He's Cerebras first term sheet investor, also the first investor in Solana and a bunch of other great companies. So we will bring in Steve from Foundation Capital from the waiting room. Steve, how are you doing there? He is doing great. How are you? Sorry to keep you waiting. Congratulations. Thank you so much for taking the time to come chat with us. How you doing? Just another day. Are you at the NASDAQ or are you calling in from home? Yeah, exactly. No, just another day. No, I'm at my hotel on my way to the dinner that Eric's also headed to. Okay, we won't keep you too long, but I would love to hear the story of you meeting Andrew Feldman in 2007, how things matured from there, how you wound up working together. Yeah, so I showed actually Andrew the email last night over dinner. But yeah, he and I and Gary met in October of 2007. They were raising money for the company that they started prior to Cerebras, which is called C Micro. Yeah. And it was kind of broadly in sort of new server architecture. So these guys have been thinking about these kinds of problems for a long time. But I passed on the investment, but stayed close. We really connected in that meeting. And then when I saw them get acquired by amd, it was about four or five years later, I was like, guys, Andrew in particular, you guys are not going to stick around this company for too long. So let's start riffing on some new ideas. And that began basically a two year conversation about a whole bunch of ideas. Actually it all started really in kind of this concept of warehouse scale computing. We were looking at companies like Mesosphere, ended up actually doing a small investment there and Coreos and a whole bunch of others. And Andrew came in in November of that year of 2014 and shared his ideas with our enterprise team. And then basically we riffed on ideas and in the spring of 20, so it was like March timeframe, we started telling them, look, we want to be your first term sheet. We've been courting each other for a while here and we got him a term sheet to lead that first financing. And then Eric stepped in and we changed the terms a little bit to make room and co lead along with Eric and Pierre from Eclipse. Yeah. And then they started it right in our office. That's amazing. Can you talk to me about. There's, you know, crypto and I feel like two wildly different technologies, but there's a ton of overlap everywhere. You see from, you know, crypto miners pivoting to neo Clouds, there's a lot of movement back and forth. And I'm wondering like what in your mind, the similarities, differences are like why you've been drawn to both over your career where, where the gap is, where there's similarities. So what I would say, the similarities which are probably in retrospect somewhat obvious, I would say the hardest problems of software and systems live in the area that we're working on in AI. So the AI infrastructure, the frontier labs as well, all the work they're doing there. And the same thing is also true at the bottom of the stack, the layer ones and the very hardest technologies over in crypto, you know, the folks that are attracted to both of those areas tend to, to be very technology driven. They love distributed systems, they love the hard problems around cryptography and elliptical curve cryptography, they love low latency computing. Like they're, they're, they're quite similar in terms of being systems thinkers. And so those are the, those are the ways in which I would say that the problems are quite similar. And in fact, here's a funny anecdote related to this. So Anatoly Okovenko, you know, co founder of Solana, part of the reason why he chose to work with us back in March of 2018, so about two years after we invested in Cerebras, was because we were investors in Cerebras. Oh, no way. He's like, you guys take hard problems seriously. He had spent 12 years at Qualcomm. That's right, yeah, distributed system, exactly. And then was at Dropbox and understood those challenges. And so he said, wow, you guys care about these kinds of hard problems and that matters to us. So we ended up doing fair bit more diligence in writing actually a larger check into that very first. Solana Financing. Yeah, can you take us back to earlier in your career pre investing? Obviously fascinating hard problems, but like where does all that come from? Does it start in high school? College, early career? Walk me through some of the early days. So I studied robotics and embedded systems, sort of the intersection between mechanical and electrical engineering in undergrad, and then came to graduate school and, and did more of that. And then my very first Friday at Stanford, I met David Kelly, who's the founder of ideo, which is a product development consulting firm that worked with the very best kind of Fortune 1000 companies. When they would hit a snag, a hard problem or want to invent a new product and they didn't often know how to, how to wrestle those challenges to the ground, they would call us and so we did a lot of work for Apple, we did a lot of work for Cisco. We did a lot of work across every industry, from health care to consumer devices to really hard problems in systems. So I worked there for five years designing products. In fact, saw one of my other products earlier today on the desk at a trading floor in NASDAQ for Cisco's VoiceOver IP phones, which I worked on now, 28 years ago. Just working on cool things, hard problems mostly where it feels like if you solve that problem, it was worth solving. There's a real prize at the end. Okay, so that's how I got started. Yeah. I want to take this full circle then, because robotics is sort of having a moment, but it still feels like it's early in terms of, as a consumer, as optimistic as I am, I just don't think I'm going to have a humanoid robot walking around my home this year. Most people we've talked to have said, yeah, it's maybe five, six, eight, ten years away. But that's like the perfect timeline for a venture capitalist to start getting involved. You don't want to be trying to build custom AI chips today. You want to start 10 years ago like Cerebras did. So how are you thinking about the, like pulling your experience from robotics into the modern era? Because if the boom isn't already here, it's probably going to be here in a decade, if not a decade, two decades. Like it's coming. Robots are going to be real. So how are you thinking about it? So we've done a fair bit of work in embodied intelligence in terms of research. And