In this segment from theCUBE + NYSE Wired’s “AI Factories – Data Centers of the Future” series, theCUBE’s Dave Vellante sits down with Rob Biederman, managing partner at Asymmetric Capital, to unpack a disciplined approach to early-stage investing amid AI-scale infrastructure shifts. Biederman explains Asymmetric’s founder-first model: writing $1–$10M checks (often via SAFEs), joining boards as they form and helping operators with go-to-market, operations, finance and strategy (not product/engineering). He shares why the firm avoided 2021’s lofty SaaS multiples in favor of backing proven builders earlier (single-digit pre-money), and highlights portfolio execution such as a cash-efficient LATAM e-commerce company scaling from ~$1-2M to about $50M in revenue. The discussion also explores Asymmetric’s subscale buy-and-build plays (e.g., pool cleaning in San Diego, sleep apnea clinics in Houston), where density, tech-enabled services and platform ops expand margins and enterprise value.
Biederman weighs in on AI economics as enterprises race to “AI factories,” cautioning that not every AI workload creates ROI and that overbuilt compute assumptions could face a reckoning. He argues that winners will prove a clear 10× value equation and avoid scaling go-to-market before product-market fit. Additional insights include early liquidity discipline (returning $0.20 on the dollar before the fund’s third anniversary), portfolio survivability (34 of 35 companies still operating; three positive exits), and guidance to founders: make your value proposition relevant, credible and differentiated. Tune in for candid perspective on how capital efficiency, ownership discipline and anti-thematic sourcing intersect with a world where GPU-dense data centers and AI-scale software are reshaping enterprise infrastructure and economics.
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Arvind Jain, Glean
In this segment from theCUBE + NYSE Wired’s “AI Factories – Data Centers of the Future” series, theCUBE’s Dave Vellante sits down with Rob Biederman, managing partner at Asymmetric Capital, to unpack a disciplined approach to early-stage investing amid AI-scale infrastructure shifts. Biederman explains Asymmetric’s founder-first model: writing $1–$10M checks (often via SAFEs), joining boards as they form and helping operators with go-to-market, operations, finance and strategy (not product/engineering). He shares why the firm avoided 2021’s lofty SaaS multiples in favor of backing proven builders earlier (single-digit pre-money), and highlights portfolio execution such as a cash-efficient LATAM e-commerce company scaling from ~$1-2M to about $50M in revenue. The discussion also explores Asymmetric’s subscale buy-and-build plays (e.g., pool cleaning in San Diego, sleep apnea clinics in Houston), where density, tech-enabled services and platform ops expand margins and enterprise value.
Biederman weighs in on AI economics as enterprises race to “AI factories,” cautioning that not every AI workload creates ROI and that overbuilt compute assumptions could face a reckoning. He argues that winners will prove a clear 10× value equation and avoid scaling go-to-market before product-market fit. Additional insights include early liquidity discipline (returning $0.20 on the dollar before the fund’s third anniversary), portfolio survivability (34 of 35 companies still operating; three positive exits), and guidance to founders: make your value proposition relevant, credible and differentiated. Tune in for candid perspective on how capital efficiency, ownership discipline and anti-thematic sourcing intersect with a world where GPU-dense data centers and AI-scale software are reshaping enterprise infrastructure and economics.
