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 data scientists) can create production-grade agents.
The conversation dives into Glean’s vision of the enterprise brain powered by an enterprise graph, highlighting the importance of deep context, human workflows and behavior to reduce “noise” and drive outcomes. Jain outlines core building blocks – hundreds of enterprise integrations and a growing actions library – that let agents securely read company knowledge and take actions across systems (e.g., CRM updates, HR tasks, calendar checks). He discusses how organizations are standing up AI Centers of Excellence, prioritizing “top 10–20” agents across functions like engineering, support and sales, and why a horizontal AI data platform that unifies structured and unstructured data – accessed conversationally and stitched together via standards like MCP – sets the foundation for AI factory-scale operations. Looking ahead, Jain says Glean’s upgraded assistant is evolving from reactive tool to proactive companion that anticipates tasks and accelerates productivity.
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Varun Chhabra, Dell Technologies & Anne Hecht, NVIDIA
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 data scientists) can create production-grade agents.
The conversation dives into Glean’s vision of the enterprise brain powered by an enterprise graph, highlighting the importance of deep context, human workflows and behavior to reduce “noise” and drive outcomes. Jain outlines core building blocks – hundreds of enterprise integrations and a growing actions library – that let agents securely read company knowledge and take actions across systems (e.g., CRM updates, HR tasks, calendar checks). He discusses how organizations are standing up AI Centers of Excellence, prioritizing “top 10–20” agents across functions like engineering, support and sales, and why a horizontal AI data platform that unifies structured and unstructured data – accessed conversationally and stitched together via standards like MCP – sets the foundation for AI factory-scale operations. Looking ahead, Jain says Glean’s upgraded assistant is evolving from reactive tool to proactive companion that anticipates tasks and accelerates productivity.
Varun Chhabra, Dell Technologies & Anne Hecht, NVIDIA
Anne Hecht
Sr. Director, Enterprise ProductsNVIDIA
Varun Chhabra
SVP, Product MarketingDell Technologies
In this interview from theCUBE + NYSE Wired: AI Factories — Data Centers of the Future, Varun Chhabra, senior vice president of ISG and telecom market at Dell Technologies, joins Anne Hecht, senior director of product marketing enterprise at NVIDIA, to talk with theCUBE's John Furrier about how AI factories are emerging as the new unit of enterprise value in the agentic AI era. Chhabra and Hecht reflect on a post-GTC landscape where agentic AI has become the defining conversation, with OpenClaw and NemoClaw signaling what Chhabra describes as a "ChatGPT momen...Read more
exploreKeep Exploring
How are enterprises reacting to the recent GTC/NVIDIA announcements about agentic AI, and what are they looking for from AI solution providers?add
How can enterprises safely and securely adopt rapidly evolving AI technologies, and what role do vendors and ecosystem partners (for example Dell, Google, and NVIDIA) play in enabling on‑prem, confidential, and distributed AI deployments?add
What do customers need from an AI partner when adopting AI, and how does Dell's AI Factory with NVIDIA address those needs?add
What trends are you seeing in how customers are deploying AI (locations and use cases), and how is the Dell AI Factory with NVIDIA addressing those needs and delivering results?add
What infrastructure and economic considerations should enterprises weigh when deploying always-on, self-evolving AI agents that consume large numbers of tokens (i.e., whether to rent or invest in their own compute)?add
What were the main announcements this year, and how do they address the primary obstacles to enterprise AI adoption—particularly around preparing enterprise data for AI and enabling models to access that data for large‑scale inferencing?add
Varun Chhabra, Dell Technologies & Anne Hecht, NVIDIA
search
John Furrier
>> Welcome back everyone to theCUBE here at our New York Stock Exchange CUBE studios. Of course, we have our Palo Alto studio connecting Silicon Valley and Wall Street. This is our AI Factory series. We talked to the leaders who are making it happen, building out the infrastructure, accelerating the enablement of the new GenAI software layer and services and human involvement in those software layers. We've got two amazing companies building the future, have Dell Technology and NVIDIA. Varun Chhabra is the SVP of ISG and Telecom Market at Dell Technologies. And Anne Hecht, senior director of Product Market Enterprise at NVIDIA. Varun, great to see you. Anne, great to see you guys back. CUBE alumnus. Thanks for coming in. What a year it's been for AI factories. I will tell you that it is now mainstream conversation. GTC at NVIDIA's conference, Anne, Jensen on stage talking. Pareto curves now have different tiers of performance, that's a portfolio. You're starting to hear productivity and monetization. And of course, everyone should have an OpenClaw strategy. This is a now tell sign that the future is a moved in transition. We've crossed the threshold from AI factories to actually real world applications. So I want to get your thoughts. Post GTC, how are you feeling? What are you seeing? What's been some of the results post GTC?
