Okay, welcome back to theCUBE's coverage here on location at Las Vegas for AWS re-invent 2023. I'm John Furrier, your host, Dave Vellantez here. He's with the analysts, he's getting more content there. We have two great guests coming on theCUBE here to break down the generative AI wave from a technology platform perspective, infrastructure, and from an entrepreneurial perspective, as well as a venture council. We've got two great guests. We've got the entrepreneur and a VC in the same segment, two CUBE alumni's, John Chouro's partner at Madrona and Luis says CEO of OctoML, both CUBE alumni. It's great to see you guys. Thanks for coming on. Great to see you. Great to see you, John. So OctoUXM was on, John. So I should have said that you know a lot about AWS being a leader and building products there. Now at Madrona, you're funding builders. As we got a founder here, been on many times. So we're in a dynamic where you're already in market with some funding. It's a pretty good funding. It's raised, what, 800 million plus? Yeah, a little bit more. So you've got some good funding. So you're in market, but now a new wave is coming in, of the entrepreneurs. And then, you know, this re-invent really speaks volumes to this next wave of GenAI. So you've been preparing for this. Got the picks and shovels. You've been funding it. What's the current state right now? Because you have people in the market. Is there a line where, if you're not in it a certain way or you're coming in, is there a table stakes to build successfully? What's the current appetite for that entrepreneurial readiness? If you had to peg that, Luis. What would you say? Wow, there's just so much. First of all, it's changing so fast, right? So the new capabilities coming from models happening almost on a daily basis, you know, the platforms evolving fast, right? So I think there's, you know, pretty clear trends now that folks want to build prototype quickly and see what works and then, you know, scale that out and put in production. We're seeing clear trends in, you know, no single model rules them all. You know, people bringing with a collection of models, say model cocktails that together form something special, right, so distance would be a popular term here. Model cocktails.  It's a great testament that, hey, open source models have capabilities that people want, right?  So, Jensen on stage, you know, talking about the new instance types, but also DGX Cloud being offered at AWS, you know, Adam talking about Trainium 2, and which is all fantastic because bringing more resource for folks to use and build awesome applications, but also more infrastructure complexity to manage, right? So, I'm a- More horsepower, more complexity with the AI workloads. Exactly. And the- For a believer that we need to manage, they make it very easy for folks to build with. I think that's one of the- I want to get into the ease of use, John. We were talking about last night about ease of use and this waiver on, but you mentioned speed. You guys have just announced in one month two major things. You did image and text products just in one month. You shipped. Yeah, we did. And we've seen people already building with it, which is really, really rewarding and awesome, right? So we have, you know, the fastest implementations of stable diffusion and Lama 2 and Mistral offerings on the market. You know, I'm not saying this just to brag, it's that latency matters here because it's a better user experience and also making it faster makes it cheaper. But more importantly, you know, making this for applications in production. Not for, you know, of course, we want people to experiment on the platform, but our end user here are folks putting in production and showing that he works and stays up and you can run an enterprise application with it. It's been a couple months since you've been in theCUBE and I know lots of change. What's your current business model now? I mean, then you've got more tech, you've got more capabilities. What are you guys doing now? What is OptoML's main focus from a product customer perspective? Absolutely, yeah. So people can run, tune and scale models and build generative applications with them, right? So it's generative AI infrastructure for application builders. And the business model is simple. You pay for what you use, right? You run a model, image. If you're an image gen, you're going to pay per image. If you're in a language model, you're going to pay per token produced, right? So very, very clear, transparent pricing and then you can use- And the person that's buying or using the service are who? Platform engineers? Are they application developers or are they business? Application developers that are not machine learning engineers, right? So there's a folks that again, abstracting complexity, right? So these are developers that now they have done some experimentation with AI. They know what they want to build and they want to put models in production. And they want to build their models, their own models. You want to choose models. Customers want choice. Like this was a common thing here. So like when we were to choose the models that do not be dependent on any specific model and choose models that have the capabilities that you need and have the right performance. They're going to be building. They're going to be developing. This is that new layer where the feeding frenzy is going to come in. They want to get into bedrock and test the long tail of power law of models and see what happens. They'll experiment, maybe discover something and then deploy it. Deploy in scale and then putting production with production quality service. Like the means of time, performance, reliability.  If you're an enterprise, show your boss, get funding. Okay, John, entrepreneurs get some experimentation. Now they want cash.  Not just cash, he's full of insights too, John.  He's an added value investor. So I didn't mean that as a compliment, you are. Okay, now I want to get funding. The pace is fast. How do you decide what's good when you look at a deal because I've seen some products out there that look really good on paper, but are just a hackathon. We can project that looks good, but is there enough differentiation there or are valuations too high? So there's a lot of action happening. What's the filter now? It's a speed game, it feels like. What's the funding look like for an entrepreneur out there? So I think there is now so much tooling, so much infrastructure, so many models available to founders now. And what could be a better environment to build in? And the question is, how can you make that good for you, not bad for you? It's by using it. It's by using OctoML to get faster inference and better access to silicon than anyone otherwise could. It's by using AWS to get infinite elastic compute and things like zero ETL to pipe your data around. And all of this is really hinting at what we've said all along are the two key things that a great company needs to have to build a great product on top of technology like this. It's really powerful data. It's really powerful talent who is well-leveraged and it's better understanding of your customers than anyone else. And when we have these infrastructure, what that can do is it can actually get you more efficiency out of the talent that you do have, which is precious, which is rare and special. The technology is now making it easier to take the data that you may have, that you do have, and actually apply that. And the customer insight, that's the thing you got to start with. I think that's a very key point because I think, if you say, okay, heavy lifting, I got OctoML as a service, transparent price, and you know what you get, so you can watch your budget. If you don't want to overdrive, if it goes viral, then you got to get to funding first and then build that back. But once you have that transparent part, you can focus on the product. The customer is the business logic. So you go to the customer and identify, that's an opportunity for an entrepreneur to actually compete and unsee the incumbent. If they can go to a customer, say, I've identified a workflow that I could get in there and leverage end to end with data, potentially is an entry. So again, this brings up the question of, can the small guy come into a market and take territory? I think the answer is absolutely yes. We see it again and again and again. I think you're about to say something, but we see it again and again and again that if you can understand your customers better than other people do, and you can make the trends around you of speed and undifferentiated heavy lifting and faster performance and better generalizability and reasoning capabilities of these models, if you can do all that and understand your customers better, you can absolutely beat Goliath. Yeah, absolutely. I just want to add, the only thing I have to add here is that there's so many pieces ready to be composed into something really compelling and magical here that definitely understanding what your customer needs, but also showing what's possible with this new technology for customers that would be a pleasantly surprise and do that very quickly, right? So it's just extremely exciting to see how much resources available to do that today. We've taught in the past, obviously you've been a professor at the University of Washington, great school, great football program this year, congratulations, but also computer science programs, phenomenal. It feels like we're at an intersection, a flash point of all the things that have been kind of hanging around the table that we want to have happen, smart homes, cars, like stuff that's been supposedly supposed to happen over the past 20 years, it reminds me of the web, you know, the dot-com bubble, it burst, but all that stuff happened. We get food online, the stuff that was supposed to happen didn't, but it happened over time. It was a slower boil. Now with the speed game, we might see a flash point with this market, with this event showing us the GNI that this might come together, the smart home, the edge, the smart car, the stuff that was kind of crawling along that might actually go faster now. It feels like we're at a point where there's enough compute, models as a service. You know, if I can offer a hot take, I would say that we haven't seen anything yet because, you know, go back a click or tune history and the App Store launches in 2008 or so, mid-2008, iOS 2.0. And we had a lot of apps, a lot of them were flashlight apps, a lot of them were simple games or direct ports from the web.  