as I'm sure you're familiar, it's always a little tricky to invest in an area that you have some operating experience. It tends to bring some scar tissue. So you might be more circumspect than if you'd had kind of a beginner's mind, I would say. I am generally not a big believer in the humanoid approach. I think there are use cases, for example, in the home companionship. Yeah. And even in that case, it's a bit of a stretch. I think you need to think about robotics more broadly and think about industrial automation and, and then look at the problems that are not necessarily kind of the, you know, the consumer level use cases, but you walk the factory floor and you see people moving around pallets and the human form factor is not good for moving pallets around. And so you wouldn't actually build a humanoid robot if you were trying to deal with that use case. So I Think when I zoom out and I say, what are robotic systems? Robotic systems are basically ways of automating human labor. And so, and in fact the greatest compliment for most of these systems is when you stop calling them a robot. You actually call them a forklift or you call it a washing machine. Oh, that's a great. And it's when that technology diffuses into the background and you just focus on what is the application. So that's how I look at it through kind of the product lens as opposed to the technology lens. Yeah, yeah. You see these demos of humanoids loading washing machines and I've been thinking in the back of my head every time I'm interacting with my washing machine, is it time just for a ground up first principles rebuild of what a washer and dryer stacked is? If you constrain it to, you have this dimension but now you have all the modern technology and your goal is to just take in dirty clothes and put out clean clothes. Can you do something better than just a big tumbler and then another tumbler, one with water, one without. I'm excited by that. Is the implication of that, that almost you would be open to talking to entrepreneurs who are maybe thinking a little bit narrower, thinking a little bit smaller, at least in the interim. And then how would you guide someone towards long term messaging around their company if they are finding a wedge, but then they want to grow at some point? Yeah. So I think it is exactly what you just described, which is, and again, sort of the applications do matter here, but the notion that you would start with something that is, let's call it sort of big enough to matter, but small enough to win. And in hardware technology being more focused is actually a huge advantage, a huge point of leverage. And then as you continue to build, you want to be able to access larger opportunities in markets. And so I really do believe that that is the way you get started with hard technologies and hardware in particular. I think there's another thing that we do and I will just say this kind of brings it to the cerebras again for a minute, which is we look at workloads. And so one of the reasons why we backed Andrew and Gary and Sean and team back in 2016 was it was quite clear and we saw this through the lens of our portfolio that the AI workloads at that time was more ML, they were ramping very, very steeply. And, and whenever you see computing workloads that are doing something new and different and this, you know, you're talking about in the, in the robotics context, and we'll get to that in a second. But when you see a workload that is spiking hard, there's often an opportunity to basically replace the compute layer. In other words, there's often sort of purpose built silicon that should exist here. And so in the case of personal computers, very clear serial programming, and you were very well suited to the x86 platform. It was actually something we saw go on and on for decades. As soon as you started to see the need for much better graphics, of course you would build a graphics processing unit that's really good at rendering graphics, at doing floating point math, at managing lots of multiple cores. And then of course, take the mobile era, and then you say, okay, wait a minute, what's going on here? I need low power, I need a smaller form factor. And so when you look at these workloads, oftentimes there is this sort of transformative opportunity. And that's exactly what we saw in 2016 was, wait a minute, like there should be purpose built silicon for this ML and AI workload. At first, of course, we started with training. Back to your point around how do you start small? And then seven years in was actually a board meeting when Sean, one of our co founders, said we got to go after inference. It's just, it's exploding. And so again, to this point, you start small and then rotate towards the much larger opportunity. Yeah, I mean, we talked to Andrew about all the ups and downs. A classic overnight success with tons of moments on of, you know, intense tumult. But I'm curious about, were you ever worried or hesitant that the company might narrow down too much? And because you've heard like, you know, YouTube has custom silicon for video encoding and there was probably an opportunity at some point to narrow the focus even more to do chip development for one specific company, be less generalized and maybe ramp the revenue a little bit faster. But was there a tension there that you were observing and like, how did you get through those moments? Say that the primary tension that relates to your question was probably around making sure we would not silo ourselves into use cases that were traditionally just high performance computing use cases. Sure. So those workloads are valuable and those markets are actually still relatively interesting, but they're, they're not growing anywhere close to the rate of the inference and specifically the reasoning part of inference where you start chaining workloads together. So we worried a little bit about that being, you know, a niche that was not interesting enough for us to build, you know, a really nodal company. If I zoom back from that and you ask sort of, what are the things we really worried about in those early, scary days? I mean, there were. I don't know if Andrew shared this, and there were like, five startups worth of hard problems for us to go after. I mean, I mean, it was absolutely. There were moments I was joking with one of the other founders last night where you had come back from a board meeting and you weren't quite sure whether we were going to figure out our way through a very