Odd Lots: Why Cerebras CEO Andrew Feldman Built The World's Largest Computer Chip

Sourced by podcast-ingest on 2026-05-21. Auto-transcribed via AssemblyAI (universal-2, en). Speakers identified by AssemblyAI Speaker Identification using the per-podcast host/regulars hints; the resulting label→name mapping is in the frontmatter. Duration: 51m. Episode page: https://omny.fm/shows/odd-lots/why-cerebras-ceo-andrew-feldman-built-the-worlds-largest-computer-chip. Audio: https://podtrac.com/pts/redirect.mp3/tracking.swap.fm/track/UVBrz8bN8aM2Xe47PEPu/traffic.omny.fm/d/clips/e73c998e-6e60-432f-8610-ae210140c5b1/8a94442e-5a74-4fa2-8b8d-ae27003a8d6b/4945a340-783e-4fba-9782-b4510023ea71/audio.mp3?utm_source=Podcast&in_playlist=982f5071-765c-403d-969d-ae27003a8d83.

Show notes (from RSS)

Size is the name of the game for the AI chipmaker Cerebras: Their chips are truly massive, about the size of a dinner plate. According to Andrew Feldman, CEO and founder of Cerebras, that is about 58 times larger than the average chip. That sheer size enables blazing fast inference for AI queries. Feldman joins us on the week of his company's IPO to talk about his core product and how it fits into the AI boom. We discuss the history of the GPU, competition between open-and closed-source models, the company's relationship with with TSMC, and more.

Read more: Nvidia Tells Skeptical Investors That AI Is Ready to Go Mainstream Trump Set to Sign AI Cybersecurity Directive as Soon as Thursday

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Transcript

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Joe Weisenthal: Hello and welcome to another episode of the Odd Lots Podcast. I'm Joe Weisenthal.

Tracy Alloway: And I'm Tracy Alloway.

Joe Weisenthal: Tracy, I have to say, unfortunately I don't have AI psychosis. I'm certain of that.

Tracy Alloway: Debatable.

Joe Weisenthal: I'm pretty sure I don't have AI psychosis. I do have to say unfortunately, like the amount of time now where it's like it feels like AI related questions, and there's many of them, are sort of like swallowing up the other thoughts that I have in my head. Whether it's questions about which model's best and why and what are the economics of inference and how much training is pre training versus post training for each model. Like it's just sort of like this blob that's growing that's taking up more and more of my thoughts.

Tracy Alloway: What is your definition of AI psychosis? Because one would argue that maybe thinking about AI literally all the time would be a form of psychosis.

Joe Weisenthal: Well, let's just say, like I'm not the type who thinks that. Like I don't like think that the AI is a friend for one thing, I'm not in love with the AI models. I don't think that in collaboration with ChatGPT, that I'm stumbling on unified theory of physics and things like that. So, like.

Tracy Alloway: But you do spend a lot of time inputting instructions, pressing the button, and seeing what comes out.

Joe Weisenthal: And seeing what comes out. I'm just saying I think I'm aware that I'm talking to a machine and that we're not establishing any great breakthroughs of which we are collaborators and partners of friends.

Tracy Alloway: Recognizing you have a problem is the first step towards healing. Joe. Seriously, though, there's a good reason to think about AI more and more, which is that a huge chunk of not just the market, but the real economy is now revolving around AI.

Joe Weisenthal: Right, Totally. So, anyway, again, within the AI conversation, there are a lot of subcategories. One of the subcategories happens to be another odd lot's favorite topic, which is chips. Of course, chips are used in multiple different ways. Chips are used in different parts of the AI supply chain. Different types of chips have different roles. And so we have to learn more.

Tracy Alloway: We have to learn more. And I have to say I'm particularly interested in the company we're about to speak to, but partly because the two things I know about them are, number one, they just had a huge IPO.

Joe Weisenthal: Yep.

Tracy Alloway: Right. Raising something like $5.5 billion at kind of insane multiple. I can't even do a price to earnings multiple because they're not profitable yet. But I think just on a sales basis, it was like 67 times forward earnings, which is pretty juicy, pretty hot. And the second thing I know about the company is they make giant wafers.

Joe Weisenthal: Yes.

Tracy Alloway: Which is just a fun image to have in your head.

Joe Weisenthal: That's right. So if you were thinking it's like, okay, there is a hot entrant in this space. What is their differentiator? Well, one fact about them is their chips are just enormous, about the size of the dinner plate. One might think you're reading an Onion article, but in fact, it's real. And apparently it actually has some real technical advantages, so.

Tracy Alloway: And it's different to what everyone else is doing. So everyone else is, I guess, doing this sort of like modular network thing where you get together a bunch of chips and you connect them together, and that's how you get more compute, more memory, more power, basically. But this company has done something different in the form of the giant Wafer.