And I would assert that you could have built Uber or Instagram in 2008, but it's not just a matter of the technical building blocks. The technical building blocks have to be there and then lightning has to strike. And so what we now see is that the infrastructure is there and we're waiting for a lightning strike. It feels static in here, doesn't it? Like the lightning's gonna hit. It does feel static, right? It does feel static, right? It does feel static, right? A lot of the craps are done. What is the lightning? Is it timing? Is it just a moment in time? What does the lightning strike look like? Is it, you know, what do you see that? I didn't know what it's coming to. It's just showing it's possible to automate things that only humans could do before. Like think about, you know, the velocity we're seeing applications that can do, like automating, producing a summary of a long email thread, you don't have to read the whole thing. This is actually working, showing meaningful progress. We actually needed people to do that meaningful in the past, right? Or automating, generating graphical content out of text. I mean, this is something that was just in its infancy like 18 months ago, and now it's not only showing that it's possible. It's actually in production. A lot of really compelling use cases. This happening so fast that I'm with John that we haven't seen anything yet, but at the same time, what we've seen already is really, really, really cool. So that's why. Two years at the App Store, maybe two months in AI. But again, this brings up the shrinking of the acceleration. Or is it at the pace of human speed at this point? Yeah, I think we're at the pace of human speed. Take the examples I just gave before. Uber, the lightning strike was Travis Calcanic, can't get a cab in Paris. And Airbnb, those guys needed, you know, they were trying to, they needed, they had an air mattress. And so for all the pace of innovation, there is, we're waiting for human inspiration. And there's a lot of people working on it, but there's some things that actually you can't rush. Yeah, there's some table stakes. I think the bedrock thing, choice and open always wins. We talked about that a little bit last night.  And it was interesting to hear the perspective and the demographics. I think we were the oldest ones in there. The young guns. I mean, if you're under, if you're an entrepreneur today and you're not excited about this environment, then maybe you shouldn't be in tech. Cause like, this is probably the best ripe opportunity. But no one knows what's coming. They know it's going to be big. That's why I was asking about what, what is good look like and how do you differentiate?  Do you get funding for that? Well, some people did. That was the dot com bubble. But I think now we're more pragmatic about that. But you know, people are asking me, where's the white space? Where's the opportunity? And so I just, I mean, I just think it's everywhere. Literally, literally every type of application and many business and your human processes that are not even applications is in scope for this kind of technology. You could probably contrive one that's not. And so I should say almost literally everything, but I can't contrive one. And so what that means is anything that, you know, founders ask, where should I focus? Where's the opportunity to focus? And I think that's the wrong question. I think the question is what customers, what problem do you know better than anybody else on this earth and have more passion for than anyone else on this earth? That is the thing. I totally agree with that. I think that's great advice. And also the point about the customer, the access to customers now are a lot easier path or to get data about a customer to say, okay, I can validate the problem that I know about. And I know what the answer looks like. Let's get going and then use AI to help you get in there and then get back and get standing up some solutions and get the iteration, the latency of the answers that you need. So there's latency questions two ways. It's like latency of speed, speed of packets and speed of creative creation. I know latency, I have confusion. So I think about latency as in how long do you wait and then velocities, how fast it's, you know, things are evolving, right? Yeah, that's a great point. All right, so what's your take of the show here? Let's take a step back. Give me the summary of your perspective of re-invent 2023. Well, I'm generating AI everywhere, not surprising.  Also showing, you know, bad rock coming together. And again, like, evolution of infrastructure on the compute side is clearly clear that compute is top of mind for everyone here. Just look at the scale of compute that we're talking about. That was announced yesterday. It's just several hours of magnitude than we've seen in a while, right? So I want to bring up with people that I don't know wake up the annoying professor in me, but one thing that's, I think it's interesting for people to internalize here is that every output that Genevieve AI produces, like a word from a language model or an image from these text image models, it's like literally many hours of magnitude more than the kind of compute requirements for any application we've ever had in human history. Right, so I think it's a moment, it's a moment of like the number of calculations required to produce a meaningful output for application Genevieve AI is orders of magnitude more than a typical application like a spreadsheet sitting in front of you or a browser. So the compute involved for that is more than people can even imagine. It's a moment to be grateful that computers are really, really fast and they're getting energy efficient, you know, so. Well, that's a great point. I mean, the energy thing is the envelope now for the constraint. It constrains everything. Yeah, and John, what's your take? That's a good point about the magic of how fast that hardware and the speed of the processors and the system around it, it's like, it's not just chips, it's what's around it. Well, I think it really brings up why we're all here at re-invent because this new wave of AI requires science and math that would not be possible without the cloud, without what happened here in 2012, what happened in Seattle in 2006. That level of compute and coordinate and cross many nodes would not have been possible. And now, back to 2023, when you see what Peter DeSantis announced in the infrastructure keynote, there's the best way to believe, the best way to know for sure that Amazon believes in AI is the way Amazon is consuming AI. For example, to make the databases more scalable and more efficient. It's really inspiring and deep, deep investment that's happening. And it's meaningful. It has a direct application to what people need. Just get that lightning strike going back to the spark. You didn't get the cab in Paris, start Uber, or I want to solve this problem. I see that no one else solved. As Steve Jobs said, half the stuff is invented by people no smarter than you, right? That's the famous line from Steve Jobs. So people can start solving the problems. You don't have to be a machine learning engineer to go build apps with unprecedented compute available for you. Yeah, and I'll add one more thing so that you might not even need to be a regular, typical application developer because now there's a lot of really meaningful ways for you to actually start building experiences without writing code. I mean, natural language becoming, no, you'd be hearing this more and more, but there's something natural language programming has been a dream of computer scientists for a long time and it's finally becoming, it's finally being realized in a way that's very different than computer scientists have thought. And it just feels magical even to them, right? So people can build stuff, more people that can build stuff, more creativity that can be unlocked very quickly, more experiences, and that's where it's a wealth creation, it's a wealth creation. That's just not money, but assets, society, benefit. Love having the professor on masterclass here and I think you always love having you on. Special time, last minute we got. Give a plug for the company and when you're looking for an investment we'll start with the company. What are you going on? Who are you hiring? You look up to hire. What's going on? Put the plug in. No, we're definitely on a roll. So come talk to AI or product to run to scale your Genitive AI models. We're a Genitive AI infrastructure for application builders. We make it very easy for folks to go and build the model cocktails and solve the problems that they want and have the models they can choose.   Madrona will write a check anywhere from the very first check through series C. Great, great stuff. Great firm. Congratulations, Dr. Mel, continue. We can't wait to get the update next. Always great to have you guys. Great to see you guys last night in the team. Thanks for coming on theCUBE. All right, we're bringing it down.  Perfect time to be out there. Of course, theCUBE's got all the action, more coverage after this short break. Back to the studio in Palo Alto.


