In this insightful video interview, John Furrier, co-founder and co-CEO of SiliconANGLE Media, hosts Andrew Feldman, founder and CEO of Cerebras Systems. The discussion, part of the Future of the Data Center Series, explores the transformative role of large-scale systems in revolutionizing artificial intelligence applications and data centers. Filmed at the NYSC's CUBE Studios, this interview provides an in-depth look into the strategic developments driving the tech ecosystem today.
Andrew Feldman brings vast experience and expertise as a leader in the AI and semiconductor industries. In conversation with John Furrier and theCUBE Research team, Feldman shares insights into Cerebras Systems' advancements, highlighting their unique approach in developing high-speed inference processing capabilities. They elaborate on how Cerebras' innovations address the accelerating demand for AI-native applications and computational infrastructure.
Feldman discusses the critical need for scalability and speed in AI inference, a domain where Cerebras makes significant strides. They assert that the surge in AI-native startup activity and the increasing reliance on large-scale data centers underscore the dynamic and rapidly evolving tech landscape. This conversation also touches on the necessity of fearless engineering and strategic investments in research and development to gain a competitive edge.
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Andrew Feldman, Cerebras Systems
In this insightful video interview, John Furrier, co-founder and co-CEO of SiliconANGLE Media, hosts Andrew Feldman, founder and CEO of Cerebras Systems. The discussion, part of the Future of the Data Center Series, explores the transformative role of large-scale systems in revolutionizing artificial intelligence applications and data centers. Filmed at the NYSC's CUBE Studios, this interview provides an in-depth look into the strategic developments driving the tech ecosystem today.
Andrew Feldman brings vast experience and expertise as a leader in the AI and semiconductor industries. In conversation with John Furrier and theCUBE Research team, Feldman shares insights into Cerebras Systems' advancements, highlighting their unique approach in developing high-speed inference processing capabilities. They elaborate on how Cerebras' innovations address the accelerating demand for AI-native applications and computational infrastructure.
Feldman discusses the critical need for scalability and speed in AI inference, a domain where Cerebras makes significant strides. They assert that the surge in AI-native startup activity and the increasing reliance on large-scale data centers underscore the dynamic and rapidly evolving tech landscape. This conversation also touches on the necessity of fearless engineering and strategic investments in research and development to gain a competitive edge.
In this theCUBE + NYSE Wired: AI Factories – Data Centers of the Future interview, Andrew Feldman, founder & CEO of Cerebras Systems, joins theCUBE’s John Furrier to unpack why inference-first AI infrastructure is resetting the enterprise playbook. Feldman shares an on-the-ground update: the inference market is “white-hot,” Cerebras delivers 10x–50x faster inference versus alternatives, and the company has opened a 10-megawatt Oklahoma City facility – its fifth U.S. data center – with immediate expansion underway. He explains how AI factories turn data center...Read more
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What is the focus of the Future of the Data Center Series being hosted by John Furrier on theCUBE?add
What is the current state of the inference market and how does it compare in speed to competitors?add
What are the reasons for the belief that the demand for data centers and computing capacity is increasing rather than indicating a bubble in the market?add
What are the challenges associated with processing AI that make it difficult for a standard-size processor?add
What is your perspective on handling the challenges and long-term efforts involved in building a successful startup?add
>> Welcome back, everyone. I'm John Furrier, host of theCUBE. We are here for our Future of the Data Center Series. This is the large-scale systems that are changing the game and we're interviewing the leaders who are powering that trend and continuing to invest in the fastest, latest and greatest enablement for the software tsunami with agents and all the new AI-native applications coming. We've been reporting on that on SiliconANGLE. Andrew Feldman, the Founder and CEO of Cerebras is back on theCUBE. Last time we were together was in Paris at the RAISE Summit. Andrew, thanks for coming in. Thanks for coming into our NYSC's CUBE Studios here as part of the NYSC Wired community. Appreciate it.
Andrew Feldman
>> Thank you for inviting me. What a cool spot you have here.>> Yeah, I mean, you got the active trade and the energy. I love it here. The energy, of course, with the Palo Alto Studio connecting Silicon Valley and Wall Street. Those environments are now merged, right?