Varun Chhabra
>> Hey, John. It's great to be here on theCUBE. Thanks for having us. Look, GTC was such an incredible event as it always is for the industry. And the word that I think is top of mind for everybody is agentic. With OpenClaw and the announcements NVIDIA made around NemoClaw, it does feel like we're at that ChatGPT moment for agentic. Everybody's asking us about how to adopt agentic faster than ever before. So certainly we'll talk a lot about that. But if I take a step back, at its core, what enterprises are looking for has not changed from us. They're looking for someone that can integrate this increasingly complex ecosystem for AI, bring it all together into one seamless solution that brings together the infrastructure, the AI models, the AI software, as well as increasingly data, approaches to data and brings them all together into a sort of a turnkey, easier to deploy, quick to adopt AI technology. Because at the end of the day, what customers and companies are looking for is an ability to get started as fast as possible and then be able to actually get value out of it, time to value as low as possible and get time to value faster than ever before. And what we've seen is while maybe there's a perception out there that it's easy to get started on in the public cloud, what we have found consistently through all of our thousands of deployments of the AI Factory with NVIDIA is that once you're operating at scale, doing it on-premises is better. It's better to do it from an economic perspective, as well as from a governance and security perspective as well.
John Furrier
>> Anne, talk about the enterprise piece, because what Varun just talked about is really something that we've been seeing for years now. It's mainstream. Distributed computing hybrid cloud is the standard. So it's not about cloud and on-prem as much anymore. It's about the systems working together. And the ecosystem NVIDIA is building with GTC was clear that it's growing so fast. That's also in the enterprise. So the enterprises now have a clear line of sight into standing up AI factories in whatever form. Take us through what you're seeing, what's changed, what's your view on this?
Anne Hecht
>> Yeah. And it's changing so fast, which I think is one of the challenges that enterprises also have. Last year, we were talking about reasoning models because DeepSeek had just dropped. And now we're talking about agents that evolve and create other agents because OpenClaw dropped. And so with every one of these waves we're, and especially working with Dell and the ecosystem, evaluating these technologies and then bringing it to an enterprise in a way that they can leverage these advancements, but safely and securely so they can always get the best advantages from AI and use these technologies that are coming out so quickly. But they need a trusted advisor to help them through that process, which I think is what we really strive to do with our ecosystems and what Dell's doing with us. And technologies like confidential computing, Jensen talked about that on stage too, that's where you see the ecosystem coming together where frontier models like Google's Gemini model can now run on-prem on a Dell server because they're building a confidential computing stack. So you see even vendors that you wouldn't have thought Google and Dell would come together for a solution, but here they are bringing enterprises a frontier model, Gemini that they can now run anywhere where they want to do their AI, to Varun's point. Enterprises are going to have their AI workloads running across a very distributed architecture and as an ecosystem, we need to build the systems to make that possible.
John Furrier
>> It's interesting. You mentioned a few things. They were seeing accelerated computing move to accelerated everything. And you mentioned the speed. One of the things we're seeing, and I would like to get both of your reactions to this, is that it's changing so fast that there's a lot of decisions that are being made over a longer horizon, multi-year. So architecture is a huge kind of conversation. So the enterprises are really challenged with the speed side of it, and they want to have reliability, they want to have confidence. How does that translate in some of these big AI Factory decisions, Varun, and, Anne, how does NVIDIA view this? Because they need to have something that's going to be not only great, but have enough headroom to grow into now that you have the Pareto curves that are now segmented by performance. You have the workloads and the software, you have confidential computing and security. So everything's being accelerated so fast. How are customers thinking about this and what do they need to see? Again, that's the number one question we're getting is I need to know what's going to change, what's real, what's legit. And of course they like Dell and like NVIDIA, you guys are world-class brands. What are their challenges with the speed game? Can you share your thoughts?
