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

You're watching TVPN and today is Thursday, May 14th, 206. We are live from the TBPN Ultradome, the temple of technology, the fortress of finance, the capital of capital. Always. Hey, Ben. That is Ben. Indispensable. You got multiple Ben over here. We have a great show. It's Cerebras day on the show. Cerebras IPO. We'll talk about that. SemiAnalysis has a fantastic deep dive on the company. We'll go through that. Doug Laughlin from SemiAnalysis has joined the show. And then the founder and CEO of Cerebras is joining the show. But we have lots more folks joining. Amy Reinhardt from Netflix, the president of ads. Can you imagine? I didn't think, you know, you think about presidents, the president, you think president of the United States. I think president of ads.

That's right. Ben Hilac, Eric Visra, Steve Valo. We got a bunch of folks coming on the show today. It's going to be a fun one. So, there's a ton of news. Let's start with Cerebras. The IPO has gone spectacularly well. Cerebras doubled their valuation basically overnight. Brandon Gell had the good fortune of writing up some of the details of the Cerebras news in the newsletter today. TBPN.com. You can go sign up. Yeah, right now it's sitting at a $64 billion market cap. Yeah. And a lot of the prediction markets, they didn't even have a category above 50, right? A lot of people were just kind of trading or betting.

And when I wrote the newsletter Friday, Monday, I said a $50 billion IPO and was sort of being optimistic and it beat those expectations, which is great news. 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, Cerebras, if you're not familiar, they make a big, big chip, big chip company. Instead of taking the wafer, putting a bunch of chips on it, cutting it up into smaller chips, they use the whole wafer. It's a genius idea. It's one of those simple ideas taken deadly seriously in some ways. But it's trading at $350 a share on its first day of public trading which values the company much higher.

300 now.

300. Okay. So it was okay. It was up at 350. It has since sold off slightly. And they raised around $10 billion. I think they were targeting six billion at one point. They've upsized that. I think there was a $3 billion raise initially, but they have a good amount of money in the bank now. 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? Picture at the NASDAQ of the Cerebras team standing on stage. Someone should make a set in LA. You know, they have those like fake private jet sets. Imagine if entrepreneurs could have a set where they put their logo in the background like they're hitting it with a hammer and there's confetti going everywhere.

Yeah. But it's for your course. Yeah. And you walk right from there to the Lambo. 1,000 students in your mastermind. Yeah. No, I had this idea back in the day when, do you remember the ice cream 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 sense of like a presidential library or, you know, the Norton Simon or the Getty or the natural history museum. It was more of like an experiential place to go and hang out. Good for first dates, good for, you know, taking kids maybe. And they would maybe give you some ice cream, but most of the museum was just like very Instagrammable things. So, there would be like a ball pit or a bunch of raining confetti and stuff and a huge fabricated statue of ice cream that was not a piece of art that would be sold.

Sprinkle ball pit.

There you go. That sounds real. I don't know if that's real, but it sounds very believable. 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, cuz they just wanted to cash in. And my idea was just the museum of Instagrammable objects. And so it would have all of those. So there would be a private jet set and then there would be a Lamborghini set. And this one would fit right in. So it's just they have a big pink wall so you can go take the pink wall photo. And then there's a beach. And then there was a gym with fake weights so you could go and look like you're maxing out and benching 500 pounds. And so it just says, 'Bring these clothes or we'll have them for you.' And then you move from room to room taking the ideal dating profile photo reveal.

Yeah. Exactly. Exactly. Oh, you had kids here. You in the hospital. You can live an entire life through this fictional museum of Instagrammable objects. More of a meme than a real business idea. But...

No, John, the Museum of Ice Cream now has seven locations.

Okay, so they're cooking.

They're global. They're doing well. Well, let us know.

Anyways, nice little tangent there. Miami is the capital of Gimmick Museums. Got to do a whirlwind tour. Anyway, let's go back to the serious stuff. Cerebras, 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, SemiAnalysis has a fantastic deep dive. It's a longer read, so we're not going to go through it 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 Laughlin from SemiAnalysis at 12:30. So, there's a bunch of interesting takeaways, some really solid positives. Cerebras chips work, which was something people were not expecting for a while. There was a lot of FUD around this company, just the idea of like, oh that'll never work. Like what if the architecture changes? What if we go away from transformers or something? What if we need something completely different? Or maybe like the yields will never work because there was this idea that if you're using the entire wafer, typically as you're etching the chips onto the wafer, sometimes there's little defects and it's not a problem if you're going to break up a wafer into like 64 chips because you just throw away one. But if there's one defect on basically every wafer, well then your yield is going to be super low. We talked to Andrew about how he solved that by 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.

You can use Cerebras chips today in Codex 5.3 Spark and so they are very fast and I think the most important thing that SemiAnalysis 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 Anthropic's 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 six times the price for two times the speed. That's disproportionately more money for what you're getting. You would think you'd pay six times the price for six times the speed potentially. But there were a lot of questions about would people really pay that much more for faster models, faster inference. And Andrej Karpathy, Sam Altman was saying like, do you want faster models or smarter models? And 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 Andrej Karpathy was like no I just want smarter, I'll just let it run overnight. I don't mind that. But that's not what everyone is feeling. Some people especially the SemiAnalysis 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 is there a 250 IQ model or is there just a much more capable model that uses tools more efficiently and is really quick.

Yeah. And that's actually important to Cerebras because as we'll get into the chips do face a hurdle with scaling in terms of longer context windows, all that stuff but SemiAnalysis shared their breakdown. They were running $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 excited, 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 SemiAnalysis 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 OpenAI is clearly very bullish on Cerebras. Cerebras has a big 750 megawatt deal with OpenAI and the chips are already serving GPT 5.3 in Codex under the name Spark as we mentioned. And I've used it. You should use it. It's a very interesting experience because I think a lot of people have interacted with LLMs and chatbots and they're sort of used to this token streaming in 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 ChatGPT. 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.