fundamental thermodynamics challenge. Okay, so when you say five problems, you're not talking about fundraising, a hard negotiation with tsmc, talking to your supplier, you're talking to all of that, too. All of that's true. I'm talking about the actual hard problems, meaning hard technology problems. Yeah, yeah, yeah. And, you know, the ones that are sort of more physical, where you have laws of physics and thermodynamics to obey and you don't get to negotiate. Andrew's a very good negotiator, but he's also learned that he can't negotiate with the second law of thermodynamics. So, no, this was how do you yield a semiconductor that's the size of a dinner plate? How do you power it? How do you cool it? How do you maintain continuity across thousands of connections? How do you put it in a system and integrate it and then in a data center and then put. Put 65 of or 64 of them in a data center together. So it was those kinds of very hard challenges where I say five startups in one. And they were, of course, also stacked, which means that the risks are now combinatorial, so even more dangerous. So you've been through taking companies public, you know, being involved with public companies several times. A lot of times the founders that you're backing is their first time becoming a public company. What are telling them? What advice can you share with a founder? Not Andrew specifically, but any founder who's going public. How will the company change? What are you telling them as they become the CEO of a public company? Yeah, so there's. There's a few things that come to mind. One is buckle up, because it's going to be, particularly in markets like the one we're in right now, where, I mean, you see the headlines change every. Every few days. I mean, there'll be another drop of another model tomorrow that could, whatever, upend the public markets. And so you don't have a lot of control over what the world thinks about your share price. And so You've got to coach your teams and your engineers in particular to know that like when, when the, when the share price is moving, it very often has nothing to do with what you're doing in the day to day. And, and you just need to steel your sense yourself against that. I think there's also a, A piece which is you just have to grow up. Like there's, there's a cadence to these businesses quarterly, unfortunately, I wish they were longer where, you know, Andrew and Bob are going to hop on an earnings call very soon and they're going to have to start talking about the business of the business, not necessarily the technology of it. And that requires a level of discipline and planning that oftentimes founders don't, you know, don't have their stuff together well enough in order to be able to sort of manage through that transition. And then the last thing I would say is actually the flip of it, which is don't forget what made you special. Because when you get into this quarterly cadence and you start to think, how do I meet the next quarter? You oftentimes lose sight of the long horizon. That was the larger opportunity for you to go after, you know, not just, you know, the opportunity right in front of you, but there's much, much larger opportunities. And we're building systems for the next gen and the gen after that and the gen after that. And so you can get tricked into being in a kind of quarterly mindset. And it's one of the most toxic ways to kill a company that's built around innovation. So you just want to, you want to make sure that, you know, there's that horizon that's still calling. That's where we need to go. I love it. Thank you so much for coming on and breaking it down. Sorry for running long. I'll let you get to the celebratory dinner. Say hello to everyone and have a great day. Awesome. Thanks so much. Talk to you soon. Have a good one. Bye.

There's no other evidence that we're finding of presidential stock traders. Well, we'll dig into it, but we have Doug o' Laughlin from Semianalysis in the waiting room. Doug, how are you doing? Welcome to the show. Good, good man. Pretty busy day. Another day, dude, honestly, every day is a busy day. Every day is a busy day. Take us through it. How do you think the market reacted to the Cerebras ipo? To the semianalysis deep dive on the company? What is the overarching story here? I think the market was obviously positive. I don't think we're quite as positive as the market, but it's a bull market baby. I think the takeaway is that Cerebras got to ipo, which at one point in time we didn't think that would happen. At the semi analysis world we've historically been very bearish on sram, but I think there's a path forward for them to be a disaggregated pre filled chip or maybe even aft chip, meaning attention feed forward disaggregation. Okay, so yeah, yeah, unpack. Sort of the competitive dynamic, like the fear around Cerebras, as far as I could tell years ago it was like will this ever be useful? Will they ever actually be able to make it? Will it have defects? Then it became certain applications demand side customer concentration. But where do you think they are now? How has that journey evolved? So first and foremost Cerebres is about sram. SRAM is like the fastest possible memory and it's kind of done on a logic process. But the problem is SRAM scaling is dead, meaning that you can't make smaller and smaller SRAM scales. So pretty much they kind of committed to this dead end process by having the biggest scale up world as a wafer size. But then the models got much bigger than just a single wafer. And so they have really really fast inference, but only at a certain size. And I think the real capability problem is can they inference models larger than a trillion parameters. And I think the answer, as we think right now, it's pretty unlikely in the near term, yes. So I, so I understand all that. I'm just wondering about the world, where should I view it more like a CPU? Because when the AI boom, the chatbot moment happened, the obvious buy was Nvidia because we're going to need a lot of GPUs. No one was really expecting a chip, a chip shortage in CPUs. But then agents wound up using CPUs for a bunch of stuff you have to keep the GPUs filled. And so CPUs are now in demand. And I'm wondering if there's this world where there's this. Yes, we're going to move past the trillion parameter models, but we're going to keep using them forever, just like we use relational databases forever. Even in an AI agentic AI world. Or you have a scenario where you have a big