 Welcome back everyone, Cube's live coverage at Google Next. I'm John Furrier, host of theCUBE. We've got Savannah Peterson here. We've got Rebecca Knight, Rob Stretchade. We've got Dustin Kirkland coming in. He's our guest analyst host, platooning in for a couple of segments. But he's also the VP of Engineering at Chain Guard, also a Cube contributor. Great to have you on Dustin. We've got two Cube alumnus, other analysts, for this analyst angles. We're all going to analyze. So we've got Andy Therai, who's with Constellation and Sargeet Chowal, founder and CEO of Stack Plane. Guys, great to have you on. Dustin, great to see you. You guys are all analysts. Dustin's certainly not an analyst, but he's got the analyst kind of mindset. He can analyze just as good as anyone else I know. Like myself. See some analysis. Like myself. I'm not an analyst, but we'll analyze the hell out of the show. First up, this show only eight months ago was in Moscone, getting all this content together in eight months with the backdrop of the industry changing so fast, and how many papers have dropped in eight months, how many new things have happened in AI at all layers of the stack? It's been quite a remarkable run. Dustin, we'll start with you, your observation on this show right now. Yeah, I actually think it's kind of refreshing to get out of Moscone and San Francisco and do something a little bit different here in Vegas, but I think the Google-y-ness of the show is definitely palpable. It's a lot of energy, a lot of startups, a lot of integrators, systems integrators here, and the AI bits have gone very much from the lab into prime time, and the demos are, I think, much less hand-wavy and much more real, much more tangible. They brought a lot of the last event to the table this year. Sarji, what's your take on the show? Real quick, hit off the gate. I think out of the gate, Google is getting into their module, which is they are greater search, they are merging search. They are grounding AI with the search, that's big. And their new AI model, Gemini 1.5 Pro is in public preview. That's huge, the demo was, I think, awesome. So that's there. I think the equation is simple for any vendor and they are getting that. So more partner-friendly, plus more developer-friendly, means more customer-friendly, and they get that. We'll see if they can pull it off. Andy, your take, what's your take on the show? Yeah, so I got to, like I said earlier, I got a bunch of yeas that they nailed it and then they got a bunch of meas, you know, I'm like, why are they even doing this, right? So a couple of things that stood out to me, we'll double-click on that, of course. The one that was about the Gemini 1.5 Pro, the context of Windows, one million tokens, I don't know if you guys realize that. Yeah, and the headroom is up to 10 million. That's the point. I was talking with them saying that, okay, why are they going to stop at one? And they were like, we can go up to 10, but the market is not very important. I'm not sure it's public information, but it is now. Now it is. You started it, I did, so I'm safe. It's a token world we live in now. So that's one, that's a massive model. But what's more impressive is because everyone else is starting to build the massive, massive and massive models, what Google has realized is that that's not the only way we're going to serve. So they also came up with distillation of models and smaller language models, so they're able to take the nano models and then that's what they put in their phone and Google Pixel phones, and now it's also running on Samsung models too. So they're going both side of the large models, as well as the nano and smallest possible models. So that's one major difference that I noticed. The second one that he was suggesting earlier, that Google has always been about, I give you everything as a platform or as API level service. They were never concentrating on developers to come in, I give you workspace and I'll give you the whole nine yards, but never went to woo the developers. Now the day two, as you have noticed that, the whole thing, the developer was completely unfocused. So those two kind of stood out for me, totally different than the previous. Well we'll unpack that and again the dust in the stack model is we've seen, obviously we know what stack is, infrastructure, middleware, applications, generically, Google, the perfect storm has kind of happened for Google, if you think about it, we're celebrating the 10 years of Kubernetes, a major success, by the way, that could have failed at any point in the first three years, okay? So that community just is celebrating and they're so awesome to see that success. Containers, serverless. You have that kind of orchestration layer and then at the bottom of the stack, Google's got full scale, always had that. The workspace, the Gmail, that suite, people use it, but with generative AI, that's the application consumption layer and then of course, the data piece with BigQuery, so let's break down what you guys think about, and by the way, the ecosystem you mentioned are the key things. Three layers of stack, they're innovating on all three with AI throughout, what's your take on that, what happened with Google, why are they getting it right, why is this working? Yeah, I'm going to start very much with Kubernetes. It was just three weeks ago, some of us were in Paris for KubeCon here on theCUBE. I look around this expo floor, many of the sessions, many of the organizations that are exhibiting here, and there's a lot of