TBPN - David Sacked by NYT, Sir Dylan Patel Joins, Kushner & Sama are Thriving | Ro Khanna, Jonathan Swerdlin, Cristóbal Valenzuela, Vincent Weisser, Ben Hylak, Alby Churven

Starting point is 00:00:00 You're watching TVPN. Today's Monday, December 1st, 2025. We are live from the TBPN Ultradome, the Temple of Technology, the Fortress of Finance, the Capital of Capital, Ramp. Time is money saved both. Easy use corporate cards, bill payments, accounting, and a whole lot more all in one place. We have a special guest. Special guest today, opening the show with us.

Starting point is 00:00:20 I'll be from the Land Down Under. Probably saw him go viral recently. But why don't you introduce yourself? Yeah. So I just actually arrived. arrived in LA on Saturday. Welcome. Yeah, I'm from Sydney or Wollongong, so about an hour and a half from Sydney.

Starting point is 00:00:37 And yeah, I've been building something called Finkel, which is basically Duolingo for Life Skills. And I just applied to YC as well with that post on X. How many views did you get on the application video? I think it got like 7.8 million, so yeah. Let's hit the gong for Albi. Well done. Well done.

Starting point is 00:01:02 So give me an example of a life skill that you can learn with your app. Yeah, I guess like entrepreneurship especially, startups and stuff. Because like in Australia, I don't know about it in the US, but school is very entry level. It's not hands on. I feel like it's very just not preparing us for life. Like you do need it if you want to be a doctor or a lawyer or something,

Starting point is 00:01:26 but some kids don't want to do that. And like, yes, you do have commerce and computer science and stuff, which I am doing as electives. But they're not hands-on, and they're very, like, outdated and, like, textbook-heavy. So I feel like actually learning life skills that you can apply now,

Starting point is 00:01:46 especially, like, with AI and everything. Like, if you don't know how to use AI now, you're sort of going to be left behind, so. Very exciting. What are you hoping to get out of your trip? You're on summer holiday right now? Well, like, my exam's just finished before I came, So there's still like two weeks left of school.

Starting point is 00:02:03 How do you think you did? I think I got like a being science and like a be in math. Focus on the game. Focus on the game. Yeah. Yeah, I guess what I'm trying to get out of it is just to like meet as many people as possible, make as many connections as possible because this trip probably won't, a trip like this probably won't happen again for a while.

Starting point is 00:02:28 So yeah, that's sort of my goal. What's the status of the Y-C? application. You've submitted it? Yeah. Have you heard back yet? No, it hasn't been. It's still like... We got a recommendation. Yeah, we got a lot here.

Starting point is 00:02:39 If you're YCL on watching this, please go leave a recommendation. Yeah. But congratulations, thanks so much. Yeah, I'm coming by. What is the stage of development of the actual application, the product itself? Are you live? Can people go downloading? So it's in demo right now.

Starting point is 00:02:55 We're getting like beta testers. But the beta should be launching soon, probably by like, the end of this year. Do you have a wait list? Are you doing email capture yet? Yeah, like waitlist beta testers. We've got like a couple hundred, but yeah. Very cool. Incredible. Well, congratulations on all the attention. I'm sure you'll convert it into a lot of opportunity and have a great trip. Yes. And good luck with the YC application. Thanks so much to stop. And looking sharp in the suit. Looking sharp in the suit. Amazing. Have a good rest of it. Thank you. Thanks for stopping by before we move on to the rest of

Starting point is 00:03:31 show. Let me tell you about Restream. One live stream, 30 plus destinations. If you want to multi-stream, go to Restream.com. And it's been three years since ChatGPT launched. I wanted to reflect a little bit. Everything changed or maybe nothing changed or maybe some amount of change in between everything and nothing. You're more on the nothing changed camp. I sort of agree with you. I was sort of reflecting on like, okay, Thanksgiving's happened. It was Thanksgiving over the weekend, you know, how different is my world? Like, there's not a humanoid robot that's cooking for me. And also, even if we had a humanoid robot, I think that would, I think Thanksgiving would be the day we let the robot sit in the closet because we enjoy, no, no, let us cook. We enjoy cooking.

Starting point is 00:04:13 Cooking is a fun family experience. And so of all the things, Thanksgiving is like the track day of cooking. Like even if you have the robot that does it, you still want to do it on Thanksgiving. You don't want to cook on a random Tuesday. When you're busy, you got lunch, you know. all this other stuff. Thanksgiving is the, is the Nureberg ring. And I was doing some dishes after Thanksgiving, and I felt like it was a good way to kind of like, it felt like walking off the pie. Yeah, totally. Totally. It wasn't walking very far. Yeah, yeah, yeah. Yeah. So that, that hasn't really changed that much for me. Um, I was thinking, I was reflecting more on the agentic commerce thing.

Starting point is 00:04:49 It feels like chatypte and open AI, they really are pushing to make revenue from agentic commerce, like, both in this holiday season. Uh, and, uh, incredible. speed of execution. Like, clearly it's a big opportunity. If you can figure out how to, you know, run ads, commerce, convert, take a cut of that. That's big. My experience actually demoing it, it was kind of interesting. Like, the actual product in ChatsyPT is pretty good, but you can see that the walled gardens are already going up. So one place that I like to go to for reviews of products, specifically around the holidays, is the wire cutter. Now, the wire cutter, their whole twist was they wouldn't rate each product. What they would do is they would pick a category and then they would just tell you

Starting point is 00:05:30 what their best product was in that category. Sort of like a cluster max of vacuums. So they would give you the platinum tier vacuum and then a budget pick. And so I've always liked the wire cutter. I think they do a very rigorous job. They were acquired by the New York Times. The New York Times is currently in a lawsuit with Open AI. And so if you go to chat GPT and say, Hey, I think they're about to be in a lawsuit with David Sachs. Maybe. Maybe. Which we will talk about on the show in a little bit. But if you go, so I went to chat chitp t and I was like, hey, okay, pull a deep research report.

Starting point is 00:06:03 Just pull everything from the wire cutter and tell me every category and every product that's top ranked because then I can just scan it really quickly and be like, oh yeah, I didn't even remember that that category existed. That would be a great gift. I'll get it. And I'll go through the wire cutter link. I'm fine with that. I'm paying chat chattchipt.

Starting point is 00:06:21 I'm happy to go and use their affiliate link on the wirecutter. That's how the wire cutter monetizes. But it couldn't do it. It couldn't do it. It said, hey, we don't, we can't touch the wirecutter. Like, it's off limits. You got to head over there yourself. Pop open a Chrome tab, brother.

Starting point is 00:06:35 If you want to get over there, like, that's on you. Or maybe an Atlas tab. I don't know. But so that had not really changed that much for me. But the one thing that did really change on Thanksgiving was the discourse. Like, the AI narrative has fully arrived to just family and friends. You mean family in the home? Yes, yes.

Starting point is 00:06:58 In people that don't work in technology, that don't, their job is not podcasting. Their favorite trough. Not that. More talking about is it a bubble. Where do you think all this stuff goes? The stuff that we've been talking about for months. You're not living in a bubble? You think the average family in America is talking about the AI bubble?

Starting point is 00:07:15 I saw multiple newsletters where the whole conceit of the newsletter going into the holidays was how to talk to your family about the AI bubble and how to talk to your family about AI generally. And I think it's real because if you've been watching your 401K over the last year, you've seen a massive spike and then a recent sell-off. And if you've turned on any news or opened up any newspaper, you've been hearing about $1 trillion. And you're like, what, a trillion dollars? That chat GPT app, they need a trillion dollars to make that thing work?

Starting point is 00:07:45 Chat. Right? Chat. And so it is a really big narrative. And so I wanted to reflect on like what has actually changed over the last three years. And specifically in the MAG 7, the MAG 7 has been on absolute tear. Just over the last three years, the value as a whole has basically tripled. It was a little under $8 trillion.

Starting point is 00:08:06 Now it's over $21 trillion. It's a lot of value created in the last three years. NVIDIA was second to last in the MAG 7 when ChachyPT launched. It was worth just $420 billion, something around there. Humble. Humble. over 10x, basically. It's $4.36 trillion. And up today.

Starting point is 00:08:30 And up today. Despite all the chaos over the weekend. Dylan Patel was trying so hard to bring that stock down, but he couldn't do it. He's coming on on the show at noon. We're going to confront him about his bear posting and whether or not the market is overreact. Funny enough, Rodcom is down today. Okay. Why is the buyer?

Starting point is 00:08:45 The maker of the TPU. Oh, yeah. I mean, a lot of these things, it's like it's already been priced in. I mean, even when you read that semi-analysis piece, A lot of it's like, we've been writing about this for months. People have already put this trade on, et cetera, et cetera. But I do think that the Nvidia, the 10X that's happened, has really created some crazy zealots and just an entire industrial complex. Because there are so many people who put, who heard AI, they tried the chat GPT thing.

Starting point is 00:09:17 And they were like, this is big. How do I get it on this? I can't buy Open AI. opening eyes running away with it. Oh, they need Nvidia chips. That's the logical next step. They went in in Nvidia and they got a 10x. And they could have gotten a 10x on like a million dollars, $10 million.

Starting point is 00:09:34 There's no amount of money because it was already a $420 billion company. So you could be, you could put your entire retirement savings in it. No problem. Complete liquidity, right? It's not, oh, you've got to get some SPV. It was really easy. Siki Chen from Runway was saying that back, I think it was 2020, 2020, 2021. He said he put an uncomfortable amount of his net worth.

Starting point is 00:09:52 to Nvidia. Yep. And obviously. And near Sayan, same story, right? Still underappreciating. The NVIDIA 10-year fund, all it does is buy NVIDIA. Just by investing in it, you can't possibly sell. God's chosen company.

Starting point is 00:10:09 Yes. That's what the, I think, title of the fund was. Oh, really? Yeah, yeah. That's hilarious. And so, I mean, yeah, there's been a ton of zealots. We're going to talk to Dylan Patel at noon about some of the zealots that have been attacking him.

Starting point is 00:10:20 previously the world's largest company in November of 2022 was Apple and at the time they had a sizable lead over Microsoft, Amazon, and Google. Now that gap has closed a bit as the hyperscalers have grown more over the last three years on the back of the AI boom. And it's interesting. I mean, you can sort the Mag 7 by market cap and today you get the following ranking. Tesla, then meta, then Amazon, Microsoft, Alphabet, Apple, and then a new. Nvidia at the top. And the big question, I think, that's on everyone's mind and kind of underpins the horse race that we cover every day on the show is, what will that ranking look like in the next three years? Is Nvidia really a monopoly? Is it impervious to attacks from the, you know,

Starting point is 00:11:07 different suppliers? What does Broadcom have to do to get into the Mag 7? I don't know. Tesla's sitting at 10 on the market cap. Yeah. Companiesmarketcap.com, which we are not affiliated with, which is just a fantastic website. Broadcom is sitting at number six above meta currently. I don't know. I mean, I think I think

Starting point is 00:11:30 several years in the $1 trillion club, like just being, you know, undeniable at that scale. There's also just like a bit of branding. Like some of the companies that made it into the Mag 7 were, I feel like the Mag 7 leaned

Starting point is 00:11:48 understandable. Like not that deep in the supply chain. Even Nvidia was the deepest. Nvidia had the least of like a consumer brand, but still a lot of people use the gaming graphics cards.

Starting point is 00:12:00 Broadcom is really tricky because there's no consumer angle whatsoever. Consumers can buy Tesla. They can use meta products. They can buy on Amazon, have a Microsoft, you know, operating system.

Starting point is 00:12:10 They can use Google. They can have an iPhone. And they can have an Nvidia gaming graphics card. The top 10 right now. Tesla is sitting at 10. TSM at 9, 8 is Saudi Aramco, 7 meta, and then 6 is Brock. I also think you have to be an American company to be in this like Mag 7 or whatever the hot ranking is, like Fang. Fang did not include, never included oil companies, never included international companies.

Starting point is 00:12:35 Because if you go there, then you could be like, oh, well, let's include like the Chinese tobacco company that's worth a trillion dollars or something like that. Like there are some crazy, there's some crazy like foreign owned companies that are, if they were independent, be worth a trillion dollars because they just have so much of the true monoply. Yeah, exactly. But it doesn't really count because it's just sitting there out in the ether. Well, let me tell you about Gemini 3 Pro, Google's most intelligent model yet. State of the art reasoning, next level vibe coding, and deep multimodal understanding. And speaking of that, Bucco Capital Bloch has a post here, Gemini app downloads are catching up to chat GPT, and Gemini users now spend more time in the app than chat GPT users.

Starting point is 00:13:17 People are going back and forth on can Gemini catch up? You know, the model clearly very good. The big bombshell in the semi-analysis piece over the weekend was this idea, which I think has been bandied about before, this idea that Open AI has not done a proper pre-trained since 40, and the 4-5 pre-trained kind of got mothballed. But there was this question about, is pre-training dead? seems like the Google folks said, no, it's not. And then they went and did a pre-trained and Gemini 3 outperformed. Anthropic also pre-trained.

Starting point is 00:13:53 I mean, yeah. We asked Cholto about this and he said, oh, yeah, we're still bullish on scaling. Yeah. I think actually, like, Cholto kind of like in the subtext said, like, the reason Opus 4-5 was good is not because it was a new pre-chain is because it was RL. Oh, that's interesting. That was your reading? Yeah.

Starting point is 00:14:11 I feel like there's still there's still juice in the level. of pre-training, but it's not scale. Like, we only have one internet. Ilya was correct about that. It's not scaling the size of the pre-trained, which is what happened with 4-5 from GPD 4.5. That was just bigger, I guess. But does seem like there's little optimizations

Starting point is 00:14:33 that you can do on the pre-training side. But I don't know. We'll have to dig into it. But I think the thing that no one is debating is the fact that the Gemini 3, as a model, with Nanobanato Pro, with V-O-3, is just like the actual foundational intelligence is plenty good to be dominant in the consumer AI category. The question is, can you actually get people to install the app, use it, can they enjoy it?

Starting point is 00:15:00 Do they not churn and go back to chat GPT? I've been fighting back and forth, left and right, going into one app and the other. I was getting a ton of disconnect errors with the Gemini app, even though the model's great, and there's some really cool features. Yeah, they need to catch up on the product. side. Exactly. Yeah, the product side. And so a lot of people are saying like, oh, Gemini team should just, the app team should

Starting point is 00:15:20 just go and, you know, copy Chat ChaptiPT's homework and, you know, copy all these little features. I put out a post that the folks over at the Gemini team actually, you know, did turn into bug reports and I think are working on. But it really does seem like it's a really, it's a sprint to actually create an app that is as sticky as Chat Chachypte because Chachypte, the app is fantastic and very, very well designed. And so the...

Starting point is 00:15:45 Yeah, and there's some reporting from similar web is what the FT is using to track average user minutes. I always find those hard to... I mean, it must be like Nielsen ratings where they're like polling people or something. Because you can't get a pixel in OpenAI. Like, you can't get a pixel into the Gemini app. And are they counting user minutes if a tab is open, but I'm not actually in? And is this just desktop? Because that's like completely separate from mobile use.

Starting point is 00:16:12 desktop and mobile web, which, again, I don't know a lot of people that are using mobile web. I don't know. I wouldn't read too much into this data specifically. I would much more look at like, what are the structural advantages that we know exist? And I mean, with Gemini, one of them is, to that point about the wire cutter, you know where the wire cutter shows up? Google search results. You know what company has one bot for scraping everything? Google. So the Google bot identifies as one entity. So you can either say, I'm allowing Google or not.

Starting point is 00:16:46 And it's a big, it's a tall order to be like, yeah, I don't want to be in Google results. And so a lot of companies are saying, yeah, I'm good with Google showing up in Google results, but that also shows up in AI search results. And there are things that companies can do to say, hey, don't put me in the Gemini, you know, like training dataset necessarily.

Starting point is 00:17:05 But in terms of just actually showing up, You've seen it in the Google, in the Gemini app. It says using Google search. And so if I go to Gemini and I say, hey, head over to the wirecutter, find me the best vacuum cleaner. Google probably can do that. I'm testing it right now. I'm testing it right now. You've tested.

Starting point is 00:17:21 Whereas Open AI is in a fight with the New York Times. Whereas Google and the New York Times, like they might not love each other, but they definitely have like an uncomfortable truce, right? A funny Gemini integration that I have used is that you've landed in a. in a hangout and you just say, who is this person? Is this real? You can actually do that? It is. It pulls up a sidebar. You can just ask, like, who, who am I meeting with right now? And it'll give you like a, uh, it's clearly, who am I meeting with? What should I say? What should I say to that? What, should I ask them? What are, what, what do they want to know about me? What, what should I tell them about me? Okay. So, Gemini was able to pull wire cutter.

Starting point is 00:18:03 Okay. Recommendations. Yeah. I, I don't know. Is it. So, I feel like, I feel like, I wonder, yeah, I wonder if, if wirecutter is actually benefiting from this in any way yet. I mean, for sure, because Google hasn't, Gemini hasn't rolled out the agentic commerce stuff that would actually, like, scrape out the referral token. And so if I'm, if I'm in Gemini and I'm saying, I'm going to do some agentic shopping or whatever, and I say, pull me the best vacuum cleaner from the wire cutter, it goes over and does that. land on the wire cutter and then I click that link, that should give the wire cutter the credit. Now, if I, as a follow-up prompt, go in Gemini and say, okay, great, the wire cutter told me the best vacuum cleaner is from James Dyson, of course, is the Dyson. Find me the Amazon link.

Starting point is 00:18:56 Well, Gemini's probably not given the wire cutter the attribution at that point. It might even be taking its own attribution. I don't know exactly how it's functioning right now, but I would imagine that that link does not get reinstantiated as the wirecutter affiliate link. And so we could say, I mean, these are all like going to be pretty existential questions for the SEO crowd, anyone who's monetizing off of SEO. We saw some screenshot that apparently site traffic to Vox properties is down 50%. And I don't know how much of that is just the shift to social media versus the shift to Yeah, how much is it their business strategy, just being like, hey, we want to do more video.

Starting point is 00:19:38 Yeah. And that'll be distributed off our site for the most part. I think a lot of people generally do not, they consume more and more content on social media platforms. They go from YouTube to their RSS player to audiobooks to Twitter to Instagram, and they kind of bounce around from one and the other. And then every once in a while they will go in and actually land on a particular site. Like you can. If you go to tbPN.com, you can get. our newsletter in your inbox every morning.

Starting point is 00:20:06 And you can also sign up for cognition. They're the makers of Devin, the AI software engineer, crush your backlog with your personal AI engineering team. Well, speaking of the New York Times, David Sacks is going to war with the New York Times. He says inside the NYT's hoax factory, calls it a hoax factory, because the New York Times posted a piece about David Sacks

Starting point is 00:20:28 saying that the headline was Silicon Valley's man in the White House is, benefiting himself and his friends. And Ryan Mack was going back and forth with Sean McGuire. Or, yeah, Sean McGuire. Ryan Mack says, today has been a good example of what X has become complaints from a subset of wealthy tech folks about a story that circulates more widely than the actual story itself. Musk bought the platform to control the message.

Starting point is 00:20:57 And he and his friends are getting just that. And Sean McGuire says, you don't get to run this headline. then write an article that doesn't validate the claim and then get away with playing the victim. We see through the ruse. And so David Sacks has responded in full to the NYT's hoax factory. He says five months ago, the five New York Times reporters were dispatched to create a story about my supposed conflicts of interest working as the White House AI and Cryptozar through a series of fact checks.

Starting point is 00:21:26 They revealed their accusations, which we debunked in detail. Not surprisingly, the published article included only bits and pieces of our responses. Their accusations ranged from a fabricated dinner with a leading tech CEO to non-existent promises of access to the president to baseless claims of influencing defense contracts. Every time we would prove an accusation false NYT pivoted to the next allegation. This is why the story has dragged on for five months. Today they evidently just threw up their hands and published this nothing burger. Anyone who reads the story carefully can see that they strung together a bunch of anecdotes that don't support the headline. And of course, that was the whole point.

Starting point is 00:22:05 At no point in their constant goal post-shifting, was NYT willing to update the premise of their story to accept it? I have no conflicts of interest to uncover. No conflicts of interest. As it became clear that NYT wasn't interested in writing a fair story, I hired the law firm Claire Locke, which specialized in defamation law. I'm attaching Claire Locke's letter to the NYT. So readers have full context on our interactions with NYT reporters over the past several months. Once you read the letter, it becomes very clear how NYT willfully mischaracterized or ignored the facts to support their bogus narrative. So Will says hiring Claire Locke for this is sick.

Starting point is 00:22:44 Cruise missile to blow up a straw hut. He's a big fan of litigation. He loves litigation. Well, people have been supportive of this broadly in tech. Let's go through some of the reaction. Sam Altman says David Sachs really understands AI and cares about. the U.S. leading in innovation. I'm grateful we have him.

Starting point is 00:23:06 Brian Armstrong. Yeah, here's here is, here's my takeaway. Yeah. If you believe that AI and crypto are, our industries that we should support in the United States,

Starting point is 00:23:23 then you want to have a czar focused on those things that generally feels positively about those things and wants to create the best possible environment for those industries to thrive in the U.S. I think that there's actually a debate on both fronts, right? Like there's people on the left that think AI and crypto are just default bad, and they want less of them. And there's people on the right that believe that too. But I think

Starting point is 00:23:50 that ultimately there's arguments for why the U.S. should bleed in stable coins, which, you know, is part of why the Genius Act is important. And a lot of, you know, the AI action plan, there's going to be debates on individual points in that. But in general, I think, you know, creating an environment in the U.S. where we can continue to lead in AI is important. So I think there wasn't,

Starting point is 00:24:21 I didn't see any sort of like smoking gun in any of the stuff. There were some allegations around the all. I don't think they smoke very much at all. I think it's mostly tequila drinking. That's true. They do. All in tequila. Although, although J-Cal does towed a gun regularly. Oh yeah. So maybe that's the smoking gun. He's a Texan. Yeah, no, I didn't see anything very specific. I mean, it's, it's all in. Like, they are super connected. If you partner with them in some ways, like you would expect to get more of a read on where they're spending time in D.C., what they're seeing. That seems, like, there are clear lines on what you can share, like what turns you into a lobbying firm and what doesn't.

