 Welcome back everyone to theCUBE's coverage here and reinvent AWS's annual conference 2023. I'm John Furrier, your host, Dave Vellante, is in the analyst session. Shelly Kramer is running around, getting through George Healbert's here. Rob Hoef, Mark Albers, and the whole team coverage and the group in Palo Alto with the live stream and studio action. I'm excited to have a great guest here from VMware program, Betty's, who've been on theCUBE before, VP marking the Tanzu division. Great to see you. Always pleasure. Great to have you. I saw you in Barcelona for VMware Explorer. I heard that was packed. Yes, it was very lively. And I saw you here too, like in a different setting, but also biggest. Great, we're in the press area. We're at our set here. We got the Palo Alto studio. We were in set yesterday at MongoDB. We've had VMware on before. Narayan came on with AWS. Fred, the VMware cloud on AWS, has produced a lot of great success. Simplification, Tanzu's doing the same thing with Tanzu application platform, Tanzu application service, plus the data and the intelligence piece kind of put together. Kind of nice clean packaging for developers. Developers and platform teams really, right? Just when you look at the ecosystem, there's so much, even just within AWS itself. Sometimes it's about how do we stitch all that together? And we know with large enterprises, they have an investment in clouds like AWS and then they have something on-prem. So stitching that across hybrid. I mean, that's really where we shine. So let's talk about what you're working on now. I mean, look at the industry. So the Tanzu thing I find is very fascinating because as VMware was restructuring with Broadcom, the clarity's clear. You got the install base, VCS, VCF, the vSphere, that whole core base. And the Tanzu was with SuperCloud and all the cloud native stuff. You got Kubernetes and Cloud Foundry and Spring, which you ran that big event, all kind of speaks to this developer. And with this show here, I want to get your reaction to what you're seeing at AWS re-invent because Adam laid out the three layer stack. We had the exclusive on silicon angle. And then it's interesting because it's like, this is a foundation model kind of stack. It's a JNAI stack. We're expecting a developer feeding frenzy to kick the tires at least, so to speak, on the LLM layer. But that means the data, and everything's got on the infrastructure, the platform engineering and data engineers merging. What's your vision for this? What are you seeing? Well, you know, what I think is there's definitely going to have to be, and you've already seen it, innovation at the infrastructure layer, right? And then infrastructure and data coming together because you need to make sure that the infrastructure can handle not only moving that data around quickly, as well as can it run the models? Can that be fast and efficient? But then also an experience layer, right? So we're doing that from enabling the application developer to write the code. But we'll also need to do something like that for like how do I then bring the AI elements, like the AI tooling into the app, as well as how do I know which data elements to attach to this, right? So I think there's going to be another wave around like the developer enablement because what Gen AI has done is like, it's taken it out of the realm of just a few highly specialized data scientists and now it's like available for everyone. It's the same kind of thing that, I'm going to go a little bit back in memory lane. Virtualization has always existed, but it took x86 and then VMware to commoditize and make it available for the masses. Linux containers, that always existed in the Linux kernel, but it took Docker to put a really easy interface so that it's available to the every person. And I think Gen AI like that has done that for this area of like doing very interesting things with mass amounts of data. So there's going to be a explosion in new tooling around it, so we're going to have to sell the same problems again. How do you secure it? How do you control the chain of data? And then how do you then allow people to like manipulate and use it effectively? I mean, we've seen this move before, you know, tool sprawl. Tools sprawl is going to come and maybe AI can help the tool sprawl. I was just talking with Eric Brandewine who rises VP and runs, yeah, he's a technology, he's part of the security team. Amazon just reorganized their security team to have one for the entire companies. And he said the problem is that some of the tools don't match. Yes. And the data sharing, it's a complicated situation. 100%. And so as you have these complex workloads, because AI will be complex under the hood, it'll look easy to the front end, that's thanks to the new interface, but there's a lot going on under the covers. Yes. So this is going to be complex. So, you know, the enterprise people, they want to solve complexity with more complexity, but that's, you can't do that here. No, we want to simplify. You can't do that, you got to simplify. Yes. Well, first, I mean, the idea of having to attack the problem set, that is complex because, you know, it's changed a paradigm in a way, right? So security in an AI model, maybe that'll be different than how we secured physical servers or virtual networks or whatever, right? Application code, but we'll still have to solve it. And then once those puzzle pieces haven't solved, you still need like the ecosystems to come together and simplified. Otherwise, you can't put that burden on the customer. That's impossible. And or the developer. Oh, 100%. I mean, yeah. I mean, the developer, the whole security paradigm, but the whole early days of shift left, how that's evolved. We're seeing, I want to hear that because I want to throw something out at you on this. So we're seeing data conversations have the similar trajectory where we hear from words like guardrails, heard that in the keynote yesterday, or hey, you get data into the pipeline so the developers can make policy and governance decisions at the point of coding and see ICD pipeline. So, okay, that's, we're seeing this same kind of movie again, security now data. So the data engineer or platform engineer, those roles are coming together. And I had a astronomer on earlier, fast growing company, they came out of Airflow, which Airbnb did an open source, which is a phenomenal. That product is