible, as easily as possible within an organization? And another value is about just the openness of the platform, building a future together. I think a platform that is not just about the platform but also for the ecosystem of partners around it, bringing the little bit of accessibility and flexibility, you need for the 10 years of that. >> Yes, so that's key, but it's not just data. It's turning data into insights. Now Benoit, you came out of the world of very powerful, but highly complex databases. And we all know that, you and the Snowflake team, you get very high marks for really radically simplifying customers' lives. But can you talk specifically about the types of challenges that your customers are using Snowflake to solve? >> Yeah, so really the challenge before Snowflake, I would say, was really to put all the data, in one place and run all the computes, all the workloads that you wanted to run, against that data. And of course, existing legacy platforms were not able to support that level of concurrency, many workload. We talk about machine learning, data science, data engineering, data warehouse, big data workloads, all running in one place, didn't make sense at all. And therefore, what customers did, is to create silos, silos of data everywhere, with different systems having a subset of the data. And of course now you cannot analyze this data in one place. So Snowflake, we really solved that problem by creating a single architecture where you can put all the data in the cloud. So it's a really cloud native. We really thought about how to solve that problem, how to create leverage cloud and the elasticity of cloud to really put all the data in one place. But at the same time, not run all workload at the same place. So each workload that runs in Snowflake at least dedicate compute resources to run. And that makes it very agile, right. Florian talked about data scientist having to run analysis. So they need a lot of compute resources, but only for few hours and with Snowflake, they can run these new workload, add this workload to the system, get the compute resources that they need to run this workload. And then when it's over, they can shut down their system. It will automatically shut down. Therefore they would not pay for the resources that they don't choose. So it's a very agile system, where you can do these analysis when you need, and you have all the power to run all these workload at the same time. >> Well, it's profound what you guys built. To me, I mean, because everybody's trying to copy it now. It's like, I remember the notion of bringing compute to the data in the Hadoop days. And I think that, as I say, everybody is sort of following your suit now or trying to. Florian, I got to say, the first data scientist I ever interviewed on theCUBE was the amazing Hilary Mason, right after she started at Bitly. And she made data science sounds so compelling, but data science is hard. So same question for you. What do you see is the biggest challenges for customers that they're facing with data science? >> The biggest challenge from my perspective is that once you solve the issue of the data silo with Snowflake, you don't want to bring another silo, which would be a silo of skills. And essentially, thanks to that talent gap between the talent and labor of the markets, or how it is to actually find, recruit and train data scientists and what needs to be done. And so you need actually to simplify the access to technology such as every organization can make it, whatever the talents by bridging that gap. And to get there, there is a need of actually breaking up the silos. I think a collaborative approach, where technologies and business work together and actually all put some of their ends into those data projects together. >> Yeah, it makes sense. So Florian, Let's stay with you for a minute, if I can. Your observation spaces, is pretty, pretty global. And so, you have a unique perspective on how companies around the world might be using data and data science. Are you seeing any trends, maybe differences between regions or maybe within different industries? What are you seeing? >> Yep. Yeah, definitely, I do see trends that are not geographic that much, but much more in terms of maturity of certain industries and certain sectors, which are that certain industries invested a lot in terms of data, data access, ability to store data as well as few years and know each level of maturity where they can invest more and get to the next steps. And it's really reliant to reach out to certain details, certain organization, actually to have built this longterm data strategy a few years ago, and no stocks ripping off the benefits. >> You know, a decade ago, Florian, Hal Varian famously said that the sexy job in the next 10 years will be statisticians. And then everybody sort of changed that to data scientists. And then everybody, all the statisticians became data scientists and they got a raise. But data science requires more than just statistics acumen. What skills do you see is critical for the next generation of data science? >> Yeah, it's a good question because I think the first generation of data scientists became better scientists because they could learn some Python quickly and be flexible. And I think that skills of the next generation of data scientists will definitely be different. It will be first about being able to speak the language of the business, meaning all you translate data insight, predictive modeling, all of this into actionable insights or business impact. And it will be about who you collaborate with the rest of the business. It's not just how fast you can build something, how fast you can do a notebook in Python or do quantity models of some sorts. It's about how you actually build this bridge with the business. And obviously those things are important, but we also must be cognizant of the fact that technology will evolve in the future. There will be new tools in technologies, and they will still need to get this level of flexibility and get to understand quickly what are the next tools, they need to use or new languages or whatever to get there. >> Thank you for that. Benoit, let's come back to you. This year has been tumultuous to say the least for everyone, but it's a good time to be in tech, ironically. And if you're in cloud, it's even better. But you look at Snowflake and Dataiku, you guys had done well, despite the economic uncertainty and the challenges of the pandemic. As you look back on 2020, what are you thinking? What are you telling people as we head into next year? >> Yeah, I think it's very interesting, right. We, this crisis has told us that the world really can change from one day to the next. And this has dramatic and profound aspects. For example, companies all of a sudden, saw their revenue line dropping and they had to do less with data. And some of the companies was the reverse, right? All of a sudden, they were online like Instacart, for example, and their business completely change from one day to the other. So this agility of adjusting the resources that you have to do the task, a need that can change, using solution like Snowflake, really helps that. And we saw both in our customers. Some customers from one day to the next, were growing like big time, because they benefited from COVID and their business benefited, but also, as you know, had to drop and what is nice with cloud, it allows to adjust compute resources to your business needs and really address it in-house. The other aspect is understanding what is happening, right? You need to analyze. So we saw all our customers basically wanted to understand, what is it going to be the impact on my business? How can I adapt? How can I adjust? And for that, they needed to analyze data. And of course, a lot of data, which are not necessarily data about their business, but also data from the outside. For example, COVID data. Where is the state, what is the impact, geographic impact on COVID all the time. And access to this data is critical. So this is the promise of the data cloud, right? Having one single place where you can put all the data of the world. So, our customers all of a sudden, started to consume the COVID data from our data marketplace. And we have the unit already thousands of customers looking at this data, analyzing this data to make good decisions. So this agility and this adapting from one hour to the next is really critical and that goes with data, with cloud, more interesting resources and that's doesn't exist on premise. So, indeed I think the lesson learned is, we are living in a world which is changing all the time, and we have to understand it. We have to adjust and that's why cloud, some way is great. >> Excellent, thank you. You know, in theCUBE, we like to talk about disruption, of course, who doesn't. And also, I mean, you look at AI and the impact that it's beginning to have and kind of pre-COVID, you look at some of the industries that were getting disrupted by, everybody talks about digital transformation and you had on the one end of the spectrum, industries like publishing, which are highly disrupted or taxis, and you can say, "Okay well, that's Bits versus Adam, the old Negroponte thing." But then the flip side of this, it says, "Look at financial services that hadn't been dramatically disrupted, certainly healthcare, which is right for disruption, defense." So the more the number of industries that really hadn't leaned into digital transformation, if it ain't broke, don't fix it. Not on my watch. There was this complacency. And then of course COVID broke everything. So Florian, I wonder if you could comment, what industry or industries do you think are going to be most impacted by data science and what I call machine intelligence or AI in the coming years and decades? >> Honestly, I think it's all of them, or at least most of them. Because for some industries, the impact is very visible because we are talking about brand new products, drones, flying cars, or whatever is that are very visible for us. But for others, we are talking about spectrum changes in the way you operate as an organization. Even if financial industry itself doesn't seem to be so impacted when you look at it from the consumer side or the outside. In fact internally, it's probably impacted just because of the way you use data to develop for flexibility you need, is there kind of a cost gain you can get by leveraging the latest technologies, is just enormous. And so it will, actually comes from the industry, that also. And overall, I think that 2020 is a year where, from the perspective of AI and analytics, we understood this idea of maturity and resilience. Maturity, meaning that when you've got a crisis, you actually need data and AI more than before, you need to actually call the people from data in the room to take better decisions and look forward and not backward. And I think that's a very important learning from 2020 that will tell things about 2021. And resilience, it's like, yeah, data analytics today is a function consuming every industries, and is so important that it's something that needs to work. So the infrastructure needs to work, the infrastructure needs to be super resilient. So probably not on trend and not fully on trend, at some point and the kind of residence where you need to be able to plan for literally anything. like no hypothesis in terms of behaviors can be taken for granted. And that's something that is new and which is just signaling that we are just getting into a next step for all data analytics. >> I wonder Benoit, if you have anything to add to that, I mean, I often wonder, you know, when are machines going to be able to make better diagnoses than doctors, some people say already. Will the financial services, traditional banks lose control of payment systems? You know, what's going to happen to big retail stores? I mean, may be bring us home with maybe some of your final thoughts. >> Yeah, I would say, I don't see that as a negative, right? The human being will always be involved very closely, but then the machine and the data can really help, see correlation in the data that would be impossible for human being alone to discover. So, I think it's going to be a compliment, not a replacement and everything that has made us faster, doesn't mean that we have less work to do. It means that we can do more. And we have so much to do. That I would not be worried about the effect of being more efficient and better at our work. And indeed, I fundamentally think that, data, processing of images and doing AI on these images and discovering patterns and potentially flagging disease, way earlier than it was possible, it is going to have a huge impact in health care. And as Florian was saying, every industry is going to be impacted by that technology. So, yeah, I'm very optimistic. >> Great, Guys, I wish we had more time. We got to leave it there but so thanks so much for coming on theCUBE. It was really a pleasure having you. >> [Benoit & Florian] Thank you. >> You're welcome but keep it right there, everybody. We'll back with our next guest, right after this short break. You're watching theCUBE.

