(mellow music) >> Hello everyone, welcome to theCUBE's coverage of AWS Startup Showcase. This is the closing panel session on AI machine learning, the top startups generating generative AI on AWS. It's a great panel. This is going to be the experts talking about riding the wave in generative AI. We got Ankur Mehrotra, who's the director and general manager of AI and machine learning at AWS, and Clem Delangue, co-founder and CEO of Hugging Face, and Ori Goshen, who's the co-founder and CEO of AI21 Labs. Ori from Tel Aviv dialing in, and rest coming in here on theCUBE. Appreciate you coming on for this closing session for the Startup Showcase. >> Thanks for having us. >> Thank you for having us. >> Thank you. >> I'm super excited to have you all on. Hugging Face was recently in the news with the AWS relationship, so congratulations. Open source, open science, really driving the machine learning. And we got the AI21 Labs access to the LLMs, generating huge scale live applications, commercial applications, coming to the market, all powered by AWS. So everyone, congratulations on all your success, and thank you for headlining this panel. Let's get right into it. AWS is powering this wave here. We're seeing a lot of push here from applications. Ankur, set the table for us on the AI machine learning. It's not new, it's been goin' on for a while. Past three years have been significant advancements, but there's been a lot of work done in AI machine learning. Now it's released to the public. Everybody's super excited and now says, "Oh, the future's here!" It's kind of been going on for a while and baking. Now it's kind of coming out. What's your view here? Let's get it started. >> Yes, thank you. So, yeah, as you may be aware, Amazon has been in investing in machine learning research and development since quite some time now. And we've used machine learning to innovate and improve user experiences across different Amazon products, whether it's Alexa or Amazon.com. But we've also brought in our expertise to extend what we are doing in the space and add more generative AI technology to our AWS products and services, starting with CodeWhisperer, which is an AWS service that we announced a few months ago, which is, you can think of it as a coding companion as a service, which uses generative AI models underneath. And so this is a service that customers who have no machine learning expertise can just use. And we also are talking to customers, and we see a lot of excitement about generative AI, and customers who want to build these models themselves, who have the talent and the expertise and resources. For them, AWS has a number of different options and capabilities they can leverage, such as our custom silicon, such as Trainium and Inferentia, as well as distributed machine learning capabilities that we offer as part of SageMaker, which is an end-to-end machine learning development service. At the same time, many of our customers tell us that they're interested in not training and building these generative AI models from scratch, given they can be expensive and can require specialized talent and skills to build. And so for those customers, we are also making it super easy to bring in existing generative AI models into their machine learning development environment within SageMaker for them to use. So we recently announced our partnership with Hugging Face, where we are making it super easy for customers to bring in those models into their SageMaker development environment for fine tuning and deployment. And then we are also partnering with other proprietary model providers such as AI21 and others, where we making these generative AI models available within SageMaker for our customers to use. So our approach here is to really provide customers options and choices and help them accelerate their generative AI journey. >> Ankur, thank you for setting the table there. Clem and Ori, I want to get your take, because the riding the waves, the theme of this session, and to me being in California, I imagine the big surf, the big waves, the big talent out there. This is like alpha geeks, alpha coders, developers are really leaning into this. You're seeing massive uptake from the smartest people. Whether they're young or around, they're coming in with their kind of surfboards, (chuckles) if you will. These early adopters, they've been on this for a while; Now the waves are hitting. This is a big wave, everyone sees it. What are some of those early adopter devs doing? What are some of the use cases you're seeing right out of the gate? And what does this mean for the folks that are going to come in and get on this wave? Can you guys share your perspective on this? Because you're seeing the best talent now leaning into this. >> Yeah, absolutely. I mean, from Hugging Face vantage points, it's not even a a wave, it's a tidal wave, or maybe even the tide itself. Because actually what we are seeing is that AI and machine learning is not something that you add to your products. It's very much a new paradigm to do all technology. It's this idea that we had in the past 15, 20 years, one way to build software and to build technology, which was writing a million lines of code, very rule-based, and then you get your product. Now what we are seeing is that every single product, every single feature, every single company is starting to adopt AI to build the next generation of technology. And that works both to make the existing use cases better, if you think of search, if you think of social network, if you think of SaaS, but also it's creating completely new capabilities that weren't possible with the previous paradigm. Now AI can generate text, it can generate image, it can describe your image, it can do so many new things that weren't possible before. >> It's going to really make the developers really productive, right? I mean, you're seeing the developer uptake strong, right? >> Yes, we have over 15,000 companies using Hugging Face now, and it keeps accelerating. I really think that maybe in like three, five years, there's not going to be any company not using AI. It's going to be really kind of the default to build all technology. >> Ori, weigh in on this. APIs, the cloud. Now I'm a developer, I want to have live applications, I want the commercial applications on this. What's your take? Weigh in here. >> Yeah, first, I absolutely agree. I mean, we're in the midst of a technology shift here. I think not a lot of people realize how big this is going to be. Just the number of possibilities is endless, and I think hard to imagine. And I don't think it's just the use cases. I think we can think of it as two separate categories. We'll see companies and products enhancing their offerings with these new AI capabilities, but we'll also see new companies that are AI first, that kind of reimagine certain experiences. They build something that wasn't possible before. And that's why I think it's actually extremely exciting times. And maybe more philosophically, I think now these large language models and large transformer based models are helping us people to express our thoughts and kind of making the bridge from our thinking to a creative digital asset in a speed we've never imagined before. I can write something down and get a piece of text, or an image, or a code. So I'll start by saying it's hard to imagine all the possibilities right now, but it's certainly big. And if I had to bet, I would say it's probably at least as big as the mobile revolution we've seen in the last 20 years. >> Yeah, this is the biggest. I mean, it's been compared to the Enlightenment Age. I saw the Wall Street Journal had a recent story on this. We've been saying that this is probably going to be bigger than all inflection points combined in the tech industry, given what transformation is coming. I guess I want to ask you guys, on the early adopters, we've been hearing on these interviews and throughout the industry that there's already a set of big companies, a set of companies out there that have a lot of data and they're already there, they're kind of tinkering. Kind of reminds me of the old hyper scaler days where they were building their own scale, and they're eatin' glass, spittin' nails out, you know, they're hardcore. Then you got everybody else kind of saying board level, "Hey team, how do I leverage this?" How do you see those two things coming together? You got the fast followers coming in behind the early adopters. What's it like for the second wave coming in? What are those conversations for those developers like? >> I mean, I think for me, the important switch for companies is to change their mindset from being kind of like a traditional software company to being an AI or machine learning company. And that means investing, hiring machine learning engineers, machine learning scientists, infrastructure in members who are working on how to put these models in production, team members who are able to optimize models, specialized models, customized models for the company's specific use cases. So it's really changing this mindset of how you build technology and optimize your company building around that. Things are moving so fast that I think now it's kind of like too late for low hanging fruits or small, small adjustments. I think it's important to realize that if you want to be good at that, and if you really want to surf this wave, you need massive investments. If there are like some surfers listening with this analogy of the wave, right, when there are waves, it's not enough just to stand and make a little bit of adjustments. You need to position yourself aggressively, paddle like crazy, and that's how you get into the waves. So that's what companies, in my opinion, need to do right now. >> Ori, what's your take on the generative models out there? We hear a lot about foundation models. What's your experience running end-to-end applications for large foundation models? Any insights you can share with the app developers out there who are looking to get in? >> Yeah, I think first of all, it's start create an economy, where it probably doesn't make sense for every company to create their own foundation models. You can basically start by using an existing foundation model, either open source or a proprietary one, and start deploying it for your needs. And then comes the second round when you are starting the optimization process. You bootstrap, whether it's a demo, or a small feature, or introducing new capability within your product, and then start collecting data. That data, and particularly the human feedback data, helps you to constantly improve the model, so you create this data flywheel. And I think we're now entering an era where customers have a lot of different choice of how they want to start their generative AI endeavor. And it's a good thing that there's a variety of choices. And the really amazing thing here is that every industry, any company you speak with, it could be something very traditional like industrial or financial, medical, really any company. I think peoples now start to imagine what are the possibilities, and seriously think what's their strategy for adopting this generative AI technology. And I think in that sense, the foundation model actually enabled this to become scalable. So the barrier to entry became lower; Now the adoption could actually accelerate. >> There's a lot of integration aspects here in this new wave that's a little bit different. Before it was like very monolithic, hardcore, very brittle. A lot more integration, you see a lot more data coming together. I have to ask you guys, as developers come in and grow, I mean, when I went to college and you were a software engineer, I mean, I got a degree in computer science, and software engineering, that's all you did was code, (chuckles) you coded. Now, isn't it like everyone's a machine learning engineer at this point? Because that will be ultimately the science. So, (chuckles) you got open source, you got open software, you got the communities. Swami called you guys the GitHub of machine learning, Hugging Face is the GitHub of machine learning, mainly because that's where people are going to code. So this is essentially, machine learning is computer science. What's your reaction to that? >> Yes, my co-founder Julien at Hugging Face have been having this thing for quite a while now, for over three years, which was saying that actually software engineering as we know it today is a subset of machine learning, instead of the other way around. People would call us crazy a few years ago when we're seeing that. But now we are realizing that you can actually code with machine learning. So machine learning is generating code. And we are starting to see that every software engineer can leverage machine learning through open models, through APIs, through different technology stack. So yeah, it's not crazy anymore to think that maybe in a few years, there's going to be more people doing AI and machine learning. However you call it, right? Maybe you'll still call them software engineers, maybe you'll call them machine learning engineers. But there might be more of these people in a couple of years than there is software engineers today. >> I bring this up as more tongue in cheek as well, because Ankur, infrastructure's co is what made Cloud great, right? That's kind of the DevOps movement. But here the shift is so massive, there will be a game-changing philosophy around coding. Machine learning as code, you're starting to see CodeWhisperer, you guys have had coding companions for a while on AWS. So this is a paradigm shift. How is the cloud playing into this for you guys? Because to me, I've been riffing on some interviews where it's like, okay, you got the cloud going next level. This is an example of that, where there is a DevOps-like moment happening with machine learning, whether you call it coding or whatever. It's writing code on its own. Can you guys comment on what this means on top of the cloud? What comes out of the scale? What comes out of the benefit here? >> Absolutely, so- >> Well first- >> Oh, go ahead. >> Yeah, so I think as far as scale is concerned, I think customers are really relying on cloud to make sure that the applications that they build can scale along with the needs of their business. But there's another aspect to it, which is that until a few years ago, John, what we saw was that machine learning was a data scientist heavy activity. They were data scientists who were taking the data and training models. And then as machine learning found its way more and more into production and actual usage, we saw the MLOps become a thing, and MLOps engineers become more involved into the process. And then we now are seeing, as machine learning is being used to solve more business critical problems, we're seeing even legal and compliance teams get involved. We are seeing business stakeholders more engaged. So, more and more machine learning is becoming an activity that's not just performed by data scientists, but is performed by a team and a group of people with different skills. And for them, we as AWS are focused on providing the best tools and services for these different personas to be able to do their job and really complete that end-to-end machine learning story. So that's where, whether it's tools related to MLOps or even for folks who cannot code or don't know any machine learning. For example, we launched SageMaker Canvas as a tool last year, which is a UI-based tool which data analysts and business analysts can use to build machine learning models. So overall, the spectrum in terms of persona and who can get involved in the machine learning process is expanding, and the cloud is playing a big role in that process. >> Ori, Clem, can you guys weigh in too? 'Cause this is just another abstraction layer of scale. What's it mean for you guys as you look forward to your customers and the use cases that you're enabling? >> Yes, I think what's important is that the AI companies and providers and the cloud kind of work together. That's how you make a seamless experience and you actually reduce the barrier to entry for this technology. So that's what we've been super happy to do with AWS for the past few years. We actually announced not too long ago that we are doubling down on our partnership with AWS. We're excited to have many, many customers on our shared product, the Hugging Face deep learning container on SageMaker. And we are working really closely with the Inferentia team and the Trainium team to release some more exciting stuff in the coming weeks and coming months. So I think when you have an ecosystem and a system where the AWS and the AI providers, AI startups can work hand in hand, it's to the benefit of the customers and the companies, because it makes it orders of magnitude easier for them to adopt this new paradigm to build technology AI. >> Ori, this is a scale on reasoning too. The data's out there and making sense out of it, making it reason, getting comprehension, having it make decisions is next, isn't it? And you need scale for that. >> Yes. Just a comment about the infrastructure side. So I think really the purpose is to streamline and make these technologies much more accessible. And I think we'll see, I predict that we'll see in the next few years more and more tooling that make this technology much more simple to consume. And I think it plays a very important role. There's so many aspects, like the monitoring the models and their kind of outputs they produce, and kind of containing and running them in a production environment. There's so much there to build on, the infrastructure side will play a very significant role. >> All right, that's awesome stuff. I'd love to change gears a little bit and get a little philosophy here around AI and how it's going to transform, if you guys don't mind. There's been a lot of conversations around, on theCUBE here as well as in some industry areas, where it's like, okay, all the heavy lifting is automated away with machine learning and AI, the complexity, there's some efficiencies, it's horizontal and scalable across all industries. Ankur, good point there. Everyone's going to use it for something. And a lot of stuff gets brought to the table with large language models and other things. But the key ingredient will be proprietary data or human input, or some sort of AI whisperer kind of role, or prompt engineering, people are saying. So with that being said, some are saying it's automating intelligence. And that creativity will be unleashed from this. If the heavy lifting goes away and AI can fill the void, that shifts the value to the intellect or the input. And so that means data's got to come together, interact, fuse, and understand each other. This is kind of new. I mean, old school AI was, okay, got a big model, I provisioned it long time, very expensive. Now it's all free flowing. Can you guys comment on where you see this going with this freeform, data flowing everywhere, heavy lifting, and then specialization? >> Yeah, I think- >> Go ahead. >> Yeah, I think, so what we are seeing with these large language models or generative models is that they're really good at creating stuff. But I think it's also important to recognize their limitations. They're not as good at reasoning and logic. And I think now we're seeing great enthusiasm, I think, which is justified. And the next phase would be how to make these systems more reliable. How to inject more reasoning capabilities into these models, or augment with other mechanisms that actually perform more reasoning so we can achieve more reliable results. And we can count on these models to perform for critical tasks, whether it's medical tasks, legal tasks. We really want to kind of offload a lot of the intelligence to these systems. And then we'll have to get back, we'll have to make sure these are reliable, we'll have to make sure we get some sort of explainability that we can understand the process behind the generated results that we received. So I think this is kind of the next phase of systems that are based on these generated models. >> Clem, what's your view on this? Obviously you're at open community, open source has been around, it's been a great track record, proven model. I'm assuming creativity's going to come out of the woodwork, and if we can automate open source contribution, and relationships, and onboarding more developers, there's going to be unleashing of creativity. >> Yes, it's been so exciting on the open source front. We all know Bert, Bloom, GPT-J, T5, Stable Diffusion, that work up. The previous or the current generation of open source models that are on Hugging Face. It has been accelerating in the past few months. So I'm super excited about ControlNet right now that is really having a lot of impact, which is kind of like a way to control the generation of images. Super excited about Flan UL2, which is like a new model that has been recently released and is open source. So yeah, it's really fun to see the ecosystem coming together. Open source has been the basis for traditional software, with like open source programming languages, of course, but also all the great open source that we've gotten over the years. So we're happy to see that the same thing is happening for machine learning and AI, and hopefully can help a lot of companies reduce a little bit the barrier to entry. So yeah, it's going to be exciting to see how it evolves in the next few years in that respect. >> I think the developer productivity angle that's been talked about a lot in the industry will be accelerated significantly. I think security will be enhanced by this. I think in general, applications are going to transform at a radical rate, accelerated, incredible rate. So I think it's not a big wave, it's the water, right? I mean, (chuckles) it's the new thing. My final question for you guys, if you don't mind, I'd love to get each of you to answer the question I'm going to ask you, which is, a lot of conversations around data. Data infrastructure's obviously involved in this. And the common thread that I'm hearing is that every company that looks at this is asking themselves, if we don't rebuild our company, start thinking about rebuilding our business model around AI, we might be dinosaurs, we might be extinct. And it reminds me that scene in Moneyball when, at the end, it's like, if we're not building the model around your model, every company will be out of business. What's your advice to companies out there that are having those kind of moments where it's like, okay, this is real, this is next gen, this is happening. I better start thinking and putting into motion plans to refactor my business, 'cause it's happening, business transformation is happening on the cloud. This kind of puts an exclamation point on, with the AI, as a next step function. Big increase in value. So it's an opportunity for leaders. Ankur, we'll start with you. What's your advice for folks out there thinking about this? Do they put their toe in the water? Do they jump right into the deep end? What's your advice? >> Yeah, John, so we talk to a lot of customers, and customers are excited about what's happening in the space, but they often ask us like, "Hey, where do we start?" So we always advise our customers to do a lot of proof of concepts, understand where they can drive the biggest ROI. And then also leverage existing tools and services to move fast and scale, and try and not reinvent the wheel where it doesn't need to be. That's basically our advice to customers. >> Get it. Ori, what's your advice to folks who are scratching their head going, "I better jump in here. "How do I get started?" What's your advice? >> So I actually think that need to think about it really economically. Both on the opportunity side and the challenges. So there's a lot of opportunities for many companies to actually gain revenue upside by building these new generative features and capabilities. On the other hand, of course, this would probably affect the cogs, and incorporating these capabilities could probably affect the cogs. So I think we really need to think carefully about both of these sides, and also understand clearly if this is a project or an F word towards cost reduction, then the ROI is pretty clear, or revenue amplifier, where there's, again, a lot of different opportunities. So I think once you think about this in a structured way, I think, and map the different initiatives, then it's probably a good way to start and a good way to start thinking about these endeavors. >> Awesome. Clem, what's your take on this? What's your advice, folks out there? >> Yes, all of these are very good advice already. Something that you said before, John, that I disagreed a little bit, a lot of people are talking about the data mode and proprietary data. Actually, when you look at some of the organizations that have been building the best models, they don't have specialized or unique access to data. So I'm not sure that's so important today. I think what's important for companies, and it's been the same for the previous generation of technology, is their ability to build better technology faster than others. And in this new paradigm, that means being able to build machine learning faster than others, and better. So that's how, in my opinion, you should approach this. And kind of like how can you evolve your company, your teams, your products, so that you are able in the long run to build machine learning better and faster than your competitors. And if you manage to put yourself in that situation, then that's when you'll be able to differentiate yourself to really kind of be impactful and get results. That's really hard to do. It's something really different, because machine learning and AI is a different paradigm than traditional software. So this is going to be challenging, but I think if you manage to nail that, then the future is going to be very interesting for your company. >> That's a great point. Thanks for calling that out. I think this all reminds me of the cloud days early on. If you went to the cloud early, you took advantage of it when the pandemic hit. If you weren't native in the cloud, you got hamstrung by that, you were flatfooted. So just get in there. (laughs) Get in the cloud, get into AI, you're going to be good. Thanks for for calling that. Final parting comments, what's your most exciting thing going on right now for you guys? Ori, Clem, what's the most exciting thing on your plate right now that you'd like to share with folks? >> I mean, for me it's just the diversity of use cases and really creative ways of companies leveraging this technology. Every day I speak with about two, three customers, and I'm continuously being surprised by the creative ideas. And the future is really exciting of what can be achieved here. And also I'm amazed by the pace that things move in this industry. It's just, there's not at dull moment. So, definitely exciting times. >> Clem, what are you most excited about right now? >> For me, it's all the new open source models that have been released in the past few weeks, and that they'll keep being released in the next few weeks. I'm also super excited about more and more companies getting into this capability of chaining different models and different APIs. I think that's a very, very interesting development, because it creates new capabilities, new possibilities, new functionalities that weren't possible before. You can plug an API with an open source embedding model, with like a no-geo transcription model. So that's also very exciting. This capability of having more interoperable machine learning will also, I think, open a lot of interesting things in the future. >> Clem, congratulations on your success at Hugging Face. Please pass that on to your team. Ori, congratulations on your success, and continue to, just day one. I mean, it's just the beginning. It's not even scratching the service. Ankur, I'll give you the last word. What are you excited for at AWS? More cloud goodness coming here with AI. Give you the final word. >> Yeah, so as both Clem and Ori said, I think the research in the space is moving really, really fast, so we are excited about that. But we are also excited to see the speed at which enterprises and other AWS customers are applying machine learning to solve real business problems, and the kind of results they're seeing. So when they come back to us and tell us the kind of improvement in their business metrics and overall customer experience that they're driving and they're seeing real business results, that's what keeps us going and inspires us to continue inventing on their behalf. >> Gentlemen, thank you so much for this awesome high impact panel. Ankur, Clem, Ori, congratulations on all your success. We'll see you around. Thanks for coming on. Generative AI, riding the wave, it's a tidal wave, it's the water, it's all happening. All great stuff. This is season three, episode one of AWS Startup Showcase closing panel. This is the AI ML episode, the top startups building generative AI on AWS. I'm John Furrier, your host. Thanks for watching. (mellow music)

>> From The Cube Studios in Palo Alto and Boston, bringing you data driven insights from The Cube and ETR, this is Breaking Analysis with Dave Vellante. >> Despite it's faster growth and infrastructure as a service, relative to AWS and Azure, Google Cloud platform remains a third wheel in the race for cloud dominance. Google begins its Cloud Next online event starting July fourteenth in a series of nine rolling sessions that go through early September. Ahead of that, we want to update you on our most current data on Google's cloud business. Hello everyone, this is Dave Vellante, and welcome to this week's Wikibon Cube insights, powered by ETR. In this session, we'll review the current state of cloud, and Google's position in the market. We'll drill into the ETR data and share fresh insights from our partner and the Cube community. So let's get right into it. You know, Google, if you think about it, was actually very early into the cloud game. Google's 2004 IPO was a milestone event for the tech industry, and in you know many ways, it really marked the end of the post-dotcom malaise. It signaled the beginning of a new era of innovation. During this time, Google was busy building out its massive, global cloud infrastructure, probably the largest in the world, with undersea cables, global data centers, and tools like the Google file system, and of course Bigtable. But it took many years for Google to pull its head out of its ad serving butt and realize the opportunity to sell its cloud services to global enterprises. Bigtable, Google's no-sequel database, for example, was released in 2005, but it wasn't until 2015 that Google made this service available to its customers. That was the same year Google brought in VMware founder, Diane Greene to begin its enterprise journey in earnest. Now Google, they have a dizzying array of services in compute, storage, database, networking, IT ops, dev tools, machine learning, AI, analytics, big data, security, on and on and on. Name a category and it's likely that Google has something in it as a cloud service. But Google, to this day, still hasn't figured out how to sell to the enterprise. It really struggles to find the right formula. So, as you know, Google brought in Thomas Kurian from Oracle, to figure this out. Of course Kurian is, he's going to go with Google's strengths like analytics and database, but it has to have differentiation, so it comes up with unique pricing models like sustained discounts, which automatically apply discount for heavy usage, as opposed to forcing users to buy reserved instances such as what AWS does. You know Google is more aggressive partnering around multi-cloud, for instance, with Anthos, and it's smartly open-sourced Kubernetes really to minimize the importance of, physically, where workloads run. The bottom-line, however, is that these moves are necessary for Google to compete because it lags behind the leaders. And it has a long way to go before it's going to be satisfied with its cloud business. Let's look at the IaaS market in context. Now, I don't want to say it's all gloom and doom for Google. Far from it. Earnings for Q2, they're going to start rolling out later this month, but this chart shows our latest estimates of IaaS and PaaS for the big three cloud players. Now, I got to caution you, as I did before, other than AWS, which reports very clean numbers each quarter on IaaS and PaaS, we have to estimate Azure and GCP revenue because they bundle in other things. I'll give an example. Google reports its overall cloud numbers which include G Suite. Microsoft reports a category they call intelligent cloud. Now that includes public, private clouds, hybrid, sequel server, Windows server, system center, GitHub, enterprise support and consulting services. And Azure, the IaaS and PaaS numbers are also in there too. So what we have to do is to squint through the earnings reports and the 10 Ks and try to get a clean IaaS and PaaS figure for these players, and that's what we show here. Now there's really two points that we want to stress with this data. First, on a trailing 12 month basis, the big three cloud players now account for nearly 60 billion dollars in IaaS and PaaS revenue. And this 60 billion dollars, on a weighted average basis, is growing in the mid 40% range. So well on its way to being a 100 billion dollar business. Just for these three firms. And as we've reported, that's eating directly into the on-premises infrastructure install base, which is a flat to declining market. And that trend is going to play out in a big way this decade. We've predicted that public cloud is going to out pace on-prem infrastructure by more that 1800 basis points over the next 10 years, from a spending standpoint. Now the second point that I want to make relates to Google IaaS and PaaS growth. We peg it at greater than 70%, based on public statements, reading the 10 Ks and ETR data, which we'll discuss in a moment. So, very healthy growth, but from a much smaller install base than, or base than AWS and Azure. But in our view it's not enough, because AWS and Azure are so large and strong still, growth wise, that we feel Google is going to remain a distant third, really indefinitely. Nonetheless, a lot of companies would be thrilled to have a four billion dollar cloud business and there's certainly good news in the data for Google. So let's look at some of that survey data. Now, as we've reported in the past, Google pushes G Suite very hard, as part of its cloud story, and it leads often times with G Suite in its messaging. You know, but to us that's never really been that compelling. So let me start with some anecdotal data from ETR. ETR runs a regular program, they call it VENN, and in the VENN they invite clients into a private session to listen to named CIOs talk about their experience with vendors and overall spending intentions. It's a facilitated session. And we've had ETR's Eric Bradley on as a guest who directs the VENN program, and does much of the facilitation, and here's a statement from a recent VENN session quoting a CIO at a midsize Telco, that I think sums it up nicely. He says Google's G Suite is fine and dandy, but I don't see that truly as an enterprise solution. And frankly, it's still not of the quality of an Office application, talking about Microsoft. All in all I really like the infrastructure-as-a-service and the platform-as-a-service components that GCP had. And I thought they were coming along very very well in that space. Now, the reason that I share this is because the IT buyers that we speak with, you know they're very serious about exploring Google. They want options other than Azure and AWS and they see Google as having great tech and as a viable alternative. So let's talk about GCP and the enterprise. We looking, when we look into the ETR data for the most recent survey, which ran in June and early July, GCP is showing strength in one really important bellwether category, the giant public and private companies. These are the largest firms in the ETR dataset and often point to secular trends. Now, before we get into that, let's look at the picture for GCP using ETR's net score up methodology. This is fundamental to the ETR approach, and remember, each quarter ETR goes out and asks its respondents, are you planning to spend more or less? In its July survey, ETR focuses on second half spending. The next chart captures results across Google's entire portfolio. So here's the breakdown for, for Google across all sectors. 14% of the respondents are adopting new, that's the lime green. 39% plan to increase spending in the second half versus the first half, that's the forest green. Then there's a big fat middle, that's flat, and you see that in the gray area. And the 7% are spending less, with 2% replacing, that's the pinkish and dark red, respectively. So, I would say this result is mixed, in my opinion. Yeah, it's not bad, don't get me wrong, and we've, we'll see once ETR comes out of its quite period, how this compares to Azure and AWR, so remember, I can only share limited data until ETR clients get the data and have time to act on it. But this calculates out to a net score of 44%, which is respectable, but frankly not overly inspiring. So let's look across the GCP portfolio using the ETR taxonomy and see what it looks like. This chart shows the net score comparisons across three different surveys, October 19, April 20, and July 20. So reading the bars left to right, you can see Google's strong suit really is machine learning and AI. Container platforms are also very strong, as are functions, or server-less, and databases, very solid, we'll talk more about that in a minute. You know, video conferencing was just added by ETR and sure it pops up with the work from home. Cloud is actually holding firm when compared to October of last year. But surprisingly, analytics is looking a bit softer. And ETR for the first time added G Suite with, it shows a 26% net score, first time out, which is pretty tepid. I mean not very impressive at all. But overall, the picture looks pretty good for Google. So let's dig further into the giant public and private sector, that bellwether I talked about. And let's peal the onion a bit and look closer at the results from the largest companies in the dataset. So this chart shows the giant public, plus private organizations. So it would include like monster public companies but also large companies like a Cargill or a Coke Industries, if in fact they responded in this survey. And you can see, in that all important sector, it's a story of a lot of green with hardly any red, so quite a positive sign for Google within those bellwethers. Here's what I think is happening here. Is these large, and often far flung organizations, have realized that they have multiple cloud vendors, and they're asking their senior IT leadership to bring some consistency and sanity to their cloud strategies. So they look at the big three and say, okay, what's the best strategic fit for each workload? So they might say for instance let's use AWS for core IaaS, let's use Azure for productivity workloads, and we'll sprinkle some Google in for machine learning and related projects. So we do see some real strength in some of the larger strongholds for Google, although interestingly ETR sort of tells me that there's softness in the midsize and smaller companies that have powered AWS for so many years. And of course this, with Google's base, but compare that to AWS and AWS is much stronger in those smaller companies, start-ups and the like, and of course COVID's the wild car in all this. You know, we have to take that into account, and we will with Sagar Kadakia, who's ETR's director of research in the coming weeks. But I want to look at Google in the all important database category. So before we wrap, let's look at database. You remember, Google's playing catch up in the cloud and its marketing takes a more open posture around partners and things like multi-cloud and you know you can contrast that with AWS for example, but look, make no mistake, Google wants you data in their cloud, and that's why database is so strategic and so important. Look, it's the mother of all lock specs. All you got to do is look at Oracle and their success. Now, as we've reported many times, there's a new workload emerging in the cloud around this idea of the modern data warehouse. I mean I don't even like that term anymore, data warehouse, because it sounds just so static. But anyway, any rate, I'm talking about workloads that bring database, machine learning, AI, data science, compute and storage along with visualization tools to deliver real-time insights and operational analytics. Database is at the heart of everything here. Win the database and everything else falls into place. Now, Google has six or seven database products and one of the most impressive, in my opinion, is BigQuery. I mean, for those who have followed me over the years you know I love the technology behind Google's banner, but BigQuery is where much of the action is around this new workload that I'm talking about. So, let's look at, deeper at Google's position in database. This chart shows one of my favorite views. On the Y axis is the net score, or spending momentum, and on the X axis is market share or pervasiveness in the ETR dataset. The chart plots various database companies and their position within the all important giant public plus private sector. So these are the companies in the ETR survey that are the largest, and oftentimes, again, are a bellwether. And you can see Microsoft and Oracle and AWS have very strong presence on the horizontal axis. Mongo, MongoDB looms large, MemSQL, they just raised 50 million dollars this past May, MariaDB just raised another 25 million this month. You can see Couchbase and Redis, they show up, and they're on my radar. I'm learning more about those companies. Folks, database is hot. VC's are pouring money in and it's something that's very important to the Cube community to look at. And of course you see Google in the chart, with a strong net score, you know, but not the type of market presence that you see from the other big cloud players. In fact, they've pulled back a little somewhat in this last ETR survey. So despite some bright spots in the enterprise in terms of spending momentum, just not quite enough presence yet. Oh, by the way, look who's right there with Google. I know I sound like a broken record, but Snowflake is everywhere. You'll find them in AWS, you'll find them in Azure and on GCP. Now remember, Snowflake is only about one tenth the size of Google's IaaS and PaaS business. But it has stronger spending momentum than all the big guys, and it continues to creep its way to the right in terms of market share or presence. You know, but Google has great database tech and BigQuery is at the heart of its strategy to support analytics at scale, and automate the data pipeline. BigQuery's very well designed, it started as a cloud native database, it's based on server-less, it's highly scalable, and it's very cost-effective. In fact, ESG, enterprise strategy group, wrote a report comparing the TCO of the cloud databases. Let me pull that up and show you. Now the report was commissioned by Google, so I got to caution you there. But it was very well done in my opinion by a guy named Aviv Kaufmann, and you can see here it compares BigQuery with the other cloud databases, and of course, you know, BigQuery wins, got the lowest TCO, but again I thought the report was really detailed and well researched. I have no doubt that Snowflake has an answer for the big brown bar, which is on-demand cloud cost. I think ESG was making certain assumptions, maybe worst case assumptions, about the need to over-provision resources for Snowflake, which I'm sure ESG can defend, but I'll bet dollars to donuts that Snowflake, you know, has an answer to that or a comeback. I'm going to ask them. But the point I want to make here is that BigQuery was designed from day one, again, as a cloud-native database. We've been talking about that a lot. It's very efficient and is going to be competitive. So you can see, there are some bright spots in the enterprise, for Google. Okay, let's wrap up. Now, having called out some of the positives, and there are many, Google is still not getting it done in the enterprise, in my opinion. I certainly would not say too little too late, but I would say they spotted the competition a huge lead, and the only reason is Google just didn't act on the opportunity staring them in the face, within the enterprise, fast enough, and they finally woke up. But enterprise sales are, they're really hard. Thomas Kurian, for all his experience, is coming from way, way behind with regard to the enterprise go to market, systems and processes, pricing, partnerships, special deals for the enterprise. Google's still learning how to sell the business outcomes and is relying far too much on its technology chops, which, while impressive, are not going to win the day without better enterprise sales, marketing, and ecosystem integration. Now I feel like for years, Google has said to the enterprise market, give me heat and I'll add the wood. Meaning we have the best tech, go ahead and use it. That strategy just doesn't work in the enterprise. Kurian knows it and I suspect that's why Google's showing some strength within these large, giant public and private companies. They're probably applying focused sales resources to nail customer success with some of its top accounts where they have a presence, and then once they nail that they'll broaden to the market. But they got to move fast. We'll learn more about Google's intentions and its progress over the next few, next few months as they try their online event experiment, and of course we'll be there providing our wall to wall coverage. Remember, these Breaking Analysis episodes, they're all available as podcasts. ETR is shortly exiting its quiet period, this week, and will be rolling out the data, so check out etr.plus. I publish weekly on wikibon.com and siloconeangle.com and as always please comment on my LinkedIn posts, I really appreciate the feedback. This is Dave Vellante for the Cube Insights, powered by ETR. Thanks for watching everyone. We'll see you next time.

