Cerebras IPO, Warsh Confirmed Fed Chair, Musk-OpenAI Trial Nears End | Diet TBPN

0:06John Coogan:There's a ton of news. Let's start with Cerebris. The IPO has gone spectacularly well. Cerebris doubled their valuation basically overnight. Brandon Gurell had the good fortune of writing up some of the details of the Cerebris news in the newsletter today, tbpn.com. You can go sign up. Yeah, and right now it's sitting at a$64 billion market cap. 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 trading or betting. And when I wrote the newsletter Friday, Monday, I said a$50 billion IPO and was sort of being optimistic. And it beat those expectations, which is great news.

0:48John Coogan:Chip design company, Cerebris, if you're not familiar, they make a big chip company 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. It's a genius idea. It's one of those simple ideas taken deadly seriously in some ways. But it's trading at$350 a share on its first day of public trading, which values the company much higher. $300 now. $300. Yeah. $300. Okay. The price on this IPO has been literally up only. On Monday, the price range was$150 to$160. Then they raised it. That was up from$115 to$125.

1:24John Coogan:and today we're seeing much higher prices. Go back to that picture. Someone should make a set in LA. You know they have those fake private jet sets? Imagine if entrepreneurs could have a set where they put their logo in the background and they're hitting it with a hammer and there's confetti going everywhere. Yeah, but it's for your course. Yeah. Yeah, and you walk right from there to the Lambo. 1 ,000 students in your mastermind. Yeah. No, 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, there was this trend, I mean, really bad news for the museum industry, but they're getting eaten alive.

2:03John Coogan:And so some entrepreneurs, I think they did very well, started something called the ice cream museum, which was not really a museum in the sense of like a presidential library, or, you know, 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. And they would maybe give you some ice cream. But 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 a huge fabricated statue of ice cream that was not a piece of art that would be sold.

2:45John Coogan:Sprinkle ball pit. There you go. That sounds real. I don't know if that is real, but it sounds very believable. Yeah, they have. They have that? Okay, yeah. 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, because they just wanted to cash in. 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.

3:15John Coogan: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. The gender reveal. Yeah, exactly. Oh, you had kids here. You in the hospital. You can live an entire life through this fictional museum of Instagrammable objects. More of a meme than a real business idea. I know, John. The Museum of Ice Cream now has seven locations. Okay, so they're cooking. They're global. They're global.

3:50John Coogan:They're doing well. Anyway, let's go back to the serious stuff. Cerebris, it's a complicated company because they are so deep in the AI supply chain, but we'll break it all down for you. So there's a bunch of interesting takeaways, some really solid positives. Cerebris chips work, which was something people were not expecting for a while. There was a lot of fun around this company. Just the idea of like, oh, that'll never work. What if the architecture changes? What if we go away from transformers or something? What if we need something completely different? Or maybe the yields will never work because there was this idea that if you're using the entire wafer, typically as you're etching the chips onto the wafer, sometimes there's little defects.

4:27John Coogan: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 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. Yeah, you can use Cerebris chips today. In Codex 5.3 Spark. And so they are very fast. And I think the most important thing that Semi Analysis points out is that token consumers, customers, businesses have shown this revealed preference and a willingness to pay for speed.

5:09John Coogan:And they sort of contextualize it and they quantify it based on their own usage and their experience with Anthropix 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 famously charges six times the price for two and a half times the interactivity, although it's now under two X faster. So effectively you're paying six times the price for two times the speed. 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. But there were a lot of questions about would people really pay for more, that much more for faster models, faster inference.

5:53John Coogan:And Andre Carpathy, Sam Altman was saying like, do you want faster models or smarter models? And he was like, I think in Sam's point was sort of like, these models are very intelligent, but using them faster is sort of more of a magical superpower. And Sam was, I felt like Sam was sort of leading it towards like speed is really important as the next leg up on productivity. And Andre Karpathy was like, no, I just want smarter. I'll just let it run overnight. I don't mind that. 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.

6:29Well, yeah. And then there's the other aspect, which is just capability. capability, speed, and intelligence. That's like the question people have had is like, okay, is there a 250 IQ model or is there just a much more capable model that can use tools more efficiently and is really quick?

6:48John Coogan:Yeah, and that's actually important to Cerebrus. 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 when their sort of expectation, as they put it, was that they would always want the smartest model. They would be very cost conscious. They were in reality saying, I'm going to hammer fast mode. I want to spend on fast mode. And then I think 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.

7:23John Coogan:But OpenAI is clearly very pilled on Cerebris. Cerebris has a big 750 megawatt deal with OpenAI. and the chips are already serving GPT 5.3 in codex under the name Spark as we mentioned and I've used it you should use it It's a very interesting experience because I think a lot of people have interacted with LLMs and chatbots And they're sort of used to this token streaming in it's sort of cute because the phone vibrates 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 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.