Andrew Feldman
>> Yes >> You see money in tech. It's not mad money. It's tech money now, right? So it's like really now a tech-enabled scene here in this market. We've been covering the crypto trailblazers, which essentially AI, I mean, decentralized infrastructure, financial infrastructure that's being dislocated and innovated on, so that's one vertical. Life sciences, I mean, every single area is popping with new discoveries, new capabilities enabled by large-scale systems that can compute and infer around things that they couldn't do years ago.
Andrew Feldman
>> That's right. New processes, new methodologies, new techniques, new ways to find insight in data. They already have guidance to which data they need to collect to gather new insights. It's really an extraordinary time.>> I want to unpack that with you because you're at the forefront of this with speed and scale. But first, give us an update on Cerebras. What's new since we last saw each other in July? Give us a quick update.
Andrew Feldman
>> Well, the inference market's continued to accelerate. It is white-hot. And speed, which is the reason we exist. As you remember, we're the fastest at inference in any model we do. Not close, 10X, 15X, 20X faster than the nearest competition. And that's important as we use inference to do more, as we use reasoning models, as we use a agentic models, as we use inference to write code. The willingness of people to wait 10, 20, 30 seconds, a minute, three minutes, nobody wants to sit and watch the little dial spin while they achieve nothing. And so by being able to deliver inference at these extraordinary rates, we found extraordinary demand.>> Talk about the demand curve. We're seeing in the news huge financing and CapEx growth, more data centers are being built, applications that are AI-native getting funded. We saw the NVIDIA going all-in on OpenAI. I think they have five major customers that represent a lot of their business, so obviously they have some big customers, but if-
Andrew Feldman
>> Concentration, it's true.>> It points to though the demand. And watching some of the hot takes on TV, obviously they're pretty obvious low-hanging fruit, obvious ones like, "Oh, the sector's hot." It speaks to the build-out-
Andrew Feldman
>> Great insight there.>> I think it's super insightful. Okay. I mean, it's hyped up, so hype and hot synonymous in that layer of analysis, but when you look at the demand curve, the infrastructure enablement is still firing away. Can you take us through your thoughts on that? Because again, on the software side, there's huge activity going on: certainly open source; on all the models, they're moving faster so the models are like hitting a flywheel; people are decoupling the app layer; they're natively infusing AI and either native AI or they're consuming AI to manage the models; and then the underlying infrastructure is decoupled from that flywheel. That's being optimized. So you have layers above and beyond the models, that's so the smart money seems very... So the software side is in massive demand. They need horsepower.
Andrew Feldman
>> I think one of the things that leads you to believe this is not a bubble is that all of us are looking for more capacity. We just opened a new data center in Oklahoma City. It's our fifth data center in the U.S. This is a 10-megawatt facility, and we immediately began expansion. We opened it, we expanded it. And we're looking for data centers around the world. And I think when OpenAI, when Anthropic, when other leaders are saying, "We need more compute," all right? When leaders in the IDE space, all right, Cognition, Cursor, Lovable, when they say, "We need more capacity," right? What you're seeing is this sort of groundswell up out of the application and it's washing over the makers of software and there is just this giant sucking sound, and that sucking sound means we need to deliver more compute to do more inference, that compute needs to live in more data centers, and it just washes back down the supply chain.>> One of the comments that's come out of some of these interviews on these Future of the Data Center is, "Well, we've had x86 for a while. We've got GPUs and XPUs." They've had that relationship for years. It's an interconnect. You obviously have a semi background as a founder. It's to just interconnect them, get the same. And then these neoclouds are just resources, assist resource capacity, not so much a game-changing software perspective. What's your take on that reaction? Is it naive or is it outdated?
Andrew Feldman
>> Yeah. Both.>> Both?
Andrew Feldman
>> Both. There are very little problems in this world where you can just add and connect for free. When you break up a problem and you separate it, there's a communication overhead. The probability of miscommunication jumps through the roof. And so as you fracture a problem and spread it out over thousands of GPUs, the problems multiply, the complexity multiplies, and that's why there's so few organizations that can train with 10,000, 15,000 or more GPUs. There may be a handful in the world. And this is complexity that, at Cerebras, we seek to eliminate. By building the largest chip in the industry, we keep more information, more data on chip, we move it less often, we use less power and we deliver answers in less time.>> Yeah. We saw the news this week of Intel has government stake in it. They'll come from the philosophy of go smaller, not bigger.
