TBPN Live - Cerebras IPO, WarshTime, General Catalyst Ad Reactions | Andrew Feldman, Amy Reinhard, Ben Hylak, Doug O'Laughlin, Eric Vishria, Steve Vassallo

Episode Date: May 14, 2026

(01:04) - Cerebras IPO (21:36) - Warsh Confirmed as FED Chair (30:51) - Amy Reinhard is the President of Advertising at Netflix, where she leads the company’s global ads business and monetization strategy. She oversees Netflix’s push into ad-supported streaming, partnerships with advertisers, and the development of new advertising products and measurement capabilities. (45:46) - General Catalyst Ad Reactions (01:03:38) - Ben Hylak is the founder and CEO of Raindrop, a relationship management platform designed to help people strengthen and maintain personal and professional connections. He focuses on building consumer software that uses AI and thoughtful design to make networking and relationship-building more natural, proactive, and human-centered. (01:29:03) - Doug O'Laughlin is an analyst and writer at SemiAnalysis, where he covers AI infrastructure, semiconductors, cloud computing, and hyperscaler economics. He is known for deep technical and financial breakdowns of GPUs, data centers, and the companies shaping the AI compute stack. (02:23:13) - Andrew Feldman is the co-founder and CEO of Cerebras Systems, an AI hardware company building wafer-scale processors designed for large-scale AI training and inference. He previously co-founded SeaMicro, which was acquired by AMD, and focuses on rethinking compute architecture for the era of massive machine learning models. (02:42:27) - Eric Vishria is a general partner at Benchmark focused on early-stage software and AI investments. Before joining Benchmark, he held product and operating roles at companies including RockMelt and Google, and is known for working closely with founders on product strategy, growth, and company building. (02:56:51) - Steve Vassallo is a general partner at Foundation Capital focused on enterprise software, AI, and frontier technologies. He works closely with technical founders building infrastructure and developer-focused companies, and is known for backing ambitious startups at the earliest stages. Follow TBPN: https://TBPN.comhttps://x.com/tbpnhttps://open.spotify.com/show/2L6WMqY3GUPCGBD0dX6p00?si=674252d53acf4231https://podcasts.apple.com/us/podcast/technology-brothers/id1772360235https://www.youtube.com/@TBPNLive

You're watching TVPN. Today is Thursday. May 14, 26. We're live from the DBPN Ultradown. The Temple of Technology. The Fortress of Finance. The capital of capital. Oh, thanks. Hey, Ben. That has Ben. Indispensable. We got multiple Ben. We have a great show. It's Cerebris Day on the show. Cerebris IPO. We'll talk about that.

Semi Analysis has a fantastic deep dive on the company. We'll go through that. Doug O'Loughlin from Semi-Alices joining the show. And then, Angel Feltman, the founder and CEO of Cerebrus is joining the show. But we have lots of 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 president, you think president of the United States. I think president of ads.

That's right. Ben Hylach, Eric Vichra, Steve Vassallo. We got a bunch of folks coming on the show. show today. It's going to be a fun one. So there's a ton of news. Let's start with Cerebris. The IPO has gone spectacularly well. Cerebris doubled their valuation basically overnight. Brandon Grell 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. 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. They deserve it. It's a true overnight success. 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, chip design company, Cerebrus, if you're not familiar, they make a big, big chip, 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 taking deadly seriously in some ways. But it's trading at $350 a share on its first day of public trading, which values. values of the company much higher? 300 now. $300. Yeah. $300.

Okay. So it was. It was up at $350. It has since sold off slightly. And they raised around $10 billion. I think they were targeting $6 billion at one point. They've upsized that. I think it was $3 billion raise initially, but they have a good amount of money in the bank now.

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. And today we're seeing, you know, much higher prices. Go back to that picture. Who's in the picture? At the NASDAQ.

A 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 set. 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 in there. Yeah, yeah. But it's for your course.

Yeah. Yeah. Yeah. Exactly. And you walk right from there to the Lambo. When you have 1,000 students in your mastermind? Yeah. 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 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 was not really a museum in the, you know. 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, most of the museum was just like very Instagramable things. So there would be like a ball pit or a bunch of raining confetti and stuff and a huge, 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.

That sounds real. I don't know if that is real, but it sounds very believable. No, they have. They had that? Okay, yeah. So, 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 Instagramable 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. Yeah. Exactly. Oh, you had kids here. You in the hospital.

You can live an entire life through this fictional museum of Instagramable objects. More of a meme than a real business idea. But, uh, John, the museum of ice cream now has, uh, seven locations. Okay, so they're cooking. They're global. They're global. They're doing well. Um, well, let us know.

Anyways, nice little tangent there. Yeah. Miami is the capital of gimmick museums. Got to do a whirlwind tour. Uh, anyway, let's go back to the serious stuff, cerebris. Uh, 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 semi-analysis has a fantastic deep dive.

It's a longer read, so we're not going to go through at all. But there are some very interesting tidbits in here that we can sort of summarize and contextualize for you. And then, of course, we'll be talking to Doug O'Loughlin from semi-analysis at 1230. So there's a bunch of interesting takeaways, some really solid positives. Cerebrous 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.

Like, what if the architecture changes? What if we go away from transformers or something? What if we need something, quote, 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. 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, will, 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. You can use it. Use cerebrose chips today. Yeah.

As 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 for and a willingness to pay for speed. 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 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 paying, you're paying six times the price

for two times the speed. That's, 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 was a lot, there were a lot of questions about would people really pay for more, that much more for faster models, faster inference?

And Andre Carpathie, 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 under Carpathie 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. Well, yeah, and then there's the other aspect, which is just capability, right? Capability, speed, and intelligence. Yeah, I think.

Like, that's the question I think people have had is like, okay, what is, is there a 250 IQ model? Or is there just a much more capable model? Yeah, the unhobbled one. Tools more efficiently. Sure. And is really quick. Yeah. And that's actually important to.

cerebrus because as we'll get into the chips do face a hurdle with scaling in terms of longer context windows, all that stuff. But semi-analysis shared their breakdown. They were run rating $10 million on AI spend in April. And they said that April was the peak, which is interesting because that was sort of, I would have expected a straight line, continued growth. But they might have, you know, been really pill, 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, 95% 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. So, 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. But opening eye is clearly very pilled on Cerebris. Cerebus has a big 750 megawatt deal with Open AI. and the chips are already serving GPD 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 the token streaming in. And 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 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. And so you can also just go talk to the model, like it's chat TPT. 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 LLM. And 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. Yeah. And for a lot of things, if they're, if there's two times faster, they do command six times the price. Because over the course of the year, a sales rep 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 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, where does the buy button go, where does certain information go, the price, the discount, all of 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, oh, I kind of forgot about what I was asking about. I didn't get my answer. And that's certainly true in business context as well. So 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 head.

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 NVL 72 racks that wire a whole bunch of NVIDIA chips together can serve these really large models. 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 agenic workloads. And it doesn't look like there's a simple solution of just scaling the wafer size larger, because TSM is set up with a standard wafer size, and adding more memory to the existing architecture, because Cerebrus's whole design, depends on a lot of S-RAM, static, random access memory, directly on the wafer, but S-RAM is no longer shrinking as much with each new semiconductor nodes.

So the last version of the Cerebrose chip, they've done WSE 1, 2, and 3. They're on 3 now, but WSE 2 had 40 gigs of memory. W.S.E.3, you would expect, oh, we want a doubling, right? We want a 10x or something. It got 44. So a 10% increase over 1.5%. process node one iteration and some 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?

To add more S-Ram, 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 So they, there might be a much harder 3D wafer bonding approaches, doing stack stuff. Like there are other potential ways, but there's not like a linear. Oh, yeah, of course, the next version is going to double again. And so that is a potential problem that they need to work through. But in an agenic 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 Cerebrus, that might be on NVL-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. 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 I was asking Ben about, like, he pulled all our guests

together. It's like, I'd love to know the geolocation 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 1,000 or 2,000 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? It might be okay to run faster on a smaller model

that works on Cerebris. And so it's hard to predict the exact mix of chips that will power large networks of agents, but these different designs, to me, they seem more complimentary than directly competitive. Like 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's been mad to say that on Stratory, but like, you know, we entered the AI age and everyone was like, oh, GPUs of 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's doing very well. And it was going to make big computers. Big computers. Big computers for sure. And so I'm extremely optimistic, obviously, and very excited to talk to Andrew Feldman, Doug O'Loughlin, Eric Mishra, a bunch of folks who have been involved with the journey.

Yeah, Eric did the Series A. Wow. Convention. Almost a decade ago, 2016. And a benchmark are sitting on, I guess as of this morning, like many, many billions of Cerebrus. It feels like they brought a huge team to the NASDAQ. Look at this photo, the second post we have. Obviously, Andrew's there.

A lot of the team members, typically the key banker. But, you know, we've been to some IPOs, and some of them have had smaller teams. This one feels extremely celebratory and feels like a very broad, inclusive crew came together. What else is in the timeline? And the class B shareholders have 99% of the class. corporate voting power. So founders in control. Founders in control. Founders in control. Founder M. Uh, Hohom says this IPO illustrates the power of an individual partner over the brand name of the firm. Pierre LeMond was a partner at both Sequoia and Kosla. 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. Matthew Siegel is giving the recovering CFA is giving some color on what happened to the order book. One third of the order book, the folks that said, I want shares in the Cerebrous IPO. One third 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 Fidelity, the State Streets, the Black Rocks, 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 at the NICIPO and went back 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 Cerebra's 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. 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 2006, the IPO. at 48.8 billion.

I can sort of just do the... Nice work from... 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 know. Incredible work from Tiger.

Yeah. Coming in a 23 post. Very, very good. And now up dramatically in just four months. Very, very good. Well, our first guest is joining us in eight minutes. Let's run through the Kevin Warsh news because he has been confirmed as the Fed chair. And we'll run through this and then we will come back to some of the other stories because we have a gap later in the show.

So 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 time line? Let's play the clip. Yeah, we have the video here from John. They really put Kevin Warsh on the map because this is what he's known for. Of course, he's at a storage career.

I heard you recently sales. I remember I showed up in your office ones. I was dressed like this. I think you screamed at one of the guys. He 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 are 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 and keeps spinning it. 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?

Okay, wait, wait, but so I have so many questions. First, we have to get him to recreate that for sure. second 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 kairon yeah no i i don't think it was actually on i think it was from palanter like that was a palanter oh okay so it was just like a random podcast and then and then when i've seen it on cnbc they were playing the clip god it i think so yeah probably a reaction stream over there um well let's go through what happened because kevin warsh has been confirmed as the new Fed chair. 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 in. That's why stagnation 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. 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. 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. There's some absolutely legends in there because some of them have really long run. 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 good. Bernanke. He is great. An absolute dog. Yeah, I don't know. Hard to pick. Hard to pick. Warsh, who is nominated for the post by President Trump. In January 1 confirmations, 54 to 45, earning support from all Senate Republicans, but just one Democrat, John Federman of Pennsylvania. Senator Christian 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 ends Friday, captured at least 80 votes in Senate confirmations for each of his two terms atop the Fed. Wow, Jerome Powell just fan favor to both teams. 80 votes in the Senate. That's pretty significant. Yeah, is he going to be looked at as a gigatchat?

Potential future podcast throughout history? Get a maybe VC fund going? What do we think? No, but when we look back, like it seems like the last two, three years, he's handled himself. Yes. I mean, he's had a really tough situation, and he's managed to plan the plane. When did Jerome Powell get, first become the Fed chair? When was he?

He's been assumed off as 2012. So I think the, wait, oh, 2018. 2018, he was right, he was right after Janet Yellen. And so I think I'm, I'm, I'm putting him in the conversation, Jorty, but I'm not giving him the goat trophy. Yeah, I'm not saying. I'm not saying that. Because the, the challenges faced, he wasn't confronted with a great recession, a dot-com bubble bursting, a black Friday.

Like, like the economy from 2018 to today. Mobile pandemic doesn't, you don't, you don't count it. shut down of large parts of the economy? I actually don't because the economy was pretty strong in 2019 and it went into 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 end, the ZERP era and the end of the ZERP era and all of those gyrations 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 die. situation that the Fed had to intervene in a meaningful way and like save the economy like in 2008.

It's a big deal. He did a great job, but he didn't, 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 like, you know, 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, nothing super bad happening is evidence that he was really good as a Fed chairman, right? 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 for the last couple of years. Potentially. He's definitely in the top 17. I'll give him that.

Anyway, Kevin Warsh. Chair Jerome Powell, whose leadership tenure ends Friday, captured at least 80 votes in the Senate. 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, 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 a difficult economic backdrop in Trump's broadsides against the Fed independence.

have set up the central bank for a thorny leadership transition. Senate committee confirmation hearing last month, Warsh faced intense questioning from Democrats over how he would maintain the Fed's independence from a president who plays his priority on personal loyalty. Warsh 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 governor, 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 Powell. Warsh 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 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 that sort of Bernanke deployed. And so that's what, those are his laurels that he will not be resting on, but he will be drawing on from experience.

So Warsh left the Fed in 2011. 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. Fed chairman Kevin Warsh 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 channel show. Amy, how are you doing?

Doing it fantastically. It is an honor to have you here. Yes. Our first ever guest from Netflix. I think so. Thank you so much for taking the time. Only took like 2,000 interviews to get you guys on here. But we're excited to me.

You need to be here first. Yes, yes. I mean, obviously big fans of both Netflix and advertising, but would love to start with a little bit of background on yourself, your experience, and just sort of your intro to how you found yourself as the president of ads at Netflix today. Sure. I've been at Netflix for about nine and a half years now in a couple of different roles. Started out first in our content organization, doing both licensing and then. overseeing production and about two and a half years ago I stepped into this role overseeing our ads here and it's been a fantastic two and a half years a lot of

excitement feels like we've been able to accomplish a lot and great company if you take us back to the initial the initial push into ads what can you tell us about the trade-offs the build versus buy debates that we're going on at the time the just maybe even the cultural changes. I think we are super, you know, we love ads. We think that's a fantastic business model. It's a way to deliver great value to customers at lower prices, and there's so many benefits.