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. Right. And for a lot of things, if they're two times faster, they do command six times the price because over the course of the year, a sales rep that sells twice as much or someone who is twice as effective at 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 may or may not be real, but it's probably been transposed so many times in think pieces. I don't know the real quote, but it goes something like every 100 milliseconds of latency costs Amazon 1% in sales. I don't know if that's the right way to think about it, but basically as Amazon was scaling, they realized that there were a bunch of things that they could do on the UI side, a bunch of things they could do on the layout side. Where does the buy button go? Where does certain information go, the price, the discount, all of this stuff, the images, they 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's happening in LLM use cases all over the place. People fire off a query and they're like, 'Ah, it's taking too long. I'll go scroll Instagram Reels. There's always an Instagram Reel.' And they'll be like, 'Ah, I kind of forgot about what I was asking about. I didn't get my answer.' And so, and that's certainly true in business contexts as well. So, this is currently playing out in AI inference. Companies are paying disproportionately more for faster inference, and this is good for Cerebras. But SemiAnalysis does point out a number of potential headwinds and problems that the team at Cerebras will have to solve or contend with over the next few years. Mainly, Cerebras 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 NVL72 racks that wire a whole bunch of Nvidia chips together can serve these really large models. That has potentially been a challenge. So SemiAnalysis says moreover the industry is trending towards larger context windows ad infinitum, 128k context will certainly not be acceptable for long especially with the prevalence of agentic workloads and it doesn't look like there's a simple solution of just scaling the wafer size larger because TSMC is set up with a standard wafer size and adding more memory to the existing architecture because Cerebras' whole design depends on a lot of SRAM, static random access memory, directly on the wafer but SRAM is no longer shrinking as much with each new semiconductor node. So the last version of the Cerebras chip, they've done WSE 1, 2, and three. They're on three now, but WSE 2 had 40 gigs of memory. WSE 3, you would expect, oh, we want a doubling, right? We wanted 10x or something. It got 44. So a 10% increase over one process node, one iteration. And SemiAnalysis 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 SRAM? You might have to sacrifice compute area because everything is being done on one wafer if you want computation or memory. There's a direct trade-off because you only have so much space on the actual wafer. And so there might be much harder 3D wafer bonding approaches, doing stack stuff like that. There are other potential ways but there's not like a linear oh yeah of course the next version's going to double again and so that is a potential problem that they need to work through. But in an agentic workflow I think it's entirely possible that you want like the biggest most powerful model like the vice president delegating things you want the vice president, junior, senior vice president, maybe just the president handling the critical work. So future models might not, and that might not be on Cerebras, that might be on an NVL72 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 Cerebras 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 a thousand 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 GPT 5.5 class model, right? It might be okay to run faster on a smaller model that works on Cerebras. 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 complementary than directly competitive. Like a year or two ago when Daniel Gross wrote AGI Bets and was sort of like is Nvidia underpriced? I don't know if Nvidia, he might have said that on Stratechery but like you know we entered the AI age and everyone was like oh GPUs are the future. Nvidia is the company, but then it was like Nvidia GPUs are good and then also CPUs are good and ARM is getting into it and Intel's doing very well and we're 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 Laughlin, Eric Visra, a bunch of folks who have been involved with the journey. Overnight success, Series A in almost a decade ago 2016 and Benchmark are sitting on I guess as of this morning like many many billions of Cerebras.

It feels like they brought a huge team to the NASDAQ. Look at this photo, the second post we have. Obviously Andrew is 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?

And the class B shareholders have 99% of the corporate voting power. So founders in control.

Founders in control. Founder. Honam says, 'This IPO illustrates the power of an individual partner over the brand name of the firm. Pierre Lamond was a partner at both Sequoia and Kleiner, but instead of those firms backing Cerebras, 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 Seagull, 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 Cerebras 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 Fidelities, the State Streets, the BlackRocks.

They have done quite well today. This picture looks wildly different than the CLA IPO last year in which only a handful of the team at CLA popped over at the NYSE, IPOed and went back home.

Yeah, it was very much just like another day at the office for the team. Yeah, that's definitely what I was contrasting it to. The Cerebras valuation every round: Series A in 2016, $100 million, Foundation, Benchmark, and Eclipse co-led. The Series B in 2016, Eclipse Capital led. The Series C in 2017, then 1.6 billion valuation in 2018, 2.4 billion in 2019. 4 billion in 2021. That was like maybe a little bit of a slump. But then 2025, Tiger and Fidelity come in at 8 billion. Then Tiger comes in at $23 billion. Then in May of 2026, they IPO at 48.8 billion. I can sort of just do the work myself. I don't need any help. I don't need the soundboard to do it. Am I doing it for real or am I using the soundboard? You don't know.

Incredible work from Tiger. Yeah. Coming in at 23 post. Very, very good. And 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 timeline?

Yeah, it should be. Yeah, we have the video here.

It really put Kevin Warsh on the map because this is what he's known for. Of course, he's recently sales. And I remember I showed up in your office once. I was dressed like this and I think you screamed at one of the guys. You said, 'Kevin's here. He looks like the guy from IBM.' And I was talking about, well, you know, we need like really finance controls and, you know, how you going to sell the product and all this stuff. Okay. But I would say you certainly built that. He's really spinning it. I didn't realize he goes back to it like four times. A dirty word anymore. He's really good at this. But somehow you grafted that on to the 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 Chiron or...

Yeah. No, I don't think it was actually on, I think it was from Palantir. Like that was a Palantir podcast.

Oh, okay. So, it was just like a random podcast and then when I've seen it on CNBC, they were playing the clip. Got it.

I think so. Yeah, probably a reaction stream over there. Well, let's go through what happened because Kevin Warsh has been confirmed as the new Fed chair. The vote was 54-45 in the Senate. The divided vote signals challenges ahead for Warsh who faces a Fed committee skeptical of rate cuts that Trump has demanded. And 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 stagflation is so difficult because if you have stagnation and low inflation you can cut rates very easily maybe the economy starts overheating a little bit you get a little bit of inflation but then you can pull back that's what we've done historically vice versa if the economy is running hot you're seeing high GDP growth and high inflation well if you raise rates you're going to pull back on both of those but in stagflation you're seeing both inflation and economic stagnation harder to deal with as a Fed chairman, which is potentially the task he will be faced with. So, the Senate confirmed Kevin Warsh as the Federal Reserve's 17th chair Wednesday in a largely party-line vote that reflected how tensions with the White House have dragged the Fed deeper into the political fray. I was looking back at the old Fed chairs. There's some absolute legends in there because some of them had really long runs. So very quickly you get back to the black and white portrait and the painting as you go back in time.

