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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Bob Beachler, Efinix
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, Bob Beachler, corporate vice president at Efinix, joins theCUBE’s John Furrier to explain why edge AI is quickly becoming the proving ground for physical AI. Beachler details how Efinix has spent more than a decade rebuilding FPGA architecture from a clean slate, aiming to deliver high performance and low latency in power-starved environments where liquid cooling is not an option. He also outlines how embedded RISC-V processors and AI acceleration are expanding what engine...Read more
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What is the unfair advantage of using AI at the edge in applications?add
What are the key factors enabling new AI applications and the challenges associated with processing various types of information for these technologies?add
What is heterogeneous compute, and how does it relate to different types of processors and efficiency in systems?add
What design considerations were taken into account for creating a new FPGA targeted at edge applications?add
What is the current and projected market size for the FPGA industry, and what factors are influencing this growth?add
What are the key markets and applications for self-driving cars and sensor processing technologies?add
>> Welcome back here to theCUBE here at our NYSE studio. Of course, we have our Palo Alto studio connecting Silicon Valley and Wall Street. I'm John Furrier, host of theCUBE. This is our AI Factory series. AI on the edge, a big theme coming up for us this year. You're going to see a lot of action around how data and at the edge and AI factories are producing the kind of AI outcomes. Bob Beachler here is the Corporate Vice President of Efinix. Bob, great to see you. Thanks for coming into theCUBE. Appreciate it.
Bob Beachler
>> Hey, John, thanks for having us. We're really excited to be here.
John Furrier
>> You guys have been, I won't say stealth mode, but kind of stealthy in your execution. The hottest trend right now is AI infrastructure. You're starting to see real benefits of these new large scale systems, AI factories. And now you look at the edge piece coming online. You got Mobile World Congress, MWC, coming up. We expect to see a lot more AI at the edge. The role of language models, large language models, custom models, certainly hot. But computer vision is also a big part of the AI theme. Tell the story of Efinix, because you guys have been operating for years in a product building mode with the technology, now kind of coming out and starting to tell the story.
Bob Beachler
>> Yeah. Yeah. The company reel has been quiet. They've been focused on building new products, disruptive products. We've assembled a dream team of people with pedigrees from the larger FPGA players, Alterra, Xilinx, Lattice, you name it. The team has been responsible for some of the most popular programmable products on the planet. And the nice thing about it is that you could take this intelligence and start from a clean slate. No baggage from previous architectures, previous technologies. So the founder, Sammy, the co-founder and CEO, Tony, the CTO, really started with this clean slate, were then able to create a new disruptive type of FPGA ideally suited for edge AI applications.
John Furrier
>> Yeah, we're seeing the role of NVIDIA. Obviously on the boards here on the offices, they're trading all the time. Tesla, NVIDIA. NVIDIA started as a graphics card and well documented in their history. And then when AI comes in, matrix multiplication, the application of what they were building then and now actually line up perfectly. FPGA, same thing. What's different now with the technology? What's it built for? How have you guys seen that? Because it's not yesterday's architecture. You mentioned that. What is the unique secret sauce? What's the unfair advantage?
Bob Beachler
>> Yeah. There's a few things. First, what we're seeing is that AI at the edge is transforming the applications. It's making existing applications better. Your medical equipment's getting more intelligent. Your safety equipment knows more about its surroundings. And that's a combination of new sensor technologies, but also running the AI at the edge. And so for FPGAs and with this disruption, we're an ideal platform for that because they're trying to integrate new sensors, they're trying to do new applications. And so it's not just the old RTL or HDL logic. Our chips have to have the logic capabilities, but we have embedded RISC-V processors. We have to be able to accelerate AI neural networks for that high performance, low latency applications so you can have real time responsiveness. So we're seeing entirely new applications. You were probably at CES. Humanoid robotics was the big thing, right? Okay, which could not have happened without AI and without edge types of chips like Efinix, which can do that high performance, low latency, but also at a power consumption that's not going to burn down the planet.