>> Announcer: From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is theCUBE Conversation. >> Hello everyone, welcome to this CUBE Conversation, I'm John Furrier, host of theCUBE I'm in the Palo Alto CUBE Studios here with the quarantine crew, doing the remote interviews during this time of COVID. Of course, we want to check in with all of our great esteemed guests and CUBE alumni. We're here with Jerry Chen, partner at Greylock. Jerry, great to see you, it's been a while. Hope you're sheltering in place, nice camera, nice set up you got there at home, thanks for coming on. >> Thanks, John. I set up all the cameras are just for you. Everybody needs their quarantine hobbies, and for me, I kind of dust off the audio visual playbook and set this up, just for theCUBE interviews. But it's good to see you. Glad you and the family are healthy and sane as well. >> Yeah, and same to you. Let's just jump into it, obviously, COVID-19 has caused the virtualization trend, virtual everything. You're no stranger to virtualization, and VMware back in the day really changed the game on server virtualization, but the whole world's becoming virtual. And it's very interesting because now people are feeling, but we in the industry have been talking about inside the ropes for a long time, which is, the future is there, it's going to be about interactions online, software, cloud scale, these things just got accelerated, and the disruption, the change of behavior, Zoom fatigue, Webexing, all this stuff that's happening, people are kind of like, "Wow! This is the future." This is a real impact, and it's mainstream, everyone's feeling about business, to personal, your thoughts? >> Yeah, I think Satya Nadella at Microsoft had this quote recently that they've seen two decade's worth of digital acceleration and transformation in just two months, and I think what we've seen the past four months, John is all the kind of first order effects of virtualization events, not just infrastructure, but like virtualization meetings and people, telemedicine, telehealth, online education, delivery of food, all those trends are just accelerated. We're buying stuff on eCommerce, and Amazon, and Instacart before hand, that's just accelerated. We're moving towards virtualized events, online education, online healthcare, that's just accelerated. So I think we're seeing the first order effects of changing not only how we work, how we communicate, but how we shop, interact, and socialize, it compress two decades within two, three months. And so I think that's changing both how you and I interact and how we build relationships, also how companies interact with their customers, and how companies interact with employees. and it's been exciting time, because one, when there's disruption, there's opportunity, but two is giving guys like you and me a chance to kind of dust off or try new skills, and you and I are both figuring out how to exist and thrive in this role where we're now interacting in this virtualized world. >> And it's still the same game personal relationships. Content is now data. This is stuff that we've been preaching on theCUBE. You've been on many times talking about, I going to get your thoughts as a venture capitalist, whether you're making bets on the future for investments, you have a 10 year horizon, and roughly speaking average on VC deals, enterprises and customers who are building a cloud and data centers, they got to make new bets or double down on stuff they've been doing, or cancel stuff that they had going on, and refactoring. So I want to to get your thoughts on one, first on the VC side, how have you guys refactored your thinking, your meetings, and your bets? >> Yeah, so I would say, three areas, one is how we operate as a VC firm what's changed? Number two, I'll talk about what we're investing in what's good or bad, and thirdly is like, what I think changes for our portfolio companies and how startups think. So first and foremost obviously, we've gone all virtual too, with shelter-in-place, our entire team is now working remotely, working from home, but we're still open for business and we're looking to find new investments, we are investing aggressively right now, and we're just doing things over Zoom. And so we're either A, doing video calls as a partnership, or doing video calls with startups that we're meeting and founders, but I'll be honest, one thing I've done John, is I've turned off the screen more or less, I've done more phone calls because I find that a video call is great for the first or second meeting, but with a founder or executive you have relationship with, it's just really nice to actually, go on a virtual walk where me and the founder of both put AirPods or take the phone to walk outside and kind of have a conversation, that's a little of a higher bandwidth. So, I think how we're operating has changed a little bit, but to your point, is the same business, connecting with a person one-on-one, reading the market, reading the founder, and making a bet. So that hasn't changed. I think on the stuff we're investing in, like you said, all the trends around cloud and APIs and SaaS, that's accelerated. So all the trends around the new workplace, SaaS companies, collaboration, going cloud that's accelerated faster, so some of our companies like Cato Networks that does software defined, wide area networks plus cloud security that just accelerated there in this market called secure access serves edge. We've seen kind of a nice tailwind from that, more and more data is going to cloud so companies like Rockset, that's a database company that you had on theCUBE, they're going to see a benefit from that because more and more data is now in the cloud. Then finally for the founders we work with, the way to go to market, the way to sell like no one's flying around selling one-on-one anymore, you're not meeting a CSO, or the CIO over steak dinner, or you're not going to a conference anymore. So a lot of our companies are figuring out how to do more online sales, bottoms ups adoption, that could be an API, that could be open source, we're trying to find a couple more of our line of business entry to the company and sell that way, versus go to a conference or for one-on-one meeting. So it's interesting, everything's moved faster, but then this slight curve ball on how you connect with your customer has changed. And so what's the Darwin line, it's not the strongest that survives, but the most adaptable. So we're seeing the companies that founders that are most adaptable right now, they're going to thrive. >> It's interesting, we've always talked about from a tech standpoint with DevOps and cloud-native, integration or horizontally scalable has been that ethos of value creation, you've talked about moats in the past, but now it's more real life, is becoming immersed into software, and so I want to get your thoughts on this, and we have a phrase here in theCUBE team is that, every company will become a media company, that's something that we believe in, and you starting to see that people are doing more Zooms, doing more digital events, you mentioned some of the other things. Can you see any other examples where a company has to become blank? Because media is just one element of the new realities of life, right? You got to broadcast, and you got to share your stories and formats, that's media, is there other areas we're seeing, that things that weren't on the radar before with COVID, where companies have to become something like, every company will be blank? Fill in the blank. >> I would say, it's trite to say one, one, was every company is a data company, people have been saying that for a while, that's more true than ever. Number two, I'll be honest, every company now is a healthcare company, right? Because be it in health insurance for employees, the current pandemic is making the reality of both physical health, and emotional health, and mental health key for employees. And so if that was a top cost factor for hiring employees, this could be even more important going forward that every company is a health care company. And thirdly, like you said, every company becomes media company, I would say every company is also either one or two things, they're a Fintech company, because every company is now going online with their content. They wanting to create a one-to-one commercial relationship with a customer, right? That could be ads, could be transaction, could be selling something, so you're now doing business directly with your customer, so every company is a Fintech company, and I would say every company's now also, like you said, content company, right? It's the media creating, but also the data you're taking, the value you add on top of the data you're creating, and then how you share that back to your customer. So you as an enterprise company or a consumer company, you collect data from users, you're to use that data to improve your product, and this could be a SaaS offering, this could be an application, but then take that data through real time analytics, then make your product better and so because of that, if you're a data company, real time data, like our database company mentioned earlier, Rockset becomes more important. If you're a Fintech company, so all things around payments or commercial banking and relationship with your customer make sense. And if a you're a healthcare company because all your employees are now caring about healthcare, just thinking about how to make communication of healthcare with employees a lot more efficient, and a part of the reason why to work for theCUBE and work for a startup is important, so I think those three things are top of mind for all employees and all employers. I think things could change the next six or nine months, but right now I see those three being front and center. >> It's interesting. I wonder if you can add real estate company to that because if you look at the work from home, it's dynamic. >> Yeah >> I had a friend who was a fellow dad with my son's lacrosse team, he lives in Los Gatos, he's been involved in Google, Tesla, building up their facilities, and he had an interesting guest post on SiliconANGLE, and he was saying, it's not just give them some extra pay for their internet access, companies got to rethink the facilities question, right? Because do you pay rent for your employees? Do you provide the VPN, beyond VPN security, for instance? So again, you start to see these new opportunities or challenges, open up new thinking, this is going to be a wave of opportunity. >> Well, that virtualization between work and home has now been blurred like you said earlier, John and so if you're a technology company that enables remote access or distribute access, like Cato Networks when the portfolio comes and Greylock around our road office, home office, that is now how to right? So I had this conversation with Jason of Austin, askSpoke, one of our companies, there's like a mass of hierarchy for working out, and at the base of the mass of hierarchy is like good internet access, right? That's the how to, you need security, right? Because if you don't have secure access, you can't work, and then you have information management, knowledge management, how to communicate, right? And then collaboration, so, you have now this new hierarchy of what is required you to work in this new world, but also the tools and the technologies, be it secured access service edge like CATO or IT Helpdesk for all employees like askSpoke, both of those things become dial tone for any remote work. Just like videoconferencing, we couldn't do this in the same way, 10, 15 years ago, that's become kind of a must have, and so I think it'd be fascinating how we went from the office world where I gave you a laptop, or a computer, or a desk to this home office world, where maybe you now I have to pay for my fancy camera setup and my VPN. >> Well certainly you're getting good ROI on your setup and sure Greylock will take care of that plenty of dough big, billions of dollars under management. And by the way, must have hire things in our houses, ping and internet access, so we fight for that ping time, I got 12 I'm like what's going on? Who's gaming? We have to get the kids off of Twitch, and whatnot. but in all seriousness, this is what the reality is. So now for the average person out there, there's a lot of discussion around mental health, you mentioned taking it off the video conferencing and going for a walk, or just talking on the phone, this speaks to the humanization aspect of what's going on, mental health, social interaction, we're social creatures, collaboration has to be re-imagined. What's your view on all this? >> I think absolutely, look, humans are social creatures by nature, and I think part of the reason why I had this conversation with my founders early during COVID-19, that it's both a healthcare crisis. It's an economic crisis with all the million and millions of people unemployed, but it's also an emotional crisis because one, we're not connected to family, friends, and loved ones, and we're sheltering home with