Starting point is 00:25:06 I think that they've stayed out of becoming a lobbying firm, and so they have clear rules on that. Yeah, I think Boz distilled it pretty well. Before we read his post, let me tell you about Adio, the AI Native CRM. Adio builds scales in the grocery company to the next level. Boss said, I don't know David Sachs, but I want more expertise in government.

Starting point is 00:25:25 Experts tend to have made money in their area of expertise, have friends in their area of expertise. If people can't have history or friends in a field before leading it, then our leaders won't know anything. And I thought this was a good distillation of like the core debate about like, should you have someone who has never participated in an industry overseeing it? Or should you, like someone who's purely academic, purely outside of it? And I believe there's some readers and probably people at the New York Times

Starting point is 00:25:55 that would like somebody that hasn't participated in either industry. to be running in a role like that and just blanket against both industries and just sort of like hold them back. So the reaction is interesting in the comments. I mean, first, the top comment is somebody like beefing with Baz over how he ran the Quest store. It's like clearly a VR aficionado who like has a axe to grind over niche VR policies. But the second post is what I want to get to because it actually addresses the core claim here. and Alex says the construct you're thinking of is called a council it's been used for a long time to allow the elected with limited knowledge on a domain to get a consensus of options from a range of experts this minimizes conflicts and prevents kleptocracy but like isn't that what a czar is i thought i thought i thought i thought sacks was a council like he's not he's not an elected official like the elected official is donald trump the president and like there's a variety of folks there and then and then and then uh, Sacks is like appointed to this czar role that is just to give his, like, his,

Starting point is 00:27:03 like he, he doesn't have the right. He doesn't have the ability to just like create legislation out of thin air, right? He, he is, he is very much accountable. I was trying to look up the history of czars, right? It is weird. Is it like, have we always had czars? I know there was a whole thing about the border czar. The first major czar was Bernard Barak, appointed by, uh, President Woodrow Wilson to head the war industry's board in 1918, the press, dubbed him the industry czar because he had sweeping powers to coordinate wartime production. During World War II, President Franklin D. Roosevelt appointed several czars to manage the massive wartime economy, including a shipping czar and a synthetic rubber czar. These roles were essential...

Starting point is 00:27:41 Synthetic rubber czar? People were most iconic... Stoked for that. People don't talk about the need for our ongoing need for synthetic rubber czar. No. These roles were essential because existing government bureaucracies were too slow to handle the urgent demands of total war. During the Nixon era, the modern concept of the czar,

Starting point is 00:28:01 a policy specialist with a specific portfolio, solidified under Nixon. During the 1973 oil crisis, Nixon appointed William Simon as the energy czar to manage fuel shortages. He also had a drug czar during the sort of like beginnings of the, war on drugs. So anyways, again, I think unless you're just blanket against these

Starting point is 00:28:32 industries, it's hard to argue that you want somebody that doesn't have any expertise in said industries. Yeah, some of these claims, here's one, it's sort of hard to track. Like, so he says, free from those, this is from the New York Times, from the actual article for a screenshot, free of those restrictions. Mr. Sacks flew. to the Middle East in May and struck a deal to send 500,000 American AI chips, mostly from NVIDIA to the UAE, the United Arab Emirates. The large number alarmed some White House officials who fear that China, an ally of the Emirates, would gain access to the technology, these people said. But the deal was a win for NVIDIA. Analysts estimated that it could make as much as

Starting point is 00:29:17 200 billion from the chip sales. And so, like, I understand, like, we've covered the debate around export controls and should NVIDIA, where should NVIDIA be able to sell things, but it's never been an open and shut case in my mind. It's never been like, oh, it's so obvious that the UAE is completely off the table. Yeah. I don't know.

Starting point is 00:29:38 Yeah, I mean, it was also just like painting the friendship between SACS and Jensen as like something that felt wrong was a little bit rough considering it's the most valuable company in the world. Yeah. One of the most important AI companies, potentially the most important AI company, if you just go by weight in various indexes.

Starting point is 00:30:04 Yeah, I don't know. I mean, it's clear that he doesn't have Nvidia bags directly. Like, that's completely debunked. So you have to do these, like, 25 different steps to get to some sort of conflict. It's a lot of, like, you know. I read this, and I think, like, this is, if you're the average New York Times subscribe, this is probably that you were, they were probably

Starting point is 00:30:27 like very excited by this story, right? Yeah, I mean, a lot of, I think a lot of people are definitely like, yeah, just riled up by the all-in podcast. Charlie in the chat says, all-in pot about to be an all-timer after this article, do you think it's possible that David

Starting point is 00:30:43 and Jason coordinated to get this hit piece done to grow all-in even further? They said, we're at such an insane scale. Oh, yeah, that was a crazy thing. Yeah, Jason, Jason said a bunch of, I mean, Jason made a lot of good arguments about this, but one thing was he was like, we would be smaller if we, what was it? He was like, he was like, we would be bigger if we didn't talk about politics.

Starting point is 00:31:08 And that seems crazy to me. I feel like politics is like the ultimate Tam Expander in the history of podcasting and media broadly. Yeah, the audience for political content is like 10 times larger. I would think so. I do believe that Jason loves talking about tech. And like, I think he's, I think he's, he's an OG. He's, he's said that multiple times. But, but I would be shocked if, if, if politics was not a, was not a Tam Expander for, for podcasts broadly. And then the other thing is that he said that they lost money on the all-in events.

Starting point is 00:31:38 I don't know how that's possible. Like, those events, obviously, they're like big budgets, but, you know, I would, I would imagine that, like, the sponsors can, and the ticket sales, they're not cheap tickets, right? I would, I would imagine that they'd be making money off that. I certainly hope so. I mean, they've been running this thing for five years. It's incredibly valuable in the ecosystem. They should be able to capture some value there. Maybe they set up their own data center to sort of manage.

Starting point is 00:32:00 They're just a new one. Yeah, we decided to bring a podcast production on-prem, and we ordered a lot of a carbox. A lot of cabbacks. Blue Al really has us by the polls. It's rough. Martin Scralli here says the sax piece illustrates the exact problem with the New York Times. Voters specifically want this type of person, not a bureaucrat who has never worked a

Starting point is 00:32:20 Real Job, Lena Con, Kay Street, public. So the issue and the reason I think this article was written is that New York Times subscribers specifically want this type of article. Yeah. Yeah, Whiskey Titans going back and forth here. Did you miss the entire part of the article? This isn't a, quote, we can't have businessmen in government. This is a we can't have the government officials who host government summits and sell

Starting point is 00:32:47 access to the president for $1 million via their podcast business. And Martin Screly says, I doubt it was Sacks who wanted to sell one million dollar passes. And Whiskey Titan says, I agree with you. I'm sure it wasn't, but letting Jason run rampant until Susie Wiles steps in isn't a great look. I happen to think Sacks is doing fine at this particular role, but I also understand the general public feelings. Like there's a lot of graft. The New York Times isn't the right conduit for that argument, though. And they're going back and forth.

Starting point is 00:33:16 The timeline truly isn't turmoil over this. Dan Primack had a good take. He had a whole breakdown of this, which I think was interesting. He said, let's kick this off. But first, let me tell you about fall, build and deploy AI in the AI video and image models. They trusted by millions to power generative media at scale. So Dan Primack said, lots of people are sending me the New York Times story on David Zax. Outside of the all-in sponsorship proposal, which feels oblivious at best, corrupt.

Starting point is 00:33:50 at worst. I'm not seeing much in there that's new, at least to those who've been following. Dan Premack says, as an aside, it's true that SACS slash Kraft still have a ton of AI investments. Thing is, all tech investments at this point are AI investments. It's kind of like internet investments at this point. If you invest in tech startups, you de facto invest in AI startups. And Jason says, we lost money on the event. The NIT knew this and deliberately published false information. And Dan Premack says, they included statement that you lost money on it. What did they print that was false?

Starting point is 00:34:27 They were somehow make that, that we're somehow making money in this or some gain. And Dan Primack says, just reread, just re-read. Doesn't claim that All In made money, said you tried to generate revenue via $1 million sponsorships, including for VIP reception that didn't end up happening, but ads that you don't know what, but as that you don't know what sponsors ultimately paid, or that it doesn't know what sponsors ultimately paid, included the statement that you lost money. Am I missing something? And Jason says, Mr. Sachs has raised the profile of his weekly podcast all in through his government role and expanded its business. Confused. I thought you were talking specifically about the White

Starting point is 00:35:11 House AI summit piece, says Dan Premack, talking in general, don't know how you would, would not quantify. SAC's role in White House deaf-raised All-In profile, at least among Normies, as for role in biz expansion. Guess you could stake your claim there. I completely disagree with this. I feel like the all-in podcast put the White House

Starting point is 00:35:31 on the map. I feel like a lot of people were like they found out about the White House and about the U.S. government. Which house? Exactly. Exactly. Because of the All-N. podcast. They were listening to the All-N podcast, and they were like, wait, wait, wait, you're telling me. There's people in Washington, D.C. They run this whole country.

Starting point is 00:35:47 They create laws. They're in charge of the rules. Yeah, they create sort of laws and framework over how our country should operate, which industries we want to, you know, support and grow. You're telling me, you're telling me that there's a group of people.

Starting point is 00:36:02 And one of my besties is up there running in it. This is amazing. I got to learn more about this. I've got to figure out what a bill is. I've got to feel how a bill turns into a law. Chat, what is a bill? Jason says, if anything going deep into politics,

Starting point is 00:36:16 has been a net negative for All In, at least in my opinion, we would be growing faster and wouldn't have lost some percentage of our left-leaning audience if we'd stuck to tech, markets, science, VC, etc. That's an interesting take. I still think that politics made all in so important. It made it so big. Well, yeah, and it made the content polarizing. Yeah. Which, but I think that polarizing in media is good. You actually get more attention, not necessarily good from all points of view.

Starting point is 00:36:46 but good from a pure just like reach. I mean, yeah, I was looking at the, I think the, I think the, the, the ratings are like the amount of viewers for, for, like, CNBC is bigger than Bloomberg by, like, a pretty significant margin. Because Bloomberg's, like, extra wonky and CNBC's a little bit, I mean, it's literally called Consumer Business News, like that's the C stands for, I believe. And then, and then you have Fox, which is even more. Fox News is political and it's much bigger ratings than CNBC or Bloomberg.

Starting point is 00:37:22 And then ESPN is like by far the biggest because it's like sports. Everyone loves sports. And so maybe that's the final form. They should go full poker and then full sports. It's just become sports center competitor. I could see it. It might be the way. A.B says,

Starting point is 00:37:36 I only learned about the Trump about Trump because Chamoth endorsed him. Yes, exactly. I had never heard this guy. Who? The vodka and social media entrepreneur? He's running for president. Okay. So Dan Primack is weighing in again, concluding it.

Starting point is 00:37:55 He says, the New York Times story was mostly a nothing burger, at least for those familiar with the situation. As for hoax, the story itself, as published, isn't being disputed. Obviously, the New York Times had info questions that Sachs lawyers answered, and disproven info wasn't included. That's how journalism works. The real complaint seems to be about the headline. Quote, Silicon Valley's man in the White House is benefiting himself and his friends.

Starting point is 00:38:20 I get the complaint, but that's not, but it's really a matter of interpretation, not true, false, slash hoax. Imagine if you had a friend and they went to the White House and they didn't try and benefit you. You'd feel, you'd feel like you'd turn. You might not be friends with them. You might not be friends with anyone. Saxon Trump and the Trump White House are pursuing let them cook AI policy. I like that, that they believe will help us win the AI race and that the rewards, outweigh the risks, others disagree. Yeah, this is so true. It's like, there is no like, oh,

Starting point is 00:38:50 like we now know the correct way to win the AI war. Like we, we know that there's a correct way. It's very obvious. It's like, no, everyone's debating this constantly, even inside of tech and SACS has one view that I think has actually played out pretty well, considering that he's been anti-dumer, anti-fast takeoff, more industrial capacity, more, you know, opportunity to grow GDP, you know, there are some elements of his takes that are a little bit more like TBDs, like what actually happens to jobs over the long term, how does it manifest in GDP growth over the long term? But so far, I think he's been correct. And I think that's what Dan Premack's saying here.

Starting point is 00:39:30 He says, only time we'll tell if Sachs is correct. What we know for sure, though, is that his deregulatory policies should help VC funds, his, those runs by his friends, those run by strangers, et cetera. Thus, the headline is defensible, albeit pushing an agenda. And that's the timeline in turmoil, folks. Let me tell you about graphite.com. Code review for the age of AI. Graphite helps teams on GitHub, ship higher quality software, faster.

Starting point is 00:39:56 I can't read this. AI Amblicus, this name. This Ilya interview will be compulsory viewing for any future student trying to understand what misallocation of capital looks like in real life. See, I completely disagree with this take. People were going back and forth on this. We talked about this a little bit over the, over the holidays.

Starting point is 00:40:16 But fleeting in bits says, can you say more just that he doesn't have any business direction or something else? And the original poster says, these are my intuitions, but for what it's worth on the micro level, he just seems adrift and a sea of possibility and not the kind of person. See, I originally read this as,

Starting point is 00:40:33 as, as the misallocation of capital that I've seen is like the 10th, 11th, 12th foundation model lab. that has like $100 to a billion dollars that is just like kind of iterating in what I already worked on, and already developed, right? And doesn't necessarily, like if they just do, if they just create a model that's like not state of the art,

Starting point is 00:40:59 like I don't know that there's gonna be incredible value in that. Meanwhile, I'm like, okay, you take the guy that, that, that, who's work led to chat GPT and you give him a few billion dollars and let him, you know, continue to iterate. And he's not just like, you know, firing a single, you know, multi-billion-dollar cannon and hoping he hits a target,

Starting point is 00:41:19 it's like this incremental research that, uh, I think is still one of the best shots at, like, developing the next paradigm, whatever comes after LLM. So, um, I, I read this. I, I, I think that your, your reading of this was right, but I initially read it the other way. And I was like, yeah, I do think this is, you know, somewhat, somewhat bearish on the, on the, on the incremental, uh, large language model lab. Yeah. I don't know. I mean, I can, I can kind of steel man both. Like, we're going to have Julian on the show in, when is he coming on? Or we're having Vincent from Prime Intellect come on the show.

Starting point is 00:41:56 And I was talking to him. We'll get more information from him. He's going to be on at 140. But Vincent was explaining that more and more and more companies and different business processes, they do need specific training runs. They do need the skill sets of a foundation. Model Lab, but there's a lot of business to be done that's not purely AGI seeking, not purely paradigm shifting. So I do think that there's some value there, if the business can be run well,

Starting point is 00:42:26 which is a big if, but there is a path where a thinking machines or one of these companies is going to go and do specific reinforcement learning, specific model development for a specific company and task, that can work out. It's a very different business than searching for the next paradigm doing science. And maybe you shouldn't even call it a lab because you're not really even trying to do foundational science necessarily. You're more productizing. Company. Yeah, it's a business, which is great. We love that. What's interesting about Ilya is that when we talked about this, like it is a venture style bet. Like let the scientist go experiment. Maybe it will work out. It's extremely high risk, probably a zero. But if it works,

Starting point is 00:43:08 it's huge, right? So the expected value is still high. What's crazy is that we're doing a a venture style bet at growth scale. And it's just massive amount of capital for something that I think, I think the consensus here is that it's, it's either he solves it and it's incredibly valuable and leapfrogs everything and it's just amazing. Or it's just you do get lost and you get a sea, you get lost in the sea of research and ideas and you never really produce anything.

Starting point is 00:43:33 So I love, I love the high risk bets. I just understand why people are saying, well, what, that scale, that's a lot of money. That's a lot of money. But that has been happening internally at Google for a long time. They probably burned a lot of money on research projects. It hasn't been that big of a deal because they had the engine for it. And if the investors are significantly diversified, they should be fine.

Starting point is 00:43:57 Yeah. Anyway, what else is in the timeline today? Finn.a.I, the AI that handles your customer support, the number one AI agent for customer service. We did get a good meme. We got a couple good memes. Cody says, when my wife asks what we should. to eat for dinner but says no to my first two suggestions. We are back to the age of research.

Starting point is 00:44:19 I like it. And then when she asks what I want for dinner from Bayeslord, the answer to that question will reveal itself. I think there will be lots of possible answers. Very true. It's a great new meme template. I like it. When my husband asks how many Amazon packages are still on the way,

Starting point is 00:44:37 the answer to that question will reveal itself. I think there will be lots of possible answers. But I think that's actually true. Like if he creates some new AI, like there's a bunch of different ways to monetize it. We know this is a fact. But of course, Ilya is now joining the ranks of Jan Lacoon and Rich Sutton and under Karpathy of sort of industry legends that are more or less saying that scaling is over and LLMs are dead. You know, on the other side, Shulte is saying scaling maybe not over. So we'll see.

Starting point is 00:45:13 This is, uh, what? This post is, yeah, this post is great. Scaling is over and LLM's are a dead end. Aw, you're sweet. Scaling is over and LMs are dead end. Hello. Human resources. I love his meme template because it's like, yeah, Jan Lecun has been saying the same thing.

Starting point is 00:45:30 He says, Jan says, for the record, my current BMI is 24. This guy rocks. It's very funny. I thought he would have dropped the, the, the, meta tag on X by now. But I guess he's still Oh, he used to wrap him? Didn't he leave?

Starting point is 00:45:49 Reporting to leave. He's like, reporting to leave. Okay, he's like on his way out more or less. Another one billion to SSI. There's a bunch of this in the SSI bucket. Let me tell you about profound. Get your brand mentioned in chat. You reach millions of consumers who use AI to discover new products and brands.

Starting point is 00:46:09 Of course, we are having Dylan Patel on the show in 12 minutes, and we should do a little bit of a run-through of the drama on the timeline. The timeline wasn't turmoil. Lots of people very, you know, upset with semi-analysis latest post. How dare you question? They take a, they took a swing at the king, which was the name of their article. They said, TPUV-7, Google takes a swing at the king. The king is, of course, Nvidia.

Starting point is 00:46:38 and they are asking, is this potentially the end of the Kudamote? Anthropics, they're talking about Anthropics, one gigawatt, TPU purchase, the more TPU meta-SSI, X-A-I,

Starting point is 00:46:52 Open-A-I, Anthropic buy, the more GPU-C-C-U-Save, next-generation TPUV-8, and they're going into what the battle between TPU and the next-generation GPU out of NVIDIA

Starting point is 00:47:06 will look like. And this upsets some people. There's a lot of folks who are long NVIDIA. Either they have invested in NVIDIA, they made a lot of money in VIA, or their whole business is tied to NVIDA or AMD, even. Or they bought the local top a month or... Potentially.

Starting point is 00:47:26 There's a whole bunch of reasons. You could also just disagree with this, and you could just think that semi-analysis, their takeaways are wrong, but I think it's a thought-provoking article. I think there's a lot of data in here. They're extremely thorough. And I think that they do leave you with a lot of new information that you can do with what you want.

Starting point is 00:47:49 And I think in general, the response to this article was very positive, but there were some folks who were very upset by it and went all over the place. And on accounts that put a noun and then capital as their name. Yes. And suddenly they're an expert on everything. Yes, yes, yes. Yeah, it was a little odd seeing the credentialism come out from the Anans. Because like, I don't think we should get in the two can play that game camp. It's a little bit rough.

Starting point is 00:48:20 But there's a little bit of interesting stuff in here. I want to read through some of this. Let's kick it off with the opening of the semi-analysis article. The two best models in the world, Anthropics Claude 4.5 Opus and Google's Gemini have the majority of their training and inference infrastructure on Google TPUs and Amazon's Traneum. Now Google is selling TPUs physically to multiple firms. Is this the end of NVIDIA dominance? The dawn of the AI era is here and it's crucial to understand that cost structure of AI-driven software deviates considerably from traditional software. Chip,

Starting point is 00:48:55 microarchitecture and system architecture play a vital role in the development and scalability of these innovative new forms of software. The hardware infrastructure on which AI software runs has a notably larger impact on CAPEX and OPEX and subsequently the gross margins in contrast to earlier generations of software where developer costs were relatively larger. Consequently, it is even more crucial to devote considerable attention to optimizing your AI infrastructure to be able to deploy software. Firms that have an advantage in infrastructure will also have an advantage in the ability to deploy and scale applications with AI.

Starting point is 00:49:32 And we've long believed that the TPU is among the world's best systems for AI training and inference, neck and neck with King of the Jungle, Nvidia. 2.5 years ago, we wrote about TPU supremacy, and this thesis has proven to be very correct. TPU's results speak for themselves. Gemini 3 is one of the best models in the world. And there's a very funny bit in here. I need to find it.