this, and Google Cloud in general, I think owes a lot to Kubernetes and Kubernetes being the backbone of what makes Google Cloud work, what makes it different. You mentioned the 10-year anniversary, it's taken 10 years to really take something that was an internal Google implementation for all of G Suite and Gmail and YouTube, to take that, put that into open source, make that available to everyone, anyone, competitors, other clouds as well, but that's very much the foundation for a lot of what we're seeing here. And they're cross-cloud, cross-network, they're calling it, what are they called, cross? Cross-cloud network. Cross-cloud networking, I get confused between what VMware is doing, but that orchestrationally fits beautifully in with a lot of that AI stuff because serverless fits in nicely. Now developers are going to get sweet from this, so the developer angle on this, Sarjeet, is strong. What's, is there a there there right now and what's the view look like from a developer standpoint or a company trying to build on top of that cloud? I think my gripe with Google was for many, for the last three, four years, definitely I've been screaming loud and clear that you don't show empathy towards enterprise developers. And developers, normally people who are like, stay at the surface, they take developers one persona. No, they're 20 plus types of developers. You know, you're gaming developer front and back and there's just so many types of developers, like data science start developers now, right? So they finally talked about enterprise language through Spring, Spring and Java are like this, right? So they talked about Java, Spring, on the main stage. I was happy to see that. A lot of people, other people are happy. My tweet, I can tell from my tweet, tweet temperature, tweet temperature tells me that people are happy with that announcement. So that's one thing. And I had a round table with the richest rotor and a few other Googlers and a few other developer focused analysts, if you will. I said this, SDKs make you more like vendor lock-in issues. That's my SDK, you code to me. Then Libs, which are in the languages, they make you more like a language dependent. But when you go to APIs, that sets you free. Developers love APIs, it's the age of APIs. If there's API, I can use any language I want, right? So it sets me free. And they have to go there. That was my sort of guidance to them a little bit. And also, it's right. Well, the APIs can help their developers code too, the whole coding thing that's going on. Yeah, the APIs, of course, that is simple thing. Like, you know, large language models are language based. And most precise language are computer languages. They're only less than 200 keywords in any programming language. It's very finite set of keywords. And it's simple stuff. Andy, so this is an event, as Dustin pointed out. They've got the package, developers are there. They don't, 600 announcers, they paired that back to 250 and they still was hard. But this show, there's no Jensen. It's just all Google. There's no window dressing out there. So they bring in a lot to the table and I think, to me, the big takeaway is, yeah, there's some man in there. I mean, I think the video stuff's pretty cool. You think it's not, but if you look at what they did, there's little things in there. They had, the bottom of the stack, they had the processor and the TPUs coming. They had the Vertex 130 models, Gemini 1.5. They had grounding in enterprise data, not just Google search. And then little things, automatic side by side, rapid evaluation, big query with vector embeds now and cross modal reasoning, okay? This is now could be the engine. So, no Jensen, they don't need that hype. They're just saying, we got it. They're saying, we got the package. So, what's your take on that because, or what's our analysis? Do they finally have the equation? Does Google finally got it? Certainly there's booths here. There's parties from vendors and ecosystem. I think so. I think they do. And as you pointed out, if you get Jensen, they get distracted. Everybody talks about Nvidia and Jensen when you get that. It's a good thing they didn't do it, right? I mean, their TPUs are equally good or even better than GPUs. But here's the problem that Google always had and not just with Google, with any cloud vendor for that matter. In order for you to train all these larger models or in order to keep those workloads, you need to have the data in your platform. So if you don't have the data, the customer is not already on their cloud. I mean, they are talking about a federated training of the models, which is a possibility, but it gets a little bit slower. But if the customer is already there, you need to keep them there instead of moving somewhere else. So some of the unknowns was what they made. For example, we talked about the code assist. I actually like that, the 1.5 Pro, the code what it generates is actually very, very good. It's much better than GitHub, from based on my conversation with the C level. So they like that. But the problem is, again, when it comes to GitHub, it's already strongly integrated with the Visual Studio, which means if you are that ID development shop, that's where we're going to go. But if you're using other things in the market, like JetBrains or, you know, even VS Code, they're integration web, if you're going to go that model, this could be an answer for them. But more importantly, that a lot of people didn't give a credibility for this. I was impressed by that when they were