 Good morning NerdFam and welcome back to Google Cloud Next. We're here in Las Vegas. It's day three of this absolutely fabulous power packed event. My name's Savannah Peterson joined by my fabulous co-host Rebecca Knight and John Furrier. John, what's been your favorite part of Vegas this week? I think the whole Gemini 1.5 proves that intelligent software is coming faster. You're seeing better software stack to merge with data. I think the question of how to run cloud at scale, high performance. JNAI will be the biggest opportunity for business to change their growth strategy. So, you know, to me the tech question is how do you run it? So this next segment will be really awesome. Yeah, how are you doing this morning Rebecca? I'm great, I'm great. And I second everything that John is saying. And what I find most fascinating based on my beat is how we are really entering this AI driven workforce and how it's really going to change people's day to day jobs. Yeah, I love it. Well, our next guest is ain't his first rodeo. Very happy to have you back, Nick. Thank you so much for being here. Thank you so much for having me on. Definitely appreciate it. Yes, absolutely. How are you feeling day three? Absolutely. Hey, you know what, not as much energy as I had on day one, but we're getting there. You're still smiling, you're bringing it, we're feeling it here on the desk with you. You just said something before we started that I love. You spent the last few years taking away the worst part of people's jobs at Harness. What does that mean now that we're in this new AI era? Look, everybody's actually talking about AI, but they're talking about removing the best part of the job. And I think that's a huge problem because that causes resistance. And what we've done for the last seven years when we came into the world as continuous delivery using machine learning and AI was actually doing it to remove that worst part. So no one wants to babysit deployments. No one wants to wait for tests to run. No one wants to write policy and do all these tasks. So let's let AI do the things that we hate doing so we can do what we love. Well, I want to dig into this because this is exactly where I like to be thinking about how people actually experience work and their day to day. How, when you are taking away the toil as you tech people call it and giving people more time to be creative, to innovate, to think big thoughts about where do I want to go do next? What's my vision? What's my strategy? Do you have any great examples of people that have actually done that at their company and said, hey, since AI took care of that, I could do this? Absolutely. I mean, if you look at United Airlines specifically, they removed, and this is, you know, you can look at the quote, 99% of their toil by using Harness. So now they give all of their operations folks, their developers, all of that time back. And now if you actually look at the performance of their applications, their mobile apps, infinitely better in that short period of time. Also allowing them to actually get to the cloud astronomically faster. Yeah, that's some serious data right there. Do you have any other customer examples you can drop like that for us? Because that was powerful. I'm here for it. No, absolutely. I think when you actually look at all these customers, like regardless of whether it's our largest financial services customer, so you look at Citibank. Same kind of thing. When you're going and you're taking away all of the provisioning, the thought process of how do I onboard and get this through. Now they're actually doing this at a rate that's 30 times faster. These are things that are massive. They're huge game-changers. And it's because what you're doing is you're providing a guarded path, right? You've made it easy for people to do the right things and you've made it hard for them to do the wrong things. The reason the cloud started was because it was easier to do the wrong thing. We would go out and spin up a service. Now, if we make it easy to do the right thing, we'll actually get people doing it the way they're supposed to. You know, one of the operational questions that's been around, ML Ops, you mentioned that earlier, it's come up and it's being redefined. I won't say redefined. Genetics forcing it to be reframed, if you will. So, how would you reframe the ML Ops opportunity as language models are introduced, multimodal, runtime is starting to get much more smarter in terms of the software side? As developers put this all together, how do you run this? What's the vision? Yeah, the vision here I think if we don't put it in a guarded path, we actually can't trust it. If we look at anything that we generate with AI, we have to take it with a zero trust methodology. We have to literally treat it as if it was a bad actor. And so we don't put it through a process that checks for all of the security options to make sure that all the tests are on are resiliency that even if we apply these, they'll actually meet not just the, you know, what is it up, but is it performant? If we're not doing all of that through the delivery, we genuinely don't achieve anything. We only add backlog and we don't actually get to production. That's a really good point. You see a lot of different customers across