Economics provides us with true laws of the type if A, then B, then C, etc. Some of these laws are true all the time, i.e. A always holds, the law of diminishing marginal utility, time preference, etc. Others require A to be established as true before the consequence can be affirmed in practice. The person who identifies economic laws in practice and uses them to explain complex economic fact is then acting as an economic historian rather than as an economic theorist. He is an historian when he seeks the causal explanation of past facts. He is a forecaster when he attempts to predict future facts. In either case, he uses absolutely true laws, but must determine whether any particular law applies to a given situation. Furthermore, the laws are necessarily qualitative rather than quantitative, and hence when the forecaster attempts to make quantitative predictions, he is going beyond the knowledge provided by economic science. It has not often been realised that the functions of the economist on the free market differ sharply from those of the economist on the hampered market. What can the economist do on the purely free market? He can explain the workings of the market economy. A vital task, especially since the untutored person, tends to regard the market economy as sheer chaos. But he can do little else. Contrary to the pretensions of many economists, he is of little aid to the businessman. He cannot forecast future consumer demands and future costs as well as the businessman. If he could, then he would be the businessman. The entrepreneur is where he is precisely because of his superior forecasting ability on the market. The pretensions of econometricians and other model builders that they can precisely forecast the economy will always founder on the simple but devastating query, if you can forecast so well, why are you not doing so on the stock market, where accurate forecasting reaps such rich rewards? It is beside the point to dismiss such a query, as many have done, by calling it anti-intellectual, for this is precisely the acid test of the would-be economic oracle. In recent years new mathematical statistical disciplines have developed, such as operations research and linear programming, which have professed to help the businessman make his concrete decisions. If these claims are valid, then such disciplines are not economics at all, but a sort of management technology. Fortunately, operations research has developed into a frankly separate discipline, with its own professional society and journal. We hope that all other such movements will do the same. The economist is not a business technologist. The economist's role in a free society then is purely educational, but when government or any other agency using violence intervenes in the market, the usefulness of the economist expands. The reason is that no one knows, for example, what future consumer demands in some line will be. Here, in the realm of the free market, the economist must give way to the entrepreneurial forecaster. But government actions are very different, because the problem now is precisely what the consequences of governmental acts will be. In short, the economist may be able to tell what the effects of an increased demand for butter will be, but this is of little practical use, since the businessman is primarily interested not in this chain of consequences, which he knows well enough for his purposes, but in whether or not such an increase will take place. For a governmental decision, on the other hand, the weather is precisely what the citizen must decide. So here the economist, with his knowledge of the various alternative consequences, comes into his own. Furthermore, the consequences of a governmental act, being indirect, are much more difficult to analyse than the consequences of an increase in consumer demand for a product. Longer chains of praxeological reasoning are required, particularly for the needs of the decision makers. The consumer's decision to purchase butter and the entrepreneur's decision about entering into the butter business do not require praxeological reasoning, but rather insight into the concrete data. The judgment and evaluation of a governmental act, e.g. an income tax, however, require long chains of praxeological reasoning. Hence, for two reasons, because the initial data are here supplied to him, and because the consequences must be analytically explored, the economist is far more useful as a political economist than as a business advisor or technologist. In a hampered market economy indeed, the economist often becomes useful to the businessman, where chains of economic reasoning become important, e.g. in analysing the effects of credit expansion or an income tax, and in many cases in spreading this knowledge to the outside world. The political economist in fact is indispensable to any citizen who frames ethical judgments in politics. Economics can never by itself supply ethical dicta, but it does furnish existential laws that cannot be ignored by anyone framing ethical conclusions, just as no one can rationally decide whether product X is a good or a bad food until its consequences on the human body are ascertained and taken into account. Thank you for watching this video, let me know what you think in the comments below. I now have a page on patreon.com, so if you'd like to support this channel and help me make it bigger and better, click the box on your screen now or the link in the video description. Thank you.


