LIVE CLIPS
EpisodeĀ 5-14-2026
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.
So let's bring in Eric. Welcome to the show, Eric. Congratulations on the progress. Thank you so much for taking the time on such a busy day. Great to meet you. Great to meet you guys. Excited to be here. Long, long overdue. Yeah. Crazy that this hasn't happened. Already have an opportunity. Well, you guys like ev. You know, so have ev on. You don't have to have. He was a former colleague, but everyone is welcome here. But I would love to just hear the story from your perspective. We just heard it from Andrew. It seemed obvious, but was it obvious to you? Yeah. Was it the most obvious deal ever? Because I was. We were talking with Andrew, I was asking him for the story of those first couple rounds, expecting him to be like, you know, it was really hard for almost a decade. It was a slog. We kept getting. We walked up and down Sand Hill row. We got nose. He's like, yeah, we got eight term sheets. So clearly it was a deal you had to win. Yeah. But take us through it. Well, you know what the hilarious thing about it is? In venture, it's very useful to be naive. And certainly I was so naive about how hard hardware actually is. Like, I can't even, I can't even describe you guys how naive I was. And we were, you know, it was 2016, deep learning was clearly going to become a thing which would obviously evolve and empower the AI that we have today. And I was looking at all of these different applications. So I was looking at like deep learning for radiology and security and other things. And it was really hard to figure out where it was going to work, like which application was going to take off. And you guys have to Remember, this is 2016. Right. The TPU hadn't been announced, the transformer paper hadn't come out yet. LLMs hadn't been born yet. And obviously not chatgpt or anything else. And so it's really early, but there was clearly something there. And when I first met Andrew, he came in and I was like, we hadn't. We're not hardware investors typically. I think our last hardware investment before that one was ambarella, which was 10 years earlier. And he came in and he said, you know, it was like the team slide, very impressive. And then, you know, the slide three was, GPUs actually suck for deep learning. They just happen to be 100 times better than CPUs. And as soon as he said it, it just like a light bulb went off. Like, of course, of course. Like, why would a graphics processing unit be the right solution for Deep learning. And then, you know, of course he proceeded to explain like why GPUs were so much better than CPUs for training and also what the like, ideal ground up solution could look like. And they had their idea of the wafer scale and everything else. As soon as he said it, it's kind of like, oh yeah, that makes sense. And we don't know what application's going to work. We should invest in infrastructure. This is an amazing team and a really provocative idea. Fast forward, that was 2016, spring of 2016, you fast forward like six, seven years and we're still slogging it out and have raised so much money and have very little revenue and, you know, and it's just, it just hadn't all come together yet. And then of course over the last two years, inference is exploding. It turns out Cerebra switches from training to inference and really focusing on inference and making inference speed where speed matters. Coding explodes where speed really matters. And so all these things kind of came together and so, you know, a, a lot of luck, a lot of naivete on my part, but for the team, just relentless grind, Never giving up, always taking feedback, but being persistent, being open minded about where the market was going. So I'm so, so proud of them. Yeah. What was your role as an investor like over the journey of the company? Because obviously Andrew and his core team, deep engineering bench. Were you focused on how you position the company, the private markets, fundraising or management? What were you focused on in terms of value add or just helping build the company alongside? I'm really the algorithm specialist. I go in there and I do that. No, I'm just kidding. I don't know anything. You're in the fab. You're the one that making the chips. That's right. I was making it up. Clean room. Yeah. It really changes a lot over the course of a company. This is, I think, the fourth company that I've worked with for more than 10 years. And so when you work on them a long time, the companies evolve a lot. Right. You start out, it's just five people. It's just the five founders originally. And so at different points in time, it's a lot of fundraising help. At points in time it's like really helping build out the broader management team. And a lot of it is also just being someone for the founder to talk to. You know, being an entrepreneur is very, the highs are very high and the lows are very low. And so someone you can talk to and be really open with that like helps moderate that and I think that's a part of it. So it's just, it's an evolving, you know, conciliary kind of role and I really love it. Actually. That's the part of the job that I love the most. And it's rare and special to have these kinds of relationships. I've had a few of them. I'm very lucky to have a few of them where I just feel really like a lot of chemistry with the founder and just feel like we have a really productive relationship. Where are you excited to invest over the next decade? Because it feels like we're still in the semis boom. There's a lot of opportunity there. You could go deeper into that side of the business. But then there's so much software gotten pitches that look like the what maybe would be the next gen and you know, maybe like I already got my horse. Yeah, well, yeah, that. But then, you know, talking to these teams that don't necessarily know what it'll actually take. Right, sure. They didn't learn the hardware is hard lesson. Yeah, yeah, totally, totally. Well, you know, one of the funny things, and I ask myself this question all the time obviously is, you know, this is a 20 for us as early stage investors and looking for, you know, really big outcomes but willing to take big swings. You, you really do have to kind of look many years forward and try to see like what's going to ripen at the right time. Right. So in 2016 you make a hardware investment and you know, Grok was I think 2017 for example. So like, you know, there were several contemporaries of them and of course Grok and Cerebras have ended up doing really well. And so. But