model that is giving sort of. Yeah. Orders. Orders, workload, delegation or something delegating to a smaller model. Yeah, I think in a perfect world where there's no silicon constraints that might be true, but obviously there's silicon constraints and I think Cerebras is really well optimized for a certain problem and we think they do a great job at answering that, which is fast inference at a certain size of model, maybe that, that that market is going to be large enough. And I mean honestly I don't think I was ever bullish to rebus the entire time. But now that we're here, like non. Ironically 1% of a very large market works and I think they got like 1% of a very large market when it first started. I was like, oh yeah, what are you going to do? 1% of very large market. That's going to be a few hundred million dollars. And that's like the classic seed seed pitch too. Ten years ago. Yeah. Is there any. For a long time there was a lot of fear around ASICS companies around architecture changes. We're going to move past the transformer and they're all going to be locked in the past. Is there any optimism around there's an architecture change that actually is to the benefit of Cerberus and makes them more relevant in the future. Do you think that's, I mean, pay grade. That's two gigabrain for me, right where I'm at. And the understanding there is a narrow path for them, I think. And I think they're going to be able to inference maybe 1 trillion framers at very small context window sizes or smaller models at very, very fast speeds. But I don't know man. Maybe, I mean like, you know, the true gigabrain take is Mythos is so good or whatever that it makes compute efficiency super easy and boom. You know. Yeah. Your model is inefficient and AGI understands. Yeah, yeah, yeah, yeah. Distill yourself so you can run on a Cerebras chip just as effectively. Okay, now we're talking Gigabran. That's, that's the Giga brain thesis. But I think I just Think that there is, there's demand. Right. Like clearly we're in a shortage. And ironically in a shortage it's not the best company who wins. I mean you can look at Nvidia's stock chart and that tells you it's the second, third, fourth best companies where the demand overflows. Right. And so we're seeing all that today. Yeah. And I think, I think the reality is the market's big enough for a lot of demand and story versus in that, in that space. Okay. So they've done a really good job and I mean it's a cool engineering problem, but we think it's kind of a solution looking for a problem because the world of LLMs blew up at a much faster scale than anyone could have ever thought of. The size I think is really the difference. Yeah, yeah. Give me a little primer on Groq. How Grok fits into the SRAM machine market, what the view is, because it felt like that Nvidia's move there with the license acquire as you put it, was defensive against Cerebras. Is that the correct framing? Like how does GROK fit in on this? Okay, so let's talk about exactly where Grok fits into the architecture. So in the transformer architecture you have like the multi heads of attention and then there's a feed forward network that's a portion of essentially the entire transformer block. And what's become really hot in the last few years, or not even two years, like probably a few months man, is you've been disaggregating all the different parts of the inferencing into subsequent specialization. So we're talking about GPUs and ASICs being a specialization over CPUs. But now we're actually starting to break essentially the constraints of inferencing into different I guess compute and memory bound like pockets. And so for example we're finding prefill ends up being pre filled, being essentially loading all the weights ends up being compute constrained. So you don't really need a lot of memory band. So why don't you just use a very flops heavy portion and you disaggregate the memory onto the decode portion which is extremely memory bandwidth limited. And so this is Grok. Where this fits in the strategic thought process here is in the GB200 rack what you can do is you can pass the activations over to the SRAM in the GROQ LPU rack and that is an extreme speed up. And so that's like a perfect example of another Break apart of the transformer architecture. Pretty technical. But that's like the thought process here is that the memory is so fast, the memory bandwidth or the speed of the IO doesn't really matter. And you don't need a huge scale up world size because you're just streaming the activations. That problem wouldn't work with the Cerebrus trip because you're kind of. It's an island, right? You think of it as an island of compute. It's really, really good at everything in the middle. But moving anything off the island is really hard versus moving something off the island onto a Groq chip because there's a plug at the end of it is a lot easier. And that's kind of the calculus, I guess. Yeah. So Cerebras, lower memory bandwidth, lower interconnect speed. Off the chip. Off the chip. But on the chip it's as fast as cell. Yeah. Okay, so what does that mean for the Grok Nvidia ecosystem? Because is this something where the default configuration is going to be a Blackwell and a Grok chip like in, you know, 50% of racks, 80% of racks, or is this like still some sort of niche application where Grok is going to be deployed, you know, sort of sparingly sprinkled into specific use cases? Do you have an idea? Yeah, I think I don't have an idea. With high precision, I think you'll find that a lot of these things, there's a lot of different ways to split up and serve your model. So expert parallelism, pipeline parallelism, tensor parallelism. Right. And so the correct optimization per hardware rack is going to kind of depend on the shape and architecture of the model. And we don't really know with high precision what is what. And there's been kind of like different road, road maps along the way in terms of what, what they wanted to do for speeding up inference. A perfect example of this is the CPX rack, which was mostly built for extra parallelism. It's kind of remains to be seen if this is like if the GROQ GB200 speed up is going to be like the way forward. But it's definitely a technology tree that I think Jensen is excited about. So I mean, we'll see. What about Lisa Su at amd? Is she excited about this technology tree? Can you give me an update on how AMD fits into all of this? So AMD is mostly just trying to get the last thing to work, which