But culturally. Yeah, what was the debate like? Yeah, what was it like internally? Yeah, well, I think it's been well publicized, you know, that not being in advertising was a strategic bet for a long time, right? And so early in 2021, 2021, when we started to talk about the notion of getting into this business, yeah, it created a lot of, I think, fair to say, you know, angst within the company for a bit of time because it was such a big shift to your point, culturally and strategically. So I would say, and then we made the announcement that we were getting into it. And in terms of the whole build versus buy, you know, we partnered with Microsoft to enter the business very quickly.

And that got us up and running. But it's been, you know, we made the decision about 18 months ago to lean into building our own tech stack. And we launched that a year ago. So we're just a year. I keep having to remind myself how nascent our tech stack is because we've been able to deliver so many developments and so much progress against that over the course of the last year. But I would say full circle, you know, we were past, we put to bed all notions that we should be in this business. I think everybody understands strategically now that it's important for us to be that. And we've been able to grow our user base because we have been able to get to a lot more consumers who are looking for that low-cost option and are fine with ads, right?

So it's been a great thing for the company, I think, and everybody's on board. And, you know, the recent news, as you heard, which we just announced our upfront yesterday, that we're expanding that. ads here into 15 more countries around the world. There you go. Everybody's on board, full speed ahead. How are you pitching the ad product today? Is this primarily brand marketing? Is there a timeline to get to more of a performance focus?

What is your pitch to advertisers? Yeah. You know, as we see in the marketplace, advertisers are oriented around outcomes, right? So we know that we need to be a full funnel solution, and we believe that we have the metrics to support that. So to your point, we've been very successful with some of the brand partnerships that we've done over the course of the last year and a half. But we've also seen really good conversions in terms of lower funnel and making sure that we're driving purchase intent and consideration. So as we build out more of our solutions, we are going after that full funnel solution. What are conversations like around how brands should, how much brands should want to associate with particular pieces of content?

Because I think some brands might come in and say, well, I'm advertising, 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 the nose directed and I care. I want my brand linked to this particular piece of content. But we've seen time and time again that once the algorithms get good enough, once dynamic ad placement can actually flourish, every company tends to see better performance there. So where are the ad buyers today in terms of those 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, powerful moments, those are oftentimes the easiest to sell. I think K-pop Demon Hunters is actually an interesting case because when it came out a year ago, we didn't know that we had a hit on our hands until about 60 days into it. And I think that's what the magic is of Netflix, is that we have so much variety and depth of content that we're programming and trying to hit all audiences that you never know where your next hit is going to come from. And so selling those audiences, selling that, you know, audience behavior, moods, targeting moods and relevance is really important to a lot of different advertisers. So, again, we just want to meet advertisers where they're at.

And some folks understand that being across a number of different programming choices is important. And some people want to tag along with those big tent holes. And we want to, you know, provide those opportunities. Yeah. I mean, it's interesting. Netflix has, like, deep, deep experience. in machine learning, AI, recommendations, all parts of, like, you know, high throughput data processing. But I'm interested in any learnings or surprises from building the proprietary ad delivery stack.

Has it been as expected? Has it been, there's new skills that you need to bring because a lot of companies have been successful at scaling content and then struggle to figure out ad delivery. You obviously haven't. But then also there's this AI boom going on, which can help with productivity, but also new algorithms and new ways to actually target content. And so I'm interested in where the buildout didn't master your expectations or surprised you. 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 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 caps, those types of things.

But we do know that there are times when we have to pivot. So I would see you, there's not just one example of a time that we've had a wrong hypothesis. We're constantly testing things out and figuring out where we, you know, where those hypotheses prove out. where we need to pivot and change swiftly. So it's hard to point to one specific moment where it felt like it's been out learning. I would say the bigger learning for us just as a company is, you know, this is a relationship business too.

And we've never this, you know, you talked about kind of getting into ad sales, right? We've never had a sales team in terms of our overall organization. So I would say there's more organizational learnings. necessarily tech learnings because we're so used to that tech cycle of testing and learning and iterating. How are you thinking about the ad product feeding back into the content production? I've noticed that Netflix has been fantastic, in my opinion, of creating more engaging content. I was watching The Rip with Matt Damon.

And you click the play button and you see Matt Davin's face within like two seconds. And it clearly confirms that you're watching the right movie and then the title card comes in. And that's a departure from 50 years ago. You 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, there was the famous, like,

fade to black and then the ad break and then fade back in and you resume and Netflix has never had to contend with that in media products, but is that going to come back? Is there a next generation pattern for creating content that can both have ads in it and not? Are you seeing glimmers of what the future, like the impact of ads might have on on like the editing structure and the timing and the pacing? Yeah, a lot of it, to be honest, depends on our creators. So, you know, working with talent who, and some of those, some of that talent may be more tech forward and are thinking through those types of things when they're writing shows. I'll give an example. Shaunders was used to writing for broadcasts and network for many, many years. So when she writes

a lot of her content, she's already thinking about where those natural breaks are. But not all writers do that. And that's okay. We can still find what are those natural breaks because we want to make sure, again, getting back to the member experience, that it's not intrusive or it doesn't come mid-sentence, right, and is cutting off any of the action. We're able to adapt to any way that our creators want to write the content and fit it into that member experience. Yeah. For U.S. markets, is this, is there any enterprise spending? I would imagine that a lot of enterprise buyers, like have been Netflix subscribers for really? long time and maybe they're not getting served any ads at all. And so this is more of a

consumer opportunity or am I thinking about that the wrong way? We've, most of our clients right now, the target segment that we're going after are enterprise top 400 clients, right? Because we think those are the ones who, uh, has been, sorry, I meant I meant B to B versus like B to C company. Oh, we think about this more as a B to C opportunity for the most part. And I think as we expand our learning and expand our offering may get into the B2B space,

but for the most part, B2C. Yeah. Yeah. I feel like all of that, like the higher upmarket, more targeted, that's all unlocked with scale once there's more learnings on responses. I'm wondering what other signals you think might be valuable

because many times, you know, 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, I imagine that the data is messy, but how important is it to sort of close that loop in an ATT era where it's a little bit trickier, but there's a lot of things that you can do on the signal side anyway. Yeah, you're absolutely right. And this is an area where I talked about the testing and iterating.

Yeah. We're leaning in a lot on the testing. What does that screen experience look like? You know, again, how do you meet the customers where they're at without being sort of intrusive? 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 taking care of our members data. But a lot more, I think, to come.

Have you been surprised by the return of the QR code in maybe podcast advertising? But I see it a lot because people are watching on, you know, they'll watch a YouTube video on a TV. And the creator will, you know, hard code in a QR code to link out. And that was something, I had completely written off QR codes and then they made a major comeback. No, I agree with you. From a member experience may not be the most simplistic thing. Understanding kind of the ad tech on the back end, I'm not surprised by it because we got to be pretty complex pretty quickly. Yeah.

And there's some, we hit some. we hit some sort of like inflection point where the, maybe it was in a certain iOS revision where the camera app became so easy to press a button and pull out and then it detects the QR code so quickly that that flow, because you used to need to like have a QR app separately to scan it, and now it's all integrated and so someone can just whip out their phone

and run right to it. Jory, you have something else? Nothing super top of mind right now. I mean, last question I had is, Do you ever expect Netflix to serve more short form vertical video style ads in something like the Clips tab? I know the Clips tab right now is focused on basically a content discovery. But I imagine in the future people will spend more time in a format like that, especially on mobile. Absolutely.

And that is one of the announcements we had at our upfront yesterday is that as we roll out this vertical video content, that we are going to be. offering that to advertisers, along with our todoom.com coverage in 2027. So, yes, we think that is a big opportunity to. Last question for me. I would love to know about the intersection between games and ads. That's been a huge growth driver with other categories and other companies. But I'm wondering where that is in the roadmap, how you're thinking about that. We haven't thought about that yet. Look, our roadmap, I could. fill our roadmap for the next two to three years based on just some of the foundational things we want to do and a lot of the innovative areas we want to lean into. But it's an area that we're

keeping an eye on. And as we watch that games engagement increase, I would never say never. I've learned to never say never at Netflix, but it's not something that's on the near-term roadmap. Okay. Thank you. Well, thank you so much for joining. This is a great to meet you, Amy. Thanks for breaking it down. We'll talk to you soon. Cheers. Have a good one. Thank you. Moving on from Kevin Warsh, who is the newest Fed chair. We have a debate. We have a debate going on on the timeline around General Catalyst's advertisement.

Some are calling it an attack ad. Particularly, Injuries and Horowitz is calling it an attack ad. We touched on this yesterday, but if you did not tune in, General Catalyst, the large-scale, what do they call it, gigafuns now? What do they call it? Platform fund. But there's something else. It's a huge, huge venture capital. They launched an advertisement which we can play again to refresh everyone.

Let's scroll it out in the beginning. Let's play it from the beginning. It's only 30 seconds. Hi, I'm G.C. And I'm V.C. Who's your friend here, V.C.? This is Wolf 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. 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.

It's easy, easy. Stay, stay. No, no. Stop, stop. I'm sure he'll be fine. Okay, tons of thoughts, but you kick it off. What's your read on this? take me through it. So the actual ad,

the way it's shot, the timing, cinematography, it's fantastic. I do, I think the ending is, I think the ending is funny, right? It makes me smile a little bit. The dog's going 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 those are not typically used as pets that I know of. I think that they are more deployed. Whenever I see a demo of a Boston Dynamics 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 and then obviously a fodder for viral videos. But yeah, step through it 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 a lot of discussion, Anjni Midehavis says, it's a bit cringe guys. Olivia Moore at Andresen 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? The biggest B2C businesses always start out looking weird. Yeah, there's a way to there's a way to do the same pitch for airbed and breakfast, which is you know that empty couch, you know that empty room in your house? What if you were to monetize it? What if you were to allow strangers to sleep in the empty?

room in your house. And of course, that was a lot of the criticism at the time, was that it never was going to work. And it was strange, but then it created a lot of positive externalities. People built some businesses, people get to travel and integrate with local communities. Yeah, so the main thing here is, like, it's, I do find it, I do find it, fascinating, considering that when you actually look at the portfolio overlap, it is insane. Almost all of their winners, 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, A16C has a very different media strategy. They're very loud. They're all saying the R word a lot.

And I think G.C. can kind of counterposition against that. But you're not going to counter position against like what companies you're backing, right? G.C. G.C. I had to look this up because I thought there's no way that it's true. GC's in both Calci and Polymarket.

Big, great companies. Right? Those have been at the center of the debates. But that is the center, those companies are at the center of the moral debate in tech, right? Certainly for the venture-packed controversial companies. Yeah.

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? Not draft kings, but Fanduel, I think, back in the day. And also, it's not to say... There's companies that start out as controversial, and then they become, like, completely normalized and everyone sort of comes around to, like, oh, that's a good thing.

Like, Anderol's a good example. Both of these companies... both Andresen and G.C. are in Anderl. 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 industry. And then on the other side, you have companies that come out as really controversial and people are like, this is the end of the world. And then they just sort of fizzle out. Like, clearly would be a good example of a company that, like, has done okay, but it's not like, oh, no one's doing home work.

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, yeah, just like some funny memes and ultimately, you know, moved on from it. And and so it's it's very hard to map like the controversiality to the ultimate outcome and where it lands. Like a lot of the yeah, a lot of the prediction markets were not controversial when they were just predicting the election. 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 Andreessen, G.C. being like the cool, hip fund 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 in investing in it, regardless. of what category it's really in. Yeah. 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 do, like, new media. Yeah. And G.C. is the one that I've always viewed as, like, more buttoned up. Yep. More behind the scenes. Yep. More traditional finance. I think of them as, like, New York, East Coast, Boston.

And I love that. Yeah, we love that. We love that. I think Payne Capital is a good example of 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 Bain's done some fun stuff. I mean Foggo to 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 we're buy the book investors we find great companies back them and uh you know Sequoia's done that too you know 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 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 Steve Ballmer. Like Steve Ballmer. I think it looks like Steve Ballmer. Or Mark Injureson. But the other thing is like. So hold on. I was looking at Mark Andreessen photos, and I could not find a single photo of him in a vest.

So I don't know about this. But clearly Mark Andreessen did take issue with it because he, he quote tweeted it something like 45 times, which really amplifies it and creates more of a conversation about it. Strategically, you might have just wanted to mute this if you don't want 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. But it does seem like messy rolling around in the mud when you're wrestling with pigs, you're going to get muddy, right?

Isn't that the same? Yeah, it's just, it's just, again, like creatively, it looks great and it's very fresh. And I think Reggie and his team did great work. But it's funny to just like take shots at a whole category of investing, basically, that is kind of the bread and butter. of G.C.'s business. Yeah. Yeah. Drawing the line in the sand. What is the actual

line? 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 seriously. Yeah, so I, so, for example, the last robot. I would be like, don't fund gambling, don't fund cannabis. like don't upset Sagar and Jetty basically. It's like the way I would pitch someone if they were trying to be like

the responsible actor fund. 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 seeing-eye dogs. Oh, interesting. Seeing-eye dogs are like incredibly expensive. Helping short supply.

You know, kind of out of reach for many people. 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 kind of endeavor. Yeah.

I don't know if it'll, you know, work or what the business will look like, but in general, like, there's a, you know, anyway, so. I'm extremely bullish on robot dogs. I think it's very complimentary. You think about, I mean, especially if it's a,

a toy for kids. Think about how many vehicles kids have these. Yeah, Gabe says service dogs can reach 60K. Whoa. And that doesn't count lifetime of all the other costs associated with dog ownership. Next time I see some with a service dog. Bow!