you're trying to say, okay, this fruit's going to ripen in six years. And so there's kind of some mention of projection right now. I think I'm really excited and continue to be really excited about a lot of the AI applications. We're investors in Sierra and Lagora and a number of others where they're obviously booming. They're selling magic to their customers and the companies are doing great. We also have these infrastructure investments like Fireworks, for example, which is also riding this enormous inference demand. And then there are kind of things that are a bit more forward looking. We invested in Star Cloud, my partner Chafing, our investment in Star Cloud, which space data centers. And we also led the initial round in Sunday Robotics, which is a home robot. And so, you know, I think those things are going to take longer. Like, you know, they're they're not going to be, you know, massively scaling revenue like next year. Like that's not, that's not what they are. So you kind of have a combination of these different things which are. But it's kind of trying to figure out when they ripen next time you come on. We got to have you debate Delian because he came on and was debating EV and hardware versus software. But you got space chips, you got everything Delian likes. Yeah, well, it's nice, it's nice to have a portfolio and I think, you know, one of the beauties of benchmark is each of the partners is attracted to different things and different types of founders. And so we, you know, you put it together and it works out really well. Yeah, yeah. Walk us through Funds seven and eight because there's chatter on the timeline as those funds being some of the best in venture history. And although this is Cerebras day, this is your first time on the show. So we do have a big gong here. Yeah, well I, you know, Fund 7. Fund 7 has or had Uber Snapchat elastic stitch fix. We work. I mean there were so many things and it was like it was such an embarrassment of riches. And I had nothing to do with that fund, just to be clear. Like I, I joined in 2014. That fund was already deployed but. And invested in. But the team, you know, that team at the time just did such an outstanding job with winner after winner. Discord is in there. I mean it's like really when you have in venture, if you catch the trend, right. And obviously work hard and get lucky, but you have the sixth or seventh company in the portfolio delivering a multiple of the fundamentals or something like that. You're in such rarefied air and it's really special. So that's fund seven. Fund eight is a very enterprise. It's our 2014 vintage I think and it's a very enterprise Y fund. And so we had Confluent which returned a bunch and Amplitude has returned a bunch. And then we have Cerebras obviously which is big but Chainalysis is in there and several others. And so it's kind of interesting how they switch. I think that's actually more interesting to me which is Fund seven was very consumer mobile and Fund eight is very enterprise Y and they're like back to back but they turn out to. They both work. And so I think that tells you a little bit about what venture is and how we all have to be really open minded about what's happening and what's the right timing for these various ideas. And then fast Forward and our 2022, I think 2022 vintage has the first round of Sierra, the first round of Fireworks, the first round of Lagora Merkor reductor. Merkor. Yes, absolutely. LangChain. And so all those are in there. And so obviously that's a totally different fund and has a different set of things, but also, you know, looks pretty interesting. So it just, it evolves. And that's what's so hard and tough about this business, is staying on your toes when you're in a very, very dynamic world. Yeah, well, it's interesting. Something that, you know, this has been talked about on plenty of podcasts, but it's worth bringing up. You guys have stayed true to the strategy and you can count on the market changing and evolving. But a lot of funds are like having to deal with markets changing and evolving while having a fun strategy that is changing and evolving. And if you keep one of those things true, it seems, at least from Benchmark's track record, that it gives you some advantage and that like, you're playing a very specific kind of game and not having to evolve your own game while dealing with changing technology trends and markets. You know, I've been at benchmark 12 years and I've thought about this a lot. And you know, you're watching your peers do all these different things and, you know, and swimming and fees and all these like, amazing things. And so you're like, wow, that's pretty, that looks pretty cool. So like, you know, kind of like, look at this stuff. But I'll tell you what I think it actually comes down to. It's. What it actually comes down to is what do you love doing? And you know, we're obviously in a very fortunate position and I inherited a amazing platform and so, and very fortunate to have done that. And so, you know, we're in this amazing position where you get to do what you really like doing. And at the end of the day, we really like partnering with early stage founders and working on these companies for a decade plus. And that's kind of what we like doing, you know. And so I think things have definitely evolved. The opportunity set is changing and evolving. And you know, more recently, I mean, just in February, we, we raised an spv, which we've never really done before, and to, to invest in Cerebras. And that was unusual, but it was, you know, you can also, we've actually, a few years ago we did public market investing when, when Covid first hit and the NASDAQ tanked. You know, all of the early stage stuff just disappeared. We were like, wait a minute. Like these publics, like, there's interesting stuff in public. So we, we started deploying a little bit in the public. So, you know, yes, we are really focused on the early stage and that's what we love doing. And then also occasionally, like, we see these special opportunities and we try to jump on them. Wow. Yeah. Well, thank you so much for coming on during a business day. Had to sneak in the SPV round of 23 billion. So congratulations on that investment. Fantastic. Another little cheeky 3x. I think you deserve a drink. Hopefully you can find Andrew. I'll definitely have some drinks tonight for sure. Yeah. Have a great time. Great. Great to finally meet you and congrats to everyone. Yeah, let's do it again soon, nasdaq. Thank you, guys. We'd love to do that. Thank you. Fantastic. Goodbye.
