Cerebras IPO, Wartime, VC Infighting, H100s in China, Robot Monkey Business

I see a large IPO on the horizon. You're surrounded by journalists. Hold your position. >> Right. There's misinformation. >> Clearing order inbound. >> Let's just roll. We are surrounded by journal. Hold your position. Come on. Get up. Trust the experts here. We are experts found. I see multiple journalists on the horizon. Stand by. >> UAV online. >> Blaze. >> Double blaze. Triple blaze. Double kill. By coming is wins. Team Deathmatch. >> We are experts. Triple blade. Let's just roll. Right. >> Hockey clearing order inbound. Come get up. >> We are surrounded by journalists. Hold your position. >> Strike one. >> Strike two. Activate golden retriever mode. >> Another one. 5. You're watching TVPN and today is Thursday, May 14th, >> 206. >> We are live from the TBPN Ultradome, the temple of technology, >> the fortress of finance, the capital of capital. >> Always. >> Hey, Ben. >> That is Ben. Indispensable. >> You got multiple Ben over here. >> Uh, we have a great show. It's Cerebrous day on the show. Cerebrous IPO. We'll talk about that. Uh uh Semi analysis has a fantastic deep dive on the company. We'll go through that. Doug Laughlin from Semi analysis has joined the show. And then the founder and CEO of Cerebras is joining the show. But we have lots more folks joining. Amy Reinhardt from Netflix, the president of ads. Can you imagine? I didn't think you know, you think about presidents, the the president, you think president of the United States. I think president of ads. >> That's right. Um, Ben Hilac, Eric Visra, Steve Valo. We got a bunch of folks coming on the show today. Uh, it's going to be a fun one. So, uh, there's a ton of news. Let's start with Cerebrus. The IPO has gone spectacularly well. Cerebras doubled their valuation basically overnight. Uh, Brandon Gell had the good fortune of writing up some of the details of the Cerebrus news in the newsletter today. TBPN.com. You can go sign up. Yeah, right now it's sitting at a $64 billion market cap. Yeah. And a lot of the prediction markets, they didn't even have a category above 50, right? A lot of people were just kind of uh trading or or betting. Uh >> and when I wrote the newsletter Friday, Monday, I I said a $50 billion IPO and was sort of being uh optimistic and uh it beat those expectations, which is great news. Uh they deserve it. It's a true overnight success. Uh we'll show you some charts of the valuation. Lots of troughs of disillusionment, but Andrew and the team powered through and wound up finding the perfect application for their technology at the perfect time during a mega cycle, which we'll go through. So, uh chip design company, Cerebrris, if you don't if you're not familiar, they make a big big chip big chip company. Uh instead of >> the biggest chip, >> instead of taking the wafer, putting a bunch of chips on it, cutting it up into smaller chips, they use the whole wafer. Uh it's genius idea. It's one of those simple ideas taken deadly seriously in some ways. Uh but it's trading at uh $350 a share on its first day of public trading which values the company much higher. >> Three 300 now. >> 300. >> Yeah. >> 300. Okay. So it was okay. It was up at 350. It has since sold off slightly. Um >> and uh and they raised around $10 billion. I think they were are targeting six billion at one point. Uh they've they've upsized that. I think there was a $3 billion raise initially, but they have a good amount of money in the bank now. Uh the price on this IPO has been literally up only. On Monday, the price range was 150 to 160. Uh then they raised it that was up from 115 to 125. Uh and today we're seeing, you know, much higher prices. >> Go back to that picture. >> Who's in the picture at the NASDAQ? >> Picture at the NASDAQ >> of the Cerebrus team standing on stage. Someone should make a set in LA. You know, they have those like fake private jet sets. Imagine if entrepreneurs could have a set where they put their logo in the background like they're hitting it with a hammer and there's confetti going everywhere. >> Yeah. But it's for your course. >> Yeah. >> Yeah. And and you walk right from there uh to to the Lambo. >> 1,000 students in your mastermind. >> Yeah. Yeah. No, I had this I had this idea back in the day when Do you remember the ice cream the ice cream museum? this whole thing. >> Oh, yeah. >> So, there were there was this trend. I mean, really bad news for the museum industry, but they're getting eaten alive. And so, some entrepreneurs, I think they did very well, started something called the ice cream museum, which uh was not really a museum in the sense of like a presidential library or uh you know, the the Norton Simon or the Getty or the you know, natural history museum. It was more of like an experiential place to go and hang out. good for first dates, good for, you know, taking kids maybe. Uh, and they would maybe give you some ice cream, but most of the most of the museum was just like very Instagrammable things. So, there would be like a ball pit or a bunch of raining confetti and stuff and uh a huge a huge fabricated statue of ice cream that was not a piece of art that would be sold. >> Sprinkle ball pit. >> There you go. Uh, that sounds real. I don't know if that's is real, but it sounds very believable. They had they had that. Okay. Yeah. So, uh and and there were a number of other kind of copycats that were trying to jump on and do like, oh, we'll do like the waffle museum or something or the pancake museum, you know, cuz they just wanted to cash in. And uh and my idea was just the museum of Instagrammable objects. And so it would have all of those. So there would be a private jet set and then there would be a Lamborghini set. And this one would fit right in. So it's just they have a big pink wall so you can go take the pink wall photo. And then there's a beach. And then there was a gym with fake weights so you could go and look like you're maxing out and benching 500 pounds. And so it just says, "Bring these clothes or we'll have them for you." And then you move from room to room taking the ideal dating profile photo reveal. >> Yeah. Exactly. Exactly. Oh, you had kids here. You in the hospital. You can live an entire life through this fictional museum of Instagrammable objects. Uh more of a meme than a real business idea. But uh >> no, John, the Museum of Ice Cream now has uh seven locations. >> Okay, so they're cooking. >> They're global. >> They're global. They're they're doing well. Um well, let us know. >> Anyways, nice little tangent there. >> Miami is the capital of Gimmick Museums. Got to do a whirlwind tour. Uh anyway, let's go back to the serious stuff. Cerebrus, uh it's a complicated, uh company because they are so deep in the AI supply chain, but we'll break it all down for you. So, uh Semi analysis has a fantastic deep dive. Uh it's a longer read, so we're not going to go through it all, but there are some very interesting uh tidbits in here that we can sort of summarize and contextualize for you. And then of course we'll be talking to Doug Laughlin from semi analysis at uh 12:30. So uh the there's a bunch of interesting takeaways, some really solid positives. Uh Cerebrous chips work, which was something people were not expecting for a while. There was a lot of FUD around this company, just the idea of like, oh that'll never work. Like what if the architecture changes? is what if we go away from transformers or something? What if we need something quote completely different? Or maybe like the yields will never work because there was this idea that if you're using the entire wafer, typically as you're as you're etching the chips onto the wafer, sometimes there's little defects and it's not a problem if you're going to break up a wafer into like 64 chips because you just throw away one. But if there's one defect on basically every wafer, well then your yield is going to be super low. We talked to Andrew about how he solved that by sp uh creating redundant cores and they don't actually activate all the cores and so they sort of built in that redundancy and got through that. But that was an early critique of the strategy. Uh >> you can use >> you can use it >> use cerebra strips today as uh >> in codeex 5.3 spark uh and so they are very fast and I think the most important thing that semi analysis points out is that uh token consumers customers businesses have ex have shown this revealed preference for and and a willingness to pay for speed. uh and they sort of contextualize it and they quantify it based on their own usage and their experience with anthropics opus models. So opus 4.6 fast mode famously I like that they use famously because it's like famous to like 100,000 people but uh famously charges six times the price for two and a half times the interactivity although it's now under 2x faster. So effectively you're you're you're paying you're paying six times the price for two times the speed. that's uh that that's disproportionately more money for what you're getting. You would think you'd pay six times the price for six times the speed potentially. Um but uh there was a lot there were a lot of questions about would people really pay for more uh uh that much more for faster models faster inference and Andre Karpathy Sam Alman was was was saying like uh do you want faster models or smarter models and uh he was like I think in Sam's point was sort of like the these models are very intelligent but using them faster is sort of more of a magical superpower and Sam was I I felt like Sam was sort leading it towards like speed is really important as the next leg up on productivity. Uh and utter karpathy was like no I just want smarter I'll just let it run overnight. I don't mind that. Uh but that's not what everyone is feeling. Some people especially the semi- analysis team leaned more towards interactivity or speed over raw intelligence power. >> Well, yeah. And then there's the other aspect which is just capability, right? >> Capability, speed, and intelligence. >> Yeah. I I think >> like that's that's like the question I think people have had is like okay what is is there a 200 >> 50 IQ model or is there just a much more capable model >> tools more efficiently and is really quick. >> Yeah. And that's actually important to Cerebris because as we'll get into uh the the the the chips do face a hurdle with scaling in terms of longer context windows all that stuff but semi analysis shared their breakdown. they uh were run rating uh $10 million on uh AI spend in April and they said that April was the peak which is interesting because that was sort of uh I I would have expected a straight line continued growth but uh they might have you know been really pelled tried it all and then eventually said oh okay well for this we can probably use a cheaper model we can probably optimize we don't want you know uh 95% of our uh of our revenue going towards tokens we want a little bit of margin to pay our team and obviously it's a business they need to make profits. Um so uh semi analysis was spending 80% of their AI spend on opus 4.6 fast. And so they were willing to pay that 6x like 80% of their spend disproportionately more even though uh when their sort of expectation as they put it was that they would always want the smartest model. They would they they would they would be very costconcious they were in reality saying I'm going to hammer fast mode. I want to spend on fast mode. And then I think uh the price was significant. And so there's probably sort of a renegotiation about when is the right time to use fast versus when do you want to leave something running overnight. But um OpenAI is clearly very pled on Cerebras. Cerebras has a big 750 megawatt deal with OpenAI and the chips are already serving GPT 5.3 in codecs under the name Spark as we mentioned. Uh and I've used it. You should use it. It's a very interesting experience because uh I think a lot of people have interacted with LLMs and chat bots and they're sort of used to this token streaming in and it's sort of uh it's sort of cute because the phone vibrates and it and it feels like you're talking to someone who's typing. But it's way better when you just land on a Wikipedia page, the full thing loads and you can just scroll however much you want. And that's the experience that I think people want and will demand across everything, especially if they're firing off a coding task. They just want the code immediately. Uh and so um you can uh you can also just go talk to the model like it's chatbt. You don't need to use codeex 5.3 spark in a coding context. You can ask it whatever you want and it will just act like a normal llm. Uh and I I personally think there will be huge demand for faster inference across all parts of the AI economy. There's this old late >> Yeah. Another another way to think about it is like if you have two employees with the same skill set, the same capability, >> but one is just five times faster, right? That person can create way more value in the organization. Yeah. Right. And for a lot of things, if they're if they're two times faster, they do command six times the price because over the course of the year, a sales rep that that that sells twice as much or someone who is twice as effective as their job might actually command a salary that's five times, six times um the the actual price. And so there's lots of other uh u context across different business lines that uh you could draw to. Uh there's also this old uh adage or saying about e-commerce that might may or may not be real, but it's probably been transposed so many times in Think Pieces. I don't know the real quote, but it goes something like every 100 milliseconds of latency costs Amazon 1% in sales. I don't know if that's the right way to think about it, but basically as Amazon was scaling, they realized that there were a bunch of things that they could do on the UI side, a bunch of things they could do on the layout side. Where does the buy button go? where does certain information go, the price, the discount, all of this stuff, the images, they they were tweaking the front end. But as they did that, they added bloat, and the pages would slow down. And what they noticed was that the slower the page was, the lower the conversion rate because people were waiting for amazon.com to load, click on the page, it takes a second, they get distracted, they go somewhere else. And I think that that's happening in LLM use cases all over the place. People fire off a query and they're like, "Ah, it's taking too long. I'll go scroll Instagram reels. There's always an Instagram reel." and they'll be like, "Ah, I for kind of forgot about what I was asking about. I didn't get my answer." And so, uh, and that's certainly true in business contexts as well. So, um, this is currently playing out in AI inference. Uh, companies are paying disproportionately more for faster inference, and this is good for Cerebras. Uh, but semi- analysis does point out a number of potential headwinds and problems that the team at Cerebris will have to solve or contend with over the next few years. uh mainly cerebrous chips are not currently as capable of holding larger models uh in the limited memory that they have or networking multiple chips together to serve larger models. We've heard about the NVL72 racks that wire a whole bunch of Nvidia chips together can serve these really large models. Uh that has potentially be been a challenge. So uh semi analysis says moreover the industry is tending trending towards larger context windows add infinitum 128k context will certainly not be acceptable for long especially with the prevalence of agentic workloads and it doesn't look like there's a simple solution of just scaling the wafer size larger because TSMC is set up with a standard wafer size uh and adding or adding more memory to the existing architecture because cerebrus' whole design depends on a lot of SRAMM static random access memory directly on the on the wafer but SRAMM is no longer shrinking as much with each new semiconductor node. So the last version of the Cerebrris chip, they've done WSSE 1, 2, and three. They're on three now, but WSE 2 had 40 gigs of memory. WS3, you would expect, oh, we want a doubling, right? We wanted 10x or something. Uh, it got 44. So a 10% increase over one process node, one iteration. And uh, so analysis is asking the question of like, okay, is there an easy way to double this? Is there a question like how will this scale as the models get bigger um to add more SRAM? You might have to sacrifice compute area because everything is being done on one wafer if you want computation or memory. There's a direct trade-off because you only have so much space on the actual wafer. And so uh they they there might be much harder 3D wafer bonding approaches doing stack stuff like that. there are other potential ways but there's not uh like a linear oh yeah of course the next version's going to double again and so that is a potential problem that they need to work through uh but in an agentic workflow I think it's entirely possible that you want like the biggest most powerful model like the vice president delegating things you want the vice president junior >> senior vice president uh maybe just the president uh handling the critical work uh so future future models might not and that might not be on cerebrus that might be on an NVL72 or TPUs or something. Uh but uh I imagine that we will quickly jump from the agentic age where you're firing the best smartest model at the full workload to the orchestration age and there will be hybrid approaches. So the biggest and best models will delegate certain tasks to smaller faster models just like they go and do database queries these days or they go and search the web these days and that's CPUbound. There will be certain workloads that the larger smarter agent model like the boss model can sort of delegate to the cerebrous speed workers, the faster workers. So if you need to I mean just yesterday we were I I was asking Ben about like uh he pulled all our guests together. It's like I'd love to know the geoloccation of every single guest. And it's like, okay, run that same inference query of like look up this company, figure out where it is across a thousand or 2,000 uh individual rows and something like that's highly parallelizable if you want that to happen really fast. It might not need a GPD 5.5 class model, right? Uh it might be okay uh to run faster on a smaller model that works on Cerebris. Uh and so it's hard to predict the exact mix of chips that will power large networks of agents. Uh but uh these different designs to me they seem more complimentary than directly competitive. Like a year or two ago when uh Daniel Gross wrote AGI Bets and was sort of like is Nvidia underpriced? I don't know if Nvidia he's might have said that on on semi on stratey but uh like you know we we entered the AI age and everyone was like oh GPUs are the future. Nvidia is the company, but then it was like Nvidia GPUs are good and then also CPUs are good and and ARM is getting into it and Intel's doing very well and it it >> we're going to make big computers >> big computers big computers for sure. Uh and so uh I'm extremely optimistic obviously and very excited to talk to uh Andrew Feldman, Douglofflin, Eric Visra, a bunch of folks who have been involved with the journey overnight success series a >> wow conviction >> in uh almost a decade ago 2016 uh and uh benchmark are sitting on I guess as of this morning like many many billions of >> cerebras. It feels like they brought a huge team to the NASDAQ. Look at this photo, the second post we have. Um, uh, obviously Andrew is there. A lot of the team members, typically the the the the key banker, but, uh, you know, we we've been to some IPOs and, uh, some of them have have had smaller teams. This one feels extremely celebratory and, uh, feels like a very broad inclusive crew came together. Um uh what else? >> And the class B shareholders uh have 99% of the corporate voting power. So founders in control. >> Founders in control. >> Founder. >> Uh Honam says, "This IPO illustrates the power of an individual partner over the brand name of the firm. Pierre Leond was a partner at both Sequoia and Kla, but instead of those firm backing Cerebras, it was Eclipse, the firm he joined at the age of 84 that backed this littleknown chip company multiple times in the early days. What a way to wrap up a career. He was born in 1930, the same year as Warren Buffett. >> Wow, that is an awesome story. I love that. Uh uh Matthew Seagull is giving