Starting point is 00:49:58 Saving. Oh, yeah, here. So this is a very spicy line. in here. He says, OpenAI hasn't even deployed TPUs yet, and they've already saved 30% on their entire lab-wide Nvidia fleet. This demonstrates how the Perf per TCO advantage of TPUs is so strong that you already get the gains from adopting TPUs even before turning one on. And so basically what he's, what he's explaining is that because of the competitive dynamic between Nvidia and Google with with the CPU now, you can use TPU as a stocking horse and say, hey, if you don't cut your prices

Starting point is 00:50:37 in video, we know that you have really high margins. Or not even cut prices, but encourage an investment. Exactly. And so that's what they're explaining here. Invidia would rather invest back into your business instead of cutting prices. Yes. And so says, we think the more realistic explanation is that Nvidia aims to protect its dominant positioned at the foundation labs by offering equity,

Starting point is 00:51:00 investment rather than cutting prices, which would lower gross margins and cause widespread investor panic. Below, we outlined the OpenAI and anthropic arrangements to show how Frontier Labs can lower GPU total cost of ownership by buying or threatening to buy TPUs. And so Open AI, Nvidia, you know, it was $22 billion per gigawatt, the rest of the system. So it's a $34 billion per gigawatt expense to Nvidia, but Nvidia is doing effectively an equity rebate of $10 billion per gigawatt

Starting point is 00:51:37 in investment. And so how that works out is a 29% partner discount. Anthropic has similar math, but a little bit higher at 44% partner discount because Microsoft is paying for a piece of it. And so it's an interesting thesis and it's unclear

Starting point is 00:51:53 exactly like, well, you know, if the claim that the investors will panic if it was actually just lower gross margins. Well, if you say the quiet part out loud like this and you have, you know, you do the math to show that there is basically a discount that margins might be coming down because of competitive dynamics. Does that wind up resulting in investor panic? I mean, certainly it didn't today.

Starting point is 00:52:21 Isn't Nvidia up today? Yeah. Yeah. Nvidia is up 1% adding a casual, you know, what, 10 trillion or 100 billion or something? 100 quadrillion. Yeah, gigajillion dollars. Yeah, I just, I mean, and again, we said this earlier on the show, but Broadcom is down almost 4% today, which I would have expected it to be the other direction,

Starting point is 00:52:46 given that to actually buy TPUs physically, you need to go through Broadcom. Yeah. Yeah, so a lot of people are going back and forth on, you know, can semi-analysis be trusted because they're writing about, you know, Invidia and Dylan. I think some people didn't understand that he was joking. Zephyr here has a post. Dylan is being tongue-in-cheek, but he's not wrong. Invidia was extremely dominant for the last three years, as we saw in the stock.

Starting point is 00:53:19 It's up 10x over the last three years. New competitors will cause a lot. reduction in market share and margin compression, but Tam is big, so revenue profits won't go down. 75% of GM is just unsustainable. Hypersalilers will also use the cheap TPU's threat to extract better deals from Jensen, priority access for Ruben Feynman or discounts on GPUs. Jensen called Altman and initiated the $10 billion deal after he saw the information about the information article about opening eye testing TPUs.

Starting point is 00:53:50 And so this is in reaction to that. that point about OpenAI hasn't even deployed TPUs yet, and they've already saved 30%. There's a decent post here from just another pod guy. They say Dylan's speed running through all the learnings of cell side research, industry capture, pissing off IR execs, gatekeeping info based on client tier, difficulty scaling beyond single star analysts, distorted MSN representation of your notes,

Starting point is 00:54:16 eventually spending too much time, marketing versus researching, amazing biz. Content though, obviously Dylan would push back. on a lot of this stuff. If you actually read through the entire article, there's nothing in the article should actually, in this article should be that surprising because so much of the article is just referencing

Starting point is 00:54:36 old semi-analysis research, some of which they did a sort of before the paywall, some of which they did under the paywall. But it felt like a kind of a culmination of everything that they've been saying for a really long time. And I think that part of, I think, the surprise here is just how much faster this conversation has really come to ahead than people may have expected. I think at least, at least like surface level on the timeline. I think people felt like the TPU threat was maybe like a 2026, 2027 conversation versus being like it's a part of these buying discussions right now and negotiations.

Starting point is 00:55:19 Yeah. Yeah. The other buried lead in the article was, of course, about pre-training. So there's a snippet in here. Open AI's leading researchers have not completed a successful full-scale pre-training run that was broadly for a new frontier model since GPT40 in May of 2024. And, you know, this is, it's so interesting that this, like, if this was wrong, you would imagine that there would be a whole bunch of reaction from Open AI.

Starting point is 00:55:51 people or like proxies or surrogates, right? People quote treating and be like, that's just not true. Wow, somebody else is cooked. But the fact that I haven't seen anyone respond to this and say like, oh, this is wrong, like we actually did. Not that, not that like that's the north star for what the business is. Like the business's job is to create profits. Right. It's not to, you know, complete successful, full scale pre-training runs.

Starting point is 00:56:14 That's not the goal. That's just something that they might do in service of making a better model, making a better product, but ultimately, it's whatever the customers want. And if the customers are happy with 4-0-level base pre-trained and a bunch of reasoning on top, that's fine. So what else is in the back and forth? People are also, I mean, it really, it does make me happy that we didn't go deeper into ranking people because it does feel like when you create a list of tears and rank a bunch

Starting point is 00:56:46 of people, you're just creating a big bucket of enemies. down at the bottom of like people who want you dead because you rank them low. But I'm sure we'll get into the discussion of ClusterMax and what how people are interpreting ClusterMax because there's a whole bunch of ways to read it. Like one way to read it is like which which stock should you buy, right? But like that's not necessarily the read. The other the other read is like which product is the best to work with as a customer. But it's like what customer are you?

Starting point is 00:57:19 There are some that are in the lower tiers that are fantastic for very specific use cases. Like this is the nature of every business. Like one of the, one of the, one of the, one of the neoclods that was particularly upset with, with Dylan, is in a very niche market. But if you're in that niche market, it's probably a great product. It's probably great for you if you're, if you satisfy like this specific list of criteria and you don't need these features, you're probably fine then. but it's a lot of fun. People are going back and forth. They're also debating whether or not Dylan is independent,

Starting point is 00:57:54 given that he lives with Shulto from Anthropic. We got to ask him why he has roommates. I'm not even concerned about a conflict. It's roommate gate. Yeah, it's roommate gate. But what about this other? This is a tinfoil hat post from Jukon? My theory is that META deliberately leaked the story

Starting point is 00:58:14 to the information about Google's about acquiring Google's TPUs. For META, it's a classic risk-free power play. The moment Jensen Wong reaches wind of meta using Google silicon, NVIDIA is likely to rush

Starting point is 00:58:28 in with an investment. They might even be negotiating as we speak. This allows meta to secure capital and shift from burning their own cash to potentially getting discounts or effectively buying NVIDIA chips with NVIDIA's own money. Plus, if they actually do secure Google TPUs, they solve their compute shortage. It covers all bases. I wonder

Starting point is 00:58:44 when other hyperscalers will catch on to this magic wand. All you have to do is hint at using TPUs. But the issue, the issue is how many red flags would be waving if Jensen was like, yeah, we're investing $20 billion in meta. We're very excited about, we're very, very excited about meta and owning a piece of... Yeah, it seems very, very odd. So he's in a position where I don't know what kind of leverage Jensen has. around in those conversations with meta because he doesn't want to discount and it's not like

Starting point is 00:59:22 an open AI where he can just announce an investment or an anthropic etc so how does any type of like rebate actually happen is a question yeah well before we bring in our next guess let me tell you about turbopuffer serverless vector in full tech search built from first principles and object storage fast 10x cheaper and extremely scalable um Let's read through some more TPU stuff to set the table. So, Clyde Chan, says, I keep seeing stuff about TPU. Has anything materially new happened? There's no evidence Google has ever trained Gemini on non-TPU hardware,

Starting point is 00:59:56 going back to pre-GBT models like Burt. TPUs predate NVIDIA's own tensor cores. Anthropic and character and SSI and Mid-Jurney have long used TPUs. I'd be surprised if META weren't looking at them. NVIDIA's moat has never been deep for the big labs. See OpenAI deciding it could do better. than CUDA and investing in Triton instead. Regularly edging out CUDNN on benchmarks.

Starting point is 01:00:20 There's nothing magical or structural about any of this, just good engineers doing good work. TPUs are not that much more efficient than GPUs, and small performance per watt difference are dwarfed by whether meta has the right kernels and systems engineering talent to pull it off. Both NVIDIAs and Google's moats are small and we are still at the point

Starting point is 01:00:40 where individual good engineers can flip the entire balance. Why was this not? priced in. This is all super old public info. I have a feeling that this, Clive Chan, who I guess is over at, was at Tesla and then Open AI, is a little bit of like first time in the public markets, first time realizing that the people who trade this stuff are not necessarily like on the super inside of the labs actually understanding the decisions that are being made inside the labs. It's a completely separate ecosystem. And that's why organizations like Semi Analysis exist.

Starting point is 01:01:18 And I believe we have Dylan Patel from Semi Analysis in the Reshoot Wendering. Let's bring him in. Dylan, how are you doing? What's happening? Fantastic. How about yourself? You know, I saw the meme image that you guys put out there for me, so I had to wear a tank talk to show you.

Starting point is 01:01:31 Let's go. Let's go. Dude, we need a bigger screen for that bicep. We'll work on it. Let's go. Where in the world are you? I'm in Florida, I was spending Thanksgiving with my family here. I'm trying to chill out a little bit.

Starting point is 01:01:49 It's nice to have the family pamper me a little bit because I broke my foot a couple weeks ago. I'm sorry. I knew that. How'd you break your foot? Tripped over at TPU. Family reunion playing football in Texas. We're American as we can get. There you go.

Starting point is 01:02:06 Well, we were just running through a little bit of the TPU article. Can you actually set the table for me on like, what do you think? think is new about it versus what has semi-analysis already been saying and this is more just like tying everything in a bow. Yeah, half of the article is just referencing research. We've been saying this for two years. We've been saying this for one year. And even referencing Google's own content about the TPU dating back even further. So. Yeah, I would say I would say the majority of this piece was if you're a client, it's already been pretty much all published. But it hasn't tied together. It hasn't had a narrative around it, right? Because when we think about like what we

Starting point is 01:02:48 put out as on the paid side versus what we put out on the newsletter, right? Our clients sort of get, you know, what changed, what happened. Here's the numbers. That's about it, right? We don't explain the technology that much because our clients are sophisticated, right? They're either in the industry or their finance pros who don't give a shit about the technical stuff. And so it's either of those two, right? And so we're just explaining, here's what's happening. Here's the change, here's the numbers, right? So for months, we've been saying Google's selling TPUs. For months, we've been saying, hey, here's TPUV-7 versus Blackwell. We've even put out updates on, here's what we think TPUV-8 is versus what we think Ruben is. And so generally it was making it

Starting point is 01:03:28 into a narrative and explaining the technology and the corporate, I would say, politics or dynamism around it, right? So that's, you know, I think there has been bits and pieces put out by other folks, right? I think the information has done great reporting on some of the stuff after we did, but in the public space, I think, you know, as an example, right, like, so other people have put out bits and pieces surrounding this, but they haven't put out the full picture. So as far as, like, what's new, it depends on where you sit in the stack, but, you know, anthropic and meta and folks like that have been talking to Google about buying TPUs for many months, right?

Starting point is 01:04:05 Whereas people externally are, you know, last week when Gemini 3 was launched, or two weeks ago, people were just learning that TPUs are trained, training Google's models, right? So it's, it's where are you in that information spectrum, right? Yeah, totally. So on that information spectrum, uh, the finance bros, they can probably just like, if they, if they read into this, oh, bullish Google or bearish and video or whatever, like they can kind of trade in and out, as, as they please. But what, on the, on the more technical side, like, are people using semi-analysis research to understand like, okay, I'm a neocloud. What? do I want a rack for next year?

Starting point is 01:04:43 Maybe I need to be putting in a TPU order? Is that how people interpret your research? Like what happens on the technical side of the house? Yeah. So as far as like some of the paid stuff we do, we have one model called the TCO model, right, which is calculating the TCO of all these different hardware performance, building up the entire cluster cost, you know, breaking it out into like a dozen plus different things, whether it's storage or networking and breaking down the cost of everything.

Starting point is 01:05:07 So there we put out research on TPS because as, as soon as Neo Cloud started getting offered, hey, you want to buy TPUs, we're like, okay, we need our own ground-up model. So when you're negotiating a big contract, what you do is called it should cost, right? You go and calculate what it costs for the company versus what it costs for me to deploy. And then you think about like, oh, what's the margins they have? What is ridiculous to offer them what is not, right? Because everyone always wants to know, like, hey, what margin are they making off of me? Can I push that down a little bit?

Starting point is 01:05:36 What is ridiculous to demand in a negotiation versus what's not? So we've already been working, you know, through this TCO model, we've put out four different updates on the TCO of TPUs, V7 and V8, because there are neoclouds out there, as well as labs who are purchasing TPUs that are using that to understand what's the cost. Now, you know, Anthropic, I will say, just already knew and figured it out because they've hired so many Google people, but other labs are also looking at it, right? And so, you know, when you say, hey, on the cost side of things, on the technical side of things, right, there's a lot of network engineers now out there who have never deployed. deployed Google hardware that are now like, okay, I need to figure out how to do this. Techs, right? Like, you know, so there's people who have DM me that are like, oh, we've been, you know, as you know, we've been thinking about deploying neoclouds, but your material on this is technically better and teaches me more than Google's own material, right?

Starting point is 01:06:24 So it's like this is helpful to people on multiple factors. Yeah. What about the software side? Google's built their own internal stack to compete with Kuda. How much of that are they going to actually give to their customers who are buying TPU? feels like you, it feels like potentially you could overrotate on, oh, well, Gemini 3 is really good, but why is it good? Is it just because of the hardware? Or is it also Google's incredible

Starting point is 01:06:52 prowess, multi-data center training, all this fancy stuff they have that they won't be giving you when they sell you the TPU? Yeah, so that's the interesting thing is some of the stuff software will remain close source, but you can still use it. Okay. Right? And then some of the software, they are trying to open source aggressively. And then some of the software, there's never going to give out there anywhere. So it sits in three kind of buckets, right? The interesting, I guess, newer thing that we did in the piece was we looked across all these different open source AI software, right, whether it's Plytorch, whether it's VLM, whether, you know, all these different open source libraries. And we calculated and counted up how many Google commits there were, right?

Starting point is 01:07:30 And you can see there's a chart in the article where the number of commits that Google's doing on TPUs has exploded over the last handful of months, right, as they've decided to shift their strategy, sell TPs externally, they also recognize software has to be open for this, right? You know, only the gigabrain that like Anthropic can figure out how to do everything themselves, right? It's those people outside of Anthropic, you know, types that need a bunch of open source software that builds on top of it, right? And what's interesting is when you look at like, hey, Nvidia, you know, the biggest argument that Nvidia doesn't really make for GPUs, but they should is that, you know, about 40% of the software that's open sourced is actually just from China, right?

Starting point is 01:08:06 on Kuda, and that's the Kuda mode, right? It's like 40% of the software is just like open source stuff, whether it's people committing to VLM or Pytorch or all these other libraries, right? ByteDance, open sourcing stuff, deep seek open sourcing stuff. And Google, you know, they don't have people open source. You know, Anthropics not going to open source software.

Starting point is 01:08:24 So Google needs to catch up not just by, hey, here's all the software we have internally, let's open source it. They also need the ecosystem to build a ton of software on top of TPUs. And so that's the real big challenge there. And there's an element, of software there that Nvidia is happy to open source and customers of Nvidia are happy to open source that Google will never open source because it's it's Google Cloud is selling the TPU. Gemini is the one actually using it and developing a lot

Starting point is 01:08:48 of the software and these two groups are not always going to be aligned. Yeah, isn't that like, I mean, what are the other kind of just problems with Google becoming an actual seller of TPU? It feels like there's obviously an opportunity because Nvidia has high margins. There's demand, it's a great chip. But culturally, like, Google tries a lot of different things. They have a lot of advantages, but occasionally, like, they fall flat on their face with just like they can't even get an RSS reader out or something like that. So, like, are there other risks to the TPU not really finding its footing for reasons that aren't just the laws of physics? Yeah, so the biggest challenge I see with them is everything is non-standard, right? Google for years,

Starting point is 01:09:35 they developed liquid cooling first, right? Sure. For AI computing. They deployed rack-scale architectures, right? Everyone's talking about GB200 rack-scale architecture. Google did it first with TPS, right? But when they did all of this stuff, they didn't give a crap about, hey, you know, this has to go in 50, 100,000 different people's data centers, right?

Starting point is 01:09:54 This has to go in my data centers that I designed myself. So everything is super vertical. The entire liquid cooling supply chain is super vertical, entire. The racks aren't even the standard width, right? So when I look at like a data center, it's like the, door, the loading bays, because there's so much wider, the Google racks are like three times as wide. It's like, maybe it might not even fit into the data center, like physically, like, they're the doors. So there's like all sorts of random, like, I wouldn't say random.

Starting point is 01:10:17 It's Google from first principles design stuff. Totally. Yeah, yeah, yeah. But if you're in cloud and you're like, the hot thing's going to be TPU next year or the year after, and I want to be able to sell into that market, it's not just flip a switch, drop in, replace with TPU. You have to maybe build a whole new building. Like, it might be able to. be that significant. Right, or do, or like knocked on some walls. And then like, you know, I need to go get liquid cooling, not from Dell and Super Micro and an HPE who I've, who serviced me already.

Starting point is 01:10:46 I need to go get it from some random supplier who's only ever sold to Google. And usually they're sitting across the table from like some gigabrain engineer who has a team of 20 people working on liquid cooling instead of like, you know, my one guy who does liquid cooling procurement and negotiations and like, also does procurement of like network stuff. Yep, yep. There was a tinfoil hat theory floating around that meta leaked their TPU interest to try to gain some sort of leverage over maybe some negotiations with NVIDIA. I don't know if you see any possibility in that, but how do you think those conversations are going? Jensen doesn't want to discount and compress his margins, but at the same time he can't do this kind of like equity rebate thing if he took a big position.

Starting point is 01:11:33 And meta. It'd be very suspicious. I'd be very concerned. I totally get the opening eye investment. That seems like it makes much, much more sense than saying, hey, we're going long meta is a $4 trillion company.

Starting point is 01:11:47 Yeah, at the end of the day, right, like TPUs have like a set of maybe 10 customers, right? Because you have to be super sophisticated. And so what really is challenging here is, is,

Starting point is 01:11:58 you know, meta looks at the numbers. You know, it's like, okay, opening eye, I'm getting 30% off because they're investing in me

Starting point is 01:12:04 as a role. obviously they get equity, but they're investing in me and I get 30% off on these GPs as a result, right? Meta, you can't do that. So meta, I don't think that they're just negotiating, right? Like, you know, are they just negotiating with InVio when they buy AMD? No, there are any engineers, right? They're developing all the software. They're actually deploying Lama 4-5B was exclusively on AMD for a number of months, right?

Starting point is 01:12:27 For inference, right? So when you look across, hey, is meta just like playing around trying to negotiate? it's like, no, no, no, they're looking out for what is best, right? And meta is power constrained, and TPUs are currently way more power efficient. Meta is compute constrained. And TPUs are potentially higher performance per watt and higher performance per dollar, right? At least that's what we believe for TPUV7, it is. So they'd be dumb not to look at it, right?

Starting point is 01:12:51 And they have the people, they have the team. Now, Invidia at the same time has to play the game of chicken, right? Yeah, sure, they could discount the pricing somewhat. And because what's funny is NVIDIA is more vertically integrated than Google is when selling hardware, right? Google has to pay Broadcom who pays TSMC, whereas NVIDIA gets to pay TSM directly, right? There's this vertical integration challenge where Nvidia could drop the price a little bit and they'll be fine, but they don't want to, right? You know, the whole point is you charge the highest price possible. And then the last thing is they've got this like, you know, they've got this view about antitrust, right?

Starting point is 01:13:23 You don't want to cut deals for specific customers because that looks bad, right? Instead, you want, you know, right now, Dell pays the same price for a GPU as Gigabyte as meta. Now, the networking hardware, there's different pricing because there's a lot more competition, and Nvidia can cut a lot more there. But on the GPUs themselves, Nvidia's pricing is very fair, right? Fair in the sense that they're making a shitload of money off of everyone, you know. Yeah. Talk about kind of Jensen's leverage that he has around Ruben allocations. as some of these customers start to at least consider TPUs?

Starting point is 01:14:05 Yeah, so as far as like next year's TPU deployments, it's pretty set in stone for the vast majority of the volume, right? Anthropics got a bunch, and then there's some sprinkled elsewhere. But as we go into 2028 where Google can actually ramp, you know, the flip side is Rubin is also ramping. And at least based on our research looking throughout the supply chain, you know, over a year ago when opening I started their chip team, they poached like 15 Google people overnight, right?

Starting point is 01:14:29 In one week, like, someone I knew I heard Gap was like, oh, yeah, I'm joining Open. And I text like another three people I know. And they're like, oh, yeah, I'm also joining Open. I'm like, what the fuck? So Google's, a lot of their best TPU engineers have left, right? They also have a ton left. And so what that's done is, you know, chip timelines are so long. That didn't affect TPPV7.

Starting point is 01:14:47 That's affecting TPP8. At the same time, Google's trying to diversify their supply chain, get from not just broadcom, but also media tech. And so Google's got a real challenge on TPUV8. in that it's good, it's an improvement. But then when you go look at what Nvidia is doing with Rubin, Rubin is so much better because invidia's just pedal to the floor, paranoid as fuck.

Starting point is 01:15:06 We have to be the best and we have to be way, way, way better than everything because how much better I am than everyone else is my margin, right? And so, InVIDIA has the sort of like, at least currently, we think Nvidia is going to be so much better that they'll be fine and they'll be able to maintain margins, right? Now, things can happen. Ruben can delay or TPUs can delay and the position looks better or worse, right? There's a lot of unknowns to go through.