John Furrier
>> Bob, it's interesting. If you look at the major trend pillars, we've kind of lived through the AI 1.0 or say phase one, large language models, frontier models, and we see the benefits. Consumers are flocking to the ChatGPT, Claude, Anthropic, and a variety of others, Gemini for Google. Then we moved into the agentic wave. That's the hype right now and you're starting to see benefits. CES, Retail Week here for theCUBE, and NRF, okay, there's going to be some middleware. I call it middleware, but agent software doing things. After that, the discussion is already being discussed now around physical AI. That's the manufacturing plant. That's the humanoid. That's the car. That's the autonomous side of it. Where the physical digital meet, that truly is where the human is. That is the edge. It's power, you've got intelligence. So we're going to see that progression. So we're in agent mode now. Physical AI is coming strong, which is an edge application. First of all, do you agree with that? And two, how does FPGA fit now? And how would you define what that does? Because the systems are being built now by engineers to get that physical AI up and running.
Bob Beachler
>> Yeah. Yeah. So what's happening is that AI, like I said, is enabling these new types of applications, these new types of products. There's still a huge problem, which is what do we see around us right here? We have all sorts of auditory information, visual information, temperature information. Being able to process that, clean it up so that an AI can act on it requires a lot of processing power. And that disruption is where FPGAs come in, because it allows the engineers to be able to do things at the hardware level and adapt to these new technologies, do the processing, do low latency response through some AI acceleration so that I can have the motor control and I can do the servos. I can make the hands actually work. You probably heard about the hand problem in humanoid robotics. So that's where it's all happening. Now, granted, the data center is a huge opportunity, obviously. And there's very large language models in the agentic AI, but AI started in vision, right? The first AlexNet was about ImageNet and about a vision application. And so that's really where the disruption started to happen. And that's why the edge and AI at the edge is so exciting.
John Furrier
>> AI factories, I've been asked many times, "Explain that to me in plain English." It's kind of like a factory. It's not cars or anything being built. Data comes in, outcomes come out, and that's essentially tokens or whatever people call it. When you look at vision, the amount of data coming in on vision is massive. So that's one point. The second point is that when you look at where computer vision is going, it's like, okay, the systems that need to run this aren't like just the chip where you have developers that work on silicon, you're starting to see the supply chain and the engineering around the chips. And now you've got a systems construct where now application developers, AI native developers, are going to start to build apps. So you've got system architecture design, large scale systems, and developers. Not just hardcore, I call silicon developers, I don't know what the actual word is, but like people who are in the weeds on silicon to actual people building AI apps. So this new abstraction layer of software is going to sit, and we see that in open source right now in AI. That's a feeding frenzy with developers. Take us through why that's important because as software comes to the edge, the apps will need to run on that. And how do you talk to that specific systems architecture play and also enabling the developer?
Bob Beachler
>> Right, right. And one of the things that people knock about FPGAs is like, "Oh, they're really hard to program." Because it's HDL. It's like I have to have a PhD in electrical engineering. Great. We're trying to make it easier for people to make their hardware, but we also have to have separate flows for the different types of developers. So yes, we have the hardware developer flow, but we have embedded processors, so C, C++ types of coding for a traditional sequential programming. And then for the guys who are writing the AI, the neural networks and the AI applications, we have another flow so that they never have to touch HDL, right? It's like, I don't want to go to computer scientists and explain to them, "Okay, this is how you make a flip flop or whatever."
So they want to stay in their environment and in their lane. So they want to use their TensorFlow or their PyTorch. They want to be able to have APIs. And so we're abstracting that so that, for those developers, they don't have to get into the weeds that the electrical engineer may be doing in terms of integrating the sensors and integrating the system.
John Furrier
>> And that's the key, this abstraction. Talk about the impact to the design side and your customers, because now you're starting to see ... We all saw the big AI systems, there are millions of dollars. Then you start to see a new kind of configuration of like, I have a little bit of a GPU, I'm going to mix the chips and put them together in a system. And those systems might be smaller for say the edge or big for big data centers. So you're starting to see ... They're not mutually exclusive. They have different use cases. Take us through that piece of it.
Bob Beachler
>> Yeah. So what you're touching on is something called heterogeneous compute, where I have maybe a CPU, maybe I have a GPU, maybe I have an embedded AI accelerator. And so in FPGA land, we live heterogeneous compute because we do have sequential risk processors. We do have this parallelized hardware design. We do have the AI acceleration. And so you have to be able to play in all those different worlds because that's the way you get efficiency. Certain types of things that you need to do in your system need to be in hardware. They need to be super fast, low latency. Some things, command and control are complex and you don't need immediate millisecond response. You can do that in sequential code. And then for your AI, you need to have certain parts of it run really fast on the edge, but then I want to take all this video back to the cloud, do analysis of it, extraction, and get some actionable content out of it over time. And so you have to be able to play in all those different domains.