Joe Weisenthal: The Giant Wafer. And if you figure that to get maximum performance, you sort of want to Lessen the distance between things, then put it all on one wafer. Anyway, we're going to learn a lot more, I'm very excited to say, about Giant wafers and more I'm very excited to say we do have the founder and and CEO of Cerebras on the podcast, Andrew Feldman. Truly the perfect guest. So, Andrew, thank you so much for coming on the podcast on the week of your ipo.

Andrew Feldman: Well, thank you so much for having me. What a pleasure.

Joe Weisenthal: Absolutely. Why don't you just start us off? The big giant chip, they're apparently real. They're as big as a dinner plate. What is the technical reason why this actually makes sense as a superior form of architecture for at least some aspect of AI?

Andrew Feldman: I think larger chips process more information in less time.

Joe Weisenthal: Okay.

Andrew Feldman: And that produces faster results. And everybody had gone to bigger chips. Nvidia had moved from 400 square millimeters to 800 square millimeters over the course of five or six years for this exact reason. And in the compute industry, wafer scale, which is building a chip, this is,

Joe Weisenthal: by the way, for those who are just listening, Andrew is now holding up the chip. And yes, it looks. It actually looks bigger than a dinner plate, to be honest. But that is a big chip.

Andrew Feldman: That's a big chip.

Joe Weisenthal: That's a big chip.

Tracy Alloway: Beautiful.

Andrew Feldman: It's 58 times larger than any other chip that had ever been.

Joe Weisenthal: Wow.

Andrew Feldman: And what it did was it allowed us to use a different type of memory.

Joe Weisenthal: Okay.

Andrew Feldman: A type of memory that there at the beginning. There are two types of memory. There's memory that can store a lot, but it's really slow. There's memory that can't store very much per square millimeter, but it's blisteringly fast. Historically, all graphics processing units used this memory that could store a lot, but was really slow. That's the reason they do inference so slowly. If you're using Claude right now or you're using anything but ChatGPT, what you'll frequently feel is you'll enter your prompt and you wait for an answer. That's because the memory is slow, and they have to move a ton of information from memory to compute. Now, by going to wafer scale, we could use this fast memory. Now, we couldn't make that memory store more information per square millimeter, but we could add square millimeters. By building this big chip, we were able to stuff it to the gills with this fast memory, and that's why we're 15 times faster than the fastest GPU. That's why on some problems we're 50, 100, even 1,000 times faster than graphics processing units.

Tracy Alloway: Wait, can you explain how you actually managed to do this? Because I know there have been previous attempts to do wafer scale, and I seem to remember there was even like an early attempt in the 1980s or something to do it. How are you able to, to pull this off?

Andrew Feldman: Yeah, it was an ambitious undertaking, that's for sure. Every previous effort in the 75 year history of our industry had failed, including Gene Amdahl, who's sort of on the Mount Rushmore of compute in our industry. He failed sort of spectacularly in the mid-80s at a company called Trilogy. Not only that, but after we succeeded, people who had visited us, who'd been in our labs, tried to copy us, and they also failed. And so what we were able to do is solve a set of really fundamental problems. And those problems cut across a wide swath of technology. They cut across lithography. So we had to collaborate closely with TSMC and they turned out to be a great partner. We had to make inventions in material and packaging. That's how you put a processor, how you put a piece of silicon on a motherboard, deliver power and IO to it. We had to make inventions in power delivery. Right. When you build a giant chip, you're going to deliver way more power to it than if you do a chip the size of a postage stamp. We had to invent ways to cool it. We had to write new types of software that ran on it. All of these had never been done before. And it was a decade long process. It took us five years and about $500 million to deliver the first one. And it's been an extraordinary run since. In December, we signed a deal with OpenAI, north of $20 billion, one of the largest contracts ever signed in Silicon Valley. And then in March, we signed a deal with AWS where they would deploy our systems in their data centers. In their AWS data centers. And so it's just been an extraordinary run, but it took a long time. It took extraordinary engineering and there were certainly long periods of time when it wasn't clear we were going to make this work.

Joe Weisenthal: Obviously you've hit this remarkable milestone. You have in fact IPO'd and so forth. And right now, markets valuing your company at $64 billion. Early days of the IPO, just for the listener to understand the chips, are they solely an inference as opposed to training? When we think about AI, we think about, okay, there's training, training the model and then answer giving. That's the inference are the chips just for inference.

