# Cerebras CEO on the Future of Data Centres, Token Costs and Memory We are Not in an Infra Bubble & Dario Got a Bad Deal with Elon for Compute · Should US Companies Sell to China & Why Most Layoffs are AI Washed with Andrew Feldman 20VC · May 26, 2026 · 62 min · 11,397 words Speakers: Andrew Feldman, Harry Stebbings Source: https://www.996.fm/episodes/20vc--ep-e411c37a/ ## Cold open **Andrew Feldman** [0:00]: We can't build data centers fast enough to keep up with demand. We have a $25,000,000,000 backlog. If demand stays high, we're gonna continue to see memory shortages for at least the next several years. I think it has been Nvidia's strategy to try and create competitors for the traditional hyperscalers. I think they have funded and backstopped and over allocated to the neo clouds. They have created a dependence, which is probably not healthy. So over time, the history of our industry is a massive reduction in the cost per unit compute. For hard problems, there is no upper bound to how much faster you wanna be. **Harry Stebbings** [0:39]: This is 20 VC ## Intro **Harry Stebbings** [0:40]: with me, Harry Stebbings, and I'm so excited to welcome a dear friend, Andrew Feldman, founder and CEO of Cerebras. Now this show just makes me incredibly happy and proud to do because this is a true testament of resilience, of grit, of building really hard things. And last week, Cerebras went public, the largest semiconductor IPO ever. The price went from $185 to $311. They got over $5,500,000,000. It was an incredible day for an incredible team. Today, we deep dive on the future of chips, the future of US China relations, the future of data centres and energy. This was an incredibly wide ranging conversation, and I just wanna say a huge thank you to Andrew for being a great friend to me over the years and for being a fantastic adviser to 20 VC. 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For labs advancing agentic systems, Turing provides the structure required to understand why these failures occur. To find out how, visit turing.com/20vc. That's turing.com/20vc. You have now arrived at your destination. ## Conversation **Harry Stebbings** [4:31]: Andrew, dude, it is so lovely to have you on the show. I have to say, I was I was quite emotional last week when I saw the IPO because you are one of the kindest, greatest people. I I love getting to know you. I so appreciate our relationship. And so to see that culminate last week with the IPO, it was really special. So congratulations for last week, dude. Thank you so much. Those were really **Andrew Feldman** [4:50]: kind words. And it was a really exciting day for for for the company and the team and the people who'd who'd believed in us and and who backed us for for a decade. It was great. Thank you for for for saying those nice things. **Harry Stebbings** [5:03]: Not at all. And I was I was thinking, like, in terms of this conversation, how I wanted to structure it. And I I always get back when I have amazing people like you on the show, which is Eleanor Roosevelt's kind of statement of not very intelligent people discuss other people, mediocre people kind of discuss current events, and then intelligent people discuss the future and ideas. So I thought I'd grapple with my own ideas and wrestle with your incredible brain to help me understand where we're at and where we're going. I wanna start with, on the one hand we look at the landscape today and it's like, oh my gosh, an AI infrastructure bubble and then on the other hand we look at Jensen Huang last night, he comes out and says we're gonna be spending 3 to 4,000,000,000,000 on AI infrastructure by 2030. How should I balance the, oh, there's an AI infrastructure bubble with this appreciation of 3 to $4,000,000,000,000 spent by 2030? I've been thinking a lot about this. I **Andrew Feldman** [5:55]: think when you look at other bubbles and you look at bubbles in the past, and I was in one in the late 90s when we built out an enormous amount of fiber optics. And you sometimes have economists who who maybe think it's relevant to look at eighteen eighties, the building out of of rail. I'm not sure that's relevant, but what I see is that there was a pension to believe that if we built it, they would come. Right? The infrastructure build out was way ahead of demand. And that was true in railroads. That was true in fiber optic cabling. And in a strange way, that is the exact opposite of where we are with AI. The infrastructure build out is behind demand. We can't build data centers fast enough to keep up with demand. We have a $25,000,000,000 backlog. Nvidia has a backlog. AMD has a backlog. Others have backlogs. They have backlogs because we can't get data centers built fast enough. And it's not that we're building on the come. We're not building ahead of demand. We're building behind demand. That is a very different observation than those who say there's a bubble. I don't think they've really gotten their head around the fact that we are trying to keep up with demand, not the other way around. And I I don't think that's a characteristic of a bubble. When you are trying with your infrastructure to keep up with what people want today, not in the future, today. **Harry Stebbings** [7:22]: And their demands are growing over time. Is it ultimately a good thing that we are meted in our ability to build out data centres? Because it almost tempers the demand. If we were able Gavin Baker said actually kind of the delays and the permitting and the challenges that are incurred today actually help. Because if you were able to have it all today, all of demand would be met with all of supply. And that would actually be a challenge. Sometimes **Andrew Feldman** [7:46]: the world is like I was in my twenties the first time I went to Vegas and went to the buffet. Right? You eat so much, you feel sick for days. It's all in front of you and you just gorge yourself. I think the market can sometimes be that. And I think Gavin is an extraordinarily thoughtful sort of guy about this. I think we are being metered. We also know that the reason you put meters on a freeway is because it makes the the freeway traffic smoother. It avoids hiccups. That's exactly what metering is designed to do. And so I think he used that that analogy extremely thoughtfully. One of the advantages that OpenAI has, and I think one of Sam's brilliances, was that he saw an exponential growth, and he saw what that would mean in a year or two to the demand for compute, and he wasn't afraid by it. He went out and took action. And perhaps others couldn't believe it, or they were looking at the same demand sort of steep exponential growth and like, well, we can't need that much. You can't need tens of gig. That my mind hurts if you do that. Whereas what OpenAI did is they went out and they're like, we're gonna contract for it here and here. We're get power. We're gonna get data centers. We're gonna sign up for hardware. And an ability to to believe your data in an exponential growth environment out **Harry Stebbings** [9:02]: a year or two or three is a superpower. Do you get rewarded for that insight if you can just buy it from Elon now on demand? I don't think they can buy the same thing from Elon on demand. They bought down rev gear. I'm sorry. I've learned from doing this show for a long time. I can ask stupid questions. They they bought down rev gear? What is that? **Andrew Feldman** [9:21]: They bought a they they got h one hundreds. They didn't get the b two hundreds. They didn't get the most current. They are a generation and a half, maybe two generations behind. This was not a great