verticals. Are you noticing any trends? Is everyone at a similar proof of concept or excited about AI? States, what are you seeing? We're seeing actually people somewhat consolidate because now they can't do this with 17 different tools. They can't string this together with scripts and parts and pieces. They actually have to use a platform to do this. They have to put policies in place and rules to make it again easy to do that right thing. And that's how they're gaining the velocity because now you have a happy path or a golden path to production. Everyone wants a happy path to production. Yeah. So here at Google Cloud Next, you are doing a session called Innovate with Confidence. I'd love to hear what are the components that you advise and how you talk to customers about how this is how we get from proof of concept to this is going to add real value to our organization. Absolutely. And even figuring out the proof of concept to start with. Sure, I think one of the things is, you know, we've always talked about move fast and break things. The problem is in the enterprise, if you move fast and break things, that doesn't work. It's a big break. It's a big break. Yeah. You can't do that at the largest financial institutions. But what you can do is you can move fast, you can fail fast in earlier environments, but you can run them for all of the tests. So I guess to that point is you have to make sure that what you deliver is guarded, that it has all of its protections. It's doing all the right tests and all the measures and it stops it and prevents you from making those mistakes. If you put that in place, now you can actually do anything. You can take ideas and they'll only make themselves as far as they're actually allowed to go in a production if they meet those requirements. So ideas become quickly iterated. The good ones make their way to production. The other ones, we can go and iterate again. I think I got to get your perspective because we've interviewed across multiple ecosystems, AWS and other clouds, obviously on-premises developing with data centers are changing, becoming AI centers. Yes. Because you can do stuff on-prem with workloads end to end. Absolutely. Big theme end to end. As an ecosystem partner, Google's presenting a pretty damn good package here. Stacks looking good, get more performance, intelligent software layer orchestration. The Kubernetes is 10 years old. That bets playing out beautifully, serverless, that abstraction, good call, and then the apps obviously infuse with NGNA. So check, check, check. As a participant, the ecosystems are critical for customer success. How is Google's ecosystem from your perspective or ecosystems in the new era of GenRvii evolving? And what are the table stakes? What are some of the key prerequisites for success? What's your opinion? I think the key to success here is actually looking at where you were, where you are and where you're going. And oftentimes people are only looking at where they're going and they make decisions based on that. The problem is if you're constantly doing that, you'll always have new tools, new policies, new procedures. And if we're not looking at where we came from and actually incurring that into where we're going, that's a big one. So if you're truly, in order to have success, I believe you have to support where you were, where you are and where you're going. And that's what'll actually launch people and it gives them an easy way to crawl, walk and run. If we move them entirely and you shift everything that they're thinking about and one day they're on Tomcat and the next they're Kubernetes or Lambda or serverless functions, then that becomes a problem. And if we can slowly give them a way to crawl, walk, run, now we can actually start achieving things and you actually take people that would've been blockers and you actually make them your champions. And better together too. Like the ecosystem is also a better together philosophy technically. So like there has to be some room for innovation strategies for you guys as participants. 100%. No, I think in the innovation side of life, if you're not innovating, you're dying. And you'll see that here. We see people actually, again, so many companies are coming together. They're either partnering or they're acquiring because now you can't do this alone. You can't do this as a single product. You have to do this as a platform to truly enable people. You can't fake AI, it's like security because, and the other thing too I'd love to get your thoughts on is came up a lot as when do I use AI and when do I build my own? Those aren't mutually exclusive. You can use a lot of other AI tools and technology and figure out where you want to build your core competency with whatever you got that could be infused. What's your opinion on that view? Seven years ago we came out with our first artificial intelligence. We built neural networks and clustering around thinking about things like your best engineers. And back then I was telling people, people say, oh we're in AI world. No, they were barely doing math. They were doing standard deviation. It's not even machine learning. And so honestly people constantly talk about this as if it is true. We've been doing this for a long period of time. We've created our own