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Autonomous By Design
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.
In this interview from theCUBE + NYSE Wired: AI Factories – Data Centers of the Future, Mika Saryan, vice president of software at Formic, joins Jewel Li, co-founder and chief officer of Tensor, to talk with theCUBE's John Furrier about how physical AI is shifting from sci-fi concept to production reality across manufacturing floors and public roads. Saryan explains how Formic delivers robotics-as-a-service to small and mid-sized manufacturers, compressing deployment timelines from nearly a year down to weeks through AI-driven configuration. Li details Tensor...Read more
exploreKeep Exploring
How is your company bringing robotics to market in the context of the new era of physical AI and autonomous systems?add
What is your perspective on the role and adoption of physical AI (robotics) in manufacturing as we approach GTC?add
What does your company do, what are you building, and which market or customers are you targeting?add
What does Formic provide, which types of manufacturers use its robotics systems, and can you give examples of customers?add
What do you think will be the main focus or big discussion at NVIDIA's keynote and sessions at this year's GTC?add
>> Hello , welcome to theCUBE Studio here in Palo Alto for a special edition pre-GTC breakdown, but also breaking down the industry as GTC from NVIDIA is their largest conference of the year. It's an industry event. It's where all the AI elite and developers, builders, investors, and operators will all be there. This is a special episode. We've got six panels. We're kicking off with the Autonomous Panel by Design, Autonomous by Design. Mika's here with Formic. Mika, great to see you. Thanks for coming on, VP of Software. Jewel, co-founder and chief officer of Tensor, the first L4 car out front of our studio, thanks for coming on, kicking off this panel for the NYSE Wired program.
Jewel Li
>> Great to be here.
John Furrier
>> So obviously GTC last year, go back past two years. Jensen has been very vocal about physical AI. I think what you guys are doing represents, to me, the execution side of what's been building up for two years. Now, it's kind of mainstream and it's kind of not a strategy risk anymore for companies, but it's more of execution risk. As people start to get the value propositions, this new industrial era is upon us. You got the car, you got the robots in the factories. Mika, let's start with you. You're bringing robotics to the world that haven't really seen them before. In some cases, if they tried it, they've seen now the modern version. What's your take on the physical AI piece as we lead into GTC?
Mika Saryan
>> Yeah, absolutely. As robotics is growing within these factories, there is a lot of differences within the manufacturing sector, how robotics is introduced. On one side, there is a lot of aerospace defense companies. And for them, they're building greenfield factories completely from ground up. And on the other side, there are a lot of small and mid-sized manufacturers. And for those factories, robotics is fairly new. And what is important for those and what started being enabled over the past couple years is the ease how the robotics could be deployed. So we create a lot of ecosystem, how we can identify their problems, and we can deploy the robotics and bring it to light-
John Furrier
>> Take a minute-...
Mika Saryan
>> in a couple of years....
John Furrier
>> to explain what you guys do so people know what you guys are building, the market you're targeting.
Mika Saryan
>> Absolutely. So Formic provides robotics systems for manufacturing and very much we're focused on small and mid-sized manufacturers. For them, a lot of the time they might not have robotic systems or they only have one or two, and we make it very easy for them to deploy the robotics. So we'll sculpt the solution. We will provide all the deployment and we will provide monitoring and maintenance. All our customers, we provide robotics as a service, so they pay hourly or monthly. So it's very easy for them to deploy it.
John Furrier
>> Give an example of the kind of company, if you can, that's using your stuff. I mean, I'm trying to visualize this so people can understand the kind of customers you have. This isn't like Ford Motor Company with all the building cars. You guys are doing something to explain it. Give an example.
Mika Saryan
>> Exactly. So some of the companies I can name, Garrett's Popcorn. We have a customer in California Mi Rancho. If you go into some of the stores, you'll see Mi Rancho Tortillas, and they provide tortillas for a lot of restaurants and stuff. So again, they're more kind of small size. A lot of the time it's brownfield factories. So they have been operating for many years. They're-
John Furrier
>> With more-...
Mika Saryan
>> family businesses-...
John Furrier
>> people labor....
Mika Saryan
>> exactly. Exactly. Very often you'll see them tracking on the clipboard, how many cases they palletize. So it's not like the automotive factory or some of these brand new defense factories. They're very kind of old school and we're making it easy for them to operate
John Furrier
>> We're going to-...
Mika Saryan
>> the robots....