Fan of. Well, without further ado, we have Andrew Feldman from Cerebras in the waiting room. Let's bring him in to the TV panel. Drum. Andrew, great to see you again. Looking sharp, feeling sharp. How you guys doing? Feeling. Congratulations. How has the day been? I would love to get just your reactions from the day. It seemed like there were a lot of people there. Take us through your. Your emotions today. Well, you know, this was better than we'd hoped for, I think. A chance to celebrate. We did bring a lot of people from the company, and we brought families. And to share with the team, we brought everybody who'd been at the company for longer than nine years and their families. You know, when you do a startup, the family is a meaningful part. It takes patience from them and. And a great deal of it. And so they came and we celebrated it. It was really an extraordinary day. We opened up, you know, we did. We priced at 185. We opened up 350, and we settled at about 320. What an extraordinary thing. We're just so proud. Yeah. Take us through some of the history of Cerberus. Has it been a straight shot? Has it been an overnight success? How do you characterize it? What were the darkest moments? What were the highlight? What are the good old days to you? What does that mean? Well, I think in the hardware business, if anybody tells you it's a straight shot, you can call bs. I just don't think that's the way our business works. I think the first time you build a chip with a new architecture, it's a little more than a prototype, a little more than a proof of concept. The second chip, you iron out your challenges and you begin to show it to customers in mass. Third one, often that really takes off. And so it's a long, long road in innovative hardware designs. And so, you know, we were founded in 2016. We're more than 10 years old. We sought to solve problems that others. That's right. Overnight success. Oh, exactly. Like a decade. I was 15 pounds lighter and weight faster. Overnight successes are. Yeah, that's right. I mean, they're just overnight because most people sort of weren't paying attention. But we tried to solve some problems that other people thought were impossible. As we showed you last time, you know, we tried to build a chip that was the size of a dinner plate, and everybody told us it was impossible. And the truth is, for a while, it was. And, you know, we didn't solve it until August of 2019. We built this extraordinary chip. We were faster than Everybody. And absolutely nobody cared. Nobody. And AI wasn't ready and it was still sort of a novelty. And nobody cares about how fast you are when it's a novelty. But starting with GPT and in 2025, the models got so darn smart, they became useful and suddenly everybody wanted to use AI and you use it with inference and business with Roland. Yeah. What were those early rounds like? I'm thinking the benchmark round, CO2, Eclipse, a bunch of others. You know, we had the advantage of the founding team had been together at our last company that had paid pretty well for the venture capitalists and the team. And so we had some wind in our sails when we went out and raised money. It's not like today where we're four guys in the word lab and you're raising at a billion pre for your A. That's not us. But we went out, we made eight calls, we got eight term sheets. We. We chose Benchmark and Foundation and Eclipse. And we got going, you know, less than a year later. I was expecting. I was expecting you to say, like, yeah, I mean, it was. It was a slog. You know, we were so. Rounds were a slog. Other rounds were a slog at the beginning, not so, you know. Thomas Lafont at CO2 came in shortly thereafter and we did a round with them. I think the truth is between about 2020 and 2023, it was much harder. AI was sort of in this situation where everybody was saying, oh, that's cool, look what this model can do. Look how big it is. But it wasn't being used anywhere. Nobody was using it. They were pointing at it. They were saying, wouldn't this be nice? And they went back to whatever they were doing before. And it wasn't until really sort of 2025 when the models got good and you just saw this tidal wave of people using AI and demand for AI compute. And that's been exceptional. It's just been an amazing thing to ride. Yeah, you mentioned, like, if you have four guys and your. Your company name ends with Lab, you can raise a billion dollars. There's a little bit of that going on in the market with just like chips, semiconductors, AI. There's not that much that needs to be expl. Were the key ideas or thesis that you needed to explain in the roadshow to investors that wanted to go a layer deep, a layer deeper than just AI chips? Yeah, I think there were. There's the first. The market size and dynamic. And I think Jensen said some time ago on. On Brad Gerstner's podcast that the demand for for inference will grow by a million X. And nobody believed him. And at the same time you saw Sam Altman displaying real vision and going out and trying to lock up huge amounts of compute and memory and data center and power because he saw it too. And I think trying to share what that means, what an exponential demand means, and that we're still so early and yet the demand for AI compute is, is overwhelming. I think sharing that was interesting and I think helpful in educating the financial community. The other thing is that there are lots of ways to do this. The GPU isn't the only way. You've got a tpu, you've got Trainium, you've got us. There are lots of different ways to build a solution here. And finally, maybe the notion that CUDA is sort of this grand lock in is overplayed and that the Gemini 3, which is an excellent model, was trained on TPUs with no CUDA, that anthropics models were trained on Trainium with no Cuda. Lo and behold, some of the best models, some of the most interesting things are being done without Cuda and that that lock in might be overplayed. And I think these three factors were really important in educating the financial community going forward. How do you think, how do you and the team think about sort of calling your shot and sort of trying to predict where and how inference demand will look in 2030 and beyond versus like working closely with the labs that now have product lines with billions of dollars of revenue and their own roadmaps that you can work with. Yeah, like the babe I'm going to point out to left field and just say, wait, this is where it's going, baby. No, I don't think that's the way it works. I think we're calling our shots every day by making big investments in data center capacity and collaborating with the leading visionaries in the field. In working not just with OpenAI to serve as sort of the cutting edge and deliver their extraordinary models, but also with AWS to make sure that we can get access to the largest enterprise customers. And instead of having to work with these enterprise customers, procurement aid, sort of organizations who provide master purchase agreements that are the size of a bible, you can say, look, why don't you buy us through aws and it'll count against your annual commitment. And so I think those are really important ideas and ways we get access to the market. And then we're taking huge amounts of data center capacity. And so that's the other bet we're making. Yeah, a lot of sense. How do you think the year will play out in terms of just broader consumer awareness of what fast inference feels like? I had