deal. It was a good deal for Elon. He had them sitting around, but they were forced to take action in a deal that I think was not the ideal deal they wanted. It was **Harry Stebbings** [9:41]: the deal that was available. Going back to what we said about kind of the delay in data centres and data centres being a constraint, I just hear everyone say, well, memory, memory is the shortage too, Harry, and that's why we're seeing it increasing cost, four, 5x in certain cases. Is that true? How should we think about memory being the shortage as well? **Andrew Feldman** [10:01]: What's happening here is there is such extraordinary growth in demand that it is putting pressure on all parts of the supply chain. Memory after TSMC, which is right after fab space, memory is number two item that's needed. And what's happened is there are only three companies that make the memories GPU use. We don't use that memory. But that HBM is made by Samsung and Micron and Hynix. They couldn't keep up. And so the prices shot through the roof. I mean, Micron producing numbers where they have 85% gross margins. I mean, they're getting software gross margins on making memory. Yeah, I think it's extraordinary. That is a limitation for all **Harry Stebbings** [10:42]: GPUs, but not us, we don't use it. Again, going back to my idea of what the future looks like, what should one expect from that? Does it ease? Does it ease over time? What happens to the cost? The challenge Harry is **Andrew Feldman** [10:54]: that these are extremely lumpy items, right? You can't just add a little bit of manufacturing capacity at a fab. You have to build a fab for $40,000,000,000 and takes five years to build. If you see demand explode, you cannot respond quickly. All you can do is fill your factory. Once your factory is filled, you gotta build another factory. Right? It's a step function in your ability to meet that demand. And the step is huge and takes years. And so if demand stays high, we're gonna continue to see memory shortages for at least the next several years. **Harry Stebbings** [11:29]: Do you think we will see a peaking of demand? You've seen so many different **Andrew Feldman** [11:34]: Not if AI continues to improve in usefulness. I mean, what's happened here, and this is something that I haven't heard others sort of talk about, is that somewhere in 2025, the models got smart enough to be really useful. Before that, Harry, these were sort of sort of a novelty. AI was like, cool, and then nobody used it. Remember, we we make AI with training and we use it with inference. And so once the the AI we made, twenty twenty five ish, got smart, we began using it. And this explosion in demand that Jensen described and that we very much agree with is happening. That's because people are using it every day, and they're using it on more and more problems. They're using it on harder problems, and it is sweeping through different demographic groups. It's not just 28 year olds in Silicon Valley. It's my 85 year old father. Right? My 11 year old niece. It is right? It is sweeping through demographic groups and they're using it all the time. That is what's driving this demand. If we continue to find ways to make the AI, the frontier models smarter and more useful. We'll keep using it. The demand will continue to **Harry Stebbings** [12:43]: on this sort of exponential curve. You've compared past cycles before in this conversation, Sarah Fry said about cloud providers can be similar into some perspective to what we're seeing today in terms of frontier models. And you said that last night. To what extent do you think you see the commoditization there and they essentially become utilities versus differentiated providers with meaningful moats? **Andrew Feldman** [13:07]: I think it has been Nvidia's strategy to try and create competitors for the traditional hyperscalers. I think that has been a strategy of theirs. I think they have funded and backstopped and over allocated to the neo clouds. They have created a dependence, which is probably not healthy. The truth is is that what what AWS and Azure offer is extremely useful for most enterprises. They offer credibility and legitimacy. They offer security. They offer layers of different software for different parts of your organization. If you'd like to enter in the AWS world, you can enter with Bedrock. You can use tools like SageMaker. You have a collection of different ways to enter. And you can store your data there. You you have your s three instance. I mean, you you can have an entire offering. I think that is really valuable to a segment of the market. I think there might be other segments of the market. They're like, give me cheap compute. I don't care about anything else. In that case, your strength as a hyperscaler becomes your weakness. You have the security. You have the other layers of software, and you have some of the costs that are associated with that. And if people don't want that, if you don't care about leather seats, and there are leather seats in the truck, there's extra cost in the truck. And when you buy the truck, you find somebody who's got a truck that's got Naugahyde seats. Our business, just because it's wrapped up in technology, is no different than any other business. It's segmented, there's value, that value comes at a cost, you have to make that value. The hyperscalers make the value. They make the value through software, through security, through having rules about their data centers, the security, physical security, the various security checks they put in. Those are enormously valuable to most parts of the market, but not all. **Harry Stebbings** [14:48]: You said about the costs there. When we look forward, how do the costs of your business change significantly over time? We we spoke about the cost of memory going up firebacks. If we look at the COGS in five years' time, how do you think they will look most significantly different? **Andrew Feldman** [15:04]: Well, look, the increase in memory has been very good for us because we don't suffer it. Right? This has given us opportunity. We use SRAM, and there's no shortage of SRAM. The cost of SRAM hasn't changed. And, you know, no SRAM maker, because TSMC etches it into your chip while they're making the logic. There are no extra margins to pay the HBM maker. And so we have been advantaged in this environment. We have been advantaged by the fact that there are constraints on COOS at TSMC. We don't use COOS. We are advantaged by the fact that we're at five nanometer, and the three nanometer node is the most oversubscribed. Our supply chain is advantaged on these dimensions and others are paying the price. The price of **Harry Stebbings** [15:49]: GPUs has gone through the roof. And so to my question on COGS, do we see like a plateauing of COGS in terms of it can't get cheaper and this is the stable state? Do we see a meaningful reduction? **Andrew Feldman** [16:01]: I think what happens over time, Harry, is all of us, we improve our designs. The designs deliver more tokens per unit time. They deliver faster tokens. We are 15 x faster because of architectural reasons. We will continue to to improve over time. Nvidia, they'll continue to improve over time. I believe the gap will widen between our performance and their performance. But all of us, the whole industry, Us, Nvidia, AMD, Qualcomm, ARM, everybody's chips will be better in three or four years than they are today. They will produce more per unit power, and they will produce more per dollar cost. So over time, the history of our industry is a massive reduction in the cost per unit compute. **Harry