In this interview from theCUBE + NYSE Wired: AI Factories – Data Centers of the Future event, Glean co-founder and CEO Arvind Jain joins theCUBE’s John Furrier to unpack what’s really working in enterprise AI today and what comes next. Jain explains why knowledge access remains the first successful AI use case at scale and how Glean’s enterprise search brings AI into everyday work. He details the past year’s lessons with AI agents – from the need for guardrails, security, evaluation and monitoring to democratizing agent building so business owners (not just d...Read more
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What is the background and current focus of the company Glean and its CEO, Arvind Jain?add
What are some successful use cases of AI in enterprises today?add
What are the challenges and processes involved in vibe coding applications within an enterprise setting, particularly in relation to API functionality and the role of the Glean enterprise graph?add
What are the key components and capabilities needed to build an effective AI agent that automates business processes?add
What is Glean focusing on in relation to understanding tasks and human behavior within the company?add
What is the current attitude of enterprises towards deploying AI?add
What is the process for deploying Glean as a customer, and what considerations should be taken into account?add
What are the anticipated advancements in AI tools and their potential impact on workplace productivity and user adoption over the next few years?add
>> Hello, I'm John Furrier here at the NYC CUBE Studio here in New York City. This is our East Coast hub. Of course, we've got our Palo Alto studio, connecting tech and business money here, Wall Street and Silicon Valley. Arvind Jain is the Co-Founder and CEO of Glean, a rocket ship startup we've covered many times. If you've watched theCUBE, you know Arvind's been on multiple times. They're continuing to innovate, bringing hardcore deep tech into the enterprise. They already cracked the code on search in the enterprise, doing a lot more. Arvind, great to see you. Thanks for being part of the NYC Wired program in theCUBE and our AI factory focus and our mixture of expert series. Thanks for coming on.
Arvind Jain
>> Yeah, excited to be here.>> We have been hyper-focused on the enterprise AI for a long time, as you know. But obviously the people are looking at the market, the reports going, "Oh, 10 out of nine, 10 out of 10 projects are failing." Okay, so first of all, not sure the stats are right, but certainly not the explosive growth on the production holy grail workloads. That being said, know search is working, what you've been doing, because they have data. So, this is a progression in my mind. So we're predicting 2026, we'll see a surge of growth. Enterprises aren't hyperscalers, they got high bars for resilience, they don't have the CapEx budget to throw GPUs and everything. So take us through the enterprise AI market, because certainly there's a robust data business. But as enterprise start to see value, what's your take on where we're at, because you're doing very well? What's the state of the enterprise AI market?
Arvind Jain
>> Yeah, well, so everybody's seen these studies which are showing that AI projects are failing, stalling. But I think we actually see really good progress, not just with our product, but they're actually a few specific use cases that enterprises are seeing good success with today. So first, the biggest application of AI of course has been knowledge access so far. So enterprises, they're deploying good search solutions like Glean. And this becomes a really important tool for everybody in the business. You build a product like Glean, give it to all of your employees, and now you're bringing AI in the day-to-day work of every individual. So, that's actually the first thing that enterprises are doing because what they're thinking about is that, look, AI is happening. And I need to actually bring AI tools to all of my employees and get that education going. So, good progress on that part.>> And we covered that before, that the RAG, the retrieval augmentation generation market was booming. Why? Because they have data. Now when you start to scale it across the enterprise, and this is why I like your news that you guys have announced, making it personal. I think you hit that last time, or all the times we were on, personalization's a killer app, but search too. The user experience is changing and agents, the promise of agents are certainly hyped up, but I think it's legitimately hyped up. But the infrastructure's got to get taken care of first. You can't just wish yourself or manifest agents. Talk about that importance 'cause you guys have specific new things you're talking about here.