Starting point is 01:15:29 But as far as like what is Jensen's leverage is, look, I'm going to make the best hardware and plus my software advantages. And I'll be able to continue to be dominant and dominate the market, right? There's curveballs that could go, which is like, oh, Google software, they could open source enough software that actually their software ecosystem is not far behind. InVIDIA, maybe they don't want to, right? Or, hey, they could execute everything. Invidia has a three, six month delay. And now all of a sudden they're a lot more competitive, right? And so all these things are still open questions, but it's, it's,

Starting point is 01:15:59 Nvidia can play the allocation game as well, of course, right? Hey, I'm going to give all of the GPUs initially to companies that probably could buy TPUs, but that ends up being all the AI labs and hyperscalers, right? At least, you know, like meta, right? And bite dance, people that would actually be willing to buy TPUs. And then you end up with this like weird situation where, okay, well, that's like 75% of the GPU market anyways. When I look at the AI labs through the Neal clouds, right?

Starting point is 01:16:25 when there's, you know, Nebius and Iris Energy and all these other, you know, Correweave and all these folks are deploying for Open Eye anyways, right? You know, this sort of ends up being like, well, sure, I could stiff like some people in the allocation, but at the end of the day, everyone who is a potential customer for TPUs is sophisticated enough to be where they were going to be on the beginning of the allocation anyways, right? That makes sense. How are you framing ClusterMax these days? Is it for customers who want to buy services from neoclouds?

Starting point is 01:16:59 Is that the primary goal of Cluster Macs? Because I feel like some people look at it and they're like, this is a buy rating, this is a sell rating on the stock. So the funniest thing is like ClusterMax v1, the title of it was Cluster Max, How to Rent a GPU, right? Because we discussed all of that. And then in Cluster Max V1, I believe we put Iris Energy and underperform. At the same time, the research side of the business side of the business,

Starting point is 01:17:23 business. We explicitly were like, dude, they've got these data centers. It doesn't matter if they suck it running GPUs. They've got these data centers. They've got this power. If you just value them on a watts per, you know, how much money they could make, it's a long. At the same time as like, Jordan, he's running ClusterMax is like, Iris kind of sucks. And it was other people in the technical team before him. You know, it's like, it's like Jeremy who's running the data center side and I think he's been on TBPN is like, dude, Iris Energy is a stock. Right. So it's like, it's kind of like, You know, it's like what the technical side of the house does versus what the, you know, research side of the house does. Yes, they talk to each other.

Starting point is 01:18:00 Jeremy did ask the team like, hey, what do you think of Irish energy? I think it's a log. And the team working on ClusterMax is like, I don't know. Like, you know, it's a bad cloud. And it's like, that doesn't matter. So Cluster Max has nothing to do with the stock, right? Now, obviously there's going to be some correlation with how good is a stock versus, you know, who's going to want to rent from them? Yeah.

Starting point is 01:18:20 But at the end of the day, right? Like Cluster Max is, the goal purpose, sole purpose, it will be explicitly say in there is it's for people renting anywhere from like, you know, hundreds of GPUs to, you know, right below the AI lab scale, right? The AI lab scale, there's different considerations. But in that range, tens of thousands of GPUs all the way down to hundreds of GPUs, that's who we're targeting. Plus, we're giving a bunch of feedback for people to make the cloud ecosystem better.

Starting point is 01:18:44 The unsung hero between ClusterMax v1 and V2 is that we move the bar up, right? You know, what it required to be in gold, like, was much more. What it required to be in silver was much more because everyone improved so much, right? And as we continue to, like, increase the requirements, make it harder and harder. You got to keep moving the gold pose. Right. People keep improving the ecosystem. And actually, you know, this is the funny thing.

Starting point is 01:19:09 It's like ClusterMax is evil. It's like, when we look at the quotes and we've got hundreds of quotes on Clustermax. com. All these companies are like, dude, I love this. This one specific bug that this Neocloud had, they fixed it as soon as you wrote about it. it, right? Or like, hey, help me understand the reliability. Help me understand this or that. People are like love clusterbacks. And, you know, altruistically, like I think we're generating billions of dollars in value just from, hey, like, all these clouds are more efficient and there's less failures. And it's easier to get your workload running on any random GPU cloud and the market is more efficient. Now, I'm not making any money off of that. How am I making money off of ClusterMax? I'll be very clear. Is people who hire us to do due diligence, right? So people who want to acquire a NeoCloud. People who want to sign a massive, massive deal that's not just like thousands of GPUs, but tens of thousands of GPUs.

Starting point is 01:19:55 And then lastly, it's people who want to invest in an EEO cloud. Those are the three areas where we're making money off of quote unquote cluster max, but not really. We're not selling ratings. Sure. You know, we're in fact, like a customer will do a consulting project with us or want to buy some research from us. And I'll explicitly put in our Slack shirts, Slack, or I'll send an email to the CEO like,

Starting point is 01:20:17 like, dude, just so you know the people working on this or not the people, who are doing Cluster Max rating, right? You know, the people who are by, you know, the research on like, these data centers are there and this is the power ramp or here's the accelerators or here's the TCO. That's not the people doing ClusterMax, right? And I don't care about, you know, whether you buy it or not. I, you know, at the end of the day, Google and Amazon and Microsoft are way bigger customers than, you know, Fluke DAC and like, you know, those kind of companies, right?

Starting point is 01:20:44 And yet one, some of those are ranked in silver and some of those are ranked in platinum and gold. And that's because technically what matters not, hey, you know, obviously when we talk about who buys our research, the biggest companies in the world are going to pay me more than the mid-sized companies in the world. Okay. Question from the chat. And the price is discriminated based on that. Would you change the rating of a neocloud if Shulto promised to do the dishes for two weeks straight? You know, there was an argument. I saw someone as like, who does the chores? And it's like, brother, we live together by choice. You know, we pay someone to come once a week. If you cook something, you do your own.

Starting point is 01:21:17 dishes but like you know um frankly the we're working so much and I think like you know I think I think I think Dorcasch has ordered pizza from the same spot three nights in the row before right like it's it's wait so so is is being an adult man with roommates underrated um so I haven't lived with people in years and then I when I moved to SF this year this is crazy I moved with I moved to SF this year you know I'm like oh you know I should live with friends just so it's more fun and the first house kind of fell apart. So I moved into this house with these guys.

Starting point is 01:21:51 And we've been talking about it for months. I love it, right? It's like, look, we have, you know, if you think about, oh, what if we all rented our own places that were good? And then we pulled that budget together. We have a nice place. Yeah. Right?

Starting point is 01:22:02 And then in that place, we have plenty of space for ourselves. Yeah. We pay for someone to come and clean once a week, right? So at the end of the day, what is the negative here is like, well, we're living with our friends. It's just guys being due. to where like, and the beauty is if you,

Starting point is 01:22:18 if you do bunk beds, you have more room for activities. Exactly. Anyway, no, no, sorry, sorry,

Starting point is 01:22:24 actual question from the chat. When is TPU going on inference max? We got to know. So we're working on it, right? We're working with Google, technical folks. You know,

Starting point is 01:22:35 funnily enough, actually, we triggered a security warning for this Google engineer. Kimbo went to a, a Jack's conference, right? Jack's is the opposite is like PyTorch for but for TPUs is the most simple it's it's Google's

Starting point is 01:22:48 own internal thing right um that people do use externally um he went to this pie torch or this jack's conference a Google engineer presented something he's like can I get the slides they send it to him and then Google security alert like locks him out of his computer because he sent us like some technical like information and like for three days the guy can't work and he's freaking the fuck out and I'm like email Jeff Dean I'm like bro this is like do not fire this guy he sent me stuff that you presented at a public conference. He's like, oh, okay, yeah, yeah, I'll get that fixed. But anyways, like, we're working with, we're trying to, you know, implement it.

Starting point is 01:23:17 We have access to some TPUs. The software stack is different, right? Yeah. You know, just as much as much as a rewrite or re-implement inference max, like, like the code that actually runs. I won't say it's that much work, like, as much as, like, completely redoing inference max, but there's a ton of work, right? Okay.

Starting point is 01:23:32 So we're moving as fast as we can. Yeah, internal target is this year. Okay. You know, I don't hold us to it. Then the obvious question is, is, is, is, Like, I feel like inference max is my north star for TCO relative in AMD versus Invidia land. There was a bar chart of TCO for GPU versus Nvidia. It looked like TPU was doing really well on that chart.

Starting point is 01:23:55 The bars were very low. Where did those numbers come from? Do you have confidence in those numbers? Do you think the numbers will change once you actually get TPU on inference max? Yeah, so inference max shows performance TCO, right? You know, it's great, great. Like, you know, like, guess what? Like, you know, TCO of like a Raspberry Pi is incredible.

Starting point is 01:24:15 It's like five bucks, right? Sure. You know, versus a GPU is $50,000. Performance divided by TCO is what matters. So that bar chart is saying, look, TPs are cheaper. And at least on quoted specs, you know, now let's make some assumptions around utilization. And in the article, we explicitly said, look, we don't know what the utilization is. It's going to change customer to customer.

Starting point is 01:24:34 Here's a range. Worst case, it's like a little bit worse than GPU's. Best case, it's way better than GPU. right? And so inference max will tell us what the actual performance is in inference because we don't know yet, right? Currently, the open source software for TPUs is not good enough for good enough for us to just take the open source software and say that's the performance, right? Because that's obviously like not real, right? Anyone who like is actually buying TPUs is going to spend engineering hours to work on it. And so we're trying to work with Google to get a real performance number that is

Starting point is 01:25:03 achievable by people, you know, and will be upstreamed into the open source software because this is an in-progress thing, right? No one cares what TPV-7 can do today. It's about what it does in six months. And so, you know, obviously we don't want to be, you know, today, TPUs, if you're using VLM, or worse performance TCO than GPUs, without a doubt. But the target is moving very fast. And, you know, there's a ton of, like, low-hanging fruit for us to implement before we actually put a number out there, right? And so where does Google sit there? We'll see. I personally believe the TCO side of things, the total cost of ownership, is based on what we know on supply chain, right? How much do the chips costs? How much do the racks costs? How much is the liquid cooling

Starting point is 01:25:42 cost? How much does the memory cost? How much do the cables cost? Et cetera, et cetera, et cetera, right? That's based on our estimates up and down. So I think the TCO side of things we're pretty confident. It's the performance side of things where we don't know, right? There is a wide range, and that's what we sort of tried to state in the article, right? Performance is a wide range. Can you, can you explain more about Google and broadcom's relationship? Max Hodak from Neurlink and Science was asking on the timeline last week, why have Broadcom as a middleman? Couldn't Google do the design and place the orders from TSM themselves. But what's your read on that relationship and how durable it is?

Starting point is 01:26:23 Yeah, so when you think about chip design, there's a few different stages, right? There's defining the architecture. And then there's actually like implementing that architecture onto a process technology. there's laying out that architecture into gates on the chip. And then there's like the whole supply chain side of things, right, negotiating contracts, getting allocations, et cetera. That takes like 18 months, right? Isn't that like an 18 month process, basically?

Starting point is 01:26:47 Yeah, 18 months or more, right? I would say actually like Nvidia is faster side and Google's on the slower side just because, you know, Nvidia's been doing it for longer. They have a bigger team, right? But at the same time, Intel has the biggest chip design team, and they move even slower than that, right? They take like four years.

Starting point is 01:27:05 At least that's what they did a year or two ago. We'll see what the new CEO can get into the, you know, reorgate, right? But as far as like Google, you know, when they first started the TPU, it was a very few people and they relied heavily, heavily, heavily on Broadcom to do everything, right? They just defined the top level architecture and Broadcom did everything I said below, right? Negotiating with supply chain, figuring out how to lay out gates, everything, right? As time has moved forward, Google has taken on more and more of this, right? Now they use, you know, they've talked a lot about alpha chip where they use AI to help floor plan the chip, right?

Starting point is 01:27:37 Once you have the architecture, how do I physically lay it out onto the chip, right? They've done more and more and more there. They haven't taken over everything yet, but that's sort of the point. But Broadcom has this like super big advantage, right? Invidia, they acquired Melanox, you know, call it five, six, seven years ago. Huge acquisition. Who's the biggest networking company in the world? Broadcom, right?

Starting point is 01:27:58 Broadcom is the biggest networking company in the world. And, you know, when you talk about AI, it's the AI, it's the architecture of the actual processing elements. It's memory, which you're buying from, you know, the memory companies, right? Hynix and Samsung and Micron. And then it's networking, right? When you try and boil it down to the most simple things in software, right? The networking side of things is so important. And the, let's say, technical competence of everyone around the world besides Broadcom and Invidia in networking is so low, or rather it's just not as good as them.

Starting point is 01:28:28 They're actually good. but it's like Broadcom and Invidia are just so good. And Broadcom is better than Nvidia in many ways at networking that, you know, when you think about what is Google doing, yes, they're defining how the network topology is, but when you're talking about the physical network surrety's, you know, how packets get transferred, all these different things. Broadcom has heavy, heavy influence there. So to this day, right, Broadcom is still charging margins like they did three, four years ago,

Starting point is 01:28:51 even though Google has taken up more and more of the work. But at the same time, Google can't leave until they figure out how to do, the networking and supply chain themselves or with a partner. And so what are they doing on TPUVA that is potentially a distraction that's slowing down their execution is they're working with MediaTech, right? Media Tech at times has helped Cisco with their network chips. Media tech has a lot of work on some of this networking stuff. They're nowhere close to Broadcom, right, on revenue, right?

Starting point is 01:29:17 For, you know, that's one metric on technical competence. You know, that's another metric. I think Media Tech is good, right? But, like, they're just nowhere close to Broadcom. So now Google is having to work with, you know, I don't want to say subpar vendors, but inferior vendors to Broadcom. And that's just to increase their margin on TPU8. I would even say their angle when they started this project was never we're going to sell TPs externally.

Starting point is 01:29:40 It was, dude, we're paying a 3x markup to Broadcom. And half the cost of this chip is memory. Like, what the fuck are we doing? Right? You know, at the same time, it's like, well, sure, physically the cost for the networking is not that much. But what value does the networking bring is, you know, sort of Broadcom. And then Broadcom is also doing the, like, game theory. not science of like, well, you can't really leave us, so we're going to charge you what we think

Starting point is 01:30:02 is fair or what we think we can charge. And Google's like, oh, no, we're stuck to you, right? So Media Tech is taking way, way, way less margin. They're not passing the memory through them, right? And so, you know, this ends up being like, hey, that's a huge advantage for them. Flipside is like, well, they've got to engineer all this work that Broadcom was doing instead of working on a way better architecture. They've got to work with a worse vendor, right? Objectively worse, although MediaTech, like I said, is very good, to try and implement TPUs more directly with CSMC with less Broadcom sort of in the middle. Very helpful.

Starting point is 01:30:38 And Google, you know, because it's risky, is going down both paths, right? They're continuing to work with Broadcom on TPV8 and then separate TPV8 project that's working with Media Tech, right? Because they can't risk, you know, find whatever 30 points of margin, 40 points of margin, 50 points of margin. I can't risk the TPU being late because ads runs on that, Gemini runs on that. Yeah. Can you give any takes on the NVIDIA's $2 billion investment in synopsis that got announced this morning? I don't know if you saw it. I'm assuming you did.

Starting point is 01:31:06 Yeah, so in a time where, you know, let's say the two biggest chip makers, Broadcom and NVIDIA are making more money than ever and everyone else in the supply chain. And all the hyperscaler are trying to design more and more chips. Everyone's sort of working on that. You've got, you've got the EDA vendors are at the lowest possible valuations or lowest valuations that they've had. They're still very expensive, but lowest valuations they've had on a earnings multiple basis for a long time. And this is on the eve of, hey, like, objectively, are there going to be more chip designs or less chip designs in five years? A lot, lot more, right? Now, the flip side is AI chip design is coming. There's 20 plus companies doing AI chip design.

Starting point is 01:31:44 We've got a really long article coming on that soon. That will sort of explain the landscape, but AI chip design is going to shake up everything. To be clear, this is AI, this is AI chip design. Correct. AI helping chip design, whether it's for AI chips or for like power chips. Okay. Yeah. And so the question is like, you know, Nvidia has a lot of tools internally, right?

Starting point is 01:32:07 The dirty, the thing about EDA is that there's three companies that own 95% of the revenue. But at the same time, Google and Nvidia and Broadcom and all these guys also designed a lot of their tooling internally, although there are massive customers of all three vendors. Right. So it's kind of like an oligopoly where the customers also contribute a lot. And so, NVIDIA's whole goal here is like, how do I get every EDA flow working on GPUs? Because today a lot of it is running on FPGA, a lot of it's running on CPUs. And AI, chip design is going to get a lot more AI influenced. How do I get everything working on GPUs in terms of like the operation of it, even if it's helping people design not GPUs?

Starting point is 01:32:44 Right. And I don't have enough engineers to work on all the software. They've open sourced a lot of software, right? Like cool it's software for lithography, right? And they've got all this software up and down the chain all the way from lithography to laying out chips and all this other things. They just want to make it all run on GPUs. And so that's what their goal here is, right? And now they've given synopsis a huge, huge.

Starting point is 01:33:07 They're buying synopsis at the lowest valuations that synopsis has ever had with all this cash that they were going to give away in dividends or buybacks anyways. And they're getting synopsis to now make GPU's first class, right? And so I think this is a win-win for synopsis and Nvidia. Well, we can go way longer, but I know what's on your calendar. You've got to hit the gym. Thank you so much for coming by and chatting with us. This is really helpful. Have a great rest of your day.

Starting point is 01:33:35 Enjoy the holidays with your family. Great catching up. We'll talk to you soon. Cheers. Goodbye. See you guys. See you. Let me tell you about public.com investing for those who take it seriously.

Starting point is 01:33:45 They got multi-asset investing and they're trusted by millions. We have Rokana in the Restream waiting room. Let's bring him in to the TBPN Ultradome. Roe, good to meet you. Welcome to the show. How are you doing? I'm doing well. You guys have become quite the celebrities in my district.

Starting point is 01:34:03 Everyone is tuning into your podcast. I'm glad to hear it. I'm glad to hear it. And we're happy to have you on the show. Thank you so much for all in the time. What was that? Are the all in guys jealous or do they respect you? I think that they have left.

Starting point is 01:34:16 They're in the stratosphere. They have left the, you know, the low... We're picking up the scraps compared to them. I think like any great Silicon Valley startup, you're lifting at their heels. Well, it's funny. We had a New York Times piece around us. It was a very nice, you know, just like, here's what TBPN is doing. Just kind of an explainer piece.

Starting point is 01:34:39 David Sachs, of course, got a little bit more of the investigative journalism treatment, got five reporters. We only got one. And so I think that tells you about the relative importance of the shows. But anyway, I'm sure we'll get into that. I would love for you just to kind of set... Normally on our Gus joins, join, and they're wearing a suit, we say thank you. But I think this is, I'm assuming it's one of your daily drivers.

Starting point is 01:35:01 Yeah. Well, you know, I'm not back in my district. It'd be the only way I'd lose my seat is if I started to show up with a suit to like... The place was there. Oh, it's a suit guy. It's going to be uniform. Yeah. It's the uniform in D.C.

Starting point is 01:35:16 But I was hoping you could... you could sort of take us through a little bit of the prehistory, since it's the first time on the show, just explain how you wound up in this position, a little bit of your backstory. And then obviously there's so many hot topics that I want to talk about in artificial intelligence and tech broadly. And I want your opinion on everything that's going on. But I'd love to kick it off with a little bit of like how you wound up in Congress. Sure. Well, I am the son of immigrants.

Starting point is 01:35:44 My parents came from India in the late 19th, 60s. My grandfather spent four years in jail alongside Gandhi as part of the Indian independence movement. And that really inspired my love of public service. When I came out to Silicon Valley, I had a professor, Larry Lessig, he said, if you care about policy, go out to Silicon Valley. That's where the interesting things are happening. That's where the big things are happening. So I went out. And I ran when I was 27 against the Iraq war, and I got killed. I got crushed. 71 to 19, but came to the attention of folks as someone willing to stand up for against the war. And then I worked as a tech lawyer.

Starting point is 01:36:29 I supported President Obama. I got to go work for President Obama. And then I wrote a book about what we needed to do to build new manufacturing across this country in 2012, what we needed to do to really have the modern economy in different parts of the country. You were one of the first American beginning of the American dynamism movement, would you say? Yeah, I was going to say to President Trump, he stole all my ideas in terms of manufacturing. But, you know, he... Well, that's good. That's good, then, right?

Starting point is 01:36:59 We just want to... Yeah, no, look, I support the American dynamism movement. I'm a fan of sort of what Mark Andreessen wrote in a Wall Street op-ed about, like, how do we not make masks in America? How do we not make basic things in America? When my parents came to this country in the 60s, we were the place to be. We were humming. We were brimming with confidence. Kennedy said to go to the moon.

Starting point is 01:37:21 And in my first book was about why manufacturing still matters. I think it was a colossal mistake to let China eat our lunch on so many key industries, especially now with rare earth and metals and magnets. I mean, we should have a Manhattan project to do that in the United States or New Zealand, Australia, Chile. But, you know, so after my time of the Obama administration, after I wrote this book, I said, technology is going to shape so much of the future of this country. I have a vision of how we can make sure that it helps everyone in my district and around the country. And maybe I have something to offer to Congress.

Starting point is 01:37:56 So I ran against an incumbent again, lost again. California is a machine-dominated state. It's very hard to break in when I persisted and won on my third try. There you go. Third time's a charm. And so for this year in 2025, how would you frame, you know, your top priorities? There's this weird, there's this weird disconnect between that we've been tracking on, like, how relevant is AI? It's so dominant in tech.

Starting point is 01:38:24 And yet, if you talk to somebody at Apple, they'll be like, we didn't want to focus on AI this year at all. We wanted to focus on battery life because that's what helped us sell phones. and AI was actually not a driver of iPhone sales, for example. It's a deeply pervasive discussion point, and yet it's not necessarily, and yet it's widely used, but also widely hated. It's such a unique technology. But just in terms of political priorities, what's been on the top of the stack for you this year? I want to answer your question on AI. But obviously in the last few months, what's been the highest priority is getting these Epstein files released.