Varun Chhabra
>> John, you're absolutely right. The speed of innovation in the AI space continues to accelerate, and it has been the case for the last three or four years. And yes, keeping up with the latest infrastructure, the latest models and the capabilities of those models and what the software that surrounds this ecosystem can do can be incredibly hard. But to me, it actually puts a finer point on what we've been seeing, what NVIDIA and Dell have been seeing over the last three or four years since we announced AI Factory, which is customers want a trusted advisor. It's not just about the technology. The technology is an important part of it, but the journey starts much earlier, right? It's about thinking through what your use cases are. What's going to get you the maximum impact? How do you decide from hundreds or even thousands of use cases that keep coming out of the woodwork as there's so much excitement around AI in every single organization? How do you get your data strategy right? The foundation that your AI infrastructure sits on top of is the data. So that's also very, very important. And of course, you want the confidence to your point that the solution that you're adopting is going to be scalable, is going to keep up with the needs that you have over all the evolutions that are going to happen in the next few years, as they have been over the last few years. And I think the AI Factory, Dell AI Factory with NVIDIA has really, really been built with a lot of those key principles in mind. We've got a very, very big use case focus that we announced at GTC. A lot of solutions that are specifically aligned around things like coding assistant, knowledge assistant, computer vision, et cetera, et cetera. Things that we know are going to help customers get to value faster. We announced the Dell AI Data Platform with NVIDIA, which accelerates all aspects of the data life cycle, starting from ingest to faster inferencing and governance as well. And then of course, we continue to adopt the latest innovation that NVIDIA has on the infrastructure side. We announced a lot of new platforms on the compute side that take advantage of the innovation on the Vera Rubin platforms, as well as what's happening with networking that NVIDIA is accelerating so fast now. All of these things, the partnership that we have with NVIDIA, the close engineering relationship means that customers can rest assured that as things change, and of course, as you pointed out, they are changing and they will continue to change. They have the confidence that the two companies are working faster than ever before to kind of continue to deliver the latest innovation to customers on their platforms so they can feel confident about how they'll be able to keep up with the pace of change as they move forward.
John Furrier
>> Varun, I'm really glad you brought that up because one of the things we're seeing is people who try to cobble together an AI factory, I won't say cobble, maybe it's a bad word, cobble together, or design their own and do piece parts. It's challenging because it is a system, Anne you mentioned that system piece. I think you had over 30 innovation announcements at GTC. 30. You're kind of being humble there. And talk about this relationship dynamic because one of the things that's key to NVIDIA has been the extreme co-design. This is a huge ... I mean, it's a very nuanced point, but this is what separates the quality. Can you share your thoughts on the co-design and then how that helps customers and the enterprise get that confidence?
Anne Hecht
>> Yeah. So we co-design across our stack, and there's six, seven chips in the stack, depending on how you want to add them all up. And so we design across that whole hardware stack to optimize performance, whether it's at the networking layer, the compute layer. Obviously, we're bringing up to CPU with the introduction of Vera. And Dell is with us along the way building out the systems that take advantage of that co-design and a full server stack. I think the other point I want to make, which is really important to this audience is a lot of times when Jensen's on stage, he's talking about the NVL72. It's a huge system. A lot of those Prado curves, that's what they're based on. And those are amazing systems, but not everybody, not every enterprise, many enterprises in fact, are not going to be able to start there. They need to start with a PCIE, a form factor of a GPU and a server that fits into their data center and their compute infrastructure. And we've done co-design in that stack as well, and we've brought that to market as well with Dell, with the software and the full stack. So they get great tokenomics on that system. And that system, because it is PCIE, it fits in with some of the infrastructure and probably standardizations in terms of Kubernetes and operating system that they've already made. It supports all of those standardized IT form factors. And so an enterprise can really easily start their AI journey. They don't have to start with the big gorilla, NVL72, which is amazing. We want everyone to have those, but not everybody can. Our IT team does not run our IT operations on NVL72. They have a bunch of RTX 6000s and B200s and B300s. So I think the breadth of options is really important to talk about with the enterprise customers because there is one that fits their environment. And the other thing Varun and us have started to talk about is the AI Factory for an enterprise and an organization, of course it's going to be a server form, if you will, but we also have these great compute options for individual developers and for running inference at scale even on a GB10, on a station system that actually can even run a Kimi K2 model. So your token generation doesn't necessarily just happen from a factory or from the cloud, but it can be happening at somebody's desk and really be temporal and dedicated to a developer or a user even that's doing AI really quickly and wants that dedicated access.