a really magical moment using cerebras in GPT 5.3, Spark and Codex. And even outside of coding tasks, just talking to the model and having it respond instantly was sort of, it felt like a new breakthrough or new paradigm. And I feel like this hasn't fully diffused, but it also feels like when it does, there will be potentially like entirely new ways of working, entirely new paradigms that might emerge. How are you thinking about actually diffusing the technology? We think that's exactly right. And we think that the experience of engaging with a real time AI will encourage people to do more things, to stay longer, to work on harder problems and to invent new things. I mean, if you remember, you know, when Netflix started, they delivered DVDs and envelopes. Yeah, right. And when the Internet got fast, they became a movie studio. And they didn't get better at DVD delivery, they became something completely different, something that had never been in existence before. A movie studio that delivered directly to your home. I think that's exactly what's going to happen. And you can just sit back and you can ask yourself, how big is the market for slow search? Zero. How big is the market for dial up Internet? I mean, how much would I have to pay you to swap out broadband at home and bring in dial up? 1000amonth, 1500amonth, 2000amonth? I mean, no way. I mean, it just wouldn't be worth it. And so the community is going to engage with inference in the same way. And that fast inference is going to be all of the market. Yeah. So you make the chips. I believe you also make cooling infrastructure as well, cooling units. Are there other products on the roadmap that you think will be required to roll out and scale Cerebras over the next couple years? No, I don't think so. I think right now we build the chip and the system. And the system includes, it's about the size of a dorm room fridge there. You put two of them in a standard data center rack and the cooling infrastructure is built into the system. And I think that's where we want to focus. We want to be measured on our ability to build AI computers that are faster than anybody else. Yeah. How are you thinking about scaling on chip memory? It feels like there's some, there's some concern about, well, what if the models go to 10 trillion parameters? What if it gets too Big. How are you thinking about that challenge? Or maybe it's an opportunity. It is an opportunity. I think a 10 trillion parameter model is hard for everybody. It's actually easier for us. Okay. Right. There's a reason we're not a 10 trillion. It's because it's really hard and expensive to sell for everybody. I think one of the things that we've been able to do for the larger models is to tie together a bunch of these systems in parallel and run them as a pipeline. And that way we can train and do inference on trillion, multi trillion parameter models in ways that I think are much more intuitive than on GPUs that have much smaller, smaller compute. They have off chip memory, but their problem is the compute. They don't have enough compute per chip. And then how are you talking to customers about potentially bringing Cerebras in not as a full replacement to their entire semiconductor supply chain or stack, but as a complement to everything else that they're running? Because I have this vision of the next generation of AI agents. You get this genius model, but it needs to use a small model over here, an open source model over there, a super fast model for a certain thing. If it's looping through something. Way you hire, you have a superstar employee, you don't necessarily want them doing every single task themselves. It's like, yeah, you should be able to delegate. Yeah, delegation. How are you thinking about that? Yeah, I think that is sort of a notion of a confederacy of models. Right. That there's a collection of different models. And one of the things we thought about early on was how to interoperate in that environment. And we connect in via standard 100 gigabit ethernet. Nothing fancy, nothing proprietary. We are deployed in many places where they've got GPUs from Nvidia or GPUs from AMD, they've got x86 compute from Dell or HP. And so that's not a problem at all. We're eager for those environments. Yeah. What do you think the company would look like today if you guys had had access to today's frontier models when you started the company? Like are you feeling and how do you think about just like the speed up at the company today due to how good the models have gotten? We use frontier models every day in coding, in running our gna. I think if you start a company today, you build a very different organization. I think there are whole departments that look different in the next nine to 18 months. I think much of what HR does, much of what training does is solved by some form of AI. I think a lot of the work in finance, closing the books, a bunch of what they do is checking. And those were all done by agents. I think what it is to be selling or doing recruiting. Those change. I think for a long time what recruiting was was hunting through or writing scripts for LinkedIn. I think that changes substantially. And so when we look out, we see sort of fundamental changes. The obvious ones, of course, are a year ago, engineers were using approximately zero tokens and now they're using, you know, $10,000 worth of tokens a month. And the rate of change and the rate of new PR requests, new pull requests, is just extraordinary. And so AI is having fundamental changes. Obviously it usually starts in Silicon Valley and sort of works in waves to other areas. But that's what we're seeing right now. Since the last time we talked about, there's been a ton of movement in the space data center market, a lot of energy. Just yesterday, Space X and Google eyed a launch deal in the Wall Street Journal. Have has any of your thinking changed? Like what is your current thesis on space data centers and how it might fit into your business plan over the next decade even? Well, one of the hardest things in a space data center is communicating across chips, one chip the next. And we solve that. Right. One of the great parts about a big chip is that you have to communicate from one chip to the next less frequently. It's a huge advantage for us in space. I think that this is an idea like self driving where the last 10% takes 80% of the time. Sure. Right. And that we're not three or five years away where eight to 12 years away. That doesn't mean we shouldn't be working on it or thinking about or making progress to it. Because if you don't do that, it's 25 years away. Yeah. But I don't see data centers in space in the next three or four years. Yeah. And arguably you've solved the key problems that you would be asked to solve. And so you'll be ready if demand shows up. But there's not that much for you to do individually to advance that. That's exactly right. Yeah, that's exactly what. Well, we're hoping for it. It'd be exciting, but plenty, plenty work to do here on the grand trail. Congratulations to you and the whole team on this incredible milestone. We're honored that you would spend time with us. We really appreciate it. Work day for the company. Yeah. And let me hit the go to watch your progress and I look forward to your next appearance. And enjoy the rest of the evening. Enjoy the rest of the evening. We'll talk to you soon. Thank you. Guys, it's time for a cocktail. Be welcome. Fantastic. Enjoy it. You deserve it.