Well good afternoon everybody. My name is Gary for those of you who aren't at any of our earlier sessions welcome and thank you for coming to Cisco's sponsored room for the afternoon for our first of two afternoon sessions. I got a great panel lineup for you today. I'm going to do just some very very quick first name introductions. I'll let the panelists each introduce themselves in a little bit more detail before they dive in. Also just want to remind everybody that the little card that we handed out to you as you came in, please fill that out. We will be doing a drawing at the end of today's session for an Apple Watch. Cisco people be careful about filling out your cards. And with that I'm going to do some very very quick across-the-stage introductions. Our moderator today Dwayne, panelists Lou, Uri, Daryl and JL. And with that gentlemen I turn it over to you. Thank you. Thank you Gary. Good afternoon and welcome to a panel discussion on real-world solutions for network function virtualization or NFE. I'm Dwayne DeCaput, Director of Product Management for OpenStack and Cisco's Cloud and Virtualization Group and we're excited to be hosting this panel today. We know that NFE is top of mind with virtually all service providers. A recent survey by Infinetics indicates that 93% of all service providers have either started to deploy NFE or want to deploy it in the next 12 months. So we know that NFE is on everybody's mind and in this panel today this is everyone's panel. I mean we're going to ask the tough questions. What are the barriers to adoption of NFE and then we're going to open it up to questions for Q&A at the end. So Cisco on behalf of Cisco and our good friends with Intel and Red Hat we're excited to be hosting this panel today. Cisco is dedicated to OpenStack. We've been involved in the OpenStack Foundation since its inception. Our Cloud CTO and Vice President Lou Tucker is also Vice Chair of the OpenStack Foundation. We're a top five member company in terms of number of memberships and essentially have been involved since the beginning of the OpenStack Foundation. So who here has been to more than one OpenStack Summit prior to today? All right what about more than three? What about more than five? Awesome. This is my sixth. You don't count Ken. So this is my sixth Lou and team has basically been there since the beginning and we've been contributing code across all the major services. Nova for compute, Horizon the dashboard, the drag-and-drop GUI curvature for Liberty that's from Cisco. I've done a lot of investment in Neutron and now we're expanding beyond Neutron working with a lot of components based on containers. COLA OpenStack services and container as well as Magnum networking containers and applications on containers. We've also made a large investment in engineering for Cisco OpenStack solutions and plugins to OpenStack products like Cisco UCS and Nexus which is nice because UCS and Nexus their data center staples. They're part of the flex pod architecture, the block and now we're bringing them into OpenStack environments. We've also done a lot of innovation around things that make OpenStack better with open source projects. Analytics and virtualization or AVOs. Cef early warning system to detect before a Cef pool fails. Smart scheduling, a better Nova. We also do a lot of work with validation like CVD, Cisco validated designs. We take Cisco, Red Hat and Intel products, we put them in a lab, we scale it up and we validate it and we stand behind the solution. We also do a lot of work with customers helping them with best practices and for them to be successful with OpenStack. Work with several large customers including Comcast, WebEx, PhotoBucket, some of the newer customers with our partnerships of Red Hat and Intel. The Eli and Edith Broad Institute of MIT and Harvard were the Broad Institute. This is genomics, gene sequencing, literally changing people's life with OpenStack, Cisco, Red Hat and Intel and also FICO, one of the user candidates today, also a good Cisco, Red Hat and Intel, OpenStack customer. We also have a rich ecosystem around NFEI featuring Red Hat and Intel. We've done a lot of investment in terms of other open source communities like Open Daylight and Open NFE. We've also contributed more than 40 applications to the open source community. We've done a lot of work with validation. We recently had a report from the European Advanced Networking Test Center or ENTC. They put out phase one of their report earlier this year talking about how Cisco VPP vector packet processing along with Intel DPDK can get 10 gigabits per second with a single core. Phase two was released just prior to the OpenStack Summit, which talks a lot about Cisco's NFEI solution. So NFEI, it's kind of the best of both worlds. You have the flexibility of network function virtualization, yet it's embedded within the infrastructure for a turnkey solution. We have Cisco UCS for compute and storage as well as Red Hat as a software overlay. Also with Cisco Nexus, a wide variety of networking controllers, APIC, OSC, Open Source Controller or Open Daylight, VTS, Virtual Topology System for a nice VX LAN overlay. A better OpenStack, which is detailed in the ENTC report, how it's easy to get started, easy to scale, high availability, very secure, all with a single pane of glass for management and integrated infrastructure monitoring. We have a great go to market between Red Hat, Intel, and Cisco, all certified, a very high performing solution. The entire solution is validated, all with a single support point of contact within Cisco. So without further ado, let's get into it. To my left, we have Lou Tucker, our vice president and CTO. Massive amount of experience, thinking machines, remember that scene from Jurassic Park, right? Sun, a little thing called Java on the core team for that, SunCloud. I'm also to my far left, we have Udi Ellsner from Intel, chief technologist in the SDN division on the Open Daylight board as well as technical steering committee, also on the IETF network service headers or NSH co-editor. You'll also be at the IETF in Yokohama next week as well. To my right, Daryl Jordan Smith from Red Hat, or DGS, as you're known within Red Hat, runs a vice president service provider sales, and in my far right, J.L. Valente, extensive experience up and down the valley, including CEO, venture capitalist experience. So let's get into it. We'll ask the first tough question. We'll send this over to you, if you don't mind, because you're to my far right. Okay. So is NFV really ready for prime time? What are some of the biggest challenges facing adoption and rollout of NFV and SDN in the industry today? Well, it's a good question. But just for those in the room, DJS is the short for my name, or people like to say barrel double barrel. So just add that. But no, in terms of is NFV real? Is it being deployed? Is it ready for prime time? I think we're beginning to see a number of the use cases out there that actually demonstrate that NFV and OpenStack is ready for prime time. It's the reason why we partnered extensively with Cisco in order to provide the VNFs and the stability around the platform is necessary to make it a viable solution with all the sales and support services from an operational perspective that allow you to deploy it within the network. I think some of the key things that we're seeing in terms of challenges that we're working collectively on upstream around security. I think that's a big topic that we're going to be continuing to develop. Service chaining, I think that's going to be another huge topic that we're going to bring together over the next 12 to 18 months. And you're talking a little bit about the availability of VNF and applications that sit in the cloud in terms of state less and stateful applications at Red Hat. We call them Mode 1 and Mode 2 applications. Mode 1 is really where you just virtualize an application that just sits on a virtual machine. Mode 2 is really where you have a stateless application that actually is able to replicate itself or self heal itself and migrate across the cloud in a dynamic manner. So I think those are the interesting things from our perspective in terms of where we see things going and the acceleration of the marketplace and coming back to your comment earlier about Telco's, you would probably, everyone in the room would understand that Red Hat Enterprise Linux is pretty well deployed in every Telco in the world. I mean, they've migrated a lot from Unix to Linux. And out of all of those customers that we have today, there isn't one that I can't tell you that isn't looking at NFE. So I completely collaborate what you're saying there. Excellent. Anyone else care to comment? Great. Is this one? Okay, great. So I think we are making very good progress. I guess this panel is a good opportunity to talk about multiple things that are happening in OpenStack, in standards, other open source projects as well and specifically also work that is being sponsored driven by the three companies involved. Now, on one hand, we are actually seeing not only trials, but also some deployment, which is good, but probably more interesting for you is if I go and describe some of the things that we as a community and specifically in this event as OpenStack, we still