is the rack scale up. And I think they're going to do a good job of 450. I think what's going to happen is that like, you know, it's a compute shortage. Right. So you're talking about overflow demand. I think Lisa's going to figure it out. But on the inference serving side, I think there's definitely some demand or desire to probably match the Nvidia roadmap. And I wouldn't be surprised to see if there's some kind of fast SRAM offload FFN chip in the, in the next 12 months. But the thing is, the number of candidates there is actually like pretty low. I think Intel's really, Intel's going for Sanbornova, which is a little clever. There's like HBM2, there's a few other players out there too, that pursued SRAM scaling. But I think that in this specific case, Lisa's mostly just focused on the last thing and I think AMD is definitely good enough right now. Okay, on intel, what is the latest there? It feels like the roundtable has been assembled and sort of everyone has held hands and decided to maybe jump across the transom at the same time, take the leap of faith. But it also feels like lithography machines are majorly backlogged. Like there's a whole supply chain that they have to answer to that's backlogged. And so really high expectations. But also what is the next milestone for them after they actually get these deals with Apple and Elon Musk. Amazon and Elon Musk. Yeah, and the gigafab sort of like once they get those signed, like what does the next couple of years look like? I think it's about execution. It's kind of crazy to me that I think the stock price is ahead of the technical turnaround. And I think that I think Liquid and clearly has like righted the ship and gotten the right people onto the party, if that makes sense. And I think, I really do think the government intel deal was a stroke of genius because Pat Gelsinger spent, you know, three years trying to build a bottom up demand to essentially come to the Fab and, and Trump's like, yeah, none of this. I'm going to sign the deal from the top and what's going to happen is you're going to come play because we're in the United States government or else. And so I think, I think people are there, I think the customers are there. I think the process is good enough, I think 14 will be also good enough given how much of a shortage M3 at TSMC is. And it's all execution. Risk from here, but the historical intel has quite a bit of execution problems, so we'll see. Okay, before we move on to tsmc, which I want to go to next, are there any other interesting ASIC projects on the, on the horizon? We've talked to a few of these companies, but I'm interested in like the shape of the differentiation, like you explained, a little bit of the divergence and strategies between Groq and Cerebras. But there's Etched and a bunch of other companies that are working on new chip designs and I'm wondering if any of them stick out to you as particularly differentiated. I'm not going to go too into the details because I feel like some of them are even still figuring out their roadmap. I think Maddox is kind of interesting the way that they're kind of trying to pursue the memory problem. I think Etched, I'm excited about the kind of yolo bed, if it makes sense, just make a big systolic array. But I think there might be niche cases. I think the problem is at the end of the day, Nvidia's bus is still really good for the majority of cases. And you're going to have to start to make really opinionated bets on the ASIC to find what niche market ends up being all like a diverter of demand into their asic. And so the ASIC specialization from here, I feel like you have to make some pretty big brain bets in order to make your bets come pay off. And I think most of the bets that would have guessed when you, like when you originally did them didn't really wouldn't have paid off. And the ones I didn't expect did like. It's kind of crazy. Yeah, it is. It is a very weird market dynamic where a couple of years ago we saw ASIC and new chip companies, new silicon companies raising hundreds of millions of dollars or $500 million. And it was like, well, for that you're going to need this massive market. Are you really going to flip Nvidia or something? And then the market grew so much that the 1% of a huge market sort of potentially maths out for some of these companies now. It's a, it's a fascinating development. Jordan, do you have something China trip? Yes. Oh yeah. What are you tracking on the H100? Oh, so honestly, do you guys see the parade? You know, Trump loves a parade. Oh yeah, they're winning them over. Good parade. I was like, dude, I think I'm not much of a parade guy. But I was like, dude, if they show, if they showed up in that parade was for me, I'll be like, these guys could be friends. Yeah. My, my impression is that the executive, the executive branch really wants a deal. And I think, you know, you saw the H200 list, the verified H200 list. I probably more lightening up on the executive branch. Something that's really interesting is if you look on the legislative branch, there's actually more export control bills going through the House than ever in history time. So there's kind of this tension. But I do think Trump's a businessman, he loves the deal. I expect a deal. Yeah. Somewhat related tsmc Ben Thompson was writing that potentially they weren't ramping capex fast enough. What are you tracking on TSMC being a potential bottleneck for the AI buildout? Just as more and more Cerebrus is now trying to get allocation, it feels like a particularly sharp, elbowed place to do business. Yeah. So I think at the end of the day TSMC is kind of a kingmaker in terms of supply and there's no reason for them to really that the market go out over its skis. And I think they're happy with the pace of what they're, they're expanding out because like, hey, they're growing their capex like whatever, 40% but in absolute dollars. These are big numbers. We're going to run out of TSMC engineers in the island of Taiwan pretty pretty soon here. So I think, I think this is all kind of good on the margin for overflow demand which is actually it's Intel, Intel's, you know, definitely reflecting some of that. But I think the shortages specifically at TSMC is driven by clean room. It's a long lead time item. It takes three to five years or let's just say three years to bring a clean room up. And so in order for them to have like figured out and like perfectly match demand two years ago they would have to been like we have