John Furrier
>> come back to this in the panel because I want to put a pin in that because the... I won't say small, medium-sized business, but the mid-market is a huge adopter in this new hyperconverged, the new physical AI will come back to not just the big enterprises or the startups. We'll come back to that. Jewel, you got the car out front. Autonomous vehicles, people think of it, they think of Waymo, they see that. Robotaxis.
Jewel Li
>> Yeah.
John Furrier
>> You guys have built I call pure autonomous vehicle from the ground up. Explain Tensor, the car. Why is it different? It kind of looks like a Waymo, but it's not a retrofit. Explain what you guys do.
Jewel Li
>> Yeah. Tensor Robocar, that's our product name. It's the first robocar out there that is built from the ground up for personal ownership and beyond just robotaxi ridesharing. The car is a ground-up-designed vehicle so that when the car rolls off the assembly line, it is L4-capable already. And the whole vehicle is homologated or certified as a product by itself and can be sold to consumers. It is the world's first vehicle that has a folding steering wheel. That only existed in sci-fis before, where you can enter Level 4 mode and the steering wheel goes away, and you can take over and drive by yourself by taking the steering wheel out anytime you want.
John Furrier
>> So this is hard problem. So people who have been following this might know L4. Explain the L4 and what is different around some of the other approaches.
Jewel Li
>> Yeah. I think in our car, in a Tensor, it's very clear what L4 means versus a L2 or L3. The steering wheel goes away. There is no way for the person, the driver's seat person to control the car anymore. Of course, there are other ways where you can tell the robot what to do, like a pullover, you can talk to it. There are buttons to push, but it's a totally different level of autonomy where we, as the manufacturer, as the autonomous driving provider, we take full responsibility. That's what's important. That's the key difference between L4 versus lower layers.
John Furrier
>> As we look at the physical AI, which is these are great examples, scaling and building realities kick in. So take us through a story or approach that you guys have done to get this into the market because in some people's minds or even the realities could be years in some use cases, but it's happening pretty fast. Can you guys just share the time it takes to build these physical AI systems? I mean, you guys have been working on it for a while.
Jewel Li
>> Yeah, a while. We've been working on it for 10 years, and autonomous vehicles are not a new concept. People have been talking about it and seen it for almost a decade. So in the past years, what happened is on the product and hardware side where the industry is shifting to a volume production Level 4 autonomous vehicle, no longer retrofit, but volume production for scale. And on the other hand, it's recent development with physical AI and generative AI that makes this technology can scale faster and make decisions and act more human-like in long tail .
John Furrier
>> What's been the breakthrough that has accelerated the timing recently? Can you put your finger on the issue or what's been the key driver?
Jewel Li
>> Sure. I mean, there are foundation models where there are basically still generative models, but they don't generate text or image. They generate actions. We use foundation models on our vehicle. We also use vision language action models for a slower thinking to solve very, very challenging, complex scenarios that takes longer reasoning. There are also world models where you can simulate the whole world, train the robot, validate the robot, let them self-reinforce themselves and get better.
John Furrier
>> You mentioned sci-fi, that's great. I mentioned earlier about the wheel going away. It seems like a sci-fi movie. Mika, sci-fi might come to mind when someone's working in a factory, could be mid-market you mentioned. What's the timing on your end look like? Because they're now seeing product that was literally sci-fi. If you go back a decade, it's like no one thought a popcorn manufacturer or any kind of mid-size manufacturing or business would have robotics.
Mika Saryan
>> Right, exactly. And there's a lot of changes in the last few years how quickly we can deploy. Traditionally, there has been robots in the factories for a while, but it took a very long time to make those deployments. Very often, the lead times would be like 50 weeks, a year to make a deployment. Now with all the software advancement and AI advancement, we're able to deploy them much faster because we can quickly configure it for specific factories. So we went from almost a year, many months to weeks we can bring the system. And that's what's very important for a lot of this smaller plants because they're very flexible. Their products change, their line change, so it's very important for them to say< Oh, I want the robot to do this." And we're able to actually change it very quickly. And then the next week, they might be using some other products or making other products. We can quickly adapt and change it. And that's what software and AI allows us to do in the past couple of years.
John Furrier
>> And why the mid-market so hot right now? Is it because they're the ones who get the best benefit out of it, price point, capability?
Mika Saryan
>> Exactly. So they get a lot of benefits from lower prices on the robotic side, but also its capabilities. Before it was like automotive plant, they would build the plant for many years and then would operate. But for this mid-sized factories, again, things change weekly. And next week they may be making other product, they may be lost the contract and they need the ability to quickly change what the robotics does.