Andrew Feldman
>> Yeah, how's it work for them in AI?>> I mean, explain because it's not... They would probably throw shade on what you're doing.
Andrew Feldman
>> No->> What's the net-net of going bigger with the wafer? Because you have cracked the code on speed, okay?
Andrew Feldman
>> First, Labuza, an extraordinarily knowledgeable and seasoned executive and he was one of our early seed investors, so he knows exactly what the benefit is. I think that the notion that there's a one-size-fit-all in technology or that there's a single silver bullet is always garbage. It's just never the truth. The interesting parts about AI are the challenges that make the AI work difficult for a processor is several. First, you need a lot of very simple calculations, more than generally fit on a small, standard-size processor. That means you need to tie them together. So that means you've got this crazy communication pattern of moving intermediate results. Final thing is you need to get to memory, and we are 2,500 times faster at getting to memory, and getting to memory is the speed factor in inference.>> I mean, SRAM was the holy grail.
Andrew Feldman
>> SRAM's the holy grail and by built->> How much can you put in there?
Andrew Feldman
>> Well, that's why you make a big chip. That's why your chip's 56 times larger than the largest GPU. It's so you can stuff it to the gills with the fastest type of memory on earth. And this is this architectural decision we made, these inventions, this is where the advantage comes from.>> Yeah, we talked about that big bet on our last segment. So folks watching, check out that interview from the RAISE Summit with Andrew. He went into great detail on the speed there, so I'll just put that out there. Talk about the data centers because inference is where the money's being made. Everyone I talked to, "Oh, inference is where the money is." Now, let's unpack that. Certainly on the infrastructure side, you're building inference centers. My word, not yours. Maybe that's the wrong word, but-
Andrew Feldman
>> That's fine.... >> they're inference.
Andrew Feldman
>> Massive data centers filled with machines->> To do inference....
Andrew Feldman
>> that are running inference. Correct.>> And the software writers, whether it's written by a machine or a human or both, they want to deploy software that's intelligent. They're going to need to have that capability. Talk about where that goes because I think this is where I'm connecting the dots with Cerebras because, okay, I'm a software, I don't really care who's got what.
Andrew Feldman
>> That's right.>> I just want the fastest.
Andrew Feldman
>> That's right.>> No one says, "I want the slower product."
Andrew Feldman
>> Right. Nobody's ever said, "Can you make my results appear slower on the screen?" Right?>> Yeah, no one says that.
Andrew Feldman
>> No one says that.>> Yeah, totally. So take me through that enablement because I think this is where I think the crossover is on the moneymaking on both sides, by the way.
Andrew Feldman
>> So what happened in early 2024 was for the first time, AI moved from being a novelty to being valuable in the sense that before that, the emphasis was on training. There was a little bit of inference, but we couldn't do useful things with it. And so it was cool, you could do this or that, but could it be useful? And starting in the middle of 2024, what we saw was the rise of AI being an extremely useful tool. And it could write code, all right? It could generate text, it could deliver thoughtful answers, it could do deep research, it could survey vast amounts of information, synthesize, summarize it, and deliver it back. And so what's happened is software writers want to take advantage of that technology. They want to bolt on to AI, they want to use AI, use a chat service inside their application. And what's happened is it's become dead simple to move. It requires no special coding. You can move from a competitor to Cerebras in 10 keystrokes, all right?>> Yeah.
Andrew Feldman
>> And once you do that, you're getting results 10, 15, 20, 50 times faster. That's what's driving this is adoption across all app makers are including some form of AI and they want it dead fast, and that's where we come in.>> I want to get your thoughts on the AI hype and to reality because I think if you go back to 2024 into 2025, every company, "We're an AI company." I mean, no one's going to say they're not an AI company because that's like, I mean, unless you clearly aren't an AI company. Construction equipment. That probably has some IoT AI in it actually too. But then now we're starting to see the rise of AI-native startups, people who are thinking AI-first, who are building AI into the products from day one, not bolting it on.
Andrew Feldman
>> That's right.>> What does AI-native mean to you? What does that definition mean? Because we saw cloud-native. Cloud-native really powered the SaaS market.
Andrew Feldman
>> It did.>> Is AI-native real? Because agents have workflow knowledge, and you mentioned things like moving data around, I'm paraphrasing, moving data, access to memory. The bigger chip does all that. That handles a lot of that velocity.
