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

26:12Is that what it's called? Kimmy and Quinn. I'm sorry to press you on this point. But if you had to make a bet, like in 20 years, is the dominant aim AI model going to be a cheap open source thing or a more expensive, incrementally better closed source model? I don't think there's going to be one. There's not one SaaS software. There's some big dogs. There's Salesforce. There's some other giant players. And there are lots of other specialists. I can't think of many markets where we've settled onto one player. If you look at the semiconductor market, you've got x86 where you've got two major players in AMD and Intel.

26:51And 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 will be open source. I don't think any of those go away. 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?

27:30Yeah. software stack that goes with it. What's your take on that? 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? 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 Tan 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.

28:18Just 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, Quad, and GPT. Gemini, built by Google, on TPUs, trained on TPUs, served on TPUs, no CUDA. Anthropics models, trained on Tranium, no CUDA, served on TPUs, on Tranium, 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 a 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 in inference and shrinking in its role in training.

29:31Tracy Alloway:You know, since we're talking about the economics of inference and all this stuff, I would love to get your take. One of the things that 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 buy some capacity, the H100 benchmark, et cetera, and people want maybe theoretically hedging it. But 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.

30:18Tracy Alloway:Do you think the market is going to evolve in such a way that there will be significant demand for financial instruments that allow inference providers to hedge their price exposure? I don't know. I'm not a financial engineer is the first thing. OK. But we can look a little bit at history. OK. The guys at Corweave 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 neocloud space. And that was an innovation in financial engineering and extremely creative.

31:04Others 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 sort of CO2 was one of the first to loan money against GPUs for CoreWeave, this was really innovative. And not only does Corweave 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.

31:48And as sort of more and more people jumped in and these could be regularized, they could be more easily priced. And then once it's regularized and you have a market, then derivatives of that market are easy to make historically. And that's sort of 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. Since we brought up finance, I was looking through the IPO filing and looking at some of the actual numbers in there.

32:29And 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? 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 Adnock. They're a leading oil company.

33:19It's used by G42's nine operating companies. The deployments to date have been in the US. We have 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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37:04Tracy Alloway: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 of days announces some new thing. Oh, we have a new markdown file that could do this for tax 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 use data centers and inference providers that aren't the model themselves?

37:48Well, first, I think there is a type of professional, a type of job that is most directly under threat from AI. Okay. And they're almost always white collar. And they required you to have expertise over a body of knowledge, right? That's what an accountant is, right? You have expertise over a body of knowledge of rulings, of previous examples of tax case law, etc. That's exactly what AI is good at right now. Exactly. So lawyers, accountants, 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.

38:49There 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. Yeah. 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. Right. And these are companies that have spent 20 or 30 years spending tens of billions of dollars a year gathering data, right?

39:44Patient care records or test results for drug design. They're going to mine the insight in this work, and they're going to find extraordinary things. And those are much more protected because the insight's in the data, and they have the data. 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 efforts 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 it?

40:30Yeah. A, is it happening? And then B, why does it seem so difficult to actually make happen? Right. The first thing is difficult because it's a difficult problem. 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, right? 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.

41:24Third 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, them, 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 well.

42:09And 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. 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 U.S. should place some limitations on their use abroad. And so we've seen things like export controls, export restrictions.

42:53You're an actual chip company. And so I'm very curious at an operating level what your experience of these kind of export controls 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? 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.

43:34I 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, is that there are differing views about the right way to do this. And there are differing views on, you know, the right way to achieve the goal, which is to not give your most precious technology to your industrial enemy.

44:14And 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 we're 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 they're 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.

45:05And I'm okay with that.

45:07Tracy Alloway:Just quickly on the sort of like current business stuff, you mentioned the deal with AWS. How does that work? Can customers right now, like, can customers of AWS pay them to have inference served specifically on one of your chips? Not yet, but soon. Okay. They will be, it will be served in Bedrock, which is their AI as a, service offering. And they will, yes, be able to go down 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 tranium for some of the inference work and using the Cerebris technology in our systems called the CS3 for other parts of the work.

45:53Tracy Alloway:And presumably someone who scrolls down and selects that, they would pay some premium for that ultra fast inference? I think they will pay a premium. We will see this as entirely as Amazon wishes to price it. This is their product. 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 2024 about 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.

46:34Tracy Alloway: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

46:55Tracy Alloway:I'm a cynic. So I wonder if the participation, if 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. I wish it were that easy. No, it had no role at all. We resolved all CFIUS issues in March of 2025. I believe that was before we took money from 1789. Okay. 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 are 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.

47:39They 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. 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. No, I think the honest truth is it was a big nothing for me. I had some wealth before and have some wealth after.

48:22I 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. 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.

49:17And 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? First, we think the opportunity for innovation based on our way for 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.

50:09When 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. 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.

51:00You 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'll 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.

51:40And I like working with them for exactly that reason.

51:43Tracy Alloway: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 they're 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. 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.

52:17Yeah, and they're right next to each other. Right. Carbon is the heart of all other life. And artificial life will be founded. At least the intelligent part will be founded on silicon. Right below silicon is germanium.

52:29Tracy Alloway:Maybe the next—I don't know. What does that mean, Joe? Ask your dad. 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. Thank you guys for having me. And I really appreciate it. Look forward to seeing you again soon.

52:59Tracy Alloway: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? I just like looking at the giant. It's so cool. It really does seem like an onion thing, doesn't it? It's like company solves inference with a giant chip. By building the biggest chip in the world. But it is interesting. We did that episode, of course, with Ray Wang from semi-analysis and talking about the role like memory as being this really important part of this sort of cutting edge chipsets.

53:37Tracy Alloway:And it's interesting to think it's like, OK, 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. Yeah. I mean, I do imagine there are some downsides to having giant chips, you know, 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 like I can also imagine a world where maybe it's not that important.

54:15you know 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 that extra speed

54:28Tracy Alloway:i think it really this is like one of those things where it probably really depends what you're what you're using it for right so it's like if you're like you know what i'm really curious uh 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 is not that important. I would wait five minutes for the chat bot to tell you you're wrong, Joe. You just, buddy, you just don't really care that much. But if you're doing some sort of like a gent decoding thing or whatever, et cetera, then like, yeah, that definitely adds up.

55:00Tracy Alloway:And I will say like, as you use it more, like, 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 a patient 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. But to me, this feels like this is the crux of the A.I. 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?

55:43How 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 was pretty upfront about closed versus open source. But I think on the speed question too, like we're going to find out.

56:02Tracy Alloway:We're going to find out. And 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? Like 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, OK, what really needs to be served fast? What really needs to be served on the most premium closed source models?

56:38Tracy Alloway:And companies are probably going to get a lot more skilled at allocating from, you know, different forms of inference depending on the need. 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? Let's leave it there. This has been another episode of the All Thoughts podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway. And I'm Joe Weisenthal. you can follow me at The Stalwart. Follow our producers, Carmen Rodriguez at CarmenArmandDashelBennett at DashBot, Kale Brooks at KaleBrooks, and Kevin Lozano at KevinLloydLozano.

57:15Tracy Alloway:And for more OddLots content, go to Bloomberg.com slash 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 slash OddLots. And if you enjoy OddLots, 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're 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.

58:19We'll see you next time.

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