8:00John Coogan:They just want the code immediately. You can also just go talk to the model like it's ChatTPT. You don't need to use Codex 5.3 Spark in a coding context. You can ask it whatever you want, and it will just act like a normal LLF. 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 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, right? Yeah. And for a lot of things, if they're two times faster, they do command six times the price.

8:38John Coogan:A sales rep that sells twice as much or someone who's twice as effective as their job might actually command a salary that's five times, six times the actual price. And so there's lots of other context across different business lines that you could draw to There's also this old adage or saying about e-commerce that might may or may not be real But it's probably been transposed so many times and 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.

9:19John Coogan:Where does the buy button go? where does certain information go, the price, the discount, all this stuff, the images, 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, oh it's taking too long, I'll go scroll Instagram Reels, there's always an Instagram Reel and they'll be like, I've kind of forgot about what I was asking about, I didn't get my answer.

9:54John Coogan:And that's certainly true in business context as well. This is currently playing out in AI inference. Companies are paying disproportionately more for faster inference, and this is good for Cerebris. 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. Mainly, Cerebris chips are not currently as capable of holding larger models in the limited memory that they have or networking multiple chips together to serve larger models. We've heard about the NBL 72 racks that wire a whole bunch of NVIDIA chips together can serve these really large models.

10:30John Coogan:That has potentially been a challenge. So Semi Analysis says, moreover, the industry is trending towards larger context Windows ad 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, or adding more memory to the existing architecture, because Cerebrus's whole design depends on a lot of SRAM, static random access memory, directly on the on the wafer, but SRAM is no longer shrinking as much with each new semiconductor node.

11:10John Coogan:So the last version of the Cerebris chip, they've done WSE1, 2, and 3. They're on 3 now, but WSE2 had 40 gigs of memory. WSE3, you would expect, oh, we want a doubling, right? We want a 10x or something. It got 44. So a 10 % increase over one process node, one iteration. Is there an easy way to double this? Is there a question, like how will this scale as the models get bigger 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.

11:49John Coogan: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? Senior or junior? Senior Vice President, maybe just the president, handling the critical work. So future models might not, and that might not be on Cerebris, that might be on NBL 72 or TPUs or something. But 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 CPU bound.

12:35John Coogan: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. A year or two ago when Daniel Gross wrote AGI Bets and was sort of like his NVIDIA underpriced. I don't know if NVIDIA, he might have said that on Stratechery, but 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 ARM is getting into it and Intel is doing very well. We're going to make big computers. Big computers. Big computers for sure.

13:13John Coogan:Ho Nam says, this IPO illustrates the power of an individual partner over the brand name of the firm. Pierre Lamond was a partner at both Sequoia and Kostla. But instead of those firm backing Cerebris, it was Eclipse, the firm he joined at the age of 84, that backed this little-known 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. But one third of the order book, the folks that said, I want shares in the Cerebrus IPO, one third of the book got zero. I guess the top 25 investors took 60%.

13:48John Coogan:That's probably the big investment funds, the Fidelity's, the State Streets, the BlackRock. They have done quite well today. This picture looks wildly different than the Klarna IPO last year, in which only a handful of the team at Klarna popped over, hit the NYSE, IPO'd, and then went back home. Yeah, it was very much just like another day at the office for the team. Yeah, that's definitely what I was contrasting it to. The Cerebris valuation every round, Series A in 2016,$100 million foundation, benchmark, and eclipse. CO2 led the Series B in 2016. VY Capital led the Series C in 2017. then 1.6 billion valuation in 2018, 2.4 in 2019, 4 billion in 2021.

14:35John Coogan:That was like maybe a little bit of a slump, but then 2025, Atreides and Fidelity come in at 8 billion. Then Tiger comes in at$23 billion. Then in May of 2026, the IPO at 48.8 billion. Let's run through the Kevin Warsh news because he has been confirmed as the Fed chair. Kevin Warsh, who 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 timeline? Yeah, it should be. Okay, let's play the clip that really put Kevin Warsh on the map. 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 control and you know how you're going to sell the product and all this stuff.

15:37John Coogan: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 and keeps spinning it. Sales isn't a dirty word anymore. He's really good at this. But somehow you grafted that on to the strange company that can produce these products. How's that transition been if I've got it right? I have so many questions. First, we have to get him to recreate that for sure. Second, 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. Yeah, no, I don't think it was actually on.

16:07I think it was from Palantir. Like that was a Palantir. Oh, okay.