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.
Guest Ben Hylack from Raindrop joins. I believe he's in the waiting room so we'll let him come in. He's the co founder and cto. We've had him on the show before. Welcome back Ben. How you doing? Doing well man, how are you? Fantastic. Great to see you. Long time in your world. Reintroduce the company quickly and then tell us the news. Sure. Raindrop. We make observability for agents so the main thing we do is self healing agent. So what it means is that when your Raindrop hits a problem in production, we detect it, we fix it. How do you do it? That's a good question. So at the end of the day we consider ourselves like the intelligence for your intelligence. What that means is that we are the best, fastest way to essentially look at anomalies. So what that means is that let's say you make a change, right? We're able to very, very quickly find out that like oh, users all started complaining about something or the trajectory, the traces are kind of starting to evolve into a different pattern. And so it's kind of a combination of agents but also more like classic ML techniques, a lot of like custom trained models for every customer. Walk me through the shape of the agent market right now. Like the way you're talking about it, you know, sort of illustrates the broad diffusion of agents and custom agents. I think that a lot of people think cloud code and codex and I don't know if you're doing enterprise deals with those firms or that's the goal. But I imagine that every startup, many legacy companies have built some sort of agents, some sort of harness and I'd love to know the shape of how broadly diffusing like custom agents are in companies versus is it the domain purely of startups that create an agent for legal or an agent for sales and then they ven that into a company? Yeah, so I would say that there's two kind of categories of customers. We started with super high growth startups at the time startups. So those are companies like clay, for example, framer, speak.com, some of the fastest growing companies in the world. And those were some of our earliest customers and we're lucky they grew a ton. So you know, that has helped our growth always helps. It always helps. Yeah, someone once mentioned that like, you know, this kind of business is a lot like early stage seed investing. Actually it's kind of interesting like you know, we, you have to be pretty, pretty picky not to work with companies that are going to die because if, like, especially analytics, like, all these sort of things, like, they. You succeed as a company when your customers succeed, like, if all of your customers are terrible, it's like everyone's like, well, why do I. Why are you. Yeah, I had a. I had a portfolio company that was working on, like, agent infrastructure, like, roughly two years ago and pivoted because he was like, okay, this is clearly going to be a big thing someday. But right now he's looking at all the underlying companies and he's like, I don't believe that any of these, like, agents in their current iteration are going to work. No, maybe there's. I think it was very counterintuitive at the time, but I think we chose to find companies like Clay.com, right, which are. We're clearly on a insane trajectory, but at the time weren't necessarily as large. And so I think a lot of our customers now are pretty large, but at the time weren't necessarily as large. And then in the last few months, we've been moving into Fortune 50s, Fortune 1002s, and a lot of amazing things happening there. And again, it's kind of like two shapes of a product. One is in our bread and butter is companies that are redefining the way people interact in different verticals. But then there are Fortune 50s, Fortune 1000s that are also deploying agents internally. I think the shape of that looks very interesting, and it's something that. Being on the forefront of understanding how these companies are deploying things, there's not that much I can talk about right now. But, yeah, always very interesting. What do you think is a generally under hyped agent category right now? I'm sure you're seeing the future a little bit. It's a really good question. I think that. I mean, you know, I. So this is a tough question. I. What I want to do, actually, is pivot the question a little bit because I want to talk about our launch today, if that's okay with you guys. Yeah, I'll tell you my questions, you tell me your answers. Okay. Okay, Sounds good. Does that mean that you want me to not answer this? No, no, no, no. I'm just messing around. Go for it. Okay, cool. Yeah, I botched it. The joke is, what questions do you have for my answers? It sounds good. CEOs act like that, where it's like you could ask them anything and they're just going to direct you. But it's fine. I want to hear about the launch today, so just tell us about it. Great. Let's talk about it. Okay, so guys, there's been this crazy thing that has been missing for a very, very long time. That's why I want to talk about it. So like people have been building agents. You're building them locally. Like you're using some sort of SDK. It could be OpenAI, it could be Vercels, whatever SDK it is. And what do you mean there's no way it actually has to run on, like you're just on your laptop, right? Like before you push to production. Sure. Right. Sure it's on your laptop. Yeah, yeah, yeah. There's no way to see what it's doing. Like no standard way, nothing. Like so people will send those traces out to like a server. Like Raindrop is like one of those, you know, and there's a bunch of others. Yeah. But they might also just like drop the logs in like a non relational database. Sure. They'll just print it to, you know, console log. Like, oh, here's what was happening. It's like that bad? Yeah, yeah, yeah. And the other problem there is like, so you can't see like a nice trace or you're sending it to some server and it takes like seconds to see everything. I'm like, whatever. It looks terrible. But then also your coding agent can't see the traces either. Then when you hit a problem and you're like, hey, this response was wrong. Blood code will just make shit up. It'll just be like, oh, I think that maybe this tool was wrong. Or I think maybe this happened because it doesn't have any of that data, doesn't actually know what the coding agent did. So I think that as someone building agents, as our company building agents, it's actually kind of embarrassing how long it took us to solve this problem. No one else solved it either, but. But yeah, that's what we launched today. Free local open source tool Raindrop AI Workshop. And it's completely free, it's just open source. Why open source? That's a really good question. I mean I think the genuine answer and I think part of why our competitors haven't done it, I mean there's probably other reasons for that as well, but I think it's that it can be right so someone else can do it. You know what I mean? I think that it running locally is the best experience for people. And to be clear, there's still things that it enables if you connect it to your production Raindrop, which is like you can pull in a remote trace and replay it and then claude code or codecs can just Keep doing that loop until