(bright, upbeat music) >> Hi, I'm Peter Burris, and welcome to another CUBE Conversation. We've got another great guest this week, Ed Bugnion, who's a professor of computer science at EPFL, a leading technical university in Switzerland. Ed, welcome to theCUBE. >> Thanks for having me. >> So Ed, you do at EPFL, you are leading research on the future of the data center. What I want to do, is I want to talk about the near term of the data center, 'cause a lot of people have questions about what's going to happen over the next few years. Let's posit that the data center's not going to go away any time soon, and instead talk about inside the data center. What's going to happen with the organization of technology inside data centers? >> Well it's always been a chase about how to reduce complexity. You always start with basically having a number of moving parts and then the business requirements keep increasing, and at some point, the complexity just overwhelms the operational model. So I was involved in virtualization. I've been working virtualization for close to 20 years. Right, virtualization was about reducing the complexity for the servers, and basically moved from having to manage servers one by one, separate the physical from logical and sort of solving that problem. Now what we actually did, as a side effect, is we actually pushed the remaining aspects of that complexity elsewhere. The servers were mobile, they were flexible, they could v motion across a cluster, they had to be stored on a storage area network so as a result, we ended up having this entire operational complexity around the management of storage area networks for very large amounts of data and as the increase in virtualization became more and more important, that became bigger, more of an issue. So then I actually got involved into into networking and networking was about the fact that a decade or so ago there was a proliferation of incompatible networks inside the data center. I was involved at Cisco in the pure storage, unified conversion networking with the UCS product so we could both do storage and regular TCPIP networking on the same on the line firework. This was about reducing complexity, but we didn't address all the complexity problems, we created other bottlenecks so it's always this ever shifting issue with dealing with scale. >> So as we virtualized the servers, we virtualize the storage and now we're virtualizing the network, that suggests that we can start bringing these things together in new novel ways have I got that right? >> Yeah so we first virtualized the network access, right, the storage access and the SANs and then now we're obviously with hyperconvergence we're about disaggregating storage and rethinking storage because of these new requirements. That's solves a number of the problems, right? It's actually now proving out to be sort of an industry-wide accepted model that we move away from storage arrays into hyperconverged models and hyperconvergence alone if the only thing you're doing is moving blocks around is again only solving part of the problem, you still need to worry about DR, you still need to worry about backup, you still need to worry about offsite. You still need to worry about locality, right, because having completely filed storage is a gross violation of the locality principal and the locality principal actually does come back and matter at some point in time. So it's really about finding the balance between the space and feeds, what needs to be co-located and what can be disaggregated and then what use-case must be addressed. >> And I presume, how much control can be bought from a single point of presence, console, onto the underlying infrastructures, is that how the rest are worried about? >> Yeah so I think there's, you're going to have to separate two things. One is the physical building blocks and the other one are the operational consoles, right, and the physical building blocks, the number of people providing these physical building blocks is small and if anything, shrinking. If you think about the operational console, the different panels, right? If you think about the different software companies providing technology, they actually themselves offer different panels to different constituencies. The silos have not completely disappeared in the IT operation model today, they're, communication is much better, tools facilitate this communication but silos not completely gone. So you still have these different panels, they can come from one vendor, different vendors, the same vendor can actually provide multiple capabilities but the theme is do you actually want to move away from having to deal with the complexity of having completely different universes into having much more coherent elements to talk to each other? >> So if we have this more coherence, presumably that means these more coherent elements can actually support each other in providing, as you said earlier, some of the crucial features of what a complex, large, scalable system needs to perform. You mentioned backup restore for example. How do you anticipate that the requirements of what constitute as systems, before it was scale compute and now we're actually worried about making sure that all those other issues from an automation from a business requirement standpoint and increasing impinging upon what we regard as design, like, having data protection. How do those new constraints start to impact folks to think about what to buy, what to use now? >> Yeah it's actually fascinating that tape, right, as we know it and as we knew it which largely has not changed, right, is actually still present. Tape obviously is a sequential approach, it's not by any stretch not the most easy way technology to operate and yet it still has sort of a presence so moving away from this, and the interesting observation is and you can now move away from these classic approaches of backup to object-based solutions. These object-based solutions, provide that you have the appropriate kind of connectivity assumptions can either be offsite or onsite and it's a very fluid and transparent model. And these object-based solutions are actually now designed into scale and can be used to either store primary data and stream data also to store backups of data and so this convergence between using object storage between what is backup and what is live data is one of the interesting themes. >> So we're talking about convergence of the hardware elements, but now we're also talking about convergence of the services and the capabilities associated, all within the same console, all within the same platform, utilizing specialization where it makes sense, have I got that right? >> Yes I mean you obviously have different use cases right? One of the things that is always goes back to the question of what is the API right? If you have an API and it is really you know gets and sets on an object model, that is designed to operate transactional objects right, you effectively are in a particular mindset. If you actually want to guarantee retention, you actually want a different set of APIs right, one of the things that's really important is to make sure that the data is actually safe and that the API won't prevent a catastrophic misuse and deletion of the data, for example. >> So there's one bit of advice you can offer someone who's sitting in a data center today and thinking about what they should be doing to increase the returns on their data assets and what they provide to the business, what would that kind of one thing that you'd leave them with be? >> Almost depends on where you start from, right? >> Peter: Okay good point. >> But having said that, there are sort of two general approaches, one is sort of the incremental approach which is you try to catch up with the technology trends and the other one is to say, okay what are actually my problems that I'm trying to solve purely from an infrastructure perspective and how do I actually solve these problems in a reasonable timeframe? It's actually if you think about the pros and cons between the two approaches, the first approach is this pragmatic, it's going to be better this quarter than last quarter, but you may never be able to catch up the other approach requires a little bit more thinking, sometimes process re-engineering, sometimes thinking about things differently. Changing the operational model, how your teams operate within the IT organization, sometimes it actually delivers the right solution. >> And we do have a model for how to do this, the big hyper-scalers are doing just that second approach and it's having a consequential impact on the industry isn't it? >> Yeah well storage, the storage industry has always been a fascinating industry, it was static for a few years, it's now extremely dynamic industry, there's a lot of companies that went public in the storage space over the last few years as we all know. They went because there was new technology, right? Flash sort of was transforming to the landscape. Now object and hyperconverged and post-hyperconverged solutions are actually also completely transforming the landscape because now, we think about storage different because it's not, the paradigm is no longer the same. >> Thinking about computing entirely differently. Storage plus everything else. >> Well at the end of the day, this is purely, this is infrastructure right? >> Right. >> And infrastructure is never for infrastructure's sake. Infrastructure is to deliver a new capabilities, new applications. The combination of you know phenomenal increases in primary memory, in Flash memory, and NVME, all these technologies are sort of transforming our expectation with respect to responsiveness and access to data. And then the changes on the compute side and the huge specialization going on in hardware in A-six that we know how to process data in much more efficient way and this is, we haven't talked about AI yet but fundamentally when you think about all these AI-based improvements, it is about being able to put massive amount of computational capabilities onto mass amounts of data. >> So you've been part VMware, you've been part of Neva, you've been part of a lot of different companies, if you look out, what types of foci, what types of centers of innovation amongst, in the valley do you look to for leadership? (laughing) >> The nice thing is, I was in the valley, i was in the industry and now I'm. >> And now you're out. (laughing) >> So I actually don't have to take a position. It's actually nice to be able to look at it much more from a principal perspective rather than to look at is as to which of the existing players are, the agenda they're trying to push. They each have legitimate agendas because they're driving their business and the evolution of their business for their customer and trying to deliver value to their customer. Obviously the customers have to choose. When I look at it sort of from my perspective both academically and so simply from an IT perspective as I operate a fair amount of IT EDPFL, it's really this notion of what problems are we trying to solve? And whether the boundaries that we traditionally had between the classic large vendors still make sense in this sort of hyperconverged environment. >> Alright well, Ed Bugnion, Professor of computer science at EDPFL, thanks again for being on theCUBE and this is Peter Burris and once again, great CUBE conversation and hope to see you soon. (bright upbeat music)

(upbeat music) >> Welcome everyone to theCUBE's presentation of the AWS Startup Showcase AI Machine Learning Top Startups Building Generative AI on AWS. This is season 3, episode 1 of the ongoing series covering the exciting startup from the AWS ecosystem to talk about AI and machine learning. I'm your host, John Furrier. I'm joined by two great guests here, Adam Wenchel, who's the CEO of Arthur, and Chief Scientist of Arthur, John Dickerson. Talk about how they help people build better LLM AI systems to get them into the market faster. Gentlemen, thank you for coming on. >> Yeah, thanks for having us, John. >> Well, I got to say I got to temper my enthusiasm because the last few months explosion of interest in LLMs with ChatGPT, has opened the eyes to everybody around the reality of that this is going next gen, this is it, this is the moment, this is the the point we're going to look back and say, this is the time where AI really hit the scene for real applications. So, a lot of Large Language Models, also known as LLMs, foundational models, and generative AI is all booming. This is where all the alpha developers are going. This is where everyone's focusing their business model transformations on. This is where developers are seeing action. So it's all happening, the wave is here. So I got to ask you guys, what are you guys seeing right now? You're in the middle of it, it's hitting you guys right on. You're in the front end of this massive wave. >> Yeah, John, I don't think you have to temper your enthusiasm at all. I mean, what we're seeing every single day is, everything from existing enterprise customers coming in with new ways that they're rethinking, like business things that they've been doing for many years that they can now do an entirely different way, as well as all manner of new companies popping up, applying LLMs to everything from generating code and SQL statements to generating health transcripts and just legal briefs. Everything you can imagine. And when you actually sit down and look at these systems and the demos we get of them, the hype is definitely justified. It's pretty amazing what they're going to do. And even just internally, we built, about a month ago in January, we built an Arthur chatbot so customers could ask questions, technical questions from our, rather than read our product documentation, they could just ask this LLM a particular question and get an answer. And at the time it was like state of the art, but then just last week we decided to rebuild it because the tooling has changed so much that we, last week, we've completely rebuilt it. It's now way better, built on an entirely different stack. And the tooling has undergone a full generation worth of change in six weeks, which is crazy. So it just tells you how much energy is going into this and how fast it's evolving right now. >> John, weigh in as a chief scientist. I mean, you must be blown away. Talk about kid in the candy store. I mean, you must be looking like this saying, I mean, she must be super busy to begin with, but the change, the acceleration, can you scope the kind of change you're seeing and be specific around the areas you're seeing movement and highly accelerated change? >> Yeah, definitely. And it is very, very exciting actually, thinking back to when ChatGPT was announced, that was a night our company was throwing an event at NeurIPS, which is maybe the biggest machine learning conference out there. And the hype when that happened was palatable and it was just shocking to see how well that performed. And then obviously over the last few months since then, as LLMs have continued to enter the market, we've seen use cases for them, like Adam mentioned all over the place. And so, some things I'm excited about in this space are the use of LLMs and more generally, foundation models to redesign traditional operations, research style problems, logistics problems, like auctions, decisioning problems. So moving beyond the already amazing news cases, like creating marketing content into more core integration and a lot of the bread and butter companies and tasks that drive the American ecosystem. And I think we're just starting to see some of that. And in the next 12 months, I think we're going to see a lot more. If I had to make other predictions, I think we're going to continue seeing a lot of work being done on managing like inference time costs via shrinking models or distillation. And I don't know how to make this prediction, but at some point we're going to be seeing lots of these very large scale models operating on the edge as well. So the time scales are extremely compressed, like Adam mentioned, 12 months from now, hard to say. >> We were talking on theCUBE prior to this session here. We had theCUBE conversation here and then the Wall Street Journal just picked up on the same theme, which is the printing press moment created the enlightenment stage of the history. Here we're in the whole nother automating intellect efficiency, doing heavy lifting, the creative class coming back, a whole nother level of reality around the corner that's being hyped up. The question is, is this justified? Is there really a breakthrough here or is this just another result of continued progress with AI? Can you guys weigh in, because there's two schools of thought. There's the, "Oh my God, we're entering a new enlightenment tech phase, of the equivalent of the printing press in all areas. Then there's, Ah, it's just AI (indistinct) inch by inch. What's your guys' opinion? >> Yeah, I think on the one hand when you're down in the weeds of building AI systems all day, every day, like we are, it's easy to look at this as an incremental progress. Like we have customers who've been building on foundation models since we started the company four years ago, particular in computer vision for classification tasks, starting with pre-trained models, things like that. So that part of it doesn't feel real new, but what does feel new is just when you apply these things to language with all the breakthroughs and computational efficiency, algorithmic improvements, things like that, when you actually sit down and interact with ChatGPT or one of the other systems that's out there that's building on top of LLMs, it really is breathtaking, like, the level of understanding that they have and how quickly you can accelerate your development efforts and get an actual working system in place that solves a really important real world problem and makes people way faster, way more efficient. So I do think there's definitely something there. It's more than just incremental improvement. This feels like a real trajectory inflection point for the adoption of AI. >> John, what's your take on this? As people come into the field, I'm seeing a lot of people move from, hey, I've been coding in Python, I've been doing some development, I've been a software engineer, I'm a computer science student. I'm coding in C++ old school, OG systems person. Where do they come in? Where's the focus, where's the action? Where are the breakthroughs? Where are people jumping in and rolling up their sleeves and getting dirty with this stuff? >> Yeah, all over the place. And it's funny you mentioned students in a different life. I wore a university professor hat and so I'm very, very familiar with the teaching aspects of this. And I will say toward Adam's point, this really is a leap forward in that techniques like in a co-pilot for example, everybody's using them right now and they really do accelerate the way that we develop. When I think about the areas where people are really, really focusing right now, tooling is certainly one of them. Like you and I were chatting about LangChain right before this interview started, two or three people can sit down and create an amazing set of pipes that connect different aspects of the LLM ecosystem. Two, I would say is in engineering. So like distributed training might be one, or just understanding better ways to even be able to train large models, understanding better ways to then distill them or run them. So like this heavy interaction now between engineering and what I might call traditional machine learning from 10 years ago where you had to know a lot of math, you had to know calculus very well, things like that. Now you also need to be, again, a very strong engineer, which is exciting. >> I interviewed Swami when he talked about the news. He's ahead of Amazon's machine learning and AI when they announced Hugging Face announcement. And I reminded him how Amazon was easy to get into if you were developing a startup back in 2007,8, and that the language models had that similar problem. It's step up a lot of content and a lot of expense to get provisioned up, now it's easy. So this is the next wave of innovation. So how do you guys see that from where we are right now? Are we at that point where it's that moment where it's that cloud-like experience for LLMs and large language models? >> Yeah, go ahead John. >> I think the answer is yes. We see a number of large companies that are training these and serving these, some of which are being co-interviewed in this episode. I think we're at that. Like, you can hit one of these with a simple, single line of Python, hitting an API, you can boot this up in seconds if you want. It's easy. >> Got it. >> So I (audio cuts out). >> Well let's take a step back and talk about the company. You guys being featured here on the Showcase. Arthur, what drove you to start the company? How'd this all come together? What's the origination story? Obviously you got a big customers, how'd get started? What are you guys doing? How do you make money? Give a quick overview. >> Yeah, I think John and I come at it from slightly different angles, but for myself, I have been a part of a number of technology companies. I joined Capital One, they acquired my last company and shortly after I joined, they asked me to start their AI team. And so even though I've been doing AI for a long time, I started my career back in DARPA. It was the first time I was really working at scale in AI at an organization where there were hundreds of millions of dollars in revenue at stake with the operation of these models and that they were impacting millions of people's financial livelihoods. And so it just got me hyper-focused on these issues around making sure that your AI worked well and it worked well for your company and it worked well for the people who were being affected by it. At the time when I was doing this 2016, 2017, 2018, there just wasn't any tooling out there to support this production management model monitoring life phase of the life cycle. And so we basically left to start the company that I wanted. And John has a his own story. I'll let let you share that one, John. >> Go ahead John, you're up. >> Yeah, so I'm coming at this from a different world. So I'm on leave now from a tenured role in academia where I was leading a large lab focusing on the intersection of machine learning and economics. And so questions like fairness or the response to the dynamism on the underlying environment have been around for quite a long time in that space. And so I've been thinking very deeply about some of those more like R and D style questions as well as having deployed some automation code across a couple of different industries, some in online advertising, some in the healthcare space and so on, where concerns of, again, fairness come to bear. And so Adam and I connected to understand the space of what that might look like in the 2018 20 19 realm from a quantitative and from a human-centered point of view. And so booted things up from there. >> Yeah, bring that applied engineering R and D into the Capital One, DNA that he had at scale. I could see that fit. I got to ask you now, next step, as you guys move out and think about LLMs and the recent AI news around the generative models and the foundational models like ChatGPT, how should we be looking at that news and everyone watching might be thinking the same thing. I know at the board level companies like, we should refactor our business, this is the future. It's that kind of moment, and the tech team's like, okay, boss, how do we do this again? Or are they prepared? How should we be thinking? How should people watching be thinking about LLMs? >> Yeah, I think they really are transformative. And so, I mean, we're seeing companies all over the place. Everything from large tech companies to a lot of our large enterprise customers are launching significant projects at core parts of their business. And so, yeah, I would be surprised, if you're serious about becoming an AI native company, which most leading companies are, then this is a trend that you need to be taking seriously. And we're seeing the adoption rate. It's funny, I would say the AI adoption in the broader business world really started, let's call it four or five years ago, and it was a relatively slow adoption rate, but I think all that kind of investment in and scaling the maturity curve has paid off because the rate at which people are adopting and deploying systems based on this is tremendous. I mean, this has all just happened in the few months and we're already seeing people get systems into production. So, now there's a lot of things you have to guarantee in order to put these in production in a way that basically is added into your business and doesn't cause more headaches than it solves. And so that's where we help customers is where how do you put these out there in a way that they're going to represent your company well, they're going to perform well, they're going to do their job and do it properly. >> So in the use case, as a customer, as I think about this, there's workflows. They might have had an ML AI ops team that's around IT. Their inference engines are out there. They probably don't have a visibility on say how much it costs, they're kicking the tires. When you look at the deployment, there's a cost piece, there's a workflow piece, there's fairness you mentioned John, what should be, I should be thinking about if I'm going to be deploying stuff into production, I got to think about those things. What's your opinion? >> Yeah, I'm happy to dive in on that one. So monitoring in general is extremely important once you have one of these LLMs in production, and there have been some changes versus traditional monitoring that we can dive deeper into that LLMs are really accelerated. But a lot of that bread and butter style of things you should be looking out for remain just as important as they are for what you might call traditional machine learning models. So the underlying environment of data streams, the way users interact with these models, these are all changing over time. And so any performance metrics that you care about, traditional ones like an accuracy, if you can define that for an LLM, ones around, for example, fairness or bias. If that is a concern for your particular use case and so on. Those need to be tracked. Now there are some interesting changes that LLMs are bringing along as well. So most ML models in production that we see are relatively static in the sense that they're not getting flipped in more than maybe once a day or once a week or they're just set once and then not changed ever again. With LLMs, there's this ongoing value alignment or collection of preferences from users that is often constantly updating the model. And so that opens up all sorts of vectors for, I won't say attack, but for problems to arise in production. Like users might learn to use your system in a different way and thus change the way those preferences are getting collected and thus change your system in ways that you never intended. So maybe that went through governance already internally at the company and now it's totally, totally changed and it's through no fault of your own, but you need to be watching over that for sure. >> Talk about the reinforced learnings from human feedback. How's that factoring in to the LLMs? Is that part of it? Should people be thinking about that? Is that a component that's important? >> It certainly is, yeah. So this is one of the big tweaks that happened with InstructGPT, which is the basis model behind ChatGPT and has since gone on to be used all over the place. So value alignment I think is through RLHF like you mentioned is a very interesting space to get into and it's one that you need to watch over. Like, you're asking humans for feedback over outputs from a model and then you're updating the model with respect to that human feedback. And now you've thrown humans into the loop here in a way that is just going to complicate things. And it certainly helps in many ways. You can ask humans to, let's say that you're deploying an internal chat bot at an enterprise, you could ask humans to align that LLM behind the chatbot to, say company values. And so you're listening feedback about these company values and that's going to scoot that chatbot that you're running internally more toward the kind of language that you'd like to use internally on like a Slack channel or something like that. Watching over that model I think in that specific case, that's a compliance and HR issue as well. So while it is part of the greater LLM stack, you can also view that as an independent bit to watch over. >> Got it, and these are important factors. When people see the Bing news, they freak out how it's doing great. Then it goes off the rails, it goes big, fails big. (laughing) So these models people see that, is that human interaction or is that feedback, is that not accepting it or how do people understand how to take that input in and how to build the right apps around LLMs? This is a tough question. >> Yeah, for sure. So some of the examples that you'll see online where these chatbots go off the rails are obviously humans trying to break the system, but some of them clearly aren't. And that's because these are large statistical models and we don't know what's going to pop out of them all the time. And even if you're doing as much in-house testing at the big companies like the Go-HERE's and the OpenAI's of the world, to try to prevent things like toxicity or racism or other sorts of bad content that might lead to bad pr, you're never going to catch all of these possible holes in the model itself. And so, again, it's very, very important to keep watching over that while it's in production. >> On the business model side, how are you guys doing? What's the approach? How do you guys engage with customers? Take a minute to explain the customer engagement. What do they need? What do you need? How's that work? >> Yeah, I can talk a little bit about that. So it's really easy to get started. It's literally a matter of like just handing out an API key and people can get started. And so we also offer alternative, we also offer versions that can be installed on-prem for models that, we find a lot of our customers have models that deal with very sensitive data. So you can run it in your cloud account or use our cloud version. And so yeah, it's pretty easy to get started with this stuff. We find people start using it a lot of times during the validation phase 'cause that way they can start baselining performance models, they can do champion challenger, they can really kind of baseline the performance of, maybe they're considering different foundation models. And so it's a really helpful tool for understanding differences in the way these models perform. And then from there they can just flow that into their production inferencing, so that as these systems are out there, you have really kind of real time monitoring for anomalies and for all sorts of weird behaviors as well as that continuous feedback loop that helps you make make your product get better and observability and you can run all sorts of aggregated reports to really understand what's going on with these models when they're out there deciding. I should also add that we just today have another way to adopt Arthur and that is we are in the AWS marketplace, and so we are available there just to make it that much easier to use your cloud credits, skip the procurement process, and get up and running really quickly. >> And that's great 'cause Amazon's got SageMaker, which handles a lot of privacy stuff, all kinds of cool things, or you can get down and dirty. So I got to ask on the next one, production is a big deal, getting stuff into production. What have you guys learned that you could share to folks watching? Is there a cost issue? I got to monitor, obviously you brought that up, we talked about the even reinforcement issues, all these things are happening. What is the big learnings that you could share for people that are going to put these into production to watch out for, to plan for, or be prepared for, hope for the best plan for the worst? What's your advice? >> I can give a couple opinions there and I'm sure Adam has. Well, yeah, the big one from my side is, again, I had mentioned this earlier, it's just the input data streams because humans are also exploring how they can use these systems to begin with. It's really, really hard to predict the type of inputs you're going to be seeing in production. Especially, we always talk about chatbots, but then any generative text tasks like this, let's say you're taking in news articles and summarizing them or something like that, it's very hard to get a good sampling even of the set of news articles in such a way that you can really predict what's going to pop out of that model. So to me, it's, adversarial maybe isn't the word that I would use, but it's an unnatural shifting input distribution of like prompts that you might see for these models. That's certainly one. And then the second one that I would talk about is, it can be hard to understand the costs, the inference time costs behind these LLMs. So the pricing on these is always changing as the models change size, it might go up, it might go down based on model size, based on energy cost and so on, but your pricing per token or per a thousand tokens and that I think can be difficult for some clients to wrap their head around. Again, you don't know how these systems are going to be used after all so it can be tough. And so again that's another metric that really should be tracked. >> Yeah, and there's a lot of trade off choices in there with like, how many tokens do you want at each step and in the sequence and based on, you have (indistinct) and you reject these tokens and so based on how your system's operating, that can make the cost highly variable. And that's if you're using like an API version that you're paying per token. A lot of people also choose to run these internally and as John mentioned, the inference time on these is significantly higher than a traditional classifi, even NLP classification model or tabular data model, like orders of magnitude higher. And so you really need to understand how that, as you're constantly iterating on these models and putting out new versions and new features in these models, how that's affecting the overall scale of that inference cost because you can use a lot of computing power very quickly with these profits. >> Yeah, scale, performance, price all come together. I got to ask while we're here on the secret sauce of the company, if you had to describe to people out there watching, what's the secret sauce of the company? What's the key to your success? >> Yeah, so John leads our research team and they've had a number of really cool, I think AI as much as it's been hyped for a while, it's still commercial AI at least is really in its infancy. And so the way we're able to pioneer new ways to think about performance for computer vision NLP LLMs is probably the thing that I'm proudest about. John and his team publish papers all the time at Navs and other places. But I think it's really being able to define what performance means for basically any kind of model type and give people really powerful tools to understand that on an ongoing basis. >> John, secret sauce, how would you describe it? You got all the action happening all around you. >> Yeah, well I going to appreciate Adam talking me up like that. No, I. (all laughing) >> Furrier: Robs to you. >> I would also say a couple of other things here. So we have a very strong engineering team and so I think some early hires there really set the standard at a very high bar that we've maintained as we've grown. And I think that's really paid dividends as scalabilities become even more of a challenge in these spaces, right? And so that's not just scalability when it comes to LLMs, that's scalability when it comes to millions of inferences per day, that kind of thing as well in traditional ML models. And I think that's compared to potential competitors, that's really... Well, it's made us able to just operate more efficiently and pass that along to the client. >> Yeah, and I think the infancy comment is really important because it's the beginning. You really is a long journey ahead. A lot of change coming, like I said, it's a huge wave. So I'm sure you guys got a lot of plannings at the foundation even for your own company, so I appreciate the candid response there. Final question for you guys is, what should the top things be for a company in 2023? If I'm going to set the agenda and I'm a customer moving forward, putting the pedal to the metal, so to speak, what are the top things I should be prioritizing or I need to do to be successful with AI in 2023? >> Yeah, I think, so number one, as we talked about, we've been talking about this entire episode, the things are changing so quickly and the opportunities for business transformation and really disrupting different applications, different use cases, is almost, I don't think we've even fully comprehended how big it is. And so really digging in to your business and understanding where I can apply these new sets of foundation models is, that's a top priority. The interesting thing is I think there's another force at play, which is the macroeconomic conditions and a lot of places are, they're having to work harder to justify budgets. So in the past, couple years ago maybe, they had a blank check to spend on AI and AI development at a lot of large enterprises that was limited primarily by the amount of talent they could scoop up. Nowadays these expenditures are getting scrutinized more. And so one of the things that we really help our customers with is like really calculating the ROI on these things. And so if you have models out there performing and you have a new version that you can put out that lifts the performance by 3%, how many tens of millions of dollars does that mean in business benefit? Or if I want to go to get approval from the CFO to spend a few million dollars on this new project, how can I bake in from the beginning the tools to really show the ROI along the way? Because I think in these systems when done well for a software project, the ROI can be like pretty spectacular. Like we see over a hundred percent ROI in the first year on some of these projects. And so, I think in 2023, you just need to be able to show what you're getting for that spend. >> It's a needle moving moment. You see it all the time with some of these aha moments or like, whoa, blown away. John, I want to get your thoughts on this because one of the things that comes up a lot for companies that I talked to, that are on my second wave, I would say coming in, maybe not, maybe the front wave of adopters is talent and team building. You mentioned some of the hires you got were game changing for you guys and set the bar high. As you move the needle, new developers going to need to come in. What's your advice given that you've been a professor, you've seen students, I know a lot of computer science people want to shift, they might not be yet skilled in AI, but they're proficient in programming, is that's going to be another opportunity with open source when things are happening. How do you talk to that next level of talent that wants to come in to this market to supplement teams and be on teams, lead teams? Any advice you have for people who want to build their teams and people who are out there and want to be a coder in AI? >> Yeah, I've advice, and this actually works for what it would take to be a successful AI company in 2023 as well, which is, just don't be afraid to iterate really quickly with these tools. The space is still being explored on what they can be used for. A lot of the tasks that they're used for now right? like creating marketing content using a machine learning is not a new thing to do. It just works really well now. And so I'm excited to see what the next year brings in terms of folks from outside of core computer science who are, other engineers or physicists or chemists or whatever who are learning how to use these increasingly easy to use tools to leverage LLMs for tasks that I think none of us have really thought about before. So that's really, really exciting. And so toward that I would say iterate quickly. Build things on your own, build demos, show them the friends, host them online and you'll learn along the way and you'll have somebody to show for it. And also you'll help us explore that space. >> Guys, congratulations with Arthur. Great company, great picks and shovels opportunities out there for everybody. Iterate fast, get in quickly and don't be afraid to iterate. Great advice and thank you for coming on and being part of the AWS showcase, thanks. >> Yeah, thanks for having us on John. Always a pleasure. >> Yeah, great stuff. Adam Wenchel, John Dickerson with Arthur. Thanks for coming on theCUBE. I'm John Furrier, your host. Generative AI and AWS. Keep it right there for more action with theCUBE. Thanks for watching. (upbeat music)

foreign [Music] my name is Savannah Peterson and I am very excited to be coming to you today from the cube in Palo Alto we're going to be talking about kubecon giving a little preview of the hype and what you might be able to expect in Detroit with the one and only co-founder and CEO of the cube and siliconangle John ferriere John hello how are you today thanks for hosting and doing the preview with me my goodness a pleasure I we got acquainted this time last year how do you think the ecosystem has changed are you excited well first of all I missed kubecon Valencia because I had covid I was so excited to be there this big trip plan and then couldn't make it but so much has gone on I mean we've been at every kubecon the cube was there at the beginning when openstack was still going on kubernetes just started came out of Google we were there having beers with Lou Tucker and a bunch of The Luminaries when it all kind of came together and then watch it year by year progress through and how it's changed the industry and mainly how open source has been really the wave behind it combining with the Linux foundation and then cncf and then open source movement and good kubernetes has been amazing and under it all containers has been the real driver and all this so you know Docker containers Docker was a well-funded company they had to Pivot and were restructured now they're pure open source so containers have gone Supernova on top of that kubernetes and with that's a complete ecosystem of opportunity to create the next operating system in in software development so to me kubecon is at the center of software software 2030 what do you want to call it super cloud it's that it's really action it's not where the old school is it's where the new school is excellent so what has you most excited this year what's the biggest change from this time last year and now well two things I'm looking at this year uh carefully both from an editorial lens and also from a sponsorship lenses where is the funding going on the sponsorships because again a very diverse ecosystem of Builders but also vendors so I'm going to see how that Dynamics going on but also on the software side a lot of white space going on in the stack or in the map if you will you know the run times you've got observability you got a lot of competition maybe projects might be growing some Rising some falling maybe merge together I'm going to see how that but there's a lot of white spaces developing so I'm curious to see what's new on that area and then service meshes is a big deal this year so I'm looking for what's going on so it's been kind of a I won't say cold war but kind of like uh you know where is this going to go and because it's a super important part of of the of the orchestration and managing containers and so be very interested to see how service mesh does istio and other versions out there have been around for a while so that and also the other controversy is the number of stars on GitHub a project may have so sometimes that carries a lot of weight but we're going to look at which ones are rising which ones are falling again um which ones are getting the most votes by the developers vote with their code yeah absolutely well we did definitely miss you down in Los Angeles but it will be great to be in Detroit what has you most excited do you think that we're going to see the number of people in person that we have in the past I know you've seen it since the beginning so I think this year is going to be explosive from that psychology angle because I think it was really weird because La was on they were a bold to make that move we're all there is first conference back it was a lot a lot of like badges don't touch me only handshakes fist pumps but it was at the beginning of the covid second wave right so it was kind of still not yet released where everyone's was not worried about it so I think it's in the past year in the past eight months I mean I've been places with no masks people have no masks Vegas other places so I think it's going to be a year where it will be a lot more people in person because the growth and the opportunities are so big it's going to drive a lot of people in person just like Amazon reinvent those yeah absolutely and as the most important and prominent event in the kubernetes space I think everyone's very excited to to get back together when we think about this space do you think there that anyone's the clear winner yet or do you think it's still a bit of a open territory in terms of the companies and Partnerships I think Red Hat has done a great job and they're you know I think they're going to see how well they can turn this into gold for them because they've positioned themselves very well open shift years ago was kind of waffling I won't say it in a bad way but like but once they got view on containers and kubernetes red has done an exceptional job in how they position their company being bought by ibms can be very interesting to see how that influences change so if Red Hat can stay red hat I think IBM will win I think customers that's one company I like the startups we're seeing companies like platform nine Rafi systems young companies coming out in the kubernetes as a service space because I think whoever can make kubernetes easier because I think that's the hard part right now even though that the show is called kubecon is a lot more than kubernetes I think the container layer what docker's doing has been exceptional that's the real action the question is how does that impact the kubernetes layers so kubernetes is not a done deal yet I think it hasn't really crossed the chasm yet it's certainly popular but not every company is adopting it so we're starting to see that we need to see more adoption of kubernetes seeing that happen it's going to decide who the winners are totally agree with that if you look at the data a lot of companies are and people are excited about kubernetes but they haven't taken the plunge to shifting over their stack or fully embracing it because of that complexity so I'm very curious to see what we learn this week about who those players might be moving forward how does it feel to be in Detroit when was the last time you were here I was there in 2007 was the last time I was in that town so uh we'll see what's like wow yeah but things have changed yeah the lions are good this year they've got great hockey goalies there so you know all right you've heard that sports fans let John know what you're thinking your Sports predictions for this season I love that who do you hope to get to meet while we're at the show I want to meet more end user customers we're gonna have Envoy again on the cube I think Red Hat was going to be a big sponsor this year they've been great um we're looking for end user project most looking for some editorial super cloud like um commentary because the cncf is kind of the developer Tech Community that's powering in my opinion this next wave of software development Cloud native devops is now Cloud native developers devops is kind of going away that's killed I.T in my opinion data and security Ops is the new kind of Ops the new it so it's good to see how devops turns into more of a software engineering meet supercloud so I think you're going to start to see the infrastructure become more programmable it's infrastructure as code so I think if anything I'm more excited to hear more stories about how infrastructure as code is now the new standard so if when that truly happens the super cloud model be kicking into high gear I love that let's you touched on it a little bit right there but I want to dig in a bit since you've been around since the beginning what is it that you appreciate or enjoy so much about the kubernetes community and the people around this I think there are authentic people and I think they're they're building they're also Progressive they're very diverse um they're open and inclusive they try stuff and um they can be critical but they're not jerks about it so when people try something um they're open-minded of a failure so it's a classic startup mentality I think that is embodied throughout the Linux Foundation but CNC in particular has to bridge the entrepreneurial and corporate Vibe so they've done an exceptional job doing that and that's what I like about this money making involved but there's also a lot of development and Innovation that comes out of it so the next big name and startup could come out of this community and that's what I hope to see coming out here is that next brand that no one's heard of that just comes out of nowhere and just takes a big position in the marketplace so that's going to be interesting to see hopefully we have on our stage there yeah that's the goal we're going to interview them all a year from now when we're sitting here again what do you hope to be able to say about this space or this event that we might not be able to say today I think it's going to be more of clarity around um the new modern software development techniques software next gen using AI more faster silicon chips you see Amazon with what they're doing the custom silicon more processing but I think Hardware matters we've been talking a lot about that I think I think it's we're going to shift from what's been innovative and what's changed I think I think if you look at what's been going on in the industry outside of crypto the infrastructure hasn't really changed much except for AWS what they've done so I'm expecting to see more Innovations at the physics level way down in the chips and then that lower end of the stack is going to be dominated by either one of the three clouds probably AWS and then the middle layer is going to be this where the abstraction is around making infrastructure as code really happen I think that's going to be Clarity coming out of this year next year we should have some visibility into the vertical applications and of the AI and machine learning absolutely digging in on that actually even more because I like what you're saying a lot what verticals do you think that kubernetes is going to impact the most looking even further out than say a year I mean I think that hot ones Healthcare fintech are obvious to get the most money they're spending I think they're the ones who are already kind of creating these super cloud models where they're actually changed over their their spending from capex to Opex and they're driving top line revenue as part of that so you're seeing companies that wants customers of the I.T vendors are now becoming the providers that's a big super cloud Trend we see the other verticals are going to be served by a lot of men in Surprise oil and gas you know all the classic versus Healthcare I mentioned that one those are the classic verticals retail is going to I think be massively huge as you get more into the internet of things that's truly internet based you're going to start to see a lot more Edge use cases so Telecom I think it's going to be completely disrupted by new brands so I think once that you see see how that plays out but all verticals are going to be disrupted just a casual statement to say yeah yeah no doubt in my mind that's great I'm personally really excited about the edge applications that are possible here and can't wait to see can't wait to see what happens next I'm curious as to your thoughts how based given your history here and we don't have to say number of years that you've been participating in in Cape Cod but give them your history what's the evolution looked like from that Community perspective when you were all just starting out having that first drink did you anticipate that we would be here with thousands of people in Detroit you know I knew the moment was happening around um 2017-2018 Dan Coney no longer with us he passed away I ran into him randomly in China and it was like what are you doing here he was with a bunch of Docker guys so they were already investing in so I knew that the cncf was a great Steward for this community because they were already doing the work Dan led a great team at that time and then they were they were they were kicking ass and they were just really setting the foundation they dig in they set the architecture perfectly so I knew that that was a moment that was going to be pretty powerful at the early days when we were talking about kubernetes before it even started we were always always talking about if this this could be the tcpip of of cloud then we could have kind of a de facto interoperability and Lou Tucker was working for Cisco at the time and we were called it interclouding inter-networking what that did during the the revolution Cloud yeah the revolution of the client server and PC Revolution was about connectivity and so tcpip was the disruptive enable that created massive amounts of wealth created a lot of companies created a whole generation of companies so I think this next inflection point is kind of happening right now I think kubernetes is one step of this abstraction layer but you start to see companies like snowflake who's built on AWS and then moved to multiple clouds Goldman Sachs Capital One you're going to see insurance companies so we believe that the rise of the super cloud is here that's going to be Cloud 3.0 that's software 3.0 it's software three what do you want to call it it's not yesterday's Cloud lift and shift and run a SAS application it's a true Enterprise digital digital transformation so that's that's kind of the trend that we see riding in now and so you know if you're not on that side of the street you're going to get washed away from that wave so it's going to be interesting to see how how it all plays out so it's fun to watch who's on the wrong side it is very fun I hope you all are listening to this really powerful advice from John he's dropping some serious knowledge bombs on us well holding the back for kubecon because we've got we got all the great guests coming on and that's where all the content comes from I mean the best part of the community is that they're sharing yeah absolutely so just for old time's sake and it's because it's how I met your fabulous team last year Define kubernetes for the audience kubernetes is like what someone said it was a magical Christmas I heard that was a well good explanation with that when I heard that one um you mean the technical definition or like the business definition or maybe both you can give us an interpretive dance if you'd like I mean the simplest way to describe kubernetes is an orchestration layer that orchestrates containers that are containing applications and it's a way to keep things running and runtime assembly of like the of the data so if you've got you're running containers you can containerize applications kubernetes gives you that capability to run applications at scale which feeds into uh the development uh cycle of the pipelining of apps so if you're writing applications and you want to scale up it's a fast way to stand up massive amounts of scale using containers and kubernetes so a variety of other things that are in the in the in the system too so that was pretty good there's a lot more under the hood but that's the oversimplified version I think that's what we were going for I think it's actually I mean it's harder to oversimplify it sometimes in this case it connects it connects well it's the connective tissue between all the container applications yes last question for you John we are here at the cube we're very excited to be headed to Detroit very soon what can people expect from the cube at coupon this year so we'll be broadcasting Wednesday Thursday and Friday we'll be there early I'll be there Monday and Tuesday we'll do our normal kind of hanging around getting some scoop on the on the ground floor you'll see us there Monday and Tuesday probably in the in the lounge too um come up and say hi to us um again we're looking for more stories this year we believe this is the year that you're going to hear a lot more storytelling coming out of this community as people get more proof points so come up to us share your email your your handle give us yours give us your story we'll publish it we think we think this is going to be the year that cloud native developers start showing the signs of the of the rise of the supercloud that's going to come out of this this community so you know if you got something to say you know we're open to share stories so we're here all that speaking of John how can people say hi to you and the team on Twitter at Furrier at siliconangle at thecube thecube.net siliconangle.com LinkedIn Dave vellantis they were open on all channels all right signal Instagram WhatsApp perfect well pick your channel we really hope to hear from you John thank you so much for joining us for this preview session and thank you for tuning in my name is Savannah Peterson here in Palo Alto at thecube Studios looking forward to Detroit we can't wait to hear your thoughts do let us know in the comments and let us know if you're headed to Michigan cheers [Music] thank you