Who's your favorite Fed chair?

Volcker.

Yeah, Volcker is pretty goated. Bernanke is crazy.

An absolute dog. Yeah, I don't know. Hard to pick. Hard to pick. Warsh, who was nominated for the post by President Trump in January, won confirmation 54 to 45, earning support from all Senate Republicans, but just one Democrat, John Fetterman of Pennsylvania. Senator Kirsten 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 at the top of the Fed. Wow. Jerome Powell just fan favorite of both teams. 80 votes in the Senate. That's pretty significant.

Pretty cool.

Yeah. Is he going to be looked at as a future podcast throughout history? Get a maybe VC fund going. What do we think?

No, but 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... When did he land the plane? When did Jerome Powell first become the Fed chair? When was he? He assumed office 2018. So I think the, wait, 2018. He was right after Janet Yellen. And so I'm putting him in the conversation, Jordy, but I'm not giving him the GOAT trophy nearly because the challenges faced he wasn't confronted with a great recession, a dot-com bubble bursting, a Black Friday. He, like, the economy from 2018 to today...

Global pandemic doesn't, you don't count a shutdown of...

No because, no, I actually don't because the economy was pretty strong in 2019 and it went into 2020 with pretty strong consumer balance sheets, low debt, there wasn't a shadow banking economy, there was no bomb in the US economy waiting to explode. And so although we saw high unemployment briefly and we did have to stimulate the economy, that's not his job. His job was to set rates. There was a little bit of like, I mean maybe you put the inflation, you know, the end the ZIRP era and the end of the ZIRP era and all of those gyrations on him but those problems that were downstream of both the ZIRP era and the end of the ZIRP era were suffered mostly and benefited mostly on like tech companies and Silicon Valley companies that had really long cash flow horizons. And so there was not a moment where it was a dire situation that the Fed had to intervene in a meaningful way and like save the economy like in 2008. Like it's a big deal. He did a great job but he wasn't faced with the same challenges of a Bernanke for example. That's what I would say. Tyler, what do you think?

Yeah, I think that's reasonable. 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 the 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 plays because he's just shut down cornerback for the last couple 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 and 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 places 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 governors. Defying Trump's insistence that he leave, he says, 'You're going to have to drag me out of here. I'm staying at the Fed,' says Jerome 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 Bernanke deployed and so 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 show. Amy, how are you doing?

Doing well. How are you doing?

Fantastically. It is an honor to have you here. Our first ever guest from Netflix, I think. So, thank you so much for taking the time. Only took like 2,000 interviews to get you guys on here, but we're excited to meet you.

To be the first.

Yes. Yes. I mean obviously big fans of both Netflix and advertising. But would love to start with a little bit of background on yourself, your experience, and just sort of your intro to how you found yourself as the president of ads at Netflix today.

Sure. I've been at Netflix for about nine and a half years now in a couple of different roles. Started out first in our content organization doing both licensing and then overseeing production. And about two and a half years ago I stepped into this role overseeing our ads tier and it's been a fantastic two and a half years. A lot of excitement feels like we've been able to accomplish a lot and great company. So.

If you take us back to the initial push into ads, what can you tell us about the trade-offs, the build versus buy debates that were going on at the time, the just the maybe even the cultural changes? I think we are super, you know, we love ads. We think it's a fantastic business model. It's a way to deliver great value to customers at lower prices and there's so many benefits, but culturally... What was the debate like? What was it like internally?

Yeah. Well, I think it's been well publicized, you know, that not being in advertising was a strategic bet for a long time, right? And so, early in 2021, 2022, when we started to talk about the notion of getting into this business, yeah, it created a lot of I think fair to say, you know, angst within the company for a bit of time. Because it was such a big shift. To your point, culturally and strategically. So I would say and then we made the announcement that we were getting into it and in terms of the whole build versus buy, you know, we partnered with Microsoft to enter the business very quickly. And that got us up and running. But it's been, you know, we made the decision about 18 months ago to lean into building our own tech stack and we launched that a year ago. So, we're just a year. I keep having to remind myself how new 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 put to bed all notions that we should be in this business. I think everybody understands strategically now that it is important for us to be that and we've been able to grow our user base because we have been able to get to a lot more consumers who are looking for that low-cost option and are fine with ads. So it's been a great thing for the company I think and everybody's on board and you know the recent news as you heard we just announced at our upfront yesterday that we're expanding that ad tier into 15 more countries around the world in 2026. There you go. Fantastic. Everybody's on board. Full speed ahead.

How are you pitching the ad product today? Is this primarily brand marketing? Is there a timeline to get to more of a like performance focus? Like what 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 whole 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 this is the most on-brand and 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 trade-offs?

You know there's a full spectrum. So absolutely we get advertisers who want to be associated with K-Pop Demon Hunters like McDonald's or with Stranger Things, right? Like those big moments, those are often times 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 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 tentpoles and we want to 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 match your expectations or surprised you.

Um, you know, as a tech company, we do a lot of testing and we go into things with hypotheses. So, we're constantly testing things 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 say 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 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 a learning. I would say the bigger learning for us just as a company is, you know, this is a relationship business too. And we've never, you know, you talked about kind of getting into ad sales, right? We've never had a sales team in terms of our overall organization. So, I would say there's more organizational learnings...