Stebbings** [16:47]: I was chatting to a friend who's a phenomenal mind, and he said that Google will become the lowest cost producer of tokens because they own the full stack from TPUs to data centres, networking, power procurement. Do you think that's right? That that full stack ownership will lead to their highest margin, lowest cost stability? **Andrew Feldman** [17:05]: There are pros and cons of that strategy. The pro is you have everything from the ground, land, all the way up to tokens. The downside, you can only sell your TPU to yourself. And historically, volume mattered a lot. And so your market is constrained by your own demand. Whereas if you were able to sell to the whole market, you might have more more demand and be able to drive down the cost. It's an open question. Google is threatening that argument. I think your friend's argument is reasonable, but there has historically been a challenge if you only have one customer yourself for your hardware. That has historically limited the size of the opportunity landscape for you. **Harry Stebbings** [17:51]: Do you think they should sell to external customers? **Andrew Feldman** [17:54]: I think you are already seeing them step outside of their own data centers for this exact reason. What it says in your friend's construction is our ability to sell hardware is constrained by our ability to build data centers. One can imagine a world where you don't want that constraint. You would like to be able to sell hardware to anybody's data center. These arguments are extremely complicated, rarely unfold in a simple form. But it is true that when Google or when Cerebras puts our equipment in our own data center, we have a significant advantage over Neo Cloud because Neo Clouds are buying hardware with gross margins of 80% for Nvidia. So the hardware in those data centers, and then they have to make their margin. That's not what Google's doing. **Harry Stebbings** [18:41]: That's not what we're doing. Does that mean they're dramatically overvalued? When you look at a Nabbings or CoreWeave or any of the others, like I think CoreWeave has been an **Andrew Feldman** [18:49]: extraordinarily innovative company. I think they've solved a series of financial challenges with really innovative sort of financial engineering. They were the first to use debt in a very innovative way. I get enormous credit for that. They have been extremely good at sort of rapid deployment, which itself is a really important skill in in this environment. I don't know about the others, but all of us have challenges as our business grows. I think they have produced really interesting things through creativity. Now it's it's different creativity than than than what I have, but they've gotten paid for real innovation in financial thinking. **Harry Stebbings** [19:30]: Speaking of real innovation, I saw the post about you running Kimi K 2.6, 6.7 x faster than the next fastest GPU cloud. We posted **Andrew Feldman** [19:43]: it while one bozo at an analyst firm was on TV saying we couldn't do it. I mean, if ever there was an example of being empirically proven dead wrong, To have these numbers posted while you are on TV saying they can never do it. It was perfect. I enjoyed that. I'm a collector of examples of people being dead wrong. My wife has a list of when I'm dead wrong. So I've sort of embraced this and collected. You should be a venture investor, my friend, with a portfolio of **Harry Stebbings** [20:14]: 30, you'll be dead wrong a lot. **Andrew Feldman** [20:16]: You have, if you're **Harry Stebbings** [20:17]: lucky, 80% of your portfolio where you were dead wrong. You should do if you're doing it right. Yeah, I agree with that. How important was that for you? And is there a stage where actually it doesn't matter being that increment more important? Like 6.7 times. This is so much more important. It's not 20% more important. That's right. **Andrew Feldman** [20:37]: I think for hard problems, there is no upper bound to how much faster you wanna be, nor the value of speed. I think that if in three minutes we can solve problems that take others twenty minutes, then think of all the extra problems we get solved. Think of if I'm your competitor and I'm solving your hard problems in three minutes and you're taking 20. Imagine over a day or a week, I mean, you get smoked. You will be smoked in this example. That is the way this is going. Speed is of the essence and it's true in coding. It's true in agentic flows. It's true in every part of the AI landscape. I mean, let me just ask you this question. How big is the market for slow search? Really? How how it's zero. How big is the market for dial up for slow Internet? Right. How much would I have to pay you? Let's try turn it around and say, there's a negative market here. If I gave you a thousand dollars a month to have slow Internet in your home, you wouldn't take it. A thousand dollars a month. That's how impossible it is to engage with an important technology slowly. Why do we **Harry Stebbings** [21:42]: believe that inference will be any different? I am kinda pushing them. When you power codecs and you're able to be so much faster, if you're core coder, you're not like, ah, bugger. They are. I think you have to be. You have to be. Are you able to sell to them also? Again, please tell me to sort off. **Andrew Feldman** [21:56]: Oh, no. No. No. Look. Right now we are digesting one of the largest deals in the history of Silicon Valley. **Harry Stebbings** [22:02]: You're like, for fuck's sake, Harry. Give me a break. I've just signed a $20,000,000,000 deal. You want more? **Andrew Feldman** [22:08]: You know, while we were on the road, some investors would ask. They'd say, oh, you're heavily concentrated. You have a big portion of your business with OpenAI. We say, I talked to you a year ago when I had a billion dollar deal with g forty two. And you said, you're heavily concentrated. You have a billion dollar deal with g I I come back to you in a year with a 20 plus billion dollar deal, and you tell me the same thing. But with a different customer as well. With a different customer. With a different customer. And I tell everybody that the way you get good and the way you have succeed with many customers of size is first you win one. The way to catch big customers is first catch one and learn, build the muscle, change your supply chain, learn how to work with a large customer. Then you're in a position when the next one comes to have a chance to win. And what's more chance to keep them happy once you won. And then once you have that muscle, you're in a position to go out and **Harry Stebbings** [23:01]: win the next one. It is a huge deal. What are the biggest challenges in fulfilling it? With the greatest of respect, do you go to sleep at night going, oh, that is quite a lot. **Andrew Feldman** [23:12]: Look, I think what has happened, and Sam said this, he said the first time people use GPT, I think it was what, four something. They said, oh, this is amazing. And the next day they're like, how come it's not faster? The rate at which you get accustomed to something and then want better is amazing in our industry. And it used to be the case that 20 megawatts was a lot, and then a 100 megawatts was a lot, and then a gigawatt was a lot. And now we're running around looking for multi gigawatt facilities. And that's in any other time, 750 megawatts would have been a mind boggling amount. And now we're like, yeah, we got that. Right? It is the change in mentality over the last year or two for everybody in our industry has been sort of extraordinary. Five years ago, if