Andrew Feldman: So a couple things, I think you framed it exactly right. Training is how we make AI and inference is how we use AI. And so what happened was that in sort of 2025, in the first part of 2025, the models we made were smart enough to be useful. And there was an explosion of use. And we use AI by doing inference. So there was this sort of tidal wave of demand on inference. And that has continued in 2026. And we think it will continue for years and years to come. And so that's what had happened in 2015. When we began thinking about the company. We knew that AI was on the horizon and it would eat a huge amount of compute. Right? And we made sort of two fundamental bets. We bet that it would need dedicated silicon and graphics had needed dedicated silicon. That's how you got the graphics processing unit. Mobile compute had needed dedicated compute. That's where you got ARM processors. We made that bet. And we made a bet that modifying the GPU architecture wouldn't be right. You needed to start with a clean sheet of paper. And so what we started with was a new vision. And that vision could do training and it could do inference, and it was orders of magnitude faster at both. But right now what we're seeing is such an explosion in demand for inference that a lot of the business this minute is inference, even though we're just as fast at the same amount faster than GPUs on training.

Joe Weisenthal: That's interesting. Maybe we'll get more to the theoretical training market a little later. Just real quick on inference. Ben Thompson, who writes a newsletter about tech, he wrote a piece in which he distinguishes between answer inference and agentic inference. So answer inferences like, you know, format by resume or whatever, or write me an essay on X or Y, or answer some questions. And then agentic inference is like, okay, here's this thing that's going to go around. Do you distinguish and do services for you not producing visual answers? Do you distinguish between those two? Is that a real divide in your view? And can your chips do both?

Andrew Feldman: Our chips can do both. I think it is a divide. I think speed matters equally in both. I think if you are engaged with the AI, if you're writing code, which is agentic, if you're writing code or you're doing work, nobody wants to wait. I mean, we could just turn the question around and say, well, how big is the market for slow search? Zero. How big is the market for dial up Internet? Zero. Why is that? Because nobody wants to Wait. So if you're engaged with the AI, speed is of the essence, but if the AI is doing agentic work and your competitor gets 3 times, 5 times, 10 times as much work done in 20 minutes than you do, you're going to get smoked. And so this notion somehow that Ben proposed that speed isn't very important in agentic flows is dead wrong. That speed is important in all aspects of productive work and that your ability to get more done in less time is a fundamental advantage that accrues over time. Right. If while your competitor is doing one unit of work, you can do three, and in the next time they do one unit of work, you do six, this adds up over time to. And you beat them in any line of work. And so speed, which is sort of our specialty, is important across the board.

Tracy Alloway: What do giant wafers and speed in general actually mean for, I guess, the economics of tokens? Because one way I think about it, I have this sort of vision in my head, like, okay, if I'm out shopping for toothpaste, I know I need toothpaste every once in a while, and I go into, like a CVS store, I get one thing of toothpaste, and then maybe a week later I get some more toothpaste. Or, or I could go to Costco and buy a giant thing of toothpaste and take it home, probably at a cheaper cost. And that's sort of how I think of the giant wafers. Maybe it's a bad analogy, but what does speed actually mean for the cost of tokens?

Andrew Feldman: Well, I think there are a couple observations. I think people have chosen so far to price speed a little higher. For example, Anthropic offered a premium service in which they offered tokens twice as fast and charged six times as much, and they sold it out and they couldn't meet the demand. Now, just to give you an idea, we're 15 times faster than they're twice as fast. And so people value speed because it allows them to do more work and they value their time. And when you can do more work in less time, you are making people more productive. That's why people have chosen to price them at a premium. They don't cost more to make. In fact, the GPU architecture is an extremely good architecture and extremely efficient at building very slow tokens. And if you don't mind slow, the cost per token on a GPU is extremely low. But the GPU has a characteristic that as you try and go faster, the cost and the power used per token increase. Sort of like as you go faster in your car, your miles per gallon decrease, right? So what happens is as you try and get fast enough to be useful, fast enough to be interesting, fast enough to keep users intelligence focused on this product, they become extremely expensive and extremely power hungry. And so the question is not just what people are paying for a token, what people are choosing to price them at, but what they actually cost to make. And GPUs make very slow tokens very cheaply and they're unbelievably expensive at fast tokens, we make fast tokens vastly less expensive than GPUs and we use a tiny fraction of the power.

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Joe Weisenthal: let's say we stipulate that this is all true and everyone wants the fastest and everyone's like you know what? This is the solution that the Cerebras technology, one big chip. This is really where it's at. How much of like your market share for the inference market when you look out next year, the year after, etc. How much is your market share going to be dictated by your ability to get capacity at TSMC fabs? How much is that a gating mechanism for growth?

Andrew Feldman: You know TSMC is a huge part of the supply chain.

Joe Weisenthal: Yeah.

Andrew Feldman: But we have some real advantages. There are three areas right now that are limiting vendors and building AI Compute number one is HBM memory is this memory we described earlier that can store a lot, but it's really slow. That's made by three companies approximately Samsung, Hynix and Micron. And it's under unbelievable supply pressure. It's extremely difficult to get. There are very long lead times. It's unbelievably expensive right now. We don't use it. The second part that's limiting is a process inside of TSMC called COAS. And this is the process that Nvidia and other GPUs use. We don't use it. The third thing is that at TSMC the factory that is under most pressure is their 3 nanometer factory. We don't use it. We use 5 nanometer. So we have managed to avoid some of the most binding supply constraints. Now TSMC still has to give us a meaningful allocation. And they've been an extraordinary partner from the get go. And they are the greatest manufacturing company on earth by far. A FAB is sort of a modern pyramid. It's an unbelievable thing and I highly recommend you or any of your listeners if you get a chance to go to Taipei, go and see them. They are just extraordinary.