Than necessarily tech learnings because we're so used to that tech cycle of testing and learning and iterating. How are you thinking about the ad product feeding back into the content production? I've noticed that Netflix has been fantastic in my opinion of creating more engaging content. And I was watching the Reel with Matt Damon and you click the play button and you see Matt Damon's face within like two seconds and it clearly confirms that you're watching the right movie and then the title card comes in and that's a departure from 50 years ago. You 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 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 that talent may be more tech forward and are thinking through those types of things when they're writing shows. I'll give you an example. Shonda Rhimes was used to writing for broadcast and network for many many years. So when she writes a lot of her content, she's already thinking about where those natural breaks are. But not all writers do that and that's okay. We can still find what are those natural breaks because we want to make sure again getting back to the member experience that it's not intrusive or it doesn't come mid-sentence, right? And isn't cutting off any of the action. Yeah. We're able to adapt to any way that our creators want to write the content and fit it into that member experience.

Yeah. For US markets. Is there any enterprise spending? I would imagine that a lot of enterprise buyers like have been Netflix subscribers for a really long time and maybe they're not getting served any ads at all and so this is more of a consumer opportunity or am I thinking about that the wrong way?

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...

Oh sorry I meant B2B versus like B2C companies.

Oh, we think about this more as a B2C opportunity for the most part and I think as we expand our learning and expand our offering may get into the B2B space but for the most part B2C.

Yeah. Yeah. I feel like all of that like the higher up market more targeted that's all unlocked with scale once more there's more learnings on responses. I'm wondering what other signals you think might be valuable because many times 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 ad era where it's a little bit trickier, but there's a lot of things that you can do on the signal side anyway.

Yeah, you're absolutely right. And this is an area where I talked about the testing and iterating. We're leaning in a lot on the testing. What does that screen experience look like? 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. Yeah. 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 hardcode in a QR code to link out and that was something I had completely written off QR codes and then they made a major comeback.

No, I agree with you from a member experience may not be the most simplistic thing. Understanding kind of the ad tech on the back end. I'm not surprised by it because it gets to be pretty complex pretty quickly.

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

Nothing super top of mind right now.

I mean last question I had is do you ever expect Netflix to serve more short form vertical video style ads and something like the clips tab? I know the clips tab right now is focused on basically like content discovery. But I imagine in the future people will spend more time in a format like that especially on mobile.

Absolutely. And that is one of the announcements we had at our upfront yesterday is that as we roll out this vertical video content that we are going to be offering that to advertisers along with our Tudum.com coverage in 2027. So yes, we think that is a big opportunity too.

Last question for me I would love to know about the intersection between games and ads. That's been a huge huge growth driver in other categories and other companies. But I'm wondering where that is in the road map how you're thinking about that.

We haven't thought about that yet. Look, our road map I could fill our road map for the next 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 wouldn't never say never. I've learned to never say never at Netflix, but it's not something that's on the near-term road map.

Okay. Thank you. Well, thank you so much for joining. This was a really great to meet you, Amy. Thanks for breaking it down. Progress for us. We'll talk to you soon. Cheers. Have a good one. Thank you.

Moving on from Kevin Warsh, who is the newest Fed chair. We have a debate. We have a debate going on in the timeline around General Catalyst's advertisement. Some are calling it an attack ad. Particularly Marc Andreessen 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? Gigafunds now what do they call it? Platform fund but there's something else. It's a huge huge venture capital firm. They launched an advertisement which we can play again to refresh everyone. Let's scroll down to the beginning audio. It's only Thursday.

Hi, I'm GC. And I'm VC. Who's your friend here, VC? This is Woof AI, an AI native companion platform that combines robotics and machine learning. You'll never want a real dog after this. Well, I think people like dogs as they already are, though. VC, you don't need to walk it. You never need to tell the kids you sent Milo to the farm. We're leading the seed and could probably make room for you. Well, I'd love to hear more, but we actually have a really high bar around responsibility for these things. Is Wolf AI okay? Of course, he's fine. Oh, sorry, buddy. Easy, easy. Stay. Stay. No. No. Stop. Stop. Stop. Stop. I'm sure you'll be fine.

Okay. Tons of thoughts, but you kick it off. What's your read on this? Take me through it.

So, the actual ad, the way it's shot, the timing, cinematography. It's fantastic. I do I think that 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 Dynamic robot dog, it's like walking through a nuclear power plant that you don't want someone walking into. It's like an industrial product for the most part. And then obviously 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 lot of discussion. Any Midhoff says it's a bit cringe guys. Olivia Moore at Andre says if I was a consumer founder I would run for the hills watching this weird take from a fund that returns so much money from Airbnb and Snap because is the idea that like robot dog is a weird idea but so was airbed and breakfast or Snapchat, right? The biggest B2C businesses always start out looking weird. Yeah, there's a way to do the same pitch for air bed and breakfast which is you know that empty couch, you know that empty room in your house? What if you were to monetize it? Yeah. What if you were to allow strangers to sleep in the empty 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 businesses, people get to travel and integrate with local communities. Yeah. So the main thing here is like it's I do find it fascinating considering that when you actually look at the portfolio overlap it is insane. Almost all of their winners, especially in the modern era. Sure. They are in both companies, right? Of course. And so they're backing a lot of the same companies. So it's super hard to counter position against them. Now granted, A16Z has a very different media strategy. They're very loud. They're all saying the R word a lot. And I think GC can kind of counterposition against that, but you're not gonna counterposition against like what companies you're backing, right? GC I had to look this up because I thought there's no way this is true. GC's in both Kalshi and Polymarket big great companies, right? Those have been at the center of the debates. But that is the center that those companies are at the center of the moral debate. Yeah. In tech, right? Certainly for the venture-backed controversial companies, there are some that are controversial that are not. Yeah. And all the funds have backed various like betting trading related companies over the years. I think it was like GV did what was it not DraftKings but FanDuel I think back in the day. So it's not to say there's companies that start out as controversial and then they become completely normalized and everyone sort of comes around to like oh that's a good thing like Anduril is a good example. Both of these company, both Andre and GC are in Anduril. 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 Cluey would be a good example of a company that has done okay, but it's not like oh no one's doing homework. It's completely upended education and it's so successful and it's bad. It's like no, it's like 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 just like some funny memes and ultimately you know moved on from it. And so it's very hard to map like the controversiality to the ultimate outcome and where it lands. Like a lot of 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 GC 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 investing in it regardless of what category it's really in right. And then the other thing is that historically like A16Z is the firm that's trying to be like hip and cool and loud and do like new media and GC is the one that I've always viewed as like more buttoned up, more behind the scenes, more traditional traditional fin. And I love that we love that.