either ourselves or just a handful of people. And so we're trying to figure out ways to like, recreate social connections, and that's a phone call, it's a video call, it's Zoom dinners, it's Zoom dinners, the Zoom parties, is key. I think, going on socially just in walks is another thing to kind of like, play and experience things together. But my two cents is if you're a startup, right now, it can help connect people work-wise or socially, that's just going to be super critical for the new experience. And I think people are discovering new ways to use technology, so Zoom was never meant to be used the way it is today, I think that's amazing. I think how people think about voice video, and email, and chat are changing as well. So I'll finding new ways to like, play games online with my nieces, or communicate with them. And I think as an employer in these companies, like HR software, and how you like manage, and coach, and lead your employees is going to change as well. And so, you have this world where we're all in one building, and think about how you as a CEO, or as a leader now can actually coach, develop, and enable your employees across the world. >> I want to get your thoughts on cloud, we've had many conversations around cloud computing as to rise of AWS, I remember one it was a big Twitter conversation, I think about last year where what enabled Amazon and I think one of the things that came out of it was virtualization enabled them to have all these different servers. What do you see coming out of this virtualization of our lives with the COVID-19, as people start to figure out beyond the triage of stabilization, and as they get foundationally set up in COVID, coming out of it, companies and people have to have a growth strategy, whether it's life or business, people want to come out of this on the upside, whether it's emotional or with their business, what do you see being enabled? What needs to be in place? What kind of scale? What kind of environment? Because this is where I think the entrepreneurs are really going to sharpen their energy on their creativities looking at the expectations and experience needed coming out of this, it may look completely different than what we were talking about a year ago. What's your thoughts? >> Well, I think individually, people can use this time to prove their skills in different ways. So I think as an employee, as CEO, as a founder, you take the time to like invest in new skills, and that could be, "Hey, how do our community collaborate and manage my team remotely?" So I think CEOs and founders that can understand how to motivate, educate, train their employees in this new world, well, those are skills going forward. So communication has always been a great skill John, for any leader, any founder, it's 10X more important in this new virtualized work role, communication, motivation, and leading people over remote work is going to be a new skill that people have. Managing remote teams, managing fully distributed teams or half distributed, half headquarters, so understanding how to organize and lead your team in this kind of half in the office half out of the office role, that's going to be a challenge as well. So any tools, technology and tips there, but I think in terms of the founders that can now hire employees, find customers, sell customers, and manage a distributed team, those three things in this new world, even post COVID-19, we're not going back to the way we were, so the ability to actually use skills around email, creating content, Slack, Zoom, video chat, online conferences, what was that? "Video Killed the Radio Star", the first MTV Video. So, COVID-19, and Zoom, and video collaboration, what's that do to the old skills or the old founders? And what do they enable? So just like TV replaced radio as a medium, and now this virtualized world is going to replace kind of the medium we had beforehand, so, there'll be new generation of founders and investors coming out of this generation that would be for the next 10, 15 years, and I'm excited to be part of that. >> Yeah, and it's super big opportunity, because you have these kind of medium changes, new protocols get developed, new responsibilities and roles emerge, value creation capture, equations change, right? So you're looking at things like online events, for instance, they don't happen anymore, and even when they do come back they'll probably be hybrid anyway. So you got virtual, hybrid, public it sounds like a cloud play to me, public events, hybrid events, and private events, I guess. >> Yeah, virtual private events, but the same thing holds, just like cloud internet increased the reach, right? So all of a sudden, you can reach a bigger audience than just radio, TV, or the newspaper. Now you have these virtualized events like say private events, public events, hybrid events, you as a company or a media property, like theCUBE can now reach a larger audience, right? It's global, you don't have to be there in person, you're going to have the remote audience as a first class citizen, now more than ever, it's just like the internet replacing newspaper and print, people really care about print and newspaper, but really the reach online is always a magnitude larger than print, so all of a sudden you thought more about the print, so the online audience more than print audience. So now going forward, you're going to think about the virtual audience that's remote versus the physical audience. And so you're going to have to create experiences that are their world class or both properties. So just like the cloud, you think about the big three cloud providers, private cloud, as a technology company, you think about all three venues, all three infrastructures as a first class citizen. It's not going to be all one cloud, it's not all going to be one note, if you will. So it forces everyone to think, not just kind of one path, but multiple paths, so like classic problems a lot of founders think, okay, I'm going to do an enterprise private cloud strategy only or I'm going to do a cloud only SaaS strategy. Now founders of this do both the same time, I got to address the private cloud on premise business at the same time as the cloud business, and not just one cloud, three or four clouds around the world. So it forces founders to be able to do more things at one time and the ability for a company to attack multiple venues or multiple territories at the same time, they'll be successful. And the days where I can just do one cloud or one venue, or one audience, those are gone, and so, folks like yourself, John, and what you've built here at theCUBE with everyone else, they can reach multiple audiences at the same time, that's going to be very powerful. >> And we're going to be marketing and doing a lot more online events, like you said, it's going to be easier to tap into our 7000 plus alumni to get people together to create great content. And again, content value to remote audience is interesting. So that shifts into the conversation that everyone talks about the remote worker. Well, what about the remote customer, the remote prospects? So this is going to change how companies have to be change of behaviors. And it's going to be driven by developers, because it's not like one app can solve it, 'cause you got to integrate, you got to have some integration points. So this is the question, are we moving away from that monolithic SaaS app? Or is it going to be some SaaS apps that need to integrate with others? Will there be an abstraction layer of innovation around? Because at the end of the day, these new workloads and new apps going to be built. If you're going to run an event, if I'm a SAP or a big company, I'm not going to rely or may not want to rely on a vendor. In fact, the CEO of SAP said, 'cause their site crashed for their event, "I'm not going to rely on a third party to run my business event." 'Cause their business model is the event, not just a supplier selection for a SaaS app. So interesting kind of new surge of online activity might tip the scales for the supplier side. >> I think you're right John, I think because now the, just like the IT technology is now your business, you're going to basically do one or two things, one, vet the IT technology provider that much higher or harder. But number two to your point, I think the way you sell and you reach companies is going to be through developers and yes, you're going to have these large monolithic SaaS apps before, but almost every SaaS app now has APIs for integration, and so to your point, is that integration and the ability to have multiple companies work together, and share data, and collaborate, that's going to be more important. And so really at Greylock and myself, I've been investing in developer-led technologies and developer-led adoption, or API, or open source-led adoption, for seven plus years now. And the truth of matter is, that's going to be even more powerful going forward. Nassim Taleb would say that's anti-fragile, right? So having one giant app is fragile, but having a bunch of small apps, or a bunch of APIs, or a bunch of developers using your open source technology, or using your API technology to build an application, that's anti-fragile, because at the end of the day, that's going to be more reliable for your customer than a single point of failure, which can be one giant application. So all the big apps like Salesforce, have now other platforms, right? They have APIs, they have extensibility, they understand that there's a long fat tail of solutions needed to build. And all the new startups are doing open source, or API-led adoption 'cause they understand that the fastest route to create value for the customer, is also the most robust technology stack that a customer can build upon. I think that's super insightful, in fact, that is, I think so compelling, because if you think about it, that's the formula for great investments from a startup standpoint. But now, because of COVID, you said, everything's been pulled forward and accelerated at the same time, there's a collision, not all the enterprises are that strong, they're not that developer-led. So I think, to the point about acceleration, now, the enterprises, and we've seen pockets of this with cybersecurity where they have their own, in-house teams doing a variety of different development. The customers have to be developer-led, because that's where the value is, so they have to have a supplier with the right stack and integration frameworks. Now, the customers who haven't really been developer-led, have to be developer-led, what's your take on that? >> Absolutely true. 20 years ago, the CIO of a company that used to be the monopoly supplier technology for the company, they decided what hardware to use, what servers, what stores to use, what applications to buy. And then all of a sudden, like Amazon came around and said, "Well, look, here's a set of APIs, go build what you want." And so the competition for kind of like the centralized decision making became Amazon. And guess what? CIOs reacted, they got better, they got smarter, and those that embrace kind of like an API developer-led adoption, became the CIOs you wanted to have in the company. So I think, CIOs in this cloud mobile era have adopted that philosophy that, look, my job now as the CIO is to enable my developers, my employees, which really the assets of the company is the people, to have the right tools. So you're asked a bunch of cloud APIs, like Rockset or whatever for data, or here's a bunch of resources, or open source technologies for you to pull. So like I invested in a company recently called Chronosphere, it's an open source technology around metrics and monitoring. So, "Hey, use this open source time series database for monitoring your cloud and build upon that," and they're not going to say, "We're going to pick one large vendor that's monolithic," we're going to say, "Here's an open source tech company or a cloud API, go build upon that." And the companies that are embracing that philosophy of API-led or developer-led, John, they're going to be far ahead the better CIOs, the better companies, because the rate of digital adoption has just gone exponential, so we were on this super fast path already, and with quarantine in COVID, we've accelerated all that digital transformation, so every brick-and-mortar retailer now has to be eCommerce retailer. So they're making a slow digital transformation to go from brick-and-mortar stores to online stores. Now like brick-and-mortar retail is pretty much not happening, and probably won't come back to the same levels for a while, they need to accelerate their move towards digital transformation, right? >> And IT certainly exposes the people who haven't really made those investments, because literally action and the mandate, now take action, make those changes, totally want to dig into this developer-led vision, because I think that's very real. And the new decision is going to be made on what to do. I'm happy to see the DevOps thinking, the agile, speed become the table stakes. So with that, this week, Google is having their nine-week