it works. So there's still benefits for us, but also the truth is that we want people to hack it. We want people to. To meld it into whatever works for them. So we use a lot of open source things here. Right. So it makes sense to contribute back as well. Yeah, that's great. Yeah. I'm wondering about other just like, predictions about the next breakout category of AI agents. What you're seeing feels like we're so close to being able to book a flight, but maybe no one wants that. I don't know. I mean, I'm not sure if you guys saw. I had a little bit of a thing with Ryan Chesky earlier about Airbnb. Oh, yeah, Talk about that. Talk about that. No, I think, like, you know, I use Airbnb a lot. I love Airbnb. I think if I had to guess, I would say Brian Chesky knows a lot more about airbnb than I do and probably a lot more about being a founder than I do as well. And so I think there's probably a lot that I'm not considering. That being said, I think it's fresh. Like, if Airbnb had an API, I would use it and I would book Airbnb with it, like through cloud code. Right. So it's like, I know I would do it. I find Airbnb very, very hard to search. And I think that there's a lot of. I think the tough part and what I see industry wide right now, everyone's trying to figure out is you see companies almost reducing themselves into an API with absolutely no mode. You look at Photoshop, Illustrator, et cetera, they're like, oh, we have a cloud code integration now, mcp. At the point where people are just using Photoshop, Illustrator, et cetera, as like an mcp, they've sort of lost the game. Right? Like, if no one's actually touching the UI anymore, I think that right now companies have to do that increasingly because they have no other choice. I think that there will be a point where the incentives don't make sense anymore. I can give an anecdote from when I was at Apple, you know. Do you guys remember app clips? Yeah. Yeah. Very few people. Where did those go? I only see them. Where did those go with, like, parking meters sometimes. Yeah. Right. So like, one of like the hero ideas there was like, oh, you know, like, imagine you're in line at Starbucks. You don't have the Starbucks app downloaded. Like, well, why not just, you know, scan something, have an app, come order your drink. And it's like, turns out Starbucks doesn't want that. Right? Sure. That's the last thing in the world Starbucks wants. Starbucks wants you to download the app. They want you to have stars, an entire. Like, there's a reason why DoorDash and Uber Eats and like, whatever, you know, God knows other apps exist. It's not because they need to, but because each have companies and money and goals and like, so, so why would they reduce themselves into an easily interchangeable API? It doesn't actually make sense. Yeah, but, but I think it's using, I think it's important to be careful around using like a tool like Photoshop interchangeably with like a, a retail store like Starbucks or like a marketplace like Airbnb or DoorDash. Because I really think that these marketplaces provide, you know, an exceptional amount. All the value is not in the ui. Right, I agree, I agree. And like, the value of Starbucks is not that it's a pretty app. It's because they have specific drinks that they can make pretty much anywhere. You know, someone would be, I think of that company, Buy the Drink company. They got started during the direct to consumer boom. Obviously they would have some beautiful Shopify website. They didn't. They just went direct to retail and they had Amazon. You could order it online and if you went to their website it would just say, go to Amazon. And they did. Fine. Billion dollar company. And because like the value is not in the E commerce experience, they didn't play like the Stars game. Of course Starbucks is maybe sacrificing a piece of that business model, but it's not giving away the whole cow. I don't know. There are going to be ways to monetize this, right? Like, there are going to be successful business models built on top of this sort of layer. And to be honest, as Raindrop goes into the future, like, that's the future we're building towards. That's the future we want. I mean, we're going to be announcing a partnership with a really large one, like the large coding companies soon as far as like integrating with them more where it's like, I don't see Raindrop as a company that's going to submit PRs and production to people's code bases, like someone else is me doing that. We're going to be the layer that's really good at finding those issues, diagnosing them and tracking them. So I just think it's going to be interesting into the future how much companies are willing to sort of just like be the API with like without all those hooks, without the, you know, knowing everyone's email, like having the mailing list, like all that sort of stuff. So that. That's a very interesting trend. I feel like you're generally on the frontier and cutting edge of like, adopting all these tools. You mentioned your cloud code use, and I'm wondering about. Give me a reality check, a health check on your experience computer use, because you're lamenting the fact that Airbnb doesn't have an API. And I imagine you could create a scraper or download the HTML and interact with it, treat the front end as the API, effectively puppeteer the computer through computer use. Where are you on the AGI moment in computer use from what you've seen? Where does it work? Where doesn't it work? Where would you recommend people get started if they want to play around with it? Yeah, that's a really good question. There's other places where it works. I think that Codex has done a very good job of implementing browser use, actually, both for debugging applications that you're working on and in general. This is something that Claudica just doesn't do. Creates a really, again, that kind of like, I think the next couple months, the thing you're going to keep hearing from me, but also everyone in the world is like self healing loops, loops, loops, loops. How do you create loops where it's. How do you close the loop, how do you have flawed code, make a UI change, see that it sucks, and then just keep going. Right. And a lot of like, do we have AGI or not? Is how many loops in a row can you do? It's all loops before things just end catastrophically. Right. Yeah. Because there is sort of this, like, it gets worse. Right. In many cases. So, yeah, I think there's a lot of ways to answer this. I'm a fairly, like, security conscious person. I think that, like the, you know, I'm not like an open claw guy. I'm not going to give all of my, like, cookies to some, you know, agent, et cetera, et cetera. But, yeah, I think tbd. Yeah. Cool. Well, yeah, new challenge book. An Airbnb with an agent. Can it be done? Is that where the goal posts need to be set? Let's figure it out. Anyway, thank you so much for coming on the show. Great to see you, Ben. Congrats to the team. Congrats. Of course. Last thing, if you want a hat, we have a new Cli. You can run Raindrop Drip, you can get a hat, an umbrella, a couple other things. Ooh That's a fun. That's a fun way to give out merch. I like that. That's very creative. Thanks for coming on the show. Great stuff. We'll talk to you soon.
