 Hello, and welcome back to theCUBE's live coverage here. Google Next, we're at day three of our wall-to-wall three days coverage. I'm John Furrier, host of theCUBE, joined with Savannah Peterson, Rebecca Knight, Rob Streche, breaking down all the action as, as usual, theCUBE, our flagship program to go out to the events. Extract the signal from the noise. Day three, we got the analyst angles segments and a lot of great innovators coming on. As we look back on the first two days of the show, pretty much all the big announcements are out there. We've got Sanjeev Mohan here, the principal of owner and CEO of Sanjeev Mohan. Great analyst, deep in data, deep in cloud, deep in data, former legendary Gartner analyst, and now your successful firm. And member of theCUBE Collective, great to always have you on theCUBE. Good friend, and love your work, your research is phenomenal. You always got your finger on the pulse and getting more data. So I got to ask you, well, first of all, welcome to theCUBE. Thank you so much, always a pleasure to be here. So this is data show, hiding in plain sight with a cloud show. I mean, everything about AI is about data. This has been a great move for Google Next. Now, remember, eight months ago, they had the other Google Next. Hasn't even been a year. So we know Google's been working really hard, but I was kind of like not skeptical, but curious of how it would play out following on so fast, because one, eight months is not a short time for pulling content together, building product, but the market was changing so fast. So what's your take of the show? What do you think of what's happened? How would you look at this? Give us an update on how you see how things played out. What's the coolest thing? Give us your take. So John, last year when we were at this event, I wrote a blog on, and I created this seven layer OSI stack, and I basically mentioned how Google has a full stack. And it's one of the very few companies that does from all the way hardware to applications at scale. This year, that story came together. We actually saw, I strongly feel that Google Cloud has thrown the gauntlet, and I know, I'm hearing, that a lot of other competitors are having sleepless nights, because what Google has done is, they've brought, so the entire stack is AI infused. And I don't want to overindex on AI, everything is about AI, but because now they own their own multi-model, foundation model, so it is so deeply integrated, they can handle text, they can handle audio, they can handle video, all in real time. And we saw a lot of demonstration of that, and now they have a whole built process for agents. So right from their own TPUs, their own new chip that they announced, all the way up to agents, they have a unified stack. I think that's a great point, and it's very nuanced, and I'm glad you brought that up, because what's not obvious in some of the coverage and the other analysts out there, and the news is that, they're covering all the sexy announcements. Oh, the new processor, which by the way, is big news, custom silicon. You got to have that, we had them on theCUBE. But the big query with vector embeds, with the multi-modal, and the fact that it connects to other data sources and other clouds, is a huge game changer, because Google's basically saying, hey, it's a multi-cloud world, it's a super cloud world, we'll connect, but we think it'll end up in big query anyway, because we'll have the better product, which is unique. So now you got a product that takes me all that complexity. So having big query, having all that in one place, is new for Google, and so game changer, if they can pull it off. Now again, a lot of stuff's in public preview, so we have to still see the meat come on the bone, so to speak. But directionally, powerful. So let me ask you a question. You've had a number of speakers come here. Has anyone mentioned big query omni? No, no. I was expecting that answer. They did not. Because it's not AI, and no one wants to talk about it. You know what they announced in big query omni? That did not even make the headlines, that you can have big query omni, lets you run big query, not just in Google Cloud, but in other cloud providers. They announced a cross cloud materialized view, so you can query big query on AWS much faster and cheaper. So this cross cloud materialized view is a big deal. But in all this noise about AI, no one even talked about it. Well they did, we did talk about the big query fine tuning with Vertex. That was cool. Yes. So and that is actually, okay, so let's talk about that. So this is how big query is developing. First of all, I'm a huge believer in having a unified architecture for streaming and batch. Because we are moving to streaming, we want to be able to run intelligence on the real time data, via stale data. So now what, the way big query works is, let's say a new document is loaded, that document gets into their cloud storage. Big query gets notified. Immediately it starts vector embedding it. I can run my vector search. So a lot of companies have it, but if an audio file gets uploaded, I can transcribe it in real time. I can store the transcription in BigQuery, embed it and I can do vector search. The vector search is a huge deal for multiple reasons. It makes BigQuery much stronger solution because it's got infrastructure data and you get the retrieval, it makes search available, which is going to game change. And combined with taking in other documents and reasoning them, it's going to be a huge game changer to ingest forms, procurement. I mean every vertical will be impacted. So there's going to be some goodness there. But also there's an industry perspective, Sanjeev, and remember we said on theCUBE, I think it might have been last year at MongoDB local in New York, that this whole vector thing is really powerful, but it's not a company, it's a feature. And I think Google's, again, another company that's got vector, it's a feature of something bigger with BigQuery, and so other