need to focus on and work to make them better. So one statement, and there was talking about stage one and stage two applications, there is a difference between what people expect from my cloud management system when they think about cloud applications and when they think about telco applications. And I don't want to take too much time right now. I just mentioned fewer and I guess there's going to be an opportunity with the other questions to get into more details. But as an example, people would like the stack to be career grade. What that means is different for different people, but it clearly means that it's better than what we have today. They would like to have more network awareness. They are more sensitive to the right placement in order to achieve the right efficiency in order to get higher infrastructure utilization to lower the cost to make the whole thing more feasible. They would like to see more network awareness in the placement. We have made good progress in describing what is a modern server architecture to the orchestrator. We have work to do on the networking side just to mention fewer and I'll mention more as we go. In the last couple of years, we have seen that big shift take place in OpenStack. Where as OpenStack originally was modeled purely after essentially an Amazon in-house which is a very much a compute and storage centric view of what is a cloud platform designed for web-based applications. When it seems like almost two years ago that the entire telco industry woke up and they realized they are spending too much money on fixed machines that are out there of special purpose hardware that have very long cycles and everything else like that have been harded, have been made telco-ready, carrier grade, but they just aren't keeping up with the changes in the industry and so they are moving to software. They discovered they have data centers. They looked at what platform do they move to. They chose OpenStack. From the foundation's point of view, we have had a working group on telco. We have had another working group on enterprise which is coming together because they both are trying to make now essentially a carrier grade platform that you can run these applications on. But I think the V&F themselves are lagging behind. The virtualization of the network services, that's where all the work is going to be. We can harden the platform. We can make OpenStack to be a great platform for this. We are finding these services now exist above OpenStack because they have to be orchestrated from there and then they require interfaces below OpenStack into the hardware. It is like a sandwich we are creating in which OpenStack is managing virtualized resources, but you need to have that communication between the top and the bottom and that I think is the challenge that we are all facing. Everybody is certainly very bullish. This is happening. No one is stopping this train. So now I think the race is on to really doing it in a way that we really can't deliver the kind of services with a reliability, scalability, everything we would expect from web apps. Yeah, and if I just to maybe close on this topic and move on to the next question that you may have is, yeah, it's real. The question was about NAV, you know, how much progress has been done and we discussed it yesterday with Red Hat as well is, you know, sometimes people say, well, I can't disclose names of customers and what's happening. So on overall, there are a number of organizations actually from the different vendors that have already been out there and disclosed what they're doing, what they've done, not just in park but also not just in trials but also in deployments. On our side from the Cisco standpoint, Deutsche Telekom, cloud VPN in five or six countries with something that is public. It's out there. Here in this country, Softbank was actually published a week ago or two weeks ago at the Layer 1, 2, 3 event in Dusseldorf about, you know, what they're pushing. In fact, it's interesting because many of those customers are also evolving toward the NFVPs. So they are doing physical and virtual, not just virtual, physical and virtual mix of a number of capabilities with different vendors, whether it's A10 or Cisco or 14 net, for example, and actually being able to migrate over time some of their assets to a virtual environment for enterprise services, very unique. And on top of that, it's not just actually, and we'll discuss it, OpenStack, getting to OpenStack, many of them come from VMware. You've got to be able to support that. So it's actually two modes to be able to get to OpenStack where they all want to be, but that's not where many of their infrastructure and environments and even knowledge operations are. So all of those elements here participate. There are many other customers, some many, some of them have been obviously disclosed, like Telecom Italia for us as well. So where you see actually this ramp up not just in in production by the beginning of the year or in production already at this stage. So just one short comment just to close on this maybe going back to Lou's comments. The layer above, for those of you who follow the Etsy work, etc., is the manual layer that that's a very big discussion right now in the industry. And actually, we have industry talk later this afternoon to start the community conversation here about what is it that we need to do in OpenStack in order to allow this other layer to be placed neatly on top of OpenStack. And I fully agree with the sandwich model too. Excellent point. Thank you. So speaking of SDN World Congress and Darrell, I believe you were in the house for that one in Dusseldorf. Yes. So there was an interesting report from British Telecom that came out, which fundamentally said that they're considering dropping OpenStack for virtual enterprise services in favor of enterprise or proprietary technology unless OpenStack addressed six fundamental issues. And they were pretty basic. I mean, security, upgradability, manageability. What are the panel's thoughts on on that report? I can essentially direct it originally at me. I mean, I know the individuals very well at British Telecom who made the report. I think I think that the press sensationalize a little bit about what they were really saying. I think I would be fair to say I think what they were really saying is help us as the community make OpenStack carrier grade so we can deploy it in our business. And they mapped out six areas and we're working on Intel as well. And I'm sure that Cisco has areas where they're developing solutions in those areas. But those out of those six areas five are pretty well defined in terms of blueprints and other things that we're developing upstream in conjunction actually with Cisco and Intel. And there's one that we need to work on and listen to and actually try and address very specifically. And that was around security and some of the things that they're looking to try and strive there. So from our perspective, from Red Hat's perspective, we saw it more as a call to action from a very, very important operator with very talented people who look at this every single day and work on it very vigorously. And it's an area that we're very keen to improve. And I think a lot of the efforts you're going to see as Lou was early intimating and Yuri was intimating is a lot more focus around how do we make an OpenStack carrier grade or build those extra features upstream for everyone to benefit from in terms of that sandwich that we were also talking about. When I talk to customers, they're always saying, well, they're afraid to get to the latest version of OpenStack because that's less stable and everything else. In a community development process, that's not true. The latest version is actually where all the fixes are made, where all of the security patches are made and everything else. Backporting those to the earlier releases is going to take a long time. So I think that we're going to start to see an inversion of the usual paradigm where we're going to see a lot of companies now moving as aggressively as they can to get to the latest version because that's where all of the changes that we're talking about being made are right up at the head of trunk. So that's going to be a change, I think, in terms of traditional software deployment. Yeah, since I was also in the room, I fully agree with the over-sensualization that happened with that event and to talk about some of the other challenges that Peter mentioned in his talk at that event. There are things, for instance, one of the challenges he was talking about was the idea that when you connect, disconnect, you have a nick failure, you have multiple nicks on that machine, you don't know which one is actually going to be the one that you are using. Again, all of this is community work, so I'm not going to repeat that every time, but there is work that is going on with all of you and all of us here about the more awareness of the data plane in terms of its capabilities and in terms of what's being used and in terms of what has been left. So, as Darrell pointed out, these points are already being addressed and as Lou pointed out, yes, you go the latest, you get some of it. There are areas that for NFV are somewhat challenging as we take OpenStack to new areas and one