Arvind Jain
>> Well, I mean the last year has been all about AI agents and expectations have been sky-high. And that's what when you see these reports, that's what's coming out. People thought that I could actually bring an agent, I can take any business process that's in my company and just automate that and automate that in one day. And that's actually hard. This agent technology has actually been developing over the last one year. There's a lot of infrastructure that you need to build to make sure that the right guardrails, the right security monitoring, checking if agents are actually performing with high quality. So, I think what we've seen in the last one year is enterprises deploying agents while the technology is getting built. And for that reason there's been some disappointments, but I think we're in a really good place now. And at Glean, what we have done is we've actually worked hard on making sure that piece by piece, we sort of build that technology stack, the vertical set of tools that you need to actually build agents that can actually be production grade. So when you build an agent with Glean, so first making it really easy, we are democratizing access to agent building. You could be a business owner, not a technologist, not an AI scientist, but you can still actually build something really cool with AI through our agent builders. It's becoming very natural. But then we're actually also bringing the rest of the infrastructure to help you evaluate, to test, to monitor, to put the right guardrails, make sure agents don't go wild in the enterprise. So, that's actually the progress. So actually, the industry's moving very, very fast.>> So the use case you see, the preferred future that's happening very fast is the vibe coding of apps inside an enterprise or workflows. Which by you say vibe coding because everyone knows how easy it is. Everyone sees the Replits and the Lovables of the world. But the problem is once you build your app, "Hey, look at my app.", and then you hit a button and the API doesn't work. I mean, it's hard. Even vibe coding, you get excited by the use experience, but to wire it together-
Arvind Jain
>> Is very hard.... >> very difficult. It's even harder on the enterprise. Forget the web. So, take me through how that works because if Glean, on our last interview you said that Glean's the enterprise brain. Now you've got the graph, the enterprise graph, so you have almost like neural connections to data. Take me through this process of how you guys see the enterprise, Glean enterprise graph, which is connected to the enterprise brain, and how that makes agents work.
Arvind Jain
>> Yeah. So firstly if you think about any agent, agent is doing some work, it's trying to perhaps take a business process and automate it. So as part of that, you have to do a few things. One, you have to actually work with some information that's inside your enterprise. You're going to apply AI and its brain to actually do something interesting, something that humans could do before but now AI can. And then finally, you're going to actually take that work that it just performed and you're going to probably log it, recorded somewhere, in your enterprise systems. So any agent that you build, it requires that connectivity. It requires connectivity to your enterprise knowledge. We do a good job of that because Glean comes with hundreds of these enterprise integrations to most common enterprise systems. So if you build an agent and you need some data that you need to access, it's very, very easy and seamless in Glean to do that because Glean's already connected to all of enterprise systems. The second thing is that we actually now allow you to take actions. You want to send an email, maybe you want to update an object in your CRM, you want to go and file for PTO, which requires you to sort of take some action in an HR system like Workday. So, there are all these actions that agents need to take to actually perform work. And so, Glean has actually come out with a very extensive actions library. We have thousands of actions across hundreds of different enterprise applications that you can take to make agents to work. So, those are some of the core building blocks that we are actually putting together.>> So, this action library uses the Glean enterprise graph, which is new for you guys. And is it behind the scenes? So if I'm an end user customer or I'm the user, "Hey I want to take PTO. Hey take PTO, tell people." So to me, it should be nothing to the user. What happens behind the scenes? Is it, or do I take action or just prompt?
Arvind Jain
>> Yeah. Well, you just talk to an AI system. So, Glean assistant is your sort of go-to AI companion. You go to Glean assistant, you ask it questions, you give it task, and it can actually perform that. And behind the scenes, it's orchestrating the right agents to do the right work. Now this example of like, "Hey, file a PTO for me.", this doesn't really require much intelligence. It's basically you just need the ability to actually make the right API call.>> That's pretty straightforward.
Arvind Jain
>> system.>> But another request might be, "Hey, check Arvind's calendar on September 24th. Is he available from 10 to two, and is he in the area?"
Arvind Jain
>> That's right. And that's also reasonably deterministic, so you can actually do that. But there are a lot of other agents which actually are doing work that requires judgment.>> Give an example.
Arvind Jain
>> Well, even this one for example. Let's say that you want to schedule an urgent meeting and you want to do it in the next three days. And my calendar is always full, so now you actually decide that, well, I need to actually think about is there something that we can actually push out and make room for this new important meeting that we're going to have? So when the tasks actually start to become like that, that's where the enterprise brain, the graph actually comes into play. You have to sort of deeply understand a business, how it works. And maybe I'll just add one more thing. The agents, so far there's been struggle. There are basic frameworks available, but agents have often been misfiring because they just don't have enough context. And they have not learned how humans actually do work inside the enterprise. So it's not just about connecting with the enterprise knowledge and documents.>> The mechanisms are APIs, but you're getting at context. That's different.