Andrew Feldman
>> It does.>> And it's non-linear. It's also not predictable.
Andrew Feldman
>> That's right.>> It's generative.
Andrew Feldman
>> I think what AI-native means is that AI and AI's capabilities are in the very architecture of your product. They are not an add-on. The product is built around those capabilities, right?>> Yep.
Andrew Feldman
>> And you are thinking about in every step how the product can benefit from AI, not how AI can help the product. If you're bolting on customer service or a chatbot, that's great. It can do really interesting things for you, it can save you a ton, but that's not central to your product. When the very value proposition of your product is cornerstoned with AI, then you're an AI-native product. Otherwise, you're in AI drag.>> One of the things about New York that I love and some people might not understand-
Andrew Feldman
>> The taxi ride today couldn't have been it.>> Well, you had things going on.
Andrew Feldman
>> No, no. That was not it.>> Is that they're results-oriented and Silicon Valley's a little bit of a long game on tech. As a founder and as a technical founder too, and CEO, you're in a great machine, hype machine.
Andrew Feldman
>> We are.>> How do you balance that? Because you got to balance results, speed, you certainly deliver the speed side, and then also play the long game knowing what the bet is and what you guys are going to see through. How do you handle that? Because it's easy for some founders that may not have the experience to say, "Oh, I got to get the shiny new toy. I got to be on that high horse and look at the deals I just closed." A lot of people, you have to squint through that, so we're trying to get that. How do you view that, handle that, and what's your philosophy?
Andrew Feldman
>> Well, as you know, this is my fifth startup, and over the past 27 years, there's some big highs and some deep, deep lows. And what I tell people in the startup world is you can get kicked in the stomach three or four times before lunch and have it still be a reasonable day. You are being whipsawed. And I think you add that to the fact that no great company, no company that changed the landscape did it quickly. All right? Change, fundamental change takes years, and it takes real R&D, and it takes the type of R&D that is unafraid to fail, that is working on problems that other people have refused to take upon themselves that either they've been too scared or they didn't think they could solve it. Those are the problems we're interested in. We talk about it inside the company as doing fearless engineering. And when you solve problems that matter, that are hard, that others haven't solved, that produce real benefit to your customers, it doesn't take three weeks. This isn't a 10-day sprint. These are years of extraordinary work and it produces massive results, like our innovations in chip design and in chip packaging, which lead us to be an order of magnitude, an order of magnitude and a half faster than companies that are 50 or 100 times larger than we are.>> I appreciate you sharing that because I think it's important to keep the eye on the ball and you guys are doing that. What are you guys optimizing for now? Obviously with the long-game view, what are some of those problems that you see that you're going after? What's the 20-mile stare look like for you? Obviously, you look out on the horizon. By the way, I love that line about getting punched in the stomach. Marc Andreessen's got a good one, "Getting punched in the teeth every day, multiple times, and then you're just shaking it off and moving on." And then you
Andrew Feldman
>> That's exactly right. I think if you want to be an entrepreneur, grit is one of the top several things you need. And things come at you so fast. You're going to make some mistakes, you're going to get clobbered, and it can still be a good day and it's an amazing ride.>> What's your 20-mile stare? You guys have great R&D. Again, counterintuitive, always, sometimes being misunderstood for a long time needs to be tolerated. What is the 20-mile stare? What problems are you guys working on now? What's some of the fearless engineering? Can you highlight without giving away the competitive jewels?
Andrew Feldman
>> Yes, right. I think since you brought up Marc, Ben Horowitz wrote a book, The Hard Thing About Hard Things. It's one of the most honest-feeling book for entrepreneurs. When you attack problems, they're hard because other people couldn't figure them out because the answers weren't obvious. They're hard because not just your team, but other teams across time couldn't do it, and there aren't tools that make it easy to do it. We're committed to solving that type of problem because those are the problems that unlock massive advantage. You don't get massive advantage by doing the same thing that NVIDIA's doing a little bit cheaper. All right? That's not massive advantage. You get it by identifying fundamental problems and working on them with the knowledge that these problems aren't easy to solve. They haven't been solved for a reason. That reason is a lot of thoughtful people couldn't come up with a good answer.>> Yeah, and being on the right side, on the business side, knowing the structural change and keeping the pulse, knowing what you know.