I think Capital is a good example of another 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 fund and like Bain's done some fun stuff. I mean, Fogo de Chao and they've leaned into things every once in a while, but they've never been like, oh, we're going to be the craziest brand strategy. It's been like, yeah, buy the book investors. We find great companies, back them, and 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 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 yeah Steve Ballmer looks like Steve Ballmer or Marc Andreessen but people are saying Marc Andreessen but I was looking so hold on I was looking at Marc Andreessen photos and I could not find a single photo of him in a vest. So, I don't know about this, but clearly Marc Andreessen did take issue with it because 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 gonna get muddy, right? Isn't that saying? Yeah, 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 GC'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 yeah so I for example the last robot I would be like don't fund gambling don't fund cannabis like don't upset Sager and Jetty basically it's like the way I would pitch someone if they were trying to be the responsible actor funding a 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. Seeing eye dogs are like incredibly expensive. 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 endeavor I don't know if it'll work or what the business will look like but in general like there's a you know anyway so I'm extremely really bullish on robot dogs. I think it's very complimentary. Think about I mean especially if it's a toy for kids. Think about how many vehicles kids have.

Gabe says service dogs can reach 60K.

Whoa. And that doesn't count lifetime of all the other costs associated with dog ownership with next with a service dog.

Flex like whoa buddy. You don't have to flex like that. No. The 60 Yeah. I mean, kids have like, you know, a bicycle, a tricycle, RC car that drives, and then another one. And like it's just 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 that. This is going maybe a thousand robot dog startups will flourish. And the most ironic scenario is because the industry will become so deniable. The General Catalyst and Andre both have to back it and they're like duking it out for allocation at every round and the 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? You know selfishly you know I think it's bad for the industry if you have two of our platform funds just making these somewhat petty videos that are basically ad hominems 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 you know proper Drake versus Kendrick situation, this might be a good use of A16Z new media. Yeah.

Although much of the talent there is now over at I mean, I would love a response video from Andre shot in the same way or something 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 Thiel did this very well with like stagnation is the boogeyman. Like we if we don't get the robot dog, we don't get the seeing eye dog, we don't cure cancer, we don't do the big thing, we don't visit Mars. like that is you can still have like a villain but the villain needs to be something that the industry and America and all the constituents 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 that consumers enjoy.

Yeah. Which is so funny because when you if you look at cap tables, they're almost always probably touching. They're effectively holding hands on the cap table, right? Cuz like one of them might have more of one company, right? Slightly higher. One of them might have more of the other, right? But in general, you know, they're just hanging out.

Oil did Andre at GC and then went over to Andre. And so like there's more overlap than differences. Plenty of other things to take shots at. But I mean to Andre's credit, they've done a good job of that. Focusing on geopolitical competition, focusing on China, focusing on stagnation and nihilism, all sorts of different things, that they've been aligned with like more of like an abundance view as opposed to punching down. Although, you know, there's memetics all over the place. It happens.

Yeah. Just 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 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.

Remember the last time General Catalyst went viral, Hemant CEO was on Harry Stebbings show 20VC and he said like triple triple 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 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. 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. And it might be the company that's growing 10x a year, not 2x a year. And so, that was something where it was like thought leadership from Hemant. It sparked a conversation. There was some debate around it but it was so far that take was controversial but entirely correct when you look at the growth of a lot of the most exciting companies in the industry right now.

Totally. Totally. And so 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. 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 Huang is over in China. Jason Calacanis has a photo that looks extremely real. Zero AI detected, but he's bringing two huge boxes of GeForce RTX 5090s, which are 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 US 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 Hilac from Raindrop joins. I believe he's in the waiting room, so we'll let him come in. He's the co-founder and CTO. We've had on the show before. Welcome back, Ben. How you doing?

Well, man. How are you?

Fantastic. Great to see you. What's long time in your world? Reintroduce the company quickly and then tell us the news.

Sure. Raindrop we make observability for agents. So the main thing we do is self-healing agents. So what it means is that when you're Raindrop hits a problem in production we detect it, we fix it.

How do you do it?

That's a good question. So at the end of the day like we consider ourselves like the intelligence for your intelligence. What that means is that we are the best and 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 illustrates the broad diffusion of agents and custom agents. I think that a lot of people think Claude Code and Codex and I don't know if you're doing enterprise deals with those firms or that's the goal but I imagine that every startup many legacy companies have built some sort of agent some sort of harness and I'd love to know the shape of how broadly diffusing 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 you succeed as a company when your customers succeed. Like if all of your customers are terrible, it's like everyone's like, well, why are your...

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 agents in their current iteration are going to work. No, maybe there's...

I think it was very counterintuitive at the time, but I think we chose to find companies like Clay.com, right, which were clearly on an insane trajectory, but at the time weren't necessarily as large. And so I think a lot of our customers now are pretty large, but at the time weren't necessarily as large. And then in the last few months, we've been moving into Fortune 50s, Fortune 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 our bread and butter is like you know companies that are redefining the way people interact 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 this is a tough question. What I want to do actually is pivot the question a little bit because I want to talk about our launch today if that's okay. Okay with you guys.

Yeah.

Okay.

I'll tell you my questions. You tell me your answers.

Okay. Okay. Sounds good. Does that mean that you want me to not answer this?

No. No. No. I'm just messing around. Go for it.

Okay. Cool.

The joke is Yeah, I botched it. The joke is what questions do you have for my answers? And some CEOs show up and they act like that where it's like you could ask me anything and they're just gonna redirect to a top point. But it's fine. I want to hear about the launch today. So just tell us about it. Let's talk about it.