the recovering CFA is giving some color on what happened to the order book. uh onethird of the order book, the folks that said I want shares in the Cerebrus IPO, onethird of the book got zero. And the top 25, I guess the top 25 investors took 60%. That's probably the big investment funds, the Fidelities, the State Streets, the Black. >> They have done quite well today. This picture looks wildly different than the CLA IPO last year in which >> uh only a handful of the team at CLA popped over at the NY IPOed and went back >> back home. >> Yeah, it was very much just like another day at the office for the team. Uh yeah, that's definitely what I was contrasting it to. uh the Cerebra's valuation every round series A in 2016 $und00 million foundation benchmark and Eclipse co2 led the series B in 2016 V capital led the series C in 2017 then 1.6 billion valuation in 2018 2.4 four in 2019. Uh 4 billion in 2021. Uh that that was like maybe a little bit of a slump. But then 2025 trades and Fidelity come in at 8 billion. Then Tiger comes in at $23 billion. Then in May of 2026, they IPO at 48.8 billion. I can sort of just do the work myself. I don't I don't >> I don't need any help. I don't need the soundboard to do it. Am I doing it for real or am I using the soundboard? You don't know. >> Incredible work from Tiger. >> Yeah. >> Coming in at 23 post. >> Very, very good. >> And uh now up dramatically in just four months. >> Very, very good. Well, our first guest is joining us in eight minutes. Uh let's run through uh the Kevin Walsh news because he has been confirmed as the Fed chair. Uh and we'll run through this and then we will come back to some of the other stories uh because we have a gap later in the show. So uh Kevin Walsh who uh is most famous for interviewing Alex Karp on CNBC while Alex Karp appeared to have popped a nicotine pouch and then spun a notebook on his finger. Did you ever find that clip, Tyler? Is that in the >> Yeah, it should be timeline. >> Let's >> Yeah, we have the the video here. >> It really put Kevin W on the map because uh this is what he's known for. Of course, he's >> recently sales. And I remember I showed up in your office once. I was dressed like this and I think you screamed at one of the guys. You said, "Kevin's here. He looks like the guy from IBM." And I was talking about, well, you know, we need like really finance controls and, you know, how you going to sell the product and all this stuff. >> Okay. >> But I would say you certainly built that. >> He's really spinning it. I didn't realize he goes back to it like four times. >> A dirty word anymore. He's >> really good at this. >> But somehow you grafted that on to the to to the strange company that can produce these products. How's that transition been if I've got it right? >> Okay. Wait, wait. But so, uh, I have so many questions. First, we have to get him to recreate that for sure. Um, second, uh, I I thought Tyler, I thought we were talking about that being on CNBC, but that looks like just a podcast. Like that doesn't have any Chiron or >> Yeah. No, I I don't think it was actually on I think it was from Palunteer. Like that was a Palunteer. >> Oh, okay. So, it was just like a random podcast and then and then uh when I've seen it on CNBC, they were playing the clip. Got it. >> I think so. Yeah, >> probably re a reaction stream over there. Um well, let's go through what happened because Kevin Walsh has been confirmed as the new Fed chair. The vote was 54-4 in the Senate. Uh the divided vote signals challenges ahead for Worsh who faces a Fed committee skeptical of rate cuts that Trump has demanded. And of course, we talked about the inflation news. Typically, you don't cut interest rates uh going into inflation and potentially economic stagnation. uh you definitely don't cut rates in that's why stagflation is so difficult because if you have uh stagnation and low inflation you can cut rates very easily maybe the economy starts overheating a little bit you get a little bit of inflation but then you can pull back that's what we've done historically vice versa if the economy is running hot you're seeing high GDP growth and high inflation well if you raise rates you're going to pull back on both of those but in stagflation you're seeing both inflation and st and economic stagnation harder to deal with as a Fed chairman, which is potentially the task he will be faced with. So, uh, the Senate confirmed Kevin Walsh as the Federal Reserve's 17th chair Wednesday in a largely partyline vote that require that reflected how tensions with the White House have dragged the Fed deeper into the political fray. I was looking back at the old Fed chairs. There's some absolutely legends in there because some of them had really long runs. So very quickly you get back to the black and white portrait and the painting as you go back in time. >> Who's your favorite fed chair? >> Vulkar. >> Yeah, Vulkar is pretty goated. Bernani is crazy. >> An absolute dog. >> Um yeah, I don't know. Hard to pick. Hard to pick. Uh Wars, who was known, who was nominated by for the post by President Trump in January, won confirmations, uh 54 to 45, earning support from all Senate Republicans, but just one Democrat, John Federman of Pennsylvania. Uh, Senator Kristen Gillibrand of New York did not vote. No Fed chair has been confirmed by such a narrow margin since Senate approval became a requirement for the job in 1977. Chair Jerome Powell, whose leadership tenure whose leadership tenure ends Friday, captured at least 80 votes in Senate confirmations for each of his two terms at top the Fed. Wow. Jerome Pal just fan favorite of both teams. 80 votes in the Senate. That's pretty significant. >> Pretty cool. >> Yeah. Is he going to be looked at as >> a future podcast >> throughout history? >> Get a maybe VC fund going. What do we think? >> No, but what but uh when you look when when we look back like it seems like the last two three years he's handled himself. >> Yes. >> I mean he's he's had a really tough situation and he's >> When did he land the plane? When did Jerome Powell get be first become the uh the Fed chair? Uh when when was he? He's been assumed office 2012. So I think the uh wait oh >> 2018 >> 2018. Uh he was right he was right after Janet Yellen. And so I think I I I'm I'm I'm putting him in the conversation, Jordy, but I'm not giving him the GOAT trophy. uh nearly because the the challenges faced he wasn't confronted with a great recession, a dot bubble bursting, a uh a black Friday. uh he like he like the economy from 2018 to today >> global pandemic doesn't you don't you don't count a shutdown of >> no because no I I I I actually don't because the economy was pretty strong in 2019 and it went into it went into 2020 with uh pretty strong consumer balance sheets low debt there wasn't a shadow banking economy there was no bomb in the US economy waiting to explode And so although we saw high unemployment briefly and we did have to stimulate the economy, that's not his job. His job was to set rates. They there was a little bit of like I mean maybe you put the inflation you know the the end the Zerp era and the end of the Zerp era and all of those girrations on him but those the problems that were downstream of both the Zerp era and the end of the Zerp era were suffered mostly and benefited mostly on like tech companies and Silicon Valley companies that had really long cash flow horizons. And so there was not a moment where it was a dire situation that the Fed had to intervene in a meaningful way and like save the economy like in 2008. Like it it it's a big deal. He did great job but he didn't he wasn't faced with the same challenges of a Bernani for example. That's what I would say. Tyler, what do you think? >> Yeah, I think that's reasonable. Also like you know if if if Powell was worse at his job and you saw some crazy crash because of COVID and then he brought it back like then it'd be like oh yeah he did face this massive thing but because he did you know such a good job maybe you didn't see any like massive crash. So so like the the the like nothing super bad happening is evidence that he was really good as a Fed chairman right? >> Uh yeah yeah maybe he's a defensive back. uh you know, if they don't score, he there's no great plays because he's just shut down shut down cornerback for the last couple years. Uh potentially, uh he's definitely in the top 17. I'll give him that. Uh anyway, uh Kevin Walsh, uh chair Jerome Pal, whose leadership tenure ends Friday, captured at least 80 votes in the Senate. Uh the previous chair, Janet Yellen, was a little bit more controversial, confirmed 56 to 26. Seems like not that many people showed up in 2014 to vote for Janet Yellen. uh with many senators absent because of bad weather. Interesting. I wonder what would have happened. I mean, it feels like she would have cleared it no matter what. But um a difficult economic dra backdrop and Trump's broadsides against the Fed independence have set up the central bank for a thorny leadership transition. Uh Senate committee confirmation hearing last month. Worsh faced intense questioning from Democrats over how he would maintain the Fed's independence from a president who places priority on personal loyalty. Worsh said he would preserve the central bank's monetary independence and that he had made Trump no promises about policy decisions. Powell citing concerns about political attacks on the institution plans to remain on the Fed's board of governors. Defying Trump's insistence that he leave, he says, "You're going to have to drag me out of here. I'm staying at the Fed, says Jerome Pal. Uh Walsh is 56. He's been immersed in monetary policy debates for decades, frequently as an outspoken critic of the Fed. Former Morgan Stanley investment banker. He became the youngest Fed governor in history at 35 when former President George W. Bush appointed him to the central bank's board in 2006. During the financial c crisis that struck two years later, he played a key role in tying up rescue deals. He was the bridge between Wall Street and the Fed uh that sort of Bernani deployed and so that's what that those are his laurels that he will not be resting on but he will be drawing on from experience. So Worsh left the Fed in 2011 had be he had become a critic of its direction concerned that as the economy recovered the Fed's ongoing efforts to support financial markets went too far. So, we will have to check in with the progress on uh Fed uh Fed Chairman Kevin Walsh soon, but fortunately we have our first guest of the show, Amy Reinhardt 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 >> fantastically? >> It is an honor to have you here. Our first ever guest from Netflix, >> I think. So, thank you so much for taking the time. only took like 2,000 interviews to get to get you guys on here, but we're we're excited to meet >> to be the first. >> Yes. Yes. Uh I mean obviously big fans uh of both Netflix and advertising. Uh but would love to start with a little bit of uh background on yourself, your experience, uh and just sort of uh your intro to uh how you found yourself as the president of ads at Netflix today. >> Sure. I've been uh at Netflix for about nine and a half years now uh in a couple of different roles. Started out first in our content organization uh doing both licensing and then overseeing production and about two and a half years ago I stepped into this role uh overseeing our ads tier and it's been a fantastic uh two and a half years. A lot of excitement feels like we've uh been able to accomplish a lot and great company. So, >> if you take us back to the initial uh the initial push into ads, uh what can you tell us about the trade-offs, the build versus buy debates that were going on at the time, the just the maybe even the cultural changes? Uh I think we are super uh you know, we we we love ads. We we think it'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 uh publicized, you know, that uh not being in advertising was a strategic bet for a long time, right? And so, uh early in 2021, 2022, um when we started to talk about the notion of getting into this business, uh yeah, it created a lot of I think fair to say, you know, angst within the company for a bit of time. Um because it was such a big shift. uh to your point, culturally uh 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. Um and that got us up and running. Uh 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 nent our tech stack is because we've been be able to deliver so many developments um 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 uh 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 lowcost option um and 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 we which we just announced or upfront yesterday that we're expanding that ad tier >> into 15 more countries around the world in 2020 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 like performance focus? Like what what is your pitch to advertisers? >> Yeah. Uh you know, as we see in the marketplace, advertisers are um oriented around outcomes, right? So we know that we need to be a fullfunnel 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. Uh but we've also seen really good um conversions in terms of lowerfunnel and making sure that we're driving uh purchase intent um and consideration. So uh as we build out more of our solutions, we are going after that whole funnel solution. I what are conversations like around how brands should h how much uh brands should want to associate with particular uh pieces of content because I think some brands might come in and say well I'm I'm advertising you know strollers and I know that parents will be watching K-pop demon hunters with their kids and so like this is the most on then directed and and I care I want my brand linked to this particular piece of content. Uh but we've seen time and time again that uh like once the algorithms get good enough once uh dynamic ad placement can actually flourish you every company uh tends to see better performance there. Uh so where where are the ad buyers today in terms of those tradeoffs? >> 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 often times the easiest to sell. Um, 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 hit is going to come from. And so selling those audiences, selling that uh 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 temples and we want to, you know, provide those opportunities. >> Yeah. Uh I mean it's interesting Netflix has like deep deep uh experience in machine learning AI recommendations all all parts of like you know high throughput data processing. Uh but I'm 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 you a lot of companies have have been successful at scaling content and then and then struggle to figure out ad delivery. You obviously haven't. Um 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 in what where uh where the buildout didn't match your expectations or surprised you. Um, you know, as a tech company, we do a lot of testing and we go into things with hypotheses. So, we're constantly testing things uh 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 uh you know where that those hypothesis prove out where we need to pivot and and you know change swiftly. So there it's hard to point to one uh 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. Um, and we've never this, you know, you talked about kind of uh getting into ad sales, right? We've never had a sales uh uh 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 te tech cycle of testing and learning and iterating. How are you thinking about uh the the the the ad product feeding back into uh the content production? I've noticed that uh Netflix has been fantastic in my opinion of uh creating more engaging content. And I was watching uh the RIP uh with Matt Damon and you click the play button and you see Matt Damon's face in 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 watch 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 uh 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? Is are you seeing glimmers of what the future like the impact of ads might have on 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 um 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 you an example. Um Shauna Rimes 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. >> Uh 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. Yeah. >> Um we're able to adapt to to any way that our creators want to to write the content and fit it into that member experience. >> Yeah. uh for US markets. Is this uh is is there enter any enterprise spending? I would imagine that a lot of enterprise buyers like have been Netflix subscribers for a really long time and um maybe they 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? Um we've most of our clients uh right now the the target segment that we're going after are enterprise top 400 clients right because we think those are the ones who uh >> Oh sorry I meant I meant I meant B2B versus like BTOC companies. >> Oh um we think about this more as a BTOC opportunity uh for the most part and I think as we expand our learning and expand our offering uh may get into the B2B space but for the most part B toC. >> Yeah. Yeah. I I I I feel like all of that like the the higher up market more targeted that's all unlocked with scale once more there's more learnings on responses. Uh I'm wondering what other signals you think uh might be valuable because uh many times you know advertises 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 like how I it I imagine that the data is messy, but how important is it to sort of close that loop in an AT era where it's a little bit trickier, but there's a lot of things that you can do on the 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. Um what does that screen experience look like? Um you know, again, how do you meet the customers where they're at without being sort of intrusive? um a lot of testing going on in the 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 and taking care of our members data. >> Yeah. >> Uh but a lot more I think to come. Uh, >> have you been surprised by the return of the QR code in uh maybe podcast advertising, but I see it a lot because people are watching on uh you know you they'll watch a YouTube video on a TV and the creator will you know hardcode in a QR code to link out and that was something I 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 the uh 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. Now it's all integrated and so someone can just whip out their phone and run right to it. something else. >> Uh nothing nothing super top of mind right now. I I mean uh last question I had is uh do you ever expect Netflix to serve uh more short form vertical video style ads and something like the clips tab? I know the clips tab right now is focused on uh basically like content discovery. Uh but I imagine in the future people will spend more time in in a format like that especially on mobile. >> Absolutely. And that is one of the announcements we had at our upfront yesterday is that uh as we roll out this vertical video uh content that we are going to be offering that to advertisers uh along with our todom.com um coverage in 2027. So yes, we think that is a a big opportunity too. >> Uh last question for me uh I would love to know about the intersection between uh games and ads. uh that's been a huge huge uh growth driver in other with other categories and other companies. Uh but I'm wondering uh where that is in the road map how you're thinking about that. >> We haven't thought about that yet. Look, our road map I could fill our road map for the next uh two to three years based on just some of the foundational things we want to do and and a lot of the innovative areas we want to lean into. >> Um but it's an area that we're keeping an eye on. Um, and as we watch that game's engagement uh increase, uh, I wouldn't never say never. I've learned to never say never at Netflix, but it's not something that's on the near-term road map. >> Okay. Thank you. Uh, well, thank you so much for joining. This was a really >> great to meet you, Amy. Thanks for breaking it down. Progress for us. >> We'll talk to you soon. >> Cheers. Have a good one. >> Thank you. >> Uh, moving on from Kevin Wars, who is the newest Fed chair. Uh, we have a debate. We have a debate going on in the timeline around general catalysts advertisement. Um some are calling it an attack ad. >> Uh particularly Andre Horwitz is calling it an attack ad. We touched on this yesterday. Uh but if you did not tune