a 10,000 square foot house and we need to buy a 50,000 square foot house with conviction. Right. It wasn't that clear two years ago. And so I'm going to expect supply to kind of lag over and over and over. But demand signals will continue to essentially command premiums, move up wafer pricing, move up orders. And that's what's going to make TSMC invest more next year and the year after. But they're going to do it in a like in a incremental, not a revolutionary way, but like an evolutionary way. They are very like methodical and do steps one at a time. Okay. Clean room fungibility. When you say it takes five years to build a clean room, I immediately go to space X. I imagine that Elon can build big things quickly. Is there some world where that partnership accelerates intel Regardless of your timeline for the mass driver fab on the moon, all the crazy long term stuff, but just having Elon around the table to say oh we need to build something big and it needs to be, you know, capable of operating as a fab. Like is there something where he brings more to the table than just dollars? Potentially. So I definitely think Elon is the man to do it. I forgot who said this, but like Elon makes the impossible late. I don't expect it to be on time. You know, talking about the cigar in the terrafab, I'm really kind of doubtful. It's you know, I guess from first principles it's easier to just clean the entire room than to make like really hyper concentrated pockets. And that's what I would guess the bet is. But I still think by the time Elon figures it out, the supply response will have reacted already. We're still two, three years out and there is some cleaner fungibility. But you've already seen this actually Micron bought an old power fab. I think the PSMC deal people are buying display fabs. Essentially every bit of clean room that is not accounted for in the world is being snatched up and retrofitted to kind of meet the supply demands. Interesting. Yeah, I mean that's happening all over. Didn't Ford just announced some sort of AI play today? Stocks up on something. It's all over the place. I am interested in terms of like 6% getting, getting powered shells. Ford is worth more than figure now because last year around a year ago I remember figure your robotics was worth more than the Ford Motor Company. But now they're both AI companies. I guess. But what are you tracking on the American data center build out domestically or terrestrially before we move on to space capabilities? Yeah, basically. Oh, go for it. No, no, no, just. I'm just curious about. I mean we're starting to see glimmers of pushback at the municipal level. Different data center bans. And I'm wondering about what are the big levers that are that need to get pulled to actually continue to bring capacity online in America? Yeah, I think that's a good question. And you're already seeing the first level of this is the delays. My favorite clickbait is 50% of all data centers in America are Delayed or canceled, implying 50% is canceled, when it's really just everything is delayed. That's like my favorite clickbait. I got to steal that in the future. But I think it's going to be local, municipal and people have to really believe and demand and desire the jobs. And I think one of the ways that we're seeing this is like, you know, capitalism works and effectively the dollar per megawatt has been going up. It's like a one way train. In the same way that like, you know, the power per rack has been going up, the cost of making these data centers have gone up. And one of the ways that happens is it leaks into labor. Right. So essentially you're super against it, but all of a sudden it offers 3,000 new jobs to your home and you're like, well maybe, maybe I'll take it. And I think that with enough economics, oftentimes, you know, money finds a way. And that's kind of, that's kind of how I would guess. But it's going to be like, it is a, it is like a county by county fight. Right. And some places are just going to say, hell no. Yeah. On that note, we were debating this earlier today. There's been a couple of examples in like viral photos and articles about like they, I bought a beautiful house in the countryside and then they built a data center right next to it. And you know, no matter how pro AI you are, it sounds annoying to have a huge building that's an eyesore and maybe noisy, maybe smoky next to you. But have you been tracking like, how feasible is it just to throw the data center like truly in the middle of nowhere? It feels like America has a lot of land. But what goes into selecting data sites or data center sites these days? Do you have something else? Yeah. So I think pretty much two fiber pairs is the big desire. Got it. Essentially it's like you're, you're more than willing to go to where the power is because you have to go to what the biggest actual bottleneck is. And power is the biggest bottleneck. So you can just in the past you're talking about like, hey, having these inference, or rather like let's say point of presence near local cities. Right. But power was never constrained in that world. It was just, you know, the biggest constraint was getting this video from TikTok to your phone as soon as possible. If the biggest constraint and the largest part of the cost is going to be power, why not move the data center to power and then, then like, you know, essentially hook it up with fiber. And so I think that we're going to put them in the middle of nowhere. That's just how it's going to work to a certain extent. There's going to be more densification in some of the inference near the population. But I still think the ROI makes the most sense to kick out in the middle of nowhere. Yeah. Has the political backlash pushback updated your thinking at all around the viability of space data centers? I remember, you know, we talked as this idea has gained popularity. You guys have like consistently said yeah, technically you can do that, but like maybe it won't be takes a long time for the. There are space data center players now that are kind of loving the pushback against terrestrial data centers because they're like the more pushback there is, the more it could make sense for us to put this, put these up in space. But what's your view? I still think economics is going to win out. You know something, a pound on earth is probably 10 times more expensive in space. And it's really hard for us to go to like essentially beat that out with a new completely specialized supply chain