you'd have said, we're engaged in a multi gigawatt build out, or you think about what Cursor is doing or think about some of the other cool companies, what SoftBank Power is doing, what some of these groups are doing, and you say, that'd be delusional five years ago. And right now it's like, oh, another one? That makes sense. I mean, we should try and get her UAE stargated five gig. Oh, Yeah. No problem. That seems reasonable. I think that's what's happened. It's this extraordinary sort of change in thinking. **Harry Stebbings** [24:29]: If we are nonchalant to multi gigawatt build outs today, what are we in five years time? It's difficult to imagine. And by the **Andrew Feldman** [24:37]: way, that is exactly where I think Sam is the best in the world, maybe Elon, is where everybody else's brain shuts down. Right? When you're trying to think about a 100 gigawatts or 500 gigawatts, those guys, they have sort of this ability to not be constrained by the way the world has always been. And that is such an extraordinary power. **Harry Stebbings** [25:01]: When you have such scale as the multi gigawatt, you mentioned that the 500 gigawatt, does energy not just become the core crux and bottleneck that enables winners and losers? **Andrew Feldman** [25:12]: I certainly think that people like Sam and Elon and others have said that's what they believe. That at the end, we're in the business of turning electricity into intelligence. Therefore, the limiting factor is electricity. I don't know if I agree with that, but that is certainly a very reasonable view from **Harry Stebbings** [25:28]: from where we are. What's the bad case against that? What's the alternative argument? It doesn't have to be yours, but it **Andrew Feldman** [25:34]: happens. No. No. That you bump into something else. That what happens in fact is our models can't keep getting smarter. That you hit something that has an assumption built in that the models keep getting smarter and you keep feeding them more energy. And at the end of the day, the models are either smart enough or keep getting smarter so that it makes sense to keep feeding them energy. That might be true. I don't know. **Harry Stebbings** [25:54]: Do you think we'll be able to build out data centres in the way that we need to and build out capacity in the way that we need to when we see that 40 out of a 100 data centres are now not being built out even post approval because of local municipalities permitting, like disruption. AI is not popular. **Andrew Feldman** [26:12]: Harry, I think it's hilarious. People say, oh my god, the data centers are late. Oh my god, there are delays. Have you built a kitchen? Your contractor was late. Right? Pick a little tiny project in your home. Was it built on time and on budget? No. Now imagine building something the size of 50 football fields and requiring interaction with local municipalities and and power companies and regulated industries. These things aren't gonna be delivered on time historically. People's mind explodes, and they never think about their own experience in their own homes. Does your contractor show up every day? No. Does he do exactly what he says he's gonna do? Rarely. Do the materials, the tiles, or whatever for your home, do they sometimes delayed? Yes. All that same thing happens when you build a data center. The generators, sometimes they're late. Sometimes they fall off a truck, literally. They fall off a truck and damage is done to them. Are the transformers late? Sometimes the transformers I mean, everybody suddenly sort of throws their arms up and says, oh, everything's late, or they have to deal with localities. Anybody who's built anything big knows this is par for the course. This is what building means. **Harry Stebbings** [27:22]: So you're not concerned then about bluntly local neighborhoods seeing data centres as a city think of **Andrew Feldman** [27:28]: our industry did a shitty job of engaging the community properly. And I think Brad Smith put out a post a while ago that should have been the way we all work from the get go. And it was, these can be clean, they can make jobs, they can be good for communities, we can do this thoughtfully. These create thousands of local jobs. And thousands of local jobs means restaurants and lunches and hotels. And the way they were done was, I don't know if sneaky is the right word, but sort of shielded and and wasn't open, and they weren't good neighbors. Now there is no reason why we can't be good neighbors. There's no reason why the we we can't add these to to communities and have the community benefit from it. And we have to do some thinking. Right? We have all the the heavy equipment out there. Build a football field for the for the local school. Build a school. At a church or synagogue to the community. Right? We can be good neighbors at very, very low cost. We can pay our own way. Don't try and use sort of loopholes in the way power companies have historically amortized the cost of new power lines over thirty years and push that on the community. That's BS. We ought to pay our own way. We ought to look after our neighbors. When we do that, I think that the neighborhoods that that embrace this will benefit enormously. But we didn't do a great job. I mean, we didn't do a great job as an industry at all. **Harry Stebbings** [28:52]: It feels like kind of the cartels in Colombia, were. It's like they built the churches and they built the schools and they had such good businesses and such high margins that they could kind of get away with it because of that. And hey, great. I don't think that's **Andrew Feldman** [29:05]: right. I I I think these localities have have a resource that that isn't being used. They have power. Many of these land is cheap because nobody wants it. We are not going to parts of Metro New York. Everybody wants that chunk of land. You're going to places where the the price of land is depressed, you are near power resources. And my position is that we ought to be good neighbors and we ought to be transparent. We ought to pay our own way. Now this seems not to be very controversial in my mind. I think the best and most concise description is is what Microsoft has put forward. And we should have been doing that from the get go. Most communities are comfortable when their neighbors pay all their own way. And it's only when groups tried to shift costs or not pay for the full resources they're using. Our data centers don't need to use a ton of water, they can recycle it. You can have a closed loop. We can pay our own way. We can upgrade substations. We can upgrade grids and pay for it in its entirety. We shouldn't be pawning that off on local communities. **Harry Stebbings** [30:09]: Do you worry about AI as a brand? And you see matter lay off a huge amount of people today. It's challenging to see four AM emails from Zuck and, you know, jobs being lost. Yeah. **Andrew Feldman** [30:20]: I do worry about it. Those are those are people, and they have families. And I think there are sort of two views, Harry. I I I think to date, most of the layoffs were AI washed. They were because we did boneheaded hiring during COVID. It is actually because a great deal of productivity gains has been have occurred over the years that we're just now harvesting. The ability to gather information from across the organization to synthesize it and put it in one place is now changing what it means to be middle management. The role of information gatherers and presenters is being eliminated. The ability for us to automate roles. None of this is AI yet. That is really 90%, 95% of what the in my view, what