in uh General Catalyst, the largecaled what do they call it? Gigaf funds now what do they callatform? Platform fund but there's something else. It's a huge huge venture capital firm. Uh they launched an advertisement which we can play again to refresh everyone. Let's scroll down to the beginning audio. It's only Thursday. >> Hi, I'm GC. >> And I'm VC. >> Who's your friend here, VC? >> This is Woof AI, an AI native companion platform that combines robotics and machine learning. You'll never want a real dog after this. >> Well, I think people like dogs as they already are, though. VC, >> you don't need to walk it. You never need to tell the kids you sent Milo to the farm. We're leading the seed and could probably make room for you. Well, I'd love to hear more, but we actually have a really high bar around responsibility for these things. >> Is Wolf AI okay? >> Of course, he's fine. >> Oh, sorry, buddy. >> Easy, easy. >> Stay. Stay. No. No. Stop. Stop. Stop. Stop. >> I'm sure you'll be fine. >> Okay. Tons of thoughts, but you kick it off. What's your read on this? Take me through it. >> So, the the the actual ad, >> the way it's shot, >> Mhm. >> the timing >> Mhm. >> cinematography. >> Mhm. >> It's fantastic. Uh I do uh >> uh I think that I think the ending is I think the ending is funny, right? It makes me smile a little bit. The dog's going uh haywire. The robot dog's going haywire. My first thought is I actually think there's a huge opportunity for a robot dog that is 10 times better than existing robot dogs. There are robot dog toys out there. So I don't >> specifically in the toy market because this is a Boston this is probably I mean it seems like it's a Boston Dynamics robot dog and um those are not typically used as pets that I know of. Uh, I think that they are uh more deployed. Whenever I see a demo of a Boston Dynamic robot dog, it's like walking through a nuclear power plant that you don't want someone walking into. It's like an industrial product for the most part. Uh, and then obviously a a fodder for viral videos. Um, but uh, yeah, step through it. Uh, because it is a crazy back and forth from both firms. First, the stats. General Catalyst. Oh, they're over a thousand likes now, but two million views. So lot of discussion. Any midhoff says it's a bit cringe guys. Olivia Moore at Andre says if I was a consumer founder I would run for the hills watching this weird take from a fund that returns so much money from Airbnb and snap because is the idea that like robot dog is a weird idea but so was airbed and breakfast or Snapchat, right? Um the biggest B TOC businesses always start out looking weird. Yeah, there's a way to there's a way to do the same pitch for air bed and breakfast, which is you know that empty couch, >> you know that empty room in your house? >> What if you were to monetize it? Yeah. >> What if you were to allow strangers to sleep in the empty uh room in your house? Uh and and uh of course that was a lot of the criticism at the time was that that it never was going to work. uh and it was strange but then it created a lot of positive externalities. People built >> businesses, people get to travel and integrate with local communities. Um yeah. So so the main thing here is like it's it's um >> I do find it uh I do find it fascinating considering that when you actually look at the portfolio overlap it is insane. Almost all of their winners >> Yeah. Yeah. almost all of their winners, especially in the modern era. Sure. >> They are in both companies, right? >> Of course. >> And so they're backing a lot of the same companies. So it's super hard to counter position against them. Now granted, >> A16Z has a very different media strategy. They're they're very loud. They're all saying the R word a lot. Uh, and I think GC can kind of counterposition against that, but you're not gonna counterposition against >> like what companies you're backing, right? GC GC I I had to look this up because I thought there's no way this is true. GC's in both uh Ky and Poly Market >> uh big great companies, right? >> Those have been at the center of the debates. >> Um but that is the center that those companies are at the center of the moral debate. >> Yeah. >> In tech, right? Certainly for the venture-packed controversial companies, there are some that are controversial that are not. >> Yeah. And all the funds have backed various like betting trading related companies over the years. I think it was like GV did >> what was it um uh not DraftKings but FanDuel I think back in the day. >> So it's not to say >> there's companies that start out as controversial and then they become true like completely normalized and everyone sort of comes around to like oh that's a good thing like Ander is a good example. Both of these company, both Andre and GC are in anderol. When it launched, it was like, wait, you're building defense technology? That's insane. And now the whole Silicon Valley community has come around to the idea that it's really important to have a functional defense uh industry. Uh and then on the other side, you have companies that come out as really controversial and uh and people are like this is the end of the world and then they just sort of fizzle out. Like Cluey would be a good example of a company that like has done okay, but it's not like oh no one's doing homework. It's completely upended education and it's so successful and it's bad. It's like no, it's like it was there was a lot of saber rattling around that. Sora went through a similar thing where everyone was like this is going to wirehead everyone and then it was like h just like some funny memes and ultimately you know moved on from it. Uh and and so it's it's very hard to map like the controversiality to the ultimate outcome and where it lands. Like um a lot of the a lot of prediction markets were not controversial when they were just predicting the election. That that was not what was controversial. Tech started being controversial about it or saying it's controversial once it got into sports betting. >> Yeah. So the ad is interesting from a couple ways. one is trying to counter position against >> GC being like the cool hip fund >> that is that that wouldn't back the robot dog company even though when you look at the portfolios right there's enough overlap that you can safely say if a company is ripping or has a lot of potential they're probably both interested investing in it regardless of what category it's really in right um and then the other thing is that like >> historically like A16Z is the firm that's trying to be like hip and cool and loud and and do like new media and GC is the one that I've always viewed as like more buttoned up, more behind the scenes, more traditional traditional fin. And I love that we love that. >> I think Capital is a good example of another another firm that's been like pretty straight laced but still has like aura behind it and it's like they have a private equity fund attached to the venture the venture fund and like Bane's done some fun stuff. I mean, Fogo de Chow and they've leaned into things every once in a while, but they've never been like, "Oh, we're going to be the, you know, the the the craziest brand strategy." It's been like, "Yeah, buy the book investors. We find great companies, back them, and uh, you know, Sequoia's done that, too. You know, that their whole pitch is very austere, and that's worked for a long time. It doesn't need to pivot the brand, which is maybe what this is like signaling for towards. This is the first time I mean it's very rare for any VC to like take shots directly at another VC just because they're all syndicating deals to each other. >> It's way more direct. I mean a lot of people clocked it but because the actor could look I guess if you were far away enough like >> yeah Steve Balmer looks like Steve Balmer >> or Mark Andre >> but people are saying Mark Andre >> but I was looking so hold on I was looking at Mark Andre photos and I could not find a single photo of him in a vest. So, I don't know about this, but clearly uh Mark Andre uh did take issue with it because he uh he quote tweeted it something like 45 times. Uh which which, you know, really amplifies it and creates more of a conversation about it. Uh strategically, you might have just wanted to mute this if you don't want uh this to become a thing. But maybe he does. Maybe he's like, "Yeah, actually, I do want to fight because like this is dumb and I'm going to fight back and I'm going to win." So, you know, that's the strategy. Uh but it does seem like messy rolling around in the mud when you're when when you're uh when you're wrestling with pigs, you're gonna roll you're gonna get muddy, right? Isn't that saying? Yeah, it's it's just it's just um again like creatively it looks great and it's very fresh >> and I think Reggie and his team did did great work, but it's funny to just like take shots at a whole category of investing basically. That is >> Yeah. >> kind of the bread and butter of GC's business. >> Yeah. Yeah. Drawing drawing the line in the sand. What what is the actual line? the uh the really high bar for responsibility around these things like because >> first off it's just odd because like robot dog feels very very low on like the responsibility like if some VC was like okay we take responsibility really >> yeah so I for example the last robot >> I would be like don't fund gambling don't fund uh cannabis like don't upset Sager and Jetty basically it's like what the way I would the way I would pitch someone if they were trying to be the responsible actor fun and robot dog would be fine. >> The last robot dog pitch deck I saw was a company that wanted to use robot dogs as a replacement for actual uh seeing eye dogs. >> Seeing eye dogs are like incredibly expensive. um you know uh kind of out of out of reach for for uh many people and uh and so the opportunity for a robot dog that can maybe you know travel with you like on a plane without you know barking and needing to eat right like that's actually like a very world positive um >> uh uh kind of uh endeavor I don't know if it'll you know work or what the business will look like but uh but in general like there's a you know anyway so >> I'm extreme really bullish on robot dogs. I think it's very complimentary. Think about I mean especially if it's a it's a toy for kids. Uh think about how many vehicles kids have. >> Gabe says service dogs can reach 60K. >> Whoa. >> And that doesn't count lifetime of all the other costs associated with dog ownership with >> next with a service dog. >> Flex like whoa buddy. You to flex like that. Um, no. Uh, the the 60 uh the Yeah. I mean, K kids have like, you know, a bicycle, a tricycle, RC car that drives, and then another one. And like it's just like like you you throw the robot dog in the mix. I feel like that's going to be in addition to a dog. Robot dog rips is what you said when I said that. This is going maybe a thousand robot dog startups will flourish. And uh and the most ironic scenario is because it the industry will become so deniable. The general catalyst and Andre both have to back it and they're like duking it out for allocation at every round and the robot and the future robot dog founder out there is like I I just do nothing win or build a great company and win. Um was there anything else going on with the robot dog debate? Uh I you know selfishly uh you know I think it's bad for the industry if you have two of our platform funds uh you know just making these somewhat petty videos that are basically ad hominins >> uh that you know st basically um throwing stones from glass castles uh as >> uh one one could put it. Uh, so I think it's generally bad, but for entertainment purposes, if they want to keep going at it and turn this into, you know, proper Drake versus Kendrick situation, this might be a good use of a 16Z new media. Yeah. >> Although much of the talent there is now over at uh >> I mean, I would I would love Yeah, I would love like a like a response video from Andre shot in the same way or something like there there is an opportunity for like a rap battle here. That's a beef that's like very very entertaining. Uh that I would absolutely love. Um uh the flip side though is that I agree with you like uh if you're a VC you're much better uh picking a villain that is uh something around like a lack of technological progress. Like Teal did this very well with like stagnation is the boogeyman. Like we if we don't get the robot dog, we don't get the seeing eye dog, we don't cure cancer, we don't do the big thing, we don't we don't visit Mars. like that is you can still have like a villain but the villain needs to be something that the the industry and America and all the constituents can can align around instead of like picking fights where like these two firms are obviously aligned on like 99% of like where the future goes and and what the goal is of building a business that delivers a value that consumers enjoy. >> Yeah. Which is so funny because when you if you look at cap tables, >> they're almost always probably touching. They're effectively holding hands on the cap table, right? Cuz like one of them might have more of one company, right? Slightly higher. One of them might have more of the other, right? But in in general, you know, they're just hanging out. >> Oil did Ander at GC and then went over to Andre. And so like there's like there's more overlap than uh than than uh than differences. Uh plenty of plenty of other things to take shots at. Uh but I mean uh to Andrea's credit, they've done a good job of that. Focusing on on uh geopolitical competition, uh focusing on China, focusing on stagnation and nimism, all sorts of different things, uh that they've been aligned with like more of like an abundance view as opposed to punching down. Although, you know, there's there's mometics all over the place. It happens. >> Yeah. Just it seems like GC has an opportunity to be the buttoned up >> Yeah. >> platform. I think so. uh you know they don't they don't need to say the rword uh they don't they don't need to be super loud right they can just focus on >> the craft of investing uh uh I wrote about it in the newsletter but you know run some ads you know you've been saying this forever run some ads in the Economist Financial Times Wall Street Journal >> uh just >> there's a way to counterposition yourself without attacking >> your remember the Last time General Catalyst went viral, >> Hamont CEO was on Harry Stebings show 20BC VC and he said like triple triple double double double is no longer good enough. Uh we want to see 10xing every year because that's what's happening in the AI era. And it was a brash statement. It was bold. Uh it's smart to debate looks >> but look at the results like you know like there are companies that are doing this. like we talk to them every day and and sure he and and he clarified it on our show. He clarified it on other shows. He was not saying that like you shouldn't build a business that only triples revenue every year or only doubles revenue every year. He was just saying that the reality of the market right now is that there are power law companies that are growing uh exceptionally fast, un unprecedentedly fast. And so, uh, you as a venture capitalist have to adjust your benchmarks and think about how you're allocating funds, what companies you're investing in, make sure you're in the the best company in the category that's actually going to win. And it might be the company that's growing 10x a year, uh, not 2x a year. And so, uh, that was something where it was, uh, like thought leadership from Hamont. It sparked a conversation. There was some debate around it but it was >> so far that take was controversial but entirely correct when you look at the growth of >> a lot of the most exciting companies in the industry right now. >> Totally. Totally. And so uh that that sort of more more narrow f staying in the lane view. Uh it got a lot of attention. It did break through. It caused a conversation but it still didn't sacrifice it. It didn't feel like oh he's taking shots at someone specific right? It was just like a market analysis from someone in a position to give that exact analysis. So anyway, uh Jensen Wong is over in China. Jason Calacanis has a photo that looks uh extremely real. Zero AI detected, but he's bringing two huge boxes of GeForce RTX 5090s, which are >> This is a picture from when he was in Alaska, too. >> Uh Jason says, "Never stop selling." I I agree. Uh there is some news which we will cover later uh in the week around uh the dynamics around H100 sales uh and blackwells what's actually happening. It's all in flux as the Trump China summit uh plays out on the front page of the Wall Street Journal every day this week because it is headline news. High stakes US China summit kicks off. Um, there was another drama in the tech world yesterday, but we'll come back to it after our next guest, Ben Hilac 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 on the show before. Welcome back, Ben. How you doing >> well, man. How are you? >> Fantastic. >> Great to see you. >> What's long time in your world? Uh, reintroduce the company quickly and then tell us the news. >> Sure. uh raindrop uh we make observability for agents. So the main thing we do is self-healing agents. So what it means is that when you're raindrop hits a problem in production we detect it, we fix it. >> How do you do it? >> That's a good question. Uh so at the end of the day like uh it's we consider ourselves like the intelligence for your intelligence. M >> what that means is that we are the best and fastest way to uh essentially look at anomalies. So >> what that means is that like 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 uh starting to evolve into a different pattern. >> Um and so it's kind of a combination of agents but also more like classic ML uh techniques a lot of like customtrained models for every customer. uh walk me through the shape of the agent market right now like the way you're talking about it uh you know sort of illustrates the broad diffusion of agents and custom agents. I think that a lot of people think cloud code and codeex and I don't know if you're doing enterprise deals with those firms or that's the goal but I imagine that uh every startup many legacy companies have built some sort of agent some sort of harness and I'd love to know the the shape of how broadly diffusing like custom agents are uh in companies versus um uh is it the domain purely of startups that create an agent for legal or an agent for sales and then they vend that into a company. >> Yeah. So I would say that there's two kind of categories of customers. Uh we started with super high growth uh startups at the time startups. So those are companies like Clay for example, uh Framer, Speak.com, some of the fastest growing companies in the world and uh those were some of our earliest customers and we're lucky they grew uh a ton. Uh 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 h you have to be pretty pretty picky not to work with companies that are going to die. Um because if like especially analytics like all these sort of things like they you 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 your >> I had a I had a portfolio company that was working on like a 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 I think it was very counterintuitive at the time, but I think we chose to chose to find companies like Clay.com, right, which are uh were clearly on a insane trajectory, but at the time were, you know, weren't necessarily uh as large. And so I think a lot of our customers uh now are pretty large, but at the time weren't necessarily as large. Um and then in the last few months, we've been moving into Fortune50s, Fortune 100s, and like a lot of amazing things happening there. And again it's kind of like two shapes of a product like one is like in our our bread and butter is like you know companies that are redefining the way people you know interact you know in different verticals. Uh but then yeah there are like fortune50s fortune 100s that are also deploying agents internally. I think the shape of that looks very interesting and like it's something that uh like being on the forefront of like understanding how these companies are deploying things like there's not that much I can talk about right now but um yeah always very interesting. Uh, what do you think is a generally underhyped agent category right now? I'm sure you're seeing the future a little bit. It's a really good question. Um, I think that I mean uh you know I I this