Okay. So guys, there's been this crazy thing that 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, it could be for cells, whatever SDK it is. And what do you mean locally like it actually has to run on your laptop or right like before you push to production? Sure. Right. Sure. It's on your laptop. Yeah. Yeah. Yeah. There's no way to see what it's doing. Like no standard way nothing like so people will send those traces out to like a server. Like Raindrop is like one of those, you know, and there's a bunch of others.

Yeah. But they might also just like drop the logs in like a non-relational database.

They'll just print it to console.log like, oh, here's what was happening. It's like that bad. And the other problem there is like so you can't see like a nice trace or you're sending it to some server and it takes like seconds to see everything and like whatever it looks terrible. But then also your coding agent can't like see the traces either. So then when you hit a problem and you're like, hey, you know, this response was wrong. Claude Code will just make stuff up. Like it'll just be like, oh, I think that like maybe this tool was wrong or I think maybe like this happened. Cuz it doesn't have any of that data. It doesn't actually know what the coding agent did. Yeah. So, I think that like as someone building agents, as like our company building agents, we're like it'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. Raindrop.ai 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 the genuine answer and I think part of like 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 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 Raindrop, which is like you can pull in a remote trace and replay it. And then Claude 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 I mean we use a lot of open source things here, right? So it makes sense to contribute back as well.

Yeah, that's great. I'm wondering about other just like predictions about the next breakout category of AI agents, what you're seeing. Feels like we're so close to being able to book a flight, but maybe no one wants that. I don't know.

I mean, I'm not sure if you guys saw I had a little bit of a thing with Brian Chesky earlier about Airbnb. Oh, yeah. Talk about that. We talk about that. No. I think like you know, I use Airbnb a lot. I love Airbnb. I think if I had to guess, I would say Brian Chesky knows a lot more about Airbnb than I do and probably a lot more about being a founder than I do as well. And so I think there's probably a lot that I'm not considering. That being said, I think it's like if Airbnb had an API, I would use it and I would book Airbnbs with it like through Claude Code, right? So it's like I know I would do it. I find Airbnb very very hard to search. And I think that there's a lot of I think the tough part and like what I see industry wide right now, everyone trying to figure out is you see companies almost reducing themselves into an API with like absolutely no moat. Like you look at like Photoshop, Illustrator, etc. They're like, oh, we have a Claude Code integration now. MCP at the point where people are just using Photoshop, Illustrator, etc. as like an MCP, they've sort of lost the game, right? Like if no one's actually touching the UI anymore. I think that right now companies have to do that increasingly because they have no other choice. I think that there will be a point where the incentives don't make sense anymore. Like I can give an anecdote from when I was at Apple. You know, do you guys remember App Clips?

Yeah. Yeah. Here's my app. Very few. Where did those go? I only see them go with like parking meters sometimes, I think.

So, like one of the hero ideas there was like oh, you know, like imagine you're in line at Starbucks, you don't have the Starbucks app downloaded. Like, well, why not just, you know, scan something, have the app go order your drink, and it's like, turns out Starbucks doesn't want that, right? Like, that's the last thing in the world Starbucks wants. Starbucks wants you to download the app. They want you to have stars. They have an entire like there's a reason why 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 why would they reduce themselves into an easily interchangeable API doesn't actually make sense.

Yeah. But 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 an exceptional amount all the value is not in the UI right.

I agree and where and like the value of Starbucks is not that it's a pretty app it's because they have specific drinks that they can like pretty much anywhere you know someone would be.

Yeah, I think of that company by the drink company. They got started during the direct to consumer boom. Obviously, they would have some beautiful Shopify website. They didn't they just went direct to retail and they had Amazon. You could order it online and if you went to their website, it would just say go to Amazon. And they did fine. Billion dollar company. And because like the value is not in the e-commerce experience. They didn't play like the stars game. Of course, Starbucks is maybe sacrificing a piece of that business model, but it's not giving away the whole cow. I don't know what the...

There are going to be ways to monetize this, right? Like, there are going to be successful business models built on top of this sort of layer. And to be honest, as Raindrop goes into the future, like that's the future we're building towards. That's the future we want. I mean, we're going to be announcing a partnership with a really large one of like the large coding companies as soon as far as like integrating with them more where it's like I don't see Raindrop as a company that's going to submit PRs and production to people's code bases. Like someone else is be doing that. We're going to be the layer that's really good at finding those issues, diagnosing them, and tracking them. So, I just think it's going to be interesting into the future how much companies are willing to sort of just like be the API without all those hooks without knowing everyone's email like having the mailing list like all that sort of stuff. So that's a very interesting trend I'm looking at.

I feel like you're generally on the frontier and cutting edge of like adopting all these tools. You mentioned your Claude Code use and I'm wondering about give me a reality check a health check on your experience with computer use because you're lamenting the fact that Airbnb doesn't have an API and I imagine you could create a scraper or download the HTML and interact with it like treat the front end as the API effectively you know puppeteer the computer through computer use like where are you on like 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 definitely places where it works. Like I think that Codex has done a very good job of implementing like browser use actually. Both for like debugging applications that you're working on and in general like this is something that Claude just like doesn't do like creates a really again that kind of like I think the next couple months the thing you're going to keep hearing from me but also everyone in the world is like self-healing loops loops loops loops, right? Like how do you create loops where it's how do you close the loop? How do you have like Claude Code make a UI change, see that it sucks and then just keep going, right? And a lot of like do we have AGI or not is how many loops in a row can you do before 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 ways to answer this like I'm a fairly like security conscious person. I

I think that like the, you know, I'm not like an open claw guy. I'm not going to give all of my like cookies to some, you know, agent, etc., etc. But yeah, I think...

DVD. Yeah.

Yeah. Cool. Well, yeah. New challenge. Book an Airbnb with an agent. Can it be done? Is that where the goal posts need to be set? Let's figure it out. Anyway, thank you so much for coming on the show.

Great to see you, Ben. Congrats to the team.

Congrats on course. Last thing, if you want a hat, you can. We have a new CLI. You can run raindrop drip. You can get a hat, an umbrella, a couple other things.

That's a fun, that's a fun way to give out merch. I like that. That's very creative.

Thanks for coming on the show. Great stuff. We'll talk to you soon.