talking about it. The tool said what they released for SREs about finding the root cause analysis, keeping the lights on, having it up and running, optimize it on the cloud. That used to be extremely complicated. Now they were able to do a conversation, natural language conversation, find that up and running. I thought that was pretty good. Yeah, and that brings up the model aspect. Dustin and Sarju, I want you to weigh in on this. Do they have the package now? And the word MLOps has only spoken a few times on theCUBE here, but they're saying not only machine learning ops, model ops, right? So now you have models coming in. So the operating angle's interesting here. Now you got the Kubernetes piece as well. So you got an operating concept, the business transformation about building new products. So do they have the package and is MLOps back and different? It's super interesting that you bring that up. I mean, Google's brand is strongest with consumers. And now we've got this Google Cloud thing that's much more enterprise focused. I don't think Google has yet brought those two together. I think some of the work around models is what's going to make what Google Cloud does uniquely. Actually bring that home to the way it affects not just developers, but absolutely everyone. Through developers, through those models, but absolutely everyone. I'd love to see at some point a show or a combination of expos that combines the Google Cloud story and the Google's consumer business story as well. Yeah, I think those lines are a little blurred and that confuses the heck out of the market when they're sending the message. Like we are talking with the workspace and the enterprise, like developers in the same sort of keynote, it gets a little harder, right? And also another side note is that CTO not being on the stage was like also people pointed out who's your CTO, right? So I think their key main leaders have to be on the main stage as well so people know their personalities, right? So because they compare that with, you know, AWS and Warner is like killing it, you know, on the day three or day two of re-invent and here we have some weakness. So like, I think they have, they are getting- Do they have the package? They have the package. Like I just said in the beginning, like more partner friendly plus more developer friendly. That means you are more customer friendly. I think that's it. But having said that, they still have to do a lot more work on the enterprise-y side of things, you know? Like having empathy for the legacy workloads and not talking about the green field all the time. So they have a reliance that they have to bring in the partners. There's a concept I call, I talk about feature proximity, right? AWS has a wide width of features, right? But Google was seen as best of the breed in a couple of categories. And Abhishek Singh from Everest Group and I had some discussion during the breaks here during Analyst Summit and I think they are addressing that problem. Now they look like a best platform versus best of the breed. We know the data science, you know? Like you come to us for- We'll see. Well, look at, remember, a lot of these announcers were in preview. Yeah, that's true. But remember, we look at AWS, what's happened to them. They do so many announcements every eight reinvents, like zillion, and then some of them just get abandoned or some don't follow through. But you've got to worry about what's going to come out and what's going to be implemented. You talked about MLops. And MLops is an area of coverage that I do. And I ask them some tough questions about how do you do the model decay monitoring and model measurement and the model, you know, drifting and, you know, even the data drifting and all that concept drifting and the whole line, yes. They have some pretty decent answer for it. It's not in working yet. It's in a public preview. People can use it. But I thought it was pretty cool. They were able to do comparison of that, do evaluation of that. You could do a model monitoring, evaluation and, you know, measure it. They have some measures in place. Like you said, which one's of this going to see the light of the market and which one is not? Who knows, right? So that's where the problem is going to be. Oh, I mean, the models are going to be important. I think the clear trend for me that validates this show, well, coming from the show is that we've been speculating about models will integrate with each other. You'll see small models that are small, but very valuable. Enterprise will have their own models that are going to be proprietary to their IP, not proprietary, I guess, the proprietary word. I think the clouds will differentiate themselves to some extent with what models are natively available, best tuned, and just there, already there. Sure, you can bring your own, but you know, where do you go shopping for models? So here's the thing about your model thing, right? So when you take a larger model, when you distill into small models, one of the things what they're doing is called a model cascading. So you don't have to have a bigger model at the edge locations to do something with that. You can have a model that'll work well for you in that particular location. You can cascade it to another model either on cloud or next location. So that's going to be huge, not only with mobile, but with IoT when the models move there. So I think they got a leg up on that because I haven't seen anybody else doing it successfully, we'll see. That's a good point. Let's bring up a