Tracy Alloway: Can you do FAB tours?

Andrew Feldman: You can actually. Yeah, you can do FAB tours. You can go. And they have a museum of innovation and it is an extraordinary thing. They are the sort of the national champion of Taiwan. But I think today TSMC has given us as many wafers as we've needed. Business today is constrained by data centers. And that's the grand irony. You invent technology that has been unbuildable, never been invented for 75 years. In the history of compute, you write software that is extraordinary. You built a product that is vastly faster than the incumbent. And what are we all constrained by? Buildings. Data centers right now are building. Are everybody's constraint in the entire industry. Powered buildings. So real estate. It is an amazing thing right now. And that is true sort of across the board. And that will not change for the next 15 or 18 months, for sure.

Tracy Alloway: I mean, since we're talking physical constraints, I guess I should ask you. We did an episode about helium recently, a helium shortage, given the situation in the Strait of Hormuz. And one of the things that helium is used for is lithography on semiconductor chips. Has that affected you at all or is that something that you're monitoring?

Andrew Feldman: We monitor, but there's not a lot we can do and there's plenty of stuff to worry about that we can't affect. We obviously are in communication every day with tsmc. We're in communication with our entire supply chain every single day. And we stay abreast of the various issues. But it has had no impact on us. And we put that in the bucket of things that our manufacturing partners worry about also and that we can't help.

Joe Weisenthal: You know, so in addition to manufacturing these chips, you actually, I didn't realize this. You have your own cloud.

Andrew Feldman: We do.

Joe Weisenthal: Or you have your own cloud services. We do. Which I have a bunch of questions about that. But you have your own cloud services through which a user can actually get access to various open source models and so forth. It looks a little bit, sort of visually it looks a lot like the open router interface. Roughly the same environment, except it's all like the open source. What I'm something I'm curious about and maybe you could speak to this, you know, in traditional software, open source. One nice thing about open source is you don't have to pay for it. So it's free. It's a little bit different when we're talking about there's no really such thing as like free AI software. Because even if it's like free, you still have to like pay for the depreciation of the chips and you have to pay for the electricity to run them. So there is no real such thing as like free open source AI software. But what I am curious about, in your experience as a cloud vendor, are the open source models cheaper on a per unit of intelligence basis? If we had some way of saying levelized cost of intelligence, which I don't know if the industry has yet, are open source models cheaper per IQ point? However, we want to measure intelligence.

Andrew Feldman: Yes. By a lot.

Joe Weisenthal: Really?

Andrew Feldman: Yeah. I think in the closed source world you're paying a lot for that extra little bit of intelligence. Right. The open source models, there are no open source models that are as good as the closed source models. Okay. Think of it as 3, 4%, 5% different.

Joe Weisenthal: Okay.

Andrew Feldman: Something in that range. It could be a little more. It could be a Little less. But the cost to you using them. You can jump up right now and run Kimike 2. It's a 1 trillion parameter model. It's an open source model on cerebras where 10 or 15 times faster than others. And what you're paying for is the cost of our power and some cost of the compute that took to calculate it. What you're not paying for was the cost to train it. And, and that's a battle that is underway in the market. You have OpenAI with their coding software, you have Anthropic with their coding software. And you've got companies like Cursor and Cognition that are using open source. We power OpenAI and we power Cognition. You have a battle underway between closed source and open source. And I think that the winners of that battle is yet to be determined. What is clear is that the closed source is strictly better by a little bit, by how much varies and it's more expensive.

Tracy Alloway: Yeah, I think we've talked about this before, but I've heard of a lot of big companies in the US who have been very quietly shifting from some of the closed source models to the open source models like the Chinese ones, like Kimi. Is that what it's called?

Andrew Feldman: Kimi?

Tracy Alloway: And Quinn, I'm sorry to press you on this point, but if you had to make a bet in 20 years, is the dominant AI model going to be a cheap open source thing or a more expensive, incrementally better closed source model?

Andrew Feldman: I don't think there's going to be one. Right. There's not one SaaS software. Right. There's some big dogs right there. Salesforce, there's some other sort of giant players and there are lots of other specialists. I can't think of many markets where we've sort of settled on to one player. Right. If you look at the semiconductor market, you've got x86, where you've got two major players and AMD and Intel and then you've got a whole adjacent market owned by ARM and the companies that build ARM parts. And then you've got custom silicon around that. I think that's the way you're going to have this. We're going to have, you know, OpenAI is going to continue to do extraordinary things. They will be competitors to them and they'll be open source. I don't think any of those go away.