John Furrier
>> We saw that at MWC. A lot of that mid-market is underserved. So these advances come in. Jewel, I don't think you're targeting the mid-market for your product at this point. You're kind of a luxury buy because-
Jewel Li
>> Yeah....
John Furrier
>> I think what you're doing is a scientific breakthrough. You got the AI, you have all those things you just mentioned. How are you thinking about your go-to-market with Tensor? Obviously, it's a product that no one's really seen before. I mean, I can imagine like my iPhone's got all this capability. It's been converged, iPod originally, now it's got a computer. Now, cars are going to have this personal experience, which is-
Jewel Li
>> Yeah....
John Furrier
>> new. You're targeting...
Jewel Li
>> That's a very good analogy. We're trying to build the iPhone in this age, not a Blackberry. I think one of the thing that is very unique as well for the user experience with a Tensor Robocar is that it's not only autonomous. Autonomous is one thing. We have seen... Well, Tensor have been driverless since 2020. There are other companies who has been driverless as well, but what kind of user experience are you bringing for the initial adopters, for the high-end customers? This car is not only autonomous, it's also agentic. So this morning, I could tell my Tensor, "I want to go to the NYSE Wired and theCUBE Studio shooting," and it will be able to use tools, open my calendar, get the event, grab the address, send it to the routing, and enter Level 4 and drive me here by itself. When it's arrived, I can also say, "Tensor, drop me off at the building by the front door and go find a parking spot," and it will do that by itself.
John Furrier
>> And it's also play your favorite songs or do the briefing, review the material-
Jewel Li
>> For sure....
John Furrier
>> so some work or have some play. I mean, this is a whole nother level. What's been the reaction as you guys... I know you've been doing a lot of touring around with the product. What has been some of the reaction?
Jewel Li
>> Oh, we've seen it's very rewarding to see people's reactions when they first see the car. Everyone have their own points that they like and they point it out for us. But I think a lot of the times we see people, they've very, very happy with the agentic side because this is a very high-tech product. A lot of times it's a little intimidating as well, like, "How do I use this?" And nobody read the 300-page user manual. So, we will-
John Furrier
>> Maybe an agent for that....
Jewel Li
>> yes. We have an agent where it can understand your natural languages and it's just so much easier and more accessible.
John Furrier
>> The Industrial Agent wrote a post yesterday, just posted at previewing GTC. We haven't seen this kind of advancement both on the investment side, CapEx investment coming in and breakthroughs since like the cloud era, but that was a software revolution. This is an industrial wave. It's physical and digital. So it's like this is seeing this from anywhere from crypto infrastructure all the way through the AI world that the physical and digital coming together is happening. So I have to ask you guys, what do you think is going to happen at GTC this year? Because last year I thought was probably his best event for Jensen and NVIDIA, and they had a good event in D.C. It's going to be hard to top that one, but I'm sure they will. Any thoughts on what you guys think is going to be the show, format, big items?
Jewel Li
>> Well, I think everyone is definitely looking forward to the Vera Rubin architecture. And for us as well, compute is one of the largest problems, either inference on the edge or training in the cloud. We needed both. So the Vera Rubin architecture is set to be about 10 times more powerful than what we have right now. And Tensor already have 8,000 TOPS in the car. It's like a supercomputer on wheels. I can't imagine if we can have like 10 times that.
John Furrier
>> Yeah. I mean, it's an AI factory on wheels. It's got everything in there. Networking. I mean, talk about some of the tech in the car. Well, you brought that up. So let's go there. What's in the car? What's the coolest? How much NVIDIA, how much components are in there? What's the architecture look like?
Jewel Li
>> Well, it has over 100 sensors on the car, on the exterior and on the interior as well because we need to use it not only for autonomous driving. We need it for corner cases. We also need it for in-cabin agent use. That's why there are like over a hundred. Therefore, there is a large ton of data that is generated every frame, every second. And we push all of that data and process it through 8,000 TOPS of compute. That is like four to eight times larger compute than previous generation AVs and maybe 50 to a hundred times more than a regular assisted-driving L2 vehicle.
John Furrier
>> Do you guys think about like the upgrade process? And I'm kind of nerding out here in real time, but the cars got all that horsepower with compute, networking, all that AI in there. Is it like an upgrade cycle? Like, "Okay, Vera Rubin swap-out." It's hard to swap out components. What's your thoughts around the car's headroom, so to speak?
Jewel Li
>> That's a really good question. And that's why we really pushed to the limit to have 8,000 TOPS there. Actually, we have to have 8,000 TOPS, we have eight ThorX superchips. And when we presented our solutions to NVIDIA, they were even surprised. They didn't even know that this could be done. Exactly for the reason that you said, a car is a physical product. Once it's delivered to a consumer, you can't upgrade the hardware like a software. So we have to build for the future, and models are getting larger and larger every year.