John Furrier
>> Yeah. I think it's a great point. And tokens are the key. And hey, I don't care where I get the tokens as long as it fits. I mean, you're seeing a demand on the hyperscalers, the neo clouds. I know Dell, you've been very successful there, Varun, with the Dell AI Factories, but at the end of the day, the enterprises may get service from multiple providers and on-prem because that's where the data is. Also, you can manage costs. Why not have your own factory managing all your tokens? I don't need to pay for tokens if I'm going to be doing a lot of tokens on-prem. And nice scenarios. So you have a lot of diversity seeing that. I guess my question is, can you share what customers are doing with this now? Can you give some examples post GTC? Where's been the adoption? What's been the orientation? Where's their sentiment? Can you share some examples of where they're deploying? Do they have a preference? Are they leaning to neoclouds as a service? Are they bringing on-prem? What are some of the behaviors that customers are doing right now?
Varun Chhabra
>> Yeah, absolutely. Look, as it's always been in the industry, it's not a one size fits all, John, right? You and I have talked about this many times before, and it continues to be the trend that every customer is different. Every customer is unique. Some customers are optimizing for one location. Some customers, many customers are looking at multiple locations, whether it's public cloud, their own data center, as Anne mentioned, increasingly desk side, and then also the edge. So that's a huge part of this. So we believe that the future is going to continue to be hybrid, and certainly the success we've had jointly with NVIDIA, with the Dell AI Factory with NVIDIA, 4,000 customers for the Dell AI Factory with NVIDIA in just two years since Jensen announced it on stage at GTC two years ago. And this momentum is only happening because of the unique value we're delivering, the ability to really scale across different use cases. And I'll give you a couple of examples, kind of in very different industries. So McLaren Racing, everybody knows about them. McLaren Racing is using AI to accelerate their design cycles and elevate the performance that their cars are getting on the track. They were able to cut the component design time for new car components by 90% using the AI Factory with NVIDIA, which is an incredible bar for how fast you can move. And then on a completely different business, Lowe's home improvement stores, AI is being deployed across a variety of use cases from developers that are using AI assisted code development, like we're seeing a lot of people do, or AI assisted code reviews, all the way to retail associates in the Lowe's stores that are helping customers in store with their questions, latest information at their fingertips, insights, all delivered through AI. You can't think of two more different businesses, but the gamut just shows you the kind of locations, development, use cases, et cetera, that the Dell AI Factory with NVIDIA is able to help customers with.
John Furrier
>> Anne, what's your take on this? At GTC, when we had our meeting with Jensen, we weren't allowed to take notes, but he did say, "Coding is great to have AI do, but software engineering isn't the task of coding, it's the task of solving problems." And he was pointing to the AI impact or the intelligence in all areas. This is a factor in some of the customers. Can you share how that's translated post GTC because that was a provocative statement. And he actually said, "I wish all coding went away." As long as they still solve problems, it's all good.
Anne Hecht
>> Yeah, yeah. And I think I was talking to some people, I think he said coding is going to go away like seven years ago and now we have agents that are doing coding. But you still need people who are going to review those agents, who are going to validate and audit and verify the quality of the work that the agents are doing. So I also don't want people to think that all of a sudden we don't need any software developers, we don't need to understand software development. Of course we do. We have to be the experts, the humans, the adults in the room to manage, but then also take advantage of all these great advancements. The other thing Jensen says when he talks about AI, and especially when he refers to our use of AI, coding was actually a really important use that we adopted very early on and made a lot of sense for our business and we use it for chip design and our whole chip design process, as well as we use AI for our supply chain. And some of that's even on Dell servers, as Varun knows. And what Jensen will say to enterprises is, "Think about your core competency in your business." Varun talked about some of the use cases that they're developing, and there's a variety of ways we can help customers get going with AI. But we need the customer to think about, "Where will I get the most value? Where is the core part of my business that I can accelerate and bring a higher level of intelligence to?" And that's where the revenue and the token that ... When we talk about tokens, that is really just a way to quantify intelligence that's coming out of the data and the AI from the factory, and that's driving direct impact to P&L. And you're going to get the best impact when you focus that AI investment on optimizing your core competency and the core part of your business. Yeah.