Tracy Alloway: Since we're on the topic of software, one of the things you often hear when talking about, you know, new chip entrance going up against Nvidia is this idea that, well, you know, like Nvidia chips, they're great and all, but the real moat of Nvidia's business is Cuda. Right. That software stack that goes with it. What's your take on that? Like, is that a realistic concern for someone who's trying to go up against a company as big and I guess as embedded in the software system as Nvidia currently is?

Andrew Feldman: Nvidia is probably the greatest company in the first part of this century. Right. Jensen's one of the great CEOs of our era, along with Hawk 10 at Broadcom and maybe Lisa at AMD. Just extraordinary. And CUDA was really important in the creating of the AI landscape, but it's not important now and it has no role whatsoever in inference. If you want to move from running a model on GPUs today to running it on us, we can move it in 10 keystrokes. Just move point to our API. So that's the first part. The second part is that a year ago every major Frontier Lab model had been built on a Cuda foundation and today two of three haven't. So they lost 70% market share. There are three leading frontier models, Gemini, Claude and GPT. Gemini built by Google on TPUs, trained on TPUs, served on TPUs, no CUDA. Anthropics models trained on Trainium, no CUDA, served on TPUs, on Trainium and, and on GPUs and OpenAI's GPT trained on GPUs in the CUDA environment. So two of the three leading models today use no CUDA. That's a hemorrhaging of share. And so I think what was true three or five years ago, in which CUDA had a dominant position with Central, has shrunk significantly and not important at all at inference and shrinking in its role in training.

Joe Weisenthal: You know, since we're talking about the economic, since we're talking about, you know, the economics of inference and all this stuff, I've actually I would love to get your take one of the things that Pete that like literally in the last couple of weeks there's been this flurry of announcements of these attempts to financialize the market for compute. And so it's like oh you're going to like buy some capacity, the H100 benchmark, et cetera and people on maybe theoretically hedging it. I'm not entirely convinced. It still seems to me like I. It's not like maybe but on the other hand like an inference provider can lock in a very long term relationship bilaterally with a Data center and so forth, and no need for like these spot hedging markets. Do you think the market is going to evolve in such a way that there will be significant demand for for financial instruments that allow inference providers to hedge their price exposure?

Andrew Feldman: I don't know, I'm not a financial engineer is the first thing. But we can look a little bit at history. The guys at coreweave were enormously innovative in how to fund some of their massive deployments. They were some of the first to use a debt instrument that had a backstop with the gpu. And this enabled them to really leap out and sort of have first mover advantage in the NEO cloud space. That was an innovation in financial engineering and extremely creative. Others followed. And now there's a big and active debt market in funding the building and the fit out of data centers. When you have a market that is that big and that active, you have people who want to make bets on either side. And I think over time those bets normalize and regularize and you can wrap them up and you can make it easy to make the bet. When CO2 was one of the first to loan money against GPUs for Core Weave, this was really innovative. And not only does Core Weave get credit for the creating of the instrument, but so does the other side of the deal for doing it and making a successful, innovative bet. And as more and more people jumped in and these could be regularized, they could be more easily priced then once it's regularized and you have a market, then derivatives of that market are easy to make. Historically, that's the way I see this unfolding, that as this market for data centers and compute matures, there'll be people making bets on either side and financial instruments will be created to do it. Whether it's a good idea or not, I have no opinion at this time.

Tracy Alloway: Since we brought up finance, I was looking through the IPO filing and looking at some of the actual numbers in there. And I know you have the OpenAI deal now, but a huge chunk of your revenue comes from this company called G42 in Abu Dhabi. And I think they're both like your biggest customer and also a major Investor. What does G42 actually do with all these chips?

Andrew Feldman: Sure. Last year they were a really important chunk of our business, a lot of it. They're a minority investor. They are the national champion, the national AI champion of the uae. And they build a cloud that is used across the UAE's ecosystem. So it's used by leading universities there. It's used by leading companies there, companies like Adnoc. They're leading oil company, it's used by G42S9 operating companies. The deployments to date have been in the us we have data centers that massive data centers that run equipment for G42 here in Santa Clara, but also in Minneapolis, in Dallas, Texas, soon in Toronto. And so they're doing training and they're doing inference. The training they're doing. They have pioneered some of the leading English Arabic models, they've done genomic work, they are doing serving of models and they're operating as a cloud, particularly for the UAE ecosystem, but also for global companies.

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Joe Weisenthal: Do you think that over time corporate users, and perhaps individual users, but corporate users will want inference served from a company that's separate from the model maker such that they can be certain that they are not revealing and thus training the company that might replace them? I mean, look, Anthropic every couple days announces some new thing. Oh, we have a new markdown file that could do this for taxes or that could do this for whatever. And then a bunch of companies fall like, are companies that use AI increasingly going to want to want to use data centers and inference providers that aren't the model themselves?