It's like, whoa, buddy. You know, deflects like that. No, the 60, the, yeah, I mean, kids have like, you know, a bicycle, a tricycle, a, a, a RC car that drives and then another one. And it's like, like, 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.

This is going, maybe a thousand robot dog startups will flourish. And the most ironic scenario is because the industry will become so denial that general catalyst and injuries and both have to back it. And they're like duking it out for allocation at every round. and the future robot dog founder out there is like, I just do nothing win or build a great company and win. Was there anything else going on with the robot dog debate? I, you know, selfishly, you know, I think it's bad for the industry

if you have two of our platform funds, you know, just making these somewhat petty videos that are basically ad hominence. That, you know, basically throwing stones from glass, castles as one could put it. So I think it's generally bad, but for entertainment purposes, if they want to keep going at it and turn this into proper Drake versus Kendrick situation, this might be a good use of a 16s E new media, although much of the talent there is now over at a I mean, I would love, yeah, I would love like a response video from Andreessen, shot in the same way or something. And like, there is an opportunity for like a rap battle here that's a beef that's like very, very entertaining that I would absolutely love.

The flip side, though, is that I agree with you. Like, if you're a VC, you're much better picking a villain that is something around like a lack of technological progress. Like, Teal did this very well with like stagnation is the boogeyman. Like 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

what the goal is of building a business that delivers a value the consumers enjoy yeah it's just 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? Because 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 general, you know, they're just hanging out together.

I mean, Katha Boyle did Anderl at GC and then went over to Andresa. And so like there's like there's more overlap than than, than differences. Plenty of plenty of other things to take shots at. But I mean, to Andreessen's credit, they've done a good job of that focusing on on geopolitical competition, focusing on China, focusing on stagnation and nimbism, all sorts of different things,

that they've been aligned with more of like an abundance view as opposed to punching down, although, you know, there's memetics all over the place. Yeah, it seems like GC has an opportunity to be the buttoned up platform. I think so. You know, they don't need to say the R word.

Yeah. They don't need to be super. loud, right? They can just focus on the craft of investing. 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. Just there's a way to counterposition yourself without attacking your rival. I mean, do you remember the last time General Catalyst went viral? Hamont, CEO,

was on Harry Stebbings show, 20BC, and he said, like, triple, triple, double, double, double, double, is no longer good enough. 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, it's smart to debate. But actually 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 sure, he clarified it on our show.

He clarified it on other shows. 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 exceptionally fast, unprecedentedly fast. And so 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 best company in the category that's actually going to win. It might be the company that's growing 10x a year, not two X a year.

And so that was something where it was like thought leadership from Vermont. It sparked a conversation. There was some debate around it, but it was from a position. And 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 that sort of more narrow staying in the lane view, it got a lot of attention. It did break through. It caused a conversation, but it still didn't sacrifice. 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, 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 G4s RTF 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. High stakes U.S. China Summit kicks off. there was another drama in the tech world yesterday, but we'll come back to it after our next guest,

Ben Hylach from Rangrop 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 it on the show before. Welcome back, Ben. How you doing? Doing well, man.

How are you? Fantastic. Great to see you. What's new in your world? Reintroduce the company quickly and then tell us the news. Sure. Rangrop, we make observability for 80s. So the main thing we do is self-healing agents.

So what it means is that when you're a raindrop hits a problem in production, we detect it, we fix it. How do you do it? That's a good question. So at the end of the day, like, we consider ourselves the intelligence for your intelligence. What that means is that we are the best, fastest way to essentially look at anomalies. So what that means is that, 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 starting to evolve into a different pattern. And so it's kind of a combination of agents, but also more like classic ML techniques, a lot of, like, custom-trained models for every customer. Walk me through the shape of the agent market right now. Like, the way you're talking about it, you know, sort of illustrates the broad diffusion of agents and custom agents. I think that a lot of people think Claude Codex and Codex.

And I don't know if you're doing enterprise deals with those firms or that's the goal. But I imagine that every startup, many legacy companies have built some sort of agents, some sort of harness. And I'd love to know the shape of how broadly diffusing, like, custom agents are in companies versus, is it the domain purely of startups that create an agent for legal or an agent for sales, and then they vend that into a company? Yeah, so I would say that there's two kind of categories of customers. We started with super high growth startups, at the time startups. So those are companies like Clay, for example, Framer, speak.com,

some of the fastest growing companies in the world. And those were some of our earliest customers, and we're lucky they grew a ton. So, you know, that has helped our growth. Always helps. It always helps. Yeah. Someone once mentioned that like, you know, this kind of business is a lot like early stage seed investing, actually. It's kind of interesting.

Like, you know, you have to be pretty picky not to work with companies that are going to die. Because if like, especially analytics, like all these sort of things, like they, you, you succeed as a company when your customer succeed. Like, if all of your customers are terrible, it's like, well, why do you? Yeah. Yeah. I had a, I had a portfolio company that was. working on like agent infrastructure like roughly two years ago and pivoted because he was like, okay, this is clearly going to be a big thing someday, but right now he's looking at all the

underlying companies and he's like, I don't believe that any of these like agents in their current iteration are going to work. Now, maybe there's an argument. I think it was very counterintuitive at the time, but I think we chose to, chose to find companies like clay.com, which are, we're clearly on a insane trajectory, but at the time, we weren't necessarily as large. And so I think a lot of our customers now are pretty large, but at the time weren't necessarily as large. And then in the last few months, we've been moving into Fortune 50s, Fortune 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 bread and butter is like, you know, companies that are redefining the way people, you know, interact. you know, in different verticals. But then, yeah, there are like Fortune 50s, Fortune 100s that are also deploying agents internally. I think the shape of that looks very interesting.

And like, it's something that, 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, yeah, always very interesting. 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

I think that I mean you know I so this is a tough question I what I want to do actually is pivot the question a little bit because I want to talk about I want to talk about our launch today if that's okay you're okay with you guys yeah yeah

I'll tell you my questions you tell me your answer okay sounds good does that mean that you want me to not answer this No, no, no, no. I'm just messing around. Go for it. Okay, cool. 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're like that where it's like, you'd ask me anything and they're just going to direct to it. I'm going to redirect to a topic point. But it's fine. I want to hear about the launch today. So just tell us about that. Great.

Let's talk about that. Okay. So, guys, there's been this crazy thing that has been missing for a very, very long time. That's why I want to talk about it. So, like, people have been building agents. you're building them locally. Like you're using some sort of SDK, it could be open AI,

could be for cells, whatever SDK it is. And there's no way... Like it actually is to run on like a developer's machine. 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, Rain Drop is like one of those. You know, and there's a bunch of others. Yeah, but they might also just like drop the logs in, like a non-relational database.

Sure. They'll just print it to, you know, console. log, like, oh, here's what was happening. It's like that bad. Yeah, yeah, yeah. And the other problem there is like, so you can't see like a nice trace or you're sending it to some server and it takes like seconds to see everything. I'm like, whatever, it looks terrible. But then also your coding agent can't like see the traces either.

So then when you hit a problem and you're like, hey, you know, this response was wrong, plot code will just make shit up. Like it'll just be like, oh, I think that like maybe this tool. was wrong or I think maybe like this happened because it doesn't have any of that data. It doesn't actually know what the coding agent did. So I think that like as someone building agents, like our company building agents were like, it's actually kind of embarrassing how long it took us to solve this problem. No one else solved it either.

But yeah, that's what we launched today. Free local open source tool, brainjob. a.ai slash workshop and it's completely free like it's just open source. Why open source? That's a really good question. I mean, I think that genuine answer, and I think part of why competitors haven't done it, I mean, there's probably other reasons for that as well.

But I think it's that it can be, right? Like, someone else can do it. You know what I mean? Like, I think that it running locally is the best experience for people. And to be clear, like, there's still things that it enables, you know, if you connect it to your production range drop, which is like you can pull in a remote trace and replay it. And then Cloud Code or Codex can just keep doing that loop until it works.

So there's still benefits for us. But also the truth is that we want people to hack it. We want people to meld it into whatever works for them. So we use a lot of open source things here, right? So it makes sense to contribute back as well. Yeah, that's great. Yeah, I'm wondering about other just like predictions about the next breakout category of AI agents, what you're seeing.

Feels like you'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 Brian Chesky earlier about Airbnb. Oh, yeah. Oh, yeah. Talk about that. You're talking about that. No, I think, like, you know, I use Airbnb a lot.

I love Airbnb. I think if I had to guess, I would say Brian Chesky knows a lot more about Airbnb than I do and probably a lot more about being a founder than I do as well. And so I think there's probably a lot of, a lot that I'm not considering. That being said, I think it's fresh. Like, if Airbnb had an API, I would use it and I would book Airbnb with it, like through Cloud Code, right? So it's like, I know I would do it.

It's, I find Airbnb very, very hard to search. And I think that there's a lot of, I think the tough part and like what I see industry wide right now, everyone's trying to figure out is you see companies almost reducing themselves. into an API with like absolutely no mode. Like you look at like Photoshop Illustrator, et cetera. They're like, oh, we have a cloud code integration now at MCP. At the point where people are just using Photoshop Illustrator, et cetera, as like an MCP, they've sort of lost the game, right?

Like if no one's actually touching the UI anymore, I think that right now companies have to do that increasingly because they've 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, you know, do you guys remember app clips? Yeah. Yeah. Where did those go? I only see them where did those go with like parking meters sometimes.

Yeah, right. So like one of like the hero ideas there was like, oh, you know, like imagine you're in line at Starbucks. You don't have the Starbucks app downloaded. Like well, why not just, you know, scan something, have an app clip order your drink? And it's like, turns out Starbucks doesn't. not that's right sure that's the last thing in the world Starbucks want you to download the app they want you to have stars they have an entire like there's a reason why DoorDash and Uber Eats and like whatever you know God knows other apps exist

it's not because they need to but because each have companies and money and goals and like so so why would they reduce themselves into an easily interchangeable API 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 retail store like Starbucks or like a marketplace like Airbnb or DoorDash because I really think that these marketplaces provide, you know, an exceptional amount. All the value is not in the UI, right? I agree. I agree. And like the value of Starbucks is not that it's a pretty app. It's because they have specific drinks that they can make pretty much anywhere, you know, someone would be. Yeah. I think of that company

buy the drink company. They got started during the direct-to-consumer boom. Obviously, they would have some beautiful Shopify website. They didn't. They just went direct to retail, and they had Amazon. You could order it online, and if you went to their website, it would just say, go to Amazon. And they did fine, a billion-dollar company. And because, like, the value is not in the e-commerce experience.

They didn't play, like, the Stars game. Of course, Starbucks is maybe sacrificing a piece of that business model, But it's not giving away the whole cow. I don't know. There are going to be ways to monetize this, right? Like, there are going to be successful business models built on top of this sort of layer. And to be honest, as Rain Drop goes into the future, like, that's the future we're building towards. That's the future we want.

I mean, we're going to be announcing a partnership with a really large, one of the large coding companies as soon as far as, like, integrating with them more. Where it's like, I don't see Rain Drop as a company that's going to submit PRs and production to people's code bases. Like, someone else has been doing that. We're going to be the layer that's really. good at finding those issues, diagnosing them and tracking them. So I just think it's going to be interesting into the future how much companies are willing to sort of just be the API without all those hooks, without knowing everyone's email, like having the mailing list, all that sort of stuff.

So that's a very interesting trend. I feel like you're generally on the frontier and cutting edge of adopting all these tools. You mentioned your cloud code use. And I'm wondering about give me a reality check, a health check on your health check on. your experience of 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 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. there's other places where it works. I think that Codex has done a very good job of implementing browser use actually, both for debugging applications that you're working on and in general.

This is something that Cloudco just doesn't do. Creates a really, again, that kind of like, I think the next couple months, the thing you're going to keep hearing from me, but also everyone in the world is like self-healing loops, loops, loops, loops, right? How do you create loops where it's, how do you close the loop? How do you have like CloudCode, make a UI change, see that it sucks and then just keep going, right?

And a lot of like, do we have AGI or not is how many loops in a row can you do? It's all loops. Right? Before things just end catastrophically, right? Because there is sort of this like, it gets worse, right? In many cases. So, yeah, I think like there's a lot of like ways to answer this. Like I'm a fairly like security conscious person.

I think that like the, you know, I'm not like an open claw guy. I'm not going to give all my like cookies to some, you know, agent. etc, et cetera. But yeah, I think TBD. Yeah, cool. Well, yeah, new challenge. Book an Airbnb with an agent.

Can it be done? Is that where the goalposts need to be set? Let's figure it out. Anyway, thank you so much for coming on the show. Great to see you, Ben. Congrats to the team. Congrats on the launch. Last thing, if you want a hat,

we have a new CLI. You can run raindrop drip. You can get a hat and umbrella a couple other things. That's a fun way to give out merch. I like that. That's very creative. Thanks for going 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. 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 it's fantastic. It was 24 hours. He 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 teleoperated, 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 teleop. 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. He doesn't have like a logical explanation or conclusion. And so a lot of people are asking. Well, it does have a semi-logical conclusion, which is that Brett is claiming when it reaches a, a,

cross 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 halfway up, 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 teleoperated, which is something Brett has said it's fully autonomous? I feel like that means no humans in the loop, but Tior 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, 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 would... 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 orangutan in a VR headset puppeteering the robot via tele

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 chimpanzee is running its own. It has somewhat of a neural, almost like a neural network. Yes, yes.