John Furrier
>> Yeah. What's interesting, you guys have been 13 years, kind of under the radar, but not stealth, like it's a secret. In the industry, people know who you are in the circles that you've been executing. You just have not been doing a lot of marketing. Now you're going to start to come out and tell the story. What is the secret sauce? How do you look at that story? Now that you've got the market developing, again AI infrastructure is super hot, NVIDIA is educating everyone. You got the military applications with and others. This is now a nice stuff because people are hungry. They're building new stuff. The devices have software. They're going to have to be specialized and high performant, high velocity data, low latency. All this is coming to the edge.
Bob Beachler
>> Yeah. Yeah. So the secret sauce that the team had was that really, at a blank slate, redesign, reimagine what an FPGA can be. So we really flipped the script in terms of the architecture, which allows us to be significantly lower power and significantly smaller. Smaller means cheaper. These edge applications are brutal in their form factors. I have a box this big. I can only have a couple watts. I can't blow air over it. Liquid cooling's right out. Write that one off.
John Furrier
>> Good for the data center, not good for the edge.
Bob Beachler
>> That's right. So we have a different set of constraints, and the company focused on that from day one. We've said, we're not going to make the biggest piece of silicon we have that's going to be 150 watts that you're going to have to blow air over. It's like we want to be able to enable the engineers at the edge. So we're talking about milliwatt to five watt types of chips so that they fit in these form factors. We also integrate memory inside of our packages, so it's super small form factor. And so we can enable things that people can't do. One of the greatest examples is that we had a customer go and make a thermal camera. In 14 days, they had a thermal sensor. They used our FPGA to do the thermal processing to do recognition, run AI on it, and then send it back. 14 days.
John Furrier
>> That's amazing.
Bob Beachler
>> From conception to deployment.
John Furrier
>> Scope that in terms of the alternative. Without the technology now, how long would that have taken? Cycle wise? I mean not 14 days.
Bob Beachler
>> Sure. I mean, if you were going to do a custom ASIC for something like that, A, you better make sure you have the volumes for it. Two, do you have the money to do the mass costs? Depending on the process note, that can be $30 million. And it's 18 months development cycle. And one of the things we see is that-
John Furrier
>> And by the way, no product market fit yet. That's just the tax to get into the game.
Bob Beachler
>> Exactly.
John Furrier
>> Basically unattainable. This is my point. This is where I'm seeing massive energy in this market because the enablement that you guys are enabling is going to be awesome. Explain for people who don't know what FPGA is. What does it actually do? There's been a lot of discussions certainly in the AI factories, the role of the chips, the processors, the HBM, high bandwidth memory, SSD. So it's re-imagined memory architecture, storage architecture, compute, architecture. What does FPGA mean? What does it fit?
Bob Beachler
>> Yeah.
John Furrier
>> What's it do?
Bob Beachler
>> Yeah. So the acronym is Field Programmable Gate Array. Back in the day, there's something called a gate array, which was basically an ASIC. The thing about our technology is that it's a programmable ASIC. So we ship it, it's blank slate. So the hardware engineer has infinite possibilities of what they want this chip to do. And they can design it and compile it and get it running in hours. And so the velocity of which people can innovate, that's what we're enabling. And that's really the key to it. And because AI is disrupting things, anytime there's turmoil, people are re-imagining architectures, there's new sensors coming out, there's new motor controls, they have different protocols. FPGAs are the logical-
John Furrier
>> So to translate that, if I understand this correctly, you are enabling a programmable platform goes to hardware to enable a software abstraction for that application.
Bob Beachler
>> Right.
John Furrier
>> Did I get that right?
Bob Beachler
>> Yeah, exactly. Exactly. So you're writing, quote unquote, "Software." It's a parallelized software in a language called HDL, hardware description language. But you're programming the chip. But instead of running sequential C code, it's parallelized, so it goes all over the chip. So that gives you tremendous performance increases, low latency, as opposed to running a bunch of code.
John Furrier
>> And then developers can just write apps on that and then you guys can connect it through. Okay, great. Let's talk about money, the market opportunity, because this is where we're seeing a lot of custom apps, whether it's retail, healthcare. Anywhere where there's a camera or any kind of AI capability with data, this is a hot area. What is the market opportunity? How do you see this?