the the terminations have been about. It's easy to to to put them under the umbrella of AI. Now AI is starting just now to have meaningful enterprise impact. But if you are an engineering organization that can't see how to take advantage of vastly more productive engineers, I don't think you're long for this world. I mean, the list of things I want our engineers to do is 50 times as much as we have engineers. As we get more productive, we do more things, we're gonna hire more engineers. We're not gonna hire less engineers. **Harry Stebbings** [31:37]: Can I ask you, saw Benio say that he spends 300,000,000 a year on Anthropic, which equates to about 3.8% of developer salaries on Anthropic? To make it justify the valuations that we're seeing for these companies, it needs to be 20%. Do you have any concern in that movement from 3.8% to 20%? No. **Andrew Feldman** [31:57]: I mean, think if you look at I've never done this in any detail, but if you look at what we pay hardware engineers and you look at what the tools, we the EDA tools they use, I bet you're much closer to 15% or 20% than 2% or 3%. What's happened is historically software engineers use very low cost tools, and hardware engineers used extremely expensive EDA tools. And so that's interesting, isn't it? I mean, the cost of bugs in hardware is so high that we became accustomed to using many expensive tools. In software, we threw people at the problem rather than tools. As AI becomes more productive, I certainly don't see a problem where software engineers are using 50 a 100,000 a year each in tokens. There are 47,000,000 software engineers in the world. I mean, that's $5,000,000,000,000 just in software engineering token use. **Harry Stebbings** [32:52]: We mentioned hardware engineers, we mentioned software engineers. What role does not exist today that you think will be incredibly commonplace in three to five years? **Andrew Feldman** [33:00]: I've been a part of, over the past twenty five or thirty years, several technical transformations that produced jobs in companies that didn't exist. Prior to the mid nineties, the role of CIO didn't exist. CIO arose as a role with Cisco, with their sort of rise to dominance. Prior to the mid nineties, the amount of enterprise networking was de minimis. There was a role that was often VP at telco infrastructure. That job is gone. We don't have a phone system. In fact, we don't have phones on people's desks. They call me on my cell phone. That job disappeared completely. Gone. And companies that built the PBXs like Rome and all these other because that business has shrunk to nothing. Now later, what happened in the two thousands with the rise of Palo Alto Networks and these other security? The role of CSO never existed prior to that. And what you're gonna see is the rise of roles that reflect the governance of AI in companies. Now some companies have chief AI officers. I don't know if that's what it is. But as these technologies become important in companies' life, new jobs emerge, jobs that never existed before. New organizations exist where there were none. Previous ones disappear. I think the role of HR changes fundamentally. The part of a r HR that just that answered questions, that provided information about benefits, that disappears. AIs can can answer all your questions. They can provide better answers, faster answers, more thoughtful answers. It becomes something different. The management of people becomes something different. I think there are all sorts of other parts of organizations that have fundamental changes because AI can answer the **Harry Stebbings** [34:42]: questions that they used to answer. Do you agree the biggest inhibitor to enterprise adoption of AI is data structure structure and data cleanliness preventing No. No. **Andrew Feldman** [34:50]: The biggest are are lawyers. No. Really. I think the security apparatus and the lawyers who, when they don't understand the technology, say, no. We can't do it. They're in the saying no business. Entrepreneurs are in the getting it done business. There's a reason for this, that your security apparatus and your lawyers I mean, every they're they're in jobs that everyone just blames them. No credit, no credit, failure, blame. No credit, no credit, failure, blame. I mean, that's their life, and it's brutal. It's brutal. You're selling it so well. For any of you aspiring lawyer. If you're listening, go into being a CISO. **Harry Stebbings** [35:25]: It's brutally hard. We had a year where nothing happened. Well done. That **Andrew Feldman** [35:32]: is their dream. I mean, every day their phone doesn't ring, they're just like, woah. Made it to another day. But when confronted with new technology, because their payoff structure is such that they're in the business of trying to avoid risk. They are a drag on adoption of new things, and you see this across the board. Lawyers don't have a contract for it. There's no precedent. That's in a business of backward looking precedent. You wanna make a lawyer uncomfortable? I know your girlfriend's a lawyer. Ask her to work in an area with no precedent. They don't know what to do. Their whole training is about what has everybody else done before? How do we synthesize this? How do we work within those rules? I think the wide scale adoption and use of AI in organizations is today limited by security and legal. Once they agree, we need to do this. Here are the rules we will use. There's a huge amount of productivity to be gained. Then you are immediately constrained by the way you chose to husband and marshal data. The way you chose to organize data over years. And so companies like organizations like Mayo Clinic that have been on a thirty year quest to organize data. They are at a huge advantage. Same with companies like GalaxoSmithKline. And other companies who haven't perhaps been as disciplined, as thoughtful about the organization of their data **Harry Stebbings** [36:48]: are at a disadvantage. On the security and the provisioning side, do you think we will see industries tip like legal has done, where the biggest firms in the world are now going, oh, shit. We need AI. Our clients are saying we need AI, Harvey or Agora. And I'm not gonna get into which one, but like, there's two options, boom. Do you think all industries will follow the tipping, or do you think most will follow the slow agreement that it's the new normal? What's happening **Andrew Feldman** [37:13]: is the leaders are tipping. Right? And I I think even Jensen told the story that that he was battling with his own internal lawyers around the use of I think it was Cursor. And finally, he just decreed. We're gonna we're gonna do it. I think at some point, leaders weigh the productivity gains against the unseen boogeyman of brisk. And the problem with unseen boogeyman is sometimes they're actually real. Right? Not often, but sometimes. That's the problem. What does he call them in in John Wick? Baba Yaga? John Wick is the guy you send to kill Baba Yaga? **Harry Stebbings** [37:48]: I'm just I you know, I think for me, I'm not that young anymore, but I'm definitely capable of exuberance. You look **Andrew Feldman** [37:54]: good. I know you're in your sixties, but you look good. **Harry Stebbings** [37:56]: Yeah. And it's it's facial moisturizing routine. We mentioned security, permissioning, legal, everything in between. They get even more freaking nervous when it's open source. They shit the bed. How do you think about that? I see more and more companies, especially in the Valley, really push the boundaries on with Frontier and then try and get as close as possible with