(gentle music) >> Hello everyone, and welcome back to day two of AWS re:Invent 2021, theCUBE's continuous coverage. My name is Dave Vellante, I'm here with my co-host, David Nicholson. We've got two sets. We had two remote sets prior to the show. We're running all kinds of activities and we've got AWS executives, partners, ecosystem technologists, Scott Weber is here as the director and an AWS partner, ambassador from PwC. Scott, good to see you. >> Nice to meet you guys. Thanks for letting me be here. >> Well, so your expertise is around application modernization. It's a hot theme these days. If you're a company with a lot of legacy debt, you've got a big complex application portfolio. I would think, especially with the forced match to digital over the last year and a half, two years. Now is really a time when you're probably too late to really start thinking about rationalizing your portfolio. What are you seeing in this space? >> Definitely, we're seeing the customers that have reached that point. I view modernization as sort of the second wave of cloud that's coming. So you had your first wave, the early adopters that lifted and shifted into the cloud. We still have people looking at getting into the cloud, but for those that went early, now, they're saying, "How do I get more out of the cloud? How do I get closer to cloud native?" And that's what we're starting to see around this modernization move is, I want to start to utilize those higher level services from AWS and the cloud providers. I want to get a better return, I want to stop worrying about running infrastructure and hardware. >> So when you think about, I go back all the way back to Y2K, that was like a boondoggle for IT to spend a bunch of doh and do some cool stuff. And then of course the .com crashed, but today it's different. It's really about the business impact the business outcome that you can drive in transforming your digital business. So how do you as a technology agnostic consultant help a company understand what they should leave alone or sunset? What they should aggressively migrate? What's the process that you use to do that? >> In some ways we go back, we can reuse sort of those 6Rs that maybe got a customer to the cloud, or as they're on that cloud journey, right? And you really want to focus on where can you optimize ROI. And you're going to come across those things that are going to be like, look, maybe it's a vendor COTS solution. There's not a lot we can do there. You're just going to have to continue down that path. Unless we can look to move that to a SaaS service. Maybe the vendor has gone to a SaaS offering. Or we get into looking at they've done development in house, but that development is still monolithic running on virtual machines, either in the data center or in AWS, but it's a critical system to that business. It's maybe it's become fragile. How can we now modernize that? Because that's where there's going to be a great return on investment for that customer, and it's also going to allow business agility for those customers. As we can get them to microservices and Lambda and function as a service, the blast radius for changes become smaller, allows the customer to move faster than what they're doing. So it's the rationalization becomes what's driving the business forward? What's critical to the business? But what's holding them back as well? So that the customers can start to move faster. >> So it's a formula of okay, what's the business value of those applications essentially? You can kind of rank that, but then it's a formula there's a cost equation. That's pretty straightforward to figure out the s is and the 2b but then there's a speed. Like an ongoing time to value from a developer standpoint and then I guess there's risk. Have you got your core jewels? Maybe you don't want to touch those yet. Is that kind of your algorithm? >> It is and on that sort of cost and value piece, that's where we can really see some interesting things happen, where as we get customers away from licensed OSS proprietary databases, that return on investment can be huge. So we've helped customers migrate from running .net applications on top of a typical Microsoft Windows stack and SQL server stack. All the way to taking those workloads, all the way, either to Linux containers or all the way to serverless if we're going to take all the steps to rewrite, you can drive 60, 70, 80% of the cost of operating at that platform out of it, then you start this flywheel effect of reinvesting that money back into the next project to help the customer move forward. >> And it's quick follow up, but I know you want to jump in. >> Yeah, yeah. >> Why wouldn't a customer, that's a Microsoft customer just run that on Azure? Why AWS? >> I mean, that's a good question and that sort of gets into a lot of philosophical, like discussion we talk about for a long time. The fact of the matter is the majority of your Windows workloads still run on top of AWS today. I would argue AWS has some pretty superior things in their underlying architecture, they're nitro architectures and things like that. But I think it's also choice. And, the whole move of .net to Linux, Microsoft started that they put the ability to, you can run SQL server on top of Linux. Well, if I run SQL server on top of Linux, I take out 20% of my costs right there. They put the support in for .net core to be able to run on Linux or on containers, but that's to help the developers move faster, that's to help us get to microservices. So that cloud provider choice, I think is becomes a bigger discussion, but a lot of people are choosing AWS because they're not just doing Microsoft workloads . Again, we could get very deep into like, trade-offs on why one over the other, but customers are choosing AWS for a lot of these words. >> Diversity and better cloud, better infrastructure. >> Yeah, and philosophical is an interesting way to look at it when it becomes a hostage negotiation. I'm not sure there was a lot of philosophy involved when server and SQL 2008 were being end of support life. And people were told, move it to Azure and we'll take care of you. Don't move it to Azure, you're on your own. But something on the subject of ROI. ROI is typically measured over time. How do you rectify and address the sort of CIO dilemma, which is that if ROI is being delivered fantastically in four years, but the average tenure of a CIO is 2.7 years, how do you address that? What is the sweet spot for timeframes that you're seeing for people to actually implement when you consider as was mentioned today, the keynote that somewhere around 15% of IT spend is in cloud today, which leaves 85% of it on premises. So what do we do about that? >> Yeah, that's a great question. So, I think, I like to get small wins. So find a very big pain point for that customer. How can we start to get them some small wins and start that flywheel effect going of like you saved money here, now, can we reinvest and start to show some wins, but we've engaged in projects where we've completely rewritten a whole application stack that was the core service for a business in a year and a half, and we took them from a run rate of somewhere between 40 and $60,000 a month. Had they been running that in AWS, they were running it in a data center today. So that was our estimate to less than $5,000 a month to run that application on a serverless platform inside of AWS. >> So when you talk about modernizing an application environment, that's typically not thought of as low hanging fruit. So does that mean that all the low hanging fruit has been consumed? Are all the net new things that are developed in a cloud native format, have they already been done? Is this the only frontier for opportunity now? >> No, it's not the only frontier. I mean, there's a lot of customers that are still just trying to get into the cloud. >> Lots of applications out there? >> Yeah, and you look at things like mainframe as well. That's I think a coming area where customers are finally starting to say, "Enough with the mainframe, we saw it in the keynote today of a new sort of service offering around helping customers rationalize how to do, to start to do things with the mainframe." So, but sometimes you can get those easy wins. Like we find a scalability issue. And we can inject scalability and pull back costs very rapidly. 'Cause you run in that scenario, there provision for max capacity that may happen 10% of the year. Now they're vastly overpaying. So we can still get some easy wins with slight tweaks to the platform while we help them rationalize those longer built times. I think the other thing we're starting to see is a shift in CIOs that are coming more from a software background too. That aren't from the pure infrastructure background and as we see those software dBase CIO start to come in. They're starting to understand the game that can be had of making the investment in the software and those upgrades to the software. >> And their tenure is elongating 'cause, CIO career is over was the joke. Now you're losing CIO, is cause they're going onto a bigger and better. They getting more options. I mean, they're becoming rockstars again. I want to ask you just as a side about that mainframe compatible runtime that they announced 'cause it sounds like you've got some experience in converting mainframe. >> Yeah. >> 'Cause I've always been a skeptic. We've seen this movie before where people have to freeze code, they've got to freeze code for 18 months. It takes 24 months, but now it's cloud, Adam Selipsky said, we can cut migration time, which is critical here by two-thirds 'cause that's the key. If you can reduce the time of which you have to freeze the code or maybe not even freeze the code. Again, I'm a skeptic, but what are you seeing with practical experience? >> So at PwC, we're seeing a lot of customers, start down this path and the ROI is pretty amazing when once you get in and you really start to dig in of what it can be if to go down this path. And there's a lot of tools out there, there's a gentleman on our team that's a real genius with this and he's helped multiple customers go down this path. There's tools that can start to do code conversion for you. I mean, we all get a little skeptical on those things cause we never know what the machine is going to try to make the code look like, but it's the starting point. But there is more. >> Like a prewash? >> Yeah, (Dave laughs) there's more and more design patterns coming out to help us down those pathways. But it goes back to agility for the business cause a lot of these customers running mainframes today are looking at a six month release cycle if they want to make any changes to their environment. If we can get them into an agile mindset to a microservice, they can get to two weeks or less for release cycles. So it's a big win for the company overall. Yes, there's a risk, but I think you can take, you can try to de-risk it as much as you can, you don't take the core, the absolute core critical piece of that mainframe. You start to pick away around the edges and you get comfortable with what you're doing. >> And going back to the concept of ROI, specifically in the mainframe space, there have been some not so subtle nudges from the marketplace that changed the dynamics associated with staying on your mainframe. Because if I tell you that the tax to stay on your mainframe is going to triple or quadruple over the next several years, that changes the balance. So you have the old guard in the software business who will remain nameless, jacking up the prices because they feel like, you know what, "What are you going to do? What are you going to do other than write me a cheque?" And the answer is, "Well move," right?. >> Yep, it's reached a point like the companies are moving. And what I think companies start to see too is, when we talk about purpose-driven databases, Adam was talking about that in the keynote today too. And we've seen that with customers when we've done builds, what's the right database for this data? And now you can start to get things moving even faster. And you unleash new ways of thinking. And I mean, some of the vendors are doing things like that and the companies aren't happy about it. >> Well, yes, but look, you're talking about Oracle in particular. (group chattering) That's one of them, but Oracle invests in its database and it's two different theories. Adam, today's the right tool for the right job, API and primitives and Oracle takes the kind of Swiss army knife approach. But they do invest if you have hard core mission critical, recovery is everything. There's a risk factor involved there, but if you want to go fast and you're a developer, you're not going to necessarily knock on Oracle's door, you're going to go to get an AWS. But it gets to my question, having done a lot of TCO analysis, it used to be labor, was always two-thirds of the cost. Now with automation, especially in Oracle environments, software license costs are the dominant component and it's maybe less true for SQL server, certainly true for Db2. I remember the early days of the flash, we used to tell customers, install flash. You're going to be able to consolidate, reduce your Oracle licenses when they come up. So that was a preferred strategy, but what are you seeing in terms of the ability? First of all is that a correct premise that software licenses is still a big component or an increasingly large component, and how do you unshackle from that? >> Yeah, so definitely software licensing costs for the OSS and for the databases are huge. I mean, there's numbers out there that like for SQL server enterprise, if you can get somebody off the SQL server enterprise and get them to an open solution like Aurora Postgres or something like that, it's a 90% ROI, and the numbers are similar for Oracle. And I talked to a lot of customers are like, "But we don't know Postgres," but it's not really that different. It's still data modeling. And when you get to these managed services platforms like RDS and Aurora, you free up those DBS to do the higher value things. The ROI of a DBA is not managing memory and desk and babysitting the servers, it's helping the developers build better data models. And those sorts of things that are higher value. So it is a big thing and we're seeing customers saying like, "Help us reduce this licensing cost," and help us be more efficient because the open platforms now, especially in the relational database area, are on par in a lot of ways with the Oracles and the SQL servers. So then you start to say, "Well, what am I gaining by paying and being sort of held hostage to these numbers?" So we definitely see customers making this transition. >> I mean, the point about Postgres is a good one because you're going to get enterprise class recoverability but even EDB would say okay, don't start with your mission critical core, pick around the edges just what he's saying over and over time, you're going to become more cloud native and get to the point, can you get to that point where everything's cloud native, everything is a service, maybe not a 100%, but a large part of your application portfolio can get there, right? >> Yeah, you're going to find those, that goes back to doing that application tiering and evaluation and ROI. So, we have a case study that we did with Constellation Brands, where they really needed a B2B type ordering portal solution. And they looked at sort of the typical vendors in a packaged solution if you will, a cottage type solution. And we proposed doing a full custom solution, soup to nuts and building it natively in AWS. And it was built completely on top of platform services. There was no servers in that environment and we were done. We were using AWS Fargate to run their containers on top of, we were using RDS Postgres, we were using Lambda and in some places we were using DynamoDB for holding inflate orders. And so the whole environment is deployable from one cloud formation template. So it completely changed how we even went through the testing of the thing. 'Cause you ran the same cloud formation template to deploy to a different environment. And you knew you were getting the same exact thing. And so they went from, they no longer had to worry about securing underlying compute, secure the containers, run on top of Fargate, use a platform service for your databases, and it was a beautiful solution for them. >> Yeah, you got to taste of that and your eyes open up and say, "Wow, what's possible?" >> Yeah, its a game changer. >> We heard that from NASDAQ this morning. An amazing story. She said, our first Amazon bill was 20 bucks. I bet it's higher now, but first hits free kind of thing. But the point is when people talk about the AWS bill, et cetera, no question, you should try to optimize that. But at the end of the day, it's about the business value Scott, isn't it? >> Scott: Yeah, it is. >> Hey, thanks so much for coming to theCUBE. It was great perspectives, >> No, thank you guys. I appreciate having you guys on. >> Thank you very much. >> Keep it right there, Dave Nicholson and I will be right back. You're watching theCUBE's coverage of AWS re:Invent 2021. (gentle music)

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante five publicly traded u.s based companies have market valuations over or just near a trillion dollars as of october 29th apple and microsoft topped the list each with 2.5 trillion followed by alphabet at 2 trillion amazon at 1.7 and facebook now meta at just under a trillion off from a tie of 1.1 trillion prior to its recent troubles these companies have reached extraordinary levels of success and power what if anything could disrupt their market dominance in his book seeing digital author david micheller made three key points that i want to call out first in the technology industry disruptions of the norm the waves of mainframes minis pcs mobile and the internet all saw new companies emerge and power structures that dwarfed previous eras of innovation is that dynamic changing second every industry has a disruption scenario not just the technology industry and third silicon valley broadly defined to include seattle or at least amazon has a dual disruption agenda the first being horizontally disrupting the technology industry and the second as digital disruptors in virtually any industry how relevant is that to the future power structure of the digital industry generally in amazon specifically hello and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we welcome in author speaker researcher and thought leader david michela to assess what could possibly disrupt today's trillionaire companies and we're going to start with amazon dave good to see you welcome thanks dave good to see you yeah so dave approached us about a month or so ago he was working on these disruption scenarios and we agreed to make this a community research project where we're going to tap the knowledge of the cube crowd and its adjacent communities and to that end we're initiating a community survey that asks folks to rate the likelihood of seven plus one disruption scenarios so we have a slide here that sort of shows what that survey structure is going to look like and so as i say there's seven plus another one which is kind of an open open-ended and we're going to start with amazon as the disruptee so dave you've been writing about the technology industry for decades and digital disruption and china and automation and hundreds of other topics what prompted you to start this project yeah it's a great question you know as you said that the whole history of our business has been you know every decade or so you have a new set of leaders ibm digital microsoft the internet companies etc but when i started looking at it you know that seems in some ways to have actually stopped that you know microsoft is now 40 years old amazon is what 1995 is getting towards 30. you know google's been a dominant company for 20 years and you know apple of course and facebook more recently so so whatever reason this sort of longevity of these firms has been longer than we've seen in the past so i sort of say well is there anything that's going to change that so part of it and we'll get into it is what could happen to disrupt those big five but then the sort of second question was well maybe the uh disruptive energies of the of the tech business have moved elsewhere they've moved to crypto currencies or they've moved to tesla and so you start to sort of broaden your sense of disruption and when you talked about that dual disruption agenda that whole ability of tech to disrupt other sectors banking health care insurance automobiles whatever is sort of a second wave of disruption so uh we started coming out all right what sort of scenarios are we really looking at over say for the 2020s what might shake up the big five as we know them and how might disruption spread to sort of more industry specific parts of the world and that was really the the genesis of the project and really just my own thinking of all right what scenarios can i come up with and then reaching out to companies like yourselves to figure out okay how can we get more input on that how can we crowdsource it how can we get a sense of of what the community thinks of all this it's great love it and as you know we're very open to do that so we're going to crowdsource this we're going to open it up to virtually anyone and use multiple channels so let's go through some of the scenarios all of them actually and explain the reasoning behind their inclusion the first one the govern government mandated separation divestment and or limits on amazon's cloud computing retail media credit card and or in-house product groups it probably no coincidence that this was the first one you chose today but why start here well i think the government interest in doing something to get back at big tech is is pretty clear and probably one of the few things that has bipartisan support in washington these days and also government interventions have always been an enormous part of the tech industry's history the the antitrust efforts against ibm and att in particular and more recently microsoft a smaller one but it's it's always been there there's a vibe to do it now and when you look at all the big ones but particularly amazon you can see that potential divestments and breakups are sitting there right in front of you the separation of retail and aws uh perhaps breaking out credit card or music or media businesses these sorts of things are all on the surface at least relatively clean things to do and i think when you look at the formation of an alphabet or a meta those companies themselves are starting to see their own businesses as consisting of multiple firms yeah so i just want to kind of drill into the cloud piece just to emphasize the importance of aws in the context of amazon amazon announced earnings thursday night after the close aws is now a 64 billion revenue run rate company and they're growing at 39 percent year over year that's actually an accelerated growth rate from q3 2020 when the company was grew at 29 it's astounding think about a company this size moreover aws accounted for more than actually but 100 of amazon's operating profit last quarter so the aws cloud is obviously crucial as a funding vehicle and ecosystem accelerant for amazon and i just wanted to share some data points dave before we move on to these other scenarios yeah and just on that uh i think that is the fundamental point it's very easy to see aws on its own as a powerhouse but i think you know if you figure how much freedom aws money has given the retail business or the credit card business or the music businesses to launch themselves and to essentially make no money for very long periods of time uh you see that you know if you're a walmart trying to compete with amazon as a retailer well that money from aws is is an awful big problem and and so when they look at separation that's the sort of stuff people talk about right so i just want to i want to put that into context just in in terms of the the cloud business so this chart is one from our etr surveys that isolates the four hyperscale cloud providers and adds in oracle and ibm we both own public clouds but don't you know don't have nearly the the scale we don't have apple or facebook they have clouds as well and we can talk about that in a moment but the chart shows net score or spending momentum on the vertical axis and market share or pervasiveness in the survey on the horizontal axis it's it's really mentioned share not dollar market share but it's an indicator and the red line is an indicator of elevated spending momentum and you can see azure and aws they're up and to the right i mean amazon is 64 billion you know uh azure will claim larger because they're including their application business but just their their their i asked business obviously smaller than amazon's but you can see in the survey the respondents define cloud they include that sas business so they they both impressively have this high spending momentum on the vertical axis well above that 40 line despite their size google obviously well behind those to the left and then alibaba which has a small sample in the etr survey it's you know it's not as prominent in china but even though it's ias cloud businesses larger than google's by probably a couple billion dollars now the point is these four hyperscalers and there really are only four in my view anyway they have a presence that allows them to build new businesses and disrupt ecosystems and enact that dual disruption agenda should they choose to do so at least in the case of amazon oracle and ibm are not in a position to do that it's not part of their agenda they don't they don't have that scale but dave can you talk about your dual disruption scenario very clearly amazon fits in there and i would think alibaba as well but what about microsoft facebook apple google yeah i mean you know people often say what's the biggest difference between microsoft and amazon from from a cloud point of view and the answer is pretty clear that microsoft goes out of its way to assure its customers that it really doesn't have any interest in competing directly about them so you don't see microsoft going into the retail business or the banking business or the healthcare business all that seriously in contrast that's really what amazon is all about is taking its capabilities to essentially any industry it likes and therefore as one is as great as the service aws provides it's often being provided to people who amazon is actually competing with at least some degree or another and you know that's a huge part of microsoft's sales pitch and it's certainly a potential vulnerability down the road uh it's very hard in the end to be an essential supplier and a direct competitor at the same time but so far they've managed to do that yeah so we put together just another sort of aside here this little thought experiment to see what aws would look like as a separate entity and so it's a chart that looks at a number of tech companies and lays out their revenue run rate the growth rates gross margin probably should have done operating margin might have been more relevant but market cap and revenue multiple again given the size of aws at 64 billion run rate and accelerating growth trajectory it's just it's remarkable and so we we figured this out based on industry norms and today's valuations it's not inconceivable that aws could be you know in the trillionaire club or close to it so based on that discussion we had earlier amazon amazon's dual disruption agenda being funded by empowered by aws as we just discussed dave yeah and just keep in mind nothing that you or i are saying are predictions or saying that anything is going to happen they are possible scenarios of what might happen that seem to make some plausible sense so that when amazon is making the sort of profits that it's making aws naturally that's going to attract other companies because there's margin to to be had there and similarly you know look at uh you look at microsoft for all those years the profits it made in windows or in office software allowed it to do all kinds of other things and essentially that's what amazon is doing today but if a google or a microsoft could cut into those profits through some sort of aggressive pricing and perhaps we'll talk about that you know that would have a lot of impact on amazon as a whole all right so let's quickly go through the other description scenarios and maybe make some comments the next one sort of major companies increasingly choose to do their own cloud computing and or sell their products directly for competitive cost security or other reasons so dave i saw this and look at a company like walmart and others no way they're going to run their business on aws walmart as we know is building out its own cloud and maybe it doesn't have the size of a hyperscaler but it's very large it's got the technical chops it can most likely do it a lot cheaper than renting cloud space what was your thinking in this scenario yeah the broader thing here is essentially one of that computing paradigms have been proven to go in cycles you know a long time ago people shared computers and called timeshare and then people ran their own and now they're sharing again through the cloud and who knows it's possible that the cycle could shift again through some innovation and you know a lot of companies today look at the bills they're getting for cloud or for various sas services and some of them are pretty high and a lot of them will look at and say hey maybe we actually can do some of this stuff cheaper so the scenario is that essentially the the cycle shifts once again uh and it makes more sense to do stuff in-house again that's not a prediction but uh certainly something that's happened before and couldn't plausibly happen again yeah there's a lot of discussion about that in the industry of martine casado and sarah wong wrote that piece about the you know the trillion dollar basically sucking sound basically saying the the scenario was the the the premise rather was the that that sas companies their cost of goods sold are increasingly going to be you know chewed up by cloud costs and then of course mark andreessen says every company is going to be a sas company so as the sassification of business occurs that's something to consider okay next scenario is environmental policies raise costs change packaging delivery recycling rules and or consumer preferences can you comment dave on your thinking on this scenario yeah first i'll just back up a bit we're used to thinking of technology is the great disrupter and clearly that's still important but there are now other forces out there china which will talk about uh the environment uh various cultural forces and and here with the environment you see all kinds of things that could change that you know if you look at amazon and its model of very high levels of packaging lots of delivery vehicles and all the things it is doing are those necessarily the best environmentally and will there potentially be various taxes carbon metrics or things that might work against that model and tend to favor more traditional stores where people go to pick them up that seems to be a plausible scenario and i think everybody here knows that desire to do something in the in the climate environmental spaces is pretty strong and you know if you look at you know just throws aside the recycling industry itself has arguably been quite a failure in that much of what is so-called recycled is basically put in tankers and shipped to the third world which no longer wants it uh and so the backlog of packaging and concerns about packaging and uh what to do with all that you know those those issues are rising and and will be real and i i don't know whether amazon has a good answer to that they're you know they obviously are very aware of it they're working very hard to do everything they can in that space but their fundamental model of essentially packaging every good in its own little box or envelope or whatever is arguably not the greenest way of doing business got it thank you so okay so the next one is price in slash trade wars with the u.s and or china cloud and e-commerce giant so protectionism favors national players so we talking here about for example google bombing prices or alibaba or trade policy making it difficult for amazon to do business in certain parts of the world can you add some color on this one yeah all those things and i would just start with with china itself you know you could argue that covet has been the biggest disruptor of the last couple years but if you look out the next five or eight you had to look at all these things you'd probably say china the size of the chinese market the power of its vendors players like alibaba clearly can rival amazon in many different ways uh you know it's no secret that it'd be hard for amazon to they're not going to be a big success in china uh but you can see it in harder ways that you imagine across asia or other markets where alibaba is strong and you're in today's sort of environment where there's scarce goods and maybe certain products well maybe they go chinese may probably go to alibaba first and you want to buy that product well amazon doesn't have it but alibaba has it you know those sort of scenarios if you get into a sharp trade war with china or even if the current tensions continue it's quite easy to see how that could uh play some havoc with amazon's supply chains in many ways the whole amazon retail model is based on a steady flow of goods manufactured in china and that clearly is not as stable as it was right got it the next one actually caught my attention and this is a big part of the reason why we want to survey the community to see how plausible folks think this is in its its technology related scenario so that would potentially disrupt aws and by fault by default hit amazon so that's major computing innovations such as quantum edge machine machine would obsolete today's cloud architectures okay so so here what you're thinking just as aws changed the game in i.t some future innovations or new business models that we haven't conceived yet could disrupt the prevailing cloud computing model right yeah absolutely i mean you know again we'll go back to where we started that new technologies have always been the main disruptors and here we're looking at some potentially very powerful uh new technologies you know your guess is good in mind about what's gonna happen with quantum is clearly a very different way of computing quite possibly led by other vendors possibly even led by china which would be a huge issue you look at the cloud well cloud's not very good at sort of edge stuff or machine to a machine stuff or sort of near field things out cars in the highway talking to each other uh you know again amazon's totally aware of these things and they are working on it but they have a huge investment in other ways of doing things and historically that inertia that need to protect existing bases of activity and practices has made it difficult for a lot of companies to adjust to new things and so that could happen again uh and there's certainly a puzzle but yeah in all these cases so far amazon has been aware of it is trying to do it but you can still see the scenario playing out and in a truly disruptive technology it's not always possible for the incumbent to effectively cope with it okay the next scenario speaks to i think some of the work that you've done in automation and related areas software replaces centralized warehouses as delivery services are directly connected to suppliers and factories so dave this is like cut out the middle man right software and automation changes the nature of the route absolutely i mean you know in a world of ubiquitous delivery services and product standardization metrics and products being built and shipped from all over the world the concept of running them all through a centralized warehouse is at least at a minimum uh seems like something that might be uh obsoleted and replaced and you know imagine if google built a significant taxonomy of of core products that could be traced directly to where they are either manufactured supplied or brought into the country from whatever company that tries to sell them and the delivery service connected directly to that uh and so that model has always been out there i think at various times people have looked at it it hasn't happened so far and i think amazon itself is is is looking at this particularly as it gets more into food that the idea of shipping all fresh food any sort of centralized warehouse is a pretty bad idea uh and so you know that model of software essentially replacing giant automated warehouses uh is out there and and seems to me uh likely and i just say that you know alibaba for the record doesn't really use that warehouse model it uses a network of suppliers and does it that way and and there do seem to be uh some efficiencies that would likely come with that the next one is was really interesting from a historian's perspective and it's the penultimate uh scenario and that's the proverbial self-inflicted wound and you and i certainly remember ibm's you know fateful decision to outsource the microprocessor and operating system to intel and and and and and microsoft sorry ibm's decision to do that lotus you might recall it refused to allow 123 to run on windows back in the day novell buying word perfect jim barksdale a lot of young people the audience won't of course remember this but jim barksdale poo-pooing microsoft's decision to bundle internet explorer into the operating system all those were kind of self-inflicted or blind spots so this one is complacency arrogance blindness abuse of power loss of trust so much more than the examples i gave consumer and or employee backlash you're seeing some of that at facebook now and i guess this is taking their eye off the customer ball losing the day zero in amazon's case forgetting that customer obsession formula they're working backwards culture and i think this is a big reason why andy jassy was put in charge so this wouldn't happen but we've seen time and time again as the examples i just gave blind spots have absolutely killed companies haven't they dave absolutely he listed many of the most famous but perhaps my favorite of all was kennels and the founder of digital equipment corporation one of the great tech visionaries of his time who stated over and over again why would anybody want a home computer or eunuch's snake oil was his other beautiful all of those things and and so there's the blindness uh there's the area ibm who just came to the view that they and att both came to the view that they were invincible and nothing could ever crack their control of their customer base so we've seen all that i think uh more recently i think some of these things can actually go from the bottom up and you know what's happening to facebook today well they're being hurt by former employees speaking out uh you know this never really happened too much to in the ibm and t days but people calling into question amazon's work labor practices and such things is certainly a possible scenario and the whole sort of you know in the end you know people talk about a cultural backlash against technology i'm not sure i believe it'll happen but it certainly is possible that people will start to rebel against these firms you see it more likely with facebook is fairly well along there uh amazon's still popular but you know in the end and as you i think you said the the core thing that companies routinely fail on is they lose their customer focus and they get caught up in other things their financial numbers their their power inside their position of their company but they they lose track of staying close to the customer has need and terrific job of staying close to the customers over the years uh so if anyone you know was maybe less vulnerable that they they would be well along that that line but it can happen to anyone and new management is often you know one of the real tests and there's many examples of that through history when a new executive comes in will they have that same focus that same thing particularly you know as the first generation's employees get wealthy and retired in a new set of people come in you know you look at microsoft the new people who came in well they're not going to be multi-millionaires they may have missed the great runs they're there to work and and the culture of companies changes when you get to that state the m is not that there yet but you can envision that comings soon enough so you know cultural issues have always been a factor and it's hard to imagine there won't be some sort of factor going forward well and you know you talk about that the the succession of founders and ceos i mean that's what to me makes microsoft so astounding because during the bomber years it was unclear that they were ever going to become relevant again and so nadella has done a masterful job but of course they had the margins from the pc software business that allowed them to buy that time but look at intel and the troubles it's going through uh and so many other examples of companies that just sort of said all right well we're going to pack it in and either sell the company or which is again what i think makes think companies like oracle and dell which you know founder-led ceos not ceo in the case of oracle but still running the business uh so quite uh significant yeah yeah and you know we've talked a lot about things that might hurt answers but you gotta recognize how in many ways how amazing they are and most tech companies a lot of them anyways have essentially been one trick ponies i mean google still makes overwhelming amount of its money selling ads and the things it's tried to do in cars and healthcare and various things you know they've often struggled you know apple still makes the core of its money around it's it's cell phone platform amazon's one of the few that continually generates entirely new huge businesses and and you have to give them an enormous amount of credit for that you know microsoft uh was a they failed repeatedly over and over again with internet stuff and phone stuff and all these things and it really wasn't until you know satya came in and really focused on their customers and their need for enterprise services that he that he really got the company on the right track so you know amazon has always been good listeners customers and if they continue to do so it bodes well but history says other stuff comes along okay and the last scenario is open-ended dave included uh you know what did we miss is there another scenario that we haven't put forth that you could feel it could be disruptive to amazon right i mean you've got to have the at least what'd we miss yeah i mean you know these are things that me and you and i just sort of made up the top of our head these are things we see that that might happen but you know in your huge audience of people in this community every day i'm sure there are other people out there who have thoughts of what might shake things up or even doing things that might shake things up already uh and you know one of the things you do for you guys is get this sort of material out there and and see what ideas surface so hopefully people will uh participate in this and we'll see what comes out of it all right so what happens from here is we're going to publish the the link to the survey in this video description and in our posts we ask you to take the survey please tell your friends we're going to publish the results as always we do in an open and free david michelle thanks so much for putting your brain power on this and collaborating with us i'm really excited to see the results and and and run through the other giants with you as well once we see what this survey says yeah thanks david great and yeah if we can make this one work be fun to do it for for google and microsoft and facebook and apple and see where it all goes thanks a lot all right okay that's it for today remember these episodes are all available as podcasts wherever you listen just search breaking analysis podcast i publish each week on wikibon.com and siliconangle.com etr.plus is where all the cool survey data lives they just dropped their october survey with some great findings so do check that out you can reach me on twitter at d velante he's at d michelle or comment on my linkedin post or email me at david.vellante at siliconangle.com this is dave vellante for dave michelle thanks for watching thecube insights powered by etr be well and we'll see you next time

>>Hello, I'm John Frey with the queue here for cube conversation with Devon Reed, senior director of apex offer product mentioned Dell technologies. They have a great to see you. Congratulations on apex and the momentum and the big news. >>Yeah. Thank you for having me here, John. It's a, it's a pleasure to be here with you and I can't wait to talk to you about the stuff. >>So we chatted last Dell technologies world about apex in great length. Um, first given update on what's the new news and to where's it come from since Dell tech DEC world. What's, what's the big update on the product and the news you're launching today. >>Yeah, so it's been a, it's been a fantastic journey here, John. And, um, you know, since Dell technologies world, we've learned a ton from our customers and the reception has been extremely positive. We're seeing a ton of interest from our customers. We're building demand, um, and we're learning a lot, but I think if we boil it down to what we're, we're really learning here is that customers are living in a cloud first world. And what that means is that customers want to move, uh, you know, to the public cloud because the public cloud brings simplicity, agility, the ability to pay for only what they use and they don't need to manage their infrastructure. However, what we're hearing from our customers as well is that they're, um, a little hesitant to move all of their workloads to the public cloud, because there are certain performance requirements, latency requirements, and security requirements that are, uh, still, um, held from on-prem infrastructure vendors. And that's, that's the beauty of apex to bring that the, the simplicity of the public cloud and the security and the performance of the private cloud in one with it. >>I want to get your thoughts real quick before I move on to the news, because this comes up a lot in conversations. In fact, I just had a conversation this morning on camera and also off-camera around virtualization of data, right? So, and how on premises? The bare metal growth is there, right? So you starting to see from a performance standpoint, when you security, we get that. There's not a lot of on premises reasons why to be on premises for the security reason, but performance, you brought that up. Talk more about that real quick, because I think this is really becoming quite more traction than people thought there's a performance gain. On-premise with some of the new tech, what's your reaction to that? >>Yeah, exactly. I think that's a, it's a great call out John. And especially as you get into some of these new applications where the computation needs to be directly next to, uh, the data in which is processing latency and performance is extremely important. We hear that day in and day out from our customers. And that's why it's really, it's really important to focus on not only on public cloud environments, but on-premise infrastructure. And that's what apex really, really helps customers, um, bridge that bridge, that gap. >>And for the folks watching there's a great interview, search his name, Devon's name, and look at last year's announcement. We covered it in detail with apex. So some great content there. Go check that out. I got to ask about the news. You had some new announcements at VMworld earlier today. What can you tell us about the news? >>Yeah, yeah, we did. John. This is, this is an amazing year for Dell at VM world in general. Um, there's a ton of announcements that have come out with collaboration with VMware and Dell, but for apex specifically. And that's what I'm here to talk to you about is that we're introducing a new offer to the apex portfolio. And this offer, we call apex cloud services with VMware cloud. And what this really is, is it's a full infrastructure as a service stack and it's utilizing Dell's, uh, hyper-converged infrastructure. So it's integrated storage, networking compute, and we combine that with, um, the virtualization stack from, uh, VMware virtualization stack and the services. It's a solution that's managed by Dell, it's designed for six nines of availability. And again, going back to what customers are asking for, it allows customers, the performance, the security, and it also provides those consistent operations across their multi-cloud environments. >>What's the driver behind the customer requirements than this. Is there a specific use case that jumps out off the page on, on the managed service? Could you share why the traction? >>Yeah. You know, um, this space is growing really rapidly and it's the new space. And as we talk to more and more customers, we learned there's a ton of different use cases, a ton of different deployments that are really coming to the forefront. But if I really boil it down, there are a few that are kind of rising to the top year. And I think first and foremost, we see a lot of deployments in VDI and really the driver behind that is some of those, those environments are complex. And what the customers are trying to do is really offload those it administrative tasks and have companies like Dell manager. And that's what we're doing for them. Another one is, um, you know, really around that latency, latency and security, really trying to drive applications to not suffer from, you know, that hate latency and security kind of benefits. >>Now, um, what we've seen is we have a lot of interest from very large enterprises that actually want to build and modernize their data centers. So they're either consolidating their data centers or they're trying to move to a fully automated, uh, hybrid cloud situation, right. And I'm talking very large deployments of, uh, VMware based, um, private cloud, uh, capabilities. And I say one other place that we're seeing a lot of interest in these sort of capabilities is large distributed kind of edge use cases. So think, um, you know, think use cases where you have, um, hundreds of remote office locations or a thousands of retail locations that is very difficult for customers to manage. And we take that burden away from our customers. >>Uh, thanks for laying out the customer scenarios and the use case. Good stuff I got to ask you about the solution now appears that it was jointly developed with VMware. Is that right? And if so, can you tell me more about that? >>Yeah, yeah, exactly. John, this is, this is amazing. The amount of collaboration that has gone into this solution with VMware is incredible and really it's based on customer feedback. And we saw, you know, based on this feedback, we've saw a real need to basically take the best of VMware software and their services capabilities, and our, you know, Dell's world-leading infrastructure capabilities and really combine it with the simplicity and agility that apex apex provides. So we've been working with VMware very tightly, uh, over the past year and more to really develop the solution. It's been a great journey, been spending a ton of time with the VMware team building this and, um, you know, customers really love what VMware cloud enables and customers love apex. So it's a really powerful combo and we think it's, it's really the next, uh, kind of rocket ship for, you know, the combined companies here. Yeah. >>I think the VDI piece and these use cases, you mentioned only get more relevant and complex at the same time with the whole shifting in the working environments, you know, the work from home, the future of work, you know, you have the blurring of the lines between private, you know, home versus corporate network. It's like, I mean, we thought it was hard before it's going to get even more complicated. So the pressure's on to abstract away the complexity. So, so totally relevant. Yeah. >>And demand for these kinds of solutions we're seeing, you know, the interest is, is doubling. Uh, it seems like almost every six months, you know, there's a lot more interest, especially as we progress through this pandemic and the, and uh, this environment that we're living in you. So, okay. >>So I got to ask you going forward again. Great progress from our last time we chatted at Dell technology worlds last year, um, 2021, um, what's ahead for Dell and the VMware partnership. Tell us more, how does that look? Um, extending is what's the trajectory look like, and you share any specifics, what can we expect? What's the headroom? What should customers expect? >>Yeah, yeah. You know, we get that question a lot and really, um, you know, nothing is really, although we are going to be separating as, as different entities, you know, the collaboration and the, the level of, um, joint development that we have between the two companies, uh, couldn't be stronger now. And we don't, we do not expect that to change. And we're just getting started on this thing and there's a lot more to come for sure. >>What's the biggest thing that you're, you're excited about. Obviously apex has been a good, it's a trajectory. The progress has been great. The market's in your favor, what's, what's exciting for you right now. Where do you see the action? Um, you know, where's, where's the fun for you in this what's that what's, uh, what's your take? >>You know, it always, for me, the fun always comes down to customers and understanding what the customers want, understanding what the solution, where the solution works, where the solution doesn't work, really working with our customers to really understand their problems and really try to work. So that's where I, I get my energy, uh, in this whole thing and to see the, see the pipeline grow and the sales coming in, that's just, it's really exciting for me, you know, as we're kind of embarking on this new, as a service, uh, world for the, for the multicloud world, it's, it's just, it's fantastic, John, >>You know, the one-click buy as you go consumption-based, this is the trend and infrastructure as code, which is a cloud ethos, and you may not have any on premises with security and now performance, it seems like we're seeing the second wave of virtualization kick in on premises where now that you're in a cloud operating model from storage compute, networking, kind of almost a reboot, almost a reset or an extension or a real-life, it seems like it's another second life of, of, of, of, of innovation. What's your reaction to that? >>Yeah. I, I definitely agree with you, John, and, you know, from a, from a vision perspective, we're just, we're just starting to, uh, you know, we're just starting out there and we, you know, if we think about the power, uh, in the breadth of the portfolio that Dell has, it is unmatched in the industry. So first and foremost, you know, there's a lot more from a, from a solution perspective that we can bring to the floor. So I think that's, that's really exciting. I like the position that we have there and in terms of collaboration with VMware, we're just getting started there too. And, uh, I spend, uh, almost a half of my day with VMware employees, which is incredible amount of collaboration. And there's so much more that we've talked about in our roadmap, uh, to really build out this vision when you start thinking about not just virtualization, but you start to talk about, um, you know, these, these new operating environments, including Kubernetes and Tansu capabilities. And, um, you know, how do you, how do you hit different, uh, use cases with, um, not only hyper converged and hyper-converged infrastructure, but different types of infrastructure as well. And then you start to span, uh, not only the prem, but the co-location facilities and, and the edge, and you bring this all together under the apex console. And I like our future >>Console based provisioning, easy, uh, congratulations on the big news apex cloud services with VMware cloud, um, for the folks watching, that's going to come in and maybe adopt the solution, the managed service, what can they expect from Dell? >>Uh, what you can expect is a very simple experience. So, uh, everything starts and ends with what we call our apex console. So a customer from the time they, they want to learn about our services to, um, you know, getting quotes on them, to actually transacting the, uh, the service, um, to operating the infrastructure from that. And then we provide a full set of, uh, services under the cover where a customer doesn't need to worry about the actual infrastructure management. And we provide customer success managers for every account. So we, we are there with you, uh, along every step of the journey to make this as seamless and easy as possible. So it's a fantastic, uh, experience for our customers. And that's, that's one of the things that they really love about the apex is that, um, you know, kind of white glove service that we're providing >>Devin. Great to see you, Devin Marine, senior director of Dell apex offer product management. He's only getting the product to see and congratulatory success, apex cloud services with VMware clouds, the big news here at VMworld with Dell technologies, I'm John furrier cube conversation, breaking it down and bringing the news to you. Thanks for watching.