Bob Beachler
>> Yeah. So if you were to look at traditional FPGA market, depending upon which market research firm you look at, it's 6, $7 billion a year in revenue. And certainly we play in that business, but what we're seeing is that with the disruption and edge AI, there's a much bigger marketplace involved because you're enabling new applications. That marketplace doesn't include things like humanoid robotics. We haven't really seen autonomous vehicles deployed in millions of units worldwide, factory automation, that type of stuff. So we see that the actual market size can be on the order of $25 billion in the next five to seven years, right? And so that's a tremendous opportunity.
John Furrier
>> Yeah. It could be higher. We don't know what we don't know. Certainly in the use cases, take us through some of the obvious low hanging fruit use cases, automotive. We all see self-driving cars, a lot of opportunity there. Where are the key markets you're seeing right out of the gate?
Bob Beachler
>> Yeah. And because we ship a blank chip, we can use in a lot of different applications. The company has shipped over 1,500 customers in its history. We've shipped over 50 million units. So it's very diverse. But you mentioned autonomous vehicles, LiDAR processing. Where we sit, we do the sensor processing, AI, and then the motor control. Used a lot of medical equipment. Because we're small, low form factor, low power for endoscopy, portable ultrasound. Anytime you have a lot of algorithms, you have a lot of processing that you need to do in hardware, that's kind of where we sit.
John Furrier
>> So capability-wise, you hit the new use cases, but you're also, in a way, the platform for autonomous. Explain that because this is where I'm connecting the dots. Okay, I see some immediate benefits. Autonomous is going to be the big, AI to autonomous. Like we went from on paper to the internet and mobile and cloud. Now we got AI to autonomous, and everything in between.
Bob Beachler
>> Right. So autonomous, as we talked about before, you have so much sensory information and you have to have the data ready to go for your artificial intelligence to be able to make snap decisions, to be able to respond to what's going on in its environment. And it may not have the luxury to be able to go back and talk to the cloud and come back. So you got to put it all in this small form factor. So what we do is the sensor integration, processing it, cleaning it up, fusing, for example, thermal and imaging together along with some tactile. Put that all together, run some neural networks, control the motors to be able to respond immediately to your changing environment. And that's huge. We talk about LLMs. It's like, it's text. There's one input, one output. In the truly autonomous case where you're looking at this type of environment, you have so much more data. It's dramatically orders of magnitude more that you have to process.
John Furrier
>> So you guys are the road to physical AI, if I had to put it in a sentence, because physical AI is the robotics, whether it's manufacturing or humanoids, autonomous cars, vision with language and software. Would that be a fair assessment to say?
Bob Beachler
>> It would be. And physical AI, like I said, it's disrupting the edge. And so whether you go back 20 years where it was just a simple machine vision camera that was checking ... I had one customer that was checking the labels on beer bottles, making sure that they were on straight and that the fill level was right. And they were having to do that with deterministic programming and trying to do vector analysis to make sure ... Now, I can train a very rudimentary AI to do that in a day, and now I have a new machine inspection system.
John Furrier
>> I love interviewing you guys because, one, 13 years doing the work, got the product market fit. You're shipping 50 million units, you got customers, you got some good revenue. I'm sure you can say the number, it would be great, but I'm sure you can't. You want to volunteer the revenue number?
Bob Beachler
>> I can't.
John Furrier
>> Okay.
Bob Beachler
>> Can't comment on it.
John Furrier
>> No comment. Looking good off the tee, as they say in golf.
Bob Beachler
>> Yeah.
John Furrier
>> We're here at the New York Stock Exchange. So IPO in your future?
Bob Beachler
>> Again, we can't comment on what our future plans are in the capital markets. What we're focused on is building the business. We have a great foundation. We have a better mousetrap as it were in terms of the existing competitors, but we see the tailwinds of the edge AI and the disruption there allows us to show growth. So we're really focused on growing our revenue, our customer base, creating new products, delighting our customers with our support, and the rest will take care of itself.
John Furrier
>> It's the really great Silicon Valley story. Focus on the product. Now you go to market, you got to build out the team. Talk about your focus right now, team building out for you.
Bob Beachler
>> Yeah.
John Furrier
>> What's the priorities? Share what you got going on.
Bob Beachler
>> Sure. So team building is key, because a company's lifeblood is its people. So they had a foundational team, like I said, a dream team of engineers that have been there, done that in programmable logic. I recently joined the company as one of the key marketing corporate development hires. We're going to be expanding our sales force. We're expanding our corporate governance in terms of finance, those types of things. Because you got to transition from being, "Okay, this is great. We've gone to zero to one." Well, this company is going from zero to 100. X millions to X hundreds of millions, right?