is a tough question. Uh I I what I want to do actually is pivot the question a little bit because I want to talk about I want to talk about our launch today if that's okay. Okay with you guys. >> Yeah. >> Okay. Uh >> 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. I'm I'm just messing around. Go for it. >> Okay. Cool. >> The joke is Yeah, I botched it. >> The joke is what questions do you have for my answers? And some CEOs show up and they act like that where it's like you could ask me anything and they're just gonna redirect to a top point. But it's fine. I want to hear about the launch today. So just tell us about it. Let's talk about it. >> Okay. So guys, there's been this crazy thing that 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. >> Mhm. >> You're building them locally. Like you're using some sort of SDK. It could be open, it could be for cells, whatever SDK it is. And >> what do you mean locally like like it actually has to read on your laptop or >> 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-reational database. >> They'll they'll just print it to to you know console.log log like, oh, here's what was happening. It's like that bad. >> 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 and like whatever it looks terrible. >> But then also your coding agent can't like see the traces either. So then when you hit a problem and you're like, hey, you know, this response was wrong. Flawed Code will just make [ __ ] up. Like it'll just be like, oh, I think that like maybe this tool was wrong or I think maybe like this happened. Cuz it doesn't have any of that data. doesn't actually know what the coding agent did. Yeah. >> So, I think that like >> as someone building agents, as like our company building agents, we're like it it's actually kind of embarrassing how long it took us to solve this problem. No one else solved it either, but um but yeah, that's that's what we launched today. Free local open source tool. Raindrop.aiworkshop. >> And uh it's completely free. Like 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 like why competitors like haven't done it. I mean, there's probably other reasons for that as well, but um I think it's that it can be right like like someone else can do it. You know what I mean? Like uh uh I think that it it running locally is the best experience for people. Mhm. >> And to be clear, like there's still things that it enables, you know, if you connect it to your production raindrop, which is like you can pull in a remote trace and replay it. Uh, and then cloud cloud code or codeex can just keep doing that loop until it works. So there's still benefits for us. Um, but also the the truth is that we want people to hack it. We want people to to meld it into whatever works for them. So um, I mean we use a lot of open source things here, right? So it it makes sense to to contribute back as well. Yeah, that's great. >> Um, yeah, I I'm I'm wondering about other uh just like predictions about the next breakout category of AI agents, what you're seeing. Uh >> 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 Chesy earlier about Airbnb. Oh, yeah. Talk about that. >> We talk about that. No. Um I think like you know, I use Airbnb a lot. I love Airbnb. Um I think uh if I had to guess, I would say Brian Chesy knows a lot more about Airbnb than I do and probably a lot more about being a founder than I do as well. Um and so I think there's probably a lot that I'm not considering. >> That being said, I think it's fr like uh if Airbnb had an API, I would use it and I would book Airbnbs with it like through cloud code, right? So it's like I know I would do it. >> Um it's I find Airbnb very very hard to search. Um, and um, I think that there's a lot of I think the tough part and like what I see industry widewide right now, everyone trying to figure out is you you see companies almost reducing themselves into an API with like absolutely no mode. Like you look at like Photoshop, Illustrator, etc. They're like, "Oh, we have a cloud code integration now. MCP at the point where people are just using Photoshop, Illustrator, etc. as like an MCP, they 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 um because they have no other choice. I think that there will be a point where the incentives don't make sense anymore. Like I can give an anecdote from from when I was at Apple. Uh you know, do you guys remember app clips? >> Yeah. >> Yeah. >> Here's my app. Very few. >> Where did those go? I only see them go with like parking meters sometimes, I think. >> 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 the app go order your drink, and it's like, turns out Starbucks doesn't want that, right? Like, that's the last thing in the world Starbucks wants. Starbucks wants you to download the app. They want you to have stars. They have an entire like there's a reason why Door Dash 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 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 Door Dash because I I really think that uh these marketplaces provide you know an exceptional amount all the value is not in the UI right >> agree and where and and like the value of Starbucks is not that it's a pretty app it's because they have specific drinks that they can like pretty much anywhere you know someone would be. >> Yeah, I think of that company by the the drink company. Uh 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 on the online and if you went to their website, it would just say go to Amazon. Uh and they would and they did fine. Billion dollar company. Uh and because like the value is not in the e-commerce experience. They didn't play like the 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 what the >> 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 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 uh one of like the large coding companies as 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 be doing that. We're going to be the layer that's really good at finding those issues, diagnosing them, and tracking them. So, um 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 those all those hooks without the you know knowing everyone's email like having the mailing list like all that sort of stuff. Um so that that's a very interesting trend I'm looking at. >> 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 uh give me a a reality check a health check on your experience with 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 like treat the front end as the API effectively you know puppeteer the computer through computer use like where are you on like uh the AGI moment in computer use from what you've seen like 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. Um, there's definitely places where it works. Like I think that Codeex has done a very good job of implementing like browser use actually. Um, both for like debugging applications that you're working on and in general like this is something that Claude just like doesn't do like 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, right? like how do you create loops where it's how do you close the loop? How do you have like cloud code make a UI change, see that it sucks and then just keep going, right? >> Um and a lot of like do we have AGI or not is how many loops in a row can you do before >> right before things just end catastrophically, right? >> Um because there is sort of this like uh uh it gets worse, right, in many cases. Um >> so um yeah, I think like there's a lot of like ways to answer this like I I'm a fairly like security conscious person. I think that like the uh 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 uh etc. etc. Um but yeah I think >> DVD. Yeah. >> 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. Uh anyway, thank you so much for coming on the show. >> Great to see you, Ben. Congrats to the team. >> Congrats on course. Last thing, if you want a hat, uh you can. We have a new CLI. You can run raindrop drip. You can get a hat, an umbrella, a couple other things. >> That's a fun that's a fun way to give out merch. I like that. That's very creative. >> Uh, thanks for coming on the show. Great stuff. We'll talk to you soon. >> Okay, back to the debate around Figure. We've had Brett Adcock on the show before and he had a live stream. We talked about it a little bit. Uh, watch a team of humanoid robots running a full eight hour shift at human performance levels. And Brett Adcock said, "This is fully autonomous running Helix 2." >> All right. Pull up pull up this post from Pete. >> Yes. And the the stream did fantastically. It was 24 hours. It got 3.4 million views. But at a certain point during the stream, there was some questions about whether or not the humanoid robot was in fact >> back to the beginning. Back to the beginning. >> Okay, let's play this. >> All right, so it's cooking. I mean the speed is actually >> and we were extremely impressed by this. This was remarkable. Remarkable. >> Even if it's tea operated, it's extremely impressive. >> Yeah. Yeah. Yeah. Like the robot's clearly working. This is very >> But they're saying that it's not teop. >> Okay. So then the robot starts missing things being a little bit like an inch off and then reaches up and touches the robot's head. The robot, which is something that wouldn't normally be necessary. It doesn't have like a logical explanation or conclusion. And so a lot of people are asking >> it does have a semi-logical conclusion which is that >> uh Brett is claiming when it reaches across its body to go to the right that it puts its hand up here to get the hand out of the way. That's what I was thinking was that if the hand is is halfway up >> the it might be blocking the sensor the camera sensor and so even though like you might the robot might reach the hand up further to move out of the view so then the robot can look at the next package. So that's one possible explanation, but a lot of people are asking even harder questions saying that potentially was there a human in the loop? Was this tea operated? Which is something uh Brett has said it's fully autonomous? I feel like that means no humans in the loop. But uh Teor Taxes has an artist's representation of Helix 2 figures in-house neural network running entirely on board and it of course is a human in a VR headset. Very uh very debatable. We'll let we'll see where you stand. But there is there is a third option which I have shared which is potentially no humans involved. I don't know if you'd call it autonomous, but you would call it no humans in the loop >> because you have >> Well, it is an autonomous system, right? It just sort of runs. >> Yeah, I would I would consider this autonomous. It's the It's the image that I shared in the production chat. It's not of a human and it's not quite robotic, but there's no human in the loop. And so, this could explain it's the system is running with no humans in the loop. If you make that claim and you follow this, >> I think this qualifies as no humans in the loop. If you have a giant orangutang in a VR headset puppeteering the robot via tea operation, you could say that this system is does not have a human in the loop. And you could make that >> and I could make the argument that it's autonomous. Yes, the chimpanzeee is running its own. It has somewhat of a neural network >> like a neural network. >> Yes. Yes. Uh no, no, no. No one knows. >> Says, "I think there was a human physically inside." >> Oo, physically. >> That is another option. >> Yeah. I mean, the the chime in >> the thing that I'm So, I I want to talk with somebody at a place like Amazon >> who I imagine does this kind of thing all day long. >> Yeah. >> And are they asking for a humanoid to do this process? >> Yeah. Yeah, >> like this seems like something that that um e-commerce fulfillment >> and logistics companies have been doing for many many many many years. >> Yes. >> Uh is there not a purpose-built robot that sits right there and make sure that uh yes >> the packages are in the right orientation? Does it have to >> you know? >> Yeah. If you watch an episode of how it's made, you will see every variety of uh of custommade machine for flipping around sorting packages uh that type of activity. There are customuilt machines that run at scale, they might cost like $10,000, but they last 50 years. And anytime you see a a uh you know a Diet Coke factory or gum manufacturing line, all these things like the the gum that you have there uh comes off and the the gum rattles down and is sorted into the the the pockets of the of the the the the the packaging and then the the the sleeve is wrapped around and glued and all of that is done autonomously but just with you know uh a bunch of machinery that was built in probably like a hundred years ago. Honestly, uh if it works, don't fix it. But uh you can clearly ste see how this type of task package sorting would be like on the curve to a more economically valuable humanoid robot. Uh and like if I was going to buy a humanoid robot to do my dishes and you showed me this video and it was in fact fully autonomous, uh that would be an encouraging demo to me. That would be something that I would look at and say, "Oh, well, like if it can do this successfully for hours and hours and hours, uh, I'd probably trust it to put some laundry in the in the washing machine. That doesn't seem well beyond the scope of capabilities." Um, >> it's so interesting how quick it is when it's just sorting packages there and then it does the bite and walk on the way off. >> Like if you rewind for a second. >> Yeah. Yeah. Yeah. The walk is not >> walk I only use that terminology because that's the terminology that that >> is what used. Yeah. >> Like look why does like >> if you were able to shuffle like this so fast and so fast. You'd think that you'd be able to hustle a little bit. But maybe that's V2. Maybe that's less less relevant for this particular task. You know there's a lot of different options but uh we will dig into it. Uh Brett launched day two. I mean, regardless, putting up views, sorted 32,000 packages. Day two is live. Uh, and he shared more details on what's going on. The original goal was an 8-h hour run. After zero failures yesterday, we decided to keep going. We're now over 24 hours of continuous autonomous operation without failure. This is uncharted territory. The task is small package sorting. Uh, F.03 03 detects the barcode, picks up the package, and reorients it. Barcode face down onto the conveyor. Humans average around three seconds per package. F03 is now around human parody. The robots are reasoning directly from camera pixels. Uh the robots are fully autonomous uh using Helix 2, our in-house neural network uh running entirely on board F03. There's no tea operation. Every action comes directly from Helix02. Okay. Well, I feel like that rules out the monkey business. I think uh I I I I think tea operation would fall if if you had a monkey puppeteering this thing, I think it would count as tele operation. So, he is denying that allegation from the timeline. Yeah. But the timeline seems convinced. Um YouTuber commenters started naming the robot Bob Frank Gary yesterday. So, they added name tags to each robot. And if the robot gets stuck or the AI policy goes out of distribution, Helix triggers an automatic reset. You'll occasionally see this happening during the live stream. If a robot or soft has a software or hardware issue, it autonomously leaves for maintenance and another robot takes over. We run our labs and figure this way to maximize uptime. If we haven't had a failure yet, we haven't had a failure yet, but statistically we probably will at some point. So, um, very very fun going back and forth. Who who else is chiming in? people are uh I'm the LA Dar says I'm the last person I'd expect to rush to figure's defense and I'm looking forward to hearing Brett's take here and in uh here and in any and all cases I stand with uh PBD King but IMO this demo seems authentically autonomous and could see this being learned behavior from teleoperators that collected the data for this model with their VR headsets. uh and uh PBD sucks who who uh broke the story or or went viral first time said he actually has a pretty reasonable sounding excuse but doesn't give me tons of confidence on the model's brittleleness for crossbody research uh for crossbody reach the policy lift its ar lifts its arm to avoid hitting the metal shoot nice try I wasn't I wasn't sure if he was going to if he was going to reply to this and sort of engage or just sort of let the let the timeline run wild with it but the metal plate does seem like a piece of what's going on. Um, but people are still hungry for uh teleoperation bombshells. It sort of cuts both ways. I remember Jason Carman did a uh did a video maybe with one X and and everywhere in the video they said this is teleoperation. We're doing teleoperation. We're bullish on teleoperation. Put it at the bottom in the text in the description like so told everyone. And still people were quote tweeting and being like this is telleration. And so people uh you know are are sort of grappling with like what is real, what is fake constantly. Um well, is there anything else on the figure story that you'd like to dig through? >> Uh no. >> Switches hands after working more than four hours straight. Huh. Well, we can >> there is some we've in just a few minutes. A new a newly released uh OGE form, Office of Government Ethics 278T, discloses that President Trump filed 3,642 trades involving stocks of public companies between January 1st and March 31st. Transactions include hundreds of stocks and ETFs such as Nvidia, Microsoft, Broadcom, Amazon, Apple, Alphabet, Meta, Goldman Sachs, AMD, Airbnb, Palanteer, Netflix, Costco, Walmart, JP Morgan, Door Dash, and others. individual purchases of Nvidia, Microsoft, Broadcom, Amazon >> individual. >> So he's averaging around roughly 40 trades a day. >> 40 trades a day. >> Check my math there. >> Um >> uh that is in Q1. So >> a lot of trading activity. We talked about this like should you should you just give Jane Street right access to the federal, you know, government? Should they just be able to change the laws to optimize for max GDP growth? and feels like we're one step closer, one step closer to the the economic singularity of the hedge fund running the country. Uh anyway, we have Oh, yeah. What else? >> Uh I'm trying to find the history of presidential day trading. >> I don't know if there is one. Uh Jimmy Carter famously divested from his peanut farm because he was worried about conflicts of interest. But we are in a new era. Anyway, uh we'll have to figure out if Trump is long or short the Cerebrus IPO. He's probably watching right now to hear Doug Olaflin's take on it to understand what's George W. Bush with Cerebris says not a day trader but had a famous controversial stock sale. He sold 200,000 Harkin Energy shares in 1990 before bad news came out. >> Okay, interesting. >> Uh >> well, >> and there's no no other evidence that we're finding of presidential stock traders. Well, we'll dig into it, but we have Doug Olaflin from Semi analysis in the waiting room. Doug, how are you doing? Welcome to the show. >> Good, good, man. Um, you know, pretty busy day, another day, dude. Honestly, every day is a busy day. >> Every day is a busy day. Uh, take it take us through it. How do you think uh the the market reacted to the Cerebrus IPO to your uh to the semi analysis uh deep dive on the company? Uh what is the overarching story here? So, I think the market was obviously positive. I don't think we're quite as positive as the market, but uh it's a bull market, baby. Um I I think the takeaway is that Cerebras uh got to IPO, which at one point in time we didn't think that would happen at the semi analysis world. We're very we've historically been very bearish on SRAMM. Okay. >> But I think there's a path forward for them to be a disagregated pre-filled chip or maybe even AFT chip, meaning attention feed forward disagregation. >> Okay. So yeah. >> Yeah. Uh unpack sort of the cont the competitive dynamic like what the the the fear around cerebras as far as I I could tell years ago it was like will this ever be useful? Will they will they ever actually be able to make it? Will it have defects? Uh then it became certain applications demand side customer concentration. But uh where do you think they are now? Where how has that journey evolved? >> So first and foremost uh sur is about SRAM. SRM is like the fastest possible memory and it's kind of done on a logic process. Um, but the problem is SRAM scaling is dead, meaning that you can't make smaller and smaller SRAMM scales. >> So, pretty much they like kind of committed to this dead-end uh process by having the biggest scaleup world uh 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 imprint 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 I so I understand all that. I I'm just wondering about the world where should I view it more like a CPU? Because when the AI boom, the chatbt moment happened, uh the obvious buy was Nvidia because we're going to need a lot of GPUs. No one was really expecting a chip short 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 the the trillion parameter models, but we're going to keep using them forever, just like we use relational databases forever, even in an AI a agentic AI world. Or you have a scenario where you have a big model that is that is giving sort of >> orders orders workload >> delegation something delegating to a smaller model. >> Yeah. I think 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 um Cerebrus 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's going to be large enough and I mean honestly I don't think I was ever bullish 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. Uh when it first started, I was like, "Oh yeah, what are you going to do? 1% a very large market. That's going to be a few hundred million." >> And that's that's like the classic seed seed pitch, too. You know, 10 years ago, they're >> Yeah. Is there any is there any uh uh for a long time there was a lot of fear around AS6 companies around architecture changes. We're going to move past the transformer and they're all going to be locked in the past. Uh is there a is there any optimism around there's an architecture change that actually is to the benefit of cerebrus and makes them more relevant in the future? Do you think you know that's I mean might be my pay grade that's too gigab brain for me right where I'm at and the understanding uh there is a narrow path for them I think and I think they're going to be able to inference maybe one trillion framers at very small context window sizes or smaller window uh smaller uh models at very very fast speeds. Yeah, >> but um 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 your model is inefficient and AGI understands. >> Yeah. Yeah. Yeah. Yeah. Distill yourself so you can run on a cerebrus chip just as effectively. Okay, now we're >> talking that's that's the gigab brain thesis. But I think >> I just think that there is um there's demands, right? Like clearly we're in a shortage and uh 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 of that today and I think I think the reality is the market's big enough for a lot of demand and is in that uh in that space. 