John Furrier
>> And that's why he mentioned monetization. Varun, I want to go back to you because the coding task that Anne was talking about that Jensen's comment really points to the value creation and extraction in revenue, not just cost cutting. Okay, engineers aren't going away, they're going to solve problems. But the coding use case is a precursor to agents. And so let's talk about agents for a second. We think it's going to be a big year for agents this year, the agentic infrastructure layer, MCP, all the kind of like DevOps-like things that Kubernetes solved in the cloud native world we're going to see in the agentic world, and then the Cambrian explosion of AI native apps. That's a pressure point for you guys to pump out more factories with NVIDIA. So agents, take us through your state of the market post GTC on the agentic, because it was certainly main stage.
Varun Chhabra
>> Absolutely. And as I said upfront, it is something that is top of mind in every AI conversation we're having today, John. I think customers and organizations can see the value and where agents can help them with automation, with streamlining their operations, with unlocking a different level of productivity. And like you said, it's not just about cost cutting. It's actually about increased revenue operations and new opportunities. The vision and the promise is there for everybody to see. What I think customers are now realizing is that it takes work and preparation to get there. And I think that's where we are right now. We're going to see a massive acceleration. But getting your data right, thinking through your use cases, thinking through your technology stack, those things remain as important as ever. And I think one thing that will be very interesting to see is already, John, on the coding example, even without agents being fully mainstream in production environments, the most common challenge you hear on GitHub boards or all kinds of even conversations that we have with customers is the most common example I hear is some variant of, "Well, I used up my monthly token allocation" or, "My developers used up our monthly token allocation." Let's say it was a month and they used it up in a week or two weeks. And as these technologies get better and better, we're going to see that happen even more because more people are going to adopt it, more people are going to see the value. So I think from that perspective, it's really, really important for customers to think about, yes, the cost of poor token is going down, but the Cambrian explosion is actually going to be an adoption. So we think that customers are actually going to see that AI builds rise even with the decline of poor token economics or poor token costs. This economic question's going to be even more important than ever. And then the other conversation we're having with customers about agentic is security governance, right? As we've seen with OpenClaw, these things are incredibly, incredibly powerful, incredibly capable. How do you think about the right sandbox? What are the trade offs between what control you provide the agent with the trade-off of the productivity you get out of it and how much it can connect your systems? I think we're very early days in that, but people are starting to see that those are going to be conversations and decisions that need to get made before we can roll out agents across all workloads and all production.
John Furrier
>> Anne talk about the NVIDIA side, because two years ago, RAG, search, retrieval augmentation generation was hot, check, easy, low hanging fruit. Obviously then you have obviously the compute piece and AI factories. Now with coding, that's the first marker in the enterprise. Agents unlocks enterprise appetite and tokens feed the agents. They eat tokens for breakfast, lunch, and dinner. They can have a huge appetite for tokens. Talk about the impact from your perspective on the token availability to enterprise growth.
Anne Hecht
>> Yeah. I think Varun hit on a lot of it, but these agents that now are self-evolving, creating agents, you can set an agent to do a task and then come back in the morning and it's done it. And it's done research. It's a report. It can take actions on your part with your approval, of course. And it's generated and burned through a bunch of tokens to do that. And so you need systems that actually can support that level of inference, like almost always on inference at scale. And you need to, Varun's point, look at the economics of that and very quickly, you can pay off, and we have found within months, someone will pay off a GP10. It's like depending on their workloads, a developer might pay that off in two or three months in terms of the amount of tokens that they're generating. And then all the work after that is just gravy because they've already paid off the investment in the system they're running that workload on. So I think enterprises have to look at the variable costs of their AI that they have right now. And if capital investment in infrastructure makes more sense for them, to Varun's point, so that they can have all that headroom to create tokens 24/7. And they also don't have to depend on availability to that infrastructure. And enterprise can manage the availability and prioritize those workloads, which sometimes is harder depending on your contract relationship and where you're renting your AI Factory if you're using a third party and not owning your own infrastructure.