John Furrier
>> Yeah.
Bob Beachler
>> And so there's other things that you have to do and new skill sets that you have to develop for.
John Furrier
>> Well, put a plug in for who are you looking to hire and who your target customers are, who's buying the product? Is it device folks? Is it software engineers? Who's the customer? And what are some of the hires you're going to do?
Bob Beachler
>> Yeah. So our customers really are the people that are making the systems in terms of edge applications. So whether it's in machine vision, autonomous vehicles, humanoid robotics, medical equipment. So we really cater to these people that make their own hardware and they want to differentiate based on their hardware. And so in that regards, what we're hiring for are people who have a technology expertise and can interact with those types of customers. We don't sell to the software developer. We sell to the hardcore engineers, and so you need to really understand their language and their lingo so that you can talk to them.
John Furrier
>> You mentioned heterogeneous computing earlier. I've been commentating here in theCUBE that the AI factory is all around the big data centers, which is going on now. That's its current market. I've always said a car is a AI factory on wheels. AI at the edge is an AI factory powering an edge, just different constraints. Bandwidth, form factor environment, no liquid cooling at the edge. A car, it could be self-contained, it could be contained within the car and then talk to the internet. So all kinds of new AI factory kind of super computing capabilities, but it's distributed computing.
Bob Beachler
>> Yeah.
John Furrier
>> What's your reaction to that?
Bob Beachler
>> It's absolutely the case. And what you'll see is that it's a spectrum. People have talked about, "Oh, well, AI's going to move from the data center to the edge." Well, they kind of have that backwards. It started in the edge. Like I said, the original AI was AlexNet. It was a vision application that's, in this day and age, a network about this big. But you have the supercomputers in the data center, they're good for certain things, but you can't put everything there. So you have the supercomputer in the trunk for the autonomous vehicles. You have the supercomputer on the factory floor to do your machine inspection, to deal with your vision guided robotics. You have your supercomputer in the brain of a humanoid robot, because it has to be autonomous. It has to be able to function on its own. Because heaven forbid, we just had a cell phone outage right yesterday. It's just like, so if your robot is carrying grandma and it's requiring a cell phone connection, and all of a sudden that goes down, what happens?
John Furrier
>> Hyper convergence at the edge has to be in place. I've been in the computer industry over 30 years and it's fun. We've never seen a super computing like capability as you described it. It's enabling new things. As the edge, which is obvious to me, and I think most of the insiders, that once that gets converged and it's fully operational, it's going to open up and unlock massive use cases. You've seen many cycles, you've been through the big companies at the chips, startups. What's your vision on what opens up at the edge? What are some of the things that you'd see that we might see that we might not see now?
Bob Beachler
>> The sky's the limit. AI was such an inflection point, almost like the inflection point of the invention of the transistor, the invention of CMOS processes. AI just unlocks things that we could never have done before. And so that's why we're seeing things, and we're just in the early innings. Will humanoid robotics take off in the next three to five years? I don't know. But in the next 5 to 10 years, are you going to have more AI powered devices in your home, at your work? Absolutely. It's transformed even my workflow at what I'm doing. It's like I'm using AI every day for what I need to do. And so it's really going to transform things, and there won't be a part of your entire existence that isn't touched by some AI powered type of device.
John Furrier
>> Bob, thanks for coming on, and appreciate Sammy coming in as well. And congratulations to the team.
Bob Beachler
>> Thank you.
John Furrier
>> 13 years, now you're going to bust out and get the word out. We'll be tracking you. And thanks for coming and contributing your expert opinion on theCUBE in our AI Factory series.
Bob Beachler
>> Thanks. Really great to be here.
John Furrier
>> I'm John Furrier, host of theCUBE's AI Factory series, and it's going to go to the edge super fast this year. We think it's going to be a hyper converged edge. We'll open up and connect in a heterogeneous, distributed way and open up all new use cases. Certainly knocked down stuff that's obvious, but it's going to open up opportunities for entrepreneurs, systems designers, and developers to bring in a new era as we go from AI to fully autonomous capabilities, grounded in security and performance and latency and velocity, all happening in this new supercomputer environment. We're doing our best part to keep up and track that with you. Thanks for watching.
John Furrier