>>from around the globe. It's the Cube presenting Cuban cloud brought to you by silicon angle, everybody to Cuban cloud. My name is Dave Volonte, and I'll be here throughout the day with my co host, John Ferrier, who was quarantined in an undisclosed location in California. He's all good. Don't worry. Just precautionary. John, how are you doing? >>Hey, great to see you. John. Quarantine. My youngest daughter had covitz, so contact tracing. I was negative in quarantine at a friend's location. All good. >>Well, we wish you the best. Yeah, well, right. I mean, you know what's it like, John? I mean, you're away from your family. Your basically shut in, right? I mean, you go out for a walk, but you're really not in any contact with anybody. >>Correct? Yeah. I mean, basically just isolation, Um, pretty much what everyone's been kind of living on, kind of suffering through, but hopefully the vaccines are being distributed. You know, one of the things we talked about it reinvent the Amazon's cloud conference. Was the vaccine on, but just the whole workflow around that it's gonna get better. It's kind of really sucky. Here in the California area, they haven't done a good job, a lot of criticism around, how that's rolling out. And, you know, Amazon is now offering to help now that there's a new regime in the U. S. Government S o. You know, something to talk about, But certainly this has been a terrible time for Cove it and everyone in the deaths involved. But it's it's essentially pulled back the covers, if you will, on technology and you're seeing everything. Society. In fact, um, well, that's big tech MIT disinformation campaigns. All these vulnerabilities and cyber, um, accelerated digital transformation. We'll talk about a lot today, but yeah, it's totally changed the world. And I think we're in a new generation. I think this is a real inflection point, Dave. You know, modern society and the geo political impact of this is significant. You know, one of the benefits of being quarantined you'd be hanging out on these clubhouse APS, uh, late at night, listening to experts talk about what's going on, and it's interesting what's happening with with things like water and, you know, the island of Taiwan and China and U. S. Sovereignty, data, sovereignty, misinformation. So much going on to talk about. And, uh, meanwhile, companies like Mark injuries in BC firm starting a media company. What's going on? Hell freezing over. So >>we're gonna be talking about a lot of that stuff today. I mean, Cuba on cloud. It's our very first virtual editorial event we're trying to do is bring together our community. It's a it's an open forum and we're we're running the day on our 3 65 software platform. So we got a great lineup. We got CEO Seo's data Practitioners. We got a hard core technologies coming in, cloud experts, investors. We got some analysts coming in and we're creating this day long Siri's. And we've got a number of sessions that we've developed and we're gonna unpack. The future of Cloud computing in the coming decade is, John said, we're gonna talk about some of the public policy new administration. What does that mean for tech and for big tech in General? John, what can you add to that? >>Well, I think one of the things that we talked about Cove in this personal impact to me but other people as well. One of the things that people are craving right now is information factual information, truth texture that we call it. But hear this event for us, Davis, our first inaugural editorial event. Robbo, Kristen, Nicole, the entire Cube team Silicon angle, really trying to put together Morva cadence we're gonna doom or of these events where we can put out feature the best people in our community that have great fresh voices. You know, we do interview the big names Andy Jassy, Michael Dell, the billionaires with people making things happen. But it's often the people under there that are the rial newsmakers amid savory, for instance, that Google one of the most impressive technical people, he's gotta talk. He's gonna present democratization of software development in many Mawr riel people making things happen. And I think there's a communal element. We're going to do more of these. Obviously, we have, uh, no events to go to with the Cube. So we have the cube virtual software that we have been building and over years and now perfecting and we're gonna introduce that we're gonna put it to work, their dog footing it. We're gonna put that software toe work. We're gonna do a lot mawr virtual events like this Cuban cloud Cuban startup Cuban raising money. Cuban healthcare, Cuban venture capital. Always think we could do anything. Question is, what's the right story? What's the most important stories? Who's telling it and increase the aperture of the lens of the industry that we have and and expose that and fastest possible. That's what this software, you'll see more of it. So it's super exciting. We're gonna add new features like pulling people up on stage, Um, kind of bring on the clubhouse vibe and more of a community interaction with people to meet each other, and we'll roll those out. But the goal here is to just showcase it's cloud story in a way from people that are living it and providing value. So enjoy the day is gonna be chock full of presentations. We're gonna have moderated chat in these sessions, so it's an all day event so people can come in, drop out, and also that's everything's on demand immediately after the time slot. But you >>want to >>participate, come into the time slot into the cube room or breakout session. Whatever you wanna call it, it's a cube room, and the people in there chatting and having a watch party. So >>when you're in that home page when you're watching, there's a hero video there. Beneath that, there's a calendar, and you'll see that red line is that red horizontal line of vertical line is rather, it's a linear clock that will show you where we are in the day. If you click on any one of those sessions that will take you into the chat, we'll take you through those in a moment and share with you some of the guests that we have upcoming and and take you through the day what I wanted to do. John is trying to set the stage for the conversations that folks are gonna here today. And to do that, I wanna ask the guys to bring up a graphic. And I want to talk to you, John, about the progression of cloud over time and maybe go back to the beginning and review the evolution of cloud and then really talk a little bit about where we think it Z headed. So, guys, if you bring up that graphic when a W S announced s three, it was March of 2000 and six. And as you recall, John you know, nobody really. In the vendor and user community. They didn't really pay too much attention to that. And then later that year, in August, it announced E C two people really started. They started to think about a new model of computing, but they were largely, you know, chicken tires. And it was kind of bleeding edge developers that really leaned in. Um what? What were you thinking at the time? When when you saw, uh, s three e c to this retail company coming into the tech world? >>I mean, I thought it was totally crap. I'm like, this is terrible. But then at that time, I was thinking working on I was in between kind of start ups and I didn't have a lot of seed funding. And then I realized the C two was freaking awesome. But I'm like, Holy shit, this is really great because I don't need to pay a lot of cash, the Provisional Data center, or get a server. Or, you know, at that time, state of the art startup move was to buy a super micro box or some sort of power server. Um, it was well past the whole proprietary thing. But you have to assemble probably anyone with 5 to 8 grand box and go in, and we'll put a couple ghetto rack, which is basically, uh, you know, you put it into some coasting location. It's like with everybody else in the tech ghetto of hosting, still paying monthly fees and then maintaining it and provisioning that's just to get started. And then Amazon was just really easy. And then from there you just It was just awesome. I just knew Amazon would be great. They had a lot of things that they had to fix. You know, custom domains and user interface Council got better and better, but it was awesome. >>Well, what we really saw the cloud take hold from my perspective anyway, was the financial crisis in, you know, 709 It put cloud on the radar of a number of CFOs and, of course, shadow I T departments. They wanted to get stuff done and and take I t in in in, ah, pecs, bite sized chunks. So it really was. There's cloud awakening and we came out of that financial crisis, and this we're now in this 10 year plus boom um, you know, notwithstanding obviously the economic crisis with cove it. But much of it was powered by the cloud in the decade. I would say it was really about I t transformation. And it kind of ironic, if you will, because the pandemic it hits at the beginning of this decade, >>and it >>creates this mandate to go digital. So you've you've said a lot. John has pulled forward. It's accelerated this industry transformation. Everybody talks about that, but and we've highlighted it here in this graphic. It probably would have taken several more years to mature. But overnight you had this forced march to digital. And if you weren't a digital business, you were kind of out of business. And and so it's sort of here to stay. How do you see >>You >>know what this evolution and what we can expect in the coming decades? E think it's safe to say the last 10 years defined by you know, I t transformation. That's not gonna be the same in the coming years. How do you see it? >>It's interesting. I think the big tech companies are on, but I think this past election, the United States shows um, the power that technology has. And if you look at some of the main trends in the enterprise specifically around what clouds accelerating, I call the second wave of innovations coming where, um, it's different. It's not what people expect. Its edge edge computing, for instance, has talked about a lot. But industrial i o t. Is really where we've had a lot of problems lately in terms of hacks and malware and just just overall vulnerabilities, whether it's supply chain vulnerabilities, toe actual disinformation, you know, you know, vulnerabilities inside these networks s I think this network effects, it's gonna be a huge thing. I think the impact that tech will have on society and global society geopolitical things gonna be also another one. Um, I think the modern application development of how applications were written with data, you know, we always been saying this day from the beginning of the Cube data is his integral part of the development process. And I think more than ever, when you think about cloud and edge and this distributed computing paradigm, that cloud is now going next level with is the software and how it's written will be different. You gotta handle things like, where's the compute component? Is it gonna be at the edge with all the server chips, innovations that Amazon apple intel of doing, you're gonna have compute right at the edge, industrial and kind of human edge. How does that work? What's Leighton see to that? It's it really is an edge game. So to me, software has to be written holistically in a system's impact on the way. Now that's not necessarily nude in the computer science and in the tech field, it's just gonna be deployed differently. So that's a complete rewrite, in my opinion of the software applications. Which is why you're seeing Amazon Google VM Ware really pushing Cooper Netease and these service messes in the micro Services because super critical of this technology become smarter, automated, autonomous. And that's completely different paradigm in the old full stack developer, you know, kind of model. You know, the full stack developer, his ancient. There's no such thing as a full stack developer anymore, in my opinion, because it's a half a stack because the cloud takes up the other half. But no one wants to be called the half stack developer because it doesn't sound as good as Full Stack, but really Cloud has eliminated the technology complexity of what a full stack developer used to dio. Now you can manage it and do things with it, so you know, there's some work to done, but the heavy lifting but taking care of it's the top of the stack that I think is gonna be a really critical component. >>Yeah, and that that sort of automation and machine intelligence layer is really at the top of the stack. This this thing becomes ubiquitous, and we now start to build businesses and new processes on top of it. I wanna I wanna take a look at the Big Three and guys, Can we bring up the other The next graphic, which is an estimate of what the revenue looks like for the for the Big three. And John, this is I asked and past spend for the Big Three Cloud players. And it's It's an estimate that we're gonna update after earning seasons, and I wanna point a couple things out here. First is if you look at the combined revenue production of the Big Three last year, it's almost 80 billion in infrastructure spend. I mean, think about that. That Z was that incremental spend? No. It really has caused a lot of consolidation in the on Prem data center business for guys like Dell. And, you know, um, see, now, part of the LHP split up IBM Oracle. I mean, it's etcetera. They've all felt this sea change, and they had to respond to it. I think the second thing is you can see on this data. Um, it's true that azure and G C P they seem to be growing faster than a W s. We don't know the exact numbers >>because >>A W S is the only company that really provides a clean view of i s and pass. Whereas Microsoft and Google, they kind of hide the ball in their numbers. I mean, I don't blame them because they're behind, but they do leave breadcrumbs and clues about growth rates and so forth. And so we have other means of estimating, but it's it's undeniable that azure is catching up. I mean, it's still quite distance the third thing, and before I want to get your input here, John is this is nuanced. But despite the fact that Azure and Google the growing faster than a W s. You can see those growth rates. A W s I'll call this out is the only company by our estimates that grew its business sequentially last quarter. Now, in and of itself, that's not significant. But what is significant is because AWS is so large there $45 billion last year, even if the slower growth rates it's able to grow mawr and absolute terms than its competitors, who are basically flat to down sequentially by our estimates. Eso So that's something that I think is important to point out. Everybody focuses on the growth rates, but it's you gotta look at also the absolute dollars and, well, nonetheless, Microsoft in particular, they're they're closing the gap steadily, and and we should talk more about the competitive dynamics. But I'd love to get your take on on all this, John. >>Well, I mean, the clouds are gonna win right now. Big time with the one the political climate is gonna be favoring Big check. But more importantly, with just talking about covert impact and celebrating the digital transformation is gonna create a massive rising tide. It's already happening. It's happening it's happening. And again, this shift in programming, uh, models are gonna really kinda accelerating, create new great growth. So there's no doubt in my mind of all three you're gonna win big, uh, in the future, they're just different, You know, the way they're going to market position themselves, they have to be. Google has to be a little bit different than Amazon because they're smaller and they also have different capabilities, then trying to catch up. So if you're Google or Microsoft, you have to have a competitive strategy to decide. How do I wanna ride the tide If you will put the rising tide? Well, if I'm Amazon, I mean, if I'm Microsoft and Google, I'm not going to try to go frontal and try to copy Amazon because Amazon is just pounding lead of features and scale and they're different. They were, I would say, take advantage of the first mover of pure public cloud. They really awesome. It passed and I, as they've integrated in Gardner, now reports and integrated I as and passed components. So Gardner finally got their act together and said, Hey, this is really one thing. SAS is completely different animal now Microsoft Super Smart because they I think they played the right card. They have a huge installed base converted to keep office 3 65 and move sequel server and all their core jewels into the cloud as fast as possible, clarified while filling in the gaps on the product side to be cloud. So you know, as you're doing trends job, they're just it's just pedal as fast as you can. But Microsoft is really in. The strategy is just go faster trying. Keep pedaling fast, get the features, feature velocity and try to make it high quality. Google is a little bit different. They have a little power base in terms of their network of strong, and they have a lot of other big data capabilities, so they have to use those to their advantage. So there is. There is there is competitive strategy game application happening with these companies. It's not like apples, the apples, In my opinion, it never has been, and I think that's funny that people talk about it that way. >>Well, you're bringing up some great points. I want guys bring up the next graphic because a lot of things that John just said are really relevant here. And what we're showing is that's a survey. Data from E. T. R R Data partners, like 1400 plus CEOs and I T buyers and on the vertical axis is this thing called Net score, which is a measure of spending momentum. And the horizontal axis is is what's called market share. It's a measure of the pervasiveness or, you know, number of mentions in the data set. There's a couple of key points I wanna I wanna pick up on relative to what John just said. So you see A W S and Microsoft? They stand alone. I mean, they're the hyper scale er's. They're far ahead of the pack and frankly, they have fall down, toe, lose their lead. They spend a lot on Capex. They got the flywheel effects going. They got both spending velocity and large market shares, and so, but they're taking a different approach. John, you're right there living off of their SAS, the state, their software state, Andi, they're they're building that in to their cloud. So they got their sort of a captive base of Microsoft customers. So they've got that advantage. They also as we'll hear from from Microsoft today. They they're building mawr abstraction layers. Andy Jassy has said We don't wanna be in that abstraction layer business. We wanna have access to those, you know, fine grain primitives and eso at an AP level. So so we can move fast with the market. But but But so those air sort of different philosophies, John? >>Yeah. I mean, you know, people who know me know that I love Amazon. I think their product is superior at many levels on in its way that that has advantages again. They have a great sass and ecosystem. They don't really have their own SAS play, although they're trying to add some stuff on. I've been kind of critical of Microsoft in the past, but one thing I'm not critical of Microsoft, and people can get this wrong in the marketplace. Actually, in the journalism world and also in just some other analysts, Microsoft has always had large scale eso to say that Microsoft never had scale on that Amazon owned the monopoly on our franchise on scales wrong. Microsoft had scale from day one. Their business was always large scale global. They've always had infrastructure with MSN and their search and the distributive how they distribute browsers and multiple countries. Remember they had the lock on the operating system and the browser for until the government stepped in in 1997. And since 1997 Microsoft never ever not invested in infrastructure and scale. So that whole premise that they don't compete well there is wrong. And I think that chart demonstrates that there, in there in the hyper scale leadership category, hands down the question that I have. Is that there not as good and making that scale integrate in because they have that legacy cards. This is the classic innovator's dilemma. Clay Christensen, right? So I think they're doing a good job. I think their strategy sound. They're moving as fast as they can. But then you know they're not gonna come out and say We don't have the best cloud. Um, that's not a marketing strategy. Have to kind of hide in this and get better and then double down on where they're winning, which is. Clients are converting from their legacy at the speed of Microsoft, and they have a huge client base, So that's why they're stopping so high That's why they're so good. >>Well, I'm gonna I'm gonna give you a little preview. I talked to gear up your f Who's gonna come on today and you'll see I I asked him because the criticism of Microsoft is they're, you know, they're just good enough. And so I asked him, Are you better than good enough? You know, those are fighting words if you're inside of Microsoft, but so you'll you'll have to wait to see his answer. Now, if you guys, if you could bring that that graphic back up I wanted to get into the hybrid zone. You know where the field is. Always got >>some questions coming in on chat, Dave. So we'll get to those >>great Awesome. So just just real quick Here you see this hybrid zone, this the field is bunched up, and the other companies who have a large on Prem presence and have been forced to initiate some kind of coherent cloud strategy included. There is Michael Michael, multi Cloud, and Google's there, too, because they're far behind and they got to take a different approach than a W s. But as you can see, so there's some real progress here. VM ware cloud on AWS stands out, as does red hat open shift. You got VM Ware Cloud, which is a VCF Cloud Foundation, even Dell's cloud. And you'd expect HP with Green Lake to be picking up momentum in the future quarters. And you've got IBM and Oracle, which there you go with the innovator's dilemma. But there, at least in the cloud game, and we can talk about that. But so, John, you know, to your point, you've gotta have different strategies. You're you're not going to take out the big too. So you gotta play, connect your print your on Prem to your cloud, your hybrid multi cloud and try to create new opportunities and new value there. >>Yeah, I mean, I think we'll get to the question, but just that point. I think this Zeri Chen's come on the Cube many times. We're trying to get him to come on lunch today with Features startup, but he's always said on the Q B is a V C at Greylock great firm. Jerry's Cloud genius. He's been there, but he made a point many, many years ago. It's not a winner. Take all the winner. Take most, and the Big Three maybe put four or five in there. We'll take most of the markets here. But I think one of the things that people are missing and aren't talking about Dave is that there's going to be a second tier cloud, large scale model. I don't want to say tear to cloud. It's coming to sound like a sub sub cloud, but a new category of cloud on cloud, right? So meaning if you get a snowflake, did I think this is a tale? Sign to what's coming. VM Ware Cloud is a native has had huge success, mainly because Amazon is essentially enabling them to be successful. So I think is going to be a wave of a more of a channel model of indirect cloud build out where companies like the Cube, potentially for media or others, will build clouds on top of the cloud. So if Google, Microsoft and Amazon, whoever is the first one to really enable that okay, we'll do extremely well because that means you can compete with their scale and create differentiation on top. So what snowflake did is all on Amazon now. They kind of should go to azure because it's, you know, politically correct that have multiple clouds and distribution and business model shifts. But to get that kind of performance they just wrote on Amazon. So there's nothing wrong with that. Because you're getting paid is variable. It's cap ex op X nice categorization. So I think that's the way that we're watching. I think it's super valuable, I think will create some surprises in terms of who might come out of the woodwork on be a leader in a category. Well, >>your timing is perfect, John and we do have some questions in the chat. But before we get to that, I want to bring in Sargi Joe Hall, who's a contributor to to our community. Sargi. Can you hear us? All right, so we got, uh, while >>bringing in Sarpy. Let's go down from the questions. So the first question, Um, we'll still we'll get the student second. The first question. But Ronald ask, Can a vendor in 2021 exist without a hybrid cloud story? Well, story and capabilities. Yes, they could live with. They have to have a story. >>Well, And if they don't own a public cloud? No. No, they absolutely cannot. Uh hey, Sergey. How you doing, man? Good to see you. So, folks, let me let me bring in Sergeant Kohala. He's a He's a cloud architect. He's a practitioner, He's worked in as a technologist. And there's a frequent guest on on the Cube. Good to see you, my friend. Thanks for taking the time with us. >>And good to see you guys to >>us. So we were kind of riffing on the competitive landscape we got. We got so much to talk about this, like, it's a number of questions coming in. Um, but Sargi we wanna talk about you know, what's happening here in Cloud Land? Let's get right into it. I mean, what do you guys see? I mean, we got yesterday. New regime, new inaug inauguration. Do you do you expect public policy? You'll start with you Sargi to have What kind of effect do you think public policy will have on, you know, cloud generally specifically, the big tech companies, the tech lash. Is it gonna be more of the same? Or do you see a big difference coming? >>I think that there will be some changing narrative. I believe on that. is mainly, um, from the regulators side. A lot has happened in one month, right? So people, I think are losing faith in high tech in a certain way. I mean, it doesn't, uh, e think it matters with camp. You belong to left or right kind of thing. Right? But parlor getting booted out from Italy s. I think that was huge. Um, like, how do you know that if a cloud provider will not boot you out? Um, like, what is that line where you draw the line? What are the rules? I think that discussion has to take place. Another thing which has happened in the last 23 months is is the solar winds hack, right? So not us not sort acknowledging that I was Russia and then wish you watching it now, new administration might have a different sort of Boston on that. I think that's huge. I think public public private partnership in security arena will emerge this year. We have to address that. Yeah, I think it's not changing. Uh, >>economics economy >>will change gradually. You know, we're coming out off pandemic. The money is still cheap on debt will not be cheap. for long. I think m and a activity really will pick up. So those are my sort of high level, Uh, >>thank you. I wanna come back to them. And because there's a question that chat about him in a But, John, how do you see it? Do you think Amazon and Google on a slippery slope booting parlor off? I mean, how do they adjudicate between? Well, what's happening in parlor? Uh, anything could happen on clubhouse. Who knows? I mean, can you use a I to find that stuff? >>Well, that's I mean, the Amazons, right? Hiding right there bunkered in right now from that bad, bad situation. Because again, like people we said Amazon, these all three cloud players win in the current environment. Okay, Who wins with the U. S. With the way we are China, Russia, cloud players. Okay, let's face it, that's the reality. So if I wanted to reset the world stage, you know what better way than the, you know, change over the United States economy, put people out of work, make people scared, and then reset the entire global landscape and control all with cash? That's, you know, conspiracy theory. >>So you see the riches, you see the riches, get the rich, get richer. >>Yeah, well, that's well, that's that. That's kind of what's happening, right? So if you start getting into this idea that you can't actually have an app on site because the reason now I'm not gonna I don't know the particular parlor, but apparently there was a reason. But this is dangerous, right? So what? What that's gonna do is and whether it's right or wrong or not, whether political opinion is it means that they were essentially taken offline by people that weren't voted for that. Weren't that when people didn't vote for So that's not a democracy, right? So that's that's a different kind of regime. What it's also going to do is you also have this groundswell of decentralized thinking, right. So you have a whole wave of crypto and decentralized, um, cyber punks out there who want to decentralize it. So all of this stuff in January has created a huge counterculture, and I had predicted this so many times in the Cube. David counterculture is coming and and you already have this kind of counterculture between centralized and decentralized thinking and so I think the Amazon's move is dangerous at a fundamental level. Because if you can't get it, if you can't get buy domain names and you're completely blackballed by by organized players, that's a Mafia, in my opinion. So, uh, and that and it's also fuels the decentralized move because people say, Hey, if that could be done to them, it could be done to me. Just the fact that it could be done will promote a swing in the other direction. I >>mean, independent of of, you know, again, somebody said your political views. I mean Parlor would say, Hey, we're trying to clean this stuff up now. Maybe they didn't do it fast enough, but you think about how new parlor is. You think about the early days of Twitter and Facebook, so they were sort of at a disadvantage. Trying to >>have it was it was partly was what it was. It was a right wing stand up job of standing up something quick. Their security was terrible. If you look at me and Cory Quinn on be great to have him, and he did a great analysis on this, because if you look the lawsuit was just terrible. Security was just a half, asshole. >>Well, and the experience was horrible. I mean, it's not It was not a great app, but But, like you said, it was a quick stew. Hand up, you know, for an agenda. But nonetheless, you know, to start, get to your point earlier. It's like, you know, Are they gonna, you know, shut me down? If I say something that's, you know, out of line, or how do I control that? >>Yeah, I remember, like, 2019, we involved closing sort of remarks. I was there. I was saying that these companies are gonna be too big to fail. And also, they're too big for other nations to do business with. In a way, I think MNCs are running the show worldwide. They're running the government's. They are way. Have seen the proof of that in us this year. Late last year and this year, um, Twitter last night blocked Chinese Ambassador E in us. Um, from there, you know, platform last night and I was like, What? What's going on? So, like, we used to we used to say, like the Chinese company, tech companies are in bed with the Chinese government. Right. Remember that? And now and now, Actually, I think Chinese people can say the same thing about us companies. Uh, it's not a good thing. >>Well, let's >>get some question. >>Let's get some questions from the chat. Yeah. Thank you. One is on M and a subject you mentioned them in a Who do you see is possible emanate targets. I mean, I could throw a couple out there. Um, you know, some of the cdn players, maybe aka my You know, I like I like Hashi Corp. I think they're doing some really interesting things. What do you see? >>Nothing. Hashi Corp. And anybody who's doing things in the periphery is a candidate for many by the big guys, you know, by the hyper scholars and number two tier two or five hyper scholars. Right. Uh, that's why sales forces of the world and stuff like that. Um, some some companies, which I thought there will be a target, Sort of. I mean, they target they're getting too big, because off their evaluations, I think how she Corpuz one, um, >>and >>their bunch in the networking space. Uh, well, Tara, if I say the right that was acquired by at five this week, this week or last week, Actually, last week for $500 million. Um, I know they're founder. So, like I found that, Yeah, there's a lot going on on the on the network side on the anything to do with data. Uh, that those air too hard areas in the cloud arena >>data, data protection, John, any any anything you could adhere. >>And I think I mean, I think ej ej is gonna be where the gaps are. And I think m and a activity is gonna be where again, the bigger too big to fail would agree with you on that one. But we're gonna look at white Spaces and say a white space for Amazon is like a monster space for a start up. Right? So you're gonna have these huge white spaces opportunities, and I think it's gonna be an M and a opportunity big time start ups to get bought in. Given the speed on, I think you're gonna see it around databases and around some of these new service meshes and micro services. I mean, >>they there's a There's a question here, somebody's that dons asking why is Google who has the most pervasive tech infrastructure on the planet. Not at the same level of other to hyper scale is I'll give you my two cents is because it took him a long time to get their heads out of their ads. I wrote a piece of around that a while ago on they just they figured out how to learn the enterprise. I mean, John, you've made this point a number of times, but they just and I got a late start. >>Yeah, they're adding a lot of people. If you look at their who their hiring on the Google Cloud, they're adding a lot of enterprise chops in there. They realized this years ago, and we've talked to many of the top leaders, although Curry and hasn't yet sit down with us. Um, don't know what he's hiding or waiting for, but they're clearly not geared up to chicken Pete. You can see it with some some of the things that they're doing, but I mean competed the level of Amazon, but they have strength and they're playing their strength, but they definitely recognize that they didn't have the enterprise motions and people in the DNA and that David takes time people in the enterprise. It's not for the faint of heart. It's unique details that are different. You can't just, you know, swing the Google playbook and saying We're gonna home The enterprises are text grade. They knew that years ago. So I think you're going to see a good year for Google. I think you'll see a lot of change. Um, they got great people in there. On the product marketing side is Dev Solution Architects, and then the SRE model that they have perfected has been strong. And I think security is an area that they could really had a lot of value it. So, um always been a big fan of their huge network and all the intelligence they have that they could bring to bear on security. >>Yeah, I think Google's problem main problem that to actually there many, but one is that they don't They don't have the boots on the ground as compared to um, Microsoft, especially an Amazon actually had a similar problem, but they had a wide breath off their product portfolio. I always talk about feature proximity in cloud context, like if you're doing one thing. You wanna do another thing? And how do you go get that feature? Do you go to another cloud writer or it's right there where you are. So I think Amazon has the feature proximity and they also have, uh, aske Compared to Google, there's skills gravity. Larger people are trained on AWS. I think Google is trying there. So second problem Google is having is that that they're they're more focused on, I believe, um, on the data science part on their sort of skipping the cool components sort of off the cloud, if you will. The where the workloads needs, you know, basic stuff, right? That's like your compute storage and network. And that has to be well, talk through e think e think they will do good. >>Well, so later today, Paul Dillon sits down with Mids Avery of Google used to be in Oracle. He's with Google now, and he's gonna push him on on the numbers. You know, you're a distant third. Does that matter? And of course, you know, you're just a preview of it's gonna say, Well, no, we don't really pay attention to that stuff. But, John, you said something earlier that. I think Jerry Chen made this comment that, you know, Is it a winner? Take all? No, but it's a winner. Take a lot. You know the number two is going to get a big chunk of the pie. It appears that the markets big enough for three. But do you? Does Google have to really dramatically close the gap on be a much, much closer, you know, to the to the leaders in orderto to compete in this race? Or can they just kind of continue to bump along, siphon off the ad revenue? Put it out there? I mean, I >>definitely can compete. I think that's like Google's in it. Then it they're not. They're not caving, right? >>So But But I wrote I wrote recently that I thought they should even even put mawr oven emphasis on the cloud. I mean, maybe maybe they're already, you know, doubling down triple down. I just I think that is a multi trillion dollar, you know, future for the industry. And, you know, I think Google, believe it or not, could even do more. Now. Maybe there's just so much you could dio. >>There's a lot of challenges with these company, especially Google. They're in Silicon Valley. We have a big Social Justice warrior mentality. Um, there's a big debate going on the in the back channels of the tech scene here, and that is that if you want to be successful in cloud, you have to have a good edge strategy, and that involves surveillance, use of data and pushing the privacy limits. Right? So you know, Google has people within the country that will protest contract because AI is being used for war. Yet we have the most unstable geopolitical seen that I've ever witnessed in my lifetime going on right now. So, um, don't >>you think that's what happened with parlor? I mean, Rob Hope said, Hey, bar is pretty high to kick somebody off your platform. The parlor went over the line, but I would also think that a lot of the employees, whether it's Google AWS as well, said, Hey, why are we supporting you know this and so to your point about social justice, I mean, that's not something. That >>parlor was not just social justice. They were trying to throw the government. That's Rob e. I think they were in there to get selfies and being protesters. But apparently there was evidence from what I heard in some of these clubhouse, uh, private chats. Waas. There was overwhelming evidence on parlor. >>Yeah, but my point is that the employee backlash was also a factor. That's that's all I'm saying. >>Well, we have Google is your Google and you have employees to say we will boycott and walk out if you bid on that jet I contract for instance, right, But Microsoft one from maybe >>so. I mean, that's well, >>I think I think Tom Poole's making a really good point here, which is a Google is an alternative. Thio aws. The last Google cloud next that we were asked at they had is all virtual issue. But I saw a lot of I T practitioners in the audience looking around for an alternative to a W s just seeing, though, we could talk about Mano Cloud or Multi Cloud, and Andy Jassy has his his narrative around, and he's true when somebody goes multiple clouds, they put you know most of their eggs in one basket. Nonetheless, I think you know, Google's got a lot of people interested in, particularly in the analytic side, um, in in an alternative, hedging their bets eso and particularly use cases, so they should be able to do so. I guess my the bottom line here is the markets big enough to have Really? You don't have to be the Jack Welch. I gotta be number one and number two in the market. Is that the conclusion here? >>I think so. But the data gravity and the skills gravity are playing against them. Another problem, which I didn't want a couple of earlier was Google Eyes is that they have to boot out AWS wherever they go. Right? That is a huge challenge. Um, most off the most off the Fortune 2000 companies are already using AWS in one way or another. Right? So they are the multi cloud kind of player. Another one, you know, and just pure purely somebody going 200% Google Cloud. Uh, those cases are kind of pure, if you will. >>I think it's gonna be absolutely multi cloud. I think it's gonna be a time where you looked at the marketplace and you're gonna think in terms of disaster recovery, model of cloud or just fault tolerant capabilities or, you know, look at the parlor, the next parlor. Or what if Amazon wakes up one day and said, Hey, I don't like the cubes commentary on their virtual events, so shut them down. We should have a fail over to Google Cloud should Microsoft and Option. And one of people in Microsoft ecosystem wants to buy services from us. We have toe kind of co locate there. So these are all open questions that are gonna be the that will become certain pretty quickly, which is, you know, can a company diversify their computing An i t. In a way that works. And I think the momentum around Cooper Netease you're seeing as a great connective tissue between, you know, having applications work between clouds. Right? Well, directionally correct, in my opinion, because if I'm a company, why wouldn't I wanna have choice? So >>let's talk about this. The data is mixed on that. I'll share some data, meaty our data with you. About half the companies will say Yeah, we're spreading the wealth around to multiple clouds. Okay, That's one thing will come back to that. About the other half were saying, Yeah, we're predominantly mono cloud we didn't have. The resource is. But what I think going forward is that that what multi cloud really becomes. And I think John, you mentioned Snowflake before. I think that's an indicator of what what true multi cloud is going to look like. And what Snowflake is doing is they're building abstraction, layer across clouds. Ed Walsh would say, I'm standing on the shoulders of Giants, so they're basically following points of presence around the globe and building their own cloud. They call it a data cloud with a global mesh. We'll hear more about that later today, but you sign on to that cloud. So they're saying, Hey, we're gonna build value because so many of Amazon's not gonna build that abstraction layer across multi clouds, at least not in the near term. So that's a really opportunity for >>people. I mean, I don't want to sound like I'm dating myself, but you know the date ourselves, David. I remember back in the eighties, when you had open systems movement, right? The part of the whole Revolution OS I open systems interconnect model. At that time, the networking stacks for S N A. For IBM, decadent for deck we all know that was a proprietary stack and then incomes TCP I p Now os I never really happened on all seven layers, but the bottom layers standardized. Okay, that was huge. So I think if you look at a W s or some of the comments in the chat AWS is could be the s n a. Depends how you're looking at it, right? And you could say they're open. But in a way, they want more Amazon. So Amazon's not out there saying we love multi cloud. Why would they promote multi cloud? They are a one of the clouds they want. >>That's interesting, John. And then subject is a cloud architect. I mean, it's it is not trivial to make You're a data cloud. If you're snowflake, work on AWS work on Google. Work on Azure. Be seamless. I mean, certainly the marketing says that, but technically, that's not trivial. You know, there are latent see issues. Uh, you know, So that's gonna take a while to develop. What? Do your thoughts there? >>I think that multi cloud for for same workload and multi cloud for different workloads are two different things. Like we usually put multiple er in one bucket, right? So I think you're right. If you're trying to do multi cloud for the same workload, that's it. That's Ah, complex, uh, problem to solve architecturally, right. You have to have a common ap ice and common, you know, control playing, if you will. And we don't have that yet, and then we will not have that for a for at least one other couple of years. So, uh, if you if you want to do that, then you have to go to the lower, lowest common denominator in technical sort of stock, if you will. And then you're not leveraging the best of the breed technology off their from different vendors, right? I believe that's a hard problem to solve. And in another thing, is that that that I always say this? I'm always on the death side, you know, developer side, I think, uh, two deaths. Public cloud is a proxy for innovative culture. Right. So there's a catch phrase I have come up with today during shower eso. I think that is true. And then people who are companies who use the best of the breed technologies, they can attract the these developers and developers are the Mazen's off This digital sort of empires, amazingly, is happening there. Right there they are the Mazen's right. They head on the bricks. I think if you don't appeal to developers, if you don't but extensive for, like, force behind educating the market, you can't you can't >>put off. It's the same game Stepping story was seeing some check comments. Uh, guard. She's, uh, linked in friend of mine. She said, Microsoft, If you go back and look at the Microsoft early days to the developer Point they were, they made their phones with developers. They were a software company s Oh, hey, >>forget developers, developers, developers. >>You were if you were in the developer ecosystem, you were treated his gold. You were part of the family. If you were outside that world, you were competitors, and that was ruthless times back then. But they again they had. That was where it was today. Look at where the software defined businesses and starve it, saying it's all about being developer lead in this new way to program, right? So the cloud next Gen Cloud is going to look a lot like next Gen Developer and all the different tools and techniques they're gonna change. So I think, yes, this kind of developer ecosystem will be harnessed, and that's the power source. It's just gonna look different. So, >>Justin, Justin in the chat has a comment. I just want to answer the question about elastic thoughts on elastic. Um, I tell you, elastic has momentum uh, doing doing very well in the market place. Thea Elk Stack is a great alternative that people are looking thio relative to Splunk. Who people complain about the pricing. Of course it's plunks got the easy button, but it is getting increasingly expensive. The problem with elk stack is you know, it's open source. It gets complicated. You got a shard, the databases you gotta manage. It s Oh, that's what Ed Walsh's company chaos searches is all about. But elastic has some riel mo mentum in the marketplace right now. >>Yeah, you know, other things that coming on the chat understands what I was saying about the open systems is kubernetes. I always felt was that is a bad metaphor. But they're with me. That was the TCP I peep In this modern era, C t c p I p created that that the disruptor to the S N A s and the network protocols that were proprietary. So what KUBERNETES is doing is creating a connective tissue between clouds and letting the open source community fill in the gaps in the middle, where kind of way kind of probably a bad analogy. But that's where the disruption is. And if you look at what's happened since Kubernetes was put out there, what it's become kind of de facto and standard in the sense that everyone's rallying around it. Same exact thing happened with TCP was people were trashing it. It is terrible, you know it's not. Of course they were trashed because it was open. So I find that to be very interesting. >>Yeah, that's a good >>analogy. E. Thinks the R C a cable. I used the R C. A cable analogy like the VCRs. When they started, they, every VC had had their own cable, and they will work on Lee with that sort of plan of TV and the R C. A cable came and then now you can put any TV with any VCR, and the VCR industry took off. There's so many examples out there around, uh, standards And how standards can, you know, flair that fire, if you will, on dio for an industry to go sort of wild. And another trend guys I'm seeing is that from the consumer side. And let's talk a little bit on the consuming side. Um, is that the The difference wouldn't be to B and B to C is blood blurred because even the physical products are connected to the end user Like my door lock, the August door lock I didn't just put got get the door lock and forget about that. Like I I value the expedience it gives me or problems that gives me on daily basis. So I'm close to that vendor, right? So So the middle men, uh, middle people are getting removed from from the producer off the technology or the product to the consumer. Even even the sort of big grocery players they have their APs now, uh, how do you buy stuff and how it's delivered and all that stuff that experience matters in that context, I think, um, having, uh, to be able to sell to thes enterprises from the Cloud writer Breuder's. They have to have these case studies or all these sample sort off reference architectures and stuff like that. I think whoever has that mawr pushed that way, they are doing better like that. Amazon is Amazon. Because of that reason, I think they have lot off sort off use cases about on top of them. And they themselves do retail like crazy. Right? So and other things at all s. So I think that's a big trend. >>Great. Great points are being one of things. There's a question in there about from, uh, Yaden. Who says, uh, I like the developer Lead cloud movement, But what is the criticality of the executive audience when educating the marketplace? Um, this comes up a lot in some of my conversations around automation. So automation has been a big wave to automate this automate everything. And then everything is a service has become kind of kind of the the executive suite. Kind of like conversation we need to make everything is a service in our business. You seeing people move to that cloud model. Okay, so the executives think everything is a services business strategy, which it is on some level, but then, when they say Take that hill, do it. Developers. It's not that easy. And this is where a lot of our cube conversations over the past few months have been, especially during the cova with cute virtual. This has come up a lot, Dave this idea, and start being around. It's easy to say everything is a service but will implement it. It's really hard, and I think that's where the developer lead Connection is where the executive have to understand that in order to just say it and do it are two different things. That digital transformation. That's a big part of it. So I think that you're gonna see a lot of education this year around what it means to actually do that and how to implement it. >>I'd like to comment on the as a service and subject. Get your take on it. I mean, I think you're seeing, for instance, with HP Green Lake, Dell's come out with Apex. You know IBM as its utility model. These companies were basically taking a page out of what I what I would call a flawed SAS model. If you look at the SAS players, whether it's salesforce or workday, service now s a P oracle. These models are They're really They're not cloud pricing models. They're they're basically you got to commit to a term one year, two year, three year. We'll give you a discount if you commit to the longer term. But you're locked in on you. You probably pay upfront. Or maybe you pay quarterly. That's not a cloud pricing model. And that's why I mean, they're flawed. You're seeing companies like Data Dog, for example. Snowflake is another one, and they're beginning to price on a consumption basis. And that is, I think, one of the big changes that we're going to see this decade is that true cloud? You know, pay by the drink pricing model and to your point, john toe, actually implement. That is, you're gonna need a whole new layer across your company on it is quite complicated it not even to mention how you compensate salespeople, etcetera. The a p. I s of your product. I mean, it is that, but that is a big sea change that I see coming. Subject your >>thoughts. Yeah, I think like you couldn't see it. And like some things for this big tech exacts are hidden in the plain >>sight, right? >>They don't see it. They they have blind spots, like Look at that. Look at Amazon. They went from Melissa and 200 millisecond building on several s, Right, Right. And then here you are, like you're saying, pay us for the whole year. If you don't use the cloud, you lose it or will pay by month. Poor user and all that stuff like that that those a role models, I think these players will be forced to use that term pricing like poor minute or for a second, poor user. That way, I think the Salesforce moral is hybrid. They're struggling in a way. I think they're trying to bring the platform by doing, you know, acquisition after acquisition to be a platform for other people to build on top off. But they're having a little trouble there because because off there, such pricing and little closeness, if you will. And, uh, again, I'm coming, going, going back to developers like, if you are not appealing to developers who are writing the latest and greatest code and it is open enough, by the way open and open source are two different things that we all know that. So if your platform is not open enough, you will have you know, some problems in closing the deals. >>E. I want to just bring up a question on chat around from Justin didn't fitness. Who says can you touch on the vertical clouds? Has your offering this and great question Great CP announcing Retail cloud inventions IBM Athena Okay, I'm a huge on this point because I think this I'm not saying this for years. Cloud computing is about horizontal scalability and vertical specialization, and that's absolutely clear, and you see all the clouds doing it. The vertical rollouts is where the high fidelity data is, and with machine learning and AI efforts coming out, that's accelerated benefits. There you have tow, have the vertical focus. I think it's super smart that clouds will have some sort of vertical engine, if you will in the clouds and build on top of a control playing. Whether that's data or whatever, this is clearly the winning formula. If you look at all the successful kind of ai implementations, the ones that have access to the most data will get the most value. So, um if you're gonna have a data driven cloud you have tow, have this vertical feeling, Um, in terms of verticals, the data on DSO I think that's super important again, just generally is a strategy. I think Google doing a retail about a super smart because their whole pitches were not Amazon on. Some people say we're not Google, depending on where you look at. So every of these big players, they have dominance in the areas, and that's scarce. Companies and some companies will never go to Amazon for that reason. Or some people never go to Google for other reasons. I know people who are in the ad tech. This is a black and we're not. We're not going to Google. So again, it is what it is. But this idea of vertical specialization relevant in super >>forts, I want to bring to point out to sessions that are going on today on great points. I'm glad you asked that question. One is Alan. As he kicks off at 1 p.m. Eastern time in the transformation track, he's gonna talk a lot about the coming power of ecosystems and and we've talked about this a lot. That that that to compete with Amazon, Google Azure, you've gotta have some kind of specialization and vertical specialization is a good one. But of course, you see in the big Big three also get into that. But so he's talking at one o'clock and then it at 3 36 PM You know this times are strange, but e can explain that later Hillary Hunter is talking about she's the CTO IBM I B M's ah Financial Cloud, which is another really good example of specifying vertical requirements and serving. You know, an audience subject. I think you have some thoughts on this. >>Actually, I lost my thought. E >>think the other piece of that is data. I mean, to the extent that you could build an ecosystem coming back to Alan Nancy's premise around data that >>billions of dollars in >>their day there's billions of dollars and that's the title of the session. But we did the trillion dollar baby post with Jazzy and said Cloud is gonna be a trillion dollars right? >>And and the point of Alan Answer session is he's thinking from an individual firm. Forget the millions that you're gonna save shifting to the cloud on cost. There's billions in ecosystems and operating models. That's >>absolutely the business value. Now going back to my half stack full stack developer, is the business value. I've been talking about this on the clubhouses a lot this past month is for the entrepreneurs out there the the activity in the business value. That's the new the new intellectual property is the business logic, right? So if you could see innovations in how work streams and workflow is gonna be a configured differently, you have now large scale cloud specialization with data, you can move quickly and take territory. That's much different scenario than a decade ago, >>at the point I was trying to make earlier was which I know I remember, is that that having the horizontal sort of features is very important, as compared to having vertical focus. You know, you're you're more healthcare focused like you. You have that sort of needs, if you will, and you and our auto or financials and stuff like that. What Google is trying to do, I think that's it. That's a good thing. Do cook up the reference architectures, but it's a bad thing in a way that you drive drive away some developers who are most of the developers at 80 plus percent, developers are horizontal like you. Look at the look into the psyche of a developer like you move from company to company. And only few developers will say I will stay only in health care, right? So I will only stay in order or something of that, right? So they you have to have these horizontal capabilities which can be applied anywhere on then. On top >>of that, I think that's true. Sorry, but I'll take a little bit different. Take on that. I would say yes, that's true. But remember, remember the old school application developer Someone was just called in Application developer. All they did was develop applications, right? They pick the framework, they did it right? So I think we're going to see more of that is just now mawr of Under the Covers developers. You've got mawr suffer defined networking and software, defined storage servers and cloud kubernetes. And it's kind of like under the hood. But you got your, you know, classic application developer. I think you're gonna see him. A lot of that come back in a way that's like I don't care about anything else. And that's the promise of cloud infrastructure is code. So I think this both. >>Hey, I worked. >>I worked at people solved and and I still today I say into into this context, I say E r P s are the ultimate low code. No code sort of thing is right. And what the problem is, they couldn't evolve. They couldn't make it. Lightweight, right? Eso um I used to write applications with drag and drop, you know, stuff. Right? But But I was miserable as a developer. I didn't Didn't want to be in the applications division off PeopleSoft. I wanted to be on the tools division. There were two divisions in most of these big companies ASAP. Oracle. Uh, like companies that divisions right? One is the cooking up the tools. One is cooking up the applications. The basketball was always gonna go to the tooling. Hey, >>guys, I'm sorry. We're almost out of time. I always wanted to t some of the sections of the day. First of all, we got Holder Mueller coming on at lunch for a power half hour. Um, you'll you'll notice when you go back to the home page. You'll notice that calendar, that linear clock that we talked about that start times are kind of weird like, for instance, an appendix coming on at 1 24. And that's because these air prerecorded assets and rather than having a bunch of dead air, we're just streaming one to the other. So so she's gonna talk about people, process and technology. We got Kathy Southwick, whose uh, Silicon Valley CEO Dan Sheehan was the CEO of Dunkin Brands and and he was actually the c 00 So it's C A CEO connecting the dots to the business. Daniel Dienes is the CEO of you I path. He's coming on a 2:47 p.m. East Coast time one of the hottest companies, probably the fastest growing software company in history. We got a guy from Bain coming on Dave Humphrey, who invested $750 million in Nutanix. He'll explain why and then, ironically, Dheeraj Pandey stew, Minuteman. Our friend interviewed him. That's 3 35. 1 of the sessions are most excited about today is John McD agony at 403 p. M. East Coast time, she's gonna talk about how to fix broken data architectures, really forward thinking stuff. And then that's the So that's the transformation track on the future of cloud track. We start off with the Big Three Milan Thompson Bukovec. At one oclock, she runs a W s storage business. Then I mentioned gig therapy wrath at 1. 30. He runs Azure is analytics. Business is awesome. Paul Dillon then talks about, um, IDs Avery at 1 59. And then our friends to, um, talks about interview Simon Crosby. I think I think that's it. I think we're going on to our next session. All right, so keep it right there. Thanks for watching the Cuban cloud. Uh huh.