Mongo has vector search, so vector search is not a company, it's a feature. And so here you go, well, again, another check box, BigQuery with vector, I can run it there. Yes, but here there's a difference in what Google is doing. It is not just vector search. If it was just vector search, it'd be fine. So the story I was telling about documents come in, audio comes in, now a video comes in. So you can start tagging that video and embedding it. Let's say it's an insurance company that wants to look at not just claims, but they also want to look at the video. But in the video there's a license plate. That license plate should not be visible to the general public, so you can obfuscate that. You need computer vision, you need to have multimodal. Correct, so there's vision API. So it's no longer just about search, you can take that data that you brought in, embedded it, and you can fine tune a model, you can train a new model. No, no, this is a good point. Let's expand on this, I think this is a great point you're bringing up. So what you're saying is, this is really, I wanted to put it on the table, it's not just search, bring it in, do some retrieval, augmentation generation, which is unstructured data, it's multimodal, meaning you're creating the ability to address data so that at runtime, when you're generating answers or reasoning, you can do it. So it's not just text, you have to look at all the things. So for example, I'll give you another example, multimodal, we were talking about some of the biometric stuff, I mean, biology stuff around healthcare and health sciences, and DNA is stored. That's a mode, modal too. DNA is multimodal, it's not text. So multimodal is a really important concept because a license plate in a form is an image. A video has audio. So this is just all about making it automated. So see, let's talk about DNA, this is all, I'm just literally thinking on my feet here. You know, CRISPR has been such an amazing technology, it lets you do gene editing, and now you can simulate, you can try to find cure for cancer, and Alzheimer and all that. What if all that information is hiding in plain sight in 20 million documents from New England Journal of Medicine, CDC, WHO, with Generative AI, I can now find these relations, these semantic search that are similarity, like distance close to each other, right? So this is where I see Gen AI having the biggest impact, not summarization, great, not call center automation, IVR. Easy stuff. Yeah, but in finding, what is in my 20 million, 40 years of unstructured data, and then finding new cures and... Yeah, and so that brings up the whole context window. Gem and I 1.5's got one million tokens. Correct. Remember Jensen at NVIDIA, and again good point about Jensen not being here, there's no Jensen here. Right, so. Yeah, I was telling you that before, we didn't talk about that. See, I think this is a, if you look back, every conference you and I went to, there was obligatory presence by Jensen, by CEOs of other companies. Who came to this conference? Nobody. We had videos of Uber CEO and customers, but they didn't come on the stage, and this is the power of Google. What Google is saying is that we are giving you the whole package, simplified, unified, but we'll give you optionality. You can go to model garden, you can get an open source model, you can get Apache Icework, so you can keep your data on just a bud. We don't need NVIDIA to be successful. We don't even need OpenAI to be successful. So at your point, again, so your summary of the show is, Google put together the package. Yes. All in the stack. Correct. Bottom to top. Correct. Yes. And where AI will be introduced and scaled, and then you get the Kubernetes container piece orchestrated together. Correct, correct. And that's now a full workable package for enterprise. Yes, build and runtime. Runtime through Kubernetes, you know. Google Run is getting a lot of traction. Yep, yeah. A lot of these new pieces we are looking at, like BigQuery, Canvas, for instance, for like natural language, it's all running. Okay, so as a research analyst, you do a lot of events. Let's just zoom out. The folks watching who didn't attend the win, maybe they kind of checked out the Twitter stream. They see all the sizzle. Where's the steak on this show? What's the, where's the beef on this show? What's the, how would you assess the show? Give us a quick rundown of what you think, what the big points were, and what it means for customers looking at their cloud business transformation with AI. So this is the biggest Google Cloud Next I've ever been to. Actually, it's twice the size of Moscone Center by just moving here. And the number of analysts who were in my two days of analysts went 120 from 38 different companies. So we, so I see this new level of confidence in Google. They think their moment in the sun has arrived and they think they made the right bets. All these years you've had like people come on the show and say, well, I don't know about Google Cloud. They're not really enterprise friendly. They go to Market's Week. They don't have an ecosystem. They're not really have, they're not bringing their tech to the table. I mean, I've been very critical of Google in a very positive way. Like, and I remember saying years ago if they brought that tech to the table and cleaned up their emotions with customers and built an ecosystem, they'd be great. Now this is pre-Gen AI. Now guess what, they get a lucky strike. Gen AI comes and they got a user interface with Workspace. They have big iron back end scale, just add GPUs and TPUs. And then they've been working on Kubernetes for 10 years. Yep, so Kubernetes is a big thing. All the infrastructure that they've laid out under submarine cables and data centers, that's world-class because YouTube, search ads all run on that at planet scale. So no question about the hardware. Security. This is a week we are hearing about what happened to Azure. You know how many security incidents Google has reported? They had one