segment we're going to do, we've never done on theCUBE before. We'll do it here because you just made me think of that with your answer. It's something new you learned. Yeah, there you go. The question for each of us is, what did you learn at this show that's on your radar that you wasn't before? I'll start. What's on my, I'm going to continue to pursue. This whole performance game of token price performance, how many tokens per second is being gained. So I'm watching and I've been sniffing around and trying to understand this. It's energy per token, jewels per token. So there's a lot of price performance games going on. Also the nanometer game, seven nanometer, three nanometer on the semi side. So, but what's on the table for me now I'm looking at is that, what are the claims around performance? Because with tokens, the pricing is going to be on how fast you can do it. And there's claims out there. We do X tokens per second. Well, hell, if you're back loading it with a lot of energy. So that's on my radar that wasn't before I came in. I got a lot of validates, that's new information and we're going to keep an eye on that token angle. Dustin. I think you just put that on my radar now. Okay. I mean, I'll react to that more than, but yes, the pricing I think has started to come into focus. How are we going to be charged for AI insights, AI model tuning. The back end infrastructure and what the cost is there. Yes, there's some capital costs, but ultimately it ends up being very much, you know, the operational costs. You mentioned ML Ops, it's the people and the energy it takes to keep that running. Gaming that, that's interesting, but I guess inevitable if that's how, if that's how we're going to be charged for it. Well, that's the constrained energy. I mean, so it's like, who's got the better chip? What's on your plate? Yeah, it's not new, but it solidifies my thinking. The models are going to be commodity. It's just, right now there's a lot of noise around models, everybody's talking about AI models. All or some. I would challenge that all models are going to be commodity. Yeah, most of us, the bigger. You're not even me, Commodization. David had a big argument on our part on this last part. You're throwing more data, better model, everybody's training it, it's the same algos, but you still have to write an application. You know, application is codification of business rules. There are laws in every country, every city has laws. That is the codification. That's how we build the systems. All the examples you will see on the main stages here AWS re-invent, they show you one thing. Oh, it does this. So what's the issue that's on the table? What's new for you? Yeah. The models? No, no, it's not new. It's the solidification of the fact that programming is important than coding. System thinking, architecture is super important and you cannot ignore that. That's a reminder to myself that, hey, we have to remind the market and everybody else that, hey, this is noise. Noise is part of the game, I usually say that. But, like, it... Well, my feeling on this whole commodity thing is all models are evolving so fast, it's going to be an obsolescence game. Because if your model's obsolete or stale and not relevant, you're done. Now, I think the large language models, like OpenAI will probably be commodity because it's going to be vanilla. I think the specialty models is where the IP will be because the workload and the data will happen. So if Miesville comes out with a better crawl than, say, OpenAI, if they don't innovate and iterate on their model, they're obsolete. Is that commoditizing? So it's not a race to zero because it's a power law, right? So they're either going to be big and commodity-valuable or whatever. By the way, it takes decades... For the long tales where the value is. It takes decades to make the change. During 2009, 2009 after, during the dot com, we wanted to bring XML and commerce one and change the world and kill EDI. But no, we're still doing that. It just takes four hours. So we're just in the beginning. There's a lot of noise. Be careful. Make sure you focus on your application. This group here should do a podcast. It's because it's an hour. We're going to be popping in time. Andy, what's new for you on the radar that it wasn't before that you've, that you cleaned out of the show that's important and you're going to track? I'm surprised none of you mentioned that agents is pretty big deal, right? So what you talked about, the model sizing and go big or go home. So if they drop today, your 1.54 million into token, next week somebody's going to come and beat it. The week after that someone else is going to beat both of them. So it's not the bigger model is going to perform well. How are you going to specialize the models to do that? Agent is one option and wish you can customize the agents to do your job. So take the whole enchilada and then specialize it specifically. That's one differentiator. The second one is the model efficiency that I love it. Like I said, don't go with big. You create a big model, big enchilada and then after that you make smaller models out of that, especially smaller models, deploy wherever you want nanomodals and model cascading. The model efficiency is going to be big. So those are the two that came out. So agents is the new focus you see on your right now? I mean there are others who are doing it too equal enough co-pilers, but there's agents and building efficient agents and building efficiency, model efficiency because the building of bigger