Andrew Feldman: Well, first, I think there is a type of professional, a type of job that is most directly under threat from AI.

Joe Weisenthal: Okay.

Andrew Feldman: And they're almost always white collar. And they required you to have expertise over a body of knowledge. That's what an accountant is. You have expertise over a body of knowledge of rulings of previous examples of tax case law, et cetera. That's exactly what AI is good at right now. Exactly. So lawyers, accountants, this sort of. These professionals who have stood between the ordinary person who doesn't know anything about IRS tax rules and the tax rules that is under threat. And that is something that it will be very easy for companies like OpenAI and Anthropic to chew through. There are other areas like say drug design, genetics, genomics, where companies like GlaxoSmithKline have remarkable and unique data sets. This is true for one of our large customers, Mayo Clinic. It's true for GlaxoSmithKline and other of our pharma customers. They have unique data and they will be able to find insight in that data and they will be able to get value from that data. And they will certainly not want to share that data with the foundation model makers unless they are guaranteed that it will not sort of make the general model smarter. And these are companies that have spent 20 or 30 years spending tens of billions of dollars a year gathering data. Right. Patient care records or, or test results for drug design. They're going to mine the insight in this work and they're going to provide find extraordinary things. And those are much more protected because the insights in the data. And they have the data.

Tracy Alloway: You know, you were talking about fabs in Taiwan earlier and I'm now regretting not going on a fab tour when I was in Taipei, but it just didn't cross my mind at that time. Next time. Yeah, hopefully. And there have been various eff under the CHIPS act and some other industrial policies to try to build more chip making capacity in the U.S. in your view, what's the big, I guess, impediment to actually doing. Yeah, A, is it happening? And then B, why does it seem so difficult to actually make happen?

Andrew Feldman: Right. The first thing is difficult because it's a difficult problem to. They're hard. They cost 30 or 40 billion dollars and take five or six years to build. So that amount of money in that amount of time cuts across administrations. And that's a problem with the politics in the US Is it's hard to make policy that's durable across administrations and across time. It's the first thing. The second thing is these are remarkably complicated buildings and we have a sort of a hodgepodge, a sort of strange latticework of local regional building codes that a fab maker has to negotiate. Third is we're trying. TSMC has dedicated tens of billions of dollars to their fabs in Arizona and have committed hundreds of billions more. Samsung has dedicated tens of billions of dollars and committed hundreds of billions more to their fabs in Texas. But they take a long time. And we have to remain committed to building not just the fab, but the surrounding ecosystem, not just for three or five years, but for 20 years or 25 years. Because you want not just one fab, but you want a whole trajectory of fabs. You want them working at today's cutting edge, but tomorrow's and next year's and in 10 years, cutting edge as. And those are things that have proven really challenging in the US And I think we need it. They're strategic assets. And I think we need to find ways to collaborate with those that have the expertise and to find ways to build policy that is durable over a length of time that can build a vibrant ecosystem in the fab and the associated elements.

Tracy Alloway: So the other big political economy theme, I guess, when it comes to semiconductors is this idea that they are in fact a strategically important technology. And so the US should place some limitations on their use abroad. And so we've seen things like export controls, export restrictions, you're an actual chip company. And so I'm very curious at an operating level what your experience of these kind of export controls and has actually been like, how much time does that take up for you? And then also given that one of your biggest customers is an international firm in Abu Dhabi, how important is the trajectory of those export controls to your future business?

Andrew Feldman: I think three or four years ago I would have said not important at all. I think today they're really important in the last administration I got to know the leadership in the Department of Commerce and in the BIS Division of Commerce, which oversees the licensing. I think this is an extraordinarily difficult job. And we saw really hardworking, smart people doing a job that is very, very difficult. I got to know the people in this administration and I found the same. Every single one of them is earning a tiny fraction of what they could earn in the private sector and is doing this because they believe that this is an important mission. The problem is that there are differing views about the right way to do this and there are differing views on the right way to achieve the goal, which is to not give your most precious technology to your industrial enemy. And I think we can agree that today, in today's environment, China is an industrial enemy. Good, well meaning people can disagree on whether the right strategy is to limit them from gaining access. Others argue, as those at Nvidia have argued, is that the right strategy is to give them access and to keep them working on our product, on us made, on us sort of designed product. I come down on the other side of that argument. I understand there are good arguments in both directions. I think limiting the distribution, the diffusion of our most precious technologies makes sense and I think we have to do it thoughtfully and we have to recognize that means some markets will be foreclosed to us. And I'm okay with that.

Joe Weisenthal: Just quickly, on the sort of like current business stuff, you mentioned the deal with aws, how does that work? Could customers right now, can customers of AWS pay them to have inference served specifically on one of your chips?