little incident in public sector. It was, I don't know what Google's, but it was kind of weird on the side. But it was a nation-state attack. So it was definitely different. But I would agree with you. And Dave and I called us on theCUBE years ago and we said Apple and Google have the best security on the planet because they have a consumer business. Google had security advancements at the biometric level with Android hardware level. And look at Apple, same thing. Now the big thing that happened at World World Congress this year as we get into the supercomputing AI infrastructure is it's devices into the cloud. So IoT or handhelds, handheld devices. So your end-to-end themes is coming up too. So to me, I want to ask you this because the theme that's jumping out at theCUBE is a continuation of the same theme of end-to-end workloads can be an advantage to scope those out now, lock them in and scale them up. That has come big. So from device in, so you need the full stack to manage that in your AI, so to your point about full stack, I'm a company. I don't have to redo my entire IT and take one workflow that has an app at the end point that users use and that whole workload can be optimized. That's a huge theme here, workload optimization, end-to-end. They're using AI both inside and outside, inside being workload optimization. FinOps has been a very big topic. I don't know if you've had anyone talk about FinOps? Not yet, not here, not today. So there's an entire FinOps division. They have launched a bunch of new features for FinOps, a new single pane of glass. So there's a lot of movement going on. So John, I really feel personally that the keynote did more on agents than, it did so much on agents that all this other amazing stuff got hidden. Well, they had too much announcement. Like always, they were trying to check the box. Like AWS event, there's so many announcements. And I heard from the whispers in the hallway, there was over 600 announcements that had to be paired down to 200. They just couldn't get the volume out. So there's a lot more. In eight months, 600 new innovations in eight months. So I gave Google really high marks on this event. One big event, they pulled the content together. Again, all this work they're doing in eight months and overlay that on the industry change that's been significant. So it's been a moving train on the rapid change in the industry combined with getting this word out. So they built on model guard and they got model builder and then agent builder. So they got now 130 miles in vertex. Gemini 1.5, I've been playing with it. It's pretty good. It's got cross modality analysis and reasoning. That to me is a huge deal. And I think that's going to be this secret gem that pops out of the show. So we saw Gemini at work yesterday at the developer keynote. During the developer keynote towards the end, they took all the video and they sent it through. So that was 627,000 tokens. Sort of a million, they used 627, but it just literally took a couple of minutes and they were asking questions. I mean, it takes a lot of guts. Okay, final question to wrap up here. And first of all, thank you for your time. I know you're busy. What's your advice to the customers is you recommend you pull the data in from the show. You're going to probably do a big report. You're going to synthesize it, reason all the data. What's going to be your kind of, what's your early directional position for the posture for customers when they look at Google Next as a viable cloud? How should, how are you thinking about? So I think all these years that we've been questioning viability of Google Cloud are now behind us. I think this is the first year. I think we are now going to see an acceleration. So I would say we are already hearing how 90% of the startup unicorns already use Google Cloud. I think Google Cloud for, even for enterprise is now a very viable option. I would totally agree. I would add one more thing to that analysis, at least from my perspective is the doubters can, now that's all gone, it goes viable. Ecosystem has been really performing well. They're standing tall with their booths. They're paying up for the sponsorship. They're having parties. If Google can maintain this year and make the ecosystem successful and not overdrive that piece, then it's the last piece of the puzzle. They got to get the ecosystem 100% on board. No cognitive dissonance. Because right now I'm sensing like, did I buy the right car? You know, like I love this car. It's almost too good. Everyone has those kinds of doubts. So Google should have to reinforce, you know, we're here for you. Go to market, support them, drive business through them. That will be key. And again, public sector, a whole nother great position that they got. So love the public sector. And I think the ecosystem is the last piece of the puzzle. And of course, I'm a big fan of what BigQuery is. I think that's the unsung hero because that's the engine. The cross modality reasoning will be the big piece of the puzzle that will make everything work. And of course, the glue up top is the orchestration with Kubernetes containers and serverless. And by the way, one really critical piece when I was on KubeCon three weeks ago in Paris, on the Kube, we were talking about the metadata layer. Everything that goes into Google also goes into Dataplex, which is there and it gets tagged and classified and you can apply security on this, role-based access control and attribute-based access control. That metadata layer is that secret sauce to how this whole thing works. Sajji, it's great to have you on the Kube. Where can people get ahold of you? So the best way is through LinkedIn. So please feel free, follow me on LinkedIn. Also my Medium blogs and my YouTube podcast. All right, Sajji Mohan, Kube contributor, industry analyst. This is the space, your wheelhouse data and cloud. This is the Kube, bringing in analyst angles. Day three of our coverage. We'll be right back with more after this short break.