John Furrier
>> Well, as you guys move the needle on the AI infrastructure, AI factories and the agents, it's going to unlock the floodgates for adoption, we predict. I know you're tight on time, but I want to get one more question, if you don't mind. I know you guys do a lot of customer briefings that shape a lot of the deals and the announcements that go in. What specific things shaped this year's GTC announcements, Varun, on your side? And Anne, how did NVIDIA play in that? And then second part of the question is, what are you guys looking at now for feedback for customers?
Varun Chhabra
>> I can get started. So for us this year, as you said, 30 announcements, John, across the board covering the core infrastructure, so compute, networking, data, even on the solution side. So we certainly covered the wide gamut, but I would call out two things that I think are really tied to what customers are seeing today as big blockers of enterprise adoption. One is data. So how do you get your data ready for AI? How do you get your AI models and your AI applications to be able to see the vast amount of enterprise data that exists structured, unstructured in the public cloud, in the private cloud, out at the edge, as Anne mentioned, increasingly desk side? How do you get AI models and applications to really get a sense of all of that to process it and make it available for inferencing at scale? This is a very, very non-trivial problem. So the Dell AI Data Platform with NVIDIA was a huge investment from our joint companies to really help customers with accelerating the data life cycle for AI workloads. We work closely with NVIDIA with innovations like NVIDIA had with QDF and QVS to really bring acceleration into the data processing lifecycle for the first time perhaps. So that was one. The second thing was really this focus on, as we started the conversation upfront, how do you deliver turnkey solutions? So we're doing a lot of work with the Dell Automation Platform, working closely with NVIDIA to actually create blueprints that can help accelerate the deployment of the entire AI stack, whether it's infrastructure, the software that NVIDIA is delivering, models, as well as capabilities on top of that, all of those things, how do you deliver them in an automated fashion? And then as the ecosystem grows, how do you actually get more and more of these third party ISVs and latest software innovations from NVIDIA supported on top of the AI Factory with NVIDIA? That was another area of focus for us to really help automate the deployment of the entire stack.
John Furrier
>> Anne, what's your perspective on the feedback from customers that fed into the relationship?
Anne Hecht
>> So what we're hearing for enterprises is they're really excited about what's happening in the market, they want to take advantage of it, but they want to do it in a very safe and secure way. And they also want to do it in an economical way that fits into their business practices, complies with their governance, is stable, reliable, and that's the work we're doing with Dell and the solutions that we're building with Dell and taking out to market. So what we're hearing from enterprises is a sense of urgency and passion to get started on AI and take advantage. And many of them are already doing it, but they're trying to move from a chatbot prompt response type AI that they might already have and move it into agentic, which obviously is much more dynamic. And there's a lot of different variables they have to take into account as they build out this infrastructure. And that's really the opportunity and the challenge that Dell AI Factory is addressing.
John Furrier
>> Well, we really appreciate you guys for coming on and sharing. Of course, the more intelligent, the faster the game goes, faster innovation happens. And of course, we got sovereign cloud, sovereign AI, sovereign hybrid, you got geographic all managed via the intelligence. So the faster we can get those factories up and running, faster that software and systems can be deployed, the more greatness for everyone. Thanks for coming on theCUBE. Really appreciate the input.
Anne Hecht
>> Thanks, John. Thanks, Varun.
Varun Chhabra
>> Thanks for having us.
John Furrier
>> All right. I'm John Furrier. This is the AI Factory series. Really, the AI Factory is bringing in the next generation infrastructure super fast. This is the AI infrastructure powering the growth in agents, coding, human solving problems, they're in the loop, societal change and it's going to impact how we work and play. And of course, theCUBE's doing its part to bring you the coverage here from the NYSE CUBE studios. Thanks for watching.
John Furrier