No, no, no, no. No one knows. And he'll says, I think there was a human physically inside. Ooh, physically inside. Yeah, I mean, the, the thing that I'm so, 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. Like this seems like something that, that e-commerce, fulfillment and logistics companies have been doing for many, many, many, many years. Yes. Is there not a purpose-built robot that sits right there and make sure that 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 custom-made machine for flipping around, sorting packages, that type of activity. There are custom-built machines that run at scale. They might cost like $10,000, but they last 50 years. And anytime you see a, you know, a Diet Coke factory or gum manufacturing line, all these things, like the gum that you have there comes off and the gum rattles down and is sorted it into the pockets of the of the the the the the the the the the the the the the the

sleeve is wrapped around and glued and all of that is done autonomously but just with you know a bunch of machinery that was built in not probably like a hundred years ago honestly if it works don't fix it but you can clearly see how this type of task package sorting would be like on the curve to a more economically valuable humanoid robot and 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, 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, I'd probably trust it to put some laundry in the washing machine. That doesn't seem well beyond the scope of capabilities. I am interested about power.

interesting how quick it is when it's just sorting packages there and then it does divide and walk on the way off. Hmm. Yeah. If you rewind for a second. Yeah, yeah, yeah. The walk is not as... I only use that terminology because that's the terminology that... That is what Red used. Yeah. Like, look. Why does it look like that? Yeah. If you're 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 we will dig into it. Brett launched day two. I mean, regardless, putting up views, sorted 32,000 packages. Day two is live, and he shared more details than what's going on. The original goal was an eight-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. F.03, detects the barcode, picks up the package, and reorientance it, barcode face down onto the conveyor. Humans average around three seconds per package.

F-03 is now around human parity. The robots are reasoning directly from camera pixels. The robots are fully autonomous using Helix 2, our in-house neural network, running entirely on board F-03. There's no tele-operation. Every action comes directly from Helix 02. Well, I feel like that rules out the monkey business. I think teleoperation would fall. If you had a monkey puppeteering this thing,

I think it would count as teleoperation. So he is denying that allegation from the timeline. But the timeline seems convinced. 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 has a software or hardware issue, it autonomously leaves for maintenance and another robot takes over. We run our labs at 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 very, very fun going back and forth. Who else is chiming in? People are, I'm the last, Dar says, I'm the last person I expect to rush to figure's defense. And I'm looking forward to hearing Brett's take here and in here and in.

In all cases, I stand with 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. And PBD sucks, who broke the story 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 brittleness. For cross-body research, for cross-body reach, the policy lifts its arm, 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. But people are still hungry for teleoperation bombshells. It sort of cuts both ways. I remember Jason Carmen did a 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 tax in the description, like, so told everyone, and still people were quote tweeting and be like, this is teleoperation. And so people, you know, are sort of grappling with like what is real, what is fake constantly.

Well, is there anything else on the figure story that you'd like to dig through? No. Switch his hands after working more than four hours straight. Huh. Well, we can't get into this there. There is some new news. We've dug a little released 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 in ETS, such as Nvidia, Microsoft, Broadcon, Amazon, Apple, Alphabet, Meta, Goldman Sachs, and, AMD, Airbnb, Pallanteer, Netflix, Costco, Walmart, JPMorgan, DoorDash, 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. That is in Q1. It's a lot of trading activity. We talked about this.

Selling. Should you just give Jane Street right access to the federal government? Should they just be able to change the law? laws to optimize for max GDP growth and it feels like we're one step closer, one step closer to the economic singularity of the hedge fund running the country. Anyway, we have any other. Well, yeah, what else? I'm trying to find the history of presidential day trading.

I don't know if there is one. Jimmy Carter famously divested from his peanut farm because he was worried about conflicts of interest, but we are in a new era. Anyway, we'll have to figure out. if Trump is long or short, the Cerebrus IPO. He's probably watching right now to hear Doug O'Loughlin's take on it to understand what's happening with Cerebrus. Chatsh-B-D 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. And there's no other evidence that we're finding of

presidential stock traders. Well, we'll dig into it. But we have Doug O'Loflin from Semi-Nalysis in the waiting room. Doug, how are you doing? Welcome to the show. Good. Good, man. You know, pretty busy day. Another day, dude. Honestly, every day is a busy day. Every day is a busy day.

Take us through it. How do you think the market reacted to the Cerebrus IPO, to the semi-analysis deep dive on the company? 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 it's a bull market baby. I think the takeaway is that Cerebrus got to IPO, which at one point in time we didn't think that would happen at the semi-analysis world.

We've historically been very bearish on S-RAM, but I think there's a path forward for it for them to be a disaggregated pre-filled chip or maybe even an AFT chip, meaning attention, feed-forward disaggregation. Okay. So, yeah. Yeah. Unpack sort of the competitive dynamic. Like, the fear around cerebrus, as far as I could tell years ago, it was like, will this ever be useful? Will they ever actually be able to make it? Will it have defects?

Then it became certain applications, demand side, customer concentration. But where do you think they are now? How is that journey evolved? So first and foremost, Cerebris is about S-RAM. S-RAM is like the fastest possible memory, and it's kind of done on a logic process. But the problem is S-R-R-M scaling is dead. that you can't make smaller and smaller S-RM scales. So pretty much they like kind of committed to this dead-end process by having the biggest scale

up world as a wafer size, but then the models got much bigger than just a single wafer. And so they have really, really fast inference, but only 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, is pretty unlikely in the near term. Yes. So I understand all that. I'm just wondering about the world where should I view it more like a CPU?

Because when the AI boom, the Chachapiti moment happened, the obvious buy was Nvidia because we're going to need a lot of GPUs. No one was really expecting 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, You were going to move past the trillion parameter models, but we're going to keep using them forever, just like we use relational databases forever, even in an AI, agentic AI world.

Or you have a scenario where you have a big model that is giving sort of orders, orders, workload, delegation or 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 Cerebus 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 market's going to be large enough. And I mean, honestly, I don't think I was ever bullish Cerebus the entire time.

But now that we're here, like, not ironically, 1% of a very large market works. And I think they got like 1% of a very large market. When it first started, I was like, oh, yeah, what are you going to do? 1% of very large market that's going to be few hundred million dollars and that's and that's that's like the classic seed seed pitch to yeah yeah 10 years ago they're there there yeah is there any is there any uh for a long time there was a lot of fear around A6 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

change that actually is to the benefit of cerebris and makes them more relevant in the future. Do you think that's possible? Is that a mean, two gigabrain? That's two gigabrain for me, right? But where I'm at and the understanding, there is a narrow path for them. I think, and I think they're going to be able to inference maybe one trillion framers at very small context window sizes or smaller windows, uh, smaller 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 a compute efficiency super easy. And, you know, yeah, your, your model is inefficient and AGI understand. Yeah, yeah, yeah, yeah. Distill yourself so you can run on a cerebris chip just as effectively. Okay.

Now we're talking to gigabrain. That's the gigabrain thesis. But I think, I just think that there is, there's demands, right? Like, clearly we're in a shortage. Yeah. And ironically, in a shortage, it's not the best company who wins. I mean, you can look at Nvidia's stock chart and that tells you, it's the second, third, fourth, best companies where the demand overflows, right?

And so we're seeing all that today. And I think the reality is the market's big enough for a lot of demand and so reverses 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 world of LLMs blew up at a much faster scale than anyone could have ever thought. of. The size, I think, is really the difference. Yeah, yeah. Give me a little primer on GROC, how GROC fits into the S-RAM machine market,

what the view is, because it felt like that NVIDIA's move there with the license acquire, as you put it, was defensive against Cerebrus? Is that the correct framing? Like, how does GROC fit in on this? Okay, so let's talk about exactly where GROC fits into the architecture. So in the transformer architecture, you're like the multi-heads of attention, and then there's a feed-forward network. That's a portion of, you know,

essentially the entire transformer block. And what's become really hot in the last few years, or not even two years, like probably a few months, man, is you've been disaggregating all the different parts of inferencing into subsequent specialization. So we're talking about GPUs and A6 being a specialization over CPUs, but now we're actually starting to break the, essentially the constraints of imprinting

into different, I guess, compute and memory bound like pockets. And so, for example, we're finding pre-fill ends up being, pre-filled being, you know, essentially loading all the weights, ends up being compute-constrained. So you don't really need a lot of memory band. So why don't you just use a very flops-heavy portion and you disaggregate the memory onto the decode portion, which is like extremely memory bandwidth limited.

And so this is GROC, where this fits in the strategic thought process here, is in the GV-200 rack, what you can do is you can pass the activations over to the S-RAM in the GROC LPU rack, and that is an extreme speed-up. And so that's like a perfect example of another, like, break apart of the transformer architecture. Pretty technical, but that's like the thought process here

is that the memory is so fast, the memory band or the speed of the I.O. doesn't really matter, and you don't need a huge scale-up world size because you're just streaming the activations. That problem wouldn't work with the Cerebus trip because you're kind of, it's an island, right? If 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 GROC chip because there's a plug at the end of it is a lot easier, and that's kind of the calculus, I guess. So Cerebrus lower memory bandwidth, lower interconnect speech. off the chip. But all the chip, it's as fast as well.

Yeah, okay. So what does that mean for the grok-invideo ecosystem? Because is this something where the default configuration is going to be a Blackwell and a GROC chip in, you know, 50% of racks, 80% of racks? Or is this like still some sort of niche application where GROC is going to be deployed, you know, sort of sparingly sprinkled into specific use case? Do you have an idea? Yeah, I think I don't have an idea with high precision.

I think you'll find that a lot of these things, there's a lot of different ways to split up and serve your model. So expert parallelism, pipeline parallelism, tensor parallelism, right? And so the correct optimization per hardware rack is going to kind of depend on the shape and architecture of the model. And we don't really know with high precision what is what. And there's been kind of like different road maps. along the way in terms of what they wanted to do for speeding up imprints.

A perfect example of this is the CPX rack, which was mostly built for extra perilism. It kind of remains to be seen if this is like, if the GROC GV 200 speed up is going to be like the way forward. But it's definitely a technology tree that I think Jensen is excited about. So, I mean, we'll see. What about Lisa Sue at AMD? Is she excited about this technology tree? Can you give me an update on how AMD fits into all of this?

So A&D is mostly just trying to get the last thing to work, which is the rack scale up. And I think they're going to do a good job of 450. I think what's going to happen is that, like, you know, it's a compute shortage, right? So you're talking about overflow demand. I think Lisa's going to figure it out. But on the imprint serving side, I think there's definitely some demand or desire to probably match the invidia roadmap. And I wouldn't be surprised to see if there's some kind of fast S-RAM offload FFN chip in the next 12 months. But the thing is the number of candidates there is actually like pretty low.

I think Intel's really, Intel's going for Sanbanova, which is a little clever. There's like HVM2. There's a few other players out there too that pursued us for Amscaling. But I think that in this specific case, Lisa's mostly just focused on the last thing. And I think AMD is definitely good enough right now. Okay. On Intel, what is the latest there? It feels like the roundtable has been assembled and sort of everyone has held.

held hands and decided to maybe jump across the transom at the same time, take the leap of faith. 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 really high expectations, but also what are they, what is the next milestone for them after they actually get these deals with Apple and Elon Musk? Amazon and Elon Boss. Yeah. And the gigafab. Sort of like once they get those signed, like what does the next couple of years look like?

I think it's about execution. It's kind of crazy to me that I think the stock price is ahead of the technical turnaround. And I think that I think Liputan clearly has like right of the ship and gotten the right people onto the party. If that makes sense. And I think I really do think the government intel deal was a stroke of genius because Pat Gelsinger spent, you know, three years trying to build a bottom up demand to essentially come to the fab. And 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 people are there. I think the customers are there. I think the process is good enough. I think 14A will be also good enough given how much of a shortage N3 at TSM is. And it's all execution risk from here. But the historical intel has quite a bit of desksitution problem. So we'll see.

Okay. Before we move on to TSM, which I want to go to next, are there any other interesting ASIC projects 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 divergence and strategies between GROC 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. I'm not going to go too into the details, because I feel like some of them are even, like, still figuring out their roadmap. I think Maddox is kind of interesting, the way that they're kind of trying to pursue the memory problem. I think, 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 ASICs to find what niche market ends up being all like a like a diverter of demand into their ASIC. And so the ASIC specialization from here, I feel like you have to make some pretty big brain bets in order to make your bets come pay off. And I think most of the best that would have guessed when you, like, when you originally did them, didn't really, wouldn't have paid off. And the ones I didn't expect did, like, it's kind of crazy.

Yeah, it is a very weird market dynamic where a couple of years ago, we saw ASIC and new chip companies, new silicon companies, raising hundreds of millions of dollars or $500 million. And it was like, well, for that, you're going to need this massive market. Are you really going to flip Nvidia or something? and then the market grew so much that the 1% of a huge market sort of potentially maths out for some of these companies now? It's a fascinating development.

Jordy, do you have something? China trip. Oh, yeah. What are you tracking? On the H-100? Oh, so honestly, do you guys see the parade? You know Trump loves a parade. Oh, yeah.

They're winning them over. Good parade. I was like, dude, I was thinking, I'm not a much of a parade guy, but I was like, dude, if they show up if they showed up in that parade was for me, I'll be like, these guys could be friends. Yeah.

My impression is that the executive branch really wants a deal. And I think, you know, you saw the H200 list, the verified H200 list. I expect probably more lightning 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, never in the history time. So there's kind of this tension. But I do think, you know, Trump's a businessman. He loves a deal.

I expect a deal. So yeah, somewhat related. TSM, Ben Thompson was writing that potentially they weren't ramping KAPX fast enough. What are you tracking on TSM being a potential bottleneck for the AI buildout? Just as more and more, Cerevus is now trying to get allocation, it feels like a particularly sharp elbowed place to do business. Yeah, so I think at the end of the day, TSMC is kind of, I'm 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 expanding out because, like, hey, they're growing their CAPEX, like, whatever, 40%.