>> From around the globe, it's The Cube, presenting Cube on Cloud, brought to you by SiliconANGLE. >> Welcome back to Cube on Cloud. I'm Paul Gillin, enterprise editor of SiliconANGLE. As we look ahead at what is in store for the cloud this year, one of the intriguing possibilities that has emerged is the rise of vertical clouds. IBM has been a leader in this area with its launch in late '19 of the IBM Financial Services cloud. That's a services-ready public cloud with exceptional security, as well as a policy framework for certifying compliance and services from the IBM subsidiary, Promontory. Now, with the IBM Financial Services cloud, that has been a major focus of our next guest, Hillery Hunter. She is the Vice President and CTO of IBM Cloud, an IBM Fellow, and a veteran of, I believe, three previous appearances on The Cube. Am I right, Hillery? >> Yep, sounds about right. Great to be back here today. >> Thanks for joining us. So let's start with getting an update on the IBM Financial Services cloud. What progress have you made in signing up customers and your ecosystem of partners? >> Yeah, we've made really significant progress advancing the IBM Cloud for Financial Services since we last talked, and we're really at that place of establishing a trusted platform for the industry. Just in some specifics, in addition to Bank of America which we had talked about as our U.S. anchor partner for the program, we've announced several global banks that are partnering with us for the global expansion of the program, including BNP party bar, which is one of Europe's largest banks. More than 70 ASVs are signed up with us now as part of the program and adopting IBM Cloud for Financial Services. This level of ecosystem is exciting because it means that banks will have the opportunity to transform what they're doing, but do so in a way which is driven by security and compliance so that they can be confident in those deployments on IBM Cloud for Financial Services. We also released the IBM Cloud policy framework for Financial Services. This is both the security and compliance posture of the environment, as well as guidance on controls, reference architectures, automation to help people onboard. And so both ISVs and banks now are able to onboard to this environment, and offer their wares and deploy their workloads. So it's a really exciting state for us on the program, and we're really in a place where there'll be an ongoing cadence of additional releases and announcements of additional partnerships and clients. So it's an exciting time in the program. >> One of the distinctive features, I think, of this launch is that you're working actively with your customers. They're working with you on building policy frameworks, as well as, I imagine, the features that you're offering on the cloud. How do you orchestrate all of these different customers and get them involved in, actually, co-development. >> Yeah, the ecosystem conversation and the partnership conversation are two of the fundamental aspects of the program. Like you said, this isn't just us sitting off in a bubble and inventing the future. We're working internally with partners within IBM, like IBM Promontory, which is a consultancy that has deep, deep regulatory expertise in jurisdictions globally, with IBM Security Services, and then with these individual partners and banks and clients. One of the ways that we bring everything together is through our council. So our council, our Cloud Council for Financial Services, is where we have global, systemically important financial institutions partnered with us and working together with one another, and that covers CIOs, it covers chief security officers, risk officers, et cetera. So we have some formality around how we work with all of these partners, really, as a body and as a group. >> And what have you learned from this experience? If you were to go into other vertical clouds, what have been the lessons? >> Ecosystem is so important. As I look at this space, I see that everyone has an existing business. They have a platform they're running, they have clients they're trying to service, but the software providers into this space are looking, themselves, to transform. They're looking to transform from being software vendors to being SaaS providers. The banks and financial institutions themselves are looking to transform from working on their own premises to benefit from the elasticity and the scale and the optionality that being in public cloud provides. So there's a lot of parties themselves that are trying to transform, and a lot of vendors into the financial space that are looking to transform. And in that time of a lot of change, ecosystem is absolutely key. And so the ISE and SaaS providers providing their wares on the cloud for financial services is really just as important as those financial services institutions, so that everyone can make that transition together, and so that banks that are looking to digitally transform can leverage partners that are really at the forefront of that change and that innovation in platforms for the industry. >> Would you say that there are- Is this the first of many? Are there going to be other vertical IBM clouds, or is the range of industries that really need that kind of specificity, limited? I think it's actually not limited, though I will say that within the space of industries that are heavily regulated, there's obviously a deeper need for specific cloud embodiments and cloud implementation, so regulated industries like insurance, like telco, health care, et cetera, these are the ones, I think, where there's the greatest opportunity to do verticals that are specific to industry. But as we look at this, this is absolutely part of an IBM Cloud strategy to deliver industry-specific clouds. And this comes from our decades of expertise. Even in financial services, being able to leverage those other entities within IBM that I mentioned, our regulatory background with companies, having helped them address regulatory needs for specific industries, and then translating that into cloud and cloud technologies. And then coming up from the other side, in terms of the technologies themselves, we've partnered with key industries to deliver security, and data protection, and cryptography technologies, and such, on premises, and we're contextualizing that now for cloud and public cloud deployments. And so it brings together the pieces of decades of expertise in platforms, and technology, and regulations, and contextualizes it into cloud, and I absolutely think that's an opportunity for other industries as well. >> Can you give us a bit of a preview? Do you have specific industries in mind? Is there a timeframe? >> Yeah, so late last year we did announce a second industry-specific cloud initiative, and that was IBM Cloud for Telco. So we have in that ecosystem, now, over 40 partners that are now, that are working with IBM and with Red Hat, especially with clients and partners that are looking to help with that transition into 5G and increasing use of IOT. 5G is really this disruptive opportunity for that industry, and also just for many other different types of companies and institutions that are looking to deploy with more efficiency, better operational efficiency, deploy with AI capabilities, really being able to do things at cellular network EDGE, and the places that they're doing business using IOT devices, and 5G will enable much of that to really transform and flourish. So a couple of the partners, initially, in addition to that ecosystem that I mentioned in Cloud for Telco, we've got Samsung working with us, Nokia, AT&T, et cetera, and so these partnerships and capabilities around network EDGE and specific capabilities in Cloud for Telco are that second public announcement that we've made around industry-specific cloud. >> And as far as your competitive position is concerned, are you taking away business from your competitors when you partner with these telcos and these banks, or is this an entirely new line of business that was not previously in the cloud? >> Yeah, these are really, I think, in, by and large, new opportunities. As we look at, for example, how we, as customers, expect to engage with our bank, we are looking to increasingly engage with a bank in a digital way, use our applications, use mobile devices. We're looking for individual bank outlets, branch outlets of a banking institution, to be increasingly smart, to service our needs more quickly, et cetera. And so as we look at 5G and telco EDGE, it's about delivery of smarter capabilities and such. I think much of it really is about, in this digital transformation space, about creating new capabilities, creating new experiences, creating new ways of engagement, and engagement and opportunity to customize and personalize, I think most of those are new experiences and new capabilities for most companies. >> So speak about IBM's positioning right now. You're not one of the big three cloud providers, unlikely to become one, but you do have a big cloud business, and you've got the verticals, you've got the multicloud. I know IBM has been a big champion of multicloud. How is IBM distinctively positioned in the cloud market right now? >> Yeah, we are all-in on hybrid cloud and AI, and if you listen to our CEO and chairman, you'll hear that. It is a really consistent message since he came into his role as as our CEO. So being all-in on hybrid cloud and AI, we really are looking to help our clients transform into holistic cloud architecture. So when I say all-in on hybrid cloud, I mean that there's been a lot of, I jokingly say, random acts of cloud usage. People have ended up using cloud because there's some SaaS function that they want, or some particular line of business has been highly motivated to pursue some service on a particular cloud. And hybrid cloud is really about taking a step back, having a holistic architecture for cloud consumption. And in that sense, clouds are IBM's partners. And we're really looking to enable our clients to have consistency in their deployments, to consolidate across their IT estate and across their cloud deployments so that they can have a common platform, so they can have efficiency in how their developers deploy capabilities, so they can deploy more quickly with security and compliance patterns, and have oversight over everything that's going on in a consistent way that really enables them to have that velocity in their business. And so when we then position things like industry cloud, we're leveraging IBM-specific technologies to deliver differentiated capabilities in data privacy, data protection, security and compliance, for these industries, in public cloud, yes, but it's in the context of helping our clients overall across all the different things, some of which may not need all of that data privacy or be leveraging particular SaaS content. We're looking to help them really have cloud architecture, have a holistic conversation across hybrid cloud, and yet to still be able to choose particular cloud deployments on our cloud for industries that enables data protection and policy for the most sensitive and enterprise grade things that they're looking to do at the core of their business. >> So speaking of hybrid hybrid cloud, the major cloud providers, Amazon, Microsoft, Google, Oracle's another one, all have on-premises offerings right now. Several of them are working with telcos to expand their reach out into co-location and into telecom data centers, all of this being to enable this distributed cloud fabric, a hybrid cloud fabric. What's IBM's play in this area? Do you have a similar strategy or is it different? >> Yeah, I really think, and I think you maybe wanted to get a little bit into trends and predictions here in this conversation, and we absolutely see that need for distributed cloud, for cloud to really be alive in all the places where it needs to be, in all the places that someone is doing business, and in a consistent way across cloud environments, to be one of those major trends that's emerging as a really hot conversation. We have introduced IBM Cloud Satellite, that is IBM's hybrid cloud, as a service platform. It enables our clients to leverage OpenShift and Kubernetes environments, developer tooling, consistency in a cloud catalog, visibility and control over all their resources across different environments, and to be able to run end-to-end with consistency from on-premises, to EDGE, to different public cloud providers, and this is absolutely something that, across industries but within, also, those industries, that we're focused on in particular, that we're seeing a lot of interesting conversations emerge, because if cloud is everywhere, if cloud is distributed and can be on premises and in public cloud, it enables this consistency and this parity, really that brings together that seamlessness, not just the random acts of cloud usage. It means that using cloud can be something that drives speed of release of new product. It means that you can deliver more capability and functionality into a retail outlet where you're doing business, or a banking brick-and-mortar location. You can have AI for IT ops and understand what's going on across those different environments, and ensure things are kept secure, and patched, and updated, and you're responding to incidents in efficient ways. And so, really, having a consistent cloud environment and a distributed cloud environment across different locations, it's really key to leveraging the promises of what everyone had originally hoped to get out of cloud computing. >> And of course, one of IBM's distinctive advantages in this area is, you've got a huge hardware install base out there. How do all those 360 mainframes figure into this? >> Yeah, with the OpenShift capabilities and our (audio skips) relations with Red Hat in this area, we are able to actually help our clients leverage Kubernetes, and Linux, and all those things, even on the mainframe. So across the mainframe family, the IBM power family, where folks may also have AIX or IBM i deployments, people can now do Linux, they can do OpenShift, they can do Kubernetes, and we have core technologies that enable that really to be stitched together, and I think that's one of the unique perspectives that IBM has in this whole conversation about hybrid cloud. There are many different definitions of hybrid cloud, but we really view it as stretching from the traditional enterprise IT, like you said, there's a lot of IT out there, and being able to also incorporate OpenShift and Kubernetes in a common cloud platform, on traditional enterprise IT, on private cloud, on fresh deployments, on private cloud, Amazon public cloud, that really is the whole IT estate. So when we talk about hybrid cloud, when we talk about distributed cloud, we're really talking about the entirety of the IT state, not just new deployments of SaaS, or something like that. >> So as someone who's on the front lines of what customers are asking about cloud, do you see customer, the questions that they're asking, changing? Are their decision criteria changing for how they choose a cloud provider? >> Yeah, I think that there's definitely a lot more conversation, and especially in this current era where there's an accelerated rate of cloud adoption, there's a lot more conversation around things like security, data protection, data privacy, being able to run in an environment that you trust not just, is it a cloud and what does it do, but can I trust it? Do I understand how my data is protected, how my workloads are secured? That's really why we started Cloud for Financial Services, because that industry shepherds such vital data, so the reason that they are highly regulated is because of the importance of what they are stewarding, very important data and financial information. So we began there with the Cloud for Regulated Industries, there with with Financial Services, but I see that across all industries. I was participating on a panel with a bunch of CIOs, and I was there interviewing some CIOs who were from a much more consumer-facing, and also from from foods industry, et cetera, and their conversation was exactly the same as I have with many other clients, which is that their cloud choices, their efficiency in cloud deployment, now are largely driven by the ability to get to a secure posture and the ability to demonstrate to their internal security and risk teams that they understand their data protection and data privacy posture. So we are seeing lots of pickup and conversation opportunity around confidential computing, specifically, and that's really about enabling our clients to have full authority and privacy in their computing, in their code, in their data, even when running in a cloud environment. And so I do see a shift. Everyone's more concerned about security, and I think we have great technologies, and we've been working with core partners to establish, and harden, and create generations of technology that can really answer those questions. >> I have to ask you about that term, confidential computing. I haven't heard that before. What does that involve? >> Yeah, it is a buzzword to watch out here for in 2021. So confidential computing means being able to run in an environment where there are others, in a cloud computing environment, for example, but still have full privacy and authority over what you're doing. So you are effectively in an enclave. Imagine yourself protected and secured. And so our confidential computing technology is, we're actually on, basically, our fourth generation of the hardware and software technologies to create that strong degree of isolation. This enables us to deliver a really rich portfolio, frankly, the richest portfolio in the industry, of actual services delivered using confidential computing and secure enclaves. And so we can enable our customers to solution things in a way, for example, where their data can not even be visible to our cloud operators, or where they retain full control over a database, and have full privacy as they're running in that environment. These are really great considerations, but they impact everything from health care, financial services. We have other partners and clients who are working to protect consumer data through these means, et cetera. And so across different industries, everyone's really looking at this topic of data privacy and data protection. And so we have a whole suite and a whole family of confidential computing-based services that we're able to offer, to offer those assurances and that privacy to them in their cloud computing. >> I do have to ask you about the multicloud, because this is a topic of constant debate in the industry of whether customers want to shift workloads across multiple clouds to protect themselves from lock-in. Is that a fantasy? Is that too restrictive? This has been a key part of IBM strategy is enabling the multicloud. How do you see customer attitudes developing right now? How do they want to use multiple clouds, or, in fact, do they? Are they concentrating perhaps more of their workloads in one or two? >> Yeah, we believe vendor lock-in goes against the true spirit of hybrid cloud, that desire to have consistency across environments, that desire to- and the business need to have continuity and resiliency and operations, et cetera, and so I do see this as a really important topic. From the perspective of managing environments, I think in multicloud, I think folks are starting to realize that multicloud isn't necessarily a strategy, it's a reality. People have deployments in lots of different cloud environments that happen somewhat organically, in many cases, and so the key question is how to then get to visibility and control over those resources. I think two of the core topics in that are multicloud management, being able to understand clusters, and virtual machines, and other things that are deployed across different environments, and manage them with a common set of policies, for example. And then, in addition to multicloud management, AI for IT operations is another really important topic in multicloud, being able to respond to incidents, understand and analyze and leverage AI for understanding what's going on across those environments is another really core topic. And then as you said, distributed cloud is a means of getting that consistency. Having a common control and deployment plane across those different environments can help it not just be accidental usage of multiple cloud environments, but very intentional deployment, based on the needs of particular workloads to the environment that they're best suited to. And that's really what you want to aim for. Not that multicloud is necessarily, I guess I would say, is a- It is a complexity that is manageable through these new types of technologies and multicloud management, and such like that, and distributed cloud. >> Well, Hillery 'tis the season for predictions, it's January. Everyone's prognosticating about what the future will look like. What do you think are going to be the main trend lines in cloud this year? >> Yeah, I sprinkled a few in there as we were talking, but I really do think that the conversation around hybrid cloud, number one, how to have an open innovation ecosystem for cloud, where you have consistency across environments, not just random acts of cloud usage, but intentional and holistic architecture. I really see that as the transition, as the second wave of of cloud adoption. And then, secondly, is we were talking earlier about security. Everyone is wondering about data policy, data privacy. We've always taken a strong stance that our client's data is their data. We are not going to be using their data to further develop our AI services on our cloud, or something. We have deployed technologies in confidential computing that enable them to keep full control over their keys so that even our cloud operators don't have access to data, computing in secure enclaves where they have a strong degree of isolation and full privacy and authority over their workload. I really think these two topics, open and secure hybrid computing and with consistency across environments, with distributed cloud technology, and secondly, security. I think these are really important topics for 2021. And they may seem a little bit obvious, but I think it's important as people look at this to look for technologies that are multiple generations into this journey, partner with folks who are committed very clearly to an open ecosystem and open source innovation on the one hand, and secondly, when we talk about security and data protection, you want to know that that provider is several generations into that journey so you really know that that technology has been vetted out, is at production scale, and has a stable basis. And so I think this is the year when folks are transitioning from cloud adoption to consistency in cloud, and security and privacy in cloud. >> A final question, and it has nothing to do with cloud. You're an IBM Fellow, and I see that term turn up occasionally with other other people I've spoken to from IBM. What is an IBM Fellow, how do you become one, and what privileges and responsibilities does it entail? >> Yeah, it's an exciting opportunity to be an IBM Fellow. There's about a hundred active IBM Fellows right now, so there aren't too many of us, but there is a small community of us. IBM Fellow is IBM's highest technical designation within our technical population, so I do have a role within our cloud business, but as one of our technical leaders, get to interact with the other Fellows, work on strategy for IBM in technology overall as a company, and I also get to be a trusted advisor to many of our clients, and so I get to work with CTOs and CIOs and VP of Application Development profiles, and VP of IT, and things like that, in our different clients, and really help them wrestle through those struggles of future IT transformation. And so part of what I enjoy most about the role, and the Fellow role, is being able to be that trusted advisor to many of our clients. There's been so much change in this last year for everyone, and being able to also help our technical population through that, in various means, and then help our clients through all of that change, and really being able to take and grasp onto the opportunities that this last year has had in the way that we work has changed, and the way that companies are looking to deliver capabilities has changed. So that's, for me, the exciting part of the role. >> Well, you're one in a hundred, then, and you do a great job of articulating the IBM strategy, and also the cloud landscape. Hillery Hunter, VP and CTO, excuse me, CTO of IBM Cloud, thank you so much for joining us today on Cube on Cloud. >> Thanks so much for having me. It was a pleasure. >> I'm Paul Gillin, stick with us. (upbeat music)

>>From around the globe. It's the cube presenting cube on cloud brought to you by Silicon angle. >>Welcome back to coupon cloud I'm Paul Gillan enterprise editor of Silicon angle. You know, as we look ahead at what is in store for the cloud this year, one of the intriguing possibilities that has emerged is the rise of vertical clouds. IBM has been a leader in this area with its launch in late 19 of the IBM financial services cloud. That's a services ready public cloud with exceptional security, as well as Polly, a policy framework for certifying compliance and services from the IBM subsidiary. Promintory now with the IBM financial services cloud, uh, that has been a major focus of our next guest, Hillary Hunter. She is the vice president and CTO of IBM cloud and IBM fellow and a veteran of, I believe, three previous appearances on the cube. Am I right Hillary? >>Yep. Sounds about right. Great to be back here today. >>Thanks for joining us. So let's start with getting an update on the IBM financial services cloud. What progress have you made in signing up customers and your ecosystem of partners? >>Yeah, you know, we've made really significant progress, uh, progress in advancing the IBM cloud for financial services since we last talked, you know, and, and we're really at that place of establishing a trusted platform for the industry, just in, you know, some specifics in addition to bank of America, which we had talked about as our us anchor partner for the program. Um, we've announced several global banks, um, that are partnering with us for the global expansion of the program, including BNP party, you know, which is one of Europe's largest banks. Um, more than 70 ASVs are signed up with us now as part of the program and adopting IBM cloud for financial services, this level of sort of ecosystem is, is exciting because it means that, you know, banks will have the opportunity to, to transform what they're doing, but do so in a way, which is driven by security and compliance, um, so that they can be confident in those deployments on IBM cloud for financial services. >>We also released the IBM cloud policy framework for financial services. This is both the sort of security and compliance posture of the environment, as well as, you know, guidance on controls, reference architectures automation to help people on board. And so both ISBNs and banks now are able to, um, onboard to this environment and offer their wares and deploy their workloads. So it's a really exciting state for us on the program. And we're really in a place where there'll be, you know, an ongoing cadence of, you know, additional releases and announcements of additional partnerships and clients. So it's an exciting time in the program. >>Uh, one of the distinctive features I think of this, uh, of this launch is that you're working actively with your customers. They're working with you on building policy frameworks, as well as I imagined the features that you're offering on the cloud. How do you orchestrate all of these different customers and get them involved and actually co-development >>Yeah. You know, it's the ecosystem conversation and the partnership conversation are two of the fundamental aspects of the program. Like you said, this isn't, you know, just us sitting off in a bubble, inventing the future. Um, you know, we're working internally with partners, uh, within IBM like IBM Promintory, um, which is a consultancy that has deep, deep regulatory expertise and in jurisdictions globally with IBM security services. And then with these individual partners and banks and clients, one of the ways that we bring everything together is through our councils. So our council, our cloud council for financial services, um, it's where we have global systemically important financial institutions partnered with us and, and working together with one another. And, and that covers, you know, CIO is it covers chief security officers, risk officers, et cetera. Um, so we have some formality around how we work with, um, all of these partners, uh, really as a body and as a group. >>And what have you learned from this experience? I mean, if you were to go into the, uh, into other vertical clouds, what have been the lessons >>Ecosystem is so important, right? It's as I look at this space, I see that, you know, everyone has an existing business, they have a platform they're running, they have clients they're trying to service. Um, but those, the software providers into this space are looking themselves to transform their they're looking to transform from being a software vendors, to being SAS providers, the banks and financial institutions themselves are looking to transform from working on their own premises to benefit from the Alaska city and the scale and the optionality of, you know, that being in public cloud provides. So there's a lot of, um, parties themselves that are trying to transform and a lot of vendors into the financial space that are looking to transform. And in that time of a lot of change ecosystem is, is absolutely key. And so, um, the ISE and SAS providers, you know, providing their wares on the cloud for financial services is, is really just as important as those financial services institutions so that everyone can make that transition together. Um, and so that banks that are looking to digitally transform can, can leverage partners that are really at the forefront of that change in that innovation and in platforms for the industry. >>Would you say that there are, is this the first of many, I mean, are there going to be other vertical financial or other vertical IBM clouds or is the range of industries that really need that kind of specificity limited? >>I think it's, it's actually not limited, you know, though, I will say that within the space of industries that are heavily regulated, there's obviously a deeper need for sort of specific cloud embodiments and cloud implementation. So regulated industries like insurance, like telco healthcare, et cetera. Um, these are the ones I think, where there's the greatest opportunity to do verticals that are specific to industry. Um, but you know, as we look at this, this is absolutely part of an IBM cloud strategy to deliver industry specific clouds. And, and, and this comes from our decades of expertise, right? Even in financial services, being able to leverage, you know, those other entities within IBM that I mentioned, right. You know, our, our regulatory, um, background with companies, you know, having helped them address regulatory needs for specific industries, and then translating that into cloud and cloud technologies. Right. And, and then coming up from the other side, you know, in terms of the technologies themselves, we've partnered with key industries, um, to deliver security and data protection and cryptography technologies and such on premises. And we're contextualizing that now for cloud and public cloud deployments. And so it kind of brings together the pieces of decades of expertise and platforms and technology and regulations and contextualizes it into cloud. And I absolutely think that's, you know, an opportunity for, for other industries as well. >>Can you give us a bit of a preview? I mean, do you have specific industries in mind? Is there a time? >>Yeah, so, so, uh, late last year we did announce a second industry specific cloud initiative and that was IBM cloud for telco. So we have in that ecosystem now over 40 partners that are announced, that are working with IBM and with red hat, especially with, um, clients and partners that are looking to help with that transition into 5g and increasing use of IOT. 5g is really this disruptive opportunity for that industry. And, and also just for many other different types of companies and institutions that are looking to deploy with more efficiency, better operational efficiency, deploy with AI capabilities, really being able to do things that like cellular network edge, um, and the places that they're doing business using IOT devices and 5g will enable much of that to really transform and flourish. So a couple of the partners, initially, in addition to that ecosystem that I mentioned in cloud for telco, um, you know, we've got Samsung working with us, Nokia ATNT, et cetera. Um, and so, you know, these, these partnerships and, and capabilities around network edge, um, and specific capabilities in cloud for telco, um, are sort of that second, you know, public announcement that we've made around industry specific cloud, >>As far as your competitive position is concerned. I mean, are, are you taking away business from your competitors when you partner with these, these telcos and these banks, or is this an entirely new line of business that was not previously in the cloud? >>Yeah. You know, these are really, I think in, by and large new opportunities as we look at, you know, for example, how we as customers expect to engage with, um, you know, our bank, right. You know, we are looking to increasingly engage with a bank in a digital way, use our applications, use mobile devices. We're looking for, you know, individual bank outlets, uh, branch outlets of, of a banking institution to be increasingly smart, to service our needs, you know, more quickly, et cetera. Um, and so as we look at, you know, 5g and telco edge, it's about delivery of sort of smarter capabilities and such. I think much of it really is about in this digital transformation space about, you know, creating new capabilities, creating new experiences, creating new ways of engagement, um, and engagement and an opportunity to customize and personalize. Um, I think most of those are sort of new experiences and new capabilities for most companies. >>So speak about IBM's positioning right now. I mean, you're not one of the big three cloud providers to, to become one. Uh, but you do have as a big cloud business and, uh, you've, you've got the verticals, you've got the multi-cloud, uh, I know IBM is big, has been a big champion of multi-cloud. I mean, how is IBM distinctively positioned in the cloud market right now? >>Yeah. You know, we are all in, on hybrid cloud and AI. And if you listened to our CEO and chairman, you'll hear that it is a really consistent message. And he, since he came into his role as, as our CEO, um, so being all in, on hybrid cloud and AI, you know, we really are looking to help our clients transform into holistic cloud architecture. Right? So, so when I say all in, on hybrid cloud, I mean that, you know, it's, there's been a lot of sort of, I jokingly say random acts of cloud usage, right? People have ended up using cloud because there's some SAS function that they want, or some particular line of business has been highly motivated to pursue some service on a particular cloud. And hybrid cloud is really about taking a step back, having a holistic architecture for cloud consumption. And in that sense, you know, uh, clouds, uh, are IBM's partners. >>Um, and we're really looking to enable our clients to have consistency in their deployments to consolidate across their it estate and across their cloud deployments so that they can have, um, a common platform, so they can have efficiency in how their developers to like capabilities. So they can deploy more quickly with security and compliance patterns and have oversight over everything that's going on in a consistent way that really enables them to have that velocity in their business. And so when we then, you know, positioned things like industry cloud, we're leveraging IBM specific technologies to deliver differentiated capabilities and data privacy, data protection, security compliance, where these industries in public cloud. Yes. But it's in the context of helping our clients overall across all the different things. Some of which may not need all of that data privacy or, or, or be leveraging particular SAS content we're looking to help them really have cloud architecture have a holistic conversation across hybrid cloud. Um, and yet to still be able to choose particular cloud deployments on our cloud for industries, um, that enables data protection and policy for the most sensitive and, and enterprise grade things that they're looking to do at the core of their business. >>So speaking of hybrid hybrid cloud, I mean the major cloud providers, Amazon, Microsoft, Google, Oracle, and other one all have on premises offerings right now. Uh, several of them are working with telcos to expand their reach out into, uh, into co-location and into telecom, uh, data centers. Uh, all of these things were to enable is this distributed cloud fabric kind of a hybrid cloud fabric what's, IBM's play in this area. Uh, do you have a similar strategy or is it different? >>I really think, and I think you maybe wanted to get a little bit into sort of, you know, trends and predictions here in this conversation and, and, and, you know, we, we absolutely see that need for distributed cloud for cloud to really kind of be alive in all the places where it needs to be in, in all the places that someone is doing business and in a consistent way across cloud environments, um, to be one of those major trends, that's emerging as a really hot conversation. We have introduced IBM cloud satellite, um, that is IBM's hybrid cloud as a service platform, um, and enables our clients to leverage, um, uh, OpenShift and Kubernetes environments, developer tooling, uh, consistency in a cloud catalog, visibility and control over all their resources, um, across different environments. And to be able to run end, to end with consistency from on-premises to edge to different public cloud providers. >>Um, and this is absolutely something that across industries, but, you know, within also those industries that we're focused on in particular, um, that we're seeing a lot of interesting conversations emerge because if cloud is sort of everywhere, if cloud is distributed and can be on premises and in public cloud, it enables this consistency in this parody, um, really that sort of brings together that, that seamlessness, not just the random acts cloud usage, right? I mean, it means that using cloud, um, can be something that, that drives, you know, speed of release of new product. It means that you can deliver more capability and functionality into, you know, a retail outlet where you're doing business or a banking, you know, brick and mortar location. Um, you can have, you know, AI for it ops and understand what's going on across those different environments and ensure things are kept secure and patched and updated, and you're responding to incidents in efficient ways. Um, and so really having a consistent cloud environment and a distributed cloud environment across different locations, um, it's really key to leveraging the promises of what everyone had originally hoped to get out of out of cloud computing. >>Of course, one of IBM's distinctive, uh, advantages of this area is you've got a huge hardware install base out there. I mean, how do all those three 60 mainframes figuring it out, figure into this, >>Um, with the OpenShift capabilities in our Clara operations with red hat in this area, we are able to actually help our clients leverage Kubernetes and Linux and all those things, even on the mainframe. So across the mainframe family, the IBM power family, um, you know, where folks may also have AIX or IBMI deployments, people can now do Lennox, they can do open shifts, they can do Coopernetti's. Um, and we have core technologies that enable that really to be stitched together. And I think that's one of the unique perspectives that IBM has in this whole conversation about hybrid cloud. Um, there are many different definitions of hybrid cloud, but we really view it as stretching from the traditional enterprise. It, like you said, there's a lot of it out there and being able to also incorporate OpenShift and Kubernetes in a common cloud platform, um, on traditional enterprise, it on private cloud, on fresh deployments, on private cloud, Amazon public cloud, that really is the whole it estate. So when we talk about hybrid cloud, when we talk about distributed cloud, really talking about the entirety of VIT state, not just sort of new deployments of, of SAS or something like that. >>So as someone who's on the front lines of, you know, what customers are asking about cloud, do you see customer the questions that they're asking changing? Are they, are they their decision criteria changing for how they choose a cloud provider? >>Yeah. You know, I think that, um, there's definitely a lot more conversation, especially in this current era where there's an accelerated rate of cloud adoption. Um, there's a lot more conversation around things like security, um, data protection, data, privacy, being able to run in an environment that you trust, not just is it a cloud and what does it do, but can I trust it? Do I understand how my data is protected, how my workloads are secured? Um, you know, that's really why we started cloud for financial services because that industry shepherds such vital data, right? So the reason that they are highly regulated is because of the importance of what they are stewarding very important data and financial information. Um, so, you know, we began there with the cloud for regulated industries there with, with financial services, but I see that across all industries, I was participating on a panel, um, that was, uh, with a bunch of CEOs. >>And I was there interviewing some CEOs who were from a much more sort of consumer facing and also from, from foods industry, et cetera. And their conversation was exactly the same as I have with many other clients, which is that their cloud choices, their efficiency and cloud deployment now are largely driven by the ability to get to a secure posture and the ability to demonstrate their, to their internal security and risk teams that they understand their data protection, data, privacy posture. So we are seeing lots of pickup and, and conversation opportunity around confidential competing specifically. Um, and you know, that's really about enabling, uh, our clients to have full authority and privacy in their computing, in their code and their data, even when running in a cloud environment. And so I do see a shift everyone's more concerned about security, and I think we have great technologies and we've been working with core partners to establish and harden and, and create, um, generations of technology that can really answer those questions. >>I have to ask you about that term confidential computing. I haven't heard that before. What, what does that involve? >>Yeah. You know, it's, it is a buzzword to watch out here for an in 2021. So confidential computing means being able to run in an environment where there are others in a, in a cloud computing environment, for example, um, but still have full privacy and authority over what you're doing. So you are effectively in an enclave, uh, imagine yourself sort of protected and secured. And so our confidential competing technologies, um, we're actually on basically our fourth generation of, of, of the hardware and software technologies to create that strong degree of isolation. Um, this enables us to deliver a really rich portfolio. Um, frankly, the, the, the richest portfolio in the industry of actuals services delivered, um, using confidential, competing and secure enclaves. And so we can enable our customers to solution things in a way, for example, where their data, you know, can not even be visible to our cloud operators or where they, uh, retain, you know, full control over, you know, a database and have full privacy as they're running in that environment. Um, these are really great, um, you know, considerations, but they impact everything from health care financial services. Uh, we have other partners and clients who are working to protect consumer data, um, you know, through these means et cetera. And so, um, across different industries, everyone's really looking at this topic of data, privacy, data protection. Um, and so we have a whole suite and whole family of confidential competing based, uh, services that we're able to offer to, uh, offer those assurances and that privacy to them in their cloud competing. >>I do have to ask you about the multi-cloud because this is a topic of constant debate in the industry of whether customers want to move shift workloads across multiple clouds to protect themselves from lock-in. I mean, is that a fantasy? Is that real? Is that a too restrictive? Uh, this has been a key part of IBM strategy is enabling the multi-cloud. How do you see customer attitudes developing right now? How do they want to use multiple clouds or in fact, do they, are they, are they, uh, concentrating perhaps more of their workloads in one or two? >>Yeah. You know, we believe vendor locking goes against the true spirit of hybrid cloud, right. Um, that desire to have consistency across environments, um, that desire to, uh, and the business need to have, you know, continuity and resiliency and operations, et cetera. Um, and so I do see this as a really important topic, um, from the perspective of, you know, managing environments, I think in multi-cloud, um, I think folks are starting to realize that multicloud isn't necessarily a strategy. It's a reality. Um, people have deployments in lots of different cloud environments, um, that happened somewhat organically in many cases. And so the key question is how to then get to visibility and control over those resources. Um, I think kind of two of the, the, the core topics in that are multicloud management, um, you know, being able to understand, you know, clusters and virtual machines and other things that are deployed across different environments and manage them with a common set of policies, for example. >>Um, and then in addition to multicloud management, um, I, for it, operations is another really important topic in, in multi-cloud being able to respond to incidents, understand and analyze and leverage AI, um, for what's going on for understanding what's going on across those environments, um, is another really core topic. And then as you said, you know, distributed cloud is a means of getting that consistency, having a common, you know, control and deployment plane across those different environments, um, can help it not just be sort of accidental usage of multiple cloud environments, but very intentional deployment based on the needs of particular workloads to the environment that they're best suited to. Um, and, and that's really what you want to aim for. Um, not that multi-cloud is necessarily, um, you know, uh, uh, I guess I would say is, is it is a, um, it is a complexity that is manageable, um, through these, you know, new types of technologies and multicloud management and such like that, and cloud >>Well, uh, Hillary TIS, the season for predictions is January, uh, everyone's prognostic table of what the future will look like. What do you think are going to be the main trend lines in cloud this year? Yeah, >>You know, I, I sort of sprinkled a few in there as we were talking, but I really do think that, um, the conversation around hybrid cloud number one, how to have an open innovation ecosystem for cloud, where, um, you have a consistency across environments, you know, not just random acts of cloud usage, but intentional and holistic architecture. Um, I really see that as the transition to sort of the second wave of, of cloud adoption. Um, and then secondly, as we were talking earlier about security, right, everyone is wondering about data policy and data privacy. Um, we've always taken a strong stance that, you know, our client's data is, is, is their data. We are not going to be using their data to, you know, further develop our, um, you know, AI services on our cloud or something. Um, we have deployed technologies and confidential computing that enabled them to keep full control over their keys so that, you know, even our caught operators center have access to data, um, competing in secure enclaves, where they have a strong degree of isolation and full privacy and authority over their workload. >>I really think, you know, these two topics open and secure hybrid computing and with consistency across environments, but distributed cloud technology. Um, and secondly, security, I think these are really important topics for 2021, and they may seem a little bit obvious, but I think it's important as people look at this to look for technologies that are multiple generations into this journey, right. Um, you know, partner with, um, folks who, um, are, you know, committed, uh, very clearly to an open ecosystem and open source innovation on the one hand. Um, and secondly, you know, um, when we talk about security and data protection, you want to know that that provider is several generations into that journey. Um, you know, so you really know that that technology has been vetted out is that production scale and has the stable basis. And so I think this is the year when folks are transitioning from cloud adoption, uh, to consistency in cloud and security and privacy in cloud >>Final question. And it has nothing to do with cloud. You're an IBM fellow. And I see that term, uh, turn up occasionally with other other people I've spoken to from IBM, what is it? IBM fellow, how do you become one and what right. Privileges and responsibilities as an entail. >>Yeah. You know, it's an exciting opportunity to be an IBM fellow. There's about a hundred active IBM fellows, um, right now. Um, so there aren't too many of us, but there is a small community of us. Um, IBM fellow is IBM's highest technical designation within our technical population. Um, so I do have a role within our cloud business. Um, but as one of our technical leaders, um, get to interact with the other fellows, um, you know, work on strategy for IBM in technology overall as a company. Um, and I also get to sort of be a trusted advisor to many of our clients. And so, um, I get to with CTOs and CEOs and VP of application development, um, you know, kind of, kind of profiles and VP of, of it and things like that, um, in our different clients and really help them wrestle through those struggles, um, of, you know, future it transformation. >>And so, um, you know, part of what I enjoy most about sort of the role and, and the fellow role is, is being able to kind of be that trusted advisor to many of our clients. There's been so much change in this last year for everyone. Um, and being able to, you know, also, you know, help our technical population through that, you know, in various means and then help our clients, um, through all of that change and really being able to take and grasp onto the opportunities, um, that this last year has had in the way that we work has changed. And the way that companies are looking to deliver capabilities has changed. Um, so that's, for me, the exciting part of, of the role, >>Or you're wondering a hundred then, and you do a great job of articulating the IBM strategy and also the, uh, the cloud landscape, Hillary Hunter, VP and CTO, excuse me, CTO of IBM cloud. Thank you so much for joining us today on Cuban cloud. >>Thanks so much for having me. It was a pleasure. >>I'm Paul Gillan stick with us.