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 the world of LLM blew up at a much faster scale than anyone could have ever thought of. Um the size I think is really the difference. >> Yeah. Yeah. Uh give me a little primer on Grock, how Grock fits into the SRAMM machine market. Uh what the view is because it it felt like that that Nvidia's move there with the license acquire as you put it. uh was defensive against Cerebrus. Is that the correct framing? Like how does Grock fit in on this? >> Okay, so let's talk about exactly where Grock uh fits into the architecture. So on uh 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 um you know essentially the entire transformer block and what's become really hot in the last few years or not even few years like probably a few months man is you've been disagregating all the different parts of inferencing into subsequent specialization. So we're talking about GPUs and AS6 being a specialization over CPUs, but now we're actually starting to break the the the essentially the constraints of inferencing into different um I guess compute and memory bound uh bound like pockets. And so for example, we're finding prefill ends up being um >> prefill being uh you know essentially loading all the weights ends up being compute constraint. So you don't really need a lot of memory bandwidth. So why don't you just use a very flops heavy portion and you disagregate the memory onto the decode portion which is like extremely memory bandwidth uh limited and so this is uh grock where this fits in the strategic thought process here is in the GB200 rack what you can do is you can pass the activations um over to the to the SRAMM in the Gro uh LPU rack and that is an extreme speed up and so it's like that's like a perfect example of another like break apart of the transformer architecture. Um, pretty technical, but that's like the thought process here is that the memory is so fast the memory band or the 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. >> Um, that problem wouldn't work with uh the previous trip because you're kind of it's 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 gro chip because there's a plug at the end of it is a lot easier. And that's kind of the the calculus, I guess. >> Yeah. So, cerebrous lower memory bandwidth mo lower interconnect speed >> off the chip, but on the chip, it's as fast as hell. >> Yeah. Okay. So, what so so what does that mean for uh the the Grock Nvidia ecosystem? Because I is this something where the default configuration is going to be a blackwell and a Grock chip like in you know 50% of racks 80% of racks or is this like still some sort of niche application where gro is going to be deployed you know sort of sparingly sprinkled into specific use cases? >> Do you have an idea? >> Yeah, I think I I don't have an idea with high precision. I think you'll find that a lot of these things um there's a lot of different ways to split up and serve your model. So expert parallelism, pipeline parallelism, tensor parallelism, right? And so um >> 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 uh 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 Grock GB200 speed up is going to be like the way forward, but it's definitely a technology uh tree that I think Jensen is excited about. So, I mean, we'll see. >> What about Lisa Sue at AMD? Is she excited about this technology tree? Can you give me an update on uh how AMD fits into all of this? >> So AMD is mostly just trying to get the last thing to work, which is the rack scaleup. Um 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 comput shortage, right? So you're talking about overflow demand. I think Lisa is going to figure it out. But on the infrance serving side, I think there's definitely some demand or desire to probably match the NVIDIA road map. 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 the number of candidates there is actually like pretty low. Um I think Intel's really uh Intel's going for Sanova which is a little clever. There's like HPM2. There's a few other players out there too that pursued SRM scaling, but I think that in this specific case uh Lisa's mostly just fig focused on the last thing and I think uh AMD is definitely good enough right now. >> Okay. uh on Intel uh what is the latest there? It feels like the round table has been assembled and sort of everyone has held hands and decided to maybe jump across the the transom at the same time. Take the leap of faith. Uh but it also feels like you know lithography machines are majorly backlogged like there's a whole supply chain that they have to answer to that's backlogged and so uh really high expectations but also uh what are they what what is the next milestone for them after they actually get these deals with uh Apple and uh Elon Musk and Elon Musk. Yeah. uh and uh the the gigafab sort of like once they get those signed like what does the next couple years look like? >> I think it's about execution. Um it's kind of crazy to me that I think the stock price is ahead of the technical turnaround and I think that um >> I think Liquitan clearly has like rided the ship and gotten the right people onto the party if that makes sense. And I think I I really do think the government intel deal was a stroke of genius because Pat Gelzinger spent, you know, three years trying to build a bottomup 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 148 will be also good enough given how much of a shortage N3 at TSMC is and it's all execution uh risk from here but the historical Intel has uh quite a bit of execution problem so we'll see. >> Okay. Uh before we move on to TSMC which I want to go to next uh are there any other interesting uh 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 the the the divergence and strategies between Grock and Cerebrus, 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. You know, I'm not going to go too into the details because I feel like some of them are even like still figuring out their their road map. I think Maddx is kind of interesting the way that they're they're kind of trying to pursue um the memory problem. I think um 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 like niche cases. I think the problem is like at the end of the day, Nvidia's big bus is still really good for the majority of cases and you're going to have to like start to make really opinionated bets on the ASIC to find what niche market ends up being all like a a uh 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 uh your bets come come uh pay off. And I think most of the bets that I would have guessed uh 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 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. >> Good parade. >> I was like, dude, I was like, man, if I'm 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. >> Um my my impression is that uh the executive uh the executive branch really wants a deal and I think uh you know you saw the H200 list, the verified H200 list. I expect 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 expert control bills going through the House than like ever in history time. So there's kind of this tension, but I do think um you know Trump's a businessman. He loves a deal. I expect I expect a deal. So, >> yeah. Um, somewhat related, uh, TSMC, uh, Ben Thompson was writing that potentially, uh, they weren't ramping capex fast enough. Uh, what are you what are you tracking on TSMC being a potential bottleneck for the AI buildout? Just, uh, as more and more Cerebrus is now trying to get allocation, it feels like a particularly uh, sharpeled 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 let the market go out over its skis and I think they're happy with the pace of what they're they're expanding out cuz like hey they're growing their capex like whatever 40% but in absolute dollars these are big numbers. Um we're going to run out of TSMC engineers in the island of Taiwan pretty pretty soon here. So I think um I think this is all kind of good on the margin for overflow demand which is actually it's Intel uh 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 3 to 5 year or let's just say 3 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 have to been like we have a 10,000 foot house and we need to buy a 50,000t house with conviction, right? It wasn't that clear 2 years ago. And so I'm gonna expect supply to kind of lag um 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 they are very like methodical and do steps one at a time. >> Okay. Uh clean room fungeibility. When you say it takes five years to build a clean room, I I immediately go to SpaceX. 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 of operating as a fab." like is 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. Um I forgot who said this, but like Elon makes the impossible late. >> Uh I don't expect it to be on time. >> Uh you know, talking about the cigar in in the Terapab, I'm really 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 bed is. But um I I still think by the time Elon figures it out the supply response will have reacted already. Um we're still two three years out and there is some clean room funability but and and you've already seen this actually. Micron bought an old power fab. I think this is the PSMC deal. People are buying display fabs. Essentially >> every bit of clean room that is not accounted for in the world um 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? The stocks up on something. It's It's all over the place. Uh I am interested in 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 robotics >> was worth more than the Ford Motor Company. Rough time, but now they're both AI companies, I guess. Um but what are you tracking on the American data center buildout? uh domestically or or terrestrially uh before we move on to space capabilities. >> Basically, how >> Oh, go for it. >> No, no, no. just uh I I I'm just curious about uh uh I mean we're starting to see glimmers of push back at uh the municipal level, different data center bans, and I'm wondering about uh what are the big levers that are that need to get pulled to actually continue to bring uh 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. Yeah. Um I my my favorite clickbait is uh 50% of all data centers in America are uh are delayed or cancelled. Implying 50% is canceled when it's really just everything is delayed. That's like my favorite clickbait. I got to I got to steal that in the future. Um >> but I think that >> I 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 oneway trade 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 often times um 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 a it is a it is like a county bycounty 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 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 uh next to you, but uh have you been tracking like how how 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 these data center sites these days? Do you have something else? >> Yeah. Uh so I I think um pretty much two fiber pairs is the big uh the big desire. essentially like 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 uh in the past you're talking about like hey having these imprints or rather like let's say point of presence near uh local cities right but power was never constraint in that world it was just uh you know the biggest constraint was getting this video from Tik Tok 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 um >> to a certain extent there's going to be more densific ification in some of the inference near the population, but I still think the ROI makes the most sense to kick it out of the middle of nowhere. >> Yeah. >> Uh, has the political backlash push back updated your thinking at all around the viability of space data centers? I I remember you know we talked as this idea has like gained popularity you guys have like consistently said um yeah technically you can do that but like maybe it it won't be long there are space data center players now that are kind of loving the push back against terrestrial data centers because they're like this the more push back there is the more it could make sense for us to put this put these uh up in space but what's your view >> I still think economics is going to win out. Um, you know, something a pound on Earth is probably 10 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 u adversary against the adoption in in like let's say the short run in the very long run cuz 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 ter you know we have a thousand terowatts of GPUs on earth and we're like we got to put a terowatt in space right so like in that world I think space space data centers work where a small percentage actually% so big >> it's 1% of the market again it's just like >> one and it's a trillion dollar is trillion >> VC is vindicated >> yeah yeah VCs are vindicated >> tam tam pitch deck slides vindicated >> yep yep yep Yeah, it's it's literally as big as a galaxy, bro. Just there's no end to it. Actually, think about how big the the TAM is. It's huge. Um uh so on like 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. Um, 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 bit. Um, and so that's the important part. Uh, but you know, >> and finds away. >> Is that is that sort of the the bullcase for uh sovereign AI initiatives? I was always super skeptical because like Europe didn't get like France's Google like they just use Google and 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. Uh and you didn't need a like a national champion in every consumer category or or uh there were certainly like returns to scale and a lot of the American companies just won. Um but uh 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 company, every country has some sort of uh you know excess supply of energy or space or regulatory capacity for data centers um sort of bringing that online and and just operating like a neocloud uh 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 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 like kind of push it through and there is FOMO and Europe uh did do a lot of investment in the in the internet like really late in if we're going to use 1999 as an example. Um >> I think the thing I keep thinking about is that this AI thing is going to be a big deal. Uh I continuously am shocked and surprised by the magnitude and scale. >> That's a narrative. Um, uh, >> I don't think it is right now. I I I I feel like we are in a particular moment where >> No, there's just the people calling the top and the bubbles like they're awfully quiet right now. >> And that's makes me even more scared. >> That that is okay. So, to be clear, >> you know, the 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." Yeah. Um, so >> yeah, I was not concerned about >> I was not concerned about a bubble when everyone was saying >> it's a bubble. >> It's a bubble. >> Yeah, exactly. >> I am I mean I'm 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 I've been reading >> 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. Yeah, you're just like, we're raising a major fund and we're not going to be investing in it. We're going to be burning it. >> It's actually not a bubble until semi analysis goes public and trades up 600%. >> There we go. I'd like that. >> That's the That's the real top. 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 um before it's all over. Um, but uh I also I also think I keep thinking about this is like dude this is a big >> a big technological revolution. I think it's bigger than the internet and I I firmly believe this. I don't think I believed it would be bigger than the internet when I maybe even 2 years ago but I'm pretty convinced this can be bigger than the internet. And um 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. Yeah, for example, on the sovereign AI thing, um maybe you're like, "Yeah, you don't need to fine-tune LLM." But what happens when AI becomes such an important fundamental like almost like society level institution that like a government can't control it? That becomes really like uncomfortable and weird where it's like, hey, an enthropic can just, you know, put 5% of the compute of mythos and, you know, run a really effective effective government, uh, you know, whenever you wanted. And you're like, whoa, whoa, what does that mean for us? Um, and so this 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 like 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 we we were joking about in uh like Q4 of last year is like John was like great like the bubble popped like 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 buildout 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.com peak multiples and and and in the public markets uh Google, Amazon, Apple, all the hyperscalers uh are at like pretty reasonable price to earnings multiples still even with all the capex and stuff and so uh >> your p the push back would be it's on free cash flow that because you can make earnings look good instead of free cash flow but like I think the revenue continues to be real the demand continues to to be real and until you just like see demand evaporate like >> it's hard for me it's hard for me to sit here and be like GPU prices are up a ton quad code 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 Engle's Pause or whatever like it's going to change everything like the the the amount of net output that's going to increase is going to just blow our minds. Um 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 um to not screw over the domestic economy for World War II. like it was it was a way to essentially organize the the uh 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're 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 um >> and people and it's and it's funny I think uh 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 Oh, online and then uh it wasn't maybe as big as some people had had pitched it to be even though it's been >> self-driving cars powerful and then and then even the way you're talking you're like uh you know we're we're still early you know which is like a classic uh crypto or you're saying like you know in crypto is like well like a community could have a Dow and that DAO >> could be worth a billion that community could be worth a billion dollars but there's just no way to measure that but now we have tokens and you're saying GDP um but anyways I'm trying to like unlearn I think some lessons from that cycle because >> there are a number of things that are quite different >> it's also >> what about what about >> the reflexivity that that people do have a little bit of an immune system to uh just running away with everything because you you you could 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 uh robotics? Has figure had a major breakthrough? >> I mean I one I have not been uh 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 uh 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 and it's a lot like unsexier. I think the the 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, um it can disperse very quickly and and that's what we're seeing right now. And so yeah, I I I'm I'm just not anywhere near as bullish robotics as I am the fundamental >> for I'm bullish on the next semi analysis. Uh uh I don't know what what are cluster max and uh inference max. What what are those called? Dashboards or analyses or rankings? >> Dashboards. Dashboards. Now we everything's a dashboard. >> Dashboard. Well, you need to make a new dashboard. uh GTP gross token production production is what we're measuring now >> uh this will be 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 uh you know the real the real bubble metric is if we're like you know how many tokens what's the token uh what's the token um >> replacement cost that that would be some really good bubble math where it's like yeah yeah software company has a really low token replacement costs per market cap, but like a hardware company has an extremely high token replacement cost. And then it's like, oh, no, no, it's just enterprise value divided by token replacement cost. >> Well, the real the real bubble one will be to go to the full Mary Maker uh like eyeballs metric, eyeballs multiples. 