Andrew Feldman
>> Well, we have five founders and I have tremendous faith in the guidance from the other four, the technical vision presented by them, and we've surrounded ourselves with extraordinary people, and we fostered an environment and a culture in which it's okay to disagree. It's okay to have competing opinions, but once we decide, everybody gets in the same boat and is pulling in the same direction.>> I love it. Debate in the line. Love that, love that, love that concept. Final question for you while I got you here is obviously you're playing in a world where the big dollars, CapEx spend, big centers are being built out. You've got your data centers. The enterprise market, slow to adopt. It's very well-documented. We are predicting that 2026 will start opening up as they're just a different animal, maybe smaller footprints compared to some of the larger-scale data centers being built on the hyperscalers, and then what you guys are doing with the neoclouds and everyone else. As the data center opens up for the enterprise because the on-premise activity is high because that's where the data is, it's not like they're moving from cloud. They've still got cloud, but it's distributed computing. We talked about that many times. What's your view on the enterprise market right now, just as an industry participant? Is it core for you guys? Is it something you're looking at hard? Is it developing fast enough? Is it about to explode open?
Andrew Feldman
>> Right. I think it is about to open up. I think it's about to open up because it is the traditional enterprise that is most under threat by newly formed AI-native companies. It is traditional banks that are under threat by Klarnas. It is traditional insurers that will be under threat by this new way of insurers and FinTech companies. It is the way we used to do business that is under threat by the way we might do business in the future, and if they don't recognize this, I think they get smoked.>> Yeah. And I think, I love the old expression, it's cliche to say it now, but when we started theCUBE 16 years ago, Hadoop was big. Remember the big data days? The phrase was, "Data's the new oil." You could laugh, but the enterprises have the oil. It's called the data. So if you're a large insurance company, you've got a lot of data that you could reason against. They have the ability to thrive and survive.
Andrew Feldman
>> It's interesting. I just thought of something as you said that. I mean, data's the new oil. Midstream in the oil industry is extremely profitable. Turning it from sweet crude into a collection of things that are actually usable in the world is an enormously profitable business. That's where AI lives. AI takes this data and it can break it into a collection of insight, things that are valuable. And they might have the data, but if you don't have processing->> They do.
Andrew Feldman
>> Exactly. It has to be converted into something valuable.>> Yeah, and then you could build an engine for anything.
Andrew Feldman
>> Then you could->> And slice it.
Andrew Feldman
>> Slice it and dice it. You can find insights in it you didn't know you have. You can... And that's what we're seeing with the most forward-looking pharma companies. The companies who are our customers, like Mayo Clinic, like GlaxoSmithKline, are using data in ways to find insight, all right, and then make decisions that might otherwise not have been made.>> And they're making discoveries because they don't have to implement some hardened manual process migration.
Andrew Feldman
>> That's right. That's exactly right.>> They just integrate into their workflows that they got.
Andrew Feldman
>> They can narrow the number of hypotheses they need to test with better AI. There's a whole range of things that leading-edge companies are doing to make their data more useful and to find insights in it.>> Andrew, always a pleasure. Thanks for coming on. As usual, great unpacking of some of the key things that are creating value, and frankly extracting value. And I think the agents point to what's coming: extraction.
Andrew Feldman
>> Extraction, I think utilization, extraction. I think these are extraordinarily exciting domains. When we look out there, we see I think coding is just on fire. The IDE space is just plain ripping right now.>> Yeah. Productivity gains are off the charts.
Andrew Feldman
>> Productivity gains that are off the charts.>> Yeah, it's amazing.
Andrew Feldman
>> And I think we're proud to partner with leaders there.>> Andrew, looking forward to our next time and continuing to get stuff thrown at you really fast.
Andrew Feldman
>> All week.>> Take a couple punches, get back up, continue the mission. Great to see you. As always, a pleasure.
Andrew Feldman
>> Not a business for a glass jaw.>> If you want to do a startup, it's not for the faint of heart, but the rewards are there. They're all structural changes. The future of the data center is going to look completely different. It's going to be a systems game, it's going to be a data game, it's going to be a software game. Again, everything's up in the air. When it lands down, there'll be winners and losers. There'll be the right side of history and the wrong side of history. Of course, we're doing our part to share that data with you here at theCUBE. I'm John Furrier, your host. Thanks for watching.