Arvind Jain
>> Context and actually... So one thing is of course, reading the knowledge, reading the documents, understanding how the company works. But the other thing is actually understanding humans and their behavior and taking day-to-day tasks. And how do people actually complete those tasks? So, that's the new level of understanding that now Glean is building. Which is where we are going and understanding every job that is to be done inside the business. And we're looking at how humans are actually performing those tasks. And then you use those as sort of the baseline, as the learnings that AI can now use and learn from human intelligence, and actually start to do that work itself.>> I want to ask you about signal versus noise. In fact, SiliconANGLE's original motto in 2009 was, "Extracting this signal from the noise", was our famous line. Signal to noise ratio, you kind of pointed that out in that example of, "Hey, I might want to push something out. What's more urgent?" That's contextual too because you have dependent variables going on here. So, I want to ask you about that ratio, signal to noise. Also, if you go back to the old search days, which you know, you invented it with Google in those days, there was always two variables, context and behavior, contextual and behavioral. Some would optimize for one or the other, whether you're a search. But now you have both going on the behavior of the work and the people and the context are two critical variables.
Arvind Jain
>> That's right.>> How do you look at that? Now, it's complicated in the enterprise. You want to schedule something and figure out what your priorities are, what your signal noise is, so how does it know what your noise is and what my signal is? Or is it just in the data or is there more work that needs to get done?
Arvind Jain
>> Yeah. I mean look, enterprises have tons and tons of data. If you have 10,000 people and they're working eight hours every day. And the amount of information that gets produced, the amount of decision making that happens, it's actually at a very, very large scale. And you can build certain things as core abstractions inside the company. For example, you can say that, "Hey look, I want to actually identify the hundred most common jobs where our enterprise collectively is maybe spending 30% of all of their human time." And so, you sort of get those hero use cases, the key tasks which are very, very repetitive in nature. And for those you start to build very deep understanding of how those things happen so that now AI can actually take them over and do them independently. But a lot of those things actually are... AI is not going to automatically build. It's not going to be the case that you just suddenly from today to tomorrow, you say that today humans are doing all the work and tomorrow AI is doing all of it. It's not going to be like that. You'll have to actually still go and take specific tasks, specific business processes, and you're going to have humans that are actually going to go and build that agent and actually inform AI. There's a lot of data that's available, but there's still some instruction that's going to come from humans .>> Well, that's what I was getting at with the signal of noise. So context of behavior, you've got to nail that in the data. That's reasoning, superintelligence, road to superintelligence for the enterprise. But then it's the individual because you're announcing the upgrade to the assistant. You've got agents now and you've got the data platform with the graphs, on top of everything you've got. That means that you're going to be in a position to set the table for that use case. Where are you seeing the enterprises on their journey there? I mean that's a great north star, but I mean you're going to ship this product? What's your hopes and ambitions for the product? Immediately? Are they ready, basically? That's my question. Are enterprises ready for this?
Arvind Jain
>> Enterprises are very eager to deploy AI. This is one thing that's universal. It doesn't matter what industry vertical you are in, everybody knows that AI is going to change how we work, and everybody wants to be prepared. So, there's a lot of appetite for bringing AI into their company. And even though it's hard, it's difficult, and you're not exactly getting all the ROI on day one, but that's not actually making anybody put brakes on their AI initiative. So, there's a lot of appetite. And companies are thinking of it in a few different steps. So first is that, "Hey, can I just get AI education to happen?", as we talked about. So, let's bring some tools that people can make part of their day-to-day work and they start to learn AI and leverage it. And then you'll like, typically what we've seen is businesses are actually now creating an AI center of excellence. The AI center of excellence basically goes and works with all the different business units and departments.>> They're rushing, they're accelerating there, basically.