Andrew Feldman: Not yet, but soon.

Joe Weisenthal: Okay.

Andrew Feldman: It will be served in Bedrock, which is their AI as a service offering. And they will, yes, be able to go down to the click down menu and get super fast inference which will be delivered via a combination of what's called a disaggregated solution, which is using some trainium for some of the inference work and using the Cerebra technology in our Systems called the CS3 for other parts of the work.

Joe Weisenthal: And presumably someone who scrolls down and selects that they would pay some premium for that ultra fast inference.

Andrew Feldman: I think they will pay a premium. We will see. This is entirely as Amazon wishes to price it. This is their product.

Joe Weisenthal: So you IPO'd this week. It's May 2026. This is not the first time that you've tried to or look towards going to the IPO market. There were headlines going back to 2020 wanting to try for the IPO market. And then there were headlines last year, especially because of the relationship with G42 about CFIUS and some of the national security concerns. And maybe that was an issue with the ipo. And then. But also last September, you got one of your looks like G round. G Round. One of the participants in the G round investor was 1789 Capital, which is of course the firm that's associated with Donald Trump Jr. Which is a lot of things. And then the IPO happens. I'm a cynic. So I wonder if the participation of Donald Trump Jr. S investment in your company made it easier to get the green light from these national security concerns to do an ipo.

Andrew Feldman: I wish it were that easy. No, it had no. No role at all. We resolved all CFIUS issues in March of 2025. I believe that was before we took money from 1789.

Joe Weisenthal: Okay.

Andrew Feldman: Moreover, I wouldn't ask. That's not who I am and that's not the way we roll. So we took money because they're a thoughtful venture firm and we don't believe that there's only one point of political view. There are lots of political views. They all have some merit, they all have some weaknesses. And so we have right leaning political, some investors, we have left leaning. The fact that this firm had some right leaning investors. We were looking only at their ability to help us build an extraordinary company. And we have asked, and we have never asked nor will we ever ask for political access or anything of the kind.

Tracy Alloway: What's it like to become a billionaire in a single day? This is something I assume will never happen to me, so I might as well ask you.

Andrew Feldman: I think the honest truth is it was a big nothing for me. I had some wealth before and have some wealth after. I think this is a very difficult way to make money. Being a tech CEO. I think what you have to do is you have to love the work, you have to love the people and you have to think every day about how to make your team rich. And far more important then sort of some change in my wealth was we made more than 800 millionaires. And that's something I'm proud of every minute of every day. And at my last company, we made 100 millionaires. And at this company through our IPO we made more than 800. And that's something that you wake up feeling good about yourself every single day.

Tracy Alloway: That was going to be my last question, but actually you just reminded me in that answer, you know, the idea that getting here, I said you became a billionaire in a day, but obviously this was the outcome of years and years and years of work. And if we think about technological hardware, one of the things most people associate it with is really long lead times and really big research and development budgets. Now that you're a public company, how do you sort of balance that quarter to quarter financial performance pressure with the idea that you still need to be investing in capex in new ways of designing chips, new improvements to the existing ones?

Andrew Feldman: Well, first we think the opportunity for innovation based on our wafer scale engine. The best work is still ahead of us. Number one, we see an opportunity for extraordinary innovation in the years ahead. To make leaps every bit as big and often bigger than what we made by building the largest chip on earth. When you love building hardware, the fact that it takes time is part of the deal, right? That what we do can't be done in a week or a month or a year. And that's what you sign up for. And that's true in every profession. You sign up for the good and the challenging. And you have to sort of make peace with that. If you're a person that wants to dive in and sort of begin iterating right away and fail quickly and code up something and look at it and throw it out in the market and see if it wins, Godspeed, that's great. And that's not for me. You know, in our business we measure twice before we cut once. And you have to put that in your soul. And you have to like it. You have to like that mistakes in our business are really expensive. And you have to like the fact that you breathe life into a chunk of silicon and you get it to do things that nobody else has ever been able to make a chunk of silicon do. And if that's for you, then this process, that takes time and money, you love that too. And so I think I would love it less if you could do it in a week. And I think the people that I love to work with, they feel the same way. And they like being engineers, not because it's a path to money, they like being engineers because they like building things and they like building hard things. And I like working with them for exactly that reason.

Joe Weisenthal: Yeah, you mentioned breathing life into a chunk of silicon. My dad, who's a physicist, always likes to point out how carbon and silicon are right next to each other on the periodic table. They are. And this sort of like here are the two things that we have closest to life and they're literally touching each other. Maybe there's something deep in that.

Andrew Feldman: I think that's a really thoughtful thing. Your father Said thank you and I think that's really cool. And nobody pointed that out to me though. We've stared at periodic tables for a long time, but I think to the extent we can make artificial life, we need silicon.