to think about things like our best engineers. However, we can use large language models to look at language infinitely better. Why am I going to recreate that? And genuinely it's about using what's there in the ecosystem, right? That gives you the scale, but making sure it's done in a secure, compliant manner. Awesome. Absolutely, well you just touched on it a little bit. The developer experience, how is AI improving that? And are you seeing, I'm curious because you just talked about buying as well within a company level. Who are the big champions of AI within an organization right now? Right now the champions are actually the business. The problem is is we don't give them a very good and easy way to actually leverage it. And so unless a company is willing to put in the guardrails to make it happen, it doesn't happen. But I think you brought up a very important point. Developer experience, right now we're in an awesome time. For the first time, look every other engineer you have at a company, chemical engineers, electrical engineers, mechanical engineers, you give them every tool they need to succeed. You would not ask them to build their own hammer. And yet our software engineers for too long we've said go build your own hammer in order to start working. And for the first time the entire ecosystem is actually looking after the people that we hire to do smart things. So what is that going to unlock in terms of potential? I'll tell you what, if you take the metrics, anyone says 25 to 30% of the time right now is used in actually writing code. If we even doubled that, right, the amount of code that can make its way to production, it's infinite. If we take that on top of AI generative code which is now what, 20 or 30, xing even those numbers, now we're taking good engineers and making them great. We're making great engineers the best. And I think that's the opportunity right now. So over the course of your career you've helped a lot of early stage companies find their market fit, devise their strategy, hire the right team. How do you see the landscape right now for startups particularly when we are at this phase of a new dawn of gen AI? I think right now startups are tough because if they're a single point product it becomes very difficult. Right now we have a massive thing in consolidation around the entire industry and so they're not part of a larger platform, it actually becomes a problem. They're just one other tool that has to be integrated into an ecosystem. That's a challenge. We see platform companies truly thriving right now which is great because we started that journey. How convenient for you? It works out. I got a question for you, Nick. Since you get to see so much action what do you hope, and you're a CUBE alum, what do you hope that you can say the next time we have you on the show that you can't quite say yet? That's a great question. I think one of the things that I would love to be able to come here and say and really be as a larger partner of Google and come here and actually show, we've got some neat things coming out and we're doing some things with them and I'd like to actually show those specific things on the CUBE specifically. Can you tell us anything about those things? Are we just going to... Right now, look, we have 13 models that do amazing things and a lot of them Google doesn't do right now. And so we want to start helping people measure and understand their door metrics, their space metrics. We want to really help people understand the business the way the CTO needs to and we're really trying to help Google do that right now so I hope so to do that soon. And Savannah, that's the key we've been saying on the CUBE. The ecosystem for Google this year is going to be the real test for them because you can't win without an ecosystem in cloud because there's so many white spaces, so many big opportunities for partners to be successful and Google can sell through them. So it's like... There's some power play. It's looking good right now, everyone's standing tall so good job. Yeah, no, I think it's really, I think it's interesting, last question for you. Do you find that this, just touching on that, do you find that there's a different energy around partnership with the AI tech revolution versus other tech moments we've had? I think this is following most revolutions and you get a massive uptick and everybody throws their name in the hat and you have to sift through the ones that are actually doing it. So we've been doing it for the last seven years since inception, it's something we've known well but I do think that will, the difference will be who actually leverages artificial intelligence well versus who just uses it for marketing. That'll be the difference. Yeah, yeah, the real players. You can't fake AI, I mean at some point, it'll be obvious, it's moving so fast. Thanks for coming on. Yeah, thank you so much, Nick. This was an absolute pleasure. I want to see some pictures of your ranch in Arizona since we talked about it. Rebecca, John, always a pleasure to share the day with you and thank all of you for tuning in to our three days of live coverage here in Las Vegas, Nevada. We're at Google Cloud Next. My name's Savannah Peterson. You're watching theCUBE, the leading source for enterprise tech news.