models, you know how much it costs to build that? And we talked about that. See, the model retuning, this is another thing that they have somewhat of a differentiator. With your model garden, they can publish a bunch of validated models. You can take that model and retrain them, retune them, fine tune them with your enterprise data. That can be done in a matter of hundreds of bucks. That could be a total differentiator as well. I'll add one completely out of left field. And it's something that I think your interview with Karen yesterday on theCUBE really brought to the forum, put on the radar for me. And that's how big of an opportunity the federal sector, the government sector, is for Google, especially around AI. So what I need to do is, yeah. So what I need to do is, we didn't talk about that, you know what I'm saying? No, they have a huge opportunity. Antiquated, the cross modality reasoning is perfect use case for the government. She pointed out that, you know, Google not doing business in China is actually an asset for Google to hear. She volunteered that China comment. She did, I watched, I saw that comment. I'm like, wow, what a hot take. And I learned that they have a board structure. That was, I think they nailed the public sector. And of course, Kevin Mandiant's on the board who's a legend with Mandiant threat intelligence. So, all right guys, thanks so much for the analyst angles. One last lightning round question that we'll break. What are we going to see next year? What do you see the progression going? What's the next dot connect? I'll start. I think ecosystem has to be successful for Google. They have to nail it. They got great commitment here. People stand in tall with their booths, their parties. Will that convert? Will they address what's kind of going on in their mind? Which I can see, and I've not heard directly, but I can sense it. There's a little bit of cognitive dissonance. Am I making the right bet? I like this new car I bought. Do my friends like it? What, so you got to address the ecosystem. Without an ecosystem, cloud doesn't work. And I think Google's got a great workspace application layer with AI infused throughout. They nail the ecosystem. The puzzle is complete. And that's what I'm going to look for next year. Dustin, what do you think is going to happen? I think there's going to be a real drag race between TPUs and GPUs. And I don't know which one is going to win. I think very much to your point, TPUs have some advantages here. The question that I think might be answered is whether or not Google takes TPUs and makes those available outside of Google Cloud or keeps that as the crown jewels and exclusive to Google Cloud. I don't have crystal ball here, but I'm watching. I'm watching with popcorn. Yeah, so, Dustin, I'm with you. I think if you look for Tensor, you know what viewers, Google Tensor, what Tensor is, it's a multi-dimensional vector. It's even more complex than the vectors. Like, you know, so Google has more secret sauce. I think they're not saying that because they don't want to piss off the market because Nvidia has a lead and they don't want to sort of shake the developer sentiment. But you think there's going to be a little game-changing chip action. I think so. I think they're holding some stuff back. But, okay, I think to win the market, you have to have practitioners in the market. You have to ground your business with the skills of gravity. So you have to train a lot of people. And another thing is we cannot mix the vendor economics with the enterprise economics. And also, the third pillar is the practitioners economics. I talk about that too. How practitioners will make money. If I get the certification on AWS, will I make more money as an individual or if I go with Google? That is important. You have, as a vendor, you have to address that. As an enterprise, you have to keep an eye on your economics, keeping in mind the vendor economics as well as the practitioner economics. So these are the three pillars of economics. Keep an eye on those and then play well. We'll keep an eye on that for next year. Andy, wrap it up. What are you going to see next year with Dots Connect for you? And Google. The reason why Google didn't become much successful in the cloud, where IBM, Oracle, SAP, and others nailed it is because they went after the industry use cases. Google is still not doing that. That's why they brought big guns in. They brought TKN from Oracle to figure out how to go after the industries. So which means you need to start building, look, anybody can build models. Even the smallest of companies, well, it's not smallest when they are unicorns, two billion valuation. Two billion, yeah. Anthropics of the world and others of the world when they come in, they could build a bigger model. But that's not going to solve the problem. Your differentiation is going to be very minor. So you're going to have to build industry-specific use cases, industry-related things to solve it. And that's one differentiator that I see that going forward. The second would be that the model, specialized models for specific industries and specific differentiators for that. I think if they moving in that angle, specific use cases for industries, they could win that. All right guys, great analyst angle segment. Thank you so much for coming on theCUBE. Went a little over, but you know we're breaking it down. We're analyzing it. We're going to look forward to a lot of great stuff for next Jump Jump 4. We'll be right back with more after this short break.