of them is, for instance, in the context of CPE, a customer premise equipment where what you have is the brain, some controller sitting someplace and it should have many tentacles out trying to reach all of those tiny deployments in multiple places. This kind of model versus the model where everything happens in the data center is a little bit new and the point that was cited by British Telecom in that case was, for instance, oh, really, I mean, each of them could potentially pick a different IP address. As they do this, I may have to punch individual holes in my firewall to enable all of those. This is no different than the work cut out that we have for ourselves in this community every day. You have new use cases, good news, so we need to add capabilities and we are all hard working at it. Yeah, in fact, we got plenty of examples of those. So I'm maybe contrarian here. I think there is a lot of work that an OpenStack needs to be done for that standpoint. Virtual CPE, absolutely. In fact, today, for most of Virtual CPE environments, when you have 100,000 or a million, you're not going to go OpenStack because of the footprint, the complexity and the cost, it doesn't make any sense, overhead is too high, but also even CMTS, virtual CMTS, there are actually a number of cases today where we still have a lot of work to do in terms of the distribution of the control versus actually the compute areas and so on, so that actually we can take, because it would be great if we can actually expand and have a coverage from the branch or from the CPE all the way to the back end data center. And there is work absolutely technically and also economically, so that service providers can take in next generation POPs. I mean, I can, in Europe there's one customer, 160 POPs. You're not going to get 160 OpenStack masters driving that. Doesn't make any sense. So there is work that needs to be done. And it'll be interesting, maybe there'll be an update at Mobile World Congress later this year. Yeah, I'm hoping that we'll somehow get together to formally answer Peter and publish a bit of a paper on that and try and move things forward. That's our plan anyway. Good, awesome. So we mentioned VNF virtual network function. So what VNFs are service providers looking to deploy first? We talked about virtual CPE and we were talking about some others yesterday as well. But what are the VNFs and are these just virtual instances of existing appliances they're deploying today? So again, I'll start. So from our perspective, I think that a lot of the use cases that we're seeing at the moment around firewall in particular. So that's a particular and load balancing. I think those are two particularly interesting areas of particular VNFs. But from a perspective more of how do you auto provision that across a very large scale environment? How do you take complexities out of provisioning that? Taking something that would take a typical operator 18 months and taking it down to hours, eight hours or whatever the number is. So we're seeing some interesting applications around virtual mobile VPN services with some of the larger operators and we're beginning to see some of those actually go live in terms of trial next year. So speaking of virtual network functions, so where are service providers looking to deploy them in the network first? Are these in the traditional pops or distributed pops? Or VNFs kind of changing the paradigm where the functions are in the network? Go ahead. I feel as I'm doing all the talking here. I'm very happy with the model. You go first. That's our pattern. So from a VNF perspective, what we're seeing from our perspective is a lot of those are being as I said earlier in the conversation being standard appliance-based applications and services that happen to have been virtualized. So typically those are typically deployed in a data center-based environment versus on the edges. I think you were, Jail was explaining earlier and the complexities associated with that. However, there are a number of operators that are looking at CPE models and trying to figure out how to address that and running into some of those challenges that we're trying to address. So from our perspective, they typically mode one state full applications that are sitting in the network.  Again, I think you were talking about the complexity and the need for that technology and certainly some of these standards bodies beginning now to look at that very seriously. So I've got a question for the people who are working here. In my view, we can't just take a VNF as it existed on a router or a switch, you know, a blade running in one of these and plop it down on a VM in a cloud and expect to get a decent performance. It's an opportunity for now for us to rethink those things. If you look at like large-scale distributed web applications, they're not designed at all the way you would have designed in enterprise vertically scaling out. They're designed to scale out right from the start. I know that we're seeing inside of Neutron, for example. We're doing a lot of things to look to see instead of Neutron itself started with a network service node, right? One node and then all the network traffic was going to go through all the tenant networks. And we know you can't scale that. And so then we're looking in the Neutron community itself how to make that truly a distributed function across all of the nodes. That seems to be the direction that the cloud is going. Can we expect to see the same thing then with the VNF themselves? Is that your mode two? Yes, my mode two. I think that's where we're going. I think that people are just rushing to get their environments virtualized first. I certainly think that these applications as they become mode two become more knowledgeable about what's happening around the application and certainly into the networking layer. I think that that's where we need to get to. There's a lot of work to drive that. And then we get into, I think Yuri was talking about what is carrier grade? Is it high availability of the application and services? Or is it hardware implementations that are highly available and completely robust? And I would like to feel that we can get to the stage mode two based applications where they live in the cloud, in the true sense, enable us to scale and drive that. And then I think coming back to your point earlier about people wanting to get to the latest version of OpenStack, the latest features are in OpenStack in the next release, certainly around real-time KVM, real-time Linux, all to facilitate cloud-based radio accessing networks. So those more interesting cloud-based applications. There is, just to that point, and it goes back actually to the manual piece, the VNF manager, if we look obviously at the piece from a manual standpoint, the VNF manager touches actually directly with, or interacts directly with actually the VM with OpenStack. And so the knowledge that you have to have at the VNF, even the descriptors, the payload, the characteristics of the VNF, whether or not the VNF is going to use some specific advanced, you know, CPU peening or whatever capabilities that they need, is going to have to be rendered at that level. And there is also, for example, on the Cisco side from a compute standpoint, we know that compute has to evolve to be able, we can't take generic compute to be able to run high performance. And actually we've demonstrated that so that actually you can take, you know, thousands of customers, enterprise customers or 100,000 subscribers, residential, because also it's going into the residential piece. And that's where, if you really want to run it as we said earlier, about 16 or 2017, if you want really to scale up those actually environments, you're going to need actually to have those design environments from the orchestration down to the computer areas. It's a game changer. So a few comments on these two, especially as computer evolution came up. But going back to the two modes or two stages, we could look at that from at least two different aspects. One of them is simply, you could even refer to this as social or simply the way new technologies progress in general. First everybody, I mean, very good analogy for that would be SDN. Somebody wanted to suggest, oh, let's do open flow based switches. Everybody goes, do what? Already making lots of money with what I have right now, too many changes. And so the mode one or the stage one really is where vendors are starting to say, yes, we actually believe, let us see what happens out there. Let us do something really quick, more for applications as they exist on a blade and start moving to more software model. Oh, that actually doesn't work quite all right, but I already have some customer interest. Then I'm moving to mode two. And now I'm really starting to really make it a little bit more stateless. I still am not done. I'm not done with scale. I'm not done with geography. Many times my application is really restricted to some, let's call it one administrative domain, be it a data center or some other place where you have that. Oh, I don't actually have the ability to do one of the things that Mano, as an example, would like you to do is, oh, we have an important sport event. I'd really like to scale up that capability and scale down another