for what's going to be a smaller market. In the near term, it's a real adversary against the adoption in like let's say the short run, in the very long run. Because I'm sure you saw the anthropic colossus thing where it's like also interested in space, right? Like the biggest maxi vision of this is like AGI, we have, you know, 30, you know, we have a thousand terawatts of GPUs on Earth and we're like we got to put a terawatt in space. Right. So like in that world, I think space, space data centers work where a small percentage 1% again, it's 1% of the market again, it's just like. And it's a trillion dollar is trillion dollars vindicated. Yeah, yeah. VCs are vindicated. Tam Tam. Pitch deck slides. Vindicate. Yeah, yeah, yeah, yeah. It's. It's literally as big as the galaxy, bro. Just there's no end to it actually. Think about how big the tab is. So I think what is more likely is if it continues to be painful to do it from a zoning perspective in America, we, it will essentially slip into other geographies probably in the Western Hemisphere. There's a lot of power in space in Brazil and I think that that's probably good enough. Right. There's definitely ways to make this work. I definitely think the only way you do it is by paying more and finding someone who's like, you know what, I'll hit the bet. And so that's the important part. But you know, chapels and pines away. Is that sort of the bull case for sovereign AI initiatives? I was always super skeptical because like Europe didn't get like France's Google, like they just used Google. And for a lot of consumer aggregator type consumer Internet companies, it's like Spotify is from Sweden, but it could be from America and It wouldn't matter. YouTube is from America and they use that over there. And you didn't need like a national champion in every consumer category or there were certainly like returns to scale and a lot of the American companies just won. But so I never really bought the whole idea that like, oh, the French need like a locally trained LLM and the Germans also need a locally fine tuned something or other. But if every country has some sort of excess supply of energy or space or regulatory capacity for data centers, sort of bringing that online and just operating like a NEO cloud could just be economically valuable for that country regardless of whether or not they're vertically integrated to the point of the consumer or the business that's running an AI agent. I think that's probably the case where at the end of the day economics is going to push it through and there is fomo and Europe did do a lot of investment in the, in the Internet, like really late if we're going to use 1999, this example. I think the thing I keep thinking about is that this thing is going to be a big deal. I continuously am shocked and surprised by the magnitude and scale that's a narrative. I don't think it is right now. I feel like we are in a particular moment where no, there's just the people calling the top in the bubbles, like they're awfully quiet right now and that makes me even more scared. That is to be clear. Yeah. You know, the true top, there's no. Everyone's bullish, right? Everyone's like, dude, it's actually going to be bigger next year. It's actually just going to be a bigger bubble. Shut up. So yeah, I was not concerned about, I was not concerned about a bubble when everyone was saying it's about it's a bubble. Yeah, exactly. I am, I mean I'm a little concerned it's a bubble, but at this point in time, I think if you look at the big. I've been reading a lot of this, honestly, here's my view, here's my view, it's not A bubble until you guys are spending 120% of revenue on tokens. Yeah. Our gross margin goes negative. You're just like, we're raising a major fund. We're not going to be investing it, we're going to be burning it. It's actually not a bubble until semianalysis goes public and trades up 600%. There we go. I like that. That's the, that's the real talk. No, no, I think there's a few things that have to happen. I think OpenAI or Anthropic, someone has to go public and it's going to be this year. Like we have, like we have to hit that keystone before, before it's all over. But I also, I also think, I keep thinking about this is like, dude, this is a big, a big technological revolution. Yeah. I think it's bigger than the Internet and I firmly believe this. I don't think I believed it would be bigger than the Internet when I maybe even two years ago, but I'm pretty convinced this can be bigger than the Internet. And if you look at the past, these big technological changes are often sometimes bigger than, I don't know, everything else. It reshapes the entire world. For example, on the sovereign AI thing, maybe you're like, yeah, you don't need to fine tune LLM. But what happens when I become such an important fundamentals, like almost like society level institution that a government can't control it. That becomes really like uncomfortable and weird where it's like, hey, Anthropic can just put 5% of the compute of Mythos and run a really effective government whenever you want it. And you're like, whoa, what does that mean for us? And so this wave is so big that I think people are going to out of fear and concern that they're going to be left behind and that the institutions that that AI will bring is going to be bigger than the original thing that we're doing. I think that that's like the problem. Right. Like the Industrial revolution changed everything. Yeah. The other thing that we were joking about in Q4 of last year is like, John was like, great, like the bubble popped. Like the bubble inflated and then it popped. But then we got agents and then you have this sort of like re acceleration of every metric across the board. And so the other thing that we're like, we're trying to comp the AI boom to the Internet, but the problem with the Internet boom is that we didn't have the Internet. So everything just took like. Or the Internet was coming online and People were getting access to it. And so the entire build out and all the capabilities and all the companies took a lot longer to sort of grow. Right. And now you have that core infrastructure. And so when you're layering on more infrastructure that accelerates all the underlying trends. Yeah, yeah. I mean the labs, the lab revenue multiples are like an order of magnitude or two off of dot com peak multiples. And in the public markets, Google, Amazon, Apple, all the hyperscalers are at pretty reasonable price to