open source given the cost advantages. Is that the future? And what does that mean? Look, I I think **Andrew Feldman** [38:22]: we as an ecosystem have made real progress in sort of the the legal gunk around open source. But the result has been a complexity that hurts your head. If you ever want to dive down a rat hole that has sort of no bottom, begin a discussion with lawyers about open source software. And there's no end to the depth and the boredom with which you will suffer as you head down this hole. This is made doubly worse by some of the best open source models were made by Chinese companies, and they are exceptionally good models. Kimik two, DeepSeq, Quan, GLM, these are extraordinarily good models. They're not quite as good as the closed source models, but they're exceptionally good models. And I I think that is a case of people trying to decide whether it makes sense to to to to save money. They have been easy for us to adopt, to demonstrate extraordinary speed on. It's a hard problem. I mean, don't envy the the the the legal team and and the security groups that are thinking about these things. The truth is the tidal wave is so big and the demand is so high. They often just get washed over. Do you think we should be selling chips to China as a result? No. I I think let's remove all of us that are self interested, even though I'm arguing against my self interest. Right? If you remove me and you remove Jensen, you remove Lisa, and you remove everybody in the chip industry, and and you say, if we sell to to somebody in the security business, and you ask this question, if we sell leading edge technology to China, will their military use it? Everybody says yes. There is no debate on that point. Their military will use it. You ask a second question, which is if you sell our leading edge technology, will their government use it through their industry to compete with us in an advantaged way? The answer is also yes. That's where I stop. There's complete agreement that those two things are true by everybody in the security business and outside of the chip business. Now, you can say that keeping them in our ecosystem is the best way to manage that problem. That's one argument. And there's some merit to that. There is keeping them from building their own their own ecosystem is something that's in our interest. There's some real merit in that. I don't agree with either of those arguments, but they're real arguments and they have real merit. They are, at least, today, our industrial adversary. As you travel the world and you see the results of some of their industrial policy, for example, the driving down the cost of solar, driving down the cost of lithium batteries, the results it's had in their auto industry, and the fact that you you travel the world and you see Chinese cars and with fewer and fewer American cars, they're an industrial adversary. I don't love that. I, for years, did business with extraordinary entrepreneurs there at Baidu and at Tencent and Didi and all these companies, and they're every bit as good as anybody in Silicon Valley. And I would love a world in which they weren't an industrial adversary, and instead we were working together to solve real problems. The state of the world is the state of the world. For me, if it's an industry, as American industry, we sold fewer chips and we didn't sell them to China, I'm just fine with that. **Harry Stebbings** [41:28]: People would argue back and say exactly as you said that if we don't sell to them, they'll build their own capabilities and they'll get very good at it, and then we won't control it. Why do you not think that's a credible argument? I think the **Andrew Feldman** [41:39]: chip industry requires you to go through TSMC, and TSMC requires you to go through ASML or Samsung. There are reasonable choke points to manage those challenges. I think the strategy in any case is even those I think who who disagree with me would would suggest that you don't wanna sell them your cutting edge technology. You wanna keep them down rev. I'd like to keep my industrial adversaries more than down rev. **Harry Stebbings** [42:08]: With that, how important is it that we onshore TSMC like capabilities and companies given Taiwan's vulnerability to China? **Andrew Feldman** [42:16]: We have problems in The US in long range policy. Policy that endures more than a single administration. Right? We have problems building infrastructure that is clearly needed and crosses municipality lines. Let's look at things China has done extremely well. Their power infrastructure is extraordinary. And in The US, we are a patchwork of nineteen fifties technology if we're lucky, and that's really bad. There are things we don't do well. One of them is thinking about long term consequences of decisions like not investing in fabs in The US. I mean, we didn't just lose the fabs, we lost the surrounding ecosystem. We lost the packaging expertise. We lost a whole set of surrounding strategic jobs and industry, and it is extraordinarily important we get it back. And I've been saying that for a decade and a half, that not the chips act, not subsidizing Intel. It's important that that we have cutting edge fabs in The US and that we surround them with cutting edge packaging technologies. These are a strategic asset. **Harry Stebbings** [43:25]: If I said that you have one policy change that you could usher through with no resistance, what would it be? **Andrew Feldman** [43:32]: I would allow TSMC and Samsung to a twenty year period free from all local ordinances, all of them, to build fabs in their desired location in The US. If that's Arizona, that's great. If that's Texas, that's great. Twenty years, no local rules. Allow them to build fabs. And I I would say that use the same rules you use in Taiwan. Don't don't build garbage. Use exactly the same construction techniques and rules, etcetera, that you that you built fabs successfully elsewhere in the world. But local ordinances are disastrous and not intended to cover pyramids. Right? Fabs are modern pyramids, Harry. They are the greatest things humans make in manufacture in the manufacturing world by far. By far. **Harry Stebbings** [44:17]: Nothing's close. Can I ask you, Andrew? I sit here in London. Should I be worried? And you have the best frontier labs in The US. You have amazing open source and amazing manufacturing capabilities in China. What does Europe really have? We've of failed on the model front. Mistral is the leader, but sadly no nowhere near others. Should I be worried? **Andrew Feldman** [44:37]: You should be worried at the pattern. The pattern of sort of lack of success across a range of technologies. It's not just that the leading AI companies are are most of them are in The US, but the leading chip companies, but the leading software companies. Right? Of course, there's some examples, SAP and and some others. But there has emerged in Europe, a sort of be afraid of it, then regulate it, tax it sort of mentality that that works against entrepreneurship. And and I think Europe this isn't true across the board, and clearly there are pockets outside of Cambridge and and in London and in Stockholm where the guys at Lovable are doing really interesting stuff. And there are all sorts of counterexamples. But on the whole, given its population, the opportunity to do vastly better on the innovation front across industries is sitting there unexercised. **Harry Stebbings** [45:31]: That I think is is a worry. How much of your business do you think will be in Europe in five years' time? **Andrew Feldman** [45:36]: I think along with this, they