one, which in TechnoSpeak would mean, let me scale up this VNF. How do you scale it up? What do you want to do? Do you want to add more compute on the resources that I have right now? Or do you want to go and fire up new VM instances? Do you have the right descriptors that come from the top that say, what are the KPIs? What are the requirements under which I'm going to go fire up new VMs? Oh, the event is over. How do I scale back down? You look at OpenStack today. Many cloud, Amazon model as it started, everything was way more static. So we need to do a few things. We need to start, A, have a way to standardize a little bit better on information models, descriptors, how we describe the service required, how we describe the infrastructure available and make the best match. That is the conversation later this afternoon. We need to do other things too. There was the notion of compute evolution. That's where the data plane capabilities and performance become really instrumental in your ability to get the max out of the infrastructure you deploy. Not only that, you also want it to be flexible. Not only that, unlike the model we have today where you have VMs that are related to different applications. In the VNF, NFV model with the VNF as one of the building blocks, you have now few VMs that are actually related to each other. They are related to each other, not only in terms of compute requirements, absolutely on the network side as well. You need to take that into account. But on any individual piece of equipment that you have out there, you may find some pretty IO slash network hungry instances of something that potentially would be competing for resources. So we put that knowledge and capabilities into our platforms, as well as educating the orchestrator that, hey, this platform has that, that platform has something different, how you take advantage of that. Good point. Quick question for Red Hat. So the user, no, that's awesome. Walter, since we're gonna be cutting this off in about five minutes, if you've got questions, please come up to the mic. So the user survey for this summit just came out and NFV is the second most area of interest next to only containers, which everybody's talking about. So Red Hat's done a lot of innovation with Project Atomic for containers. Like, what's the conversation with service providers and containers? Well, a lot of service providers are looking at container technology for density, predominantly. And they're looking for what applications they might move to containers and operate as a microservice in a container within a platform as a service, as an environment that sits on top of or in conjunction with OpenStack. We don't see customers looking for one thing or another. We probably will see a hybrid. We see a lot of interest with technologies around CEF, putting CEF into containers and managing significant amounts of storage because the latest SSDs and other technologies from a storage perspective that are coming have microprocessing actually built into the drive itself. So they become, they look almost like compute nodes that sit in the network within a container. So some interesting use cases around that. I think from a container perspective, and this is my view, I think other people in Red Hat will have a different view. You know, they think it's already wearing to go. From my perspective, certainly there's a lot of work to be done around security with containers, a lot of work with networking around containers. I think it's an interesting topic that we want to invest a lot of time and money in and that's why we've launched our ATOMIC, which is our rail-based container-based technology from Red Hat. And we're beginning to have some very interesting conversation with a lot of the operators. So we can actually talk to them about the world of virtual machines. We can talk to them about the world of cloud. We can talk to them about the world of containers and how those things might converge. And the framework is a platform as a service. Great. So we have a few minutes left. We can take a few questions from the audience. It's not, I've got a question. OK. Not for Red Hat, I hope. No, not for Red Hat. In general, when we're looking at NFD applications, I'm wondering, where cloud computing has generally been there to be a platform serving multiple tenants. Multitenancy, we have a lot of different things that are going on. Networking is generally a part of the system infrastructure. It's not considered an application. And yet, are we applying sort of the wrong model there, thinking of it just as a tenant application? Ideally, I would like to be able to think we could pull that off, but I question that because of the need for placement that you mentioned. We've done a lot on placement. Well, that's a very difficult problem. You have to know what else is going on our node. Generally, in cloud computing, a tenant does not know what's happening on a physical host. We talk about network visibility. You want to be able to have that. So either we can start to abstract all of that and keep it secure so that multiple tenants can't game each other and everything else. Or we have to say, no, these are system administration apps. And these apps, therefore, are essentially owned by the service provider or whatever. And they have special privileges. And because that's something, even in OpenStack, we have to start taking a look at to be able to build that model in. So I'm just curious, do you think how far can we take this multi-tenant view or should we think of these things as being a different class map? You must have an opinion. I wasn't going to ask. I mean, OK, CPU pinning. I mean, a lot of these things. So I think we have multiple things happening with the question of the role the network should play sometimes in OpenStack, people used to refer to this, are we turning the network into a first-class citizen yet or not? So it appears to me that the real conversation starts with the way we model everything, the way we understand what capabilities we have in whatever infrastructure you deploy. And if we simply stick to the NFV example, I believe there is a need for more network awareness than what we have today. Whether it amounts to, it is also community-driven. So it's not, many times what happens with community projects is in hindsight, you see, oh, actually, we did make the jump that you were looting to. But if you put it that way in front of the community on day one, it doesn't necessarily happen that way. I think that what we'll see is features and we are driving some of them, like the placement as an example, where awareness also closed the loopback with Cilometer. There is need for analytics to actually understand what your infrastructure is doing so you could get the best out of this. When we look back in a few years, I think we would be able to answer your question much clearer. Jail, I'm wondering, because you're dealing with a lot of our customers, what is their view? Are they viewing this as part of their system infrastructure, or are they viewing this as an application they're running on their cloud? It depends who you talk to in those organizations. To your point, placement even starts higher up. You have to decide, for example, again, back to the central office, so the box is based on, let's say, a new customer coming in with different sites. Where do you put actually the workloads dedicated to them, like you would do with mobile, for example, mobile enterprise, and taking into considerations the network aspect plus obviously the availabilities, the affinity rules or non-affinity. There are a number of things, the data, what data needs to reside if you are across country, like in Europe and so on. So all of those aspects, so back to your point, modeling from the bottom up to be able to expose and top down from the services have to be reconciled. And today, there's no good way. You've got multiple ways, you've got different ways of people trying to, I was going to say Tosca, but yeah, there are multiple ways, but you're in Yang between Tosca, between other actually ways, even within OpenStack of rendering things. I think we still have a long way to go to make those systems actually cooperate. And that is actually because we are changing a model and we are going to more converge the infrastructure, we actually have multiple industries who are looking at the same infrastructure and whether we like it or not, they come with different legacy and so they have different information models, different application models in mind as well and we are out of time to talk about containers. We are unfortunately out of time. Just real quickly, a lot of the things we're talking about today, they're in a demo in the Cisco booth downstairs, so please go to Marketplace and check it out. Also, please contact your Cisco sales rep. I saw Brian walking around before it and asked them how they can help you be successful with NFV. And also, you have an invitation to contribute. A lot of these components like Cloud Pulse and Alvos, they're on GitHub, Cisco DevNet with APIs, so please contribute. And on behalf of Cisco and our good friends at Red Hat and Intel, we thank you so much for your interest and support for NFV and we hope you enjoy the rest of the summit here in Tokyo. Thank you. Thank you.