>>Yeah, yeah. >>Welcome back. I'm so excited to introduce our next session empowerment through inclusion, reimagining society and technology. This is a topic that's personally very near and dear to my heart. Did you know that there's only 2% of Latinas in technology as a Latina? I know that there's so much more we could do collectively to improve these gaps and diversity. I thought spot diversity is considered a critical element across all levels of the organization. The data shows countless times. A diverse and inclusive workforce ultimately drives innovation better performance and keeps your employees happier. That's why we're passionate about contributing to this conversation and also partnering with organizations that share our mission of improving diversity across our communities. Last beyond, we hosted the session during a breakfast and we packed the whole room. This year, we're bringing the conversation to the forefront to emphasize the importance of diversity and data and share the positive ramifications that it has for your organization. Joining us for this session are thought spots Chief Data Strategy Officer Cindy Housing and Ruhollah Benjamin, associate professor of African American Studies at Princeton University. Thank you, Paola. So many >>of you have journeyed with me for years now on our efforts to improve diversity and inclusion in the data and analytic space. And >>I would say >>over time we cautiously started commiserating, eventually sharing best practices to make ourselves and our companies better. And I do consider it a milestone. Last year, as Paola mentioned that half the room was filled with our male allies. But I remember one of our Panelists, Natalie Longhurst from Vodafone, suggesting that we move it from a side hallway conversation, early morning breakfast to the main stage. And I >>think it was >>Bill Zang from a I G in Japan. Who said Yes, please. Everyone else agreed, but more than a main stage topic, I want to ask you to think about inclusion beyond your role beyond your company toe. How Data and analytics can be used to impact inclusion and equity for the society as a whole. Are we using data to reveal patterns or to perpetuate problems leading Tobias at scale? You are the experts, the change agents, the leaders that can prevent this. I am thrilled to introduce you to the leading authority on this topic, Rou Ha Benjamin, associate professor of African studies at Princeton University and author of Multiple Books. The Latest Race After Technology. Rou ha Welcome. >>Thank you. Thank you so much for having me. I'm thrilled to be in conversation with you today, and I thought I would just kick things off with some opening reflections on this really important session theme. And then we could jump into discussion. So I'd like us to as a starting point, um, wrestle with these buzzwords, empowerment and inclusion so that we can have them be more than kind of big platitudes and really have them reflected in our workplace cultures and the things that we design in the technologies that we put out into the world. And so to do that, I think we have to move beyond techno determinism, and I'll explain what that means in just a minute. Techno determinism comes in two forms. The first, on your left is the idea that technology automation, um, all of these emerging trends are going to harm us, are going to necessarily harm humanity. They're going to take all the jobs they're going to remove human agency. This is what we might call the techno dystopian version of the story and this is what Hollywood loves to sell us in the form of movies like The Matrix or Terminator. The other version on your right is the techno utopian story that technologies automation. The robots as a shorthand, are going to save humanity. They're gonna make everything more efficient, more equitable. And in this case, on the surface, he seemed like opposing narratives right there, telling us different stories. At least they have different endpoints. But when you pull back the screen and look a little bit more closely, you see that they share an underlying logic that technology is in the driver's seat and that human beings that social society can just respond to what's happening. But we don't really have a say in what technologies air designed and so to move beyond techno determinism the notion that technology is in the driver's seat. We have to put the human agents and agencies back into the story, the protagonists, and think carefully about what the human desires worldviews, values, assumptions are that animate the production of technology. And so we have to put the humans behind the screen back into view. And so that's a very first step and when we do that, we see, as was already mentioned, that it's a very homogeneous group right now in terms of who gets the power and the resource is to produce the digital and physical infrastructure that everyone else has to live with. And so, as a first step, we need to think about how to create more participation of those who are working behind the scenes to design technology now to dig a little more a deeper into this, I want to offer a kind of low tech example before we get to the more hi tech ones. So what you see in front of you here is a simple park bench public bench. It's located in Berkeley, California, which is where I went to graduate school and on this particular visit I was living in Boston, and so I was back in California. It was February. It was freezing where I was coming from, and so I wanted to take a few minutes in between meetings to just lay out in the sun and soak in some vitamin D, and I quickly realized, actually, I couldn't lay down on this bench because of the way it had been designed with these arm rests at intermittent intervals. And so here I thought. Okay, the the armrest have, ah functional reason why they're there. I mean, you could literally rest your elbows there or, um, you know, it can create a little bit of privacy of someone sitting there that you don't know. When I was nine months pregnant, it could help me get up and down or for the elderly, the same thing. So it has a lot of functional reasons, but I also thought about the fact that it prevents people who are homeless from sleeping on the bench. And this is the Bay area that we were talking about where, in fact, the tech boom has gone hand in hand with a housing crisis. Those things have grown in tandem. So innovation has grown within equity because we haven't thought carefully about how to address the social context in which technology grows and blossoms. And so I thought, Okay, this crisis is growing in this area, and so perhaps this is a deliberate attempt to make sure that people don't sleep on the benches by the way that they're designed and where the where they're implemented and So this is what we might call structural inequity. By the way something is designed. It has certain effects that exclude or harm different people. And so it may not necessarily be the intense, but that's the effect. And I did a little digging, and I found, in fact, it's a global phenomenon, this thing that architects called hostile architecture. Er, I found single occupancy benches in Helsinki, so only one booty at a time no laying down there. I found caged benches in France. And in this particular town. What's interesting here is that the mayor put these benches out in this little shopping plaza, and within 24 hours the people in the town rallied together and had them removed. So we see here that just because we have, uh, discriminatory design in our public space doesn't mean we have to live with it. We can actually work together to ensure that our public space reflects our better values. But I think my favorite example of all is the meter bench. In this case, this bench is designed with spikes in them, and to get the spikes to retreat into the bench, you have to feed the meter you have to put some coins in, and I think it buys you about 15 or 20 minutes. Then the spikes come back up. And so you'll be happy to know that in this case, this was designed by a German artists to get people to think critically about issues of design, not just the design of physical space but the design of all kinds of things, public policies. And so we can think about how our public life in general is metered, that it serves those that can pay the price and others are excluded or harm, whether we're talking about education or health care. And the meter bench also presents something interesting. For those of us who care about technology, it creates a technical fix for a social problem. In fact, it started out his art. But some municipalities in different parts of the world have actually adopted this in their public spaces in their parks in order to deter so called lawyers from using that space. And so, by a technical fix, we mean something that creates a short term effect, right. It gets people who may want to sleep on it out of sight. They're unable to use it, but it doesn't address the underlying problems that create that need to sleep outside in the first place. And so, in addition to techno determinism, we have to think critically about technical fixes that don't address the underlying issues that technology is meant to solve. And so this is part of a broader issue of discriminatory design, and we can apply the bench metaphor to all kinds of things that we work with or that we create. And the question we really have to continuously ask ourselves is, What values are we building in to the physical and digital infrastructures around us? What are the spikes that we may unwittingly put into place? Or perhaps we didn't create the spikes. Perhaps we started a new job or a new position, and someone hands us something. This is the way things have always been done. So we inherit the spike bench. What is our responsibility when we noticed that it's creating these kinds of harms or exclusions or technical fixes that are bypassing the underlying problem? What is our responsibility? All of this came to a head in the context of financial technologies. I don't know how many of you remember these high profile cases of tech insiders and CEOs who applied for Apple, the Apple card and, in one case, a husband and wife applied and the husband, the husband received a much higher limit almost 20 times the limit as his wife, even though they shared bank accounts, they lived in Common Law State. And so the question. There was not only the fact that the husband was receiving a much better interest rate and the limit, but also that there was no mechanism for the individuals involved to dispute what was happening. They didn't even know what the factors were that they were being judged that was creating this form of discrimination. So in terms of financial technologies, it's not simply the outcome that's the issue. Or that could be discriminatory, but the process that black boxes, all of the decision making that makes it so that consumers and the general public have no way to question it. No way to understand how they're being judged adversely, and so it's the process not only the product that we have to care a lot about. And so the case of the apple cart is part of a much broader phenomenon of, um, racist and sexist robots. This is how the headlines framed it a few years ago, and I was so interested in this framing because there was a first wave of stories that seemed to be shocked at the prospect that technology is not neutral. Then there was a second wave of stories that seemed less surprised. Well, of course, technology inherits its creator's biases. And now I think we've entered a phase of attempts to override and address the default settings of so called racist and sexist robots, for better or worse. And here robots is just a kind of shorthand, that the way people are talking about automation and emerging technologies more broadly. And so as I was encountering these headlines, I was thinking about how these air, not problems simply brought on by machine learning or AI. They're not all brand new, and so I wanted to contribute to the conversation, a kind of larger context and a longer history for us to think carefully about the social dimensions of technology. And so I developed a concept called the New Jim Code, which plays on the phrase Jim Crow, which is the way that the regime of white supremacy and inequality in this country was defined in a previous era, and I wanted us to think about how that legacy continues to haunt the present, how we might be coding bias into emerging technologies and the danger being that we imagine those technologies to be objective. And so this gives us a language to be able to name this phenomenon so that we can address it and change it under this larger umbrella of the new Jim Code are four distinct ways that this phenomenon takes shape from the more obvious engineered inequity. Those were the kinds of inequalities tech mediated inequalities that we can generally see coming. They're kind of obvious. But then we go down the line and we see it becomes harder to detect. It's happening in our own backyards. It's happening around us, and we don't really have a view into the black box, and so it becomes more insidious. And so in the remaining couple minutes, I'm just just going to give you a taste of the last three of these, and then a move towards conclusion that we can start chatting. So when it comes to default discrimination. This is the way that social inequalities become embedded in emerging technologies because designers of these technologies aren't thinking carefully about history and sociology. Ah, great example of this came Thio headlines last fall when it was found that widely used healthcare algorithm affecting millions of patients, um, was discriminating against black patients. And so what's especially important to note here is that this algorithm healthcare algorithm does not explicitly take note of race. That is to say, it is race neutral by using cost to predict healthcare needs. This digital triaging system unwittingly reproduces health disparities because, on average, black people have incurred fewer costs for a variety of reasons, including structural inequality. So in my review of this study by Obermeyer and colleagues, I want to draw attention to how indifference to social reality can be even more harmful than malicious intent. It doesn't have to be the intent of the designers to create this effect, and so we have to look carefully at how indifference is operating and how race neutrality can be a deadly force. When we move on to the next iteration of the new Jim code coded exposure, there's attention because on the one hand, you see this image where the darker skin individual is not being detected by the facial recognition system, right on the camera or on the computer. And so coated exposure names this tension between wanting to be seen and included and recognized, whether it's in facial recognition or in recommendation systems or in tailored advertising. But the opposite of that, the tension is with when you're over included. When you're surveiled when you're to centered. And so we should note that it's not simply in being left out, that's the problem. But it's in being included in harmful ways. And so I want us to think carefully about the rhetoric of inclusion and understand that inclusion is not simply an end point. It's a process, and it is possible to include people in harmful processes. And so we want to ensure that the process is not harmful for it to really be effective. The last iteration of the new Jim Code. That means the the most insidious, let's say, is technologies that are touted as helping US address bias, so they're not simply including people, but they're actively working to address bias. And so in this case, There are a lot of different companies that are using AI to hire, create hiring software and hiring algorithms, including this one higher view. And the idea is that there there's a lot that AI can keep track of that human beings might miss. And so so the software can make data driven talent decisions. After all, the problem of employment discrimination is widespread and well documented. So the logic goes, Wouldn't this be even more reason to outsource decisions to AI? Well, let's think about this carefully. And this is the look of the idea of techno benevolence trying to do good without fully reckoning with what? How technology can reproduce inequalities. So some colleagues of mine at Princeton, um, tested a natural learning processing algorithm and was looking to see whether it exhibited the same, um, tendencies that psychologists have documented among humans. E. And what they found was that in fact, the algorithm associating black names with negative words and white names with pleasant sounding words. And so this particular audit builds on a classic study done around 2003, before all of the emerging technologies were on the scene where two University of Chicago economists sent out thousands of resumes to employers in Boston and Chicago, and all they did was change the names on those resumes. All of the other work history education were the same, and then they waited to see who would get called back. And the applicants, the fictional applicants with white sounding names received 50% more callbacks than the black applicants. So if you're presented with that study, you might be tempted to say, Well, let's let technology handle it since humans are so biased. But my colleagues here in computer science found that this natural language processing algorithm actually reproduced those same associations with black and white names. So, too, with gender coded words and names Amazon learned a couple years ago when its own hiring algorithm was found discriminating against women. Nevertheless, it should be clear by now why technical fixes that claim to bypass human biases are so desirable. If Onley there was a way to slay centuries of racist and sexist demons with a social justice box beyond desirable, more like magical, magical for employers, perhaps looking to streamline the grueling work of recruitment but a curse from any jobseekers, as this headline puts it, your next interview could be with a racist spot, bringing us back to that problem space we started with just a few minutes ago. So it's worth noting that job seekers are already developing ways to subvert the system by trading answers to employers test and creating fake applications as informal audits of their own. In terms of a more collective response, there's a federation of European Trade unions call you and I Global that's developed a charter of digital rights for work, others that touches on automated and a I based decisions to be included in bargaining agreements. And so this is one of many efforts to change their ecosystem to change the context in which technology is being deployed to ensure more protections and more rights for everyday people in the US There's the algorithmic accountability bill that's been presented, and it's one effort to create some more protections around this ubiquity of automated decisions, and I think we should all be calling from more public accountability when it comes to the widespread use of automated decisions. Another development that keeps me somewhat hopeful is that tech workers themselves are increasingly speaking out against the most egregious forms of corporate collusion with state sanctioned racism. And to get a taste of that, I encourage you to check out the hashtag Tech won't build it. Among other statements that they have made and walking out and petitioning their companies. Who one group said, as the people who build the technologies that Microsoft profits from, we refuse to be complicit in terms of education, which is my own ground zero. Um, it's a place where we can we can grow a more historically and socially literate approach to tech design. And this is just one, um, resource that you all can download, Um, by developed by some wonderful colleagues at the Data and Society Research Institute in New York and the goal of this interventionist threefold to develop an intellectual understanding of how structural racism operates and algorithms, social media platforms and technologies, not yet developed and emotional intelligence concerning how to resolve racially stressful situations within organizations, and a commitment to take action to reduce harms to communities of color. And so as a final way to think about why these things are so important, I want to offer a couple last provocations. The first is for us to think a new about what actually is deep learning when it comes to computation. I want to suggest that computational depth when it comes to a I systems without historical or social depth, is actually superficial learning. And so we need to have a much more interdisciplinary, integrated approach to knowledge production and to observing and understanding patterns that don't simply rely on one discipline in order to map reality. The last provocation is this. If, as I suggested at the start, inequity is woven into the very fabric of our society, it's built into the design of our. Our policies are physical infrastructures and now even our digital infrastructures. That means that each twist, coil and code is a chance for us toe. We've new patterns, practices and politics. The vastness of the problems that we're up against will be their undoing. Once we accept that we're pattern makers. So what does that look like? It looks like refusing color blindness as an anecdote to tech media discrimination rather than refusing to see difference. Let's take stock of how the training data and the models that we're creating have these built in decisions from the past that have often been discriminatory. It means actually thinking about the underside of inclusion, which can be targeting. And how do we create a more participatory rather than predatory form of inclusion? And ultimately, it also means owning our own power in these systems so that we can change the patterns of the past. If we're if we inherit a spiked bench, that doesn't mean that we need to continue using it. We can work together to design more just and equitable technologies. So with that, I look forward to our conversation. >>Thank you, Ruth. Ha. That was I expected it to be amazing, as I have been devouring your book in the last few weeks. So I knew that would be impactful. I know we will never think about park benches again. How it's art. And you laid down the gauntlet. Oh, my goodness. That tech won't build it. Well, I would say if the thoughts about team has any saying that we absolutely will build it and will continue toe educate ourselves. So you made a few points that it doesn't matter if it was intentional or not. So unintentional has as big an impact. Um, how do we address that does it just start with awareness building or how do we address that? >>Yeah, so it's important. I mean, it's important. I have good intentions. And so, by saying that intentions are not the end, all be all. It doesn't mean that we're throwing intentions out. But it is saying that there's so many things that happened in the world, happened unwittingly without someone sitting down to to make it good or bad. And so this goes on both ends. The analogy that I often use is if I'm parked outside and I see someone, you know breaking into my car, I don't run out there and say Now, do you feel Do you feel in your heart that you're a thief? Do you intend to be a thief? I don't go and grill their identity or their intention. Thio harm me, but I look at the effect of their actions, and so in terms of art, the teams that we work on, I think one of the things that we can do again is to have a range of perspectives around the table that can think ahead like chess, about how things might play out, but also once we've sort of created something and it's, you know, it's entered into, you know, the world. We need to have, ah, regular audits and check ins to see when it's going off track just because we intended to do good and set it out when it goes sideways, we need mechanisms, formal mechanisms that actually are built into the process that can get it back on track or even remove it entirely if we find And we see that with different products, right that get re called. And so we need that to be formalized rather than putting the burden on the people that are using these things toe have to raise the awareness or have to come to us like with the apple card, Right? To say this thing is not fair. Why don't we have that built into the process to begin with? >>Yeah, so a couple things. So my dad used to say the road to hell is paved with good intentions, so that's >>yes on. In fact, in the book, I say the road to hell is paved with technical fixes. So they're me and your dad are on the same page, >>and I I love your point about bringing different perspectives. And I often say this is why diversity is not just about business benefits. It's your best recipe for for identifying the early biases in the data sets in the way we build things. And yet it's such a thorny problem to address bringing new people in from tech. So in the absence of that, what do we do? Is it the outside review boards? Or do you think regulation is the best bet as you mentioned a >>few? Yeah, yeah, we need really need a combination of things. I mean, we need So on the one hand, we need something like a do no harm, um, ethos. So with that we see in medicine so that it becomes part of the fabric and the culture of organizations that that those values, the social values, have equal or more weight than the other kinds of economic imperatives. Right. So we have toe have a reckoning in house, but we can't leave it to people who are designing and have a vested interest in getting things to market to regulate themselves. We also need independent accountability. So we need a combination of this and going back just to your point about just thinking about like, the diversity on teams. One really cautionary example comes to mind from last fall, when Google's New Pixel four phone was about to come out and it had a kind of facial recognition component to it that you could open the phone and they had been following this research that shows that facial recognition systems don't work as well on darker skin individuals, right? And so they wanted Thio get a head start. They wanted to prevent that, right? So they had good intentions. They didn't want their phone toe block out darker skin, you know, users from from using it. And so what they did was they were trying to diversify their training data so that the system would work better and they hired contract workers, and they told these contract workers to engage black people, tell them to use the phone play with, you know, some kind of app, take a selfie so that their faces would populate that the training set, But they didn't. They did not tell the people what their faces were gonna be used for, so they withheld some information. They didn't tell them. It was being used for the spatial recognition system, and the contract workers went to the media and said Something's not right. Why are we being told? Withhold information? And in fact, they told them, going back to the park bench example. To give people who are homeless $5 gift cards to play with the phone and get their images in this. And so this all came to light and Google withdrew this research and this process because it was so in line with a long history of using marginalized, most vulnerable people and populations to make technologies better when those technologies are likely going toe, harm them in terms of surveillance and other things. And so I think I bring this up here to go back to our question of how the composition of teams might help address this. I think often about who is in that room making that decision about sending, creating this process of the contract workers and who the selfies and so on. Perhaps it was a racially homogeneous group where people didn't want really sensitive to how this could be experienced or seen, but maybe it was a diverse, racially diverse group and perhaps the history of harm when it comes to science and technology. Maybe they didn't have that disciplinary knowledge. And so it could also be a function of what people knew in the room, how they could do that chest in their head and think how this is gonna play out. It's not gonna play out very well. And the last thing is that maybe there was disciplinary diversity. Maybe there was racial ethnic diversity, but maybe the workplace culture made it to those people. Didn't feel like they could speak up right so you could have all the diversity in the world. But if you don't create a context in which people who have those insights feel like they can speak up and be respected and heard, then you're basically sitting on a reservoir of resource is and you're not tapping into it to ensure T to do right by your company. And so it's one of those cautionary tales I think that we can all learn from to try to create an environment where we can elicit those insights from our team and our and our coworkers, >>your point about the culture. This is really inclusion very different from just diversity and thought. Eso I like to end on a hopeful note. A prescriptive note. You have some of the most influential data and analytics leaders and experts attending virtually here. So if you imagine the way we use data and housing is a great example, mortgage lending has not been equitable for African Americans in particular. But if you imagine the right way to use data, what is the future hold when we've gotten better at this? More aware >>of this? Thank you for that question on DSO. You know, there's a few things that come to mind for me one. And I think mortgage environment is really the perfect sort of context in which to think through the the both. The problem where the solutions may lie. One of the most powerful ways I see data being used by different organizations and groups is to shine a light on the past and ongoing inequities. And so oftentimes, when people see the bias, let's say when it came to like the the hiring algorithm or the language out, they see the names associated with negative or positive words that tends toe have, ah, bigger impact because they think well, Wow, The technology is reflecting these biases. It really must be true. Never mind that people might have been raising the issues in other ways before. But I think one of the most powerful ways we can use data and technology is as a mirror onto existing forms of inequality That then can motivate us to try to address those things. The caution is that we cannot just address those once we come to grips with the problem, the solution is not simply going to be a technical solution. And so we have to understand both the promise of data and the limits of data. So when it comes to, let's say, a software program, let's say Ah, hiring algorithm that now is trained toe look for diversity as opposed to homogeneity and say I get hired through one of those algorithms in a new workplace. I can get through the door and be hired. But if nothing else about that workplace has changed and on a day to day basis I'm still experiencing microaggressions. I'm still experiencing all kinds of issues. Then that technology just gave me access to ah harmful environment, you see, and so this is the idea that we can't simply expect the technology to solve all of our problems. We have to do the hard work. And so I would encourage everyone listening to both except the promise of these tools, but really crucially, um, Thio, understand that the rial kinds of changes that we need to make are gonna be messy. They're not gonna be quick fixes. If you think about how long it took our society to create the kinds of inequities that that we now it lived with, we should expect to do our part, do the work and pass the baton. We're not going to magically like Fairy does create a wonderful algorithm that's gonna help us bypass these issues. It can expose them. But then it's up to us to actually do the hard work of changing our social relations are changing the culture of not just our workplaces but our schools. Our healthcare systems are neighborhoods so that they reflect our better values. >>Yeah. Ha. So beautifully said I think all of us are willing to do the hard work. And I like your point about using it is a mirror and thought spot. We like to say a fact driven world is a better world. It can give us that transparency. So on behalf of everyone, thank you so much for your passion for your hard work and for talking to us. >>Thank you, Cindy. Thank you so much for inviting me. Hey, I live back to you. >>Thank you, Cindy and rou ha. For this fascinating exploration of our society and technology, we're just about ready to move on to our final session of the day. So make sure to tune in for this customer case study session with executives from Sienna and Accenture on driving digital transformation with certain AI.

>> Announcer: From around the globe, it's theCUBE, with digital coverage of AWS re:Invent 2020, sponsored by Intel, AWS and our community partners. >> Hey is Keith Townsend a CTO Advisor on the Twitter and we have yet another CUBE alum for this, AWS re:Invent 2020 virtual coverage. AWS re:Invent 2020 unlike any other, I think it's safe to say unlike any other virtual event, AWS, nearly 60, 70,000 people in person, every conference, there's hundreds of thousands of people tuning in to watch the coverage, and we're talking to builders. No exception to that is our friends at Densify, co founder and CTO of Densify Andrew Hillier, welcome back to the show. >> Thanks, Keith, it's great to be with you again. >> So we're recording this right before it gets cold in Toronto. I hope you're enjoying some of this, breaking the cold weather? >> Yeah, no, we're getting the same whether you are right now it's fantastic. We're ready for the worst, I think in the shorter days, but we'll get through it. >> So for those of you that haven't watched any of the past episodes of theCUBE in which Andrew has appeared. Andrew can you recap, Densify, what do you guys do? >> Well, we're analytics where you can think of us as very advanced cost analytics for cloud and containers. And when I say advanced, what I mean is, there's a number of different aspects of cost, there's understanding your bill, there's how to purchase. And we do those, but we also focus heavily on the resources that you're buying, and try to change that behavior. So it's basically, boils down to a business value of saving a ton of money, but by actually changing what you're using in the cloud, as well as providing visibility. So it's, again, a form of cost optimization, but combined with resource optimization. >> So cost of resource optimization, we understand this stuff on-premises, we understand network, compute, storage, heating, cooling, etc. All of that is abstracted from us in the public cloud, what are the drivers for cost in the public cloud? >> Well, I think you directly or indirectly pay for all of those things. The funny thing about it is that it happens in a very different way. And I think everybody's aware, of course, on-demand, and be able to get resources when you need them. But the flip side of on-demand, the not so good size, is it causes what we call micro-purchasing. So when you're buying stuff, if you go and turn on a, like an Amazon Cloud instance, you're paying for that instance, you're paying Rogers and storage as well. And, implicitly for some networking, a few dollars at a time. And that really kind of creates a new situation and scale because all of a sudden now what was a control purchase on-prem, becomes a bunch of possibly junior people buying things in a very granular way, that adds up to a huge amount of money. So the very thing that makes cloud powerful, the on-demand aspects, the elasticity, also causes a very different form of purchasing behavior, which I think is one of the causes of the cost problem. >> So we're about 10, 12 years into this cloud movement, where public cloud has really become mainstream inside of traditional enterprises. What are some of the common themes you've seen when it comes to good cloud management, the cost management, hygiene across organizations? >> Yeah, and hygiene is a great word for that. I think it's evolved, you're right it's been around this is nothing new. I mean, we've probably been going to cloud expos for over a decade now. But it's kind of coming waves as far as the business problem, I think the initial problem was more around, I don't understand this bill. 'Cause to your point, all those things that you purchase on-prem, you're still purchasing in some way, and a bunch of other services. And it all shows up in this really complicated bill. And so you're trying to figure out, well, who in my organization owes what. And so that was a very early driver years ago, we saw a lot of focus on slicing and dicing the bill, as we like to call it. And then that led to well, now I know where my costs are going, can I purchase a little more intelligently. And so that was the next step. And that was an interesting step because what the problem is, the people that care about cost can't always change what's being used, but they can buy discounts and coupons, and RIs and Savings Plans. So we saw that there was a, then start to be focused on, I'm going to come up with ways of buying it, where I can get a bit of a discount. And it's like having a phone bill where I can't stop people making long distance calls, but I can get on a better phone plan. And that, kind of the second wave, and what we're seeing is the next big wave now is that, okay, I've done that, now I actually should just change what I'm actually using because, there's a lot of inefficiency in there. I've got a handle on those other problems, I need to actually, hopefully make people not buy giant instances all the time, for example. >> So let's talk about that feedback loop, understand what's driving the cost, the people that's consuming that, those services and need to understand those costs. How does Densify breach that gap? >> Well, again, we have aspects of our product that lineup with basically all three of those business problems I mentioned. So there's a there's a cloud cost intelligence module that basically lets you look at the bill any different ways by different tags. Look for anomalies, we find that very important, you say, well, this something unusual happened in my bill. So there's aspect that just focuses on kind of accountability of what's happening in the cost world. And then now, one of the strengths of our product is that when we do our analytics, we look at a whole lot of things at once. So we look at, the instances and their utilization, and what the catalog is, and the RIs and Savings Plans, and everything all together. So if you want to purchase more intelligently, that can be very complicated. So we see a lot of customers that say, well, I do want to buy savings plans, but man, it's difficult to figure out exactly what to do. So we like to think of ourselves as kind of a, it's almost like a, an analytics engine that's got an equation with a lot of terms in. It's got a lot of detail of what we're taking into account when we tell you what you should be doing. And that helps you by more intelligently, it also helps you consume more intelligently, 'cause they're all interrelated. I don't want to change an instance I'm using if there's no RI on it, that would take you backwards. I don't want to buy RIs for instances that I shouldn't be using, that takes you backwards. So it's all interconnected. And we feel that looking at everything at once is the path to getting the right answer. And having the right answer is the path to having people actually make a change. >> So when I interviewed you a few years ago, we talked about very high level containers, and how containers is changing the way that we can consume Cloud Services, containers introduced this concept of oversubscription, and the public cloud. We couldn't really oversubscribe and for large instance, back then. But we can now with containers, how are containers in general complicating cloud costing? >> So it's interesting because they do allow overcommit but not in the same way that a virtual environment does. So in a virtual environment, if I say I need two CPUs for job X, I need two CPUs for job Y, I can put them both on a machine that has two CPUs, and there will be over committed. So over committed in a virtual environment, it is a very well established operation. It lets you get past people asking for too much effectively. Containers don't quite do that in the same way, when they refer to overcommit, they refer to the fact that you can ask for one CPU, but you can use up to four, and that difference is if you overcommit. But the fact that I'm asking for one CPU is actually a pretty big problem. So let me give an example. If I look into my laptop here, and I've got Outlook and Word and all these things on it, and I had to tell you how many millicores I had to give each one, or with Zoom, let's see I'm running Zoom. Now, well, I want Zoom to work well, I want to give it $4,000 millicores, I want to give it four CPUs, because it uses that when it needs it. But my PowerPoint, I also want to give 4000 or $2,000 millicores. So I add all these things up of what I need based on the actual more granular requirements. And it might add up to four laptops. But containers don't overcommit the same way, if I asked for those requests by using containers, I actually will use for laptops. So it's those request values that are the trick, if I say I need a CPU, I get a CPU, it's not the same as a virtual CPU would be in a virtual environment. So we see that as the cause of a lot of the problem and that people quite rationally say I need these resources for these containers. But because containers are much more granular, I'm asking for a lot of individual resource, that when you add them up, it's a ton of resources. So almost every container running, we see that they're very low utilization, because everybody, rightfully so asked for individual resources for each container, but they are the wrong resources, or in aggregate, it's not creating the behavior you wanted. So we find containers are a bit, people think they're going to magically cause problems to go away. But in fact, what happens is, when you start running a lot of them, you end up just with a ton of cost. And people are just starting to get to that point now. >> Yeah, I can see how that could easily be the case inside of a virtual environment. I can easily save my VM needs four CPUs, four VCPUs. And I can do that across 100 applications. And that really doesn't cost me a lot in the private data center, tools like VMware, DRS, and all of that kind of fix that for me on the back-end is magical. In the public cloud, if I ask for four CPUs, I get four CPUs, and I'm going to pay for four CPUs, even if I don't utilize it, there's no auto-balancing. So how does Densify help actually solve that problem? >> Well, so they, there's multiple aspects for that problem, ones of the thing was that people don't necessarily ask for the right thing in the first place, that's one of the biggest ones. So, I give the example of, I need to give Zoom 4,000 millicores, that's probably not true at all, if I analyze what it's doing, maybe for a second it uses that, but for the most of the time, it's not using nearly those resources. So the first step is to analyze the container behavior patterns, and say, well, those numbers should be different. And so for example, the one thing we do with that is, we say, well if a developer is using terraform templates to stand up containers, we can say, instead of putting the number 1000, in that, a thousand millercores, or 400 millicores in your template, just put a variable and that references our analytics, just let the analytics figure what that number should be. And so it's a very elegant solution to say, the machine learning will actually figure out what resources that container needs, 'cause humans are not very good at it, especially when there's 10s of thousands of containers. So that's kind of the, one of the big things is to optimize the container of requests. And then once you've done that the nodes that you're running on can be optimized, because now they start to look different. Maybe you don't have, you don't need as much memory or as much CPU. So it's all again, it's all interrelated, but it's a methodical step that's based on analytics. And, people, they're too busy to figure this out, that they can't figure it out for thousands of things. Again, if I asked you don't get your laptop, on your laptop, how many miillicores do you need to get PowerPoint? You don't know. But in containers, you have to know. So we're saying let the machine figure out. >> Yes kind of like when you're asked how many miillicores do you need to give Zoom answer's yes. >> Yeah exactly. >> (laughs) So at the end of the day, you need some way to quantify that. So you guys are doing the two things. One, you're quantifying, you're measuring how much this application typically take. And then when I go to provision it, we're using a tool like terraform. Though then instead of me answering the question, the answer is go ask Densify, and Densify will tell you, and then I'll optimize my environment. So I get both ends of that equation, if I'm kind of summarizing it correctly. >> Absolutely. And that last part is extremely important because, in a legacy environment, like in a virtual environment, I can call an API and change the size of VM, and it will stay that way. And so that's a viable automation strategy for those types of environments. In the cloud, or when you're using terraform, or in containers, they will go right back to what's in the terraform template, that's one of the powerful things about terraform is that it always matches what's in the code. So I can't go and change the cloud, it'll just go back to whatever is in the terraform template next time, it's provision. So we have to go upstream, you have to actually do it at the source, when you're provisioning applications, the actual resource specifications should be coming through at that point, you can't, you don't want to change them after the fact, you can update the terraform and redeploy with a new value, that that's the way to do automation in a container environment, it doesn't, you can't do it, like you did in a VMware environment, because it won't stick, it just gets undone the next time the DevOps pipeline triggers. So it's both a, it's a big opportunity for a kind of a whole new generation of automation, doing it, we call it CICDCO. It's, Continuous Integration, Continuous Delivery, Continuous Optimization. It's just part of the, of the fabric of the way you deploy Ops, and it's a much more elegant way to do it. >> So you hit two trigger words, or a few trigger terms, one, DevOps, two, I'm saying DevOps, CICD, and Continuous Operations. What is the typical profile of a Densify customer? >> Well, usually, they're a mix of a bunch of different technologies. So I don't want to make it sound like you have to be a DevOps shop to benefit from this, most of our customers have some DevOps teams, they also have a lot of legacy workloads, they have virtual environments, they have cloud environments. So don't necessarily have 100%, of all of these things. But usually, it's a mix of things where, there might be some newer born in the cloud as being deployed, and this whole CICDCO concept really makes sense for them, they might just have another few thousand cloud instances that they stood up, not as a part of a DevOps pipeline, but just to run apps or maybe even migrated from on-prem. So it's a pretty big mix, we see almost every company has a mix, unless you just started a company yesterday, you're going to have a mix of some EC2 services that are kind of standalone and static, maybe some skill groups running, or containers running skill groups. And there's a generally a mix of these things. So the things I'm describing do not require DevOps, the notion of optimizing the cloud instances, by changing the marching orders when they're provisioned not after the fact, that that applies to any anybody using the cloud. And our customers tend to be a mix, some again very new, new school processes and born in the cloud. And some more legacy applications that are running that look a little more like on-prem environment would, where they're not turning on and off dynamically, they're just running transactional workloads. >> So let's talk about the kind of industries, because you you hit on a key point, we kind of associate a certain type of company with born in the cloud, et cetera. What type of organizations or industries are we seeing Densify deployed in. >> So we don't really have a specific market vertical that we focus on, we have a wide variety. So we find we have a lot of customers in financial services, banks, insurance companies. And I think that's because those are very large, complicated environments, where analytics really pay dividends, if you have a lot of business services, that are doing different things, and different criticality levels. The things I'm describing are very important. But we also have logistics companies, software companies. So again, complexity plays a part, I think elasticity plays a part in the organization that wants to be able to make use of the cloud in a smart way where they're more elastic, and obviously drive costs down. So again, we have customers across all different types of industries, manufacturing, pharmaceutical. So it's a broad range, we have partners as well that use our like IBM, that use our product, and their customers. So there's no one type of company that we focus on, certainly. But we do see, again, environments that are complicated or mission critical, or that they really want to run in a more of elastic way, those tend to be very good customers for us. >> Well, CUBE alum Andrew Hillier, thank you for joining us on theCUBE coverage of AWS re:Invent 2020 virtual. Say goodbye to a couple hundred thousand of your closest friends. >> Okay, and thanks for having me. >> That concludes our interview with Densify. We really appreciate the folks that Densify, having us again to have this conversation around workload analytics and management. To find out more of, well or find out just more great CUBE coverage, visit us on the web SiliconANGLE TV. Talk to you next episode of theCUBE. (upbeat music)