digital event of 200 plus sessions, essentially, an asynchronous event, it's going to be sprinkled out, they've kind of pretty much released the videos, most of them today. Over the next eight, nine weeks, you're going to see a lot of videos. Google, one of the big three got AWS, Azure, Google, what's your assessment of the horses on the track relative to the cloud? >> I've been talking about this for seven, eight, nine years, I first met it, like in the first or second Amazon reinvent and what was the forecast? And we said, well, it's not a winner take all, but right now, it's a winner take most. Amazon's clearly the market share leader, Azure coming up quickly behind the enterprise, Google's a third but they're doing some smart things around technology. Google announced a bunch of things today, which I think are very smart. So for example, they announced BigQuery Omni, which is BigQuery that's in query, their kind of a data warehouse, also query data and private cloud Azure or Amazon. And so strategically, if you're the number three player, you're going to push a multi-cloud agenda with BigQuery Omni, or Google Anthos, which is kind of a multi-cloud platform. And for Google, I think is the right strategy. I also think it's the right strategy for most customers to be multi-cloud, because you can't be dependent upon, a single point of failure in your applications. You can't be dependent on a single cloud as well. So I think multi-cloud is probably the direction we're headed as cloud matures. And I think Google's making a bunch of the right choices around embracing multi-cloud, and today they made that choice with BigQuery Omni, and so I think they're playing catch up but they're playing that game. I think Amazon's clue is still in the lead and still it blows my mind, and it's continuing to impress me what they've done over the past 10 years in terms of improving the cloud offering and the cloud services up and down the stack, and I think the past five, six years, what Azure has done, has been super impressive in terms of, Microsoft embracing, open source embracing, cloud as an ethos against their legacy business of operating systems and servers on premise, they've done a great job of embracing the next generation. But I do think, looking around the corner this new developer-led mindset is going to matter, right? So the cloud tomorrow will be APIs, like Stripe for payments, Twilio for communication. So I see the next evolution not just being VMs and containers, but also a bunch of cloud services around data, security, and privacy. And the cloud vendors can build this next generation of database APIs, or privacy APIs, security APIs, that they're going to be in the catbird seat for the next 10 years of applications are going to be built. >> And it'll be interesting to your developer-led position, our conversation around that, if the developer is going to be leading, is it going to be an abstraction layer across multiple clouds? Or do I have to have my Google developers, and my Amazon developers, and my Azure developers? How do you see that playing out? Because I do believe developer-led is the way, the question is, how do you avoid forking resources, right? So you might want to have an (mumbles) I get that, but if I'm going to go double down on say, a cloud, I'm going to go deep, I'm going to hire developers. >> It's interesting, history suggests you have multiple teams remember, we used to have a Unix team or a Sun team inside companies, right? You had a Windows team, you had a kind of a Solaris and Linux team, and there's a Microsoft team, and a non-Microsoft team, in most companies and they didn't really work well together and they had kind of two groups in most companies. I think that was an okay way to get started, but ultimately, to your point, that was not cost effective at all, it was defeating, you see now you had to like have to rethink it, what was my data backup strategy? Okay, I have a Windows backup strategy, and a Unix Solaris backup strategy. So I think we're not going to make the same mistake again, right? I think what will happen, we'll going to have multiple clouds, Amazon, Google, Azure, and then on premise private cloud, so call it, three, four, or five clouds. And then you're going to have a set of tools that can abstract away, not 100% of the clouds, but I think the best developer tools, the best APIs will be multi-cloud. So I can get 80% or 90% of what I want to be done through this developer-led layer of APIs, be it databases or analytics. And then, 10 to 20% of the code, you can write will be able to take care of what's unique to Amazon, what's unique to Azure, what's unique to Google or what's unique to your own private cloud. But I think we're seeing a layer of technology and that's true to all the startups. With back and true to all the startups I see that lets you get most of the way done with a single platform, seamlessly AI technologies, and that's what customers want, right? They don't want to create modal fiefdoms, they want-- >> They want choice. The want choice, but the reality is they don't always get it. I want to go through a throwback to 2010 when Paul Maritz, head of the VMware our first CUBE gig, he said, there's a hardened top. Okay, the hardened top was, you don't worry about what's underneath the top, we're just going to focus on top of the stack that was classic kind of, the stack would develop and you'd had standardization. You mentioned you had Windows teams and Unix teams, but also you could argue that, back then you had Cisco and Wellfleet vendors, but you didn't have two teams of routers, you had one standard that ran the remote interoperability, and OSPF routing, or whatever you had going on, so you had some standardization, how do you view that? Because you want some standardization to have the interoperability, the SLAs and the security, at the same time you want to have flexibility, kind of above what may be called a hardened top, is there a hardened top in multi-cloud? >> I'd say hard top doesn't exist in same way. I think back in the day, you had proprietary technologies, operating systems and firmware, right? So windows was closed, a lot of the network operating systems were closed source. Now you can't get away with that. So you have open source technologies today and public APIs. And so the pressure of both one, competition, two, public APIs that people can read, copy, adjust, three, open source, and it's just customer demand not to be locked into a hard top anymore, that's largely going to go away. So I think most of the major vendors success will try to kind of more or less lock you in and keep you stuck on their platform, their technology, and that's fine, right? Every successful company should be able to do that. But I think the ability to lock you in through proprietary software or operating systems, that's not going to happen anymore. I see through cloud and open source, what we've seen is kind of interoperability, and flexibility is the default, if you can't meet those needs, customers will go other ways. There'll be proprietary technologies, proprietary extensions along the way, but 60, 70% of what you want is going to be compatible with most technologies and most clouds. If you're not going to offer choice and freedom to our customers, they'll go elsewhere. If you don't offer a flexible solution, John, someone else will, and the customers will choose a more flexible solution. >> I would agree with you. Outside of latency, which is laws of physics, value is the lock in, if you're creating value, that's really what the customers want, they get to capture that value. Well, Jerry, great to have you on. I love the new setup. We're going to have to make this more of it. We can bring you in on the podcast when we get Zooms over the weekend, maybe put a panel together. Let's get Carl Eschenbach some VMware alarms to come on, give the perspective, what's going on. And I thank you for taking the time and great to see that you're healthy and doing well. Thanks. >> Me too. Thanks, john. Anytime, I love to be on theCUBE, so I look forward to my next trip. >> All right, Jerry Chen, great CUBE alumni, our first interview over nine years ago, he brought that up. That was at the second reinvent, boy has the world changed, and it's only going to accelerate even faster. Everything's changing new bets are being made, decisions have to be evolving quickly and faster. If you're not fast, you will be in the pile of dead companies and not making it. So, Jerry Chen breaking it down as venture capitalist for Greylock. I'm John Furrier with theCUBE. Thanks for watching. (soft music)

>> Narrator: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hi everybody, welcome to this CUBE conversation. This is Dave Vellante, and as part of my CEO and CXO series I've been bringing in leaders around the industry and I'm really pleased to have Sudheesh Nair, who is the CEO of ThoughtSpot Cube alum. Great to see you against Sudheesh, thanks for coming on. >> My pleasure Dave. Thank you so much for having me. I hope everything is well with you and your family. >> Yeah ditto back at you. I know you guys were in a hot spot for a while so you know we power on together, so I got to ask you. You guys are AI specialists, maybe sometimes you can see things before they happen. At what point did you realize that this COVID-19 was really going to be something that would affect businesses globally and then specifically your business. >> Yeah it's amazing, isn't it? I mean we used to think that in Silicon Valley we are sitting at the top of the world. AI and artificial intelligence, machine learning, Cloud, IOT and all of a sudden this little virus comes in and put us all in our places basically. We are all waiting for doctors and others to figure these things out so we can actually go outside. That tells you all about what is really important in life sometimes. It's been a hard journey for most people because of what a huge health event this has been. From a Silicon Valley point of view and specifically from artificial intelligence point of view, there is not a lot of history here that we can use to predict the future, however early February we had our sales kick off and we had a lot of our sellers who came from Asia and it became sort of clear to us immediately during our sales kick off in Napa Valley that this is not like any other event. The sort of things that they were going through in Asia we sort of realized immediately that us and when it gets to the shores of the US, this is going to really hurt. So we started hunkering down as a company, but as you mentioned early when we were talking, California in general had a head start, so we've been hunkered down for almost five weeks now, as a company and as the people and the results are showing. You know it is somewhat contained. Now obviously the real question is what next? How do we go out? But that's probably the next journey. >> So a lot of the executives that I've talked to, of course they start with the number one importance is the health and well-being of our employees. We set up the work from home infrastructure, et cetera. So that's I think, been fairly well played in the media and beginning to understand that pretty well. Also, you saw I talked to Frank Slootman and he's sort of joked about the Sequoia memos, that you know eliminate unnecessary expenses and practices. I've always eliminated unnecessary expenses, keep it to the essentials, but one of the things that I haven't probed with CEOs and I'd love your thoughts on this is, did you have to rethink sort of the ideal customer profile and your value proposition in the specific context of COVID? Was that something that you deliberately did? >> Yeah so it's a really important question that you asked, and I saw the Frank interview and I a 100% agree with that. Inside the company we have this saying, and our co-founder Ajeet actually coined the phrase of living like a middle-class company, and we've always lived that, even though we have, 300 plus million dollars in the bank and we raised a big round last year. It is important to know that as a growth stage company, we are not measured on what's in the bank. It's about the value that we are delivering and how much I'll be able to collect from customers to run the business. The living like a middle-class family has always been the ethos of the company and that has been a good thing. However, I've been with ThoughtSpot for a little more than 18 months. I joined as the CEO. I was an early investor in the company and there are a couple of big changes that we made in the last 18 months, and one of is moving to Cloud which we can talk. The other one has been around narrowing our focus on who we sell to, because one of the things that, as you know very well Dave, is that the world of data is extremely complex. Every company can come in and say, "We have the best solution out there" and it can just be in the world, but the reality is no single product is going to solve every problem for a customer when it comes to a data analytics issue. All we can hope for is that we become part of a package or solution that