Arvind Jain
>> And they're picking their top, building the roadmaps, their top 10, their top 20 agents that they want to go and build. And then work starts to happen on those. And so there is still, I would say that phenomenal progress that is being made and the results are showing up in pockets. For example, when you look at engineering, software engineering, you look at customer support, you look at your business sales teams, go-to-market motions. Now, agents are everywhere actually. They're making a lot of work that we used to do, you had to have .>> And they need the data platform to do that. So I want to ask you, what do you think the future of the data platform is? If you had to just throw out your vision and statement around if someone asked you, "Arvind, what's the future of the data platform?", what would you say?
Arvind Jain
>> Yeah. Well, I think you will see... If you think about data platforms in the pre-AI world, we have these warehouses from companies like Databricks, Snowflake, Google, Microsoft. And you take your structured data and the enterprise, you sort of aggregate it from all the different places, and that has been your data platform that has powered like a lot of business intelligence. But the future data platform looks very different. It's a data platform that's meant for AI use cases. And so number one, this data platform actually aggregates unstructured information, which is 90% of the enterprise's data and data as such. So, the new data platform is actually going to contain both unstructured and structured information. It's also going to have very different paradigm in terms of how you actually access this platform. You're not going to be issuing SQL commands to retrieve data. It's going to be very conversational, very informal, where you can actually just express in natural language what you want from the data.>> And the check enabler is what, graphs? What's the key technology enabler?
Arvind Jain
>> The key technology behind that first is enterprise integrations, retrieval stacks that you build, search, search is a big part of it. And then on top of that, you still have the same, depending on... This data platform is rich, so depending on the type of the question, it actually figures out, do I need to search, assemble a bunch of things, and then do the fuzzy matching extract information? Do I need to do some kind of enumeration? Do I need to actually take the question and actually convert it to SQL and actually execute data retrieval in that fashion? So, it's actually all of that. The new data platform, that's the capability. It handles unstructured and structured data, and it has different ways to fetch that data.>> Does scale matter? Because the stack's great. Okay, that's really good definition and illustration. Now I'm thinking, "Okay, what's the minimum requirement from data access? Do I need?" Certainly the more data you get, the better the brain works. So, you want to have all connective tissue working for your advantage. Talk about scale. Do you see scale can be done in pockets? Do you have to go faster? The more, the better? Is there a roadmap? Is there a template in your mind in terms of, okay, I want the benefit of the data platform. I want enterprise superintelligence.
Arvind Jain
>> Yeah.>> What's the scale equation look like? What's the scope of that?
Arvind Jain
>> Yeah, Well, see that the right architecture is to build a horizontal AI data platform for your enterprise once. And so you're not going to actually say that, "Hey look, I have 10 different teams. They're trying to build 10 different types of agents, and they're all doing their own thing." No, that architecture's not going to work.>> It's trouble.
Arvind Jain
>> You have to actually first invest in this holistic horizontal AI data platform where you bring all the company's data and context together. And this is actually, you need the system to be really scalable. Because the future also remember that today your knowledge is basically your documents, your databases. In the future, it's all conversations, it's all meetings, every single->> It's the brain....
Arvind Jain
>> work that somebody->> It's a digital twin of the organization.
Arvind Jain
>> Exactly. So, that's lots of information and you have to actually bring it all together. Because when you bring it all together and when you connect it with each other, that's when you have the full context. That's when you can actually really power, make AI capable of making complex decisions inside your enterprise.>> I think the context angle is huge because everything's contextual from the eye of the beholder. I mean that's why I used to love search. Whether it's some kid in the dorm room or executive in the boardroom, they just got to prompt and the result has to match the context.
Arvind Jain
>> That's right.>> Yeah. And I mean, simple keywords easy to say. But now you're getting into the nuance of the platform. Is that a data lake or is that a data plane? When you say horizontal architecture, what do you mean? Obviously Glean, you'd probably be highly motivated to make your product a standard, but I see your architecture. So, explain that. And then what would be the consequences of not doing that down the road? And what's some of the benefits, what comes out of that, what's enabled from it?