Starting point is 01:39:03 Thomas Massey and I passed the Epstein Transparency Act. It was my bill passed 427 to 1, 100 to 0 in the Senate, and Donald Trump signed it. Most urgently, it's about justice for these underage girls, over 1,000 victims who were raped at Epstein's Island. But it's also about this kind of idea of elite impunity that these rich and powerful people, I call them the Epstein class, don't play by the rules, which you and I have to play by, and people are tired of it. And it also is a story of how in the world do you get some things done in Washington? How did a Bay Area progressive congressperson end up getting Donald Trump to sign his bill and getting 427 people in the House to vote for it and 100 senators? So that has been the immediate priority. But what I say to folks.

Starting point is 01:39:48 So are you optimistic that the American people will ever get a like a truly cohesive narrative on the Epstein story? or will it be our generation's JFK assassination? I'm confident we're going to get far more than we've had so far. The release is now mandated by law December 19th or December 20th. And then more names are going to fall. You've already had some high profile names fall because of their affiliation with Epstein covering up for him or being inappropriate. There are going to be other names that come out. Now, do I think that it's going to satisfy everyone?

Starting point is 01:40:27 No, there's always going to be some sense that we didn't get. of full justice, but it's going to be much better than these women who were denied justice for decades, which was not partisan. I mean, they were shafted by a justice system that didn't work. And there are a lot of rich and powerful people who got away with it. But look, what I tell people is that AI is going to matter even more than anything. And to your point about Apple, it's not AI literally as just AI as GROC or chat GPT or a technology that detects patterns and can predict the future based on patterns. It's more that AI has become a symbol for a technology revolution that people know is changing everything about their way of life and the economy

Starting point is 01:41:13 and where they feel like they don't have control, that they don't have a full say in what that's going to mean. They don't have a full say in what that's going to mean for their kids in terms of having good-paying jobs, and they're unsure if their kids are going to have as good a life as their parents had. They don't know what that's going to mean culturally. for them as citizens? Are they going to have the same sense or are they just going to be manipulated by algorithms? And they don't know what that means culturally, as their kids are on phones in school and becoming sort of creatures with machines. And so this whole concept of how technology is going to be something that empowers people and that people feel comfortable about as opposed to fearful of

Starting point is 01:41:59 is the challenge in my view of our time. And, you know, I've gotten attacked from some people in Silicon Valley saying, oh, it's kind of a Luddite. And I was like, no, I'm not a Luddite. Of course, I believe AI can do a lot of great things in medicine, in coming up with new disease and lowering costs. But I don't think we can be oblivious to people's concerns about keeping jobs and keeping social cohesion and making sure their kids are going to have a good economic future. And so I've tried to be thoughtful about how we adopt AI, how we adopt technology in a way that keeps the American Dream alive and benefits folks.

Starting point is 01:42:41 And I mean, that's such a wide remit how we adopt AI technology because you can see it implemented from a chat bot that some random person uses or kids are using AI all the way down to, you know, deeper in some, the bowels of some enterprise software product that, you know, no human was ever interacting with to begin with. And then it's just streamlined a little bit with some AI dropped in the middle of some big system. How are you thinking about creating some sort of taxonomy around AI? Do you like a divide between generative AI and more traditional machine learning workloads? Do you see a divide between consumer and B2B

Starting point is 01:43:26 applications, self-driving versus what happens in a chat bot? How are you thinking about actually breaking apart that problem? Because there's so much there when we say AI. Yeah. I would say that the key distinction is, is AI going to enhance human capability or eliminate human beings? That is the distinction. And that we need to figure out as a society how we get more AI that is enhancing

Starting point is 01:43:57 human beings as opposed to just eliminating them. Let me share two thoughts on this, both of people who influence me. Steve Jobs described a computer as a bicycle for the mind. He didn't say computers would eliminate the mind. He just said it would make the mind go really faster and better. And my view is how does AI do that? And then Darren A. Smogh won the Nobel Prize at MIT. has this idea of total factor productivity.

Starting point is 01:44:28 Let me try to explain it simply. If you just had AI replacing human beings and those human beings then becoming not productive, not only would you have frustration in a society, right? I mean, who wants to just get a check without contributing people at pride? But you also wouldn't actually maximize total production because you have all these people

Starting point is 01:44:49 who could be doing things who are not productive and or not being able to earn a living and spend money. And so what he says is that there is some savings of that for consumers and for shareholders if a technology just eliminates labor. But the best technologies, like electricity, like automobiles, don't just eliminate people. What they actually do is they increase people, workers' ability to produce, that they are technologies that increase human capability. And so you have the benefit of the synthesis of the technology and the work.

Starting point is 01:45:25 And that that is actually what transforms the lives. And he calls it total factor productivity. And so my ideas around this has been, how do we do that? How do we make sure we just don't eliminate four million commercial drivers? How do we make sure that the adoption of things is actually making us more productive and that it's being done with respect to workers and capability? Okay. So let's make it more specific because I agree at a high level.

Starting point is 01:45:55 with a lot of that. But let's talk about like a specific role or job like truck driving. You've generally come out against or have concerns around AI-based job displacement with long-haul trucking and truck drivers. On the other side of that, if I'm running a trucking company and I want to deliver the best possible service for my customers. It's possible that AI would be able to support that. What kind of like policy do you think is right in order to create? You want some guardrails around the industry, how AI should be used in trucking?

Starting point is 01:46:43 I'd love to kind of understand more. Yeah, I would say have a human in the loop. And so what does that mean? When I, on a plane, a lot of it is automated, but we still have a pilot there, and I'm glad we have a pilot. I wouldn't want to just fly in an automated plane. And so does this mean that a truck driver's job may become more appealing? Because right now, as you know, we have a shortage, actually, of truck drivers more demand. But if they have a assist from a technology that maybe allows them to rest more, that's less taxing,

Starting point is 01:47:18 They're there for the edge cases, if something is possibly going wrong. They're there to deal with maintenance. They're there to make sure that you have loading and unloading happening. We can reimagine what the role of a truck driver is going to be. And we can certainly have a temporary view for the next five years that you should have the driver there now. That doesn't mean that at some point there may not be jobs or certain parts of things that don't. require a driver, but it doesn't seem unreasonable for five years to say, you want a driver in the loop and let's rethink the types of jobs that that will be. And if we need the government to be

Starting point is 01:48:01 helping invest in the developing of this technology, fine, but do it in a way that's going to be complementary with drivers. This is kind of happening already with Waymo where there is a human in the loop and it's but you know the ratio of teleoperators to cars on the road is potentially higher than one to one right now you know according to some reports uh but over time i think the waymo team expects there to be fewer and fewer humans in the loop over time the question is how fast does that happen um and you're sort of proposing um maybe try and make that as gradual as as a as a process as possible because, I mean, you go back to, like, the elevator operator used to be a human. Now we use buttons and no one's really missing those jobs. They phased out over time. I think the main thing is

Starting point is 01:48:54 everyone is concerned about rapid job displacement, not necessarily the, if I told you, your grandson can't be a truck driver. You'd say, oh, you know, he'll find a different job. But if it's like every truck driver out of the job next year, that's obviously. much more disengaging to the U.S. economy. Is that how you think about it in terms of just timelines more than strict rules forever? I think that's thoughtful. There's a famous economist, one said, in a gender time, jobs for the father, not for the son. And by that, he meant, look, we've got to make sure that people in their 30s, 40, 50, 60s

Starting point is 01:49:34 have jobs. That doesn't mean that that's exactly what their kids are going to do or their grandkids are going to do. For a lot of these human in the loop legislation, we're talking about five years. We're not talking about 15 years. And we're talking about roles evolving, right? I mean, it may be that these evolve, and then there's less of a need to hire folks down the line. And you have a natural transition of folks. But you're taking people who are workers and making sure that they're productive and they have a good life.

Starting point is 01:50:08 let me explain why I think this matters. Phone operators, which people often give an example. And Alex, Alexis, who's at Chicago, had a great point about this. That was 2% of the workforce. Commercial drivers are 10% of the workforce. You already have an anger in the country of so many people displaced by globalization,

Starting point is 01:50:28 displaced by the concentration of wealth in some areas. And you really want to throw into this mix of rapid mass job loss displacement. And then what? Just compensate them and have people stay at home and just get a check? Like, is that the society that we think is going to be productive? Or do we rather figure out how they have some role and some say? And the transition be managed in a way that considers their interests as well. And that's, you know, and I get that it's a good nature debate.

Starting point is 01:51:03 And people say, okay, kind of you're adding some. cost to the issue. And if all you cared about was shareholder profits and minimizing consumer costs as your only holy grail and you didn't care about jobs and you didn't care about communities, then people have a legitimate critique of me. But I would argue that that was the mentality during globalization. And it's what's led to so much of the polarization, not just for politics in the United States, but in the Western world, led to things like Brexit, led to anti-immigrant sentiment. And maybe we should consider jobs and communities not as dispositive, but as a factor, just like we consider consumer costs and shareholder profits. Yeah, what's your, what's your look

Starting point is 01:51:49 back on how the Uber story played out? Because that was a weird moment where there was a big pushback from the taxi cab drivers. Those jobs still exist, but they're just way less profitable because the medallion system has kind of been undone. But if you want to make money driving someone, you can, but you're making less money. Do you think that we should have handled that differently if we could run back the time? Or do you think it happened slowly enough

Starting point is 01:52:22 that it was actually okay and delivered enough value to the consumer? Because Uber is one of those weird examples where the amount of taxi cab-like activity, ride-sharing activity, 10x and more people take these, these rides than ever before in the taxi era. And yet it did have remarkable impact on the market structure of that industry. Well, I'd be hypocritical for saying I'm against Uber. I take Uber's all the time.

Starting point is 01:52:53 Yeah, right? I'm the same way. Ex-posé, you know, next time I do an Uber. But I'll say this. We should have done more for the medallel. owners, right? I tried actually in New York. This is before Zoran Mamdani became Zoran Maldani when he was an assembly member. He was really focused on a lot of these taxi drivers who had lost their medallion value and were underwater. And what could we do to compensate them? And I'd actually reached out to Jamie Dahman, who tried to do something to his credit

Starting point is 01:53:27 through J.P. Morgan, and it ended up not working out, but, you know, we should have, as a government, done more to help those folks who had medallions who lost all their value. And that's an example of something where we could have been more proactive. And then there's a huge debate about Uber drivers and whether they're getting enough value and have enough say over their lives. I would argue that we need that. And I'd argue we need national health insurance. This is the biggest biggest area where if you're not going to be employed as a traditional employee, it would really help if people didn't have to buy health care on an exchange that has soaring premiums. So there are better things we need to be doing to help that Uber driver.

Starting point is 01:54:12 But am I glad that there is a technology like Uber? Yes, I am. It has created jobs and it has made life easier for many people. You brought up Mom Donnie. He made a post, I think it was yesterday or the day before, that a bunch of Silicon Valley types were agreeing with, which was, you haven't seen that very often. It was basically around S&B deregulation,

Starting point is 01:54:38 making it easier to get a small business off the ground. Should that be a more important conversation in every state and region? I feel like growing up in California, I've seen so many businesses like try to get off the ground and you end up seeing like a finished restaurant that just has its door closed because they're waiting on some permit or something like that. And it's obviously hard enough to start a restaurant. And it seems like oftentimes local governments can get in the way. Do you think that needs to be just a bigger part of the conversation as, you know, given that starting a business? is a great way to insulate yourself from at least some job displacement risk with AI.

Starting point is 01:55:26 Yes, it does. And look, Zoran became famous in part of this halal video where he was basically saying it takes too much regulation to have a halal stall and we need to streamline that. And so I believe, yes, we need to make it easier for people to start a small business, to be their own business owner. That's not just making the permitting easier. It's also making sure people have access to capital. A lot of times that's a barrier.

Starting point is 01:55:52 But I'll tell you one thing that I think is often a blind spot for folks in my district. I love small businesses. I love entrepreneurs. I think that there's a lot of people who want to build it all. Completely agree. We love small businesses here too. But here's the butt. Most Americans, most Americans are not going to go just start a small business.

Starting point is 01:56:16 This idea that every person in Bucks County, Pennsylvania, where I grew up or Western Pennsylvania, should start a startup or build a business. Like, my dad never did that. He had a middle class life. He worked for the same company for 30 years. And there are a lot of people who just want a decent job. And they just want a job that can support a family. And there's nothing wrong with that if they want to be in manufacturing or they want to be a nurse or they want to be a child care provider. And so sometimes our rhetoric becomes like, why can't everyone become an entrepreneur? It's like, why can't everybody become a politician? Now maybe an opt for is a better life. But like a lot of people just don't want to do that.

Starting point is 01:56:55 And they still want to have the American dream. And so all I'm saying is let's think about how to help small business owners, but let's also think about the 4 million people who are drivers and like what is their life going to look like. And it's important to have that balance. Yeah. Give me some lessons from the recent trip to China. I'm fascinated by how they're dealing with AI. Are they doing anything right?

Starting point is 01:57:20 Are they moving even faster? Do they have a solution to the job displacement problems? Is there anything good or maybe risky that you found going out there? What were your takeaways? Three takeaways. One, one third of the AI talent is in China. What does that mean? That means it would be totally counterproductive

Starting point is 01:57:41 to ban Chinese students from coming to the United States or Chinese entrepreneurs for coming to the United States. We want to have that talent come to the United States because we still have a better ecosystem for capital and for investment. Second, we need to make sure that we're developing the talent in AI here in the United States and investing in STEM and making sure that we're encouraging the local development of that. But third, And this is the most important. Guess how much youth unemployment is in China? It's nearly 20%.

Starting point is 01:58:18 It's really high. You guys are too smart on this stuff. It's really high. 20%. But that's crazy. How is that possible? I feel like, can't they just go build more bridges and create more jobs? I thought it was a command of control economy.

Starting point is 01:58:32 I don't know. They have enough to empty sky rises, I think. Maybe. I don't know. Yeah. What was your takeaway from it? As I describe it to people, you can't build dating apps in China. Right? Like, so, you know, the people who have these fancy degrees.

Starting point is 01:58:46 Is it a band? Yeah, I mean, they're in such a directed economy. They want everyone to like make stuff, manufacture stuff, not do, not do things that they would consider frivolous. Sure, sure. Sports app, a music app, all the cultural stuff that we do that improves consumer life or things about consumer needs. And, you know, so you're someone who gets this fancy education in college.

Starting point is 01:59:11 And then they're like, okay. go work at a factory. And just like we've undervalued people who want to work at factories in America, we should be having more trade schools and more respect for factory workers, they've undervalued people who don't want to work at a factory. And the reality is, like, you should have both choices. So these people, they're there and they don't want to go necessarily to build a bridge or necessarily to build a next factory of robotics. And it was hilarious because I would talk to the Premier Lee Chong or others and they'd say, well, it's a voluntary unemployment problem.

Starting point is 01:59:48 These are just folks, they should be getting doing these jobs. But what if in America we said, okay, you know, is one of the newsrooms when they were being laid off said to someone, go become an electrician? Well, that's as offensive as telling a steelworker to become a coder. Like, you know, people do things and they want to do what they aspire to do. And China is a command-directed economy that is overvalued manufacturing doesn't have that diversity. We do. Our problem has been the opposite that we undervalued making things.

Starting point is 02:00:19 We undervalued the trades. And so what we need is sort of a balance for America to have manufacturing, but also this incredible ecosystem of the service economy, which can employ people where China can't. And that's ultimately why I bet on America. I'm also one point you're sick of this argument that let's just go be like China, where they're going to eat our lunch. Really? You know, the Chinese model is a crony communism. Like, okay, G.G. Ping gets rich.

Starting point is 02:00:47 And a bunch of people who are running these companies get rich. And the rest, and then you have 20% unemployment. And you have consumer welfare declining. And look at how most people live. They don't live in nice houses with, you know, two cars. So, like, I don't want China as a model. And I'm not going to compromise every American having economic security just because we're chasing China.

Starting point is 02:01:10 China is not the model. America needs to be more like America of how we built America in the 1940s. I completely agree. Quick couple. Thank you. Want your takes on a couple of things. Housing affordability. I think a lot of people agree right now that housing affordability is sort of like upstream of

Starting point is 02:01:28 of a lot of the problems that we're facing as a country, what's your current stance on how we can improve affordability at kind of the local level and at the federal level? I'm a YIMB. I'm an abundance guy on housing. We've got to build far more housing in California. You know, I don't endorse people or sort of zero housing people in my district. We've got to realize that aesthetics matter, but economic equality of opportunity matters more. And you can't have five trillion dollar companies in my district and expect to live like where the Valley of the hearts delight. Like if you got that many companies, you've got to have housing near transit and dense housing to make sure that people can live there. And that it's not just a place where

Starting point is 02:02:18 wealthy people can live and that the working in middle class is getting shafted. We also need to stop private equity from buying up single family homes. People say, oh, this is a red herring. No, it's not a red herring. In some places, they have bought up too much single-family homes. So pro-building, pro-streamlining, making it easier to build and having zoning reform and stop private equity from buying up these single-family homes. What about international participants? Will we make progress, at least in California, on those issues in the next 10 years? Yes, because I think people realize we didn't make enough progress over the last 10 years that this is a failure of California policy.

Starting point is 02:03:00 And whoever is elected the next governor, I can't imagine it won't be on a abundance agenda when it comes to housing. And it's not going to be, okay, let me do it at the last year, try to do something of an eight-year term. It's going to be day one. How do we start to do things that it's going to build more housing? So I think it's been a wake-up call for California. That makes sense. Any quick comments on the current state versus federal AI?

Starting point is 02:03:26 regulation. We didn't get to touch on that earlier. And you had some comments recently on SB 10-1047, the bill in California. But what's your updated view on where regulation should be happening? Well, look, ultimately we need a federal regulatory framework. But the way you get good federal legislation is having legislation in the states, that's federalism. And I don't understand how you would have a moratorium on having state legislation. When federal legislation right now looks bleak, the prospects of it are bleak, it is such an unpopular position even among Republicans. So my view is build a consensus that you can have thoughtful regulations at the federal level and work on that don't stop states from regulating. And this idea that, okay, you're going to stop all the growth.

Starting point is 02:04:28 I mean, my district is $18 trillion of value. We've got five companies over a trillion dollars. East of the Mississippi, there's not a single $1 million. You know? California's undefeated. It's so good. You talk to folks in like Bucks County, Pennsylvania, where I grew up and they're like, come on, come on.

Starting point is 02:04:49 They're producing more wealth than ever before. Like, what we want to know is how are our kids going to fit into this? And I just think that that, I wish more tech leaders. You know, who sometimes gets it as Jensen Wong is talking about this. Yeah, I mean, how do we create economic development opportunities in places that have been left out? How do we make sure that everyone comes along on the AI revolution? I just think it's in tech companies' interests to embrace this in a similar way as the, economic royalist embrace the New Deal eventually. I mean, you can't have just a capitalism that is

Starting point is 02:05:27 only working for some with large chunks of the country suspicious and left out. Yeah, I just worry that we don't know the shape of what we're regulating yet. Like the unintended consequences of social media took five, ten years to develop. I mean, two years ago, we were reflecting on this. People were worried about AI killing everyone and creating the Terminator. And then what wound up happening? Well, it wasn't really political misinformation. It was much more people chatting with it for a really long time going crazy, you know, maybe overbuilding, maybe risk in the debt markets. Like the risks were very hard to predict. There were risks, but it wasn't exactly what we thought. And so I'm always, I'm a little bit like hesitant about like,

Starting point is 02:06:14 you know, maybe there should be regulations, but how, when will we be confident that we know how to regulated. Is it right, is now the right time? Do we have clarity? Because a lot of the stuff it stands on, you know, we already have fair use. We already have copyright protections. And so a lot of it can be enforced through the courts, I would imagine. But of course, if new problems come up, they need to be resolved. And that's the way we resolve them in, in a democratic society. I think that's fair. The places I focus on are jobs. Yeah. And American citizenship. And I agree with you on the jobs part, but it just feels like the, the jobs, we haven't seen a collapse.

Starting point is 02:06:53 And even people building the AI technology are like, this is going to put everyone out of jobs and that's good. And then the people that hate the technology are saying it's going to put everyone out of a job and that's bad. And it's kind of crazy because they all agree that the jobs are going away. And yet what do you get when you actually look at the jobs figures? It seems like we still have jobs. Like it seems like we actually can't delegate to the AI.

Starting point is 02:07:14 And I can't just say, hey, you know, trucker. Like I want the AI to handle this one. It's just, it's just, the technology is not there yet. And will it be a year? Will it be five years, 10 years, 100 years? There's a whole bunch of incentives to say it's coming right now. And it's hard to get a read on and predicting, predicting when things will happen is, you know, fortunes are won and lost on that, on that alone. Totally agree with you.

Starting point is 02:07:38 John Maynard Cain said we would all be working 15-hour work weeks. And he was on, you know, more about economics than any of us. So, you know, it's hard to predict. But I think what we can do is when you look at Dernay Smoglu, says, well, why don't we have a neutral tax code? So we're not incentivizing a depreciation of investment in technology and automation over hiring people. I mean, there are things we can do that make it that we prioritize having people in the loop. And then there are things we can do in our social media environment that protect us as citizens

Starting point is 02:08:10 and kids. Two things are like, let's eliminate bots, right? Elon Musk talked about doing this on X. And there's still a ton of bots. but a lot of the bots that use AI are, in my view, hurting our democracy. And then let's protect kids from some of the harms on social media. Yeah, yeah. You know, so I guess, you know, I love, I love sparring with folks,

Starting point is 02:08:31 and I appreciate sort of the criticism I've gotten from some of the tech folks for the tweets on AI and drivers. But I guess what I would hope for tech people listening to this is don't resist every form of regulation. and sort of dismiss people's anxieties, instead be part of how we get smart regulations and how we answer people's concerns because if 70% of the American people believe the American dream is dead and have a concern about AI,

Starting point is 02:09:00 like the answer to that for anyone who's been like in a relationship is not to dismiss it and say they're dumb, it's to say, okay, how do I address that anxiety so that we can move forward? And I guess my hope would be that there'll be more tech leaders like that. Victor Peng is one who was a former leader at AMD. I mean, there's some people who are thinking in that way. And I think it's in Silicon Valley's interest to have that kind of view. No, that makes sense. I think you really, you really freaked people out with the, there should be a tax

Starting point is 02:09:30 on mass job displacement. Well, there is a tax on the profits, right? Like, we tax profits. So, yeah, I mean, there's a question of like, maybe we adjust that, but it's, it's all, these are all dials that already exist. We're just discussing how we turn them, I would imagine. Yeah. I don't know. Well, thank you. A lot of times, you know, this is one thing different for me than other politicians is I toss ideas out there. If I think there's good pushback, then I adjust my views. And I'm like a politician like this.