But in absolute dollars, these are big numbers. We're going to run out of TSM engineers in the island of Taiwan pretty soon here. So I think this is all kind of good on the margin for overflow demand, which is actually it's Intel. Intel's definitely reflecting some of that but I think the shortages specifically at TSM is driven by clean room it's a long lead time item it takes three

to five years or let's say three years to bring a clean room up and so in order for them to have like figured out and like perfectly matched demand two years ago they would have to have been like we have a 10,000 square foot house and we need to buy a 50,000 square foot house with conviction right it wasn't that clear

two years ago and so I'm going to expect supply to kind of lag over and over and over. But demand signals will continue to essentially command premiums, move up wafer pricing, move up orders, and that's what's going to make TSM invest more next year and the year after. But they're going to do it in a like in a incremental, not a revolutionary way, but like an evolutionary way. They are very like methodical and do steps one at a time. Okay. Clean room fungibility.

When you say it takes five years to build a clean room, I immediately. go to 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 operating as a fab. Like, is there something where he brings more to the table than just dollars potentially? So I definitely think Elon is the mantis. do it. I forgot who said this, but Elon makes the impossible late. I don't expect it to be on time. You know, talking with the cigar in the tariffab, 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 bet is. But I still think by the time Elon figures it out, the supply response will have reacted already. We're still two, three years out. And there is some clean room fungibility. 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 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 stock's up on something. It's all over the place. I am interested in in terms of like

6% Getting powered shells Ford is worth more than figure now Because last year, around a year ago I remember figure Your robotics Was worth more than the Ford Motor Company At one point

But now they're both AI companies I guess But what are you tracking on The American Data Center Buildout domestically or terrestrially Before we move on to space capabilities? Yeah basically how

No, go for it. No, no, no. Just, I'm just curious about, I mean, we're starting to see glimmers of pushback at the municipal level, different data setter bands. And I'm wondering about what are the big levers that are, that need to get pulled to actually continue to bring capacity online in America? Yeah, I think that's a good question. And you're already seeing the first level of this is the delay. My favorite clickbait is 50% of all data centers in America or delayed or canceled. Implying 50% is canceled when it's really just everything is delayed.

That's like my favorite clickbait. I got to steal that in the future. But I think that I think it's going to be local municipal and people have to really believe and demand and desire the jobs. And I think one of the ways that we're seeing this is like, you know, capitalism works. and effectively the dollar per megawatt has been going up. It's like a one-way train. And 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 I'll take it. And I think that with enough economics, oftentimes, you know, money finds a way. And that's kind of how I would guess.

but it's going to be like, it is a, it is like a county by county fight, right? And some places are just going to say hell no. Yeah, on the know, we were debating this earlier today. There's been a couple of examples in like viral photos and articles about like, I bought a beautiful house in the countryside and then they built a data center right next to it. And, you know, no matter how pro AI you are, it sounds annoying to have a huge building that's an eyesore and maybe noisy,

maybe smoky next to you. But have you been tracking like how how feasible is it just, to throw the data center like truly in the middle of nowhere. It feels like America has a lot of land, but what goes into selecting data sites, or data center sites these days? Do you have something else? So, yeah.

So I think pretty much two fiber pairs is the big desire. Essentially, it's 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 bottom. So you can just, in the past, you're talking about like, hey, having these

inference or rather like, let's say, point of presence near local cities, right? But power was never constraint in that world. It was just, you know, the biggest constraint was getting this video from TikTok to your phone as soon as possible. If the biggest constraint and the largest part of the cost is going to be power, why not move the data center to power and then like, you know, essentially hook it up with fiber. And so I think that we're going to put them in the middle of nowhere.

That's just how it's going to work. To a certain extent, there's going to be more densification in some of the inference near the population. but I still think the R.O.I. makes the most sense to kick it out in the middle of nowhere. Has the political backlash pushback updated your thinking at all around the viability of space data centers? I remember, you know, we talked as this idea is like gained popularity, you guys have like consistently said, yeah, technically you can do that, but like maybe it won't be viable. There are space data center players now that are kind of loving.

the pushback against terrestrial data centers because they're like the more pushback there is, the more it could make sense for us to put these up in space. But what's your view? I still think economics is going to win out. You know, something a pound on Earth is probably 10 times more expensive in space. And it's really hard for us to go to essentially meet that out with a new completely specialized supply chain for what's going to be a smaller market in the near term. It's a real adversary against the adoption.

And like, let's say the short run. In the very long run, because I'm sure you saw the anthropic colossus thing, where it's also interested in space, right? Like the biggest maxi vision of this is like AGI. We have, you know, 30 terawatts of GPUs on Earth. And we're like, we've got to put a terawatt in space. Right. So like in that world, I think space data centers work where a small percentage

It's 1% again. It's 1% of the market again. It's just like, and it's a trillion dollar. VCs vindicated. Yeah. Yeah, VCs are vindicated once again. Tam pitch deck slides vindicated.

Yeah, yeah, yeah. It's literally as big as the galaxy bro. There's no end to it actually. Think about how big the tan is. Yeah. It's huge. So I think what is more likely is if it continues to be painful to do it from a zoning perspective in America.

it will essentially slip into other geographies, probably in the Western Hemisphere. There's a lot of power in space in Brazil, and I think that that's probably good enough, right? There's definitely ways to make this work. I definitely think the only way you do it is by paying more and finding someone who's like, you know what, I'll hit the bit. And so that's the important part. But, you know, tackle some plans away. Is that sort of the bull case for sovereign AI initiatives?

I was always super skeptical because, like, Europe didn't get, like, France's Google. Like, they just use Google. And for a lot of consumer aggregator-type consumer Internet companies, it's like Spotify's from Sweden, but it could be from America, and it wouldn't matter. YouTube is from America, and they use that over there. And you didn't need, like, a national champion in every consumer category, or there were certainly, like, returns to scale, and a lot of the American companies just won. But, so I never really bought the whole idea that like, oh, the French need like a locally trained LLM and the Germans also need a locally fine-tuned something or other. But if every country has some sort of, you know, excess supply of energy or space or regulatory capacity for data centers, sort of bringing that online and just operating like a neocloud could just be economically valuable for that country, regardless of whether or not they're vertically integrated to the point of the consumer or the business that's running an AI agent.

I think that's probably the case where at the end of the day, economics is going to like kind of push it through. And there is FOMO and Europe did do a lot of investment in the internet like really late. And if we're going to use 1999 in this example. I think the thing I keep thinking about is that this AI thing is going to be a big deal. I continuously am shocked and surprised by the magnitude and scale. It's a good idea. I don't think it is right now. I feel like we are in a particular moment where the people calling the top and the bubbles,

they're awfully quiet right now. And that makes me even more scared. That is, he's going to be clear. You know, the true top, there's no, everyone's bullish, right? Everyone's like, dude, it's actually going to be bigger next year. It's actually just going to be bigger bubbles. Shut up. So, welcome to the weird.

I was not concerned about, I was not concerned about a bubble when everyone was saying, 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 really long. Honestly, here's my view. Here's my view. Please.

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 it a major fuss. 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%. Oh, there we go.

I like that. That's the real tough. No, I think there's a few things that have to happen. I think Open AI are Anthropic, someone has to go public. And it's going to be this year. Like, we have to hit that keystone before, before it's all over. But I also think, I keep. thinking about this is like, dude, this is a big, a big technological revolution. Yeah, I think it's

bigger than the internet. And I firmly believe this. I don't think I believed it would be bigger than the internet when I, maybe even two years ago, but I'm pretty convinced this can be bigger than the internet. And if you look at the past, these big technological changes are often sometimes bigger than, I don't know, everything else. It reshapes the entire world. For example, on the sovereign AI thing, maybe you're like, yeah, you don't need to fine-tune LLM. But what happens when AI becomes such an important fundamental, 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, anthropic can just, you know, put 5% of the compute of mythos and, you know, run a really effective government, you know, whatever you want it. And you're like, whoa, whoa, what does that mean for us? Yeah. And so this wave is so big that I think

people are going to, out of fear and concerned that they're going to be left behind and that the institutions 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 natural revolution, change everything. Yeah. The other thing that we were joking about in like Q4 of last year

is like John was like, great, like the bubble pops. Like the bubble inflated and then it popped. But then we got agents and then you have this sort of like reacceleration 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, uh, an, uh, an, order magnitude or two off of dot com peak multiples. And in the public markets, Google, Amazon, Apple, all the hyperscalers are at like pretty reasonable price to earnings multiples still, even with all the CAPEX and stuff.

And so the pushback would be it's on free cash flow that 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 be real. And until you just like see demand evaporate, like, it's hard for me to sit here and be like GP prices are up a ton

Claudecote 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 I wrote this whole thing like Engels pause or whatever. Like it's going to change everything. Like the amount of net output that's going to increase is going to just blow of our minds. It might be bad for GDP ironically because GDP will be unmeasured.

Like we're going to like GDP might be broken as a concept. GDP got invented in the 1930s to measure how much output you could make to not screw over the domestic economy for World War II. Like it was a way to essentially organize the, the American economy. And it's a statistic. it's an estimate. Like I think all of, I think we're going to like attack in 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. And people, and it's funny. I think wasn't Ben Thompson was talking about this in a recent interview of like people are comping this like, okay, Silicon Valley like, you know, brought crypto online.

And then it wasn't maybe as big as some people had had pitched it to be. even though it's been self-driving cars went through a room, VR. And then even the way you're talking, you're like, you know, we're still early. You know, it's like a classic crypto line. But the problem about you, if you are early, you have nothing else to say. You know, in crypto is like, well, like a community could have a Dow. And that Dow 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. 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 just running away with everything because 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 robotics? Has figure had a major breakthrough? I mean

I have not been following the feet as close as I should be I just think robotics feels a little further out than the hype would let you believe I feel like robotics is much more akin to the driving car paradigm

where it's like oh yeah it's definitely going to come and automate everyone's jobs and then it takes a lot longer and it's a lot unsexier I think 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, it can disperse very quickly. And that's what we're seeing right now.

And so, yeah, I, I'm just not anywhere near as bullish robotics as I am at the fundamental board. Well, I'm bullish on the next semi-analysis. I don't know. What are cluster max and inference max? What are those called dashboards or analyses or rankings? Dashboards. Dashboards now. Everything's a dashboard. Everything's dashboard. Well, you need to be a new dashboard.

GTP, gross token production. This is what we're measuring now. This will be the output of the United States. Gross token production, GTP. We need to, I mean, I think more on this soon, actually. This is like a place we're doing some research on. But I think, you know, the real bubble metric is if we're like, you know, how many tokens? What's the token?

Yes. What's the token replacement? cost, that would be some really good bubble math. Yeah, yeah. Software company has a really low token replacement cost per market cap. But like a hardware company has an extremely high token replacement costs. And then it's like, oh, no, no, it's just enterprise value divided by token replacement costs. Well, the real bubble one will be to go to the full Merrimiker 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, and then you'll get really weird gyration. That'd be great for semi-analysis. That'd be really good for semi-nalysis. We are consuming a lot of tokens. Well, you also put in a lot of good stuff. I really enjoyed the...

Would you guys ever make a sort of political-style attack at against another research firm for having AI psychosis? Hmm. Is that a reference to the GC thing? Sorry, it's a reference to General Catalyst attacking in... In Jersey. Mark and Dresen.

And Dresen. You know, life's pretty long. I actually think if some analysis is just peerless. I don't think there's like a neck and neck with someone else. It's just you guys. I was going to say, I don't really know who our competitors are.

I don't, you know, I don't really think about it. Mark and Driesen or, you know, another research firm like that. Maybe one day, maybe we will go through AI psychosis.

Honestly, you guys need a rival. You guys need a, you guys need an arch nemesis. You need an op. Moody's. I guess it would be, Gardner had to say they're like but this is not a good off you need the same analysis

hype cycle and it's up only no no travel disillusionment straight line no access no access it's actually going backwards it's a straight line on log graph that's what it is semi analysis hype cycle I love it uh gardener doesn't stand a chance but thank you so much for coming on the show this is fantastic always full analysis full analysis yeah no more semi analysis guys would kick her ass. If they had full analysis, they'll kick her ass. It's so over. So anyways, take care guys.

Have a great day. We'll talk to you soon. Cheers. Go bye. Next, we have Andrew Feldman from Cerebus joining in 20 minutes. We'll go back to the timeline because the Open AI 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 market 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 Open AI with special guests, Microsoft, are happening today, Thursday, May 14th. 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 cologne 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 compounded gains.

Sort of recursive self-improvement. I think that's what's going on here. So, the Cal sheet. Well, Elon, when his case against opening eye, it peaked at a 58% chance. Okay. Where is it now? 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. Ostensibly, it's where theater. ends. Listening to this and being read out in court and 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. The been mic drops, but not in the good way. The mics have been dropping out. Vunky video feeds. They need to revamp this place, says Mike Isaac. LMAO, the first joke of the tweet storm. He says Musk counsel is going after opening eye execs Altman and Brockman and has the mugshot style photo of Altman on the screen again. Battle of Photoshop's of executives in this trial has been entertaining to watch. You want to depict your opponent in the worst possible light. Musk counsel going back and forth, hammering the point they made over and over the argument essentially painting a picture. Sam Allman, 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. Molo is using Larry Page, who they claim doesn't care about humanity as a foil to the noble Musk, whose only care with respect to AI is the future of humanity. Musk counsel is painting the drones, don't trust Sam picture in a bit more detail for the jury. Also, Musk's side has a picture of Elon and Altman on the screen now. Sam's looks like he's about to be processed by a U.S. Marshal. Musk's looks like he's getting ready for the Met Gallo, L.L.

Lots of Musk closing side arguments, semi-populous track of pointing at opening, 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 which is man. Ouch, opening eye counsel begins closing argument with a broadside against Musk. 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 Photoshop's. OpenAA closing remarks now in the digital displays and the monitors for exhibits. All the Open AI executives look like Olin Mills, photo shoots.