only targeted to data engineers. Not data science, data engineers. Actually, engineering is about re-architecting their pipelines. That's not for the faint of heart. So who's in charge? The data engineers or the developers? You know, I mean, we would say the developer's always in charge, but now if they don't have the data, they can't do generative AI. So the data relationship to gen AI is going to be significant flywheel. What's your opinion on all this? Yeah, I think some of this goes back to when we, I have two things here. One is around the shift left and the other one is around the cognitive load discussion. I think when as an industry, we said shift left, you know, you build it, you run it. When we shifted left, we shifted over all the stuff to do, right? To people who, honestly, it's like, they may not be specialists in all the functional areas or be a prize of like all of the policies at the company or government regulations, right? That's why we have specialists. So when we shifted it left, what we should have done is, the guardrails is a great example, that's the right example of shifting left. Like specialists in regulatory governance, specialists around like which data, you know, privacy laws, which data we can use where, what the corporate governance and security policies are, bake that in and shift that left. So you're not configuring it later, but then a developer can sit there and they can do the business logic, they can manipulate data, right? That's available to them. But then do it within the sandbox, that's approved. Instead of trying to like, you know, retrofit it later. So you're saying people were shifting left for shifting left purposes. They weren't really doing it right. And it's like, we shift left these steps and make it invisible instead of like shift it left. So like, now I'm going to have like Bob over there, do it all. And that's maybe like. It's passing the problem to the next person. Exactly, exactly. And that's not right. Well, this interesting conversation about putting the governance built in from another big conversation. I think another thing that's come up is the security benefit with Genevieve AI for the good guys and the bad guys. So that's come up a lot this week. Not so much as much as with the Q and the co-pilot kind of vibe, but the question is, okay, this is going to be an acceleration of benefits, efficiency and productivity. For example, in the keynote, Adam Sileski showed an example with Q where they essentially took a thousand Java applications and upgraded them in two days. And then he hinted at .NET's going to go to Linux next. That's going to be like, that's just game changing like speed, like converting a thousand applications in two days. I mean, it's incredible. That benefit there. And if they get .NET to Linux, think about the license savings there just alone that company can have. I mean, Microsoft's not going to be happy about that, but this is the kind of things that you can do, but it's good. Now the bad guys can do it too. Yep, and you in a different way, right? I think it's the same thing that we had when we had all the software that would automate a lot of processes for us, right? If we had our processes well worked out, well thought out, we're automating the things that are right. If we, but what automation does is, if we haven't figured out the right way to do things, makes us do things incorrectly faster and more often. So I think that similar type of logic applies with AI, right? But I got to ask you now, you've been in the industry for a long time. You've seen the waves before. What out on the landscape gets you excited? Take your Tanzu hat off for a second and VMware hat off for a second and look out on the industry. I always say, I wish I was 25 again because the market's hot for these amazing AI opportunities. Knowing what you know now, if we were 25 and we were going to go look and jump into a community and build something, what gets you excited out there? What are you seeing? I mean, I was just supposed to ease those things that you mentioned. I don't know if I'm shifting left. We can learn a lot, bring that to the table. There's a speed game, certainly the pace of play is high right now on this platform. Our hype cycles are very, very short now, right? I definitely do think what's possible with AI, we've only just scratched the surface on the ideas. We haven't thought about applying it to what kinds of data. We're focusing it from a purely individual productivity, but what bigger questions could it solve for us if we were to approach it, look at where we have large unstructured data sets? I commonly joke, is this the big data wish that we had all hoped would be true? And now we have it, right? I think combining that with some of the movements around that we've had on IoT and IoT specifically, like with all these connected homes, connected, whatever, what we're doing them, and still it's kind of like industry silos. I'm curious, like how if we brought that together along with the power of creation from developers? I think there's a, what we've just unlocked is a whole new level in this game where we're running through the simulation all over again. It's legit next level. No, it's interesting. It's legit, yes. It's interesting because you mentioned the big data and if you look at like even the dot-com bubble, when that burst in 2001, everything that was hyped up actually happened. Just later. Just later because it needed to percolate and boil up a little bit, but now you were saying, I think that's an observation. The smart home has been talked about over 15, 20 years. Automation and cars starting to see some movement. So we might be at a flash point right now where this comes together. Those can intersect and it could be really interesting. Yeah, I think that's the opportunity for our entrepreneurs and the white spaces out there. Yeah, we'll see, I mean. Funding opportunity, there you go. Yeah, hey. Yeah. The Cube goes into venture capital. We've got some openings out there. Yeah, we're one big funding machine. Betty, thank you for coming on. Great to have you. Thank you. Thanks for being part of our community. We really appreciate you and Commentator and VMware and now part of Broadcom. That chapter's closed, new ones beginning. Thanks for coming on. Always a pleasure. Okay, we back with more coverage here after the short break. Back to the studio in Palo Alto.