Fortunately, we have our first guest of the show, Amy Reinhart from Netflix in the waiting room. Let's bring her in to the TV show. Amy, how are you doing? Doing well. How are you doing? Doing fantastically. It is an honor to have you here. Yes. Our first ever guest from Netflix. I think so. Thank you so much for taking the time. It only took like 2000 interviews to get you guys on here. But we're excited to meet you. Honored to be the first. Yes, yes. I mean, obviously big fans of both Netflix and advertising, but would love to start with a little bit of background on yourself, your experience and just sort of your intro to how you found yourself as the president of ads at Netflix today. Sure. I've been at Netflix for about nine and a half years now in a couple of different roles. Started out first in our content organization doing both licensing and then overseeing production. And about two and a half years ago, I stepped into this role overseeing our ads tier. And it's been a fantastic two and a half years. A lot of excitement. Feels like we've been able to accomplish a lot and great company. If you take us back to the initial push into ads, what can you tell us about the trade offs, the build versus buy debates that were going on at the time, the just maybe even the cultural changes? I think we are super. You know, we love ads. We think that's a fantastic business model. It's a way to deliver great value to customers at lower prices and there's so many benefits. But culturally. Yeah. What was the debate like? Yeah. What was it like internally? Yeah, well, I think it's been well publicized, you know, that not being in advertising was a strategic bet for a long time. Right. And so early in 2021, 2022, when we started to talk about the notion of getting into this business. Yeah, it created a lot of, I think, fair to say, you know, angst within the company for a bit of time because it was such a big shift to your point, culturally and strategically. So I would say. And then we made the announcement that we were getting into it and in terms of the whole build versus Buy, you know, we partnered with Microsoft to enter the business very quickly and that got us up and running. But it's been, you know, we made the decision about 18 months ago to lean into building our own tech stack and we launched that a year ago. So we're just a year. I keep having to remind myself how nascent our tech stack is because we've been able to deliver so many developments and so much progress against that over the course of the last year. But I would say full circle, you know, we were past. We put to bed all notions that we should be in this business. I think everybody understands strategically now that it is important for us to be that. And we've been able to grow our user base because we have been able to get to a lot more consumers who are looking for that low cost option and are fine with ads. Right. So it's been a great thing for the company, I think, and everybody's on board. And you know, the recent news, as you heard, which we just announced up front yesterday, that we're expanding that AD tier into 15 more countries around the world. There you go. Fantastic. Everybody's on board. Full speed ahead. How are you pitching the ad product today? Is this primarily brand marketing? Is there a timeline to get to more of a performance focus? What is your pitch to advertisers? Yeah, you know, as we see in the marketplace, advertisers are oriented around outcomes. Right. So we know that we need to be a full funnel solution and we believe that we have the metrics to support that. So to your point, we've been very successful with some of the brand partnerships that we've done over the course of the last year and a half. But we've also seen really good conversions in terms of lower funnel and making sure that we're driving purchase intent and consideration. So as we build out more of our solutions, we are going after that full funnel solution. What are conversations like around how brands should, how much brands should want to associate with particular pieces of content? Because I think some brands might come in and say, well, I'm advertising strollers and I know that parents will be watching K Pop Demon Hunters with their kids. And so this is the most on the nose directed. I care, I want my brand linked to this particular piece of content. But we've seen time and time again that like, once the algorithms get good enough, once dynamic ad placement can actually flourish, every company tends to see better performance there. So where are the ad buyers today in terms of those trade offs? You know, there's a full spectrum, so absolutely, we get advertisers who want to be associated with K Pop demon hunters, like McDonald's or with stranger things. Right. Like those big moments, those are oftentimes the easiest to sell. I think K Pop Demon Hunters is actually an interesting case because when it came out a year ago, we didn't know that we had a hit on our hands until about 60 days into it. And I think that's what the Magic is of Netflix is that we have so much variety and depth of content that we're programming and trying to hit all audiences that you never know where your next is going to come from. And so selling those audiences, selling that, you know, audience behavior, moods, targeting moods and relevance is really important to a lot of different advertisers. So again, we just want to meet advertisers where they're at. And some folks understand that being across a number of different programming choices is important. And some people want to tag along with those big tent poles and we want to, you know, provide those opportunities. Yeah, I mean, it's interesting. Netflix has like deep, deep experience in machine learning, AI recommendations, all parts of like, you know, high throughput data processing. But I'm interested in any learnings or surprises from building the proprietary ad delivery stack. Has it been as expected? Has it been there's new skills that you need to bring? Because a lot of companies have been successful at scaling content and then struggled to figure out ad delivery. You obviously haven't. But then also there's this AI boom going on, which can help with productivity, but also new algorithms and new ways to actually target content. And so I'm interested in where the build out didn't match your expectations or surprised you. As a tech company, we do a lot of testing and we go into things with hypotheses. So we're constantly testing things around our member experience. And, you know, I think that's been a differentiator for us, like really leaning into reducing member friction, making sure that member experience is a good one with lower ad loads, lower frequency apps, those types of things. But we, we do know that there are times when we have to pivot. So I would you. There's not just one example of a time that we've had a wrong hypothesis. We're constantly testing