Arvind Jain
>> So the key primitives of the data platform, AI data platform has to be number one, that it is very flexible in terms of the kind of questions it needs to be able to answer. It's not restricted to some kind of a database like row scan, a simple sequel. It has to be fully natural in terms of its capability to take an arbitrary complex, a question expressed by human, and has to be able to answer that. Behind the scenes, it can use variety of technologies like visit search or database scanning and things like that. But that's sort of the key technology .>> I guess the question is... Let me rephrase because I don't want to go in the weeds too much 'cause that'd be great. I know you don't have an hour. Okay. It sounds like it's hard. Going to the cloud's easy. I move a workload, I got S3, I got EC2, got an app there. Just services there. No problem to say, "Boom, here's an architecture horizontal.", it seems really difficult. And then what do you do, just plugs stuff in? I mean make an enterprise do that, it would seem to be difficult, or am I overstating it? What would it mean to do that? If I say I'm a customer, I have a database estate, I got all this stuff, what does it mean to just make it a horizontal?
Arvind Jain
>> Yeah. I think first, we bring that solution to the market. So our platform, this has to include, number one, enterprise connectivity. You're not actually replicating making copies of all of enterprise data one more time for this data platform. So, it's sort of also virtual in design. So you bring a technology, it connects with all the different systems, and the connections could be actually through open standards like MCP and others. You don't have to always make data copies to actually power this data platform. And similarly the platform, once you build it, you have to make sure that it's accessible to everybody inside your enterprise. There's going to be, any business is going to have four or five different agent platforms.>> You want to make a frictionless to connect to data.
Arvind Jain
>> Yeah.>> Make it easy to integrate.
Arvind Jain
>> Yeah. And the way you do it is first, you are connecting through MCP, through STPIs to all the individual systems. And then you are exposing an MCP-based layer to the rest of the enterprise ecosystem. So, let's say that you go and build agents in OpenAI or Bedrock or Vertex, there's so many different frameworks. All of those Asian frameworks, they can seamlessly access this data platform through standard protocols like MCP or A2A to actually fetch that enterprise context to deliver their work.>> I bring this up because the news you guys are announcing is an extension to the personal assistant, which is great. But you're getting into agents, and this has been a big thing in the industry now. People think agents are overhyped. I mean they're definitely hyped up, no doubt about it, but there's real work being done at the foundational level, like what you guys are working on. Because it's not that hard. It's not super easy and nothing's easy in the enterprise, but it's doable, it's attainable. So, you got the agent piece and you got the context platform. That is what you guys are doing with the graph. So, the graph is key to this.
Arvind Jain
>> That's right.>> Explain the value of the Glean Enterprise Graph.
Arvind Jain
>> So, I'll give an example of an agent that you want to build. So, let's say that for my sales team, we want to prospect, we want to actually find new customers to go to. And we want to send them the right messages. So if that's my task and I want actually for a given account, find the right people and send them the right messages, I need to actually go and first understand, have we actually talked to this business before? Who are the different people that we may have made connections to? So, you need to go into your CRM systems to actually get that information. You need to understand what this business is about. See if you have actually success in similar businesses before, like if we have case studies from them, you'd actually pull them through. You need to go look in the web and see if there are new news articles on it. How can I actually give them a right relevant message which appeals to them today? So, that's the work that you have to do. And that's where the graph actually comes into play, where you actually build this, bring all of this context on this customer, use the customer 360 view to bring all of this right information. And then actually generate those amazing sort of emails that you can actually go and send to them. So when you think about work, like any agent that you build->> You got to have the pre-work done. The requirement's got to be in place.
Arvind Jain
>> Exactly. Yeah.>> All right, so how do you answer the competition question? Wait, there's so many graph databases out there. I got this graph, I got that graph. I mean, it's like vector embedding. Everyone has that now. My head's exploding. Does it interoperate? How do you answer that question?
Arvind Jain
>> Well, so Glean's solution is more meant to be turnkey and end-to-end. And so there are graph databases, like you said, there are search technologies, there's of course database technology. So, they're all there as individual technology components. We in fact don't even build many of those things. We actually leverage these technologies as they already exist in open source, for example. Our value is in connecting everything and then operating at a higher level, which is we are going to actually go and deeply understand how people work inside the company.>> You do or don't?