have been slow to adopt new technologies. Not only have they been sort of slower to invent new technologies, but they've been slower to adopt new technologies. I think the fastest adoption will not be in Europe, but in the the the two and a half to three to five year range, it it will be a meaningful portion. Is is that in line with your experience? I mean, my experience is from a long way away and from visiting regularly and talking to customers. Is that your experience? **Harry Stebbings** [46:03]: Application layer, no. I think we have some of the world's best companies, whether you're eleven Labs or you're Synthesia or you're DeepMind. I think a 100% on the infrastructure, the chip side, on the model side, unwaveringly so. So yes, in large part with a little bit of nuance, you, to be fair, added there with lovable and hotspots. So I think we're totally aligned there in respect. **Andrew Feldman** [46:27]: I think you've done real work to argue against that, and hats off to you and the others in the venture community. I think capital plays an important role. I think a culture in which it's okay to fail plays a role that is not traditionally in Europe. Careers are at one company and are long, and that breeds a a conservatism. I think one of the most powerful parts about Silicon Valley is the absence of a stigma if you try to do something extraordinary, crash and burn. VCs don't hold it against you. They ask you what you learned, and often it's great experience and a credit to you. I think that is something that I've not understand its history, but is clearly present. **Harry Stebbings** [47:05]: Can I ask you before we do a quick fight? We mentioned the IPO at the start. You timed the IPO with the greatest of respects, in my mind, to absolute perfection before a SpaceX IPO, before Anthropic or OpenAI. Was that strategic and deliberate with the greatest of respects, was it relative luck? **Andrew Feldman** [47:25]: No. Let let let me share. It was 100% deliberate. We tried to go public a year and a half earlier, and we couldn't get it done because we bumped into CFIUS. We're ten years old. We tried to get public for years. It was 100% luck and grit, sort of a relentlessness and an unwillingness to fail. **Harry Stebbings** [47:45]: But did you have in your mind the other IPOs and when liquidity would be best, excitement would be highest? Did we know **Andrew Feldman** [47:52]: when we set the date that chips would be on a run and that it was impossible for xAI and OpenAI and etcetera to get public before we didn't know any of that when we set the date. But what we did know was that we had a chance to be the first and only AI pure play in the entire market. There's only one, and that's us. We had a chance to bring an extraordinary growth story to public market investors who had been shut out. And that we tried again and again, and that's how you get lucky, Harry. It is smart, hardworking people, relentless work. They get lucky, and occasionally, they find the perfect time. **Harry Stebbings** [48:30]: Should you be investing in companies building on top of you? You said about trying and trying again. Jensen said before that he wishes there were companies he had invested in and about finally investing in the ecosystem around Nvidia. Do you think Cerebras should be investing more aggressively in the application layer built on top of you? **Andrew Feldman** [48:47]: I think that's an opportunity that is newly available to us. Probably not with venture dollars or traditional venture dollars. Right? I I I think you have to think very carefully about your investors. When you're using venture dollars, the question is, should we be investing in them or should our venture partners be invent investing in them? With public dollars, the mandate is different and your investors have different access. And so the opportunity for for us to do really interesting things with our our customers and our partners grows. That includes acquiring companies, that includes investing in companies, that includes different structures of partnerships, and we we have to explore them all. **Harry Stebbings** [49:24]: You mentioned the multiple times trying to go public and the persistence. What do you know now about going public that you wish you'd known when you were trying multiple times? Is it what you thought it would be? **Andrew Feldman** [49:36]: No. Look. I I think what happened was we bumped into a CFIUS challenge that was sort of obstructionist. There were unnamed concerns that that never got articulated that that sort of lived in the ether about some of our our large customers. And then we got a new government. Those concerns disappeared, and we were able to move through it really quickly and thoughtfully with a a really fair resolution. And by the way, a resolution that we had proposed a year earlier. **Harry Stebbings** [50:06]: Is the Trump administration unwaveringly better for business? Again, I sit here in The UK. **Andrew Feldman** [50:10]: Un unwaveringly better for business. There are things I agree with. There are things I disagree with in this administration, but unwaveringly better for business. You gotta be at bat taking swings, and you've gotta be building every day. When we got public, we were a much stronger company. We had larger sales. We had we're further down our road map. We had better customers. You you sort of have to separate. We we didn't get public because of the hippies, but we kept building the business. The business got better and better and better, and that gave us the opportunity to try again. That's I think the message to to your builders, to your audience who who who builds companies is a lot of stuff will happen that is not in your control. Right? There will be bad times. There'll be, you know, I was raising money in the summer two thousand eight. Yeah. That's right. That look on your face is exactly right. Summer two thousand eight. Bear Stearns falls apart in March. Lehman Brothers is exploding in September. VCs didn't wanna put money to work. And you know what the only thing we could do? We could keep trying and keep building. I was 11. I was playing Pokemon, dude. Yeah. When you were out of nappies, we we were out raising money. And what you can do is run with the things you can control. You are always stronger if you keep building. Always. And if you keep adding customers and you keep moving your technology forward, adding space between you and your competitors, that's what you can control. Good times, bad that's what **Harry Stebbings** [51:32]: you're in charge of. I have to move into a quick five. Number one, dude, what have you changed your mind on most in the last twelve months? **Andrew Feldman** [51:39]: I I think there there are a lot of things that that sort of as you prepare to go public, the the number of people who call you and try and sell you stuff is insane. Suddenly, developing a presentation, which should cost $20,000 is a $200,000 project. Suddenly, you get 20 emails a week about wealth management. Suddenly, you get I just the garbage. It's like when you get married, Harry, it's the same. You want a photographer to do a corporate event? $3,000. You want a photographer to do the exact same thing, only you call it a wedding? Three times as much. Same for a caterer. Same for everything. Why? Because he can't put a price on love? Maybe you're why. That's the same reason. No. Be be because they can. And that's something that I didn't expect, and it's sort of uncomfortable. The number of people trying to take a little nibble of your IPO and get paid on it. That was a surprise to me. I I didn't really think