Starting point is 02:09:59 I just talk like I talk to someone over a drink over at a bar. You know, and everyone else is like so scripted. Oh, you can't put out an idea because, you know, maybe it'll come back 10, two years later on Face the Nation. I just don't think that's what our politics are. It's like put your ideas out there. You're like, what human being doesn't have some ideas that are dumb. Like maybe, maybe Einstein didn't or something. Most of us, yeah, we put up good ideas, we put bad ideas.

Starting point is 02:10:22 I love it. I love that. I love that approach. And it certainly sparks a conversation. And it certainly fits with what we've done here today. This was really fun. We really appreciate you coming on the show and just like going all over the place and just talking through all this stuff.

Starting point is 02:10:35 It's fascinating. I'm learning a ton. And we really appreciate you taking the time to come talk to me. Yes. Thank you so much for coming on. Well, you guys are doing great. Seriously, you're elevating the conversation. and Silicon Valley and it's an honor to be on.

Starting point is 02:10:48 And I look forward to being back. Yeah, we'd love to have you back on the show and go way deeper on all of these. And I'm sure by the time the next time you're on, all of the data points will be different. And we'll be looking at and we'll be staring at new problems. And they will require new solutions and new discussions. And so thank you so much for taking the time to come talk to us. I appreciate your approach.

Starting point is 02:11:06 Thank you. Have a great time. We'll talk to you soon. Bye. Before we bring in our next guest, let me tell you about numeral.com. Let numeral worry. about sales tax and VAT compliance. Compliance handled.

Starting point is 02:11:19 So you can focus on growth. Our next guest is... Jonathan. Jonathan Swardland. No, function. Hey, sorry to keep you waiting. We were in a political quagmire. We were in the swamp.

Starting point is 02:11:33 We went to the swamp. We don't normally go to the swamp. Normally we talk about series Bs. We talk about large series Bs. He did get us going, though. He was telling us how much value has been created in his district. It's in the trillions.

Starting point is 02:11:46 It's in the tens of trillions. And we were just foaming at the mouth about the market caps. And then we said a bunch of other stuff. But thank you so much for coming on the show. For those who aren't familiar, introduce yourself. Introduce the business. Tell us what's going on. Absolutely.

Starting point is 02:11:59 Great to be here. Now you're climbing out of the swamp. We're going to talk about something a little less swampy. Thank you. Talk about health. It's great to see you guys. Well, I mean, health is like, honestly, more political than politics. It can see.

Starting point is 02:12:13 It can be. but this conversation won't be. It's funny. We actually say that biology is bipartisan, though. And I like this idea of everybody can agree that nobody likes to suffer. Yeah. You know, and everybody can agree that preventable death shouldn't happen. Yeah.

Starting point is 02:12:34 So it comes that. But of course, the nuance of how you get there can become political because who's going to pay for it, right? Yeah. Not bad, but also just. Well, that or the, well, this diet is better than that diet. That's right wing. That diet. Oh, working out, that's a right wing thing.

Starting point is 02:12:49 Or like, oh, this is left wing. Like, you know, different ingredients became politically charged over the last few years. My powder is better than your powder. For sure. And sometimes there's political influences on the right and the left who actually have the same supplier. And then they put different branding on top of it and they sell that. That's a fascinating rabbit hole to go down. But anyway, we're not here to sell supplements.

Starting point is 02:13:11 Let's talk about the business. You know, it's funny. It's like, I'm not left wing, I'm not right wing. I'm the whole bird. Otherwise, you fly around in circles is kind of the idea. I love it. I like it. I like it.

Starting point is 02:13:21 The whole bird. That's great. The whole bird. The whole turkey. So yeah, take us through the shape of the business these days. What's the value prop to consumers? What's the progress been? How big is the company kind of set the table for us?

Starting point is 02:13:33 Okay. So simple value prop is get on top of your health. It's time you have your health. So what does that start with? It starts with the new platform. It's $1. per day to join and the platform includes twice a year comprehensive lab testing at over 2,200 locations, any quest diagnostics around the country. You go and you test everything, heart,

Starting point is 02:13:51 hormones, liver, kidney, thyroid, cancer signals, you name it. Up and down, all of that data goes into a platform, into an app that explains you what's actually happening inside your body. And these are the things that you would not get in a physical. This is like a true, true deep look. What it would function is created is this entirely new standard for your health that every year, the rest of your life, you know that you're well on top of whatever's happening inside your body. You're seeing how it's changing over time. You're making sure that you're getting well ahead of disease and you're doing everything you can to feel your best. So that's the value proposition and that's what function delivers right now. We started with lab testing. Because that's like,

Starting point is 02:14:30 that's the most impactful data. 70% of medical decisions are based on lab testing. And recently we acquired a company. You might have heard about this. It's called Ezra. And as well, is an imaging business. And so Ezra does, and what has been amazing for us, we've gotten FDA cleared AIs that have reduced the time that it takes for somebody to get an MRI. Okay? So why does that matter? One, nobody wants to be cited in an MRI machine typically.

Starting point is 02:15:03 Two, it also massively reduces the cost. And it picks up the efficiency. And so what we've actually done is we've introduced lab testing. became one of the largest, most powerful lab tests from the country. And then we wanted to imaging on the imaging site and bringing down the cost. And what you're seeing actually emerges is this new standard for health. We took the most impactful parts of the health system for capturing your data. And we packaged it up and something that's really simple, understand, and really affordable for many, many people.

Starting point is 02:15:34 Talk about how MRIs were used historically. Are these things that get done when, uh, like your act, you have like acute pain or you have an issue and then you're doing it. This feels like. Terry recal. Yeah. This feels like kind of flipping it and using it as like preventative care. Is that the right read?

Starting point is 02:15:56 That's where I read. And not just preventive. I would just say responsible because this idea of preventive is great, but it's also what might be happening right now that you don't even know about. Right. And so the word preventive and the word earlier are a little tricky for me. Because the word early, it's like, why is it early detection? We just call it detection.

Starting point is 02:16:15 Can we just get rid of the word early? What MRI does is traditionally it allows somebody to look inside the body. But to do that, it's been really, really expensive. We get MRI, you know, tear your ACL, something like that. Like you basically, you have to send thousands of dollars to look inside your body. And that's the way insurance is set up and that's the way MRI set up. But what MRI can do is it can look at it. every single organ and look for tumors that are 0.2 centimeters, two millimeters.

Starting point is 02:16:47 It can look for stroke risk, aneurysm risk, endometriosis, hernius, tears, everything. So if you actually want to understand what's happening inside the body, an MRI is an incredible way to do it. But it's been so arcane and so difficult. It's never actually been architected and set up to look at the body and get well ahead of things. And it's usually been, oh, you go to a hospital, you broke something, you have an issue, you go look at this one particular area. In Functions case, you can actually look at most of the body

Starting point is 02:17:15 through an MRI and you can detect cancers early. You can detect the aneurys and stroke risk, etc. And you can do it for $499 and you can do it across almost 200 locations by the end of this year. There's never been anything like this. This is the first time in history. This has been possible. It's the first time in history. It's been possible geographically from a cost perspective, technologically and culturally it's changing. People are realizing this. What I was alluding to before, it's a really important point, is a new standard of health is emerging. And that standard includes twice a year comprehensive lab testing. It takes a 10, 15 minutes each time. You go in, you get your whole body testing. We find out what's actually happening inside.

Starting point is 02:17:57 And the second thing is now a quick MRI every year. If you do it, what you're doing is you're actually creating a baseline for your whole health. And you're seeing how things are changing over time. you're catching velocity, you're seeing bad trend lines, and you're also just flagging critical issues, as well as finding out what you can optimize and what can be better in your life. And what's crazy to me is the current standard, like the status quo, we've all done this. We've all gone into the doctor's office. They test you for like 20 things. You get a phone call in three weeks.

Starting point is 02:18:26 You're good to go. John Geordy, see you in six months a year, two years, whatever. And you move on with your day. And that's just this episodic, once in a while, very now. perspective on your health. That's gone. But they miss. They're not looking at cancer and they're really not even looking at heart disease, the two leading causes of death, let alone metabolic dysfunction, hormonal issues, thyroid issues, and function looks at all that. I'll give you a crazy stat. A new study just came out. Forty-five percent of people that were hospitalized for their first

Starting point is 02:18:56 heart attack did not have what is considered high-risk cholesterol. That should be terrifying. Why? Because if you go to a doctor's office today, a regular old physician's office for a checkup, you get your LDL checked, right? You guys have done this, yeah? Yeah. Okay. That marker was born in the 1950s. It's older than my father. Vintage. It's a vintage marker. Some people would say Lindy. Some people would say that's Lindy. Okay. Let's just steal me for a minute. They might say it's Lindy. So look, um, There is no world where any top cardiologists say, I'm just going to rely on LDL cholesterol based. What every top cardiologist will tell you, let's look at APOB, let's look at LB little A, let's look lipid particle size. For most people, those words are foreign to them. But they should be.

Starting point is 02:19:50 I mean, it's this avant-garde stuff, right? So there are way better ways to look at the heart. But we're relying on something that's back to the 1950s of status quo. And what functions done is for hundreds of thousands of, of people now, we've actually delivered a new standard of health that includes twice-year testing of everything that looks at your heart, it's looking at your kidneys, your thyroid, your hormones, everything. Women don't have to go to their doctor and ask for a horn panel and get chased around.

Starting point is 02:20:18 Instead, they can actually get a look at what's going on with their hormones. And then on the cancer side, real quick, cancer side, 400, you're four times more likely to survive cancer, we catch it early. but right now status quo is you have to wait to have symptoms to catch cancer yeah it's crazy that's okay i don't want that for my family so and now there's technology where we can actually with an MRI as well as with a grail test that we test for many many many thousands of people we can actually get way ahead of these things so i can talk about it yeah yeah so i'm i'm sold on the product uh i think it's i think it's hard hard not to be it's it's the best kind of like value offering

Starting point is 02:21:00 I think in health, like period. And I was sold, obviously, when you were raising pre-seed back in the day, or however been two and a half years ago or something like that. It feels like forever ago. Can you give us like a, I'd love to get your view on an update of like the market structure. A lot of companies have seen. You guys weren't, function wasn't the first, first lab, you know, testing and health platform like this to exist. But your guys' execution and the growth, I think you're one of the, at least growing faster than a lot of the fastest growing AI companies

Starting point is 02:21:42 that we're seeing out of the last year. Give us an update on like the shape of the market, how you see the market evolving. Because like I was saying, a lot of people are trying to like ride your coat tails. But I'm curious for, for no. update there. You know, we, the market is realizing that the word consumer health has been this like dirty word for 20 years or something. And it's not, it's, what it is is it's premise on

Starting point is 02:22:11 the most primary thing that we experience as human beings is our biology. It's our, it's our, it's our life experience. And what's the LTV of your health? Right. You'd be willing to pay anything for health. It's the most valuable thing in the world. for you. And so we're finally in a place where we can actually see technology and products broadly applied to health. And so you're looking at a Tam that conservatively is $7 trillion. And some- Give it up for $7 trillion tams. John, hit the hit the hit the hit the hit the size gong for a seven trillion dollar tan. Had to had to. Anyways, continue. I love it. I love it. I love it. No, so look, this is, this is, people have been spending absurd amounts of money on their health

Starting point is 02:23:03 through these massive service platforms like insurance companies, big health systems. And finally, people are saying, you know what, health happens outside of the doctor's office, and I'm taking it into my own hands. And what we're doing is we're bringing scientific and medical rigor directly into a platform that people, they themselves can sign them for, they themselves can manage. And so they can make decisions for themselves. helps. And that gets them way ahead of diseases, you know, as I've been saying before, like, this is, this is not a, it's not a trivial space. I think it's the, it is the most anticipated

Starting point is 02:23:39 service for AI. It is the best application of AI in the world is to our health, because that is the major experience. And so, of course, we're, we're, we're surprised that the, the category and all the, all the competitors aren't, it's not, that it's not bigger, that more people are jumping. jumping into this. Like we know that people are going to try to ride these coattails. But we just, our head is down. And our focus is in how can we deliver as much value per dollar for each one of our members? We have hundreds of thousands of members, soon millions of members. We have been growing really fast because we're at, we're actually delivering something to somebody that has real, real, real substantial value. And at a time, a lot of technology can do a lot. It's like,

Starting point is 02:24:24 what are we really paying for? And it's like, where can, where can, where? How can people get started? You mentioned it's a dollar a day. Correct. Does it take me through like the customer flow? Is it just a website and then I go to the lab explain how people can get, can get going? Okay. So it used to be $999 when we started. It was manual. It was paid. Per day. $1,000 dollars per day. No, $1,000 per year. Sign me out. $1,000. $1,000. He said per year. Don't, don't worry. John's just messing with you. So it started at $1,000 per year. Then we worked really hard. to bring up the efficiencies in tech, got it down the $499. And a couple weeks ago, we announced it's now $365. It's like when it's actually $365 per day.

Starting point is 02:25:07 Because health is an everyday thing. And it's an understandable price. And when has health care actually been deflationary? Yeah. And then, so go to Quest Labs probably twice a year. You go to Functionhealth.com. Yep. Functionhealth.com.

Starting point is 02:25:20 Functionhealth.com. You just sign right up. Yeah. Right there on the scheduler, you sign up for your lab appointment. Yep. You show up at the lab, you're blood drawn, urine collected, you walk out 10, 15 minutes later. In 24 hours, results start pouring in. Yep.

Starting point is 02:25:35 Now your app is live. Yep. And all the data is coming in and it's making sense of it. And you test every six months. Every six months. Got it. Exactly. I think people, one of the reasons people underestimated this kind of category as it was emerging is

Starting point is 02:25:48 how many people got burned on like DNA testing, open DNA, DNA, like the 23 and me, you test it once. And then there's like zero incentive to. retest, right? You did 23 and me, you have the data. Well, it depends. Are you working on your DNA or not? Have you been modifying your DNA? If you modify your DNA regularly, you should probably be testing your DNA regularly. You never know. You never know. I might have, I might have rewritten my entire DNA. All of them from start to finish. Every base pair is different now. Sign me up again.

Starting point is 02:26:18 I'm ready to go. We have to study you if that's case. We have to bring you in. John, John needs to be studied, honestly. Generally. got ridiculous. What does 25,000 Diet Coke's due to the human body? We're going to find out. What is 500 Diet Coke's a year? A dollar a day on function and four diet and at least four Diet Cokes a day for John. We're actually running a split test.

Starting point is 02:26:45 We have the exact same lifestyle. We show up at the gym every morning. We work out. We prep the show. We do the show. We hang out with our family. We're going to do that forever. but John drinks Diet Coke and I drink

Starting point is 02:26:57 Mattairemahti podcast in a click can from Andrew Huberman of course and we're going to find out. Yeah, yeah, we're going to find out. Well, thank you so much for taking the time to come chat with us. We have a small bit of breaking news

Starting point is 02:27:09 I want to get to you before our next guest. So we will be seeing you soon. Oh, one last thing. Give us the numbers in the last fundraising round. I want to ring the gong for real. Yeah, let's do it. Series B, $298 million dollars raised $2.5 billion evaluation

Starting point is 02:27:24 But the thing to think about here is that's basically a dollar for every American adult. There we go. And so what that is is that's a vote on your health. That's not just the way. I love it. I love it. Well, thank you so much for taking the time to stop by. We will talk to you soon.

Starting point is 02:27:40 We'll talk to you here, Jonathan. Long live TVPN. We'll talk to you soon. For the next one, for the sea coming in person. We've got a seat here for you. Be honored to have you in person. Be great. Let's do it, brother.

Starting point is 02:27:51 We'll talk to you soon. Great to see you. Goodbye. Let me tell you. you about Vanta, automate compliance and security. Vanta's leading AI trust management platform. Also, if you're running a NeoCloud, you got to get on Vanta because that's one of the criteria for ClusterMax. I'm not kidding. Not making this up. Sock 2 compliance is a big factor in in actually making it up the tier rankings for ClusterMax because, of course, if you're

Starting point is 02:28:17 training on customer data, you need Sok2 compliance. You need the whole process. Anyway, the The breaking news that I wanted to get to really quickly is Josh Kushner is partnering with Open AI. Open AI, he says, we are excited to announce a strategic partnership between Open AI and Thrive Holdings. Through our partnership, Open AI will become an equity holder in holdings. And collectively, we will set out to deliver frontier technology to our customers. For decades, technology has transformed the world's largest industries from the outside in. We believe the AI paradigm will be different. in that some of the most profound transformations will now occur from the inside out.

Starting point is 02:28:58 We view the businesses that we own and operate as the right reward system to build test and improve industry-specific products and models. So the race is on. Is it inside-out or outside-in transformation? What's going to happen? These are the new fast take-off short timeline, long timeline. Are you an inside-out guy or an outside-in guy? This is going to be the defining debate over the next couple days.

Starting point is 02:29:22 So get ready to lock in. We'll be covering it here. We'll probably have some people on who are digging into this, investing in this, have long takes, short takes, who knows. But I want to get to the bottom of what this outside in versus inside out transformation will look like. We've been digging in a little bit talking to some folks who are building companies, buying companies. Taylor says, deal guy, yuga. This is the deal guy, you go.

Starting point is 02:29:47 It's happening. It's happening. Well, before we bring in our next guest, let me tell you about. about Figma, think bigger, build faster. Figma helps design and development teams build great products together. We have Cristobal Valenzuela from Runway in the Restream waiting room. Let's bring him in. How are you doing? Good to see you again. Thank you so much for taking the time to come talk to us on such a big day. Kick us off a reintroduction on where the company is today. And then the news. I'd love to know about the news. Yeah, thank you for hi me again. It's been a while. Yeah. Yeah. So big, big, big,

Starting point is 02:30:22 news. We just released our latest frontier model, runway Gen 4.5, is a model we've been working on for quite some time. It's the best Vito model right now in the world, which is a pretty remarkable fit. Yes. So I think it's, it's pretty good. It's pretty fun to play with it. To be clear, that's my audio. I'm at it. I was not sure. Perfect timing. But, but let's play some of the video. I want to see. the demo videos that you put out, the examples, and I want to ask you a bunch of questions about it, because it's an extraordinary claim. Google is a serious company. They have a very serious asset in YouTube, and I'm fascinated by, so first, give me, give me the news, video arena leaderboard. That's the, that's the ranking that you're using. How is that scored? How does that actually work?

Starting point is 02:31:17 So it's kind of like a way of crowd searching performance. You basically ask people in the internet to vote against two videos and it's anonymous. So you vote left or right. And then as you keep on voting, you accumulate more votes. Once you vote, you can see who you voted for. But beforehand, you don't know. And so over the last couple of months, we've been working for like this entirely new way of, I would say, training both video models and image models in such a way that hopefully we thought it would all compete. others in the arena and

Starting point is 02:31:48 we got results a couple days ago and yes we managed to basically out compete all other video models including both Google and Open AI which is a very remarkable feat if you think about the skill of resources like I think it's the era of I was saying this is the era of research again

Starting point is 02:32:05 and I agree but it's also the year of efficiency like really good really focused teams with highly efficient like you know mandates can get really far and so yeah yeah tell me about about what you optimized for here because SORA seems it's an incredible model.

Starting point is 02:32:24 And it was for like a minute like, whoa, really mind-blowing. Then I feel like I kind of developed an immune system for it. And I can clock a SORA video. And it feels like SORA was very much trained on TikTok almost or vertical, vertical social media video. And so what have been the breakout SORA videos? it's been a lot of dash cam footage and doorbell nest camera footage and they've all aren't facing videos.

Starting point is 02:32:51 They've also degraded the model dramatic. They have degraded the model a lot. Whereas V-O-3, it felt like it had a little bit of the Hollywood polish, but it was more like Michael Bay when I looked at it. It looked very saturated. It was cool. It looked good. But what you went for, it feels a little bit more.

Starting point is 02:33:10 I want to say cinematic, even though that's kind of an overused term. but talk to me about what your goal was, or even if you have a goal when you go into a training run like this? It does. So I think there's an explicit goal and an implicit goal. I think in a way, all models, specifically video models that are more visually, like, clear, or, like, perceptible have some sort of personality behind it. And I think that personality reflects a little bit, both the point of view of the company and, like,

Starting point is 02:33:38 the way you want to train the models in the first place. So to your point, like, if you want to make, like, like, consumer. consumer slop and quick, like, shareable stuff, you're going to train the models just from the ground up very differently for the stuff that we're trying to do, which is a much more professional, like, high quality, very controllable sort of like tools. And so a lot of where you're like basically outlining as I would say the personality of the models and somehow also reflects the personality of the companies. Like if you're trying to sell ads, you're going to do a very different model from if you're trying to make creative tools. And so I don't think there's one. one single recipe or one single ingredient. It's more of a just like taste. Like I think that word gets thrown a lot in research this day, just taste. And I think taste is both the research.