Okay, back to the debate around Figure. We've had Brett Adcock on the show before and he had a live stream. We talked about it a little bit. Watch a team of humanoid robots running a full eight-hour shift at human performance levels. And Brett Adcock said, 'This is fully autonomous running Helix 2.'

All right. Pull up, pull up this post from Pete.

Yes. And the stream did fantastically. It was 24 hours. It got 3.4 million views. But at a certain point during the stream, there was some questions about whether or not the humanoid robot was in fact...

Back to the beginning. Back to the beginning.

Okay, let's play this.

All right, so it's cooking. I mean the speed is actually...

And we were extremely impressed by this. This was remarkable. Remarkable.

Even if it's teleoperated, it's extremely impressive.

Yeah. Yeah. Yeah. Like the robot's clearly working. This is very...

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. It doesn't have like a logical explanation or conclusion. And so a lot of people are asking...

It does have a semi-logical conclusion, which is that...

Brett is claiming when it reaches across its body to go to the right that it puts its hand up here to get the hand out of the way. 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 Teor Taxes has an artist's representation of Helix 2 figures in-house neural network running entirely on board and it of course is a human in a VR headset. Very debatable. We'll see where you stand. But there is a third option which I have shared which is potentially no humans involved. I don't know if you'd call it autonomous, but you would call it no humans in the loop because you have...

Well, it is an autonomous system, right? It just sort of runs.

Yeah, I would consider this autonomous. It's the image that I shared in the production chat. It's not of a human and it's not quite robotic, but there's no human in the loop. And so, this could explain 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 teleoperation, you could say that this system does not have a human in the loop. And you could make that...

And I could make the argument that it's autonomous. Yes, the chimpanzee is running its own. It has somewhat of a neural network...

Like a neural network.

Yes. Yes. No, no, no. No one knows.

Says, 'I think there was a human physically inside.'

Oo, physically.

That is another option.

Yeah. I mean, the 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. Yeah, like this seems like something 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 makes sure that, yes, the packages are in the right orientation? Does it have to, you know?

Yeah. If you watch an episode of How It's Made, you will see every variety of 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 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 into the pockets of the packaging and then 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 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 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.'

It's so interesting how quick it is when it's just sorting packages there and then it does the bite and walk on the way off.

Like if you rewind for a second.

Yeah. Yeah. Yeah. The walk is not...

Walk, I only use that terminology because that's the terminology that is used. Yeah.

Like look, why does like...

If you were able to shuffle like this so fast and so fast, you'd think that you'd be able to hustle a little bit. But maybe that's V2. Maybe that's less 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 on what's going on. The original goal was an 8-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 reorients it. Barcode face down onto the conveyor. Humans average around three seconds per package. F03 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 F03. There's no teleoperation. Every action comes directly from Helix 02.

Okay. 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. Yeah. 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 and figure this way to maximize uptime. If we haven't had a failure yet, we haven't had a failure yet, but statistically we probably will at some point. So, very, very fun going back and forth. Who else is chiming in? People are... I'm the LA Dar says I'm the last person I'd expect to rush to Figure's defense and I'm looking forward to hearing Brett's take here and in any and 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 crossbody reach. The policy lifts its arm to avoid hitting the metal shoot. Nice try.

I wasn't sure if he was going to reply to this and sort of engage or just sort of 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 Carman did a video maybe with 1X and everywhere in the video they said this is teleoperation. We're doing teleoperation. We're bullish on teleoperation. Put it at the bottom in the text in the description like so told everyone. And still people were quote tweeting and being like this is teleoperation. And so people 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.

Switches hands after working more than four hours straight. Huh. Well, we can...

There is some... we've in just a few minutes. A newly 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 and ETFs such as Nvidia, Microsoft, Broadcom, Amazon, Apple, Alphabet, Meta, Goldman Sachs, AMD, Airbnb, Palantir, Netflix, Costco, Walmart, JP Morgan, 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.

Um...

That is in Q1. So a lot of trading activity. We talked about this like should you just give Jane Street right access to the federal, you know, government? Should they just be able to change the laws to optimize for max GDP growth? And feels like we're one step closer, one step closer to the economic singularity of the hedge fund running the country. Anyway, we have...

Oh, 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 Cerebras IPO. He's probably watching right now to hear Doug O'Laughlin's take on it to understand what's...

George W. Bush with Cerebras 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'Laughlin from SemiAnalysis 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 Cerebras IPO, to your SemiAnalysis 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 Cerebras got to IPO, which at one point in time we didn't think that would happen at the SemiAnalysis world. We've historically been very bearish on SRAM.

Okay.

But I think there's a path forward for them to be a disaggregated prefill chip or maybe even AFT chip, meaning attention feed forward disaggregation.

Okay. So yeah.

Yeah.

Unpack sort of the competitive dynamic like what the fear around Cerebras as far as I could tell years ago it was like will this ever be useful? Will they ever actually be able to make it? Will it have defects? Then it became certain applications, demand side, customer concentration. But where do you think they are now? How has that journey evolved?

So first and foremost, Cerebras is about SRAM. SRAM is like the fastest possible memory and it's kind of done on a logic process. But the problem is SRAM scaling is dead, meaning that you can't make smaller and smaller SRAM scales. So, pretty much they kind of committed to this dead-end process by having the biggest scale-up world as a wafer size, but then the models got much bigger than just a single wafer. And so, they have really, really fast inference, but only at a certain size. And I think the real capability problem is can they inference models larger than a trillion parameters? And I think the answer as we think right now, it's pretty unlikely in the near term.

Yes. So I 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 ChatGPT 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, we're going to move past the trillion parameter models, but we're going to keep using them forever, just like we use relational databases forever, even in an agentic AI world. Or you have a scenario where you have a big model that is giving sort of orders, workload delegation, something, delegating to a smaller model.