Do you know who Ola? He says it's complimentary. I need to get up to speed on my photographers. Olawn Mills is a portrait, offers portrait photography. Ooh, it does look very nice if you pull up the Google images on O'Lon Mills. Anyway, short summary of the closing, Musk camp, all these open AI executives are rich as hell and all the time. Open eye camp, all that is a sideshow.

And literally all the claims Musk is bringing cannot be stood up by actual law. The Microsoft camp disappears into bushes. Dota got mentioned again. They love mentioning Defense of the Ancients. 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 ImageGen 2 or if they're doing it the old-fashioned way.

I can't wait until it's entered into evidence this afternoon so he can show us. Sort of want to buy this meme guitar, but I also have two telecast. Is that just completely side note? Gamer has entered de blog. The Dota moment has been mentioned. 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. So I was a true breakthrough in the technology. So Mike Isaac says, I played during the past. What is the timeline for the jury to meet? Is this 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 Popeyes 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 110 Pacific time. I imagine that they will wrap up by, what do you say, 3 p.m., 4 p.m. So 30-minute break.

That happened 40 minutes ago. So I imagine that. 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.

Or the jury. I might get the results. Or the jury might make a quick call, but that seems unlikely. 11 minutes ago, a lawyer for Open AI on Thursday defended the company's chief executive Sam Altman from withering character attacks by Elon Musk's legal team, as both sides delivered their closing arguments in a trial with potentially seismic implications. 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. Altman from the startups board and to stop a shift the company made last year to operate as a for-profit company. They pushed back. Sarah Eddie, member Open AI 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 Altman or Greg Brockman or OpenAI made to Mr. Musk is what she's saying. And there is a new update that just

dropped in. After the recess, William Savitt, OpenAI's lead counsel, told the jury that Musk does not have a claim against the startup unless there was a specific agreement between Musk and Open AI describing how his donations to the nonprofit should be spent. That agreement does not exist, Savitt said. So that's where, I guess, Open AI is leaving it for now. We will continue to cover the story as it evolves. Is the jury allowed to use Codex slash goal be done in one and a half hours. There's other tech problems going on. Max Zeph over at Wired has been covering

the story as well and says Musk's lawyer brought a big monitor, maybe 36 inches into the courtroom. Open AI's lawyers asked to use it. Musk's lawyer said no. The judge told Musk's lawyers that they have to let Open AI use it. Then OpenAI said it might not be possible to connect their laptops to it.

AGI is here, but we'll still need a Dongle, I suppose. Dongle has entered the courtroom Excess, actually. There's about 15 lawyers standing in the middle of the room right now talking about how to use this big monitor. This is wild.

They should have talked to OpenAI about sharing their monitor. What I always do, what 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 Open AI lawyer. Sam Wellman 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. In other news, Tim Draper says, I think I broke a record. I took 52 pitches in 52 minutes at below 40 degrees.

Welcome to my office. Hashtag Draper University. Hashtag survival training. What do we think about going in? the ice tank. How cold are ice baths typically? 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 put, um, because there's a couple companies that sell like personally when, when if you're going surfing and the water is below 45 degrees can just be very painful. Okay. like to, so even in a wetsuit. Oh, okay.

Your fingers go down. Anywhere that's not covered. A lot of people are putting gloves on, uh, booty. What do you think, Tyler? So apparently Joe Rogan's at like 34. 34. Yeah. Wow.

So that's like the cold plunge, you know. He's the top of the mountain way it comes to ice pass. He's the final boss. Uh, this, yeah, this is, this is just 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, like, like, What is this set up?

Yeah, what are all the trash bags there? And the wall is sort of decrepit. 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 properties that Mark Zuckerberg's acquiring, that big investors are acquiring. You would expect something that would be much more regal. But he's doing it the old-fashioned way. whipped this up himself, bought some track bags, and took some pitches. Yeah. And, you know,

who knows? Maybe the next founder of Cursor, Figma, Ramp is right now. Yeah, also 52 pitches in 52 minutes is crazy. A minute is crazy fast for a pitch. I mean, we do 10 minute interviews, 15 minute interviews, barely get to the meat of the interview. And this one you got four of the founders. Four founders jumping in, one minute. Four founders jumping in one minute. One minute. it. That is remarkable. No stranger to controversies, though. Yeah. Joe Lawndstale says, I am not a humble man, but this is legitimately beyond my capabilities.

Absolutely wild. Well, Versaille, Guillermo Rao, friend of the show, is apparently running an ad campaign on Lyft by 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 must love Vercl. Love Varsal or work there or something. I don't know. If he was the eighth employee at Vursell, 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 illiquid. He just like, he's just like, pay me zero, actually. I'll drive

lift. I want all equity. I'm super bullish on Versa. That's a possible. That's a possibility. That's a possibility. Well, Alex Conrad says, is your startup even sponsoring lift license plates yet? It's an outside the box strategy. Someone should pick it up. Someone should do it. Get a bunch of license plates for cars, rent them out to lift drivers.

Get those impressions. Wix is down a bunch. This seems like a very logical company to suffer in the age of vibe coding. People are vibe coding websites all the time. and Wix is a supplier service to build websites based on templates. But Wix was buying 30% of its chairs at $92 six weeks ago, but the stock is now down another 45%. And so I was wondering about this.

I almost asked Max Levchen about this yesterday. But when you're going through this world, it seemed like he was very confident about the SaaS pocket. and did not feel the need to respond or take any dramatic actions, just sort of wait and let the metrics do the talking. But I was wondering about, 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, you know, get a good deal on your stock if it's, even if it's just, you know, even if it's just, you know, you know, know, three months down, then right back on. Can 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? Yeah, totally. Not actually getting a return, but obviously, you know, decreasing. Yeah. Or increasing everyone's. Well, Wix is a $2.9 billion company now. Yeah, they acquired this company base 44. Okay.

Remember this was like a one person. Oh, yeah, that's right. One person company and they were growing, I think Base 44 has been growing revenue quite quickly. It seems like pretty much any of these vibe coding tools. Yeah. Just the experience is so magical for people that a lot of them have grown revenue really quickly. It's fascinating in stock chart if you zoom all the way out. So during COVID 2021, ZERP era, stock was at $300 a share.

It's at $52 today, by the way. 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, there has been a significant sell-off that continues today. And so rough go. I was looking to get a comp.

I looked up SquareSpace. Squarespace is no longer publicly traded. It was traded on the NICC, but it was delisted after being taken private by at $7.2 billion. That is tough timing. Taken private in October 17, 2024. Oh, interesting. And at the time, there was not a Saspocalypse narrative. You couldn't one shot a beautiful website with a single prompt. It's going to be so hard to, 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 these, yeah, I know some people that have built these web, website generator companies, and then they just keep growing and growing and just sticking around forever because once someone 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, 10 bucks a month, I'll just let it keep going. Well, yeah, so Squarespace had done around one billion of revenue in 2023. 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, 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 bat and shaleigh, shield and you're ready to rock. Did 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 all I would say is don't bring a nightstand to a gunfight. Okay. Yeah. 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, has some trouble. If you disable, Ben Thompson says, if you disable, open it 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 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 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 I.O. More if you're interested. So that's good. They did receive the feedback. Well, we should talk about Nikita Beers. I was reminded of this because he screenshot it and posted it. The greatest growth hack of his career for one of his projects.

This happened, was this a year ago? Gas or explode app? This was about a year and a half ago. pre-joining X and working with Elon Musk over at X, 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 TapGet, 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 act. He made the entity a call to X. It's genius. These little things really add up. And you've seen them all over X, 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 enjoying the latest UI feature where if you're watching a video 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 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 seeing more and more of from the X-team. and I'm a big fan up. Well, without further ado, we have Andrew Feldman from Cerebrus in the waiting room.

Let's bring him in to the TV of an outro. Andrew, great to see you again. Looking sharp. Feeling sharp. How are you guys doing? Amazing. Congratulations. How has the day been?

I would love to get just your reactions from the day. It seemed like there were a lot of people there. Take us through your emotions today. Well, you know, this was better than we'd hoped. I think a chance to celebrate. We did bring a lot of people from the company and we brought families. And to share with the team,

we brought everybody who'd been at the company for longer than nine years and their families. We, you know, when you do a startup, the family is a meaningful part. It takes patience from them and a great deal of it. And so they came and we celebrated it. It was really an extraordinary day. We opened up.

you know we did we priced at 185 we opened up 350 and we settled at about 320 what an extraordinary thing we're just so proud yeah take us through some of the the history of cerebrus has it been a straight shot has it been an overnight success how do you characterize what were the darkest moments what were the highlights what are the good old days to you what does that mean well okay i think in the hardware business if any anybody tells you it's a it's a straight shot you can call BS. I just don't think that's the way our business works. I think the first time you build a chip with a new architecture,

it's a little more than a prototype, a little more than a proof of concept. The second chip, you iron out your challenges and you begin to show it to customers in mass. Third one often that really takes off. And so it's a long, long road in innovative hardware designs. And so, you know, we were founded in 2016. We're more than 10 years old.

We sought to solve problems that others, that's right, overnight success. Oh, exactly, like a decade. I was 15 pounds lighter and way faster. As most overnight successes are, you know. That's right. I mean, they're just overnight because most people sort of weren't paying attention. But we tried to solve some problems that other people thought were impossible. As we showed you last time, you know, we tried to build a chip that was.

the size of a dinner plate. And everybody told us it was impossible, and the truth is, for a while it was. And, you know, we didn't solve it until August of 2019. We built this extraordinary chip. We were faster than everybody, and absolutely nobody cared. Nobody. And AI wasn't ready, and it was still sort of a novelty.

And nobody cares about how fast you are when it's a novelty. But starting with GPT, And in 2025, the models got so darn smart. They became useful. And suddenly, everybody wanted to use AI and you use it with inference and business was rolling. Yeah.

What were those early rounds like? I'm thinking the benchmark round, Co2, a bunch, you know, eclipse, a bunch of others. You know, we had the advantage of the founding team had been together at our last company that had paid pretty well for the venture capitalists and the team. And so we had some wind in our sales when we went out and raised money.

It's not like today where we're four guys in the word lab, and you're raising it a billion prees for your A. That's not us. But we went out. We made eight calls. We got eight term sheets. We chose benchmark and foundation and eclipse. And we got going, you know, less than a year later.

I was expecting you to say like, yeah, I mean, it was. It was a slog. You know, we were so long away or everything. Other rounds were a slog. Other rounds were a slog. At the beginning, not so. You know, Thomas LaFont at Code 2 came in shortly thereafter and we did a round with them. I think the truth is between about 2020 and 2023, it was much harder.

Yeah. AI was sort of in this situation where everybody was saying, oh, that's cool. Look what this model can do. Look how big it is. but it wasn't being used anywhere. Yeah. Right. Nobody was using it.

They were pointing at it. They were saying, wouldn't this be nice? And they went back to whatever they were doing before. And it wasn't until really sort of 2025 when the models got good. And you just saw this tidal wave of people using AI and demand for AI compute. And that's been exceptional. That's just been an amazing thing to ride. Yeah.

You mentioned like if you have four guys and your company name ends with lab, you can raise a billion dollars. There's a little bit of that going on in the market with just like chips, semiconductors, AI. There's not that much that needs to be explained. But what were the key ideas or thesis that you needed to explain in the roadshow to investors that wanted to go a layer deeper than just AI chips? Yeah, I think there was the first the market size and dynamic. And I think Jensen said some time ago on Brad Gerson's podcast that the demand for inference will grow by a million X. And nobody believed him. And at the same time, you saw Sam Altman displaying real vision and going out and trying to lock up huge amounts of compute and memory and data center and power.

because he saw it too. And I think trying to share what that means, what an exponential demand means, and that we're still so early and yet the demand for AI compute is overwhelming. I think sharing that was interesting

and I think helpful in educating the financial community. The other thing is that there are lots of ways to do this. The GPU isn't the only way. You've got a TPU, you've got Traneum, you've got us. There are lots of different ways. ways to build a solution here.

And finally, that maybe the notion that Kuda is sort of this grand lock-in is overplayed. And that, you know, the Gemini 3, which is an excellent model, was trained on TPUs with no Kuda, that Anthropics models were trained on Traneum with no Kuda. I mean, that lo and behold, some of the best models, some of the most interesting things are being done without Kuda. and that that lock-in might be overplayed. And I think these three factors were really important in educating the financial community.

Going forward, how do you think, how do you and the team think about sort of calling your shot and sort of trying to predict where and how inference demand will look in 2030 and beyond versus like working closely with the labs that now have product lines with billions of dollars of revenue and their own roadmaps that you can work with? Yeah, you know, like the babe, I'm going to point out to left field and just say,

this is where it's going, baby. I love it. No, I don't think that's the way it works. I think we're calling our shots every day by making big investments in data center capacity and collaborating with the leading visionaries in the field, in working not just with OpenAI to serve as sort of the cutting, edge and deliver their extraordinary models, but also with AWS to make sure that we can get access to the largest enterprise customers.

And instead of having to work with these enterprise customers, procurement organizations who provide master purchase agreements that are the size of a Bible, you know, you can say, look, why don't you buy us through AWS and it'll count against your annual commitment. And so I think those are really important ideas and ways we get access to the market. And then we're taking huge amounts of data center capacity. And so that's the other bet we're making. Yeah. It's a lot of sense.

How do you think the year will play out in terms of just broader consumer awareness of what fast inference feels like? I had a really magical moment using Cerebrus in GPD 5.3 Spark and codex. And even outside of coding tasks, just talking to the model and having it respond instantly was sort of, it felt like a new breakthrough or a new paradigm. And I feel like this hasn't fully diffused, but it also feels like when it does, there will be potentially like entirely new ways of working, entirely new paradigm. that might emerge, how are you thinking about actually diffusing the technology? We think that's exactly right.