earnings multiple. Still, even with all the CapEx and stuff, the pushback would be it's on free cash flow because you can make earnings look good instead of free cash flow. But I think the revenue continues to be real, the demand continues to be real. And until you just like see demand evaporate, like, yeah, it's hard for me, it's hard for me to sit here and be like gp, prices are up a ton. Quadcode is really valuable to me. I still think I'm an early adopter and you know, this is all going to end tomorrow. I envision myself using it every single day more for the rest of my life, which is kind of crazy. And I think I'm a early adopter and so I just think it's hard for me to envision this not being a ginormous deal. And it's kind of like we just got the like I really, I wrote this whole thing like angles pause or whatever. Like it's going to change everything. Like the, the amount of net output that's going to increase is going to just blow our minds. It might be bad for GDP ironically because GDP will be unmeasured. Like we're going to like GDP might be broken as a concept. GDP got invented in the 1930s to measure how much output you could make to not screw over the domestic economy for World War II. Like it was, it was a way to essentially organize the, the, the American economy. And it's a statistic, it's an estimate. Like I think all of, I think we're going to like attack and like a lot of institutions and ways that we are doing things and ways we measure are going to be attacked by this because it's like such a big change. We have to rewrite the playbook over again and people and it's, and it's funny, I think wasn't Ben Thompson was talking about this in a recent interview of like people are comping this like, okay, Silicon Valley, like you know, brought crypto online and then it wasn't maybe as big as Some people had, had pitched it to be even though it's been self driving cars, super powerful. And then, and then even the way you're talking you're like, you know, we're still early, you know, like a classic crypto. But the problem is you are early, you have nothing or you're saying like, you know, in crypto is like, well like a community could have a dao and that dao, yeah could be worth $1 billion, that community could be worth $1 billion. But there's just no way to measure that. But now we have tokens and you're saying gdp. But anyways, I'm trying to like unlearn I think some lessons from that cycle because yeah, there are a number of things that are quite. It's also. What about, what about the reflexivity that, that people do have a little bit of an immune system to just running away with everything. Because you could believe this and then bid, you know, Nvidia to 10,000 times earnings or something. And like at certain point you have to start grappling with the reality. What about robotics has figure had a major breakthrough? I mean I1 I have not been following the feed as close as I should be. I just think robotics feels a little further out than the hype would let you believe. I feel like robotics is much more akin to the driving car paradigm where it's like, oh yeah, it's definitely going to come and automate everyone's jobs and then it takes a lot longer. It's a lot like unsexier. I think the scary or positive thing about AI is since it's information work and it's already been distributed and it has the perfect network to run on, which is the Internet, it can disperse very quickly. And that's what we're seeing right now. And so yeah, I, I'm just not anywhere near as bullish robotics as I am the fundamental. Well, I'm bullish. On the next semi analysis. I don't know what are cluster max and inference max. What are those called? Dashboards or analyses or rankings? Dashboards. Dashboards. Now we. Everything's a dashboard. Everything's a dashboard. Well, you need to make a new dashboard. GTP gross token production is what we're measuring now. This will be the output of the United States gross token production. Gtp. We need to, I mean I think more on this soon actually. This is like a place we're doing some research on. But I think you know, the real, the real bubble metric is if we're like, you know, how many tokens what's the token. What's the token replacement cost? That. That would be some really good bubble math where it's like, yeah, yeah, software company has really low tokens replacement cost per market cap. But like a hardware company has an extremely high token replacement costs. And then it's like, oh, no, no, it's just enterprise value divided by token. Well, the real, the real bubble one will be to go to the full Merry Meeker. Like eyeballs, metric eyeballs. Multiples. Yes. So you will value companies purely on token consumption. You'll say, oh, well, they're consuming 10 trillion tokens, so they must be worth a billion dollars. And then you'll get really weird gyrations. That'd be great for semi analysis. That'd be really good for semi analysis. We are. We are consuming a lot of tokens. Well, you're also putting a lot of good stuff. I really enjoyed the. Would you guys ever make a sort of political style attack ad against another research firm for having AI psychosis? Is that a reference to the gc? Sorry, it's a reference to General Catalyst attacking Mark Andreessen. Andreessen. You know, life's pretty long. I actually think some analysis is just peerless. I don't think there's like a neck and neck with someone else. You guys. Yeah, I was going to say, I don't really know who our competitors are. I don't, you know, I don't really think about it. Mark Andreessen or. Or you know, another research firm like that. Maybe one day, maybe we will go through AI psychosis. Honestly, you guys need a rival. You guys need a. You guys need a. You guys need an arch nemesis. You need an op. Moody's. I guess it'd be Gartner. Gartner had to say, they're like, but this is not a good op. You need. You need a semi analysis hype cycle. And it's up. Only. No, no trough of disillusionment. Straight line. Straight line. No axis. And it just. It actually goes backwards. It's a straight line on a log graph. That's what it is. Semi analysis type cycle. I love it. Gartner doesn't stand a chance. But thank you so much for coming on the show. This is fantastic. Always full analysis. Full analysis. Yeah. No more semi analysis. Those guys would kick our ass. They had full analysis. They'll kick our ass. Anyways, take care, guys. Great. Have a great day. We'll talk to you soon. Cheers.

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