Starting point is 02:34:23 Like what do you want to work on? Like having vision, like having, okay, I want to pick this specific problems I want to work on and this is how we're going to solve them and this is what we've learned over time. That's one form of taste. And the other one more aesthetically is like what things look good.

Starting point is 02:34:37 Like and that's like. Like beavers on a construction site. This is actually very good. That's your taste. That's pure taste. Yeah. Look at the donkey. Hilarious.

Starting point is 02:34:45 Right. Look at the motion of the donkey moving, like the camera, the angles. Like the amount of data creation or team of artists and like filmmakers and like people have spent. It's not, it's not like trivial to be honest. I think that's also the taste component. Like shots like this. It's like. Some of this is horrifying.

Starting point is 02:35:04 It's amazing. It's really had to summon the demon on this one. You have you been inspired by Anthropics? at all. It feels like somebody could put you in the anthropic for video bucket and that like they're just like extreme focus on code and ignoring everything else. And meanwhile, your competitors like are putting a lot of resources towards this, but they're not betting their entire business on it in the way that you are. Yeah, I think it's a it's like a messianer versus like visionary type of like I would say bet. It's like you want to have people who who feel like very committed to the vision long term. And the way you do that is like you're very

Starting point is 02:35:42 focus on like the culture and like that culture eventually shines in the product. I think atropic has also that. You can you can tell like who works there and like how they think and it's it's all very cohesive in a way. I think we spend somehow a similar amount of time like doing that in a way. And I hope you can tell via the models themselves that like that personality comes across nicely as well. Yeah. And I agree like you don't that at the end will be perhaps the most defining part of the companies that like stay in the long run like I think if you just throw money the problem you're not going to get too far to be honest yeah um what went into the actual training run are you at a are you at a scale now where it's a meaningful capital investment to

Starting point is 02:36:29 build a model like this we saw the scaling paradigm change from like you know maybe it's a hundred million dollars to do a big frontier language model run than we were talking about a billion dollar training runs bigger and bigger training runs the results are remarkable but has it been a remarkable amount of investment to get here or are there more efficient ways to actually get to a frontier result without spending frontier money yeah I mean it's definitely not cheap like this is not like traditional SaaS like so you definitely have to spend more more money more But I think we're proven that like we're not spending tens of billions of dollars to get there and to like overcome the challenges. And look, to be honest, like the model is not perfect. There's a lot of things we're going to improve and we're going to fix and we're going to do larger training runs and we do more over time. But it's kind of a, I would say the expense, the most expensive thing is like the natural intuition the team builds around we're going to work and what doesn't work. It's kind of go back to the idea of research stays. Like you can't throw money at it. It just have to spend.

Starting point is 02:37:34 enough time. We've been working on Rwainu for almost a decade. And so there's a lot of you've learned over time about what works and what doesn't that informs a lot of the efficiencies on training. And yes, like expensive models will like, you'll need more money to train larger and bigger models. Like, if this is the worst the models will be, imagine them in like two years. Like, you're going to get there by training larger models for sure, but also knowing how to train them in the first place. And that's the part that I think is hard to quantify per se. And what I'm really excited about is not only what the models can do, but also the efficiencies are not even training, but on inference. Like this is a price point that's very comparable to our previous models.

Starting point is 02:38:14 So it's actually very useful. And hopefully you'll be using it in real time very soon. And so that level of, I would say, efficiency at inference level, we haven't yet seen it. And I think we're going to get there very soon. Yeah. Fascinating. I mean, some of those videos are very straightforward. They're pretty remarkable.

Starting point is 02:38:32 Your unlimited plan includes 2,250 credits monthly. How much video can one actually generate with that? Well, technically unlimited. Okay, I was confused because it said there's still like a credit system. No, so we have a queue. There's like we have like compute and there's a queue and you get into the queue and you generate us like the Q becomes available. If you just want to generate like fast, you pay for credits. So but depending on how.

Starting point is 02:39:02 how anxious you are with like new generations. So it's a measurement of how fast you want it. But eventually you can just literally generate unlimited. It's by the way, I think no one else has a plan like that. It's a pretty good deal. What is like what are the length of generations that are that are most commonly being done today? And is that a metric that you track? Like are people consistently, is it like a 20 second scene that's the most common today?

Starting point is 02:39:29 And I know are you trying to get to two minutes? or two hours? Like how do you think about? So well technically you can do like arbitrary durations if you want it. But like the average scene duration in like a short film or a movie is like actually two to three seconds long at the most and that's actually been trending down. Like the scene right, the scene itself, the cut is like two to three seconds long on average. And so when you actually, when people mean like I want to join a 45 minute like long thing,

Starting point is 02:39:58 you don't want 45 minutes of like one camera like effect. You want like sync cuts and world and you want the character like a shot, a medium shot, a long shot, and you have, you know, like that's a different problem from like creating one continuous long sequence. So the one continuous long sequence for me is less interesting than the like multi-shot approach where you can create much more compelling like narrative work. And I think we're not that far away from that being our reality where like you can generate consistent narrative work like really good. good visuals, really good stories, like with the level of quality of the videos that we're seeing right now here, but they're all tied together in a way that just makes it feel like cohesive with each other, you know? Yeah. And so that's a different problem, I would say, all together. Yeah. There was some debate on the, why doesn't the cursor for video exist yet? Do you

Starting point is 02:40:51 have any, any thoughts there? What's the cursor for video? Basically, a nonlinear editor, like a Premier Pro, a DaVinci Resolve, an Adobe After Effects for video, cursor for video, like replacing the actual bones of the software that the editor, that the video creator uses. There's been a couple apps that have spun up runway. Originally, the reason I was using it back in the way was for Greenscourne, for Kramer, basically. It was fantastic for that. And it feels like that, building a canvas, building an NLE,

Starting point is 02:41:26 that feels like one potential pathway to victory. It's also very difficult because you can't just fork VS code. There are no leading open source NLEs

Starting point is 02:41:37 on the flip side. If you wanted to play nice with Adobe, you could be a vendor all the way nano banana is now vended into Photoshop. And that could be a solution.

Starting point is 02:41:49 And, you know, there's a variety of ways to win. I'm interested in hearing your approach. Yeah, there's definitely an interesting question. And by the way,

Starting point is 02:41:58 shout out for you for being an OG on runway since 2019. Yeah, something about that. I love you. Yeah. So my two thoughts are first, the art of like NLE and editing and film, it's an art.

Starting point is 02:42:13 And it's just a lot of pacing and like details that are very nuanced and specific. It's about granular details and it's hard for, I would say, model to or assistant to automate that level of like decisions, that's on a purely NLE side, right?

Starting point is 02:42:28 But I would say, at least for us, more interestingly, is the question of like, do we need an NLE in the first place, right? Like, do we actually need this primitives? If you think about nonlinear editing this idea that you're like stacking frames of video against each other and like you're cutting them before it was with physical racers and now we have diesel racers, you're cutting things together. My bet is that you probably won't need like anilees. that whole paradigm will feel like a fax machine like in a few more years.

Starting point is 02:42:58 And so I feel that's somewhat what's happening with like the the devons and the clog codes and the codexes of video. I just, I do wonder if there's going to be an intermediate step or maybe it'll just be absorbed by the current NLEs. I mean, I'm sure that's what your customers are using, right? Yeah, I don't know. We'll see it play. But I'm not, I'm not too fun of like, you know, pushing. like better versions of analysts out there. I think there's something around how you make videos

Starting point is 02:43:27 and how you interact with this AI systems that just naturally allows itself with different primitives. And if you think also about the fact that very soon you'll start to see this happening real time, like when you make real time like narrative work or videos or experiences, how are you going to call them?

Starting point is 02:43:44 Like you don't need to edit things async because you're generating on the fly and you have people interact with them. And so it changes, that's what I was saying. It changes the nature of like those things in the first. first place. And there's a transitional period where, like, you're seeing, like,

Starting point is 02:43:57 analyses being augmented with AI. But I think it's that's transitory. I don't think it's going to pay out, like, in the long run. Yeah. Yeah, no, I think. Has Hollywood capitulated yet? What's going on there? We had, it's funny. I've been hearing, I've been hearing more and more about Suno from, from not just guests and friends of the show, but just, like, random people out in the world. It sounds like every single musical artists now is, like, using it in some degree even if they're not willing to talk about it. What is the case in traditional Hollywood and entertainment? You can't exactly hide that you're using AI video. It's basically out in the open immediately. And there's just so much like so much negative energy that gets focused on it

Starting point is 02:44:42 specifically from people that are within the industry. You know, I think the negative energy is like the water problem with AI, you know, like it's, it's, it's, it's. kind of this unrealistic and very noisy, not-representative sample of what's actually happening within the industry. If you go to LA, we speak with the agencies, with the Thailand, with the filmmakers, with the studios, with the production teams, they're on board on AI, like years ago, like months ago. Like, they're fans, they're using it. They understand it. Of course, there's pockets of people who are, like, more advanced than others. But I would say that the narrative publicly hasn't yet to catch up with that. I mostly because,

Starting point is 02:45:22 Some people might not want to speak about it. It's much more interesting to say, like, all the negative things, to say the positive things. I would say Hollywood already has overcome that, and they're pretty much on board. I would say gaming companies are now where Hollywood companies were like a year and a half ago or two years ago. So that's, I would say, an industry who's now catching up more

Starting point is 02:45:44 to what I can help them and how they can use it. So, yeah, I would say some of those narratives are a bit fake, to be honest. yeah well thank you so much for taking the time to come on the show on a busy day we appreciate it and uh i can't wait to play around with the new model we have a benchmark here bezel bench where we try and recreate a very complicated shot from uh that we shot practically with a bunch of different watches uh with our with our intern or gap semester tyler cosgrove and it has a very the shot's very long it pulls out, it twists around. It's a pretty complex shot,

Starting point is 02:46:22 and that's our current benchmark, and we'll be testing, and we'll let everyone know how it goes. But thank you so much for time to come to chat with us. We'll talk to the same. Thank you, thanks to the chat update. Goodbye.

Starting point is 02:46:31 Let me tell you about Julius.a. The AI data analyst that works for you, join millions who use Julius to connect their data, ask questions, and get insights in seconds. We have Vincent from Prime Intellect in the Restream Waiting Room. How are you doing?

Starting point is 02:46:48 Great to see you. It's been too long since last weekend. Thanks for having me. Congratulations. Master of figuring finding the one day that we're not live to launch your new news, tell us what happened on Wednesday. We're grateful that you did. The one day that we were off of streaming.

Starting point is 02:47:08 Yes. So excited to give you a rundown. So basically for the broader context, we're primarily kind of like our broader goals, really creating open front of models. and the infrastructure for everyone to create them. And last week we released Intellect 3, which was basically really like a scale up towards scaling RL and post-training and creating like a SOTOM model, especially for like more agentic tasks.

Starting point is 02:47:36 So basically what we did is we took GLM and did a whole SFT stage and RL stage to create kind of like a state of the art, 100 billion per meter MME model. and really kind of like that whole infrastructure is kind of quite a challenge from like the RL environments to the broader like code sandboxes and the whole stack to do post training that's basically what we built over the last half year

Starting point is 02:48:00 I think Will Brown came on the show to unpack some of it on the verifiers and the environment side so basically that's kind of like what we released last week and really proved that kind of like we got performance at a hundred billion scale that does find open source only 3,000,

Starting point is 02:48:16 to 600 billion per meter models like DeepSeek, R-on, for example, achieved before. So basically getting to better performance, actually, at a much smaller scale. And I think in general, it showcases that, like, open models are starting to catch up. Obviously, I think, quite interesting. In general, seeing the trend that not just with our model, but also more broadly, with other releases like Deepseek today and over the weekend, that actually, they are also on par with, like, the closed models. now. And I think really our goal is, so it was almost like a preview release, but already sort of,

Starting point is 02:48:52 is we basically released like our early checkpoint. And we're actually scaling it much further, also on more like agenetic capabilities, but basically really like making it sort of across like a range of task. And really, I think the foundation of this, which is quite interesting, is that we created this environment where anyone in the world can create one of these environment environments, which we ultimately then included in a training run. So basically, different people, in the open source contributed actually to the RL environments that we trained on for this model. So yeah, give me a concrete example of like this shift of businesses that need to, you know, buy a model that has been trained in a specific RL environment.

Starting point is 02:49:34 You know, we've heard the example of like someone's creating a clone of DoorDash and they're figuring out how to do DoorDash orders agentically. But what else are you seeing? what are some other good examples of when a business would pull this off the shelf from all the different opportunity, from all the different APIs that are out there, and create something, I guess, semi-custom for a specific business use case. Like, what are you seeing out there? Yeah. So I think what's interesting is like this, I think two buckets. Basically, there's a bunch of these people like creating our own environments for the labs, like the Dorish clones, et cetera.

Starting point is 02:50:11 So basically, to push really a capability. So I think, we're in this paradigm right now, obviously, we're ultimately, like, scaling RL is the main way on how these models improve, right? Like, we've seen it with Opos or with GPD5 and Gemini. Like, there was mainly, like, I think, a scale up in RL. But basically, what we are seeing are two things. It's like, on the one side, it's like, there's a lot of demand for these oral environments. But then the other side, RL is very sample efficient. So you can take an old model and, and then really create an Rural environment for the specific use case you care about. And scale capabilities for that. So I think good example of this was, for example, cursor with composer.

Starting point is 02:50:48 Like, that was like what's widely believed or not known, it's like to be a scale up of an open source model. And the URL environment was cursor. Like, like, they basically just gave it, like, the tools and the things within the harness and application of cursor itself. Yeah. But they trained basically that model and like really on getting really good at using cursor. And I think we'll see the same play out, like across all the applications, where basically the broader theory is like every application or every company will be an AI company or AI native and will have an opportunity to really post-trained and use RL to make the models work specifically on the application.

Starting point is 02:51:29 So even if you take examples of like say a Figma, right, like if they want to make their platform a GEMTIC, really they need to cut an RL environment around Figma and post-Train on that environment to be able to serve that within Figma. kind of like out of the box, like the closed models, won't be perfect at like re-navigating and making those applications agendas. So I think that that's the broader theory. I think really it's also like it's so like the capital requirements are much, much lower than I think the Big Labs want to believe you.

Starting point is 02:52:01 Like in a sense where it's like you can for like hundreds of thousands of dollars like post-trained a model, right? It's like to be much better on your application. And then also to you are able to like serve the model cheaper. One weird trick. Post-training a model for 100. K and create a better. So, I mean, that's basically what you're saying is that,

Starting point is 02:52:17 is that if I'm Figma as an example, and I could use a frontier model that's really expensive and beefy and it knows everything about, it knows some stuff about Figma, but it also knows about the Roman Empire. I can go in R.L. on just my particular application and have a smaller model that's fine-tuned on open source,

Starting point is 02:52:37 you know, open-source model, and get better performance than with the big, beefy, you know, do everything Omni model. Is that right? Exactly. And I think really you get better performance, but also at a lower price point potentially, right? Because you can really specialize the model to be extremely good for your use case. So I think you could see this with like cognition, posturing their own model with like Perse up posturing their own model composer. And composers also, it's like it's much cheaper to serve. It's much faster.

Starting point is 02:53:04 Like same for the more cognition was building. So I think what we're seeing and we've started to work with like dozens of customers on like helping them basically do post-training and RL. I think we're basically starting to see a huge pool in terms of like enterprises realizing that like if they want to get a specific capability, RL is a way to get it and ultimately enables them quite capital efficiently to train those models and search those models.

Starting point is 02:53:30 And then really get to like a point where even in deployment, like all the interactions from the user help improve the model. So I think with a curse example, like every, for example, cursor tap interaction, every yes and know that a user gives to the model is updating the model every two hours. So it's what like Darakash talks a lot about. Two hours. Like online URL.

Starting point is 02:53:49 Yeah. Like they basically retrib like continuously training the model in two hour interval and pushing updates every two hours to Percer tab. So basically every user using Percer for the last two hours is, is being post trained on, so to speak, like with kind of like an online URL loop. I think that's something which will see more and more that basically applications will do their own URL, their own post training. Actually then and it's like really how we on.

Starting point is 02:54:13 unhobble basically towards AI, where it's like, the question is like, why haven't we say automated, like specific and valuable knowledge work yet? And I think the answer that also like Schulte was speaking about, for example, with the example of like automating taxes and accounting for a member, right? It's like no one has really created our own environments, whole strain on them, and then serve the model in the application where the end user is. And then ultimately the, the end user's interaction with the agent can improve the model further. Right. So I think that's really the paradigm that we see play out, which I think is really a paradigm of like thousands of models or like millions of models that like basically continuously improve and where actually

Starting point is 02:54:52 the applications win to some extent through distribution. Ultimately, they own the end customer interaction, right, where it's like even the cursors and permissions have like an advantage there over folks who basically just model providers and who don't interact with like millions of developers. And I think we'll see the same play out across all the different applications. And it's something like from the site that I talk about also in the context of like co-pilot and Microsoft. Right. Like they own distribution.

Starting point is 02:55:20 They can like create the cursor for Excel or like PowerPoint or other things, right? And then whole strain on all those in the actions. So I think we'll see this like play out, I think across like all the different verticals. And I think it's like a border trend thing of just like every company needs to become AI native. Right. Like, and own also to keep owning the distribution. Like they don't want to give all of it up to the big Agile labs. Yep.

Starting point is 02:55:42 That makes sense. We got a question from our intern, Tyler. We can shoot over there. Yeah, I guess I saw you guys talk about this a little bit online. But is there any point of you guys training your own base model? Yeah, so basically, I think one interesting release in this context was like today we actually released, like we supported RCI in their base model release, which is like kind of like catching up to the Chinese base models. So basically we supported them in training a small MOUE base model, which achieves like pretty sort of results. So we released that, I think, like an hour ago with them.

Starting point is 02:56:23 And we're actually now like ramping up with them towards like a much bigger base model. So fully pre-trained from scratch. So we actually just had like 2,000 B300 going life, I think, yesterday to ramp up like towards like a much bigger pre-trained. I think like it's really, I think like the broader pattern is like since kind of like Lama had some reorgs and changes and Mistral became sort of like a forward deployed European enterprise play or something. I think there's really no one left outside of China right now to go end to end in the Molestack. I think others like reflection I think are trying to also pick that up. But I think there's very few players, I think outside of China. So I think that's our broader goals really is like serving like the world more globally, but also like the West and the US.

Starting point is 02:57:12 with like an end-to-end pipeline, right? It's like from data to pre-training to mid-training to post-training, like the full stack and making that accessible to like enterprises and people who are like trained normal. So I think like there's a huge, I think pool where a lot of enterprises or even like sovereign like nation-sets, et cetera, like you can't train on Chinese old models, but they also, they can't rely on closed models. So I think there's a huge gap in the market right now that we're trying to fill

Starting point is 02:57:37 of really like serving kind of like that whole segment. Do you have anything else, Jordy? No, this is great. I want to know one last question about, you know, what will the market structure look like in maybe a year or two around, like implementing these RL environments for companies? Because when I see, you know, you say every company is an AI company, I believe that's somewhat true.

Starting point is 02:58:07 And I believe every tech company, maybe every founder-led tech company under 10 years might be able. able to say, okay, yes, we're going to go and train, fine-tune a model and turn our application into an RL environment. But if I'm, you know, the Coca-Cola company, you know, I might not be at that level of like going and building RL environments for every business process. I'm probably more of a buyer of this AI as SaaS almost. So how do you see that kind of breaking out? How do you see a truly legacy, you know, non-tech company adopting a fine-tuned LLM or an RLD model? Totally.

Starting point is 02:58:49 No, I think there's like early adopters and later like later adopters. I think Coca-Cola might be more like a later adopter and might not need to adopt it early on. But I think they are even more adopting it just like in less obvious places. It's like ultimately, I think they're initially just like using the AI tools that use us, for example. In a sense where it's like say customer service, right, it's like,

Starting point is 02:59:09 is a like perfect example of like where you get a lot of gains out of post training. And then like they might put like like basically the AI native customer service platforms might use us to post train you in Coca-Cola data. Sure. To serve them a better like model. So I think what we'll see play out, I think is really,

Starting point is 02:59:26 um, just like making like a lot of that like so accessible to your point that it feels more like using SaaS where I think like one element of it is like we are like launching also like our whole like rFT platform basically and and offering to make it extremely like easy and plug and play but then there's also like a forward deployed element right where you can outsource a lot of that stuff to our team and I think the other element is like really like we're walking walk in terms of like making our own thing kind of like agentic and autonomous that you could basically just use like an autonomous AI researcher to do all that for you right like

Starting point is 02:59:59 that you basically just like plug it into your system and like the AI even like creates AI for Yeah. And I think like, I think that's the next paradigm is really making, like making in general training models, like fine-tuning models, post-training models, like as accessible as code-coting as today, right? In the sense, what's like, I think with vibe coding, like, literally every human on earth is able now to, like, code some stuff up. And I think we'll see the same play out with AI over the next 12 months.

Starting point is 03:00:25 And that's one of the big things that we're playing into. We're kind of like pushing towards like autonomous AI research. Yeah. Where I can do most of it for you. Well, thank you. much for taking the time to come and talk to us on the show. Congratulations on the project. And we will talk to you soon. Great to see you, Vincent. Goodbye. Have a good one. Let me tell you about Privy. Privy makes it easy to build on crypto.

Starting point is 03:00:45 Rail securely spin up white label wallets, sign transactions and integrate on-chain infrastructure all through one simple API. And I'm also going to tell you about adquick.com. Out of home advertising made easy and measurable plan buy and measure out of home with precision. Our last guest of the show is Ben Heilak. Did he do the Jaguar rebrand? That's him. Ben, welcome. And we'll follow him forever. Grab a seat. Hang out. Good to see. Oh, you brought hats. Fantastic. Thank you. Please, grab a seat. Introduce yourself. Introduce the company. What's the new? Yes. So my name's Ben. Highluck. Yes. Let's take a second for the flow.