solves a very specific problem, so in that context there's a lot of services involved, a lot of understanding of customer problems involved. We are not a bi-product in the sense of Tableau or click on Microsoft, but they do. We are about a use case based outcomes, so we knew that we can't be everywhere. So the second change we made is actually a narrower focus, exclusively sell to global. That class, the middle class mentality, really paid off now because almost all the customers we sell to are very large customers and the four work verticals that we were seeing tremendous progress, one was healthcare, second was financial sector, the third was telecom and manufacturing and the last one is repair. Out of these four, I would say manufacturing is the one where we have seen a slowdown, but the other verticals have been, I would say cautiously spending. Being very responsible and thus far, I'm not here to say that everything is fine, but the impact if you take Zoom as a spectrum, on one end of the spectrum, where everything is doing amazingly well, because they are a good product market fit to hospitality industry on the other side. I would say ThoughtSpot and our approach to data analytics is closer to this than that. >> That's very interesting Sudheesh because, of course health care, I don't think they have time to do anything right now. I mean they're just so overwhelmed so that's obviously an interesting area that's going to continue to do well I would think. And they, the Financial Services guys, there's a lot of liquidity in the system and after 2009 the FinTech guys or the financial, the banks are doing quite well. They may squeeze you a little bit because they're smart negotiators, but as you say manufacturing with the supply chains, and in retail, look, if your ecommerce I mean Amazon hit, all-time highs today up whatever, 20% in the last two weeks. I mean just amazing what's happening, so it's really specific parts of those sectors will continue to do well, won't they? >> Absolutely, I think look, I saw this joke on Twitter, what's the number one cost? What is in fact (mic cuts out). Very soon people will say it is COVID and even businesses that have been tried to, sort of relatively, reluctant to really embrace the transformation that the customers have been asking for. This has become the biggest forcing function and that's actually a good thing because consumers are going to ultimately win because once you get groceries delivered to you into your front doors, it's going to be hard to sort of go back to standing in the line in Costco, when InstaCart can actually deliver it for you and you get used to it, so there are some transformation that is going to happen because of COVID. I don't think that society will go back from, but having said that, it's also not transformation for the sake of transformation. So speaking from our point of view on data analytics, I sort of believe that the last three to four years we have been sort of living in the Renaissance of enterprise data analytics and that's primarily because of three things. The first thing, every consumer is expecting, no matter how small or the big business, is to get to know them. You know, I don't want you to treat me like an average. I don't want you treat me like a number. Treat me like a person, which means understand me but personalize the services you are delivering and make sure that everything that you send me are relevant. If there's a marketing campaign or promo or customer support call, make sure it's relevant. The relevance and personalization. The second is, in return for that. customers are willing to give you all sorts of data. The privacy, be damned, so to a certain extent they are giving you location information, medical information,-- And the last part is with Cloud, the amount of data that you can collect and free plus in data warehouse like Snowflakes, like Redshift. It's been fundamentally shifted, so when you toggle them together the customers demand for better actors from the business, then amount of data that they're willing to give and collect to IOT and variables and then cloud-based technologies that allows you to process and store this means that analyzing this data and then delivering relevant actions to the consumers is no longer a nice to have and that I think is part of the reason why ThoughtSpot is finding sort of a tailwind, even with all this global headwind that we are all in. >> Well I think too, the innovation formula really has changed in our industry. I've said many times, it's not Moore's law anymore, it's the combination of data plus AI applied to that data and Cloud for scale and you guys are at the heart of that, so I want to talk about the market space a little bit. You look at BI and analytics, you look at the market. You know the Gartner Magic Quadrant and to your point, you know the companies on there are sort of chalk and cheese, to borrow a phrase from our friends across the pond. I mean, you're not power BI, you're not SaaS. I mean you're sort of search led. You're turning natural language into complex sequel queries. You're bringing in artificial intelligence and machine intelligence to really simplify and dramatically expand and put into the hands of business people analytics. So explain a little bit. First of all, do I have that sort of roughly right? And help us frame the market space how you think about it. >> Yeah I mean first of all, it is amazing that the diverse industry and technologies that you speak to and how you are able to grasp all of them and summarize them within a matter of seconds is a term to understand in itself. You and Stew, you both have that. You are absolutely right. So the way I think of this is that BI technologies have been around and it's played out really well. It played it's part. I mean if you look at it the way I think of BI, the most biggest BI tool is still Excel. People still want to use Excel and that is the number one BI tool ever. Then 10 years ago Tableau came in and made visualizations so delightful and a pic so to speak. That became the better way to consume complex data. Then Microsoft came in Power BI and then commoditized and the visualization to a point that, you know Tableau had to fight and it ended up selling to the Salesforce. We are not trying to play there because I think if you chase the idea of visualization it is going to be a long hard journey for ThoughtSpot to catch Tableau in visualization. That's not what we are trying to do. What we are trying to do is that you have a lot of data on one hand and you have a consumer sitting here and saying data doesn't mean you treated me well. What is my action that is this quote, very customized action quote. And our question is, how does beta turn into bespoke action inside a business? The insurance company is calling. You are calling an insurance company's customer support person. How do you know that the impact that you are getting from them is customized. But turning data into insight is an algorithmic process. That's what BI does, but that's like a few people in an organization can do that. Think of them like oil. They don't mix with water, that's the business people. The merchandising specialist who figures out which one should become site and what should be the price what should be ranking. That's the merchandiser. Their customer support person, that's a business user. They don't necessarily do Python or SQL, so what happens is in businesses you have the data people like water and the business people who touch the customer and interact with them every day, they're like the water. They don't mix. The idea of ThoughtSpot is very simple. We don't want this demarcation. We don't want this chasm. We want to break it so that every single person who interact with the customer should be able to have an interactive storytelling with the data, so that every decision that they make takes data into insight to knowledge to action, and that decision-making pipeline cannot be gut driven alone. It has to be enabled by data science and human experience coming together. So in our view, a well deployed data platform, decision-making platform, will enhance and augment human experience, as opposed to human experience says, this data says that, so you've got to pick one. That's an old model and that has been the approach with natural language based interactive access with the BI being done automated through AI in the backend, parts what we are able to put very complex data science in front of a 20 year experienced merchandising specialist in a large e-commerce website without learning Python, without learning people, without understanding data warehouse >> Right so, a couple of things I want to pick up on. I mean data is plentiful, insights aren't. That's really the takeaway from one of the things that you mentioned and this notion of storytelling is very, very important. I mean, all business people, they better be storytellers in some way shape or form and what better way to tell stories than with data, and so, because as you say it's no longer gut feel, it's not the answer anymore. So it seems to me Sudheesh, that you guys are transformative. The decision to focus on the global 2000 and really not, get washed up in the Excel, well I could just do it in Excel, or I'm going to go get Power BI, it's good enough. It's really, you're trying to be transformative and you've got a really disruptive model that we talked about before, search led and you're speaking to the system, or, typing in a way that's more natural, I wonder if you could comment on that and particularly that disruption of that transformation. >> Remember we are selling to global 2000. Almost all of them will have Tableau or one of these power BI or one of these solutions already, so you're not trying to go right and change that. What we have done is very clearly focus on use cases. We're transforming data into action. We will move the needle for the bit, but for example with the COVID situation going on, one of the most popular use cases for us is around working capital management. Now a CFO who's been in the business for 20 or 30 years is an expert and have the right kind of gut feeling about how her business is running when it comes to working capital. However, imagine now she can do 20 what-if scenarios in the next five seconds or next 10 minutes without going to the SPN 18, without going to the BI team. She can say what if we reduce hiring in Japan and instead we focus them on Singapore? What if we move 20% of marketing dollars from Germany to New York? What would be the impact of AR going up by 1% versus AP going down by 1%? She needs to now do complex scenarios, but without delay. It's sort of like how do I find a restaurant through Yelp versus going to the lobby to talk to a specialist who tells me the local restaurant. This interactive database storytelling for gut enhances the decision-making is very powerful. This is why, customer have, our largest customer has spent more than $26 million with ThougthSpot and this is not small. Our average is around close to 700k. This week for example, we are having a webinar where Verizon's SVP of Analytics specifically focused on finance. He's actually going to be on a webinar with our CFO. Our CFO Sophie, one of our financial specialists and Jeff Noto from Verizon are going to be on this talking about working capital management. What parts ThoughtSpot is a portion of, but they are sharing their experience of how do we manage, so that kind of varies, like extremely rigid focus on use cases, supply chain, modeling different things so that someone who knows Asia can really interact with the data to figure out if our supply chain from Bangladesh is going to be impacted because of COVID can we go to Ecuador? What will that look like? What will be the cost? What's the transportation cost, the fuel cost, Business has become so complex you don't have time to take five, six days to look at the report, no matter how pretty that report is, you have to make it efficient. You need to be able to make a lightning fast decision and something like COVID is really exposing all of that because day by day situation on the ground is changing. You know, employees are calling in sick. The virus is breaking out in one place, other place. If it's not, curves are going up and down so you cannot have any sort of delay between human experience and data signs and all of that comes down to your point telling visual stories so that the organization can rally behind the changes that they want to make. >> So these are mission-critical use cases. They are big problems that you're solving and attacking. As you said, you're not all things to all people. One of the things you're not is a data store, right? So you've got a partner, you've got to have an ecosystem, whether it's cloud databases, the cloud itself. I wonder if you could talk about some of the key partnerships that you're forming and how you're going to market and how that's affecting your business. >> Yeah, I mean one of the things that I've always believed in Silicon Valley is that companies die out of indigestion, not out of starvation. You