>>From around the globe. It's the queue with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS, and our community partners. >>Hello, and welcome back to the cube live coverage of reinvent 2020 virtual. We're not there this year. It's the cube virtual. We are the cube virtual. I'm your host, John fro with Dave Alante and analyzing our take on the partner day. Um, keynotes and leadership sessions today was AWS APN, which is Amazon partner network global partner network day, where all the content being featured today is all about the partners and what Amazon is doing to create an ecosystem, build the ecosystem, nurture the ecosystem and reinvent what it means to be a partner. Dave, thanks for joining me today on the analysis of Amazon's ecosystem and partner network and a great stuff today. Thanks for coming on. >>Yeah, you're welcome. I mean, watch the keynote this morning. I mean, partners are critical to AWS. Look, the fact is that when, when AWS was launched, it was the developers ate it up. You know, if you're a developer, you dive right in infrastructure is code beautiful. You know, if you're mainstream it, this thing's just got more complex with the cloud. And so there's, there's a big gap right between how I, where I am today and where I want to be. And partners are critical to help helping people get there. And we'll talk about the details of specifically what Amazon did, but I mean, especially when John, when you look at things like smaller outposts, you know, going hybrid, Andy Jassy redefining hybrid, you need partners to really help you plan design, implement, manage at scale. >>Yeah. You know, one of the things I'm always, um, you know, saying nice things about Amazon, but one of the things that they're vulnerable on in my opinion is how they balanced their own SAS offerings and with what they develop in the ecosystem. This has been a constant, um, challenge and, and they've balanced it very well. Um, so other vendors, they are very clear. They make their own software, right. And they have a channel and it's kind of the old playbook. Amazon's got to reinvent the playbook here. And I think that's, what's key today on stage Doug Yom. He's the, uh, the leader you had, um, also Dave McCann who heads up marketplace and Sandy Carter who heads up worldwide public sector partners. So Dave interesting combination of three different teams, you had the classic ISV partners in the ecosystem, the cohesiveness of the world, the EMCs and so on, you had the marketplace with Dave McCann. That's where the future of procurement is. That's where people are buying product and you had public sector, huge tsunami of innovation happening because of the pandemic and Sandy is highlighting their partners. So it's partner day it's partner ecosystem, but multiple elements. They're moving marketplace where you buy programs and competencies with public sector and then ISV, all of those three areas are changing. Um, I want to get your take because you've been following ecosystems years and you've been close to the enterprise and how they buy your, >>And I think, I think John, Oh, a couple of things. One is, you know, Dave McCann was talking a lot about how CIO is one of modernize applications and they have to rationalize, and it will save some of that talk for later on, you know, Tim prophet on. But there's no question that Amazon's out to reinvent, as you said, uh, the whole experience from procurement all the way through, and, you know, normally you had to, to acquire services outside of the marketplace. And now what they're doing is bundling the services and software together. You know, it's straightforward services, implementation services, but those are well understood. The processes are known. You can pretty much size them and price them. So I think that's a huge opportunity for partners and customers to reduce friction. I think the other thing I would say is ecosystems are, are critical. >>Uh, one of the themes that we've been talking about in the cube as we've gone from a product centric world in the old days of it to a platform centric world, which has really been the last decade has been about SAS platforms and cloud platforms. And I think ecosystems are going to be a really power, the new innovation in the coming decade. And what I mean by that is look, if you're just building a service and Amazon is going to do that same service, you know, you got to keep innovating. And one of the ways you can innovate is you can build on ecosystems. There's all this data within industries, across industries, and you can through the partner network and through customer networks within industry start building new innovation around ecosystems and partners or that glue, Amazon's not going to go in. And like Jandy Jesse even said in the, uh, in his fireside chat, you know, customers will ask us for our advice and we're happy to give it to them, but frankly partners are better at that nitty gritty hardcore stuff. They have closer relationships with the customers. And so that's a really important gap that Amazon has been closing for the last, you know, frankly 10 years. And I think that to your point, they've still got a long way to go, but that's a huge opportunity in that. >>A good call out on any Jess, I've got to mention that one of the highlights of today's keynote was on a scheduled, um, Andy Jassy fireside chat. Uh, normally Andy does his keynote and then he kind of talks to customers and does his thing normally at a normal re-invent this time he came out on stage. And I think what I found interesting was he was talking about this builder. You always use the word builder customer, um, solutions. And I think one of the things that's interesting about this partner network is, is that I think there's a huge opportunity for companies to be customer centric and build on top of Amazon. And what I mean by that is, is that Amazon is pretty cool with you doing things on top of their platform that does two things serves the customer's needs better than they do, and they can make more money on and other services look at snowflake as an example, um, that's a company built on AWS. I know they've got other clouds going on, but mainly Amazon Zoom's the same way. They're doing a great solution. They've got Redshift, Amazon, Amazon's got Redshift, Dave, but also they're a customer and a partner. So this is the dynamic. If you can be successful on Amazon serving customers better than Amazon does, that's the growth hack. That's the hack on Amazon's partner network. If you could. >>I think, I think Snowflake's a really good example. You snowflake you use new Relic as an example, I've heard Andy Jess in the past use cloud air as an example, I like snowflake better because they're, they're sort of thriving. And so, but, but I will say this there's a, they're a great example of that ecosystem that we just talked about because yes, not only are they building on AWS, they're connecting to other clouds and that is an ecosystem that they're building out. And Amazon's got a lot of snowflake, I guess, unless you're the Redshift team, but, but generally speaking, Snowflake's driving a lot of business for Amazon and Andy Jesse addressed that in that, uh, in that fireside chat, he's asked that question a lot. And he said, look, we, we, we have our primary services. And at the same time we want to enable our partners to be successful. And snowflake is a really good example of that. >>Yeah. I want to call out also, uh, yesterday. Um, I had our Monday, I should say Tuesday, December 1st, uh, Jesse's keynote. I did an interview with Jerry chin with gray lock. He's investing in startups and one of the things he observed and he pointed out Dave, is that with Amazon, if you're, if you're a full all-in in the cloud, you're going to take advantage of things that are just not available on say on premises that is data patterns, other integrations. And I think one of the things that Doug pointed out was with interoperability and integration with say things like the SAS factor that they put out there there's advantages for being in the cloud specifically with Amazon, that you can get on integrations. And I think Dave McCann teases that out with the marketplace when they talk about integrations. But the idea of being in the cloud with all these other partners makes integration and interoperability different and unique and better potentially a differentiator. This is going to become a huge deal. >>I didn't pick up on that because yesterday I thought I wasn't in the keynote. I think it was in the analyst one-on-one with, with Jesse, he talked about, you know, this notion that people, I think he was addressing multi-cloud he didn't use that term, but this notion of an abstraction layer and how it does simplify things in, in his basic, he basically said, look, our philosophy is we want to have, you know, the, the ability to go deep with the primitives and have that fine grain access, because that will give us control. A lot of times when you put in this abstraction layer, which people are trying to do across clouds, you know, it limits your ability to really move fast. And then of course it's big theme is, is this year, at the same time, if you look at a company who was called out today, like, like Octa, you know, when you do an identity management and single sign-on, you're, you're touching a lot of pieces, there's a lot of integration to your point. >>So you need partners to come in and be that glue that does a lot of that heavy lifting that needs to needs to be done. Amazon. What Jessie was essentially saying, I think to the partner network is, look, we're not going to put in that abstraction layer. You're going to you, you got to do that. We're going to do stuff maybe between our own own services like they did with the, you know, the glue between databases, but generally speaking, that's a giant white space for partner organizations. He mentioned Okta. He been talked about in for apt Aptio. This was Dave McCann, actually Cohesity came up a confluent doing fully managed Kafka. So that to me was a signal to the partners. Look, here's where you guys should be playing. This is what customers need. And this is where we're not going to, you know, eat your lunch. >>Yeah. And the other thing McCann pointed out was 200 new Dave McCann pointed out who leads these leader of the, of the marketplace. He pointed out 200 new ISP. ISV is out there, huge news, and they're going to turn already. He went, he talked with his manage entitlements, which got my attention. And this is kind of an, um, kind of one of those advantage points that it's kind of not sexy and mainstream to talk about, but it's really one of those details. That's the heavy lifting. That's a pain in the butt to deal with licensing and tracking all this compliance stuff that goes on under the covers and distribution of software. I think that's where the cloud could be really advantaged. And also the app service catalog registry that he talked about and the professional services. So these are areas that Amazon is going to kind of create automation around. >>And as Jassy always talks about that undifferentiated heavy lifting, they're going to take care of some of these plumbing issues. And I think you're right about this differentiation because if I'm a partner and I could build on top of Amazon and have my own cloud, I mean, let's face it. Snowflake is a born in the cloud, in the cloud only solution on Amazon. So they're essentially Amazon's cloud. So I think the thing that's not being talked about this year, that is probably my come up in future reinvents is that whoever can build their own cloud on top of Amazon's cloud will be a winner. And I, I talked about this years ago, data around this tier two, I call it tier two clouds. This new layer of cloud service provider is going to be kind of the, on the power law, the, the second wave of cloud. >>In other words, you're on top of Amazon differentiating with a modern application at scale inside the cloud with all the other people in there, a whole new ecosystem is going to emerge. And to me, I think this is something that is not yet baked out, but if I was a partner, I would be out there planning like hell right now to say, I'm going to build a cloud business on Amazon. I'm going to take advantage of the relationships and the heavy lifting and compete and win that way. I think that's a re redefining moment. And I think whoever does that will win >>And a big theme around reinventing everything, reinvent the industry. And one of the areas that's being reinvented as is the, you know, the VAR channel really well, consultancies, you know, smaller size for years, these companies made a ton of dough selling boxes, right? All the, all the Dell and the IBM and the EMC resellers, you know, they get big boats and big houses, but that business changed dramatically. They had to shift toward value, value, value add. So what did they do? They became VMware specialists. They came became SAP specialists. There's a couple of examples, maybe, you know, adding into security. The cloud was freaking them out, but the cloud is really an opportunity for them. And I'll give you an example. We've talked a lot about snowflake. The other is AWS glue elastic views. That's what the AWS announced to connect all their databases together. Think about a consultancy that is able to come in and totally rearchitect your big data life cycle and pipeline with the people, the processes, the skillsets, you know, Amazon's not going to do that work, but the upside value for the organizations is tremendous. So you're seeing consultancies becoming managed service providers and adding all kinds of value throughout the stack. That's really reinvention of the partnership. >>Yeah. I think it's a complete, um, channel strategy. That's different. It doesn't, it looks like other channels, but it's not, it's, it's, it's driven by value. And I think this idea of competing on value versus just being kind of a commodity play is shifting. I think the ISV and the VARs, those traditional markets, David, as you pointed out, are going to definitely go value oriented. And you can just own a specialty area because as data comes in and when, and this is interesting. And one of the key things that Andy Jassy said in his fireside chat want to ask directly, how do partners benefit when asked about his keynote, how that would translate to partners. He really kind of went in and he was kind of rambling, but he, he, he hit the chips. He said, well, we've got our own chips, which means compute. Then he went into purpose-built data store and data Lake data, elastic views SageMaker Q and QuickSight. He kind of went down the road of, we have the horsepower, we have the data Lake data, data, data. So he was kind of hinting at innovate on the data and you'll do okay. >>Well, and this is again, we kind of, I'm like a snowflake fan boy, you know, in the way you, you like AWS. But look, if you look at AWS glue elastic views, that to me is like snowflakes data cloud is different, a lot of pushing and moving a date, a lot of copying data. But, but this is a great example of where like, remember last year at reinvent, they said, Hey, we're separating compute from storage. Well, you know, of course, snowflake popularized that. So this is great example of two companies thriving that are both competitors and partners. >>Well, I've got to ask you, you know, you, you and I always say we kind of his stories, we've been around the block on the enterprise for years. Um, where do you Mark the, um, evolution of their partner? Because again, Amazon has been so explosive in their growth. The numbers have been off the charts and they've done it well with and pass. And now you have the pandemic which kind of puts on full display, digital transformation. And then Jassy telegraphing that the digital global it spend is their next kind of conquering ground, um, to take, and they got the edge exploding with 5g. So you have this kind of range and they doing all kinds of stuff with IOT, and they're doing stuff in you on earth and in space. So you have this huge growth and they still don't have their own fully oriented business model. They rely on people to build on top of Amazon. So how do you see that evolving in your opinion? Because they're trying to add their own Amazon only, we've got Redshift that competes with others. How do you see that playing out? >>So I think it's going to be specialized and, and something that, uh, that I've talked about is Amazon, you know, AWS in the old day, old days being last decade, they really weren't that solution focused. It was really, you know, serving the builders with tooling, with you, look at something like what they're doing in the call center and what they're doing at the edge and IOT there. I think they're, so I think their move up the stack is going to be very solution oriented, but not necessarily, you know, horizontal going after CRM or going after, you know, uh, supply chain management or ERP. I don't think that's going to be their play. I think their play is going to be to really focus on hard problems that they can automate through their tooling and bring special advantage. And that's what they'll SAS. And at the same time, they'll obviously allow SAS players. >>It's just reminds me of the early days when you and I first met, uh, VMware. Everybody had to work with VMware because they had a such big ecosystem. Well, the SAS players will run on top. Like Workday does like Salesforce does Infour et cetera. And then I think you and I, and Jerry Chen talked about this years ago, I think they're going to give tools to builders, to disrupt the service now is in the sales forces who are out buying companies like crazy to try to get a, you know, half, half a billion dollar, half a trillion dollar market caps. And that is a really interesting dynamic. And I think right now, they're, they're not even having to walk a fine line. I think the lines are reasonably clear. We're going up to database, we're going to do specialized solutions. We're going to enable SAS. We're going to compete where we compete, come on, partner ecosystem. And >>Yeah, I, I, I think that, you know, the Slack being bought by Salesforce is just going to be one of those. I think it's a web van moment, you know, um, you know, where it's like, okay, Slack is going to go die on Salesforce. Okay. I get that. Um, but it's, it's just, it's just, it's just, it's just old school thinking. And I think if you're an entrepreneur and if you're a developer or a partner, you could really reinvent the business model because if you're, dis-aggregating all these other services like you can compete with Salesforce, Slack has now taken out of the game with Salesforce, but what Amazon is doing with say connect, which they're promoting heavily at this conference. I mean, you hear it, you heard it on Andy Jessie's keynote, Sandy Carter. They've had huge success with AWS connect. It's a call center mindset, but it's not calling just on phones. >>It's contact that is descent, intermediating, the Salesforce model. And I think when you start getting into specialists and specialism in channels, you have customer opportunity to be valuable. And I think call center, these kinds of stories that you can stand up pretty quickly and then integrate into a business model is going to be game changing. And I think that's going to going to a lot of threat on these big incumbents, like Salesforce, like Slack, because let's face it. Bots is just the chat bot is just a call center front end. You can innovate on the audio, the transcriptions there's so much Amazon goodness there, that connect. Isn't just a call center that could level the playing field and every vertical >>Well, and SAS is getting disrupted, you know, to your, to your point. I mean, you think about what happened with, with Oracle and SAP. You had, you know, these new emerging players come up like, like Salesforce, like Workday, like service now, but their pricing model, it was all the same. We lock you in for a one-year two-year three-year term. A lot of times you have to pay up front. Now you look at guys like Datadog. Uh, you, you look at a snowflake, you look at elastic, they're disrupting the Splunks of the world. And that model, I think that SAS model is right for disruption with a consumption pricing, a true cloud pricing model. You combine that with new innovation that developers are going to attack. I mean, you know, people right now, they complain about service now pricing, they complain about Splunk pricing. They, you know, they talk about, Oh, elastic. We can get that for half the price Datadog. And so I'm not predicting that those companies service now Workday, the great companies, but they are going to have to respond much in the same way that Oracle and SAP had to respond to the disruption that they saw. >>Yeah. It's interesting. During the keynote, they'll talk about going out to the mainframes today, too. So you have Amazon going into Oracle and Microsoft, and now the mainframes. So you have Oracle database and SQL server and windows server all going to being old school technologies. And now mainframe very interesting. And I think the, this whole idea of this SAS factory, um, got my attention to Cohesity, which we've been covering Dave on the storage front, uh, Mo with the founder was on stage. I'm a data management as a service they're part of this new SAS factory thing that Amazon has. And what they talk about here is they're trying to turn ISV and VARs into full-on SAS providers. And I think if they get that right with the SAS factory, um, then that's going to be potentially game changing. And I'm gonna look at to see if what the successes are there, because if Amazon can create more SAS applications, then their Tam and the global it market is there is going to, it can be mopped up pretty quickly, but they got to enable it. They got to enable that quickly. Yeah. >>Enabling to me means not just, and I think, you know, when Jesse answered your question, I saw it in the article that you wrote about, you know, you asked them about multi-cloud and it, to me, it's not about running on AWS and being compatible with Azure and being compatible with Google. No, it's about that frankly abstraction layer that he talked about, and that's what Cohesity is trying to do. You see others trying to do it as well? Snowflake for sure. It's about abstracting that complexity away and adding value on top of the cloud. In other words, you're using the cloud for scale being really expert at taking advantage of the native cloud services, which requires is that Jessie was saying different API APIs, different control, plane, different data plane, but taking that complexity away and then adding new value on top that's white space for a lot of players there. And, and, and I'll tell you, it's not trivial. It takes a lot of R and D and it takes really smart people. And that's, what's going to be really interesting to see, shake out is, you know, can the Dell and HPE, can they go fast enough to compete with the, the Cohesity's you've got guys like CLU Mayo coming in that are, that are brand new. Obviously we talked about snowflake a lot and many others. >>I think there's going to be a huge change in expectations, experience, huge opportunity for people to come in with unique solutions. We're going to have specialty programming on the cube all day today. So if you're watching us here on the Amazon channel, you know that we're going to have an all of a sudden demand. There's a little link on our page. On the, on the, um, the Amazon reinvent virtual event platform, click here, the bottom, it's going to be a landing page, check out all the interviews as we roll them out all day. We got a great lineup, Dave, we got Nutanix pure storage, big ID, BMC, Amazon leaders, all coming in to talk today. Uh, chaos search ed Walsh, Rachel Rose, uh, Medicar Kumar, um, Mike Gill, flux, tons of great, great, uh, partners coming in and they're going to share their story and what's working for them and their new strategies. And all throughout the day, you're going to hear specific examples of how people are changing and reinventing their business development, their partnership strategies on the product, and go to market with Amazon. So really interesting learnings. We're going to have great conversations all throughout the day. So check it out. And again, everything's going to be on demand. And when in doubt, go to the cube.net, we have everything there and Silicon angle.com, uh, for all the great coverage. So >>I don't think John is, we're going to have a conversation with him. David McCann touched on this. You talked about the need for modernization and rationalization, Tim Crawford on, on later. And th this is, this is sort of the, the, uh, the call-out that Andy Jassy made in his keynote. He gave the story of that one. CIO is a good friend of his who said, Hey, I love what you're doing, but it's not going to happen on my watch. And, and so, you know, Jessie's sort of poking at that, that, uh, complacency saying, guys, you have to reinvent, you have to go fast, you have to keep moving. And so we're gonna talk a little bit about what, what does that mean to modernize applications, why the CIO is want to rationalize what is the role of AWS and its ecosystem and providing that, that, that level of innovation, and really try to understand what the next five to seven years are gonna look like in that regard. >>Funny, you mentioned, uh, Andy Jesuit that story. When I had my one-on-one conversation with them, uh, he was kind of talking about that anonymous CIO and I, if people don't know Andy, he's a big movie buff, too, right? He loves it goes to Sundance every year. Um, so I said to him, I said, this error of digital transformation, uh, is kind of like that scene in the godfather, Dave, where, um, Michael Corleone goes to Tom Hagen, Tom, you're not a wartime conciliary. And what he meant by that was is that, you know, they were going to war with the other five families. I think now I think this is what chassis pointed out is that, that this is such an interesting, important time in history. And he pointed this out. If you don't have the leadership chops to lean into this, you're going to get swept away. >>And that story about the CIO being complacent. Yeah. He didn't want to shift. And the new guy came in or gal and they, and they, and they lost three years, three years of innovation. And the time loss, you can't get that back. And during this time, I think you have to have the stomach for the digital transformation. You have to have the fortitude to go forward and face the truth. And the truth is you got to learn new stuff. So the old way of doing things, and he pointed that out very aggressively. And I think for the partners, that same thing is true. You got to look in the mirror and say, where are we? What's the opportunity. And you gotta gotta go there. If not, you can wait, be swept away, be driftwood as Pat Gelsinger would say, or lean in and pick up a, pick up a shovel and start digging the new solution. >>You know what the other interesting thing, I mean, every year when you listen to Jassy and his keynotes and you sort of experienced re-invent culture comes through and John you're live in Silicon Valley, you talked to leaders of Silicon Valley, you know, well, what's the secret of success though? Nine times out of 10, they'll talk about culture, maybe 10 times out of 10. And, and, and so that's, that comes through in Jesse's keynotes. But one of the things that was interesting this year, and it's been thematic, you know, Andy, you know, repetition is important, uh, to, to him because he wants to educate people and make sure it sticks. One of the things that's really been he's been focused on is you actually can change your culture. And there's a lot of inertia. People say, well, not on my watch. Well, it doesn't work that way around here. >>And then he'll share stories about how AWS encourages people to write papers. Anybody in the organization say we should do it differently. And, and you know, they have to follow their protocol and work backwards and all of those stuff. But I believe him when he says that they're open to what you have a great example today. He said, look, if somebody says, well, it's 10 feet and somebody else says, well, it's, it's five feet. He said, okay, let's compromise and say it's seven and a half feet. Well, we know it's not seven and a half feet. We don't want to compromise. We either want to be a 10, Oh, we want to be at five, which is the right answer. And they push that. And that that's, he gives examples like that for the AWS culture, the working backwards, the frequently asked questions, documents, and he's always pushing. And that to me is very, very important and fundamental to understanding AWS. >>It's no doubt that Andy Jassy is the best CEO in the business. These days. If you look at him compared to everyone else, he's hands down, more humble as keynote who does three hour keynotes, the way he does with no notes with no, he memorize it all. So he's competitive and he's open. And he's a good leader. I think he's a great CEO. And I think it will be written and then looked back at his story this time in history. The next, I think post COVID Dave is going to be an error. We're going to look back and say the digital transformation was accelerated. Yes, all that good stuff, people process technology. But I think we're gonna look at this time, this year and saying, this was the year that there was before COVID and after COVID and the people who change and modernize will build the winners and not, and the losers will, will be sitting still. So I think it's important. I think that was a great message by him. So great stuff. All right. We gotta leave it there. Dave, the analysis we're going to be back within the power panel. Two sessions from now, stay with us. We've got another great guest coming on next. And then we have a pair of lb talk about the marketplace pricing and how enterprises have CIO is going to be consuming the cloud in their ecosystem. This is the cube. Thanks for watching..

>>Thank you. Thank you so much for having me. I'm thrilled to be in conversation with you today. And I thought I would just kick things off with some opening reflections on this really important session theme, and then we can jump into discussion. So I'd like us to, as a starting point, um, wrestle with these buzz words, empowerment and inclusion so that we can, um, have them be more than kind of big platitudes and really have them reflected in our workplace cultures and the things that we design and the technologies that we put out into the world. And so to do that, I think we have to move beyond techno determinism and I'll explain what that means in just a minute. And techno determinism comes in two forms. The first on your left is the idea that technology automate. Um, all of these emerging trends are going to harm us are going to necessarily, um, harm humanity. >>They're going to take all the jobs they're going to remove human agency. This is what we might call the techno dystopian version of the story. And this is what Hollywood loves to sell us in the form of movies like the matrix or Terminator. The other version on your right is the techno utopian story that technologies automation, the robots, as a shorthand are going to save humanity. They're going to make everything more efficient, more equitable. And in this case, on the surface, they seem like opposing narratives, right? They're telling us different stories. At least they have different endpoints, but when you pull back the screen and look a little bit more closely, you see that they share an underlying logic, that technology is in the driver's seat and that human beings, that social society can just respond to what's happening. But we don't, I really have a say in what technologies are designed. >>And so to move beyond techno determinism, the notion that technology is in the driver's seat, we have to put the human agents and agencies back into the story protagonists and think carefully about what the human desires, worldviews values assumptions are that animate the production of technology. We have to put the humans behind the screen back into view. And so that's a very first step in when we do that. We see as was already mentioned that it's a very homogenous group right now in terms of who gets the power and the resources to produce the digital and physical infrastructure that everyone else has to live with. And so, as a first step, we need to think about how to, to create more participation of those who are working behind the scenes to design technology. Now, to dig a little more deeper into this, I want to offer a kind of low tech example before we get to the more high tech ones. >>So what you see in front of you here is a simple park bench public it's located in Berkeley, California, which is where I went to graduate school. And on this one particular visit, I was living in Boston. And so I was back in California, it was February, it was freezing where I was coming from. And so I wanted to take a few minutes in between meetings to just lay out in the sun and soak in some vitamin D. And I quickly realized actually I couldn't lay down on the bench because of the way it had been designed with these arm rests at intermittent intervals. And so here I thought, okay, th th the armrests have a functional reason why they're there. I mean, you could literally rest your elbows there, or, um, you know, it can create a little bit of privacy of someone sitting there that you don't know. >>Um, when I was nine months pregnant, it could help me get up and down or for the elderly the same thing. So it has a lot of functional reasons, but I also thought about the fact that it prevents people who are, are homeless from sleeping on the bench. And this is the Bay area that we're talking about, where in fact, the tech boom has gone hand in hand with a housing crisis. Those things have grown in tandem. So innovation has grown with inequity because we have, I haven't thought carefully about how to address the social context in which technology grows and blossoms. And so I thought, okay, this crisis is growing in this area. And so perhaps this is a deliberate attempt to make sure that people don't sleep on the benches by the way that they're designed and where the, where they're implemented. And so this is what we might call structural inequity, by the way something is designed. >>It has certain yeah. Affects that exclude or harm different people. And so it may not necessarily be the intent, but that's the effect. And I did a little digging and I found, in fact, it's a global phenomenon, this thing that architect next call, hostile architecture around single occupancy, benches and Helsinki. So only one booty at a time, no Nolan down there. I've found caged benches in France. Yeah. And in this particular town, what's interesting here is that the mayor put these benches out in this little shopping Plaza and within 24 hours, the people in the town rally together and have them removed. So we see here that just because we, we have a discriminatory design in our public space, doesn't mean we have to live with it. We can actually work together to ensure that our public space reflects our better values. But I think my favorite example of all is the metered bench. >>And then this case, this bench is designed with spikes in them and to get the spikes to retreat into the bench, you have to feed the meter. You have to put some coins in, and I think it buys you about 15, 20 minutes, then the spikes come back up. And so you will be happy to know that in this case, uh, this was designed by a German artist to get people to think critically about issues of design, not the design of physical space, but the design of all kinds of things, public policies. And so we can think about how our public life in general is metered, that it serves those that can pay the price and others are excluded or harmed. Whether we're talking about education or healthcare. And the meter bench also presents something interesting for those of us who care about technology, it creates a technical fix for a social problem. >>In fact, it started out as art, but some municipalities in different parts of the world have actually adopted this in their public spaces, in their parks in order to deter so-called loiters from using that space. And so by a technical fix, we mean something that creates a short-term effect, right? It gets people who may want to sleep on it out of sight. They're unable to use it, but it doesn't address the underlying problems that create that need to sleep outside of the first place. And so, in addition to techno determinism, we have to think critically about technical fixes, that don't address the underlying issues that the tech tech technology is meant to solve. And so this is part of a broader issue of discriminatory design, and we can apply the bench metaphor to all kinds of things that we work with, or that we create. >>And the question we really have to continuously ask ourselves is what values are we building in to the physical and digital infrastructures around us? What are the spikes that we may unwittingly put into place? Or perhaps we didn't create the spikes. Perhaps we started a new job or a new position, and someone hands us something, this is the way things have always been done. So we inherit the spiked bench. What is our responsibility? When we notice that it's creating these kinds of harms or exclusions or technical fixes that are bypassing the underlying problem, what is our responsibility? All of this came to a head in the context of financial technologies. I don't know how many of you remember these high profile cases of tech insiders and CEOs who applied for apples, >>The Apple card. And in one case, a husband and wife applied, and the husband, the husband received a much higher limit, almost 20 times the limit as his, >>His wife, even though they shared bank accounts, they lived in common law state. Yeah. >>And so the question there was not only the fact that >>The husband was receiving a much better rate and a high and a better >>The interest rate and the limit, but also that there was no mechanism for the individuals involved to dispute what was happening. They didn't even know how, what the factors were that they were being judged that was creating this form of discrimination. So >>In terms of financial technologies, it's not simply the outcome, that's the issue, or that can be discriminatory, >>But the process that black box is all of the decision-making that makes it so that consumers and the general public have no way to question it, no way to understand how they're being judged adversely. And so it's the process, not only the product that we have to care a lot about. And so the case of the Apple card is part of a much broader phenomenon >>Of, um, races >>And sexist robots. This is how the headlines framed it a few years ago. And I was so interested in this framing because there was a first wave of stories that seemed to be shocked at the prospect, that technology is not neutral. Then there was a second wave of stories that seemed less surprised. Well, of course, technology inherits its creators biases. And now I think we've entered a phase of attempts to override and address the default settings of so-called racist and sexist robots for better or worse than here. Robots is just a kind of shorthand that the way that people are talking about automation and emerging technologies more broadly. And so, as I was encountering these headlines, I was thinking about how these are not problems simply brought on by machine learning or AI. They're not all brand new. And so I wanted to contribute to the conversation, a kind of larger context and a longer history for us to think carefully about the social dimensions of technology. And so I developed a concept called the new Jim code, >>Which plays on the phrase, >>Jim Crow, which is the way that the regime of white supremacy and inequality in this country was defined in a previous era. And I wanted us to think about how that legacy continues to haunt the present, how we might be coding bias into emerging technologies and the danger being that we imagine those technologies to be objective. And so this gives us a language to be able to name this phenomenon so that we can address it and change it under this larger umbrella of the new Jim code are four distinct ways that this phenomenon takes shape from the more obvious engineered inequity. Those are the kinds of inequalities tech mediated in the qualities that we can generally see coming. They're kind of obvious, but then we go down the line and we see it becomes harder to detect it's happening in our own backyards, it's happening around us. And we don't really have a view into the black box. And so it becomes more insidious. And so in the remaining couple of minutes, I'm just, just going to give you a taste of the last three of these, and then a move towards conclusion. Then we can start chatting. So when it comes to default discrimination, this is the way that social inequalities >>Become embedded in emerging technologies because designers of these technologies, aren't thinking carefully about history and sociology. A great example of this, uh, came to, um, uh, the headlines last fall when it was found that widely used healthcare algorithm, effecting millions of patients, um, was discriminating against black patients. And so what's especially important to note here is that this algorithm, healthcare algorithm does not explicitly take note of race. That is to say it is race neutral by using cost to predict healthcare needs this digital triaging system unwittingly reproduces health disparities, because on average, black people have incurred fewer costs for a variety of reasons, including structural inequality. So in my review of this study, by Obermeyer and colleagues, I want to draw attention to how indifference to social reality can be even more harmful than malicious intent. It doesn't have to be the intent of the designers to create this effect. >>And so we have to look carefully at how indifference is operating and how race neutrality can be a deadly force. When we move on to the next iteration of the new Jim code, coded exposure, there's a tension because on the one hand, you see this image where the darker skin individual is not being detected by the facial recognition system, right on the camera, on the computer. And so coded exposure names, this tension between wanting to be seen and included and recognized whether it's in facial recognition or in recommendation systems or in tailored advertising. But the opposite of that, the tension is with when you're over, it >>Included when you're surveilled, when you're >>Too centered. And so we should note that it's not simply in being left out, that's the problem, but it's in being included in harmful ways. And so I want us to think carefully about the rhetoric of inclusion and understand that inclusion is not simply an end point, it's a process, and it is possible to include people in harmful processes. And so we want to ensure that the process is not harmful for it to really be effective. The last iteration of the new Jim code. That means the, the most insidious let's say is technologies that are touted as helping us address bias. So they're not simply including people, but they're actively working to address bias. And so in this case, there are a lot of different companies that are using AI to hire, uh, create hiring, um, software and hiring algorithms, including this one higher view. >>And the idea is that there there's a lot that, um, AI can keep track of that human beings might miss. And so, so the software can make data-driven talent decisions after all the problem of employment discrimination is widespread and well-documented, so the logic goes, wouldn't this be even more reason to outsource decisions to AI? Well, let's think about this carefully. And this is the idea of techno benevolence, trying to do good without fully reckoning with what, how technology can reproduce inequalities. So some colleagues of mine at Princeton, um, tested a natural learning processing algorithm and was looking to see whether it exhibited the same, um, tendencies that psychologists have documented among humans. And what they found was that in fact, the algorithm associated black names with negative words and white names with pleasant sounding words. And so this particular audit builds on a classic study done around 2003 before all of the emerging technologies were on the scene where two university of Chicago economists sent out thousands of resumes to employers in Boston and Chicago. >>And all they did was change the names on those resumes. All of the other work history education were the same. And then they waited to see who would get called back and the applicants, the fictional applicants with white sounding names received 50% more callbacks than the, the black applicants. So if you're presented with that study, you might be tempted to say, well, let's let technology handle it since humans are so biased. But my colleagues here in computer science found that this natural language processing algorithm actually reproduced those same associations with black and white names. So two with gender coded words and names as Amazon learned a couple years ago, when its own hiring algorithm was found discriminating against women, nevertheless, it should be clear by now why technical fixes that claim to bypass human biases are so desirable. If only there was a way to slay centuries of racist and sexist demons with a social justice bot beyond desirable, more like magical, magical for employers, perhaps looking to streamline the grueling work of recruitment, but a curse from any job seekers as this headline puts it. >>Your next interview could be with a racist bot, bringing us back to that problem space. We started with just a few minutes ago. So it's worth noting that job seekers are already developing ways to subvert the system by trading answers to employers tests and creating fake applications as informal audits of their own. In terms of a more collective response. There's a Federation of European trade unions call you and I global that's developed a charter of digital rights for workers that touches on automated and AI based decisions to be included in bargaining agreements. And so this is one of many efforts to change the ecosystem, to change the context in which technology is being deployed to ensure more protections and more rights for everyday people in the U S there's the algorithmic accountability bill that's been presented. And it's one effort to create some more protections around this ubiquity of automated decisions. >>And I think we should all be calling for more public accountability when it comes to the widespread use of automated decisions. Another development that keeps me somewhat hopeful is that tech workers themselves are increasingly speaking out against the most egregious forms of corporate collusion with state sanctioned racism. And to get a taste of that, I encourage you to check out the hashtag tech, won't build it among other statements that they've made and walking out and petitioning their companies. One group said as the, at Google at Microsoft wrote as the people who build the technologies that Microsoft profits from, we refuse to be complicit in terms of education, which is my own ground zero. Um, it's a place where we can, we can grow a more historically and socially literate approach to tech design. And this is just one resource that you all can download, um, by developed by some wonderful colleagues at the data and society research Institute in New York. >>And the, the goal of this intervention is threefold to develop an intellectual understanding of how structural racism operates and algorithms, social media platforms and technologies not yet developed and emotional intelligence concerning how to resolve racially stressful situations within organizations and a commitment to take action, to reduce harms to communities of color. And so as a final way to think about why these things are so important, I want to offer, uh, a couple last provocations. The first is pressed to think a new about what actually is deep learning when it comes to computation. I want to suggest that computational depth when it comes to AI systems without historical or social depth is actually superficial learning. And so we need to have a much more interdisciplinary, integrated approach to knowledge production and to observing and understanding patterns that don't simply rely on one discipline in order to map reality. >>The last provocation is this. If as I suggested at the start in the inequity is woven into the very fabric of our society. It's built into the design of our, our policies, our physical infrastructures, and now even our digital infrastructures. That means that each twist coil and code is a chance for us to weave new patterns, practices, and politics. The vastness of the problems that we're up against will be their undoing. Once we, that we are pattern makers. So what does that look like? It looks like refusing colorblindness as an anecdote to tech media discrimination, rather than refusing to see difference. Let's take stock of how the training data and the models that we're creating. Have these built in decisions from the past that have often been discriminatory. It means actually thinking about the underside of inclusion, which can be targeting and how do we create a more participatory rather than predatory form of inclusion. And ultimately it also means owning our own power in these systems so that we can change the patterns of the past. If we're, if we inherit a spiked bench, that doesn't mean that we need to continue using it. We can work together to design more, just an equitable technologies. So with that, I look forward to our conversation.