Joe Weisenthal: Yeah. And they're right next to each other.

Andrew Feldman: Right. Carbon's the heart of all other life and artificial life we founded, at least the intelligent part will be founded on silicon.

Joe Weisenthal: Right below silicon is germanium. Maybe the next. I don't know. Well, let's.

Tracy Alloway: What does that mean, Joe? Ask your dad.

Joe Weisenthal: Yeah, let's keep an eye on germanium next. Andrew, thank you so much for coming on odd lots. Fascinating conversation right in the sweet spot of what we're interested. Really appreciate you taking your time.

Andrew Feldman: Hey, thank you guys for having me and I really appreciate it. Look forward to seeing you again.

Joe Weisenthal: That was really fun. I'm super interested in this topic and it does feel to me like the economics of inference in particular and the market for inference, inference capacity, speed, like it's still day one, you know what I'm saying? Yeah.

Tracy Alloway: I just like looking at the giant wings.

Joe Weisenthal: It's so cool. It really does seem like an onion thing, doesn't it? It's like company solves inference with a

Tracy Alloway: giant chip by building the biggest chip in the world.

Joe Weisenthal: But it is interesting. We did that episode, of course, with Ray Wang from Semianalysis and talking about the role like memory as being this really important part of this sort of cutting edge chipsets. And it's interesting to think it's like, okay, well here is a bottleneck that doesn't run into that they don't have. And the idea that at least as he described it, they're not fighting to get the smallest nanometer chips. And so maybe that gives them a little bit of breathing room on capacity there too.

Tracy Alloway: Yeah, I mean, I do imagine there are some downsides to having giant chips, just as there are upsides that Andrew laid out. The other thing I was wondering. I know he made the case for the reason speed is very important, but I can also imagine a world where maybe it's not that important. Like, I think at some point like the incremental speed factor just starts to become less important when weighed against like the incremental cost of generating speed.

Joe Weisenthal: I think it really. This is like one of those things where it really depends what you're. What you're using it. Right. So it's like if you're like, you know what, I'm really curious why pterodactyls aren't actually dinosaurs. Can you explain it to me then it's like, I don't care about that. Like that fraction of a second.

Tracy Alloway: I would wait five minutes for the chat bot to tell you you're wrong, Joe.

Joe Weisenthal: You just, buddy, you just don't really care that much. But if you're doing some sort of like agent decoding thing or whatever, et cetera, then yeah, that definitely adds up. And I will say, as you use it more, it's just like everything else, the treadmill of expectations. Here's some tasks that you can do in 30 seconds which maybe several years ago would have taken you 30 minutes and you get impatient in that 30 seconds and you want it in 10 seconds. And that's just like that competition to shave down seconds. I think it's always going to be there. So no one ever gets satisfied with this is my point. It always eventually becomes like, it feels like waiting.

Tracy Alloway: But to me, this feels like this is the crux of the AI valuation argument, which is like, how much of a premium are we going to place on a model that may be a closed source model that is maybe slightly better than an open source model? How much premium are we going to place on compute that is slightly faster than this other type of compute or like other use of compute like that? To me, it's an unanswered question and Andrew is pretty upfront about closed versus open source. But I think on the speed question too, like, we're going to find out,

Joe Weisenthal: we're going to find out. And you know, I think one of the things that is going to happen and there have been all these stories about sort of like token shock, like how much companies are spending on tokens. My guess is one of the things that will happen at some point is there's going to be a lot more discussion about why are we using this ultra premium model when we could have done this. There is a lot of just like throw it at the AI, rack up those bills, et cetera, and at some point there's going to be this like, okay, what really needs to be served fast, what really needs to be served on the most premium closed source models. And companies are probably going to get a lot more skilled at allocating from, you know, different forms of inference depending on the need.

Tracy Alloway: Yeah, I think that's exactly it. And at that point, like, we could well see some of the dynamics in the market start to change in terms of valuation. Shall we leave it there?

Joe Weisenthal: Let's leave it there.

Tracy Alloway: This has been another episode of the All Thoughts podcast. I'm Tracy Alloway. You can Follow me at Tracy Alloway

Joe Weisenthal: and I'm Joe Weisenthal. You can follow me at the Stalwarts Follow our producers Carmen Rodriguez at Carmen Armand, dashiell Bennett at Dashbot, Kale Brooks Kale Brooks and Kevin Lozano at Kevin Lloyd Lozano. And for more Odd Lots content go to bloomberg.com oddlots we have a daily newsletter and all of our episodes and you can chat about all these topics 24. 7 in our Discord Discord, GG Oddlauts

Tracy Alloway: and if you enjoy Odd Lots, if you like it when we talk about Giant Wafers, then please leave us a positive review on your favorite podcast platform. And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes absolutely ad free. All you need to do is find the Bloomberg channel on Apple Podcasts and follow the instructions there. Thanks for listening.

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