try to do everything. That's how you end up dying and for us in the space of data, it's an extremely humbling space because there is so much to do, data prep, data warehousing, you know a mash-up of data, hosting of data, We have clearly decided that our ability is best spent on making artificial intelligence to work, interactive storytelling for business use and that's it. With that said, we needed a high velocity agility partner in the back end and Cloud based data warehouse have become a huge tailwind for us because our entire customer deployments are on Cloud, and the number one, obviously as you know from Frank's thing, the Snowflake has actually given, customers have seen Snowflakes plus ThoughtSpot is actually a good thing and we are exclusive in global 2000 and the Snowflake is climbing up there and we are able to build a good mutual partnership, but we are also seeing a really creative partnership all the way from product design to go to market and compensation alignment with Amazon on their push on Redshift as well. Google, we have announced partnership. There is a little bit of (mic cuts out) in the beginning we are getting, and just a couple of weeks ago we started working with Microsoft on their Azure Synapse algo. Now I would say that it's lagging, we still have work to do but Amazon and Snowflake are really pushing in terms of what customers want to see, and it completely aligns with our value popular, one plus one equals three. It really works well for our customers >> And Google is what, BigQuery plus Google Cloud, or what are you doing there? >> Yep so both Amazon and Google. Well, what we are doing at three different pieces. One if obviously the hosting of their cloud platforms. Second is data warehouse and enterprise data warehouse, which is Redshift and BigQuery. Third, we are also pretty good at taking machine learning algorithms that they have built for specific verticals. We're going to take those and then ingest them and deliver better. So for example if you are one of the largest supply companies in the world and you want to know what's the shipment rate from China and it shows and then the next thing you want to know is what the failure rate on this based on last behavior when you compressed a shipment rate, and that probably could use a bit of specific algorithms and you know Google and others have actually built a library of algorithms that can be injected into ThoughtSpot. We will simply answer the question of we may have gotten that algorithm from the Google library, sort of the business use is concerned. It doesn't really matter, so we have made all that invisible and we are able to deliver democratized access to Bespoke Insights to a business user, who are too sort of been afraid to deal with the sector data. >> Since you mentioned that you've got obviously several hundred million dollars in cash. You've raised over half a billion. You've talked previously about potential acquisitions, about IPO, are you considering acquisitions? M&A at this point in time? I mean there may be some deals out there. There's certainly some talent out there, but boy the market is changing so fast. I mean, it seems to, certain sectors are actually doing quite well. Will you consider M&A at this point? >> Yes, so I think IPO and M&A are two different-- IPO definitely, it will be foolish to say that this hasn't pushed our clients back a little bit because this is a huge event. I think there will be a correction across valuation and all of that. However, it is also important for us we use this opportunity to look at how we are investing our resources and investment for long-term versus the short-term and make sure that we are more focused and more tightening at the belt. We are doing that internally. Having said that, being a private company our valuation is, you know at least in theory, frozen, and then we have a pretty good cash position of close to $300 million, which means that it is absolutely an opportunity for us to seriously consider M&A. The important thing going back to my adage of, companies don't die out of starvation. It is critical to make sure that whatever we do, we do it with clarity. Are we doing it for talent? Are we doing it for tech? Or are we doing it for market? When you have a massive event like this, it is a poor idea to go after new market. It is important to go to our existing customers who are very large global 2000 firms and then identify problems that we cannot solve otherwise and then add technology to solve those problems, so technology acquisitions are absolutely something to consider, but it needs some more time to settle in because, the first two weeks were all people who were blindsided by this, then the last two weeks we have now gotten the mojo back in sales and mojo back in engineering, and now I think it is time for us to digest and prepare for these next two, three quarters of event and as part of that, companies like us who are fortunate enough to be on a good cash position, we'll absolutely look for interesting and good deals in the M&S space. >> Yeah, it makes sense, is tell and tech and, post IPO you can worry about Tam expansion. You'll be under pressure to do that as the CEO, but for now that's a very pragmatic approach. My last question is, there's some things when you think about, you say five weeks now you've been essentially on lockdown. You must, as many of us start thinking about wow, a lot of this work from home which came so fast people wouldn't even think about it earlier. You know, some companies mandated the beehive approach. Now everybody's open to that. There are certain things that are likely to remain permanent post COVID. Have you thought much about that? Generally and specifically how it might affect your business, the permanence of post COVID. Your thoughts. >> Yeah I've thought a lot about it. In fact, this morning I was speaking with our CRO Brian McCarthy about this. I think the change will happen, think of like an onion's inner most layer, I think the most, my hope is, that the biggest change will be in every one of us internally, as a what sort of a person am I and what does my position in the world means. The ego of each one of us that we carry because if this global event in one shot did not make you rethink your own sort of position in this big universe I think that's a mess. So the first thing has to be about being a better person. The second thing is, I had this two, three days of fever which was negative for COVID but I isolated myself, but that gave me sort of an idea of dipping in the dark room where I'm hoping my family won't get infected and you know my parents are in India so I sort of also realized that what is really important for you in life and how much family should mean to you, so that goes to the first, yourself second, your relationship with family, but having said that, the third thing when it comes to business building is also the importance for building with quality people, because when things go wrong it is so critical to have people who believe in the purpose of what you are trying to build. People with good faith and unshakable faith, personal faith and unshakable faith in the purpose of the company and most importantly you mentioned something which is the story telling. People, leaders who can absolutely communicate with clarity and certainty. It becomes the most important thing to lead an organization. I mean, you are a small business owner. You know we are in a small company with around 500 people. There is nothing like sitting at home waiting to see how the company is doing over email if you're a friend line engineer or a seller. Communication becomes so critical, so having the trust and the respect of organization and have the ability to clearly and transparently communicate is the most important thing for the company and over communicating due to the time of crisis. These things are so useful even after this crisis is over. Obviously from a technology point of view, you know people have been speaking a lot about working remotely and technology changes, security, those things will happen but I think if these three things were to happen in that order. Be a better person, be a better family member and be a better leader, I think the world will be better off and the last thing I'll also tell you, that you know in Silicon Valley sometimes we have this disregard for arts and literature and fight over science. I hope that goes away, because I can't imagine living without books, without movies, without Netflix and everything. Art makes yourself creative and enriches our lives. You know, sports is no longer there on TV and the fact that people are able to immerse their imagination in books and fiction and watch TV. That also reminds you how important it is to have a good balance between arts and science in this world, so I have a long list of things that I hope we as a people and as a society will get better. >> Yeah, a lot more game playing in our household and it's good to reconnect in that regard. Well Sudheesh, you've always been a very clear thinker and you're in a great spot and an awesome leader. Thanks so much for coming on theCUBE. It was really great to see you again. All the best to you, your family and the broader community in your area. >> Dave, you've been very kind with this. Thank you so much, I wish you the same and hopefully we'll get to see face-to-face in the near future. Thanks a lot. >> I hope so, thank you. All right and thank you for watching everybody. This is Dave Vellante for theCUBE and we'll see you next time. (upbeat music)

(upbeat music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CUBEConversation. >> Hi everyone, welcome to the special CUBEConversation. We're in Palo Alto, California, at theCUBE studio. I'm John Furrier, co-host of the CUBE. We're here with Chris Yeh. He's the co-founder and general partner of Blitzscaling Ventures, author of the book Blitzscaling with Reid Hoffman, founder of LinkedIn and a variety of other ventures, also a partner at Greylock Partners. Chris, great to see you. I've known you for years. Love the book, love Reid. You guys did a great job. So congratulations. But the big news is you're now a TV star as one of the original inaugural contestants on the Mental Samurai, just premiered on Fox, was it >> On Fox. >> On Fox, nine o'clock, on which days? >> So Mental Samurai is on Fox, Tuesdays at 9 p.m. right after Master Chef Junior. >> Alright. So big thing. So successful shows. Take us through the journey. >> Yeah. >> It's a new show, so it's got this kind of like Jeopardy vibe where they got to answer tough questions in what looks like a roller coaster kind of arm that moves you around from station to station, kind of jar you up. But it's a lot of pressure, time clock and hard questions. Tell us about the format. How you got that. Gives all the story. >> So the story behind Mental Samurai is it's from the producers of American Ninja Warrior, if you've ever seen that show. So American Ninja Warrior is a physical obstacle course and these incredible athletes go through and the key is to get through the obstacle course. If you miss any of the obstacles, you're out. So they took that and they translated it to the mental world and they said, okay, we're going to have a mental obstacle course where you going to have different kinds of questions. So they have memory questions, sequence questions, knowledge questions, all these things that are tapping different elements of intelligence. And in order to win at the game, you have to get 12 questions right in five minutes or less. And you can't get a single question wrong. You have to be perfect. >> And they do try to jar you up, to kind of scrabble your brain with those devices, it makes it suspenseful. In watching last night at your watch party in Palo Alto, it's fun to watch because yeah, I'm like, okay, it's going to be cool. I'll support Chris. I'll go there, be great and on TV, and oh my, that's pretty interesting. It was actually riveting. Intense. >> Yeah. You have that element of moving around from station to station and it's dramatic. It's kind of a theater presence. But what's it like in there? Give us some insight. You're coming on in April 30th so you're yet to come on. >> Yes. >> But the early contestants, none of them made it to the 100,000. Only one person passed the first threshold. >> Right >> Take us through the format. How many thresholds are there? What's the format? >> Perfect, so basically when a competitor gets strapped into the chair, they call it Ava, it's like a robot, and basically they got it from some company in Germany and it has the ability to move 360 degrees. It's like an industrial robot or something. It makes you feel like you're an astronaut or in one those centrifugal force things. And the idea is they're adding to the pressure. They're making it more of a challenge. Instead of just Jeopardy where you're sitting there, and answering questions and bantering with Alex Trebek, you're working against the clock and you're being thrown around by this robot. So what happens is first you try to answer 12 questions correctly in less than