Arvind Jain
>> We do.>> No, or you're focusing on that.
Arvind Jain
>> We are focusing on that part.>> Not necessarily in being the best graph database. You leverage whatever you could do.
Arvind Jain
>> That's right. We leverage, yeah, technology for that.>> It's just the feature in the platform to ensure, get your mission.
Arvind Jain
>> Exactly, yeah. And so the idea, the way I think about Glean is that it's the collective human intelligence of enterprise now, and all in one place. We have learned, for example, how do people approve invoices in this company? We have learned, how does the legal team goes and red lines ?>> So the new term is ESI, enterprise superintelligence.
Arvind Jain
>> That's right.>> It's like an AGI for the enterprise.
Arvind Jain
>> Exactly. And it's fully based on observations. AI by itself doesn't know anything about their company. So, we're just observing. We're continuously looking at how your employees are working, how they actually take given tasks, how do they complete it. And then use all of those observations as hints that you can actually now in runtime give to AI when AI has to solve a similar task.>> I know we're a little short on time, but I do want to highlight at the beginning of this interview you mentioned you got to build the platform for AI. And interesting, you go back 25 years, I mean enterprise search was a nightmare 'cause IT was a problem. Not that they were a problem, it was just the way they did things. You had servers, you had databases, so the environment really wasn't about search. Now, people tried to come in and make search work and then it's like it was a nightmare. You know that business, everyone who has done anything there knows what I'm talking about. If you don't, good, don't. Today, that's the key thing. So I want to ask you again, because I think this is a super important point. My friend runs a big bank, he runs the shop over there, he's got tons of stuff going on. He's thinking, "How do I design this system for the future?" Okay, there's a lot of stuff under the covers, but they want to get to that point where they can enable the user to be the most productive user possible. And they want to not foreclose any upside, and they also want to manage costs 'cause that's the best of AI. You're trying to solve that problem. So, what would you say to that person at a dinner table or in conversation around the steps to take? Because that's what everyone's working on right now. What's the stack look like? I'm building AI factories, I'm going to have a lot of compute. I'm going to have horsepower, I'm going to have all kinds of network fabric, storage fabrics, all at the benefit to enable value.
Arvind Jain
>> Well look, for most enterprises, they have to operate at a high level. They should not be investing in data center capacity and building models, training models. That's meant for, let's say the AI companies, the technology companies. As an enterprise, you have to use turnkey technologies that are available to you, out of the box models that you can put to work for your use cases. And the investment that you have to do is making sure that your data and your context is available to AI. And you can do that by investing in a horizontal AI data platform like Glean. Which is you bring it, you connect all the systems, now all of your business data is available to any agent that anybody wants to build in the enterprise. So, that's where you need to make that investment. And you have to make it in a horizontal manner like as ->> For Glean, what's the commitment to make that investment? Because I mean, I see the demand there. Is it a huge lift to come in and bring Glean in? Take me through landing zone or commitment that to me as a customer, what would I need to do?
Arvind Jain
>> Yeah. Well Glean, we actually built it for it to be fully turnkey. My goal when I started the company was that somebody should be able to actually deploy Glean in a few hours. Now, I think we failed at that. We didn't realize->> That's a great North Star. Maybe someday with an agents.
Arvind Jain
>> Yeah, because enterprises are so complex. Technology-wise, it's very simple. We are turnkey. You can actually go into our product, you can actually go and click every single application that you want to connect with glean. And it's literally a matter of few clicks for you to get connected, connect all your systems. But enterprises of course, have their processes, change management.>> The workflows, process data management, compliance.
Arvind Jain
>> Yeah. And your security and your compliance. Exactly right. So the heavy lifting isn't sort of just going through your processes, but typically we can be up and running within a month inside a business. And we don't need engineering investment from our customers.>> And you have use case value with search that hits home right out of the gate.