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." Uh, and then you'll get really weird girrations. >> That'd be great for semi analysis. That'd be really good for semi. We are we are consuming a lot of tokens. >> Well, uh you also put in 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? >> H >> is that a reference to the G? >> Sorry, it's a reference to General Catalyst attacking uh Mark >> Andre. Um, you know, life's pretty long. >> I actually think analysis is just peerless. I I I I don't think there's like a neck and neck with someone else. Like it's just you guys. >> I'm not I Yeah, I was going to say I don't really know who our competitors are. Um, I don't, you know, I don't really think about it. Mark Andre or or, you know, another research firm like that. Maybe one day maybe we will go through AI psychosis. >> Honestly, you guys need a you guys need a you guys need an arch nemesis. You need an op. Moody's >> I guess it would be Gartner. I had to say they're like >> but this is not a good option. >> You need to sell analysis hype cycle and it's up only. >> No no no trap of disillusionment straight line ais. >> It's a straight line on a log graph. That's what it is. >> The semi analysis hype cycle. I love it. Uh Gartner doesn't stand a chance. But thank you so much for coming on the show. This is fantastic. Always great. >> Full analysis. >> Full analysis. Yeah. No more analysis. >> Those guys would kick our ass, man. If they had full analysis, they would kick our ass. It' be so over. >> So, anyways, take care guys. Great. >> Have a great day. We'll talk to you soon. Cheers. >> Goodbye. Um, next we have Andrew Feldman from Cerebrus joining in 20 minutes. We'll go back to the timeline because the OpenAI Elon Musk trial is in its final day. The trial is ending. People expected four weeks of trial. We only got three. They're cutting it short. Uh, what are the prediction markets saying about who's going to win? I want to know that. And I want to go to Mike Isaac, the rat king, because he has a breakdown of what's going on. He says, "Good morning. Closing arguments of Musk versus OpenAI with special guest Microsoft are happening today, Thursday, May 14th." Uh, again, Mike Isaac, of course, he kicks it off with what his lunch is. He's got an epic bar. He's got the bison snacks. He's got a La Colom uh latte. He's got a couple other good things. He looks like he's prepared. He's got a bunch of snacks. I feel like he's in a better position today. Learned his lesson. Three weeks of >> sort of like recursive self-improvement. >> I think that's what's going on here. >> Uh so >> the cow sheet, well, Elon win his case against opening eye. It peaked at a 58% chance. Where is it now? >> April 28th. It's now sitting at a 30% chance. >> 30% chance. Okay. Uh so right now the judge is instructing the jury on the criteria by which they should be judging the outcome of the case. important because if the jury listens and carries this out, it is a very very specific lens through which they view all the evidence. Ostensibly, it's where theater ends. Listening to this and being read out in court and for the last 23 30 minutes is very helpful because it's clarifying on how high the bar is for the plaintiff's side approving some of these claims. Uh sort of feel bad for the AV guy during this trial. There's been feedback. There been mic drops, but not in the good way. The mics have been dropping out. Vky video feeds. They need to revamp this place, says Mike Isaac. Uh, LMAO, the first joke of the tweet storm. He says Musk Council is going after OpenAI execs Alman and Brockman and has the mugshot style photo of Altman on the screen again. Battle of Photoshops of executives in this trial has been entertaining to watch. You want to depict your opponent in in the worst possible light. Musk council going back and forth hammering the point they've made over and over. the argument essentially painting a picture. Sam Alman, liar, chipping away at witness credibility has been a core strategy for the plaintiff's side. And we're back to everyone hates Google again. Uh Molo is using Larry Page who they claim doesn't care about humanity as a foil to the noble Musk who only whose only care with respect to AI is the future of humanity. Uh Musk council is painting the dross don't trust Sam picture in a bit more detail to the jury. Also, Musk's side has a picture of Elon and Alman on the screen now. Sam's looks like he's about to be processed by a US marshal. Musk's looked like he's getting ready for the Met Gallow. Lol. Uh, lots of Musk closing side arguments. Semi-pop populist track of pointing open pointing at open AI and saying these billionaires are making gobs of cash while writing a charity for the supposed good of the world. I'm curious if jury can register this argument even if it comes from Elon Musk, the world's richest man. Uh, ouch. Open AI Council begins closing argument with a broadside against Musk. every even the people who work for him. Even the mother of his children can't back his story. Oh yeah. Back to the war of the photoshops. Open closing remarks now in in the digital displays on the monitors for exhibits. All the OpenAI executives look like Olan Mills photo shoots. Do you know who Olan is? He says it's complimentary. I need to get up to speed on my photographers. Olan >> Olan Mills is a portrait offers portrait photography. >> Oo, does look very nice. you pull up the the Google images on Olan Mills. Uh anyway, short summary of the closing. Musk camp, all these open eye executives are rich as hell and lying all the time. Open eye camp, all of that is a sideshow. And literally all the claims Musk is bringing cannot be stood up by actual law. Uh the Microsoft camp disappears into bushes. Dota got mentioned again. They love mentioning Defense of the Ancients. Uh incredible Photoshop from the Open AI camp of a calendar of events complete with little characters and a timeline of events. I wonder if they're using image gen 2 or if they're doing it the oldfashioned way. I can't wait until it's entered into evidence this afternoon so he can show us. Uh sort of want to buy this meme guitar, but I also have two tele is that just completely side side note. Uh gamer has entered the blog. The Dota moment has been mentioned nearly every single day during this three-week trial. AI researcher, we got to have Mike back on the show. It's so good. Uh saw as a true breakthrough in the technology. Uh I So Mike Isaac says I played what what is the timeline for the jury to meet? Are they Is this a something they're doing today? >> They're getting a 30-minute recess. Most they've had in a month. I might actually be able to go outside and get real food. There's a Popeye's across the street. Is it a bad idea to get a bucket of red beans and rice? That's what he's thinking about doing. So not much news on when this will close. It is 1:10 Pacific time. I imagine that uh they will wrap up by what did he say 3 pm 4 pm? So 30 minute break that happened 40 minutes ago. Um so I imagine that >> and but they've been taking Fridays off is kind of what I'm getting at. >> Oh yeah, >> because this could >> So maybe this happens to Monday. This is just closing arguments. It's not necessarily the end of the trial might get the results >> or the jury might might make a quick call. Well, there was an update unlikely >> 11 minutes ago. A lawyer for OpenAI on Thursday defended the company's chief executive Sam Alman from withering character attacks by Elon Musk legal team as both sides delivered their closing arguments in a trial with potentially seismic implications. Uh the stakes are high. Mr. Musk, who was not in the courtroom on Thursday because he was in China with President Trump, is asking for more than $150 billion in damages. He is also asking the court to remove Mr. Alman from the startup's board and to stop a shift the company made last year to operate as a for-profit company. Um they push back. Sarah Eddie, member of OpenAI legal team tried in her closing argument to dull the attacks on Altman's credibility and to argue that there was never a firm agreement among the founders that could have been breached. Not one in this case other than Elon Musk has testified to any commitments or promises that Sam Alman or Greg Brockman or OpenAI made to Mr. Musk is what she's saying. Um, and there is a new update that just dropped in after the recess. William Savit, OpenAI's lead council, told the jury that Musk does not have a claim against the startup unless there was a specific agreement between Musk and OpenAI describing how his donations to the nonprofit should be spent. That agreement does not exist, Savit says. So that's where I guess OpenAI is leaving it for now. We will continue to cover the story as it evolves. Is the jury allowed to use codecs/go be done in one and a half hours? Uh, >> there's other tech problems going on. Max Zeff over at Wired has been covering the story as well and says Musk's lawyer brought a big monitor, maybe 36 in into the courtroom. OpenAI's lawyers asked to use it. Musk's lawyers said no. The judge told Musk's lawyers that they have to let Open AAI use it. Then OpenAI said it might not be possible to connect our laptops to it. AGI is here, but we'll still need a dongle. I suppose a dongle has entered the courtroom says actually there's about 50 15 lawyers standing in the middle of the room right now talking how to you talking about how to use this big monitor. This is wild. Um they they should have talked to OpenAI about sharing their monitor. What I always do I always tell you when you come in here, talk to the other side. We don't have the technology available right now, so we don't want to use the TV. We think we should just get rid of it, says the opening eye lawyer. Sam Wman just walked into the room, by the way. So, that happened four hours ago. One of Musk's lawyers carried the big monitor out of the room upside down, wire dragging behind him, defeated defeated lion retreats. That is a very, very funny story. Uh, >> in other news, >> break it down. >> Tim Draper says, "I think I broke a record. I took 52 pitches in 52 minutes at below40°. Welcome to my office. # Draper University # survival training. What do we think about going in the ice tank? >> How cold are ice baths typically? You you you've done ice baths. I feel like I did one and it wasn't as insanely difficult as people said, but then I checked the temperature and I don't think it was 40. I think it was closer to 50. >> Yeah, you can totally get closer to I I I um >> cuz there's a couple companies that sell personally when when if you're going surfing and the water is below 45° >> can just be very painful like to so even in a wet suit anywhere that's not covered. A lot of people are putting gloves on >> uh booty. What do you think? >> I So apparently Joe Rogan's at like 34. >> 34? >> Yeah. >> Wow. >> So that's like the cold plunge, you He's the he's the top of the mountain when it comes to ice baths. >> He's the final boss. Uh >> this Yeah, this this is just a crazy picture. I did think it was I did think it was AI. Uh but uh but turns out it's real. It's just funny because it looks like like what is this set? What is this setup? >> Yeah. What are all the trash bags there? And the wall is like sort of decrepit and there's piping. >> It looks like kind of like a prison ice bath. >> Yeah. This is not what you'd expect from I mean isn't he a billionaire investor? You'd expect some sort of palatial, you know, you see the the the the the properties that Mark Zuckerberg's acquiring, that big investors are acquiring, you would expect something that would be uh much more regal. Uh but he's doing it the oldfashioned way. Whipped this up himself, bought some trash bags and took some pitches. Uh yeah, and you know, who knows? Maybe the next uh the next founder of Cursor, Figma, RAMP is right now. >> 52 pitches in 52 minutes is crazy. >> A minute is is crazy fast for a pitch. I mean, we do 10-minute interviews, 15-inute interviews. Barely get to the meat of the interview. >> And this one, you got four minutes. >> Four of the founders. >> Four founders jumping in one minute. That is remarkable. Um I am not >> no stranger to controversy though. >> Yeah. Um, Joe Londale says, "I am not a humble man, but this is legit legitimately beyond my capabilities." >> Absolutely wild. Uh, well, Versel Gar, friend of the show is apparently running a an ad campaign on Lyft by, uh, buying custom license plates and deploying them through Lyft drivers. Is that what's going on here? No. You think it's random? The guy Peter the driver was like I he must love >> Versel or worked there or something. Uh I don't know. >> If he was the eighth employee at Verscell, I don't think he'd be driving. >> Hopefully not. >> Unless he just loves >> it. Truly just loves the game. Loves driving >> or he's just super illquid. He just like he's just like pay me zero. Actually, I'll drive Lyft. I want all equity. I'm super bullish on Vers. >> That's a possible possibility. That's a possibility. Well, Alex Conrad says, "Is your startup even sponsoring Lyft license plates yet?" Uh, it's an outside the box strategy. Someone should pick it up. Someone should do it. Get a bunch of uh get a bunch of license plates for cars, rent them out to lift drivers, get those impressions. Um, Wix is down a bunch. The uh this seems like a very logical company to suffer in the age of vibe coding. People are vibe coding websites all the time and uh Wix is a uh supplier you know service to build websites based on templates. Uh but Wix uh was buying uh 30% of its shares at $92 six weeks ago, but the stock is now down another 45%. And so I was wondering about this. I almost asked Max Levchin about this uh yesterday, but uh when you're going through this world, like it seemed like he was very confident about the SAS apocalypse and did not uh feel the need to respond or take any dramatic actions, just sort of wait and let the let the metrics uh do the talking. But I was wondering about uh you know are you tempted as a CEO when your stock trades down on a narrative that you know does not apply to you but you're just sort of a collateral damage are you tempted to do a quick buyback and just sort of uh you know uh get get a good deal on your stock if it's even if it's just you know three months down then right back up. Imagine being a public company CEO and buying back your stock and then getting a return on it has to be one of the most euphoric experiences. Um >> totally uh not not actually getting a return but um but obviously you know decreasing >> Yeah. >> or increasing uh everyone's >> Wix is a 2.9 billion company now. >> Yeah. They they acquired this company base 44. Remember this was like a one person Yeah. That's right. one person uh company and they were growing re I I think B 44 has been growing >> revenue quite quickly. Uh it seems like pretty much any of these vibe coding tools. Yeah. Just the the experience is so magical for people that a lot of them have grown revenue. >> Stock chart if you zoom all the way out. So uh during COVID 2021 Zerp era stock was at $300 a share. It's at 52 today by the way. Uh it traded down after Zerp era ended all the way to $50 a share $60 a share and then post chat GPT moment 2024 fantastic for the stock it gets back up all the way to 250 $200 a share but then since 2025 as AI has gotten better at coding vibe coding websites doing front-end design uh there has been a significant selloff that continues today uh and so rough go >> I was looking to get a comp. >> Uh I looked up Squarespace. Squarespace is no longer publicly traded. >> Uh it was uh traded on the NY, but it was delisted after being taken private by at 7.2 billion. >> That is tough timing. Taken private in October 17th, 2024. >> Oh, interesting. >> And uh at the time there was not a SAS apocalypse narrative. You couldn't oneshot a beautiful website with a single prompt. It's going to be so hard to uh for this firm to make money on this deal. >> Yeah, it feels like a new customer problem just because it's not the hot new technology that you're hearing about like the podcast ad conversion has to be a lot worse. But I would be very interested to know >> what is retention like because I know some people that have built businesses basically. >> Yeah. I I know some people that have built these uh web website generator companies and then they just keep growing and growing and just sticking around forever because once someone uh has the magical experience of building a little website for their company or their personal brand and then they just let it run forever and they're like ah 10 bucks a month I'll just let it keep going. Uh well >> yeah Squarespace had done around 1 billion of revenue in 2023. Uh I'm assuming they grew into 2024. We don't have the full year numbers because it was taken private in Q4, but is pretty reasonable revenue multiple. But >> if they lose out on a lot of those new customers because there's every single company in the world, every single company in the world it seems like is trying to make a box that will make you a website. >> Yeah. Yeah. Everyone. Anyway, uh you know what? Very few companies are making a nightstand that turns into a bat and a shield for defense. I like this. It looks so unassuming as a nightstand. Very believable. No one would guess, but then something happens. You grab your your bat and shield and you're ready to rock. >> Would you pick one of these up? It has a little bit of a hotel vibe to it. It doesn't And also, I like a nightstand that has >> All I would say is don't bring a nightstand to a gunfight. >> Okay. Yeah. Uh well, people are having fun with the AI generated videos showing that. Yes. In fact, it's not if it's not bulletproof, it has a uh has some trouble. Um, if you disable Ben Thompson says, "If you disable open at login for the Gemini app launcher that the Gemini app installs in the background without asking, Gemini app launch will immediately reenable open at login. I will now, needless to say, delete the Gemini app and don't intend to install it ever again." And so, this is very, very odd. Gemini login. Oh, so it automatically logs in no matter what. He says, "I'm I'm actually struggling to remember a bigger middle finger to a user from an app ever. It's bad enough to install a helper app, but to immediately undo the user's explicit setting change, incredible." And uh Josh Woodward from Google chimed in and said, "This is a bug. It will be fixed in the next release, aiming for right after Google IO more if you're interested." So, uh that's good. They did they did receive the feedback. Well, we should talk about Nikita Beers. I was reminded of this because he screenshotted and posted it uh the greatest growth hack of his career uh for one of his projects. This happened, was this a year ago? Gas or explode app? This was this was about a year and a half ago uh pre-joining X uh and working with Elon Musk over at X. Uh he launched a company called Explode or an app called Explode. and he had a very interesting growth hack where he incorporated the company as Tap Get Inc. And so in the iPhone app store description under the name of the app explode it would say tap get and then right below it would say get because it's a free app >> and it doubles down on the call to action. >> He made the entity a call to action. >> It's genius. These little these little things really add up and you've seen them all over X. um and he's done a good job of creating re-engaging areas and I just feel like the UI of X has been improving significantly. I'm really I'm really enjoying the latest UI feature where if you're watching a video uh and you want to speed it up, you can hold on the right side of the screen, which is fairly common in video apps these days. Doesn't work in the iOS native video player. I don't even know if it works on YouTube, maybe. But uh what's really cool is that if you press and hold it, you will temporarily be in 2x speed mode. But now in X, if you press hold and you're in 2x speed mode and you drag down, it fills a little circle and keeps you locked in that 2x speed mode and it actually changes the speed of the video permanently until you change it back. And so that little delightful touch is something that I'm I'm seeing more and more of from the X team and I'm a big fan of. Well, without further ado, we have Andrew Feldman from Cerebrus in the waiting room. Let's bring him in to the TV room. 