(bright music) >> Announcer: From around the globe, it's theCUBE with digital coverage of Dell Technologies World Digital Experience. Brought to you by Dell Technologies. >> Hello, and welcome back to theCUBE's virtual coverage of Dell Technologies World Digital Experience. I'm John Furrier, your host of theCUBE here for this interview. We're not face to face this year, we're remote because of the pandemic. We've got a great guest, CUBE alumni, John Roese who's the Global Chief Technology Officer at Dell Technologies. John, great to see you. Thank you for remoting in from New Hampshire. Thanks for your time and thanks for coming on. >> Oh, glad to be here. Glad to be here from New Hampshire. The travel is a lot easier this way so-- >> It's been an interesting time. What a year it's been with the pandemic, the good, bad, and the ugly has been playing out. But if you look at the role of technology, the big theme this year at Dell Technologies World is the digital transformation acceleration. Everyone is kind of talking about that, but when you unpack the technology side of it, you're seeing a technology enablement theme that is just unprecedented from an acceleration standpoint. COVID has forced people to look at things that they never had to look at before. Disruption to business models and business systems like working at home. (Furrier laughs) Who would have forecasted that kind of disruption. Workloads changing, workforces working differently with in the mid of things. So an absolute exposure to the core issues and challenges that need to be worked on and double down on. And some cases, projects that might not have been as a priority. So you have all of this going on, customers really trying to double down on the things that are working, the things they need to fix, so they can come out of the pandemic with a growth strategy with modern apps, with cloud and hybrid and multicloud. This has been a huge forcing function. I'd love to get your first reaction to that big wave. >> Yeah, no, no, I think as a technologist, sometimes you can see the future maybe a little clearer than the business people can. Because there's one thing about technology, it either is, or it isn't. Either is code or hardware and real or it's marketing. And we knew the technology evolution was occurring, we knew the multicloud world was real, we knew that machine intelligence was real. And we've been working on this for maybe decades. But prior to COVID, many of these areas were still considered risky or speculative. And people couldn't quite grok exactly why they wanted a machine doing work on their behalf or why they might want an AI to be a participant in their collaboration sessions or why they might want an autonomous vehicle at all. And we were talking about how many people autonomous vehicles that were going to kill as opposed to how many that we're going to help. Then we had COVID. And suddenly we realized that the fragility of our physical world and the need for digital is much higher. And so it's actually opened up an enormous accelerant on people's willingness to embrace new technologies. And so whether it's a predictable acceleration of machine intelligence or autonomous systems, or this realization that the cloud world is actually more than one answer, there's multiple clouds working together. Because if you try to do a digital transformation acceleration, you realize that it's not one problem. It's many, many problems all working together, and then you discover that, hey, some of these can be solved with cloud one and some can be solving with cloud two, and some of them you want to do in your own infrastructure, in a private cloud, and some might belong at the edge. And then suddenly you come to this conclusion that, hey, having strategy has to deal with this system as a system. And so across the board, COVID has been an interesting catalyst to get people to really think practically about the technology available to them and how they might be able to take advantage of it quicker. And that's a mixed blessing for us technologists because they want things sooner, and that means we have to do more engineering. But at the same time, open-minded consumers of technology are very helpful in digital transformations. >> Well, I want to unpack that rethinking with COVID and post COVID. I mean, everything is going to come down to before COVID and after COVID world. I think it's going to be the demarcation that's going to be looked at historically. Before we get into that though, I want to get your thoughts on some of the key pillars of these transformational technologies in play today. Last year at Dell World, when we were physically face to face, we were laying out on theCUBE and in our analysis, the Dell Technologies has got an end to end view. You saw a little bit at VMworld this year, the Project Monterey, is looking much more systematically across the board. You mentioned systems as consequences. The reaction of changes. But lay out for us the key areas, the key pillars of the transformational technologies that customers need to look at now to drive the digital path. >> Yeah, we cast a very wide net. We look at literally thousands of technologies, we organize them and we try to understand and predict which ones are going to matter. And it turns out that over the last couple of years, we figured out there's really six, what I'll call expanding technology areas that are actually probably likely to be necessary for almost any digital transformation. And they aren't exactly what people have been doing historically. So in no particular order, and they may sound obvious, but when you think about your future, it's very likely all six of these are going to touch you. The first is, the obvious one of being able to develop and deliver a multicloud. The cloud journey is by no means done. We are at like the second inning of a nine inning game, maybe even earlier. We have barely created the multiple cloud world, much less the true multicloud world, and then really exploiting and automating has work to be done. But that's a strategic area for us and everybody to navigate forward. In parallel to that, what we realize is that multiple cloud is no longer just present in data centers and public clouds, it's actually existing in the real world. So this idea of edge, the reconstituting of IT out in the real world to deliver the real time behavior necessary to actually serve what we predict will be about 70% of the world's data that will happen outside of data centers. The third is 5G. And that's a very specific technology, and I have a long telco background. I was the CTO of one of the largest telecom companies in the world and I was involved in 2G, 3G and 4G. (Furrier chuckles) 5G is not another G. It is not just faster 4G. It does that, but with things like massive machine type communication with having a million sensorized devices in a kilometer or ultra reliable, low latency communication. The ability to get preferential services to critical streams of data across the infrastructure, mobile edge compute, putting the edge IT out into the cellular environment. And the fact that it's built in the cloud and IT era. So it's programmable, software defined. 5G is going to go from being an outside of the IT discussion to being the fabric inside the IT discussion. And so I will bet that anybody who has people in the real world and that they're trying to deliver a digital experience, will have to take advantage of the capabilities of 5G to do it right. But super strategic important area for Dell and for our industry. Continuing on, we have the data world, the data management world. It's funny, we've been doing data as an industry for a very long time, but the world we were in was the data at rest world, databases, data lakes, traditional applications. And that's great. It still matters, but this new world of data in motion is beginning. And what that means is the data is now moving into pipelines. We're not moving it somewhere and then figuring it out, we're figuring it out as the data flows across this multicloud environment. And that requires an entirely different tool, chain, architecture and infrastructure. But it's incredibly important because it's actually the thing that powers most digital transformation if they're real time. In parallel to that, number five on the list is AI and machine learning. And we have a controversial view on this. We don't view AI as purely a technology. It clearly is a technology, but what we really think customers should think about it as is as a new class of user. Because AIs are actually some of the most aggressive producers and consumers of data and consumers of IT infrastructure. We actually estimate that within the next four or five years, the majority of IT capacity in an enterprise environment will actually be consumed at the behest of the machine learning algorithm or an AI system than a traditional application or person. And all you have to do is do one AI project to understand that I'm correct, because they are just massive demand drivers for your infrastructure, but they have massive return on that demand. They give you things you can't do without them. And then last on the list is this area of security. And to be candid, we have really messed up this area as an industry. We have a security product for every problem, we have proliferation of security technologies. And to make matters worse, we now operate most enterprises on the assumption the bad guys are already inside and we're doing things to prevent them from causing harm. Now, if that's all it is, we really lost this one. So we have an obligation to reverse this trend, to start moving back to embedding the security and the infrastructure with intrinsic security, with zero trust models, with things like SASSY, which is basically creating new models of the edge security paradigm to be more agile and software defined. But most importantly, we have to pull it all together and say, "You know what we're really measuring is the trustworthiness "of the systems we work with, "not the individual components." So this elevation of security to trust is going to be a big journey for all of us. And every one of those six are individual areas, but when you combine them, they actually describe the foundation of a digital transformation. And so it's important for people to be aware of them, it's important for companies like Dell to be very active in all of them, because ultimately what you have today, plus those six properly executed, is the digital transformation outcome that most people are heading towards. >> You just packed it all six pillars into one soundbite. That was awesome. Great insight there. One of the things that's interesting, you mentioned AI. I love that piece around AI being a consumer. They are a consumer of data, they're also a consumer of what used to be handled by either systems or humans. That's interesting. 5G is another one. Pat Gelsinger has said at VMworld that 5G, and when I interviewed him he said 5G is a business app, not a consumer app. Yet, if you look at the recent iPhone announcement by Apple, iPhone 12, and iPhone 12 Pro, 5G is at the center of that announcement. But they're taking it from a different perspective. That's a real world application. They've got the watch, they have new chips in their devices, huge advantage. It's not just bandwidth. And remember the original iPhone launch with 3G if you remember. That made the iPhone. Some are saying if it didn't have the 3G or 2G and 3G, I think it was 3G in the first iPhone. 3G, it would have not been as successful. So again, Apple is endorsing 5G. Gelsinger talks about it as a business app. Double down on that, because I think 5G will highlight some of the COVID issues because people are working at home. They're on the go. They want to do video conferencing. Maybe they want to do this programmable. Unpack the importance of 5G as an enabler and as an IT component. >> Yeah. As I mentioned, 5G isn't just about enhanced mobile broadband which is faster YouTube. It's about much more than that. And because of that combination of technologies, it becomes the connective tissue for almost every digital transformation. So our view by the way, just to give you the Dell official position, we actually view that the 5G or the telecom industry is going through three phases around 5G. The first phase has already happened. It was an early deployment of 5G using traditional technology. It was just 5G as an extension of the 4G environment. That's great, it's out there. There's a phase that we're in right now, which I call the geopolitical phase, where all of a sudden, everybody from companies to countries to industries have realized this is really important. And we have to figure out how to make sure we have a secure source of supply that is based on the best technology. And that has created an interest by people like Dell and VMware and Microsoft, and many other companies to say, "Wait a minute. "This isn't just a telecom thing. "This is, as Pat said a business system. "This is part of the core of all digital." And so that's pulled people like Dell and others more aggressively into the telecom world in this middle phase. But what really is happening is the third phase. And the third phase is a recasting of the architecture of telecom to make it much more like the cloud and IT world. To separate hardware from software, to implemented software defined principles, to putting machine interfaces, to treat it like a cloud and IT system architecturally. And that's where things like OpenRAN, integrated open networks, and these new initiatives are coming into play. All of that from Dell perspective is fantastic because what it says is the telecom world is heading towards companies like us. And so, as you may know, we set up a brand new telecom business at scale up here to our other businesses this year. We already are doing billions of dollars in telecom, but now we believe we should be playing a meaningful tier-one role in this modern telecom ecosystem. It will be a team sport. There's lots of other players we have to work with. But because of the breadth of applications of 5G. And whether it's again, an iPhone with 5G is great to do YouTube, but it's incredibly powerful if you run your business applications on there, and what you want to actually deliver is an immersive augmented experience. So without 5G, it will be very hard to do that. So it becomes a new and improved client. We announced a Latitude 9,000 Series, and we're one of the first to put out a 5G enabled laptop. In certain parts of the world, we're now starting to ship these. Well again, when you have access to millimeter wave and gigabit speed capacity, you can do some really interesting things on that device, more oriented towards what we call collaborative computing which the client device and the adjacent infrastructure have so much bandwidth between them, that they look like one system. And they can share the burden of augmented reality, of data processing, of AI processing all in the real time domain. Carry that a little further, and when we get into the areas like healthcare transformation or educational transformation. What we realize immediately is reach is everything. You want to have a premium broadband experience, and you need a better system to do that. But really the thing that has to happen is not just a Zoom call, but an immersive experience in which a combination of low bandwidth, always on sensors are able to send their data streams back. But also, if you want to have a more immersive experience to really exploit your health situation, being able to do it with holography and other tools, which require a lot more bandwidth is critical. So no matter where you go in a digital transformation in the real world that has real people and things out in the real world involved in it, the digital fabric for connectivity is critical. And you suddenly realize the current architecture's pre-5G aren't sufficient. And so 5G becomes this linchpin to basically make sure that the client and the cloud and the data center all have a framework that they can actually work together without, let's call it a buffering resistance between them called the network. Imagine if the network was an enabler, not an impediment. >> Yeah, I think you're on point here. I think this is really teases out to me the next-gen business transformation, digital transformation because if you think about what you just talked about, connective tissue, linchpin with 5G, data as a driver, multicloud, the six pillars you laid out, and you mentioned systems, connective tissue systems. I mean, you're basically talking tech under the hood like operating system mindset. These systems design are interesting. If you put the pieces together, you can create business value. Not so much speeds and feeds, business value. You mentioned telco cloud. I find that fascinating. I've been saying on theCUBE for years, and I think it's finally playing out. I want to get your reactions of this is, this rise of the specialty cloud. I called it tier-one on the power law kind of the second wave of cloud. Look at Snowflake. They went public. Biggest IPO in the history of the New York Stock Exchange of Wall Street, second to VMware. They built on Amazon. (Furrier laughs) Okay. You have the telco cloud, we have theCUBE cloud, we have the media cloud. So you're seeing businesses looking at the cloud as a business model opportunity, not just buying gear to run something faster, right? So you're getting at something here where it's real benefits are now materializing and are now visible. First of all, do you agree with that? I'm sure you do. I'd love to get your thoughts on that. And if you do, how do companies put this together? Because you need software, you got to have the power source with cloud. What's your reaction to that? >> Absolutely. I think, now obviously there are many clouds. We have some mega clouds out there and then we have lots of other specialty clouds. And by the way, sometimes you remember we view cloud as an operating model, an experience, a way to present an IT service. How it's implemented is less important than what it looks like to the user. Your example of Snowflake. I don't view Snowflake as AWS. I view Snowflake as a storage business. (Furrier chuckles) >> It's a business. >> It's a business cloud. I mean, they could lift it up and move it onto another cloud infrastructure and still be Snowflake. So, as we look forward, we do see more of the consumables that we're going to use and digital transformation appearing as these cloud services. Sometimes they're SaaS cloud, sometimes they're an infrastructure cloud, sometimes they're a private cloud. One of the most interesting ones though that we see that hasn't happened yet is the edge clouds that are going to form. Edge is different. It's in the real time domain, it's distributed. If you do it at scale, it might look like massive amounts of capacity, but it isn't infinite in one place. Public cloud is infinite capacity all in one place. An edge cloud is infinite capacity distributed across 50,000 points of presence at which each of them has a finite amount of capacity. And the other difference though, is that edge clouds tend to live in the real time domain. So 30 millisecond round trip latency. Well, the reason this one's exciting to me is that when you think about what happened at the software and business model innovation, when for instance public clouds and even co-location became more accessible, companies who had this idea that needed a very large capacity of infrastructure that could be consumed as a service suddenly came into existence. Salesforce.com go through the laundry list. But all of those examples were non-real time functions because the clouds they were built on were non-real time clouds if you take them in the end to end, in the system perspective. We know that there are going to be both from the telecom operators and from cloud providers and co-location providers, and even enterprises, a proliferation of infrastructure out in the real time domain called edges. And those are going to be organized and delivered as cloud services. They're going to be pools of flexible elastic capacity. What excites me is suddenly we're going to spawn a level of innovation, where people who had this great idea that they needed to access cloud light capacity, but they ran into the problem that the capacity was too far away from the time domain they needed to operate. And we've already seen some examples of this in AR and VR. Autonomous vehicles require a real time cloud near the car, which doesn't exist yet. When we think about things like smart cities and smart factories, they really need to have that cloud capacity in the time domain that matters if they want to be a real time control system. And so, I don't know exactly what the innovation is going to be, but when you see a new capability show up, in this case, it's inevitable that we're going to see pools of elastic, consumable capacity in the real time environment as edges start to form. It's going to spawn another innovation cycle that could be as big as what happened in the public cloud environment for non-real time. >> Well, I think that's a great point in time series. Databases for one would be one instant innovation. You mentioned data, data management, time is valuable to the latency and this maybe not viable after if you're a car, right? So you pass them. So again, all different concepts. And the one thing that, first of all, I agree with you on this whole cloud thing. A nice edge cloud is going to develop nicely. But the question there is it's going to be software defined, agreed. Security, data, you've got databases, you've got software operated. You mentioned security being broken, and security product for every problem. And you want to bake it in, intrinsic or whatever you call it these days. How do you get the security model? Because you've got access. Do you federate that? How do you build in security at that level? Whether it's a space satellite or a moving vehicle, the edge is the edge. So what's your thoughts on security as you're looking at this mobility, this agility is horizontally scalable distributed system. What's the security paradigm? >> Well the first thing, it has nothing to do with security, but impacts your security outcome in a meaningful way when you talk about the edge. And that is, we have got to stop getting confused that an edge is a single monolithic thing. And we have got to start understanding that an edge is actually a combination of two things. It is a platform that will provide the capacity and a workload that will do the job, the code. And today, what we find is many people are advocating for edges are actually delivering an end to end stack that includes bespoke hardware, its infrastructure, and the workloads and capabilities. If that happens, we end up with 1,000 black boxes that all do one thing, which doesn't make any sense out in the real world. So the minute you shift to what the edge is really going to be, which is a combination of edge platforms and edge workloads, you start your journey towards a better security model. First thing that happens is you can secure and make a high integrity the edge platform. You can make sure that that platform has a hardware to trust, that it operates potentially in a zero trust model, that it has survivability and resiliency, but it doesn't really care what's running on it as much as it has to be stable. Now if you get that one right, now at least you have a stable platform between your public and private environments and the edge. At the workload level though, now you have to think about, well, edge workloads actually should not be bloated. They should not be extremely large scale because there's not enough capacity at the edge. So concepts like SASSY is a good example, which is one of the analyst firms that coined that term. But I like the concept, which is, hey, what if at the edge you're delivering the workload, but the workload is protected by a bunch of cloud-oriented security services that effectively are presented as part of the service chain? So you don't have to have your own firewall built into every workload because you're in an edge architecture, you can use virtual firewalling that's coming to you as a software service, or you can use the SDN, the service chain it into the networking path, and then you can provide deep packet inspection and other services. It all goes back to this idea that, when you deal with the edge, first and foremost, you have to have a reliable stable platform to guarantee a robust foundation. And that is an infrastructure security problem. But then you have to basically deal with the security problems of the workload in a different way than you do it in a data center. In a data center, you have infinite computing. You can put all kinds of appendages on your code, and it's fine because there's just more compute next to you. In the edge, we have to keep the code pure. It has to be an analytics engine or an AI engine for systems control in a factory. And the security services actually have to be a function of the end to end path. More likely delivered as software services slightly upstream. That architectural shift is not something people have figured out yet. But if we get it right, now we actually have a modern, zero trust distributed, software defined, service changeable, dynamic security architecture, which is a much better approach to an intrinsic security than trying to just hard-code the security into the workload and tie it to the platform which never has worked. So we're going to have to have a pretty big rethink to get through this. But for me, it's pretty clear what we have to do. >> Now I'd say that's good observation. Great insight. I'll just double down and ask a followup on that. I get that. I see where you're going with that software defined, software operated service. I love the SASSY concept. We've covered it. But the edge is still purpose-built devices. I mean, we've talked about an iPhone, and you're talking about a watch, you're talking about a space module, whatever it is at the edge on a tower, it could be a radio. I mean, whatever it is, you seem to have purpose-built hardware. You mentioned this root of trust. That'll kind of never kind of go away. You're going to have that. What's your thoughts on that as someone who realizes I got to harden the edge, at least from a hardware standpoint, but I want to be enabled for self-defined. I don't want to have a product be purpose-built and then be obsolete in a year. Because that's again the challenge of supply chain management, building hardware. What's your thoughts on that? >> Yeah. Our edge strategy, we double click a little bit is different than the strategy to build for a data center. We want consistency between them, but there's actually five areas of edge that actually are specific to it. The first is the hardware platform itself. Edge hardware platforms are different than the platforms you put in data centers, whether it be a client or the infrastructure underneath it. And so we're already building hardened devices and devices that are optimized for power and cooling and space constraints in that environment. The second is the runtime on that system is likely to be different. Today we use the V Cloud Foundation where that works very well, but as you get smaller and smaller and further away, you have to miniaturize and reduce the footprint. The control plane, we would like to make that consistent. We are using Tanzu and Dell Technologies Cloud Platform to extend out to the edge. And we think that having a consistent control plan is important, but the way you adapt something like Tanzu from the edge is different because it's in a different place. The fourth is life cycle, which is really about how you secure, how you deploy, how you deal with day two operations. There's no IT person out at the edge, so you're not in a data center. So you have to automate those systems and deal with them in a different way. And then lastly, the way you package an edge solution and deliver it is much different than the way you build a data center. You actually don't want to deal with those four things I just described as individual snowflakes. You want them packaged and delivered as an outcome. And that's why more and more of the edge platform offerings are really cloudlets or they're a platform that you can use to extend your IT capacity without having to think about Kubernetes versus VMs versus other things. It's just part of the infrastructure. So all of that tells us that edge is different enough, that the way you designed for it, the way you implement it, and even the life cycle, it has to take into account that it's not in a data center. The trick is to then turn that into an extended multicloud where the control plane is consistent, or when you push code into production with Kubernetes, you can choose to land that container in a data center or push it out to the edge. So you have both a system consistency goal, but also the specialization of the edge environment. Everything from hardware, to control plane, to lifecycle, that's the reality of how these things have to be built. >> That's a great point. It's a systems architecture, whether you're looking at from the bottoms up component level to top down kind of policy and or software defined. So great insight. I wish we had more time. I'd love to get you back and talk about data. We were talking before you came on camera about data. But quickly before we go, your thoughts on AI and the consequences of AI. AI is a consumer. I love that insight. Totally agree. Certainly it's an application. Technology is kind of horizontal. It can be vertically specialized with data. What's your thoughts on how AI can be better for society and some of the unintended consequences that we manage that. >> Yeah, I'm an optimist. I actually, we've worked with enough AI systems for long enough to see the benefit. Every one of Dell's products today has machine intelligence inside of it. So we can exceed the potential of its hardware and software without it. It's a very powerful tool. And it does things that human beings just simply can't do. I truly believe that it's the catalyst for the next wave of business process functionality, of new innovation. So it's definitely not something to stay away from. That being said, we don't know exactly how it can go wrong. And we know that there are examples where corrupted or bad bias data could influence it and have a bad outcome. And there are an infinite set of problems to go solve with AI, but there are ones that are a little dangerous to go pursue if you're not sure. And so our advice to customers today is, look, you do not need to build The Terminator to get advantage from AI. You can do something much simpler. In fact, in most enterprise context, we believe that the best path is go look at your existing business processes, where there is a decision that's made by a human being, and it's an inefficient decision. And if you can locate those points where a supply chain decision or an engineering decision or a testing decision is done by human beings poorly, and you can use machine intelligence to improve it by five or 10%, you will get a significant material impact on your business if you go after the right processes. At Dell we're doing a ton of AI and machine learning in our supply chain. Why is that important? Well, we happen to have the largest tech supply chain in the world. If we improve it by 1%, it's a gigantic impact on the company. And so our advice to people is you don't have to build man autonomous car. You don't have to build The Terminator. You can apply it much more tactically in spaces that are much safer. Even in the HR examples, we tell our HR people, "Hey, use it for things like performance management "and simplifying the processing of data. "Don't use it to hire a bot." That's a little dangerous right now. Because you might inadvertently introduce racism or sexism into that, and we still have some work to do there. So it's a very large surface area. Go where the safe areas are. It'll keep you busy for the next several years, improving your business in dramatic ways. And as we improve the technology for bias correction and management of AI systems and fault tolerance and simplicity, then go after the hard one. So this is a great one. Go after the easy stuff. You'll get a big benefit and you won't take the risk. >> You get the low hanging fruit learn, iterate through it. I'm glad you guys are using machine learning and AI in the supply chain. Make sure it's secure, big issue. I know you guys were on top of it and have a great operation there. John, great to have you on. John Roese, the Global Chief Technology Officer at Dell Technologies. Great to have you on. Take a minute to close out the last minute here. What's the most important story from Dell Technologies World this year? I know it's virtual. It's not face to face. But beyond that, what's the big takeaway in your mind, if you could share one point, what would it be for the folks watching? >> Yeah, I think the biggest point is something we talked about, which is we are in a period of digital transformation acceleration. COVID is bad, but it woke us up to the possibilities and the need for digital transformation. And so if you were on the fence or if you're moving slowly and now you have an opportunity to move fast. However, moving fast is hard if you try to do it by yourself. And so we've structured Dell, we've the six big areas we're focused on. They only have one purpose, it's to build the modern infrastructure platforms to enable digital transformation to happen faster. And my advice to people is, great. You're moving faster. Pick your partners well. Choose the people that you want to go on the journey with. And we think we're well positioned for that. And you will have much better progress if you take a broad view of the technology ecosystem and you've lightened up the appropriate partnerships with the people that can help you get there. And the outcome is a successful digital leader just is going to handle things like COVID and ease disruption better than a digital laggard. And we now have the data to prove that. So it's all about digital acceleration is the punchline. >> Well great to have you on. Great segment, great insight. And thank you for sharing the six pillars and the conversation. Super relevant on what's going on to create new business value, new opportunities for businesses and society. I'm John Furrier with theCUBE. Thanks for watching. (bright music)

>>Hi, everyone. My name is my uncle. My uncle Poor I worked with Geo reminds you in India. We call ourselves Geo Platforms. Now on. We've been recently in the news. You've raised a lot off funding from one of the largest, most of the largest tech companies in the world. And I'm here to talk about Geos Cloud Journey, Onda Mantis Partnership. I've titled it the story often, Underdog becoming the largest telecom company in India within four years, which is really special. And we're, of course, held by the cloud. So quick disclaimer. Right. The content shared here is only for informational purposes. Um, it's only for this event. And if you want to share it outside, especially on social media platforms, we need permission from Geo Platforms limited. Okay, quick intro about myself. I am a VP of engineering a geo. I lead the Cloud Services and Platforms team with NGO Andi. I mean the geo since the beginning, since it started, and I've seen our cloud footprint grow from a handful of their models to now eight large application data centers across three regions in India. And we'll talk about how we went here. All right, Let's give you an introduction on Geo, right? Giorgio is on how we became the largest telecom campaign, India within four years from 0 to 400 million subscribers. And I think there are There are a lot of events that defined Geo and that will give you an understanding off. How do you things and what you did to overcome massive problems in India. So the slide that I want to talkto is this one and, uh, I The headline I've given is, It's the Geo is the fastest growing tech company in the world, which is not a new understatement. It's eggs, actually, quite literally true, because very few companies in the world have grown from zero to 400 million subscribers within four years paying subscribers. And I consider Geo Geos growth in three phases, which I have shown on top. The first phase we'll talk about is how geo grew in the smartphone market in India, right? And what we did to, um to really disrupt the telecom space in India in that market. Then we'll talk about the feature phone phase in India and how Geo grew there in the future for market in India. and then we'll talk about what we're doing now, which we call the Geo Platforms phase. Right. So Geo is a default four g lt. Network. Right. So there's no to geo three g networks that Joe has, Um it's a state of the art four g lt voiceover lt Network and because it was designed fresh right without any two D and three G um, legacy technologies, there were also a lot of challenges Lawn geo when we were starting up. One of the main challenges waas that all the smart phones being sold in India NGOs launching right in 2000 and 16. They did not have the voice or lt chip set embedded in the smartphone because the chips it's far costlier to embed in smartphones and India is a very price and central market. So none of the manufacturers were embedding the four g will teach upset in the smartphones. But geos are on Lee a volte in network, right for the all the network. So we faced a massive problem where we said, Look there no smartphones that can support geo. So how will we grow Geo? So in order to solve that problem, we launched our own brand of smartphones called the Life um, smartphones. And those phones were really high value devices. So there were $50 and for $50 you get you You At that time, you got a four g B storage space. A nice big display for inch display. Dual cameras, Andi. Most importantly, they had volte chip sets embedded in them. Right? And that got us our initial customers the initial for the launch customers when we launched. But more importantly, what that enabled other oh, EMS. What that forced the audience to do is that they also had to launch similar smartphones competing smartphones with voltage upset embedded in the same price range. Right. So within a few months, 3 to 4 months, um, all the other way EMS, all the other smartphone manufacturers, the Samsung's the Micromax is Micromax in India, they all had volte smartphones out in the market, right? And I think that was one key step We took off, launching our own brand of smartphone life that helped us to overcome this problem that no smartphone had. We'll teach upsets in India and then in order. So when when we were launching there were about 13 telecom companies in India. It was a very crowded space on demand. In order to gain a foothold in that market, we really made a few decisions. Ah, phew. Key product announcement that really disrupted this entire industry. Right? So, um, Geo is a default for GLT network itself. All I p network Internet protocol in everything. All data. It's an all data network and everything from voice to data to Internet traffic. Everything goes over this. I'll goes over Internet protocol, and the cost to carry voice on our smartphone network is very low, right? The bandwidth voice consumes is very low in the entire Lt band. Right? So what we did Waas In order to gain a foothold in the market, we made voice completely free, right? He said you will not pay anything for boys and across India, we will not charge any roaming charges across India. Right? So we made voice free completely and we offer the lowest data rates in the world. We could do that because we had the largest capacity or to carry data in India off all the other telecom operators. And these data rates were unheard off in the world, right? So when we launched, we offered a $2 per month or $3 per month plan with unlimited data, you could consume 10 gigabytes of data all day if you wanted to, and some of our subscriber day. Right? So that's the first phase off the overgrowth and smartphones and that really disorders. We hit 100 million subscribers in 170 days, which was very, very fast. And then after the smartphone faith, we found that India still has 500 million feature phones. And in order to grow in that market, we launched our own phone, the geo phone, and we made it free. Right? So if you take if you took a geo subscription and you carried you stayed with us for three years, we would make this phone tree for your refund. The initial deposit that you paid for this phone and this phone had also had quite a few innovations tailored for the Indian market. It had all of our digital services for free, which I will talk about soon. And for example, you could plug in. You could use a cable right on RCR HDMI cable plug into the geo phone and you could watch TV on your big screen TV from the geophones. You didn't need a separate cable subscription toe watch TV, right? So that really helped us grow. And Geo Phone is now the largest selling feature phone in India on it. 100 million feature phones in India now. So now now we're in what I call the geo platforms phase. We're growing of a geo fiber fiber to the home fiber toe the office, um, space. And we've also launched our new commerce initiatives over e commerce initiatives and were steadily building platforms that other companies can leverage other companies can use in the Jeon o'clock. Right? So this is how a small startup not a small start, but a start of nonetheless least 400 million subscribers within four years the fastest growing tech company in the world. Next, Geo also helped a systemic change in India, and this is massive. A lot of startups are building on this India stack, as people call it, and I consider this India stack has made up off three things, and the acronym I use is jam. Trinity, right. So, um, in India, systemic change happened recently because the Indian government made bank accounts free for all one billion Indians. There were no service charges to store money in bank accounts. This is called the Jonathan. The J. GenDyn Bank accounts. The J out off the jam, then India is one of the few countries in the world toe have a digital biometric identity, which can be used to verify anyone online, which is huge. So you can simply go online and say, I am my ankle poor on duh. I verify that this is indeed me who's doing this transaction. This is the A in the jam and the last M stands for Mobil's, which which were held by Geo Mobile Internet in a plus. It is also it is. It also stands for something called the U. P I. The United Unified Payments Interface. This was launched by the Indian government, where you can carry digital transactions for free. You can transfer money from one person to the to another, essentially for free for no fee, right so I can transfer one group, even Indian rupee to my friend without paying any charges. That is huge, right? So you have a country now, which, with a with a billion people who are bank accounts, money in the bank, who you can verify online, right and who can pay online without any problems through their mobile connections held by G right. So suddenly our market, our Internet market, exploded from a few million users to now 506 106 100 million mobile Internet users. So that that I think, was a massive such a systemic change that happened in India. There are some really large hail, um, numbers for this India stack, right? In one month. There were 1.6 billion nuclear transactions in the last month, which is phenomenal. So next What is the impact of geo in India before you started, we were 155th in the world in terms off mobile in terms of broadband data consumption. Right. But after geo, India went from one 55th to the first in the world in terms of broadband data, largely consumed on mobile devices were a mobile first country, right? We have a habit off skipping technology generation, so we skip fixed line broadband and basically consuming Internet on our mobile phones. On average, Geo subscribers consumed 12 gigabytes of data per month, which is one of the highest rates in the world. So Geo has a huge role to play in making India the number one country in terms off broad banded consumption and geo responsible for quite a few industry first in the telecom space and in fact, in the India space, I would say so before Geo. To get a SIM card, you had to fill a form off the physical paper form. It used to go toe Ah, local distributor. And that local distributor is to check the farm that you feel incorrectly for your SIM card and then that used to go to the head office and everything took about 48 hours or so, um, to get your SIM card. And sometimes there were problems there also with a hard biometric authentication. We enable something, uh, India enable something called E K Y C Elektronik. Know your customer? We took a fingerprint scan at our point of Sale Reliance Digital stores, and within 15 minutes we could verify within a few minutes. Within a few seconds we could verify that person is indeed my hunk, right, buying the same car, Elektronik Lee on we activated the SIM card in 15 minutes. That was a massive deal for our growth. Initially right toe onboard 100 million customers. Within our and 70 days. We couldn't have done it without be K. I see that was a massive deal for us and that is huge for any company starting a business or start up in India. We also made voice free, no roaming charges and the lowest data rates in the world. Plus, we gave a full suite of cloud services for free toe all geo customers. For example, we give goTV essentially for free. We give GOTV it'll law for free, which people, when we have a launching, told us that no one would see no one would use because the Indians like watching TV in the living rooms, um, with the family on a big screen television. But when we actually launched, they found that GOTV is one off our most used app. It's like 70,000,080 million monthly active users, and now we've basically been changing culture in India where culture is on demand. You can watch TV on the goal and you can pause it and you can resume whenever you have some free time. So really changed culture in India, India on we help people liver, digital life online. Right, So that was massive. So >>I'm now I'd like to talk about our cloud >>journey on board Animal Minorities Partnership. We've been partners that since 2014 since the beginning. So Geo has been using open stack since 2014 when we started with 14 note luster. I'll be one production environment One right? And that was I call it the first wave off our cloud where we're just understanding open stack, understanding the capabilities, understanding what it could do. Now we're in our second wave. Where were about 4000 bare metal servers in our open stack cloud multiple regions, Um, on that around 100,000 CPU cores, right. So it's a which is one of the bigger clouds in the world, I would say on almost all teams, with Ngor leveraging the cloud and soon I think we're going to hit about 10,000 Bama tools in our cloud, which is massive and just to give you a scale off our network, our in French, our data center footprint. Our network introduction is about 30 network data centers that carry just network traffic across there are there across India and we're about eight application data centers across three regions. Data Center is like a five story building filled with servers. So we're talking really significant scale in India. And we had to do this because when we were launching, there are the government regulation and try it. They've gotten regulatory authority of India, mandates that any telecom company they have to store customer data inside India and none of the other cloud providers were big enough to host our clothes. Right. So we we made all this intellectual for ourselves, and we're still growing next. I love to show you how we grown with together with Moran says we started in 2014 with the fuel deployment pipelines, right? And then we went on to the NK deployment. Pipelines are cloud started growing. We started understanding the clouds and we picked up M C p, which has really been a game changer for us in automation, right on DNA. Now we are in the latest release, ofem CPM CPI $2019 to on open stack queens, which on we've just upgraded all of our clouds or the last few months. Couple of months, 2 to 3 months. So we've done about nine production clouds and there are about 50 internal, um, teams consuming cloud. We call as our tenants, right. We have open stack clouds and we have communities clusters running on top of open stack. There are several production grade will close that run on this cloud. The Geo phone, for example, runs on our cloud private cloud Geo Cloud, which is a backup service like Google Drive and collaboration service. It runs out of a cloud. Geo adds G o g S t, which is a tax filing system for small and medium enterprises, our retail post service. There are all these production services running on our private clouds. We're also empaneled with the government off India to provide cloud services to the government to any State Department that needs cloud services. So we were empaneled by Maiti right in their ego initiative. And our clouds are also Easter. 20,000 certified 20,000 Colin one certified for software processes on 27,001 and said 27,017 slash 18 certified for security processes. Our clouds are also P our data centers Alsop a 942 be certified. So significant effort and investment have gone toe These data centers next. So this is where I think we've really valued the partnership with Morantes. Morantes has has trained us on using the concepts of get offs and in fries cold, right, an automated deployments and the tool change that come with the M C P Morantes product. Right? So, um, one of the key things that has happened from a couple of years ago to today is that the deployment time to deploy a new 100 north production cloud has decreased for us from about 55 days to do it in 2015 to now, we're down to about five days to deploy a cloud after the bear metals a racked and stacked. And the network is also the physical network is also configured, right? So after that, our automated pipelines can deploy 100 0 clock in five days flight, which is a massive deal for someone for a company that there's adding bear metals to their infrastructure so fast, right? It helps us utilize our investment, our assets really well. By the time it takes to deploy a cloud control plane for us is about 19 hours. It takes us two hours to deploy a compu track and it takes us three hours to deploy a storage rack. Right? And we really leverage the re class model off M C. P. We've configured re class model to suit almost every type of cloud that we have, right, and we've kept it fairly generous. It can be, um, Taylor to deploy any type of cloud, any type of story, nor any type of compute north. Andi. It just helps us automate our deployments by putting every configuration everything that we have in to get into using infra introduction at school, right plus M. C. P also comes with pipelines that help us run automated tests, automated validation pipelines on our cloud. We also have tempest pipelines running every few hours every three hours. If I recall correctly which run integration test on our clouds to make sure the clouds are running properly right, that that is also automated. The re class model and the pipelines helpers automate day to operations and changes as well. There are very few seventh now, compared toa a few years ago. It very rare. It's actually the exception and that may be because off mainly some user letter as opposed to a cloud problem. We also have contributed auto healing, Prometheus and Manager, and we integrate parameters and manager with our even driven automation framework. Currently, we're using Stack Storm, but you could use anyone or any event driven automation framework out there so that it indicates really well. So it helps us step away from constantly monitoring our cloud control control planes and clothes. So this has been very fruitful for us and it has actually apps killed our engineers also to use these best in class practices like get off like in France cord. So just to give you a flavor on what stacks our internal teams are running on these clouds, Um, we have a multi data center open stack cloud, and on >>top of that, >>teams use automation tools like terra form to create the environments. They also create their own Cuba these clusters and you'll see you'll see in the next slide also that we have our own community that the service platform that we built on top of open stack to give developers development teams NGO um, easy to create an easy to destroy Cuban. It is environment and sometimes leverage the Murano application catalog to deploy using heats templates to deploy their own stacks. Geo is largely a micro services driven, Um um company. So all of our applications are micro services, multiple micro services talking to each other, and the leverage develops. Two sets, like danceable Prometheus, Stack stone from for Otto Healing and driven, not commission. Big Data's tax are already there Kafka, Patches, Park Cassandra and other other tools as well. We're also now using service meshes. Almost everything now uses service mesh, sometimes use link. Erred sometimes are experimenting. This is Theo. So So this is where we are and we have multiple clients with NGO, so our products and services are available on Android IOS, our own Geo phone, Windows Macs, Web, Mobile Web based off them. So any client you can use our services and there's no lock in. It's always often with geo, so our sources have to be really good to compete in the open Internet. And last but not least, I think I love toe talk to you about our container journey. So a couple of years ago, almost every team started experimenting with containers and communities and they were demand for as a platform team. They were demanding community that the service from us a manage service. Right? So we built for us, it was much more comfortable, much more easier toe build on top of open stack with cloud FBI s as opposed to doing this on bare metal. So we built a fully managed community that a service which was, ah, self service portal, where you could click a button and get a community cluster deployed in your own tenant on Do the >>things that we did are quite interesting. We also handle some geo specific use cases. So we have because it was a >>manage service. We deployed the city notes in our own management tenant, right? We didn't give access to the customer to the city. Notes. We deployed the master control plane notes in the tenant's tenant and our customers tenant, but we didn't give them access to the Masters. We didn't give them the ssh key the workers that the our customers had full access to. And because people in Genova learning and experimenting, we gave them full admin rights to communities customers as well. So that way that really helped on board communities with NGO. And now we have, like 15 different teams running multiple communities clusters on top, off our open stack clouds. We even handle the fact that there are non profiting. I people separate non profiting I peoples and separate production 49 p pools NGO. So you could create these clusters in whatever environment that non prod environment with more open access or a prod environment with more limited access. So we had to handle these geo specific cases as well in this communities as a service. So on the whole, I think open stack because of the isolation it provides. I think it made a lot of sense for us to do communities our service on top off open stack. We even did it on bare metal, but that not many people use the Cuban, indeed a service environmental, because it is just so much easier to work with. Cloud FBI STO provision much of machines and covering these clusters. That's it from me. I think I've said a mouthful, and now I love for you toe. I'd love to have your questions. If you want to reach out to me. My email is mine dot capulet r l dot com. I'm also you can also message me on Twitter at my uncouple. So thank you. And it was a pleasure talking to you, Andre. Let let me hear your questions.