five minutes. If you do that, then you make it through to the next round, what they call the circle of samurai and you win $10,000. The circle of samurai, what happens is there are four questions and you get 90 seconds plus whatever you have left over from your first run, to answer those four questions. Answer all four questions correctly, you win $100,000 and the official title of Mental Samurai. >> So there's only two levels, circle of samurai but it gets harder. Now also I noticed that it's, their questions have certain puzzles and there's certain kinds of questions. What's the categories, if you will, what's the categories they offer? >> Yes, so the different categories are knowledge, which is just classic trivia, it's a kind of Jeopardy stuff. There's memory, where they have something on screen that you have to memorize, or maybe they play an audio track that you have to remember what happened. And then there's also sequence where you have to put things in order. So all these different things are represented by these different towers which are these gigantic television screens where they present the questions. And the idea is in order to be truly intelligent, you have to be able to handle all of these different things. You can't just have knowledge. You can't just have pop culture. You got to have everything. >> So on the candidates I saw some from Stanford. >> Yeah. >> I saw an athlete. It's a lot of diversity in candidates. How do they pick the candidates? How did you get involved? Did your phone ring up one day? Were you identified, they've read your blog. Obviously they've, you're smart. I've read your stuff on Facebook. How did you get in there? (laughs) >> Excellent question. So the whole process, there's a giant casting department that does all these things. And there's people who just cast people for game shows. And what happened with me is many years ago back in 2014, my sister worked in Hollywood when I was growing up. She worked for ER and Baywatch and other companies and she still keeps track of the entertainment industry. And she sent me an email saying, hey, here's a casting call for a new show for smart people and you should sign up. And so I replied to the email and said hey I'm Chris Yeh. I'm this author. I graduate from Stanford when I was 19, blah blah blah blah. I should be on your show. And they did a bunch of auditions with me over the phone. And they said we love you, the network loves you. We'll get in touch and then I never heard. Turns out that show never got the green light. And they never even shot that show. But that put me on a list with these various casting directors. And for this show it turns out that there was an executive producer of the show, the creator of the show, his niece was the casting director who interviewed me back in 2014. And she told her uncle, hey, there's this guy, Chris Yeh, in Palo Alto. I think would be great for this new show you're doing. Why don't you reach out to him. So they reached out to me. I did a bunch of Skype auditions. And eventually while I was on my book tour for Blitzscaling, I got the email saying, congratulations, you're part of the season one cast. >> And on the Skype interviews, was it they grilling you with questions, or was it doing a mock dry run? What was some of interview vetting questions? >> So they start off by just asking you about yourself and having you talk about who you are because the secret to these shows is none of the competitors are famous in advance, or at least very few of them are. There was a guy who was a major league baseball pitcher, there's a guy who's an astronaut, I mean, those guys are kind of famous already, but the whole point is, they want to build a story around the person like they do with the Olympics so that people care whether they succeed or not. And so they start off with biographical questions and then they proceed to basically use flash cards to simulate the game and see how well you do. >> Got it, so they want to basically get the whole story arc 'cause Chris, obviously Chris is smart, he passed the test. Graduate when he's 19. Okay, you're book smart. Can you handle the pressure? If you do get it, there's your story line. So they kind of look from the classic, kind of marketing segmentation, demographics is your storylines. What are some of the things that they said to you on the feedback? Was there any feedback, like you're perfect, we like this about you. Or is it more just cut and dry. >> Well I think they said, we love your energy. It's coming through very strongly to the screen. That's fantastic. We like your story. Probably the part I struggle the most with, was they said hey, you know, talk to us about adversity. Talk to us about the challenges that you've overcome. And I tell people, listen, I'm a very lucky guy. A lot of great things have happened to me in life. I don't know if there's that much adversity that I can really complain about. Other people who deal with these life threatening illnesses and all this stuff, I don't have that. And so that was probably the part I struggled the most with. >> Well you're certainly impressive. I've known you for years. You're a great investor, a great person. And a great part of Silicon Valley. So congratulations, good luck on the show. So it's Tuesdays. >> 9 p.m. >> 9 p.m. >> On fox. >> On Fox. Mental Samurai. Congratulations, great. Great to be at the launch party last night. The watch party, there'll be another one. Now your episode comes out on April 30th. >> Yes. So on April 30th we will have a big Bay area-wide watch party. I'm assuming that admission will be free, assuming I find the right sponsors. And so I'll come back to you. I'll let you know where it's going to be. Maybe we should even film the party. >> That's, well, I got one more question on the show. >> Yeah. >> You have not been yet on air so but you know the result. What was it like sitting in the chair, I mean, what was it personally like for you? I mean you've taken tests, you've been involved with the situation. You've made some investments. There's probably been some tough term sheets here and there, board meetings. And all that experience in your life, what was it compared to, what was it like? >> Well, it's a really huge adrenaline rush because if you think about there's so many different elements that already make it an adrenaline rush and they all combine together. First of all, you're in this giant studio which looks like something out of a space-age set with this giant robotic arm. There's hundreds of people around cheering. Then you're strapped into a robotic arm which basically makes you feel like an astronaut, like every run starts with you facing straight up, right? Lying back as if you're about to be launched on a rocket. And then you're answering these difficult questions with time pressure and then there's Rob Lowe there as well that you're having a conversation with. So all these things together, and your heart, at least for me, my heart was pounding. I was like trying very hard to stay calm because I knew it was important to stay clam, to be able to get through it. >> Get that recall, alright. Chris, great stuff. Okay, Blitzscaling. Blitzscaling Ventures. Very successful concept. I remember when you guys first started doing this at Stanford, you and Reid, were doing the lectures at Stanford Business School. And I'm like, I love this. It's on YouTube, kind of an open project initially, wasn't really, wasn't really meant to be a book. It was more of gift, paying it forward. Now it's a book. A lot of great praise. Some criticism from some folks but in general it's about scaling ventures, kind of the Silicon Valley way which is the rocket ship I call. The rocket ship ventures. There's still the other venture capitals. But great book. Feedback from the book and the original days at Stanford. Talk about the Blitzscaling journey. >> And one of the things that happened when we did the class at Stanford is we had all these amazing guests come in and speak. So people like Eric Schmidt. People like Diane Greene. People like Brian Chesky, who talked about their experiences. And all of those conversations really formed a key part of the raw material that went into the book. We began to see patterns emerge. Some pretty fascinating patterns. Things like, for example, a lot of companies, the ones that'd done the best job of maintaining their culture, have their founders involved in hiring for the first 500 employees. That was like a magic number that came up over and over again in the interviews. So all this content basically came forward and we said, okay, well how do we now take this and put it into a systematic framework. So the idea of the book was to compress down 40 hours of video content, incredible conversations, and put it in a framework that somebody could read in a couple of hours. >> It is also one of those things where you get lightning in a ball, the classic and so then I'd say go big or go home. But Blitzscaling is all about something new and something different. And I'm reading a book right now called Loonshots, which is a goof on moonshots. It's about the loonies who start the real companies and a lot of companies that are successful like Airbnb was passed over on and they call those loonies. Those aren't moonshots. Moonshots are well known, build-outs. This is where the blitzscaling kind of magic happens. Can you just share your thoughts on that because that's something that's not always talked about in the mainstream press, is that a lot of there blitzscaling companies, are the ones that don't look good on paper initially. >> Yes. >> Or ones that no one's talking about is not in a category or herd mentality of investors. It's really that outlier. >> Yes. >> Talk about that dynamic. >> Yeah, and one of the things that Reid likes to say is that the best possible companies usually sound like they're dumb ideas. And in fact the best investment he's been a part of as a venture capitalist, those are the ones where there's the greatest controversy around the table. It's not the companies that come in and everyone's like this is a no-brainer, let's do it. It's the companies where there's a big fight. Should we do this, should we not? And we think the reason is this. Blitzscaling is all about being able to be the first to scale and the winner take most or the winner take all market. Now if you're in a market where everyone's like, this is a great market, this is a great idea. You're going to have huge competition. You're going to have a lot of people going after it. It's very difficult to be the first to scale. If you are contrarian and right you believe something that other people don't believe, you have the space to build that early lead, that you can then use to leverage yourself into that enduring market leadership. >> And one of the things that I observed from the videos as well is that the other fact that kind of plays into, I want to get your reaction, this is that there has to be a market shift that goes on too because you have to have a tailwind or a wave to ride because if you can be contrarian if there's no wave, >> Right. >> right? so a lot of these companies that you guys highlight, have the wave behind them. It was mobile computing, SaaSification, cloud computing, all kind of coming together. Talk about that dynamic and your reaction 'cause that's something where people can get confused on blitzscaling. They read the book. Oh I'm going to disrupt the dry cleaning business. Well I mean, not really. I mean, unless there's something different >> Exactly. >> in market conditions. Talk about that. >> Yeah, so with blitzscaling you're really talking about a new market or a market that's transforming. So what is it that causes these things to transform? Almost always it's some new form of technological innovation, or perhaps a packaging of different technological innovations. Take mobile computing for example. Many of the components have been around for a while. But it took off when Apple was able to combine together capacitative touchscreens and the form factor and the processor strength being high enough finally. And all these things together created the technological innovation. The technological innovation then enables the business model innovation of building an app store and creating a whole new way of thinking about handheld computing. And then based on that business model innovation, you have the strategy innovation of blitzscaling to allow you to grow rapidly and keep from blowing up when you grow. >> And the spirit of kind of having, kind of a clean entrepreneurial segmentation here. Blitzscaling isn't for everybody. And I want you to talk about that because obviously the book's popular when this controversy, there's some controversy around the fact that you just can't apply blitzscaling to everything. We just talk about some of those factors. There are other entrepreneurialship models that makes sense but that might not be a fit for blitzscaling. Can you just unpack that and just explain, a minute to explain the difference between a company that's good for blitzscaling and one that isn't. >> Well, a key thing