John Furrier
>> The good news is, and I wrote this in my post, there's a paradox around hardware becoming more valuable over time. It's interesting. The old computing model was next chip, lower prices, so that the prices would go down. You're seeing the software interesting. The H100's actually just as popular in some cases. So the hardware is leverageable. We're seeing that.
Jewel Li
>> The cost per TOPS may be going down.
John Furrier
>> Yes, cost per TOPS is going down and cost per tokens. Mika, talk about your piece because when you think about your tech, GTC also talks about their ecosystem. You're seeing tons of solutions merging. Okay. You got the hardcore tech with the robotics. Great. Check. But there's now an ecosystem building. The supply chain, they're kicking butt. Now, they're growing fast ecosystem.
Mika Saryan
>> Yeah. And obviously I'm sure there'll be a lot of humanoid talks at GTC. That's a lot of videos. I've seen dancing robots, fighting robots. That's the big thing that's important today. But what we're seeing in reality and real world, the development ecosystem, it's still trying to catch up. So one of the bottlenecks right now that we see is that a lot of the companies who want to develop, let's say, humanoid robot or more flexible form factor for the robots, there is no ecosystem that already exists. A lot of the companies start connecting all the sensors together. You need to bring the vision from one part and then do the simulation. So there's still a lot to work on, on that ecosystem piece, and also on the hardware pieces to come together as well as-
John Furrier
>> And what do you think-...
Mika Saryan
>> ....
John Furrier
>> and what do you think for this year, GTC? They're really transparent. It's not like they're hiding the ball, but they're also going to have a focus. I'm curious what you think will be the big discussion from the keynote and the sessions with NVIDIA this year.
Mika Saryan
>> I think it'll be around how we bring together this humanoid robotics and make it real in this world. Because again, we need to work a lot on the hardware and the reliability, on the software infrastructure, the development ecosystem. There are a lot of pieces that need to come together for this humanoid to be available in home environment, factory environments, and what is the right form factor for those pieces as well.
John Furrier
>> And rumor has it it's going to be a Tensor car and some humanoids at the NYSE CUBE Wired party later tonight.
Jewel Li
>> Yes, we are.
Mika Saryan
>> Exactly.
John Furrier
>> Yes, okay. All right. Locking it in. All right, final question to wrap up. What's your personal goals this week? And NVIDIA, obviously there's a lot going on. It'll be very, very busy. Business development, technical recruiting, learning, squinting through the announcements, connecting the dots. What's the focus for this week? What are you looking to get out of it? Mika, we'll start with you.
Mika Saryan
>> Yeah. For me, primary goal is to identify what are the real cases where we can maybe explore and deploy humanoid robots into factories. What is the right form factor? How we can make them real, how we can find more robust solutions that will operate in this kind of factory environments because it's not easy to work for 16-hour shifts in cold environment, hot environments, dusty environments. So what kind of form factor would work there?
John Furrier
>> Yeah. And there's so much software, the agility, the ability with touching and feel-
Mika Saryan
>> Exactly....
John Furrier
>> is happening is huge.
Mika Saryan
>> Exactly.
John Furrier
>> Yeah. Well, appreciate what you do. Thanks for coming on.
Mika Saryan
>> Thank you for having me.
John Furrier
>> Jewel, wrap us up. What's your focus? What are you looking to do?
Jewel Li
>> Well, we always love those events because for a Robocar, for automotive product, we have thousands and thousands of components and suppliers, partners who we have to work with together to make it happen. And for events like this, we meet them all. It's very efficient for us to meet a lot of companies within that one week and we can know what they're working on that is cutting edge that can help us.
John Furrier
>> Yeah. For you, ecosystem for you also means supply chain-
Jewel Li
>> Yes....
John Furrier
>> partners, recruiting.
Jewel Li
>> Yes.
John Furrier
>> A lot of co-design.
Jewel Li
>> Yes. Definitely.
John Furrier
>> Stream co-design as NVIDIA calls it. Well, thanks for coming on theCUBE. Really appreciate it.
Jewel Li
>> Thanks, John.
John Furrier
>> Thanks .
Mika Saryan
>> Thank you for having us.
John Furrier
>> I'm John Furrier. This is our day one kickoff of GTC, Actually, day zero. This day starts tomorrow. Of course, the NYSE Wired celebration tonight in San Jose. Thanks for watching.