Live from Orlando, Florida, it's theCUBE. Covering Grace Hopper's celebration of women in computing, brought to you by SiliconANGLE Media. Welcome back to theCUBE's coverage of the Grace Hopper Conference here in Orlando, Florida. I'm your host, Rebecca Knight. We're joined by Pamela Rice. She is the head of technology, strategy, and innovation labs engineering at Capital One. So thanks so much for joining. Absolutely, it's great to be here, it's really good. So you've only been at Capital One for nine months. That's right. You're new to the job, new to the company. Yeah. Tell our viewers a little bit about why you chose Capital One and what your first experiences were. Yeah, absolutely. So I'm leading technology strategy and innovation and what that means is looking at how we can use emerging technologies to set our course for really creating better and differentiated products for our customers. We know that customers' expectations are changing drastically and technology is really rapidly accelerating. So using things like IoT, machine learning, streaming data, these are all ways we can connect with our customers better. And it's funny, when I first walked into Capital One doors to interview nine months back, they had been talking to me for some time and I'm like, okay, I'll talk to you. I didn't have high expectations. But I walked into- Because it's a- You thought it was a buttoned up kind of- And I've been part of FinTech and startups and I'm like, rapid, let's change the world kind of thing. And I walked into the building in Richmond and I asked the person, because I could immediately see that teams were working on things together. There was a sense of real purpose and energy. It was like you could feel the energy in their walls. They were using buzzwords like machine learning and I was like, wait a minute, I must have gone to the wrong place. And I went back to the front desk and I'm like, is this Capital One? And I really did that because it felt like a tech company. And the walls were bright and shiny and people were running around and it was a little bit of a hectic pace to it. And I just thought immediately, there's something to this place that looks different than my expectations of a bank were. So what is Capital One doing right in the sense of bringing this technology buzz into a financial institution? Yeah, I mean, I think that it's safe to say that to Capital One really understands that customer expectations are changing so fast and technology is changing so fast that they have to invest heavily in technology to really reach their customers and go where their customers are going to want to go. So looking at, and part of my job is looking at strategy, where do we think technology is going to go and how do we make big bets now so that we can meet our customer needs in the future? And I think one of the things that holds true for me is that our internal moral compass, our fiber, our DNA comes out when we talk about how we get there. We want to get there together with diverse teams and that's why it's so important for us to be here because the way we build products is the way that we will connect with our customers and we want our customers, all of our customers to feel connected to our products, not just one segment of the community. So talk to me a little bit about the process of designing a new product for your customers and with that diverse team behind it. You know it's funny because in the old waterfall ways and an idea would be born in some board room or some shadow room and then all of a sudden engineers would get a spec on their plate that would say, hey, go build this. And oftentimes they were so disconnected from the customers. We've taken that and pivoted it totally around so that we have whole teams that are filled with designers, with design thinking, with product, with engineering and they can connect with the customers in a way that really optimize towards getting a product out to market faster. And by the way, that life cycle from the time an idea is born to the time you could get something in customers' hands that needs to shorten as well so that you can focus on innovation. If we're day to day activities takes you months, if not years to get out to market, you have no time for innovation. So part of my job also is optimizing how we get things out to market faster and in a streamlined way and empower that team to connect with the customers better. And see what the customers like and what they don't like and then iterate. Exactly. What are some of the most exciting things you're working on now?  We have so many really big milestones underway. It's no secret that we have a heavy migration to the cloud. We were one of the first financial institutions that really doubled down and said we're all in, we're going there. And in fact, I think there's going to be more and more financial institutions that start going there because it's such a time saving. If I don't have to worry about all these racks and all this hardware, I spend more time connecting with the customers. Again, operational efficiency in connecting with their customers. We have a high DevOps mindset, meaning we disconnect that bridge from engineer to throwing something over the wall and we combine a team that has everything it takes to get something out to the customer and then fix something if something goes bump in the night. We also have a high culture that is, we have a goal of getting into microservices more. So instead of these big monolithic applications, really focusing on these small micro elements that have functionality that is enabled to the customer so we get things out the door faster. So when you talk about diverse teams and you're talking about a lot of cross-functional teams, so you've got teams with engineers and designers on them working together, but you're also talking about racial diversity and gender diversity. Absolutely. So how do you make that happen? You know, it's a tough problem. And I've been asked a lot of questions about how do you have diversity programs that actually work? And I will say, it is not just one program. When you're focusing on diversity, you can't just think about it from a program perspective. You have to think about it at your DNA level. Like every conversation, every way that you think about who should be promoted or who should get an opportunity or economic parity, all of these things that you should be questioning, hey, am I thinking about this through a diversity lens? And so even in conversations I have with my team or somebody should bring up something as innocuous as man-hours, I correct them. I say it's not man-hours. These are people-hours. So even if you can have those small hints, you need a program, absolutely, but you need diversity included in every conversation you have, whether it is about who's going to get promoted, who's going to get a bonus, or how we talk about people and where they spend their time.   But you also are a role model to the younger women here at Grace Hopper. What is your best advice for them if they want to have a career as an engineer? You know, it was funny. Yesterday I was on a panel and over 700 people came. I was just so honored to be part of that experience. And my role in technology and being an executive now in technology really has shifted quite a bit. And I feel like it's my job to give back to the community. There's nothing more empowering for me personally than to see somebody helped by words of advice or being connected to somebody else. And I think that my biggest words of advice are really to know that you deserve it. Know that you deserve this career. Know that you deserve to dream big.  You deserve a seat at the table. You deserve the whole table. You deserve whatever you want to dream. And if you have voices in your head or external voices that are telling you you can't have it, quiet those voices and believe in yourself because there's nothing more powerful than believing in yourself. And we are all here believing in you because these engineers deserve it and I would just tell them to believe that. That's great advice. Thank you. Pamela, thanks so much for being on our show. It's been really fun talking to you. Thank you so much. We will have more from Orlando and the Grace Hopper Conference just after this.