16:09John Coogan:So it was just like a random podcast. And then when I've seen it on CNBC, they were playing the clip. Got it. I think so, yeah. The vote was 54 to 45 in the Senate. The divided vote signals challenges ahead for Warsh, who faces a Fed committee skeptical of rate cuts that Trump has demanded. Of course, we talked about the inflation news. Typically, you don't cut interest rates going into inflation and potentially economic stagnation. You definitely don't cut rates. That's why stagflation is so difficult. because if you have 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.

16:50John Coogan: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 economic stagnation harder to deal with as a Fed chairman, which is potentially the task he will be faced with. So the Senate confirmed Kevin Warsh as the Federal Reserve's 17th chair Wednesday in a largely party-line vote 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.

17:26John Coogan:There's some absolutely legends in there because some of them have 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? Volker. Yeah, Volker is pretty goaded. Bernanke is great. An absolute dog. Yeah, I don't know. Hard to pick. Hard to pick. Chair Jerome Powell, whose leadership tenure ends Friday, captured at least 80 votes in Senate confirmations for each of his two terms atop the Fed. Wow, Jerome Powell, just fan favorite of both teams. 80 votes in the Senate. That's pretty significant. I'm putting him the conversation, Jordy, but I'm not giving him the goat trophy.

18:04John Coogan:But merely because the challenges faced, he wasn't confronted with a great recession, a dot-com bubble bursting, a Black Friday. Like the economy from 2018 to today. Mobile pandemic doesn't count a shutdown of large parts I actually don't because the economy was pretty strong in 2019. And it went into 2020 with pretty strong consumer balance sheets, low debt. There wasn't a shadow banking economy. There was no bomb in the U.S. 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. There was a little bit of like, I mean, maybe you put the inflation, you know, the Zerp era and the end of the Zerp era and all of those gyrations on him.

19:02John Coogan: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 save the economy like in 2008. It's a big deal. He did a great job, but he wasn't faced with the same challenges of a Bernanke, for example. That's what I would say. Tyler, what do you think? Yeah, I think that's reasonable. But also, 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.

19:46But because he did, you know, such a good job, maybe you didn't see any like massive crash. Nothing super bad happening is evidence that he was really good as a Fed chairman.

19:54John Coogan:Yeah, yeah, maybe. He's a defensive back. You know, if they don't score, there's no great place because he's just shut down cornerback. Anyway, Jensen Wong is over in China. Jason Calacanis has a photo that looks extremely real. zero AI detected, but he's bringing two huge boxes of GeForce RTX 5090s, which are not - This is a picture from when he was in Alaska, too. Jason says, never stop selling. I agree. There is some news, which we will cover later in the week, around the dynamics around H100 sales and Blackwells, what's actually happening. It's all in flux as the Trump-China summit plays out on the front page of the Wall Street Journal every day this week because it is headline news.

20:41John Coogan:High stakes U.S.-China summit kicks off. 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 this post from Pete. Yes, and 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 - All right, 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 insane. And we were extremely impressed by this.

21:21John Coogan:This was remarkable, remarkable. Even if it's tele-operated, it's extremely impressive. Yeah, yeah, yeah. Like the robot's clearly working. This is very, very cool. But they're saying that it's not tele-op. 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 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.

21:55John Coogan:That's what I was thinking, was that if the hand is halfway up, it might be blocking the sensor, the camera sensor. And so even though the robot might reach the hand up further to move out of the view so that 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 teleoperated, which is something Brett has said. It's fully autonomous. I feel like that means no humans in the loop. but TOR Taxes has an artist's representation of Helix 2, figures in-house neural network running entirely on board.

22:39John Coogan:And it, of course, is a human in a VR headset. Very, very debatable. We'll see where you stand. But 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 consider this autonomous. 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 the system is running with no humans in the loop.

23:18John Coogan:If you make that claim and you follow this, I think this qualifies as no humans in the loop. If you have a giant orangutan in a VR headset puppeteering the robot via teleoperation, you could say that this system 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 chimpanzee is running its own. It has somewhat of a neural network. 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. What are the prediction markets saying about who's going to win?

23:57John Coogan: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. The Cal sheet. Well, Elon, when his case against OpenAI, it peaked at a 58 % chance. Okay. Where is it now? April 28th. It's now sitting at 30 % chance. 30 % chance. Okay. 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.

24:33John Coogan:Ostensibly, it's where theater ends. Listening to this and being read out in court for the last 20, 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. Sort of feel bad for the AV guy during this trial. There's been feedback. There's been mic drops, but not in the good way. The mics have been dropping out. Funky video feeds. They need to revamp this place, says Mike Isaac. LMAO, the first joke of the tweet storm. He says, Musk Council is going after opening eye execs, Altman and Brockman, and has the mugshot style photo of Altman on the screen again.