Arvind Jain
>> That's instant gratification, right? Because you bring glean and now you have a really amazing AI tool which knows everything about your day-to-day work. And you can give it to your employees. So the value comes without... You don't have to actually go and build agents to get value. Our master agent, the glean assistant is already there to add value for all of your users.>> That's why I love this market, because you can have a great product and have instant success. If you go back in the old days, you have to do tire kicking these. So, kick the tires as an expression. You think when you go in and look at a car. But you don't have that anymore because the old days was POC and then you then have to go for the big integration. Not anymore. You can get Glean now and implement it, and then work through what your needs are, workflow process.
Arvind Jain
>> Yeah. I mean, we still have to go through POCs. People still want to test.>> Yeah, of course. It's not blind. I mean of course. But it's easier.
Arvind Jain
>> But it's definitely easier and it's easier because of how we built our product, but also because the underlying software systems have also evolved. From those 25 years back, applications running in a data center don't even know where the server is and where the data is, to now SaaS and there are APIs. So, it's definitely easier with SaaS to actually go and connect with all these different systems.>> All right. you got the news out there, you got the new enhancements. I love the graph, you know I love graphs. People know I've been fantasizing about how the value is there. It's super valuable in this market. The context, everything's lining up for you. What's next? What are you optimizing for? What's your vision on the next year? Hyperscale is going crazy, starting to see the rise of the big CapEx platforms. That's only going to bring in more capability, so faster processing. It's going to make the brain enterprise brain better, so make it smarter. What's your vision for the next year?
Arvind Jain
>> Yeah. Look, we've been beneficiaries of the innovation that's happening at the model layer. In the last six months, we've seen models develop so much more thinking capability and just allowed us to actually truly enhance our product. Our assistant, like the new version that we are launching as part of this release actually now doesn't feel like a piece of software to me anymore. Now it actually feels like a colleague who has all the context, is knowledgeable about my company. And actually , this was a sort of a shift that happened in my mind where I felt like we had great technology before, nice, very smart search engine and AI summarization capabilities. But it's always felt to me like I was talking to a machine, now little assistant feels like I'm talking to a human. So, that's a big leap that has happened. But in the next, coming to the next six months, one year, today Glean or any other AI tools, they're largely reactive tools. Like you can go to ChatGPT and go to Glean, give it some tasks, it's going to do it for you. But I think the future's going to be different. In the future, AI is actually going to walk with us, and it's going to be super proactive. And that's the vision for us with Glean assistant, is that we want it to be that true personal companion to you at work.>> And it makes the organization smarter and it makes it a thinking organization.
Arvind Jain
>> Yeah. But the most important thing here from our vision perspective, is that a lot of time AI... Habits are hard to change. And so, we see adoption of AI inside the enterprise. Some people adopt it a lot and others don't as much because this don't have time for it. But when you make AI proactive, then you actually bring the value to every single person. Because imagine this AI that actually knows everything about you. It knows your tasks, it knows what you need to do this week. And if it actually comes to you and says that, "Hey, look, I saw you had to do these 10 tasks. I knew how to do seven of those, and actually I've actually already done it for you. Just approve it and you're done.">> The old expression how to break habits is to have a reward system. And revenue and productivity is a great reward.
Arvind Jain
>> Exactly, yeah. Because well, I don't have to do this work anymore like did it for me.>> Arvind, great to see you. Again, congratulations. Been following you since the beginning of your venture. And again, I love how your perspective's changed, love this reasoning layer you're building, superintelligence for the enterprise. And again, you're not shying away from the tech either, which is also great. Thanks for coming on.
Arvind Jain
>> Thank you so much for having me.>> Appreciate it. Arvind Jain, again, serial entrepreneur. Again got a rocket ship escape velocity venture as the market shifts to the modern era of AI infrastructure that's going to enable new kinds of capabilities, new kinds of experiences, productivity, and of course good business and just overall societal outcome. Of course, that's the promise of these AI factories. And the software that's running on them. This is theCUBE doing our part to bring you the data. I'm John Furrier, thanks for watching.