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 uh just your reactions from the day. It seemed like there were a lot of people there. Take us through your uh your emotions today. >> Well, you know, this was better than than we'd hoped for. All right. I think a chance to to celebrate. We we did bring a lot of people from the company and we brought families >> and to to to to share with uh the team. We we brought everybody who'd been at the company for longer than nine years and their families. >> We you know when you do a startup the family is a is a meaningful part. It takes patience from them and and uh a great deal of it. And so they came and we celebrated. It was really an extraordinary day. We we opened up, you know, we did uh we priced at 185, we opened up 350 and we settled at about 320. What an extraordinary thing. We're just so proud. >> Yeah. Um take us through some of the the history of Cerebrus. Uh has it been a straight shot? Has it been an overnight success? How do you characterize it? Uh what were the darkest moments? What were the highlights? Uh what are the good old days to you? What does that mean? Well, look, I I think in the hardware business, if anybody tells you it's a it's a straight shot, you you you can call BS. I I just don't think that's the way our our business works. I think um the first time you 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 your your challenges and you begin to show it to customers in in mass. third one often that that really takes off and and so it's a long long road in in innovative hardware designs. >> And so, you know, we were founded in 2016. We're more than 10 years old. >> Uh we we sought to solve problems that that that other That's right. Overnight success. Thank you. Oh, exactly. Like like a decade. Like >> I was 15 pounds lighter and weight >> less overnight successes are, you know. >> Yeah. >> That That's right. I mean, they're just overnight cuz 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. >> Yeah. >> And uh everybody told us it was impossible. And the truth is, for a while it was. >> Mhm. >> And you know, we we didn't solve it until August of of 2019. We built this extraordinary chip. We were faster than everybody. And absolutely nobody cared. Nobody. and uh 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 but starting with with GPT uh 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 and business was rolling. >> Yeah. Um >> uh what were those early rounds like? become thinking the benchmark round, CO2, bunch, you know, Eclipse, a bunch of others. >> You know, we we had the advantage of the founding team had been together at a at our last company that had paid pretty well for the the venture capitalists and the team. And so we we we had some wind in our sales when we went out and raised money. It's not like today where we're where we're we're four guys and the word lab and you're raising it a billion pre for for your A. Um that that's not us. But we went out, we we we uh we made eight calls, we got eight term sheets, we we chose uh benchmark and foundation and eclipseing >> and we got going, you know, less than a year later. >> I was expecting uh I was expecting you to say like, yeah, I mean it was it was a slog, you know, we were so other rounds were a slog. Other rounds were a slog at the beginning. Um not so. you know, Thomas uh Lefant at Code 2 came in shortly thereafter and um we did around with them. I think the truth is between about uh 2020 and 2023, it was it was much harder. >> Yeah. >> Um AI was sort of in this situation where uh we everybody was saying, "Oh, that's cool. Look what this model can do. Look how big it is." But it it wasn't being used anywhere. >> Yeah. >> Right. Nobody was using it. >> Um they were pointing at it. They were saying, "Wouldn't this be nice?" and they went back to whatever they were doing before and and it wasn't until really uh sort of 2025 when the models got good and you just saw this title wave of people using AI and demand for AI compute and that that's been exceptional. It's just been an amazing thing to ride. >> Yeah. Um you Yeah. You mentioned like if you have four guys and uh 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. Uh there's not that much that needs to be explained, but what were the key ideas or thesis that you needed to explain in the road show to investors that wanted to go a layer deep but be uh a layer deeper than just AI chips? Yeah, I I I think there were there's the first the the market size and dynamic and I I think Jensen said some time ago on uh on Brad Gersonner's podcast that that the demand for for inference will grow by a millionx >> and nobody believed him. >> Yeah. And you know at the same time you saw Sam Alman you know 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. >> Yeah. >> And I I think trying to share what that means what an exponential demand means and that we're still so early and yet the the demand for AI compute is is overwhelming. Mhm. >> I I think sharing that was interesting and and I I think helpful in educating the the financial community. The other thing is that that there are lots of ways to do this. The the GPU isn't the only way. You've got a TPU, you've got Tranium, you've got us. There are lots of different ways to to to build a solution here. And finally that m maybe the the notion that that CUDA is sort of this grand lock in is overplayed and that uh you know the the Gemini 3 which is an excellent model was trained on TPUs with no CUDA that anthropics models were trained on Tranium with no CUDA. I mean that that lo and behold some of the best models, some of the most interesting things are being done without CUDA and that that that lockin might be overplayed and I I think these three factors were really important in in educating the the financial community >> going forward. How do you think uh 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 you know 2030 and beyond versus like working closely with the labs that now have you know product lines with billions of dollars of revenue and their own road maps that you can work with. >> Yeah. You know like the babe I'm going to point out to left field and and and just say wait this is where it's going baby. Um, >> um, no, I I don't think that's the way it works. Um, look, I I I I think we're calling our shots every day by making big investments in data center capacity and collaborating with with the the the leading visionaries in the field in in working not just with with OpenAI to to service sort of the cutting edge uh and and deliver their extraordinary models but also with AWS to make sure that that we can get access to the the largest enterprise customers and instead of having to to work with these enterprise customers procurement a sort of organizations who who provide master purchase agreements that are are the size of a Bible. You know, you can say, "Look, why don't you buy us through through through AWS and it'll count against your against your annual commitment." And so, I I think those are are really important ideas and and ways we we get access to the market and then we're we're taking huge amounts of data center capacity >> and so that's uh the other bet we're making. >> Yeah. Um makes a lot of sense. How do you think uh the year will play out in terms of uh just broader consumer awareness of what fast inference feels like? I had uh uh a really magical moment using Cerebrus in GPT 5.3 Spark in codecs 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 it this hasn't fully diffused but uh it 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 we think that's exactly right. >> And we think that that the experience of engaging wi with uh a real time AI >> Yeah. >> Uh will will encourage people to do more things to stay longer to work on harder problems >> a >> 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 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 I think that's exactly what's going to happen. And you can just sit back and you can ask yourself, I mean, how big is the market for for slow search? >> Zero. How big is the market for dialup internet? I mean, how much would I have to pay you to swap out broadband at home and bring in dialup? >> Right. 1,000 a month, 1,500 a month, 2,000 a month. I mean, no way. I mean, it just wouldn't be worth it. >> And so it the 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 you make the chips. I believe you also make uh cooling infrastructure as well, cooling units. Um, is there are there other products on the road map that you think uh will be required to roll out and scale Cerebrris uh over the next couple years? >> No, I don't think so. I think right now we we build the the the 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. >> Sure. >> And I 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 it feels like there's some uh there's some concern about well what if the models go to 10 trillion parameters? What if it gets too big? Uh how are you thinking about that challenge or maybe it's an opportunity? >> It is an opportunity. I 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 serve for everybody. >> I think one of the things that that we've been able to do for the larger models is to to tie together a bunch of these systems in parallel. >> Mhm. >> And run them as a pipeline. >> Mhm. And uh that way we can train and do inference on uh trillion multi- trillion parameter models in ways that I think are are are much more intuitive than than on GPUs that have much smaller compute. They have offchip memory, but their problem is the compute. They don't have enough compute per chip. Mhm. Uh and then how how are you talking to um to to customers about uh potentially bringing Cerebris in not as a full replacement to their entire semiconductor supply chain or stack uh but as a as a complement to everything else that they're running because I have this vision of like 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 some >> the same 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 I think that is uh sort of a notion of a confederacy of models, right? That that there's a collection of different models. And one of the the the the things we thought about early on was how to interoperate in that environment. And we we connect in via standard 100 Gbit Ethernet, nothing fancy, nothing proprietary. Um we we are deployed in in many places where they've got GPUs from 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 we're eager for those environments. Yeah. >> How what 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 like how and how do you think about just like the speed up in um you know at the company today due to how good the models have gotten? >> We we we use frontier models every day uh in coding in running our GNA. I I think if you start a company today you build a very different organization. I I think there are whole departments that look different in in in the next 9 to to 18 months. I think much of what HR does, much of what training does uh is solved by by some form of AI. I I think a lot of the work in finance, right, closing the books, a bunch of what they do is is checking and those are all done by agents. I I think what it is to be selling or 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 we see sort of fundamental changes. The obvious ones of course are, you know, a year ago engineers were using approximately zero tokens and and now they're using, you know, $10,000 worth of tokens a month. uh and the the rate of change and the rate of uh new PR requests, new pull requests is just extraordinary. And so um AI is having fundamental changes. Obviously, it usually starts in in Silicon Valley and sort of works in waves to to other areas, but that's what we're seeing right now. >> Since the last time we talked, there's been a ton of movement in the space data center market. A lot of energy. Just yesterday, SpaceX and Google eyed a launch deal in the Wall Street Journal. Uh, 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 from one chip to the next. And we solve that, right? I mean, one of the great parts about a big chip I >> is that you have to communicate from one chip to to the next less frequently. Yeah, >> it's a huge advantage for us in space. >> I think that um this is an idea like self-driving where the last 10% takes 80% of the time. >> Sure. >> Right. Um and that that we're not 3 or 5 years away, we're 8 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 I I don't see uh data centers in space in the next three or four years. Yeah. And arguably uh 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 makes sense. >> That's exactly right. Yeah, >> that's exactly right. >> Well, we're hoping for it. It'd be exciting. But plenty plenty work to do here on the ground. Congratulations to you and the whole team on this incredible milestone. We're honored that you would spend uh time with us on historic day for the company. Uh, and uh, yeah, to watch your progress and uh, I look forward >> to your next appearance and uh, 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 well. >> Fantastic. Enjoy. You deserve it. Goodbye. >> Uh, what if >> I love that I love that analogy. He's like, I'm Babe Ruth. I just point. >> Then he's like, no, I'm not going to do that. Uh, it's way more complicated. I've been working on this for a decade. Uh yeah, what a what a fantastic story. What a fantastic uh performance. And I'm very excited that we're bringing in Eric Vishria from Benchmark uh who was in that series A that Andrew Feldman just mentioned. Uh so we will talk to him about that in just a minute. We're going to bring him into the waiting room. Uh but there are some other posts that we can talk about in the meantime. Uh, one someone is using runway ML to uh to create let me see this a full hurricane inside a TV studio. I want to watch this clip and uh it's one minute and we will see how convincing is this. Are you going to be turning off in order to watch this >> wind? >> But wind is only the beginning. The real danger is when the storm starts moving. >> This is very cool. >> As the storm builds, ordinary things stop feeling ordinary. roof panels. >> So you think audio also like fully AI generated because the typical workflow for this is uh the the the new the host would uh stand on a green screen or LED volume and then uh all of these effects would be uh added in post or or live um through like a traditional visual effects pipeline. Uh this feels fully synthetic. I think that you'll probably use some sort of hybrid approach, but uh the the ability to prompt something like this on the fly for a small uh news organization that maybe doesn't have the budget for a huge VFX team, you're just going to see a lot more VFX like this. You're going to see stuff all over the place. Uh there are so many small news channels, local news stations that just don't have the the, you know, access to digital domain or some huge visual visual effects house. So, looks pretty good. Before our next guest, Dylan Field has a quick update. They have their Q1 results. He says, "Quick update, not dead." Uh, and putting up some insane numbers. 46% year-over-year revenue growth accelerating for the second straight quarter. They're raising 2026 revenue. 8% today, up 8.6% after hours. Congratulations to >> says design matters more than ever. the Figma team continuing to execute incredibly well. >> Fantastic news. >> 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. >> You guys like Ev, you know, so have Ev. You don't have to have >> He is a former colleague, but everyone is welcome here. Um, but I would love to just hear the story from your perspective. Uh, we we we just heard it from obvious to you. Yeah. >> Was it the most obvious deal ever? Because I was I we were talking with Andrew. I was asking him for the story of those first couple rounds expecting him to be like, you know, was wouldn't come out for for almost a decade. It was a slog. We kept getting we walked up and down San Hill Road. We got nos. He's like, "Yeah, we got eight term sheets." So clearly it was a deal you had to win. But >> yeah, but take us through it. >> Well, you know what the the the hilarious thing about it is in venture it's very useful to be naive. Um and certainly >> I was so naive about how hardware actually is. like I like I can't even I can't even describe to you guys how how naive I was and we were um you know at the the the 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 these different applications. So I was looking at like deep learning for radiology and 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 this is 2016, right? The TPU hadn't been announced. The transformer paper hadn't come out yet. Um LLMs hadn't hadn't been born yet. And and obviously not chatbt 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 Amberella 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 a hundred 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 um for training and also what the like ideal groundup solution could look like um and you know and they had their idea of of the way for scale and everything else and you know and as soon as you said it just kind of like oh yeah that makes sense and like I should you know like we don't know what applications going to work we should invest in infrastructure this is an amazing team and a really provocative idea Um, you know, fast forward like that was 2016, spring of 2016. You fast forward like six, seven years and like we're still slogging it out and um have raised so much money and have very little revenue and um you know and it's just it just hadn't all come together yet. And then of course over the last two years um inference is exploding. It turns out Cerebra switches from training to inference and and really focusing on inference and making um inference speed where speed matters. Coding explodes where where speed really matters. And so all these things kind of came together and so you know a lot of luck um a lot of naive 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 yeah, I'm I'm so so proud of them. >> Yeah. What was your role as an investor like over the journey of the company? Uh because obviously Andrew and his core team, deep engineering bench, were you focused on how you positioned the company, the private markets, fundraising or management like uh what what