Yeah. I think in a perfect world where there's no silicon constraints that might be true but obviously there's silicon constraints and I think Cerebras is really well optimized for a certain problem and we think they do a great job at answering that which is fast inference at a certain size of model maybe that market's going to be large enough and I mean honestly I don't think I was ever bullish the entire time but now that we're here like non-ironically 1% of a very large market works and I think they got like 1% of a very large market. When it first started, I was like, 'Oh yeah, what are you going to do? 1% of a very large market. That's going to be a few hundred million.'

And that's like the classic seed pitch, too. You know, 10 years ago, they're...

Yeah.

Is there any... for a long time there was a lot of fear around ASIC companies around architecture changes. We're going to move past the transformer and they're all going to be locked in the past. Is there any optimism around there's an architecture change that actually is to the benefit of Cerebras and makes them more relevant in the future?

Do you think... you know, that's... I mean, might be my pay grade, that's too gigabrain for me right where I'm at and the understanding. There is a narrow path for them I think and I think they're going to be able to inference maybe one trillion parameters at very small context window sizes or smaller models at very, very fast speeds. Yeah, but I don't know, man. Maybe I mean like, you know, the true gigabrain take is Mythos is so good or whatever that it makes compute efficiency super easy and boom, you know, your model is inefficient and AGI understands.

Yeah. Yeah. Yeah. Yeah. Distill yourself so you can run on a Cerebras chip just as effectively. Okay, now we're talking, that's the gigabrain thesis. But I think...

I just think that there's demands, right? Like clearly we're in a shortage and ironically in a shortage it's not the best company who wins. I mean you can look at Nvidia's stock chart and that tells you it's the second, third, fourth best companies where the demand overflows right and so we're seeing all of that today and I think the reality is the market's big enough for a lot of demand and is 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 LLM 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 Grok, how Grok fits into the SRAM machine market. What the view is because it felt like Nvidia's move there with the license acquire as you put it was defensive against Cerebras. Is that the correct framing? Like how does Grok fit in on this?

Okay, so let's talk about exactly where Grok fits into the architecture. So in the transformer architecture you have like the multi-heads of attention and then there's a feed-forward network that's a portion of essentially the entire transformer block and what's become really hot in the last few years or not even few 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 ASICs being a specialization over CPUs, but now we're actually starting to break the constraints of inferencing into different compute and memory bound pockets. And so for example, we're finding prefill ends up being... prefill being, you know, essentially loading all the weights ends up being compute constrained. So you don't really need a lot of memory bandwidth. So why don't you just use a very flops heavy portion and you disaggregate the memory onto the decode portion which is like extremely memory bandwidth limited and so this is Grok where this fits in the strategic thought process here is in the GB200 rack what you can do is you can pass the activations over to the SRAM in the Grok LPU rack and that is an extreme speed up and so it's like that's like a perfect example of another break apart of the transformer architecture. Pretty technical, but that's like the thought process here is that the memory is so fast the memory bandwidth or the speed of the IO doesn't really matter and you don't need a huge scale-up world size because you're just streaming the activations.

That problem wouldn't work with the previous chip because you're kind of... it's an island, right? You think of it as an island of compute. It's really, really good at everything in the middle, but moving anything off the island is really hard versus moving something off the island onto a Grok chip because there's a plug at the end of it is a lot easier. And that's kind of the calculus, I guess.

Yeah. So, Cerebras lower memory bandwidth, lower interconnect speed off the chip, but on the chip, it's as fast as hell.

Yeah. Okay. So what does that mean for the Grok Nvidia ecosystem? Because is this something where the default configuration is going to be a Blackwell and a Grok chip like in, you know, 50% of racks, 80% of racks or is this like still some sort of niche application where Grok is going to be deployed, you know, sort of sparingly sprinkled into specific use cases? Do you have an idea?

Yeah, I think I don't have an idea with high precision. I think you'll find that a lot of these things, there's a lot of different ways to split up and serve your model. So expert parallelism, pipeline parallelism, tensor parallelism, right? And so the correct optimization per hardware rack is going to kind of depend on the shape and architecture of the model. And we don't really know with high precision what is what. And there's been kind of like different roadmaps along the way in terms of what they wanted to do for speeding up inference. A perfect example of this is the CPX rack which was mostly built for extra parallelism. It kind of remains to be seen if this is like, if the Grok GB200 speed up is going to be like the way forward, but it's definitely a technology tree that I think Jensen is excited about. So, I mean, we'll see.

What about Lisa Su at AMD? Is she excited about this technology tree? Can you give me an update on how AMD fits into all of this?

So AMD is mostly just trying to get the last thing to work, which is the rack scale-up. And I think they're going to do a good job of 450. I think what's going to happen is that like, you know, it's a compute shortage, right? So you're talking about overflow demand. I think Lisa is going to figure it out. But on the inference serving side, I think there's definitely some demand or desire to probably match the Nvidia roadmap. And I wouldn't be surprised to see if there's some kind of fast SRAM offload FFN chip in the 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 Sanova which is a little clever. There's like HPM2. There's a few other players out there too that pursued SRAM scaling, but I think that in this specific case Lisa's mostly just focused on the last thing and I think AMD is definitely good enough right now.

Okay. On Intel, what is the latest there? It feels like the round table has been assembled and sort of everyone has held hands and decided to maybe jump across the transom at the same time. Take the leap of faith. But it also feels like, 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 is the next milestone for them after they actually get these deals with Apple and Elon Musk and Elon Musk. Yeah. And the gigafab sort of like once they get those signed like what does the next couple years look like?

I think it's about execution. 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 Lip-Bu Tan clearly has righted the ship and gotten the right people onto the party if that makes sense. And I think I really do think the government Intel deal was a stroke of genius because Pat Gelsinger spent, you know, three years trying to build a bottom-up demand to essentially come to the fab and 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 TSMC is and it's all execution risk from here but the historical Intel has quite a bit of execution problems so we'll see.

Okay. Before we move on to TSMC which I want to go to next, are there any other interesting ASIC projects on the horizon? We've talked to a few of these companies, but I'm interested in like the shape of the differentiation. Like you explained a little bit of the divergence and strategies between Grok and Cerebras, but there's Etched and a bunch of other companies that are working on new chip designs, and I'm wondering if any of them stick out to you as particularly differentiated.