that you need for blitzscaling is one of these winner take most or winner take all markets that's just enormous and hugely valuable, alright? The whole thing about blitzscaling is it's very risky. It takes a lot of effort. It's very uncomfortable. So it's only worth doing when you have those market dynamics and when that market is really large. And so in the book we talk about there being many businesses that this doesn't apply to. And we use the example of two companies that were started at the same time. One company is Amazon, which is obviously a blitzscaling company and a dominant player and a great, great company. And the other is the French Laundry. In fact, Jeff Bezos started Amazon the same year that Thomas Keller started the French Laundry. And the French Laundry still serves just 60 people a day. But it's a great business. It's just a very different kind of business. >> It's a lifestyle or cash flow business and people call it a lifestyle business but mainly it's a cash flow or not a huge growing market. >> Yeah. >> Satisfies that need. What's the big learnings that you learned that was something different that you didn't know coming out of blitzscaling experience? Something that surprised you, something that might have shocked you, something that might have moved you. I mean you're well-read. You're smart. What was some learnings that you learned from the journey? >> Well, one of the things that was really interesting to me and I didn't really think about it. Reid and I come from the startup world, not the big company world. One of the things that surprised me is the receptivity of big companies to these ideas. And they explained it to me and they said, listen, you got to understand with a big company, you think it's just a big company growing at 10, 15% a year. But actually there's units that are growing at 100% a year. There's units that are declining at 50% a year. And figuring out how you can actually continue to grow new businesses quicker than your old businesses die is a huge thing for the big, established companies. So that was one of the things that really surprised me but I'm grateful that it appears that it's applicable. >> It's interesting. I had a lot of conversations with Michael Dell before, and before they went private and after they went private. He essentially was blitzscaling. >> Yeah. >> He said, I'm going to winner take most in the mature, somewhat declining massive IT enterprise spend against the HPs of the world, and he's doing it and VMware stock went to an all time high. So big companies can blitz scale. That's the learning. >> Exactly. And the key thing to remember there is one of the reasons why somebody like Michael Dell went private to do this is that blitzscaling is all about prioritizing speed over efficiency. Guess who doesn't like that? Wall street doesn't like because you're taking a hit to earnings as you invest in a new business. GM for example is investing heavily in autonomous vehicles and that investment is not yet delivering cash but it's something that's going to create a huge value for General Motors. And so it's really tough to do blitzscaling as a publicly traded company though there are examples. >> I know your partner in the book, Reid Hoffman as well as in the blitzscaling at Stanford was as visible in both LinkedIn and as the venture capitalist of Greylock. But also he was involved with some failed startups on the front end of LinkedIn. >> Yeah. >> So he had some scar tissue on social networking before it became big, I'll say on the knowledge graph that he's building, he built at LinkedIn. I'm sure he had some blitzscaling lessons. What did he bring to the table? Did he share anything in the classes or privately with you that you can share that might be helpful for people to know? >> Well, there's a huge number of lessons. Obviously we drew heavily on Reid's life for the book. But I think you touched on something that a lot of people don't know, which is that LinkedIn is not the first social network that Reid created. Actually during the dot-com boom Reid created a company called SocialNet that was one of the world's first social networks. And I actually was one of the few people in the world who signed up and was a member of SocialNet. I think I had the handle, net revolutionary on that if you can believe that. And one of the things that Reid learned from his SocialNet experience turned into one of his famous sayings, which is, if you're not embarrassed by your first product launch, you've launched too late. With SocialNet they spent so much time refining the product and trying to get it perfectly right. And then when they launched it, they discovered what everyone always discovers when they launch, which is the market wants something totally different. We had no idea what people really wanted. And they'd wasted all this time trying to perfect something that they've theoretically thought was what the market wanted but wasn't actually what the market wanted. >> This is what I love about Silicon Valley. You have these kind of stories 'cause that's essentially agile before agile came out. They're kind of rearranging the deck chairs trying to get the perfect crafted product in a world that was moving to more agility, less craftsmanship and although now it's coming back. Also I talked to Paul Martino, been on theCUBE before. He's a tribe with Pincus. And it's been those founding fathers around these industries. It's interesting how these waves, they start off, they don't get off the ground, but that doesn't mean the category's dead. It's just a timing issue. That's important in a lot of ventures, the timing piece. Talk about that dynamic. >> Absolutely. When it comes to timing, you think about blitzscaling. If you start blitzscaling, you prioritize speed over efficiency. The main question is, is it the right time. So Webvan could be taken as an example of blitzscaling. They were spending money wildly inefficiently to build up grocery delivery. Guess what? 2000 was not the right time for it. Now we come around, we see Instacart succeeding. We see other delivery services delivering some value. It just turns out that you have to get the timing right. >> And market conditions are critical and that's why blitzscaling can work when the conditions are right. Our days back in the podcast, it was, we were right but timing was off. And this brings up the question of the team. >> Yeah. >> You got to have the right team that can handle the blitzscaling culture. And you need the right investors. You've been on both sides of the table. Talk about that dynamic because I think this is probably one of the most important features because saying you going to do blitzscaling and then getting buy off but not true commitment from the investors because the whole idea is to plow money into the system. You mentioned Amazon, one of Jeff Bezos' tricks was, he always poured money back into his business. So this is a capital strategy, as well financial strategy capital-wise as well as a business trait. Talk about the importance of having that stomach and the culture of blitzscaling. >> Absolutely. And I think you hit on something very important when you sort of talk about the importance of the investors. So Reid likes to refer to investors as financing partners. Or financing co-founders, because really they're coming on with you and committing to the same journey that you're going on. And one of the things I often tell entrepreneurs is you really have to dig deep and make sure you do more due diligence on your investors than you would on your employees. Because if you think about it, if you hire an employee, you can actually fire them. If you take money from an investor, there's no way you can ever get rid of them. So my advice to entrepreneurs is always, well, figure out if they're going to be a good partner for you. And the best way to do that is to go find some of the entrepreneurs they backed who failed and talked to those people. >> 'Cause that's where the truth will come out. >> Well, that's right. >> We stood by them in tough times. >> Exactly. >> I think that's classic, that's perfect but this notion of having the strategies of the elements of the business model in concert, the financial strategy, the capital strategy with the business strategy and the people strategy, all got to be pumping that can't be really any conflict on that. That's the key point. >> That's right, there has to be alignment because again, you're trying to go as quickly as possible and if you're running a race car and you have things that are loose and rattling around, you're not going to make it across the finish line. >> You're pulling for a pit stop and the guys aren't ready to change the tires, (snapping fingers) you know you're out of sync. >> Bingo. >> Chris, great stuff. Blitzscaling is a great book. Check it out. I recommend it, remember blitz scale is not for anyone, it's for the game changers. And again, picking your investors is critical on this. So if you picked the wrong investors, blitzscaling will blow up in a bad way. So don't, don't, pick properly on the visa and pick your team. Chris, so let's talk about you real quick to end the segment and the last talk track. Talk about your background 'cause I think you have a fascinating background. I didn't know that you graduated when you're 19, from Stanford was it? >> Yes. >> Stanford at 19, that's a great accomplishment. You've been an entrepreneur. Take us through your journey. Give us a quick highlight of your career. >> So the quick highlight is I grew up in Southern California and Santa Monica where I graduated from Santa Monica High School along with other luminaries such as Rob Lowe, Robert Downey, Jr., and Sean Penn. I didn't go at the same time that they did. >> They didn't graduate when they were 17. >> They did not, (John laughing) and Charlie Sheen also attended Santa Monica High School but dropped out or was expelled. (laughing) Go figured. >> Okay. >> I came up to Stanford and I actually studied creative writing and product design. So I was really hitting both sides of the brain. You could see that really coming through in the rest of my career. And then at the time I graduated which was the mid-1990s that was when the internet was first opening up. I was convinced the internet was going to be huge and so I just went straight into the internet in 1995. And have been in the startup world ever since. >> Must love that show, Halt and Catch Fire a series which I love reminiscing. >> AMC great show. >> Just watching that my life right before my eyes. Us old folks. Talk about your investment. You are at Wasabi Ventures now. Blitzscaling Ventures. You guys looks like you're going to do a little combination bring capital around blitzscaling, advising. What's Blitzscaling Ventures? Give a quick commercial. >> So the best way to think about it is for the entrepreneurs who are actually are blitzscaling, the question is how are you going to get the help you need to figure out how to steer around the corners to avoid the pitfalls that can occur as you're growing rapidly. And Blitzscaling Ventures is all about that. So obviously I bring a wealth of experience, both my own experience as well as everything I learned from putting this book together. And the whole goal of Blitzscaling Ventures is to find those entrepreneurs who have those blitzscalable opportunities and help them navigate through the process. >> And of course being a Mental Samurai that you are, the clock is really important on blitzscaling. >> There are actually are a lot of similarities between the startup world and Mental Samurai. Being able to perform under pressure, being able to move as quickly as possible yet still be accurate. The one difference of course is in our startup world you often do make mistakes. And you have a chance to recover from them. But in Mental Samurai you have to be perfect. >> Speed, alignment, resource management, capital deployment, management team, investors, all critical factors in blitzscaling. Kind of like entrepreneurial going to next level. A whole nother lesson, whole nother battlefields. Really the capital markets are flush with cash. Post round B so if you can certainly get altitude there's a ton of capital. >> Yeah. And the key is that capital is necessary for blitzscaling but it's not sufficient. You have to take that financial capital and you have to figure out how to combine it with the human capital to actually transform the business in the industry. >> Of course I know you've got to catch a plane. Thanks for coming by in the studio. Congratulations on the Mental Samurai. Great show. I'm looking forward to April 30th. Tuesdays at 9 o'clock, the Mental Samurai. Chris will be an inaugural contestant. We'll see how he does. He's tight-lipped, he's not breaking his disclosure. >> I've got legal requirements. I can't say anything. >> Just say he's sticking to his words. He's a man of his words. Chris, great to see you. Venture capitalist, entrepreneur, kind of venture you want to talk to Chris Yeh, co-founder, general partner of blitzscaling. I'm John Furrier for theCUBE. Thanks for watching. (upbeat music)