carefully about that prior to **Harry Stebbings** [52:34]: getting **Andrew Feldman** [52:34]: out the door. **Harry Stebbings** [52:35]: I mean, listen, you touched on Europe from an American's perspective. If I touch on America from a European's perspective, there's always a take. It's like it's always about the money in America. The transact the money, the money, the the e oh, it's like, oh. We we have a problem with that in our **Andrew Feldman** [52:48]: society. I think that's right. It is both the the source of some of the drive and the entrepreneurship and some of the source of the uncomfortableness. **Harry Stebbings** [52:58]: How did money change you as an entrepreneur? It changed me as an investor. I go for way bigger upside. I'm not so fearful of losing money. **Andrew Feldman** [53:05]: I grew up on the Stanford campus, and the only currency was intellectual horsepower. My dad's tennis match, he played doubles every Saturday and Sunday. And there were, like, six or eight guys in rotation. I look back and four ended up with Nobel prizes and one had a Fields Medal. Right? You know, William Shockley lived next door to us. Dude invented the trans transistor. And what we knew about him growing up was on Halloween, he gave full size candy bars. That that was what we thought about as kids. After I sold my last company, nothing changed. Nothing's changing now. I think what's made me proud what made me proud in my last company is we we made a 100 millionaires. What made me proud in this company so far is we've made 800 millionaires. 800. If you don't like doing that, you have no business being CEO. If you don't like delivering for your team, you're not a real leader. That feels good every day here. 800 millionaires? 800 millionaires. Yeah. That must feel pretty great. Well done. And these are people who bet many of them bet long periods of their career with us. Right? I mean, maybe you get thirty five years of the career as a top working engineer, and many of these guys have been with me for three or four companies. Some of them been here eight, nine, nine **Harry Stebbings** [54:19]: and a half years. We we've spoken before in off record about kind of personalized. I'm intrigued. When you are a public company CEO and you're going public, the world wants a piece of you. You're public, now you're public. Any advice on how to sustain an amazing marriage and an amazing relationship while also being a public company CEO and going through that process? **Andrew Feldman** [54:36]: Pick a wife with patience. Pick a partner, a husband or a wife, partner who understands what it is to be an entrepreneur. I don't think and I look at my cofounders and and our leaders. Every day when you're a leader of a of a startup, a pressure test on your soul. Every single day. If you're a real leader, when you are 30 people, a little a little company picnic, you look out what you see are mortgage payments and braces that need to be done that you're responsible for, and that doesn't change. If you really believe that and you you hold that in your heart every day, you carry real weight with you. You have to share that with your partner so they understand. It's really hard if they don't. I think almost everybody and and maybe your your partner has felt this. And every CEO I know has told the story of their partner telling them that they're more lonely when you're sitting next to them thinking about work. Your mind is just ripping on work than they were when you weren't in the house. I think that what we do is a family thing. There's a price to be paid in how often you see your wife. I mean, I'm on the road three weeks a month. Put it this way, Emirates Airline sends me a Christmas basket. This is an Arab airline sending a Jewish guy a Christmas basket. You know how frequently you have to fly for that to happen? It takes a toll. And I I think you have to think really hard about how to put some credits back because otherwise, they're just a scream of debits against your relationship. **Harry Stebbings** [56:03]: Final one for you, dude. What's the kindest thing anyone's done for you? You know, we see a lot of whether it's your investors publishing on, you know, IPO day and, oh, I met Andrew once at a coffee shop. We oh, I opened the door for him once. I was part of Cerebras. What's the kindest thing? **Andrew Feldman** [56:18]: I think and this is for you, Harry, and and the VCs is to have empathy for how hard our job is. I think one of the the things that I was really lucky with was we had a board that understood they didn't need to put more pressure on us. That if the pressure doesn't come from within, alright, they bet on the wrong people. You know, hardware is is extraordinarily difficult. And we had and and we attacked a problem that had never been solved. And we had an eighteen month period where we were spending 8,000,000 a month and we couldn't build it. **Harry Stebbings** [56:50]: Yeah. **Andrew Feldman** [56:50]: 8,000,000 a month we were burning for eighteen months and we couldn't solve the technical problems. You know what it's like to have a board meeting every six or eight weeks and come back and say, nah, I can't do it. Still can't do it. **Harry Stebbings** [57:01]: Did you doubt **Andrew Feldman** [57:02]: yourself at **Harry Stebbings** [57:03]: that point? Eighteen months? **Andrew Feldman** [57:04]: Of course. I think there's this myth that CEOs don't doubt their selves. It's not driven by relentless fear of failure. Of course. Of course you do. But I believed in the methodology we were using. I believed that each time we failed, we learned a little bit. And we didn't fail the same way again. Where it started, we failed in the first two seconds, and then then a year later, we were failing it at an hour. Each time, we we did a full failure analysis. Each time. I mean, every single one we failed at for eighteen months. That's some of the proudest work of my career. It was that problem. Nobody else to this day has solved it. Nobody else knows how. I think you can imagine, getting back to your previous question, I wasn't a peach at home. Right? I I wasn't chipper. I wasn't light. I wasn't happy. I was failing every day at work, every single day for a long time. And I I think if you want to attack hard problems, you have to come to grips with that. You have to learn to manage it. You have to surround yourself by people who who you believe in, who you want in the boat when the hardest problems are are present. And I had all of those things. And my **Harry Stebbings** [58:11]: wife was an extraordinary partner. Dude, listen, I so appreciate you. I so appreciate you putting up with my incredibly wayward questions from partnership. No, you're **Andrew Feldman** [58:20]: interesting. I I think Harry, you're you're an extraordinarily good interviewer. You can cut that part if you want. No. I loved it. That's fantastic. **Harry Stebbings** [58:27]: Trust me. We're gonna start the teaser. It's Harry, you're gonna show it. Thank you very much. You are really good interviewer. But before we leave you today, ## Sponsor read **Harry Stebbings** [58:36]: you have the idea, but often with AI tools, you hit a wall. Well, base 44 is where that friction disappears, turning how you talk into how you build. Full stack web and mobile apps, sites, autonomous super agents, all built in minutes, not weekends spent on damn configuration. Base forty four ships it all out of the box, the back end, the database, the authentication, and the hosting. It handles the heavy lifting, so you can just stay in the flow. 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