And we think that the experience of engaging with a real-time AI will encourage people to do more things, to stay longer, to work on harder problems, and to invent new things. I mean, if you remember, you know, when Netflix started, they delivered DVDs and envelopes. Right? And when the internet got fast, they became a movie studio. Yeah. They didn't get better at DVD delivery. They became something completely different, something that had never been in existence before,

a movie studio that delivered directly to your home. I think that's exactly what's going to happen. And you can just sit back and you can ask yourself, I mean, how big is the market for slow search? Zero. How big is the market for dial-up internet? I mean, how much would I have to pay you to swap out broadband at home and bring in dial-up? I'm not doing it. Is it $1,000 a month?

$1,000 a month? I mean, no way. I mean, it just wouldn't be worth it. Yep. And so the community is going to engage with inference in the same way, and that fast inference is going to be all of the market. Yeah. So you make the chips.

I believe you also make cooling infrastructure as well, cooling units. Are there other products on the roadmap that you think will be required to roll out and scale cerebrus over the next couple of years? No, I don't think so. I think right now we build the chip and the system and the system includes it's about the size of a dorm room fridge. There you put two of them in a standard data center rack and the cooling infrastructure is built into the system. And I think that's where we want to focus.

We want to be measured on our ability to build AI computers that are faster than anybody else. Yeah. How are you thinking about scaling on-chip memory? It feels like there's some concern about, well, what? if the models go to 10 trillion parameters? What if it gets too big? How are you thinking about that challenge or maybe it's an opportunity? It is an opportunity. I think a 10 trillion parameter model is hard for everybody. It's actually easier for us. 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 we've been able to do for the larger models is to tie together a bunch of these systems in parallel and run them as a pipeline. And that way we can train and do inference on a trillion, multi-trillion parameter models in ways that I think are much more intuitive than on GPUs that have much smaller compute. They have off-chip memory, but their problem is the compute. They don't have enough compute per chip. And then how are you talking to customers about

potentially bringing cerebris in, not as a full replacement to their entire semiconductor supply chain or stack, but as a complement to everything else that they're running. Because I have this vision of 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 tasks. Yeah, the same way you hire, you have a superstar employee. Yeah. You don't necessarily want them doing every single task themselves.

It's like, yeah, you should be able to delegate. Yeah, delegation. How are you thinking about that? Yeah, I think that is sort of a notion of a confederacy of models, right? That there's a collection of different models. And one of the things we thought about early on was how to interoperate in that environment. And we connect in via standard 100 gigabit Ethernet, nothing fancy, nothing proprietary. We are deployed in many places where they've got GPUs from NVIDIA or GPUs from AMD.

They've got X86 compute from Dell or HP. And so that's not a problem at all. We're eager for those environments. Yeah. How, 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 you know at the company today due to how good the models have

gotten we we use frontier models every day in coding in running our GNA i i think if you start a company today you build a very different organization i i think their whole departments that look different in in the next nine to 18 months i think much of what hr does much of what training does is solved by by some form of AI. I think a lot of the work in finance, closing the books, a bunch of what they do is checking. And those were all done by agents. I think what it is to be selling or doing recruiting,

those change. I think for a long time what recruiting was was hunting through or writing scripts for LinkedIn. I think that changes substantially. And so when we look out, we see sort of fundamental changes. The obvious ones, of course, are, you know, a year ago, engineers were using approximately zero tokens, and now they're using, you know, $10,000 worth of tokens a month. And the rate of change and the rate of new PR requests, new pull requests, is just extraordinary.

And so AI is having fundamental changes. Obviously, it usually starts in Silicon Valley and sort of works in waves to other areas, but that's what we're seeing. right now. Since the last time we talked, there's been a ton of movement in the space data center market, a lot of energy just yesterday, SpaceX and Google, I'd a launch deal in the Wall Street Journal. 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?

One of the hardest things in the space data center is communicating across chips, from one chip to the next, and we solve that, right? One of the great parts about a big chip is that you have to communicate from one chip to the next less frequently. It's a huge advantage for us in space. I think that this is an idea like self-driving where the last 10% takes 80% of the time. Sure. Right? And that we're not three or five years away, 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 don't see data centers in space in the next three or four years. Yeah, and arguably you've solved the key problems that you would be asked to solve. And so you'll be ready if demand shows up. But there's not that much for you to do individually to advance that. That's exactly right.

Yeah. That's exactly right. Well, we're hoping for it. It'd be exciting. But plenty of work to do here on the ground. That would cool. Congratulations to you and the whole team on this incredible milestone. We're honored that you would.

spend time with us. We really appreciate it. Short day for the company. Yeah. And yeah, it's incredible to watch your progress. And I look forward to your next appearance. Yes. And enjoy the rest of the evening. Enjoy the rest of the evening.

We'll talk to you soon. Thank you guys. It's time for a cocktail. Be well. Fantastic. Enjoy it. You deserve it. Goodbye.

Uh, what a fantastic. I love that. I love that analogy. He's like, I'm Babe Ruth. Yeah. I just point. And he's like, I'm not going to do that.

It's way more complicated. I've been working on this for a decade. Yeah, what a fantastic story. What a fantastic performance. I'm very excited that we're bringing in Eric Vichria from Benchmark, who was in that series A that Andrew Feldman just mentioned. So we will talk to him about that.

In just a minute, we're going to bring him into the waiting room. But there are some other posts that we can talk about in the meantime. One, someone is using Runway ML to, to create, let me see this, a full hurricane inside a TV studio. I want to watch this clip and it's one minute and we will see how convincing is this? Are you going to be turning off the news in order to watch this? 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 the host would stand on a green screen or LED volume, and then all of these effects would be added in post or live through like a traditional visual effects pipeline.

This feels fully synthetic. I think that you'll probably use some sort of hybrid approach, but the ability to prompt something like this on the fly for a small 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. There are so many small news channels, local news stations that just don't have the access to digital domain or some huge visual effects house. So it looks pretty good. Before our next guest, Dylan Field has a quick update. They have their Q1 results. He says, quick update, not dead. And putting up some insane numbers 46% year-over-year revenue growth accelerating for the second straight quarter.

It's up 20-26 revenue guidance for a year. It's up 8.6% after hours. Congratulations. Founder. Says design matters more than ever. The Figma team continuing to execute incredibly well. Fantastic. 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. I'm excited to have an opportunity. Well, you guys like Ev, you know, so you have Ev on. You don't have to have me. You as a former colleague, but everyone is welcome here. But I would love to just hear the story from your perspective. We just heard it from Andrew's perspective.

It seemed obvious. Let's just get to the point. Was it the most obvious deal ever? Because we were talking with Andrew. true. I was asking him for the story of those first couple rounds, expecting him to be like, you know, Chad TBT wouldn't come out for almost a decade. It was a slog. We kept getting, we walked up and down Sandhill Road. We got a nose. He's like, yeah, we got eight term sheets. So clearly it was a deal you had to win. Yeah, they take us through it. Well, you know what? The, the hilarious thing about it

is in venture, it's very useful to be naive. And certainly, I was so naive about how hard, hardware actually is. Like I can't even describe you guys how naive I was and we were. You know, it was 2016, deep learning was clearly going to become a thing, which would obviously evolve and empower the AI that we have today. And I was looking at all of these different applications. So I was looking at like deep learning for radiology and security and other things. And it was really hard to figure out where it was going to work, like which. application was going to take off. And you guys have to remember, this is, this is 2016, right? The TPU hadn't been announced. The transformer paper hadn't come out yet. LLMs haven't been born

yet, and obviously not chat EBT 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're not hardware investors typically, I think our last hardware investment before that one was Ambrella, 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 happened to be a hundred times better than CPUs. And as soon as he said it, it's just like a light bulb went off. Like, of course, of course. Like, why would a graphics processing unit be the right solution for deep learning? And then, you know, of course, he proceeded to explain, like, why GPUs were so much better than CPUs for training and also what the, like, ideal ground-up solution could look like.

And, you know, and they had their idea of the way for scale and everything else. And, you know, and as soon as he said it, it's kind of like, oh, yeah, that makes sense. And, like, we don't know what application is going to work. We should invest in infrastructure. This is an amazing team and a really provocative idea. You know, fast forward, like, that was 2016. Spring in 2016, you fast forward like six, seven years. And, like, we're still slogging it out and have raised so much money and have very little revenue.

And, you know, and it just hadn't all come together yet. And then, of course, over the last two years, inference is exploding. It turns out, Cerebra switches from training to inference and really focusing on inference and making inference. speed, where speed matters, coding explodes, where speed really matters. And so all these things kind of came together. And so, you know, a lot of luck, a lot of naivete on my part. But for the team, just relentless grind, never giving up, always taking feedback, but being persistent, being open-minded about where the market was going. So I'm so proud of them. Yeah, what was your role as an investor like over the journey of the company?

Because obviously Andrew and his core team deep engineering bench were you focused on how you position the company of the private markets, fundraising or management. What were you focused on, you know, in terms of value ad or just helping build the company alongside? I'm really the algorithm specialist. I go in there and I do that. No, I'm just talking about. I don't know anything. You're in the fab. You're the one that making the job.

That's right. I was making it up. Clean room. Yeah. It really changes a lot over the course of a company. This is, I think, the fourth company that I've worked with for more than 10 years. Wow. And so when you work on them in a long time, the companies evolve a lot, right? You start out, it's just five people.

It's just the five founders originally. And so at different points in time, it's a lot of fundraising help. At points in time, it's like really helping build out the, broader management team. And a lot of it is also just being someone for the founder to talk to. You know, being an entrepreneur is very, the highs are very high and the lows are very low. And so someone you can talk to and be really open with that, like, helps moderate that. And I think that's a part of it. So it's just, it's an evolving, you know, conciliary kind of role. And I really love it. Actually, that's the part of the job that I love the most.

And it's very, it's rare and special to have these kinds of relationships. I've had a few of them. I'm very lucky to have a few of them where I just feel really like a lot of chemistry with the founder. And just feel like we have a really productive relationship. Where are you excited to invest over the next decade? Because, you know, it feels like we're still in the semis, boom. There's a lot of opportunity there. you could go deeper into that side of the business, but then there's so much software.

I'm sure you've gotten pitches that look like what maybe would be the next gen. And, you know, maybe talking to my horse. Yeah, well, yeah, that, but then, you know, talking to these teams that don't necessarily know what it'll actually take, right? Sure. They didn't learn the hardware is hard lesson yet. Totally, totally. Well, you know, one of the funny things, and I ask myself this question all the time, obviously, is, you know, this is a 20, for us, as early, early stage investors and looking for, you know, really big outcomes, but willing to take big swings,

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 an AI hardware investment. And, you know, GROC was, I think, 2017, for example. So, like, you know, there were several contemporaries of them. And, of course, GROC and Cerebus had ended up doing really well. And so you're trying to say like, okay, this fruit's going to ripen in like six years, right? And and so there's kind of some mention of projection. You know, right now I think I'm really excited and continue to be really excited about a lot of the AI applications. We're investors in Sierra and Lagora and a number of others that, where like they're obviously booming, they're selling magic

to their customers and the companies are doing great. We also have these like infrastructure investments like fireworks, for example, which is, you know, also riding this enormous inference demand. And then there are kind of things that are a bit more forward-looking. We invested in StarCloud, my partner, Chafin, what our investment in Star Cloud, which, you know, space data centers. And we also, you know, we led the initial round in Sunday robotics, which is a home robot. And so, you know, I think those things are going to take longer. Like, you know, they're not going to be, you know, massively scaling revenue like next year. Like, that's not what they are.

So you kind of have a combination of these different things, which are, but it's kind of trying to figure out when they ripen. Next time you come on, we've 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 Deleon likes, yeah? Well, it's nice. It's nice to have a portfolio. And I think, you know, one of the beauties of benchmark is each of the, you know, 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 works out really well. Yeah. Yeah. Uh, 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 we do have a big gong here. Yeah. Well, I, you know, Fund seven, um, Fund 7 has or had Uber, Snapchat, Elastic, Stitch Fix, We Work. I mean, there were so many things. It was such an embarrassment of riches.

And I had nothing to do with that fund, just to be clear. Like, I joined in 2014. That fund was already deployed and invested in. But the team, you know, that team at the time just did such an outstanding job with winner after winter. scored is in there. I mean, it's like really, when you have, you know, you guys, like in venture, if you catch the trend right and, and obviously work hard and get lucky, but you have, you know, the sixth or seventh company in the, in the portfolio delivering a multiple of the fund or something like that, like that you're in such rarefied air and that's, there's, it's really special. So that's

Fund 7. You know, Fund 8 is a very enterprise. It's our 2014 vintage, I think. And, you know, it has, it's a very enterprisey fund. And so, you know, we had Confluent, which returned a bunch, and Amplitude had returned a bunch. And, you know, and then we have Cerebrus, obviously, which is big, but Chainalysis is in there and several others. And so it's kind of interesting how they switch. I think that's actually more interesting to me, which is Fund 7, was very consumer mobile, and Fund 8 is like very enterprising. And they're like back to back, but they turn out to, you know, they both work. And so, you know, I think that tells you a little bit about 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 and our 2022, I think, 2020 vintage has, you know, the first round of, of Sierra, the first round of fireworks, the first round of Ligora, you know, Rercor. Reductor, Mercor, yes, absolutely. Lang chain. And so, you know, all those are in there.