were you focused on uh you know in terms of value ad or just uh helping build the company alongside? I'm I'm really the algorithm specialist. Like I go in there and I do that. No, I'm just I I don't know anything. Um so I >> You're the fab. You're the one that making the >> That's right. I was making it up clean. Um >> yeah, it there's you know it it really changes a lot over the course of a company. This is um I think the the fourth company that I've um worked with for more than 10 years. Wow. >> And um >> and so when you work on them a long time, the the companies evolve a lot, right? you start out, it's just five people. It's just the five founders originally. And um and so at different points in time, it's a lot of fundraising um 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, this there's being an entrepreneur is is very the highs are very high and the lows are very low and and so someone you can talk to and be really open with that like helps moderate that and um I think that's a part of it. So it's just it's an evolving um you know consiliary kind of role and um and I really love it actually. That's the that's the part of the job that I love the most and it's very um 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 with the founder um and and just feel like we have a really productive relationship. >> Where are you excited to invest over the next decade because uh you know it feels like we're still in a semis but 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. I'm sure you've gotten pitches that look like the what what maybe would be the next gen and >> you know 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? You know, they don't they don't they didn't learn the hardware is hard lesson yet. >> Yeah. >> Totally. Totally. Well, you know, one of the funny thing 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. Um, but willing to take big swings, you're 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 AI um hardware investment and and you know, Grock was I think 2017 for example. So like you know there were there were several contemporaries um of them and of course Grock and and Cerebrus have ended up doing really well and so you you you but you're trying to say like okay this fruit's going to ripen in like six years right and and so there's there's kind of some mention of projection you know right now I think um I'm I'm really excited and continue to be really excited about a lot of the AI applications um we're investors in Sierra and Lora and a number of others that um where like they're obviously booming. They're selling magic to their customers and and the companies are doing great. We also have these like infrastructure investments like fireworks for example which is you know also um riding this enormous inf um inference demand and then there are kind of things that are that are a bit more forward looking. Um we invested in StarCloud. my partner Chath led our investment in StarCloud which you know um space data centers and and we also um you know we led the initial round in uh Sunday robotics which is um a home robot and so you know I think those things are going to take longer like that you know they're they're not going to be um 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 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 a portfolio. And I think, you know, one of the beauties of Benchmark is >> um each of the 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 it it works out really well. >> Yeah. Yeah. Makes sense. walk us through funds seven and eight because uh there's chatter on the timeline as as those funds being some of the best in venture history and although this is Cerebrus's day this is your first time on the show so didn't want we do have a big gong here. >> Yeah. Well, I I you know, Fund 7 um Fund 7 has um or had Uber, Snapchat, Elastic, um Stitch Fix, We Work. I mean, I there was so many things and it 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 um and invested in um but the team, you know, that that team at the time just did such an outstanding job with winner after winner. Discord is in there. I mean it's it's like really like when you have you know you guys like in in venture if you catch the trend right and um and obviously work hard and and and get lucky but you have you know the sixth or seventh company in the um in in the portfolio delivering a multiple of the fund or something like that like that you're in such rarified air and that's there's it's really special. So that's fund seven. You know, fund 8 is a very um enterprise. It's our 2014 vintage, I think. And um you know, it it has um it it's a very enterprisey um fund. And so you know, we had Confluent, which returned a bunch, and and Amplitude has returned a bunch, and um you know, and then we have Cerebrus obviously, which is big, but chain analysis is in there and and and and several others. And so it's kind of interesting how these how they switch. which I think that's actually more interesting to me, which is fund 7 was very consumer mobile um and fund 8 is like very enterprisey and they're like backtoback, but they turn out to, you know, they both work. And so, you know, I think that that tells you a little bit about what what venture is and how we we all have to be really open-minded about what's happening and what's the right timing for these various ideas. And then, you know, fast forward, um, and our 2022, I think, um, 2022 vintage has, you know, the first round of Sierra, the first round of Fireworks, the first round of Lora, you know, >> reductor. Yes, absolutely. Um, Lang Chain and so, you know, all of those are in there. And so, obviously, that's a totally different fund and has, um, you know, a different set of things, but also um, you know, looks looks pretty interesting. So, it just it it evolves. Um and it that's what's so hard and tough about this business. Um is staying on your toes when you're in a very very dynamic world. >> Yeah. Well, it's interesting something that uh you know this has been talked about on plenty of uh 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 and but a lot of funds are like having to deal with markets changing and evolving while having a fund 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 uh technology trends and markets. >> You know, I've been at Benchmark 12 years and I've thought about this a lot. Um, and you know, you're watching your peers do all these different things and 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." Um, so like, you know, you kind of like look at this stuff and and um, but I'll tell you, you know, what I think it actually comes down to, it's what it actually comes down to is what do you love doing? And um you know we're obviously in a very fortunate position and and and I inherited an amazing platform and so um 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 um and and working on these companies for a decade plus and and um and that's kind of what we like doing, you know, and so I think things have things have definitely evolved. The opportunity set is changing and evolving. And you know, more recently, I mean, just in February, we uh we raised an SPV, which we've never really done before, and um to to invest in Cerebras. And um that was unusual, but it was, you know, you you can also we've actually a few years ago we did public market investing when um when COVID first hit and the NASDAQ tanked, you know, all 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 to point a little bit in the public. So you know, yes, we we're we really focused on the early stage and that's what we love doing and then also occasionally like we see these special opportunities and and we try to jump on them. >> Wow. Yeah. Uh well, thank you so much for coming on during a business day. >> Had to sneak in that the the SPV round of 23 billion. Uh so congratulations on on that investment. >> Fantastic. >> Another 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. >> Well done. Great. Great to finally meet you and congrats to uh everyone. >> Yeah, let's do it again soon. >> NASDAQ. >> Thank you guys. We'd love to do that. Thank you. >> Fantastic. Goodbye. >> Uh up next we have Steve from Foundation Capital. He's Cerebras' first term sheet investor, also the first investor in Salana and a bunch of other great companies. So, we will bring in Steve from Foundation Capital from the waiting room. Steve, how you doing? >> Here 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. >> This is just another another day. You're not You didn't Are are you at uh the NASDAQ or you calling in from home? >> Uh yeah, exactly. No, just another day. No, I am uh I'm at my hotel on my way to the dinner that Eric's also headed to. >> Fantastic. Okay, we won't keep you too long. Uh but I would love to hear the story of you meeting Andrew Feldman in 2007, how things uh matured from there, how you wound up working together. >> Yeah. So uh I showed actually Andrew the email last night over dinner, but um yeah, he and I and Gary met in uh October of 2007. They were raising money for the company that they started prior to to uh Cerebrus, which was called C Micro. Yeah. >> Uh and it was kind of broadly and 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 um but stayed close. We we we really connected in that meeting. And then when I saw them get acquired by AMD, it was about four or five years later. Uh I was like guys and in particular, you guys are not going to stick around this company for too long. So uh let's start riffing on some new ideas. And that began basically a two-year conversation um about a whole bunch of ideas. Actually that it all started really in kind of this concept of warehouse scale computing. Yeah. >> Uh we were looking at companies like Mezosphere, ended up actually doing a small investment there and >> and uh Coros and a whole bunch of others and Andrew came in in November of that year of 2014 and shared his ideas to our with our enterprise team and uh and then basically we rifted on ideas and in the spring of 2016, so it was like March time frame, we started telling them, look, we we want to be your first term sheet. been like courting each other for for a while here and uh yeah, we got him a term sheet to lead that first financing and and then Eric stepped in uh and we changed the terms a little bit to uh to make room and co-lead along with Eric and and uh and and and Pierre from Eclipse. >> Yeah. >> Um Yeah. And then they started started it right in our office. >> That's amazing. >> Amazing. Uh can you c can you talk to me about uh there's uh you know crypto and AI 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 uh where where the gap is where there similarities >> so what I would say the similar ities which are probably in retrospect um somewhat obvious. >> I would say the hardest problems of software and systems uh 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. Uh you know the folks that are attracted to both of those areas tend to to be very technologydriven. They're they love distributed systems. Uh 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 um I would say that the problems are quite similar. And in fact, here's a funny anecdote related to this. So Anatoli Akaveeno, you know, co-founder of uh of Salana, 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, you guys take hard problems seriously." He had spent 12 years at Qualcomm. >> That's right. Yeah. Distributed systems, >> the brew operating system. Exactly. >> And so he and then was at Dropbox and understood those challenges. And so he said, "Wow, you guys care about these kinds of hard problems." Um, and uh, that matters to us. So we ended up doing fair bit more diligence and writing actually a larger check into that very first Salana financing. So >> yeah. Uh, can you take us back to earlier in your career, pre-investing? Obviously fascinated 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. Yeah. So I uh studied robotics and embedded systems sort of the intersection between uh mechanical and electrical engineering. Yeah. >> In undergrad and then came to graduate school and uh and did more of that. And then my very first uh Friday at at at Stanford I met David Kelly who's the founder of IDO which is a product development consulting firm that worked with the very best uh kind of fortune 1000 companies. when they would hit a snag, a hard problem, or want to invent a new product, um, and they didn't often know how to, uh, 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. >> Uh, we did a lot of work across every industry from healthcare uh to consumer devices to, um, uh, you know, really hard problems in um, in in systems. And, uh, so I worked there for 5 years designing products. in fact saw one of my other products uh earlier today on the desk uh at the a trading floor at NASDAQ for Cisco's voiceover IP phones which I worked on now >> 28 years ago. >> No way. >> So so just working on cool cool things hard problems um mostly uh where it feels like if if you solve that problem it was worth solving. There's there's a there's a real prize at the end. >> Okay. I want >> So that's how I got started. >> Yeah. I want to take this full circle then because uh uh robotics is sort of having a moment but it still feels like it's early in terms of uh as a consumer as optimistic as I am I I 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 8 10 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 Cerebra 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, uh, how are you thinking about it? >> So, uh, we've done a fair bit of work in embodied intelligence in terms of, uh, research and and as I'm sure you're familiar, it's it's always a little tricky to invest in an area that you have some operating experience. It tends to bring some scar tissue. Yeah. And so you might be more circumspect than than if you'd had kind of a beginner's mind. >> Sure. >> I would say I I am generally um not a big believer in the humanoid uh approach. I think there are use cases uh for example in the home companionship. Yeah. >> Uh and and even in that case it's it's a bit of a stretch. Uh I think you need to think about robotics more broadly and think about industrial automation and uh and then look at the the the problems that are not necessarily kind of the cons you know the consumer level um 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. Yeah. 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 uh automating human labor. And so um and and in fact the greatest compliment uh 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. 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. I was I was uh you know 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 like is it time just for a groundup first principles rebuild of what a washer and dryer stacked is like if you constrain it to like 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 like can you do something better than just a big tumbler and then another tumbler one with water one without and uh I I'm excited by that. Does is the implication of that that uh almost you would be open to talking to entrepreneurs who are maybe thinking a little bit narrower, thinking a bit a little bit smaller at least in the interim. Uh and then how how would you guide someone towards uh long-term messaging around their company if they are finding a wedge but then they want to grow at some point? >> Yeah. So I I think it it is exactly what you just described which is uh and and again the sort of the applications do matter here but the notion that you would start with something that is let's call it sort of uh big enough to matter but small enough to win. Yeah. And and in hardware technology being more focused is actually uh a huge advantage a huge point of leverage. And so and then as you continue to build you want um you want to be able to access larger opportunities in markets. And so I I I I really do believe that that is um the way you get started with uh hard technologies and hardware in particular. I think there's an another thing that we do and I will just say this kind of brings it to to Cerebras again for a minute which is we look at we look at workloads and so one of the reasons why we backed uh Andrew and Gary and and Sean and team back in in 2016 was it was quite clear and we saw this through the lens of our portfolio that the AI workloads at that time it was more ML they were ramping very very steeply 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 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 uh programming uh and and you were very well suited to the uh 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 uh that's really good at, you know, at at at at rendering graphics, at doing floatingoint math, at uh at at uh 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 often times there is this sort of transformative opportunity and that's exactly what we saw in in 2016 was wait a minute like there should be purpose-built silicon for this ML and AI workload. Yeah, >> 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 uh rotate towards the much larger opportunity. Yeah, I mean we talked to Andrew about all the ups and downs. uh a classic overnight success with tons of moments on uh of you know intense tomalt but uh I'm curious about were you ever worried or hesitant that the company might narrow down too much and because you've heard like uh you know YouTube has custom silicon for encode 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 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 uh and specifically the reasoning part of inference where you start chaining workloads together. >> Um so we we worried a little bit about that being um you know a niche that was not interesting enough for us to build you know a really uh 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 uh I was joking with one of the other founders last night where you had 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 you know thermodynamics challenge. >> Okay. So when you say five problems, you're not talking about fundraising, hard negotiation with TSMC, talking to a supplier. >> You're talking too. 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 uh you know, the the ones that are sort of more physical, you where you have laws of physics and thermodynamics to obey. >> Um and and you don't get to negotiate. Andrew is a very good negotiator, but he's also learned that he can't negotiate with the second law of thermodynamics. Yeah. So no, these were this was how do you yield um 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 uh 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. Uh and and they were of course also stacked which means that the risks are now combinatorial. Yeah. So even more dangerous. So you've been through uh taking companies public uh you know being involved with public companies uh several times. A lot of times the founders that you're backing it's their first time becoming a public company. Uh what are you 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 uh 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 uh it 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 >> uh model tomorrow that could you know whatever upend the public markets. >> Yep. >> 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 uh 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 steal your sense yourself against that. Um I think there's also a a piece which is you just have to grow up. Like there's there's a a cadence to these businesses uh orderly unfortunately. I wish they were longer. um where you know Andrew and Bob are going to hop on an earnings call uh 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 uh that often times founders uh don't you know don't have their stuff together uh well enough in order to be able to to to to sort of manage through that um 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, well, how do I meet the next quarter? >> You oftenimes lose sight of the the long horizon that was the larger opportunity for you >> to go after, you know, not just, you know, the the opportunity right in front of you, but there's much much larger opportunities. And we're, you know, 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. Uh, 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. Great. >> Talk to you soon. Have a good one. >> Bye. >> Uh, that's our show, folks. Leave us five stars on Apple Podcast and Spotify. >> Another one. >> Sign up for our newsletter at tvpn.com. See you tomorrow at 11:00 a.m. Pacific time. And have a great rest of your day. Goodbye.