things out and figuring out where we, you know, where that those hypotheses prove out and where we need to pivot and, you know, change swiftly. So it's hard to point to one specific moment where we, it felt like it's been a learning, I would say the bigger learning for us, just as a company is, you know, this is a relationship business too. And we've never. This, you know, you talked about kind of getting into ad sales, right? We've never had a sales team in terms of our overall organization. So I would say there's more organizational learnings than necessarily tech learnings because we're so used to that tech cycle of testing and learning and iterating. How are you thinking about the ad product feeding back into the content production? I've noticed that Netflix has been fantastic, in my opinion, of creating more engaging content. I was watching the rip with Matt Damon and you click the play button and you see Matt Damon's face within like two seconds and it clearly confirms that you're watching the right movie. And then the title card comes in and that's a departure from 50 years ago. You watched the Shining and you know it's a helicopter shot of a car for five minutes and they show you the full titles. And it is a different style of editing and some people lament the old style. I particularly like the new style. And I'm wondering about. We went through a period of time when television, there was the famous like fade to black and then the ad break and then fade back in and you resume. And Netflix has never had to contend with that in media products. But is that going to come back? Is there a next generation pattern for creating content that can both have ads in it and not? Are you seeing glimmers of what the future, like the impact of ads might have on like the editing structure and the timing and the pacing? Yeah, a lot of it, to be honest, depends on our creators. So, you know, working with talent who. And some of those, some of that talent may be more tech forward and are thinking through those types of things when they're writing shows. I'll give an example. Shonda Rhimes was used to writing for broadcast and network for many, many years. So when she writes a lot of her content, she's already thinking about where those natural breaks are. But not all writers do that. And that's okay. We can still find what are those natural breaks because we want to make sure, again, getting back to the member experience, that it's not intrusive or it doesn't come mid sentence. Right. And is cutting off any of the action. We're able to adapt to any way that our creators want to write the content and fit it into that member experience. Yeah. For US Markets, is there any enterprise spending? I would imagine that a lot of enterprise buyers have been Netflix subscribers for a really long time and maybe they're not getting served any ads at all. And so this is more of a consumer opportunity, or am I thinking about that the wrong way? Most of our clients right now, the. The target segment that we're going after, our enterprise top 400 clients. Right. Because we think those are the ones who. Oh, Sorry, I meant B2B versus like B2C company. Oh. We think about this more as a B2C opportunity for the most part. And I think as we expand our learning and expand our offering may get into the B2B space but for the most part. B2C. Yeah, yeah, I, I feel like all of that like the higher up market, more targeted. That's all unlocked with scale once more there's more learnings on responses. I'm wondering what other signals you think might be valuable because many times advertisements shown during a TV program are very passive, harder to track. But if someone's watching on their phone, there can actually be a call to action, a trackable link. I imagine that the data is messy, but how important is it to sort of close that loop in an ATT era where it's a little bit trickier, but there's a lot of things that you can do on the signal side anyway. Yeah, you're absolutely right. And this is an area where I talked about the testing and iterating. We're leaning in a lot on the testing. What does that screen experience look like again? How do you meet the customers where they're at without being sort of intrusive? So a lot of testing going on in this space. But the biggest thing for us is, you know, privacy safe. We want to make sure that we're leaning into again that member experience and taking care of our members data. But a lot more I think to come. Have you been surprised by the return of the QR code in maybe podcast advertising? But I see it a lot because people are watching on, you know, they watch a YouTube video on a TV and the creator will, you know, hard code in a QR code to link out and that was something I had completely written off QR codes and then they made a major comeback. No, I agree with you from a member experience may not be the most simplistic thing understanding kind of the ad tech on the back end. I'm not surprised by it because it get to be pretty complex pretty quickly. Yeah, and there's some. We hit some sort of like inflection point where maybe it was in a certain iOS revision where the camera app became so easy to press a button and pull out and then it detects the QR code so quickly that that flow. Because you used to need to like have a QR app separately to scan it and now it's all integrated and so someone can just whip out their phone and run right to it. Jordy, you have something else? Nothing super top of mind right now. I mean last question I had is do you ever expect Netflix to serve more short form vertical video style ads and something like the Clips tab. I know the Clips tab right now is focused on basically like content discovery, but I imagine in the future people will spend more time in a format like that, especially on mobile. Absolutely. And that is one of the announcements we had at our upfront yesterday is that as we roll out this vertical video content that we are going to be offering that to advertisers along with our Tudum.com coverage in 2027. So yes, we think that is a big opportunity too. Last question for me. I would love to know about the intersection between games and ads that's been a huge growth driver with other categories and other companies. But I'm wondering where that is in the roadmap, how you're thinking about that. We haven't thought about that yet. Look, our roadmap, I could fill our roadmap for the next two to three years based on just some of the foundational things we want to do and a lot of the innovative areas we want to lean into. But it's an area that we're keeping an eye on. And as we watch that game's engagement increase, I would never say never. I've learned to never say never at Netflix, but it's not something that's on the near term roadmap. Okay, thank you. Well, thank you so much for joining. Thank you. Great to meet you, Amy. Thanks for breaking it down. We'll talk to you soon. Cheers. Have a good one.