And so obviously that's a totally different fund and has a different set of things, but also, you know, looks pretty interesting. So it just, it evolves. And that's what's so hard and tough about this business is staying on your toes when you're in a very, very dynamic world. Yeah, well, it's interesting, something that, you know, this has been talked about on plenty of podcasts, but it's worth bringing up. You guys have stayed true to the strategy,

and you can count on the market changing and evolving, but a lot of funds are like having to deal with markets changing and evolving while having a fun strategy that is changing and evolving. And if you keep one of those things true, it seems, at least from Benchmarks Track record, that it gives you some advantage and that like you're playing a very specific kind of game and not having to evolve your own game while dealing with changing technology trends and markets. You know, I've been at benchmark 12 years and I've thought about this a lot and, you know, you're watching your peers do all these different things and, you know, and swimming and fees and all these like amazing things. And so you're like, wow, that's pretty, that looks pretty cool.

So like, you know, kind of like look at this stuff and, and, but I'll tell you to what I think it actually comes down to, what it actually comes down to is what do you love doing? And, you know, we're obviously in a very fortunate position and I inherited an amazing platform and so, and very fortunate to have done that. And so, you know, we're in this amazing position where you get to do what you really like doing. And at the end of the day, we really like partnering with early stage founders and working on these companies for a decade plus. And, and, and, and, and, and, and, and, and, that's kind of what we like doing. You know, and so I think things have definitely evolved.

The opportunity set is changing and evolving. And, you know, more recently, I mean, just in February, we raised an SPV, which we've never really done before and to invest in Cerebrose. And that was unusual. But it was, you know, you can also, we've actually a few years ago, we did public market investing when, when COVID first hit and the NASDAQ tanked, you know, all of the early stage stuff just disappeared. we were like, wait a minute, like these publics, like there's interesting stuff in public,

so we started deploying a little bit in the public. So, you know, yes, we're really focused on the early stage, and that's what we love doing. And then also occasionally, like, we see these special opportunities and we try to jump on them. Wow. Yeah. Well, thank you so much for coming on during a business day. I had to sneak in that, the SPV round of $23 billion. So congratulations on that investment.

Fantastic. Another little cheeky three acts. I think you deserve a drink. Hopefully you can find Andrew and cheers. I'll definitely have some drinks tonight. I'm a great time celebrating. Great. Great to finally meet you and congrats to everyone. Yeah, let's do it again soon. Thank you. Thank you, guys. We'd love to do that. Thank you. Thanks. Cheers. Goodbye. Up next, we have Steve from Foundation Capital. He's Cerebris's first term sheet investor, also the first investor in Solana and a bunch of other great companies. So we will bring in Steve from Foundation Capital from the waiting room. Steve, how are you doing? There he is. Doing great. How are you guys? Terry, can you're waiting. Congratulations.

Thank you so much for taking the time to come chat with us. How are you doing? Just another, another day. Are you didn't, are you at the NASDAQ or you're calling in from home? Yeah, exactly. No, just another day. No, I am, I'm at my hotel on my way to the dinner that Eric's also headed to. Oh, fantastic. Okay, we won't keep you too long, but I would love to hear the story of you meeting Andrew Feldman in 2007, how things matured from there, how you wound up working together. Yeah. So I showed actually Andrew the email last night over dinner, but yeah, he and I and Gary met in October of 2007. They were raising money for the company that they started prior to Cerebrus, which is called C Micro. And it was kind of broadly in sort

of new server architecture. So these guys have been thinking about these kinds of problems for a long time. But I passed on the investment, but stayed close. We really connected in that meeting. And then when I saw them get acquired by AMD, it was about four or five years later. I was like, guys, Andrew in particular, you guys are not going to stick around this company for too long. So let's start riffing on some new ideas. And that began basically a two-year conversation about a whole bunch of ideas. Actually, it all started really in kind of this concept of warehouse scale computing. We were looking at companies like Mesosphere ended up actually doing a small investment there and CoreOS and a whole bunch of others.

And Andrew came in in November of that year of 2014 and shared his ideas with our enterprise team. And then basically we riffed on ideas. And in the spring of 2016, so it was like March timeframe, we started telling them, look, we want to be your first term sheet. We've been like courting each other for a while here. And yeah, we got them a term sheet to lead that first financing. And then Eric stepped in.

And we changed the terms a little bit to make room and co-lead along with Eric and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, can you, can you, can, 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, where the gap is, where there's similarities. So what I would say the similarities, which are probably in retrospect, somewhat obvious, I would say the hardest problems of software and systems live in the area that we're working on in AI. So the AI infrastructure, the frontier labs as well, all the work they're doing there. And the same thing is also true at the bottom of the stack, the layer ones, and the very hardest technology is over in crypto. You know, the folks that are attracted to both of those areas tend to be very technology driven. They love distributed systems. They love the hard problems around cryptography and elliptical curve cryptography.

They love low latency computing. Like, they're quite similar in terms of being systems thinkers. And so those are the ways in which I would. would say that the problems are quite similar. And in fact, here's a funny anecdote related to this. So Anatoly Acovenko, you know, co-founder of Solana, part of the reason why he chose to work with us back in March of 2018. So about two years after we invested in Cerebris was because we were investors in Cerebris. 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. Not even through operating system, exactly. Yeah. And so he and then was that Dropbox and understood those. those challenges. And so he said, wow, you guys care about these kinds of hard problems. And that matters to us. So we ended up doing a fair bit more diligence and writing actually a larger check into that very first Solano financing. Yeah. Can you take us back to earlier in your career pre-investing, obviously fascinating hard problems? But like, where does all that come from? Does it start in high school, college, early career? Walk me through some of the early days.

So I studied robotics and embedded systems, sort of the intersection between mechanical and electrical engineering in undergrad and then came to graduate school and did more of that. And then my very first Friday 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 kind of Fortune 1,000 companies, when they would hit a snag, a hard problem or want to invent a new product. and they didn't often know how to wrestle those challenges to the ground, they would call us. And so we did a lot of work for Apple. We did a lot of work for Cisco. We did a lot of work across every industry, from healthcare to consumer devices to, you know, really hard problems in systems. And so I worked there for five years, designing products. In fact, saw one of my other products earlier today on the desk at a trading floor,

a NASDAQ for Cisco's voiceover IP phones, which I worked on now 28 years ago. No way. So just working on cool, cool things, heart problems, mostly where it feels like if you solve that problem, it was worth solving. There's a real prize at the end. Okay. So that's how I got started. Yeah, I want to take this full circle then because robotics is sort of having a moment,

but it still feels like it's early in terms of, as a consumer, as optimist, as optimist, as I am, I just don't think I'm going to have a humanoid robot walking around my home this year. Most people we've talked to have said, yeah, it's maybe five, six, eight, ten years away. But that's like the perfect timeline for a venture capitalist to start getting involved. You don't want to be trying to build custom AI chips today. You want to start 10 years ago like Cerebrus did. So how are you thinking about the like pulling your experience from robotics into the modern era? Because if the boom isn't already here, it's probably going to be here in a decade, if not a

decade, two decades, like, it's coming. Robots are going to be real. So how are you thinking about it? So we've done a fair bit of work in embodied intelligence in terms of research. And as I'm sure you're familiar, it's always a little tricky to invest in an area that you have some operating experience. It tends to bring some scar tissue. And so you might be more circumspect than if you'd had kind of a beginner's mind. I would say I am generally not a big believer in a humanoid approach. I think there are use cases, for example, in the home, companionship. And even in that case, it's a bit of a stretch. I think you need to think about robotics more broadly and think about industrial automation and then look at the problems that are not necessarily

kind of the consumer level use cases, but you walk the factory floor and you see people moving around pallets and the human form factor is not good for moving pallets around. And so you wouldn't actually build a humanoid robot if you were trying to deal with that use case. So I think when I zoom out and I say, what are robotic systems? Robotic systems are basically ways of automating human labor. And in fact, the greatest compliment for most of these systems is when you stop calling them a robot, you actually call them a forklift or you call it a washing machine.

Oh, that's a great. And it's when that technology diffuses into the background and you just focus on what is the application. So that's how I look at it through kind of the product lens as opposed to the technology lens. Yeah, yeah. I was, I was, you know, you see these demos of humanoid's loading washing machines. And I've been thinking in the back of my head every time interacting with my washing machine, like, is it time just for a ground up for 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 I'm excited by that. Is the implication of that that almost you would be open to talking to entrepreneurs who are maybe thinking a little bit narrower, thinking a little bit smaller, at least in the interim? And then how would you guide someone towards long-term messaging around their company if they are finding a wedge, but then they want to grow at some point. Yeah, so I think it is exactly what you just described, which is, and again, the sort of applications do matter here, but the notion that you would start with something that is,

let's call it sort of big enough to matter, but small enough to win. And in hardware technology, being more focused is actually a huge advantage, a huge point of leverage. And then as you continue to build, you want to be able to access larger opportunities in markets. And so I really do believe that that is the way you get started with hard technologies and hardware in particular. I think there's another thing that we do, and I will just say this kind of brings it to Cerebras again for a minute, which is we look at workloads. And so one of the reasons why we backed Andrew and Gary and Sean and team back in 2016 was it was quite clear,

and we saw this through the lens of our portfolio, that the AI workloads at that time was more ML, they were ramping very, very steeply. And whenever you see computing workloads that are doing something new and different, you know, you're talking about in the robotics context, and we'll get to that in a second. But when you see a workload that is spiking hard, there's often an opportunity to basically replace the compute layer. In other words, there's often sort of purpose-built silicon that should exist here. And so in the case of personal computers, very clear, serial programming, And you were very well suited to the X-86 platform. It was actually something we saw go on and on for decades.

As soon as you started to see the need for much better graphics, of course, you would build a graphics processing unit that's really good at rendering graphics, at doing floating point math, at managing lots of multiple cores, and then, of course, take the mobile era. And then you say, okay, wait a minute, what's going on here? I need low power.

I need a smaller form factor. And so when you look at these workloads, oftentimes there is this sort of transformative opportunity. And that's exactly what we saw in 2016 was, wait a minute, like there should be purpose-built silicon for this ML and AI workload. At first, of course, we started with training. Back to your point around how you start small. And then seven years in was actually a board meeting when Sean, one of our co-founder said, we got to go after inference. It's just exploding. And so, again, to this point, you start small and then rotate towards the much larger opportunity.

Yeah, I mean, we talked to Andrew about all the ups and downs, a classic overnight success with tons of moments of, you know, intense tumult. But I'm curious about, were you ever worried or hesitant that the company might narrow down too much? And because you've heard, like, you know, YouTube has custom silicon for video encoding. And there was probably an opportunity at some point to narrow the focus even more to do chip development for one specific company, be less generalized and maybe ramp the revenue a little bit faster. But was there a tension there that you were observing? And like, how did you get through those moments? I'd say that the primary tension that relates to your question was probably around making sure we would not silo ourselves into use cases that were traditionally just high performance computing use. cases.

Sure. So those workloads are valuable, and those markets are actually still relatively interesting, but they're not growing anywhere close to the rate of the inference, and specifically the reasoning part of inference where you start chaining workloads together. So we worried a little bit about that being a niche that was not interesting enough for us to build a really nodal company. If I zoom back from that and you ask sort of what are the things we really worried about in those early scary days.

I mean, there were, I don't know if Andrew shared this, and there were like five startups worth of hard problems for us to go after. I mean, it was absolutely, there were moments. I was joking with one of the other founders last night where you would 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, a hard negotiation with TSMC, talking to your supplier. You're talking about design. All of that too. All of that true. I'm talking about the actual hard problems, meaning hard technology problems. Yeah, yeah, yeah. And, you know, the ones that are sort of more physical, where you have laws of physics and thermodynamics to obey. And you don't get to negotiate. Andrew's a very good negotiator, but he's also learned that he can't negotiate with the second law of thermodynamics. Yeah. So, no, these were, this was how do you yield a semiconductor that's the size of a dinner plate? How do you power it? How do you cool it? How do you maintain continuity across? thousands of connections. How do you put it in a system and integrate it and then in a data center and then put 65 over 64 of them in a data center together? So it was those kinds of very hard challenges where I say five startups in one. And they were of course also stacked, which means

that the risks are now combinatorial. Yeah. So even more dangerous. So you've been through taking companies public, you know, being involved with public companies several times. A lot of times the founders that you're backing is their first time becoming a public. company. What are 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 telling them as they become the CEO of a public company? Yeah. So there's a few things that come to mind. One is buckle up because it's going to be, particularly in markets like the one we're in right now, where, I mean, you see the headlines change every few days.

I mean, there'll be another drop of another 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 to know that, like, when the share price is moving, it very often has nothing to do with what you're doing in the day to day. And you just need to steal yourself against that. I think there's also a piece which is you just have to grow up. There's a cadence to these businesses, orderly, unfortunately.

I wish they were longer, where, you know, Andrew and Bob are going to hop on an earnings call very soon, and they're going to have to start talking about the business of the business, not necessarily the technology of it. And that requires a level of discipline and planning that oftentimes founders don't have their stuff together well enough in order to be able to sort of manage through that transition. And then the last thing I would say is actually the flip of it, which is don't forget what made you special. Because when you get into this quarterly cadence and you start to think, how do I meet the next quarter, you oftentimes lose sight of the long horizon that was the larger

opportunity for you to go after, you know, not just the opportunity right in front of you, but there's much, much larger opportunities. And we're building systems for the next gen, and the gen after that, and the gen after that. And so you can get tricked into being in a kind of quarterly mindset. And it's one of the most toxic ways to kill a company that's built around innovation. So you just want to make sure that there's that horizon that's still calling. That's where we need to go. I love it.

Thank you so much for coming on and breaking it down. Sorry for running long. I'll let you get to the celebratory dinner. Say hello to everyone and have a great day. Awesome. Thanks so much. Let's talk to you soon. Have a good one.

That's our show, folks. Leave us five stars on Apple Podcasts and Spotify. Another one. Sign up for our newsletter at TBPN.com. See you tomorrow at 11 a.m. Pacific time and have a great rest of your day. Goodbye.