Starting point is 03:01:24 Fantastic. Thank you. This is kind of like a vintage Silicon Valley flow that you don't, somewhat of a lost art. I appreciate it. You guys have great hair as well. You know, I felt a lot of pressure. You'll notice I'm not wearing a hat today. And it's because I did notice, actually. I discovered a blow dryer, I think, around nine months ago, 10 months ago. So that was a big name. Your life has never been the same since.

Starting point is 03:01:47 But yeah, my name is Ben, Hylak, as you guys know. I'm the CTO of a company called Rain Drop. So really simply put, we monitor agents in production. So we're building a product ourselves probably around two years ago now, which was like a coding agent. and we realized that there was just this huge gap of like if you're using Century, if you're using traditional analytics, you know, they're covering like the things the users are clicking and almost everything that's happening in your product if you're making an agent

Starting point is 03:02:17 is just not covered. So you just have no idea what's going. These agents are going absolutely wild. They're going crazy. They're going haywire. You know, what's been insane, I think one of the things that's been like really kind of critical to our growth in the last couple months has been realizing that as, agents get better, this problem gets worse. So that was not necessarily intuitive to us in the

Starting point is 03:02:36 beginning. You know, you think like, oh, well, agents are going to get better. Maybe this problem becomes less important. But it's like, actually, as it become more capable, they can use more tools. More valuable. Exactly. So for example, if you take a company like Replit, it's like, you know, maybe a year ago or two years ago, or when they first launched, you know, you couldn't quite get as far, right? Maybe you could just get like a personal website or something. And so if it messes up at that point, it kind of gets stuck. It's like, okay, maybe it's not the end of the other thing. the world. But now with Replit, you're able to build just like real applications, like people are building real production applications. So now if you get to a point where it gets stuck,

Starting point is 03:03:10 something goes wrong, suddenly it's like it's a real issue. So that was not intuitive before. So agents are a pretty overloaded term at this point. I think of, you know, when I fire off a deep research report in chat GPT, that's an agentic workflow to some customer service agent that's happening completely behind the scenes and the customer might not even know that they're dealing with an agent. And then there's coding agents. There's a few that you mentioned. Are you dividing the market and trying to focus on an early landing zone first? Or do you want to do all of those? Yes. So we focus on essentially, and I will say I agree. The word agents overloaded. We're very hesitant to use it for a really long time and then we realize it actually matters.

Starting point is 03:03:53 Of course. So we focus on products that have some sort of user input. And so we focus on products that have some sort of user input. some sort of assistant output eventually. So that's sort of our focus. So what we're not focused on is, for example, like we're not gonna focus on like specific ML pipelines or things like maybe like translating text or like summarizing text even. It's like we wanna see like the user,

Starting point is 03:04:14 the user is sort of like has some sort of request, the assistant is responding to that request. And we do map essentially everything that happens in between that initial user input and to what the actually, what the assistant actually responds. And then what's the go-to-market for you? I mean, it's been a little crazy, actually.

Starting point is 03:04:34 We've had a lot of inbound. So some of our biggest customers have been inbound. A lot of it has been, like, when we first launched, I think, like, I guess this was like six months ago or seven months ago now. Agents weren't as big of a deal. And so I think in the first month or two, we had a lot of customers. We were like, okay, like, I have e-vals. I think we'll need the...

Starting point is 03:04:52 Yeah, but it didn't really make sense for them. And a lot of them came back in the last, like, a month or two, after that and we're like holy shit okay now I get it we need you like so it's actually been a ton of inbound we do what we don't really pay for advertising anything like that um you know if we see a really crazy failure in the news we'll reach out to that company obviously and be like hey this is something we can help with sure sure sure uh how are you thinking about the you know target like the best type of customer are you segmenting it by size do you want to go enterprise up front because they're implementing agents at scale or are you more likely to see immediate results

Starting point is 03:05:28 of the startup that just kind of gets it and they can hop on really quickly? Like, how are you thinking about prioritizing if you are at all? Yeah, it's a really good question. I think that we really look at the entire range. And I think that we see and have always seen startups as being a really core part of keeping our company healthy. Sure. You know, I heard a while ago that, like, Post-Hog has this metric where they look at, like,

Starting point is 03:05:50 what percentage of YC companies and every batch are using them. Sure, sure, sure. And so that's why we started with startups. Like, they're always, they're able to move fast. So for example, like when a new model comes out, it's just to give actually a very specific example. So GPT5 introduced intermediate reasoning, right? It was kind of one of the first models to do this

Starting point is 03:06:07 where like it's going to make tool calls, it's going to look at the results of those tool calls, think about it, and then make more tool calls, take that, think about it, you know, more tool calls. It sounds small or subtle, but actually it kind of means that, you know, if you architected your system, your pipelines in the wrong way, you just couldn't use that.

Starting point is 03:06:25 And it really helped. So where startups will just like just throw everything out the next day, right? And they'll ship a whole new thing in a week. You don't see like, you know, like if you look at like the biggest enterprises, they're not going to do that. Sure, sure. So you can learn really fast by it with startups. That being said, on the flip side, I think that the problem we're solving is actually most painful for enterprises. Right.

Starting point is 03:06:49 It's like the, the most critical high stakes environments are where like failures cost the most in every single sense. Yeah. How much of... Categories of agents that you're excited about that are maybe under hype today. Coding agents. Coding agents are like sufficiently hyped, I think. Coding agents are, and for good reason. Yeah, for good reason, but like, and maybe they're deserving of more hype.

Starting point is 03:07:15 Yeah, yeah, yeah. But what other category, you know, I think people have been sold on the AI, BDR, yes. Haven't exactly... Maybe companies are getting a ton of value from it, and they're getting... so much value they don't want to come on TBPN and talk about it because they don't want their competitors to know. And then obviously like CX feels sufficiently hyped. But what else are you seeing? Man, it's, there's so many different things. Like I think, you know, speak, for example,

Starting point is 03:07:45 language learning. I think the better, like as models get better, that experience just actually starts to become really, really, really viable. So like that's an example of something where it's like, yeah, it existed a year ago. It existed two years ago. But like, as, as, voice models get better, as like the models themselves get better, it's actually not just like, you know, if you try to use chat CBT, for example, to learn a language, you sort of can, but if you ask it to like critique you, for example, it just never will. Like, if you say something wrong, it just isn't going to stop and be like, hey, look, actually like, it's still glazing. You're absolutely right. Yeah, exactly. Don de Astala, Biblioteca is the most complicated Spanish

Starting point is 03:08:21 sentence. It will, it will, right? You're fluent. It's like, yeah, you're pretty much good to go. And even if you can get it to the point where, like, if you can really, really, like, prompt it into critiquing you, it'll just, like, start critiquing everything, you know, which is also not what you want as, like, you're learning a language. So, like, it turns out, and I think we see this with a lot of products that, like, getting something right is actually a lot of details and really, really understanding that domain. So I think we're seeing that in literally every domain, like, whether it's, like, marketing, whether it's, like, even just, like, the idea of having a personal assistant,

Starting point is 03:08:50 like, notably we don't have that yet, which is crazy, right? Like, we have these assistant models, but then none of us actually, have an assistant, we can just chat and be like, hey, send this email. Right? I don't. But I think we're starting to see products actually like nail that, like, smaller mostly. How are you thinking about just, I don't know if, I don't know if like if you're Century for AI agents, does Century actually handle this? But just types of AI failures that happen for more infrastructural reasons. So just the GPUs are on fire. Or like, there's just not enough GPUs in

Starting point is 03:09:23 this particular cloud and you just see a spike in demand. And so you just can't provision more. Like those types of more tactical errors. Do you help with that? Sort of would be the answer. So I think it's actually really interesting is that one thing we realized about e-vals is that they don't catch those sort of issues. Like, you know, you're kind of testing just like the model. What is the model responding? But then there's all of these things that happen in between. Like I remember really, really early on when we launched, one of the issues that a customer caught was like their file upload was broken. So a bunch of users all started complaining about like, oh, like the file uploads taking too long. It's like, okay, well, it's not

Starting point is 03:09:55 like an AI problem, but it is. Yeah. And so we see that with like tool calls. We saw one of our customers had an issue sort of what you're saying, which is that like they started having like, they have their own GPUs, they started having like an infrastructure error. And it was mixing up responses between users. And so users all started complaining like, hey, that's not what I like, what are you talking about? That's not my sister. It was like an increase in that in like. I don't know if you're talking about meta, but I think that happened in meta. It wasn't meta. They're not one of our customers yet.

Starting point is 03:10:21 But there was a situation where like people could share. It was, it was not that. bad, but it was something like, I could share my chat with you, but if I shared it with you and I didn't know that I was sharing it, it would go out everywhere. And so, yeah, stuff like that happens. Totally. There's all these sorts of things. So you can actually catch those sort of problems. It's actually one of the, one of the things is like, that ground truth is actually really, really important because if you just see like a few errors, like, let's say you have tool, like your agent calls tools. Like, yeah, that's going to error once in a while, right? Like that might not be the biggest deal, but especially once you, if you can see when

Starting point is 03:10:52 it actually starts to affect users, like, that's really, that's really powerful. Yeah. Yeah, that makes sense. What about degradation of models under the hood? I feel like people, I don't know if it's just a meme. I've noticed it here and there. I'm not benchmarking everything every night. My slop agent was degraded. But it does feel like that sometimes, right?

Starting point is 03:11:10 My drop agent. It feels like sometimes I'm like, wait a minute. I used to respond in this many tokens. Now it responds this many. It used to look HD, now it looks standard definition. I agree with you. It's real, right? I think it's real.

Starting point is 03:11:22 I know that, I can't say too much. I know that at least on one occasion that I think people were led to believe that there wasn't a thing. I know that there was. Okay. So that's it. You know what I mean? I can't say who is a big company. And because I noticed this.

Starting point is 03:11:37 I can't say whose hands were caught red-handed. I can't say which one of the-front-year. Yeah, exactly. And like, it was like, I thought it was a cursor problem. It was like some really absurd behavior. And then I went into Chachitia and it was doing the same. Oh, I just said. But anyway, yeah, like, I think that the reality is that like every single one of these,

Starting point is 03:11:54 providers are like having these sort of problems. And they're trying to optimize costs. They're trying to like make changes. So I think it's natural. And some of them I understand where I'm like, oh, okay, well, yeah, realistically, I haven't used that in a long time. I came back.

Starting point is 03:12:05 I kind of sure. I don't really mind that you put me on the lower tier. Yeah, yeah, yeah. I just hope that for the people that actually like went and built businesses around this that are using at the API level that are hopefully paying for the service at a high gross margin to you, you're not degrading the service behind their backs. 100%.

Starting point is 03:12:21 Right. So anyway. Who did the, deal. Anybody we know? You want to hit the gong? You want to hit the gong? Oh, let's do it. Yeah, yeah. Hit the gong. Tell us how much you raised. How much did you raise? How much did you raise?

Starting point is 03:12:36 We raised, so we raised $15 million total. From whom? Lightspeed. Who did the deal? Bucky? Yeah, let's go. Let's hit it again for Bucky. Let's hit it again for Bucky. This one's for you. This one's for you. Yeah, we're big fans of Bucky up here. So I just wanted to get him a shout out. Us too, us too. I think the moment we met him, we're like, okay, like he matched our energy, like a great vibe.

Starting point is 03:12:59 Yeah, yeah. He's doing. How's building the team going? It's going. I think we're really, really picky we've realized. And so it's really hard. And I think hiring in San Francisco is really hard. We have a great team.

Starting point is 03:13:13 It's honestly really, really small still. Well, if you want to get out of San Francisco, you could book a wander with inspiring views, Hotel Grady Men, and he's Toppedier's 20% of consumer service. It's a big thing. vacation home, but better. You can do an offsite there. We could do our offsite. That's beautiful. I once used a team offsite as a recruiting tactic. I said, we were going on an offsite in two weeks. Okay. Oh, yeah. You want to come? Yeah. We got an amazing hire. We'll do it. I'll do it. I'll do it.

Starting point is 03:13:39 We're doing it. So if you're watching right now, uh, well, I'll post the picture soon of, of the house. Okay. Fantastic. Fantastic. But we have an amazing. Yeah, I figure if you're, if you're picky and you're in San Francisco, it's like the most ruthless, like, talent war, constant. You know, the other thing is that, that I think when you hire amazing people, they have zero tolerance for working with people that are not amazing. And so I think you can't even fool yourself as a founder. If you start

Starting point is 03:14:04 like, you know, whether you're work telling, whatever, it's like if they're just, you know, if it doesn't fit, like everybody, everybody knows and feels that. And have you had to bring anyone's soup? Are you familiar with this? I'm not familiar with this. Okay, so apparently the AI, this is from Ashley Vance. This is a scoop. Just dropped on core memory

Starting point is 03:14:20 around the podcast. So he had Mark Chen, Open AI's research chief on the show as part of a post Gemini 3 sit down to get the update from Open AI. And he said, I knew the AI talent wars were rough, but not this rough. Zuck is out there, apparently delivering handmade soup. Wow. And open AI has soup counters. And so I guess, uh, wait, they count how much soup is. I don't even know what this means, but oh, I see. I see. Like, they count how much time. No, no, I think it's like a counter. It's just like a cafeteria. Right?

Starting point is 03:14:55 Aggressive. What exactly is this tit for tat? We can play this on the show later. But, uh, well, but, yes. No, no, my, my, my partner has, has cooked meals for someone. Yeah, yeah, you have to come up with creative ways. Like that sort of thing works. Um, yeah.

Starting point is 03:15:08 You know, we, we do typewritten, I'll write a note on a typewriter, you know, when we do our offer letter, right? So that adds something a little bit. That's good. No way. Yeah. Are you messing with that? I love, no, I'm serious.

Starting point is 03:15:18 I love typewriters. No, I like, I like, It's just a way to prove. I can't value this message. All the text is AI generated, I'm sure. Of course, yeah. You're absolutely right. I think it's like a little bit of a proof of work thing.

Starting point is 03:15:31 You're not just the newest hire. You're a revelation. This is a statement. Yeah, yeah. Having fun. Well, that's great. Congratulations and all the progress. Very excited.

Starting point is 03:15:41 I'm sure you'll be back on the show soon. I will. Giving us plenty more updates. And it's been fun because, I mean, I believe that we started tracking your journey via your viral. old joke post about doing the right from the very beginning or something we've always had fun featuring your post it's great to have you here live in person

Starting point is 03:15:58 live in person one year ago today I remember roughly one year ago I was sitting in a parking lot and I was listening to the first time I ever heard of you guys you were reading like one of my tweets yeah it was just so surreal that like people from the internet are reading my tweets like one of our customers sent it to us actually we had printed you had printed out yeah so I called my mom today I was like telling I was like I'm going to be on the I was like you're not you're not going to know what it is but remember those guys that were talking about that tweet?

Starting point is 03:16:22 This was the whole schick was like little love letters to Silicon Valley folks. Just like little messages of just, hey, we've found something that you did fun because anyone can like, anyone can read the post. You know, it's easy to send a small thing. It's very hard to actually print it out, sit down, talk about it. But we appreciate your post. We appreciate you coming on the show and hanging out today. So thanks so much.

Starting point is 03:16:43 Thank you. We're going to close out the show. We'll talk to you in just a second. while he's walking off, let me tell you about getbezzle.com. Shop over 25, 26,500 luxury watches. Super intelligent. Fully authenticated in-house by Bezell's team of experts. I also need to tell you about 8Sleep.com.

Starting point is 03:17:02 Exceptional sleep without exception. Fall asleep faster, sleep deeper, wake up energized. I had a rough night. Kids have been all over the place, but I still got maybe two. Look at this, John. 98. 98. 98.

Starting point is 03:17:15 98. That is remarkable. Well, is there There are a bunch of, yeah, we'll see if you're breaking news. Bucco Capital Bloch is on the timeline. You can feel the panic behind the urgency and intensity with which people are defending Nvidia.

Starting point is 03:17:32 It feels visceral and quite intense. You can tell how much it was riding on this. It makes a lot of sense. What else did you want to cover? I thought it was notable. Pager duty has fallen to a $1.1 billion market cap. And there was a good business. 500 million of ARRs are trading.

Starting point is 03:17:49 They're not growing anymore. They're trading a 2.1X ARR. It's profitable, according to Jason Lumpkin over at Saster. So yeah, rough time out there if you're not growing regardless of the revenue scale. Two days ago, we shared that Enron back November 29th, 2001, NVIDIA replaced Enron in the S&P 500. I saw this post go out from our incredible team, and I immediately Googled to fact that. I was like, there's no way.

Starting point is 03:18:22 Someone has made a terrible mistake on our team, and we are doing fake news unironically now. We used to have some fun, but apparently this is real. It's real. It's real. Jensen was like, I'll take that spot. November 29th. Obviously, that's not how it works.

Starting point is 03:18:37 It is much more mathematical than that, I believe. Standard in Poor's, takes the largest companies, and after certain ebbs and flows of the market, they swap folks in and out. But this went pretty viral, 5,000 likes. But what is really interesting is, of course, the Nvidia and Rod, like, comparisons are just so silly to me. Obviously, it's like, you know, the discussion is like,

Starting point is 03:19:04 will it go from being the best business in the entire history of the world to being, like, you know, somewhat competitive and have to deal with, like, minor competition from other people? does not seem like it's some ridiculous Enron situation. That's like so insane. People are just having fun with that headline. But what is incredible is this this branded shirt he's wearing. Look at this thing.

Starting point is 03:19:27 Fantastic. So awesome. I love it. Not enough people trying to go snipe, vintage, and video merch. It's a great shirt. It's a great look. And I feel like it's got to make a comeback. The button down, this is the pre-Silican Valley.

Starting point is 03:19:41 I'm just in a T-shirt era. but it's post suits, you know, it's like, we're not suits, we're working in technology, we're still going to throw on a collar, but we're going to dress it down a little bit, no tie. Guys, scroll up, scroll up on this for a second. Yeah. I'll keep going, keep going. Oh, who's not, who's not following? Tyler, you got to follow the account.

Starting point is 03:20:00 No, this is not my account. I think this is more of like a burner account situation. Oh, it's a scraper that we used to, for the show. It is, it is. You got to correct that, Tyler. Come on. That's not Gorkham over at fall

Starting point is 03:20:15 had an absolute banger This was a chart showing ASML sells fewer than 500 units per year and generates 37 billion in revenue Is there any company in the world With a wider moat And Gorkham says Series A pitch meeting

Starting point is 03:20:28 Sorry to cut you off But what happened in December 2024 Since there's like a slight dip In the chart Yeah what did happen. Why did their revenue drop in 2024? I actually don't know. Is it just so much pull forward from 2023 or something? Maybe they were developing some hubris, they decided to get complacent. Yes, I mean, I certainly understand the concept. Okay, according to the CEO, customers in Taiwan

Starting point is 03:20:59 had delays and weren't ready to take delivery yet, and orders got pushed back at the same time. China raised to get as many machines as possible before export controls tightened. Okay, that makes sense. Sosh Zatz says, Oxford Dictionary didn't get the memo. Apparently, rage bait named word of the year. What? I think it, I think it, I think they're actually right. That it would be the word of the year.

Starting point is 03:21:22 But it is so funny that you, you posted this and then Oxford Dictionary. Yeah, so this is true. According to the BBC, rage bait named Oxford Word of the year 2025. It certainly feels that way on the timeline. Your post. million views on this. 3.6,000 likes. People really, this really set the agenda for a little bit. Wow. Congratulations. What a banger essay. Should TBPN do a word of the year? I like that. Or a motion. Motion. Motion might be our word of the year. Word of the year. Motion named word of the

Starting point is 03:22:03 year 2025 by TBPN. If you have it, you'll know. You'll know. You'll know. Well, call you. Tyler has motion. In other breaking news, Keith R. Boy is taking shots at Airwalex. Airwalex is now on the other side of a billion dollars in ARR. What I love about this chart is that isn't that we hit a big milestone? This is the founder, Jack Zhang.

Starting point is 03:22:25 It's how fast the business is accelerating. It took more than six years to 100 million AR. What does Air Wallachs do exactly? I think they provide payment rails for a bunch of American fintechs to handle international. Oh, okay. Okay. And so Keith Rabei, been on the show multiple times, says, cool growth chart. Have you disclosed to U.S. customers like Rippling, Bill.com, Brax, Navon, that you're quietly sending their customers to data to China. Air Wallachs has become a Chinese backdoor into sensitive American data like from AI labs and defense contractors. You must already know this. But your China-based ops, infrastructure, and investors create legal obligations to assist with CCP, espion, upon request. Through Air Wallachs, Beijing can assess supplier payments for AI labs so they could know who's using what models, payroll data for defense contractors, personal data for employees abroad. That's obviously not good. Obviously, many companies do business in China, and that's not

Starting point is 03:23:28 inherently a bad thing, but your company has become a guaranteed vector for data transfer to the Chinese government. And that's a different thing entirely. You have multiple points of vulnerability. people, legal structure, cap table. What's happening? You route global payments for U.S. companies and critical sectors without just closing that you're under a Chinese jurisdiction. You moved your HQ to Singapore. Well, that seems like a step in the right direction, maybe.

Starting point is 03:23:51 But your largest operational footprint is in China. Okay, no, so maybe one step back. One step forward and one step back. And hundreds of your engineers in mainland China touch production payment systems. You are subject to Chinese law that requires Air Wallach's employees to support CCP intelligence request and quietly hand over data when asked you hid this from your customers, but you are well aware of your obligations to China, and that's why you insist on protection of Chinese data access to your contract. Thanks to you, the Chinese government now has direct, covert, legally enforceable

Starting point is 03:24:23 access to sensitive financial information. This is a big story. This is a crazy scoop from Keith Rabeau. And I will be interested to see where this goes, how quickly they can they can, you know, remedy this. This popped up a couple of years ago during the clubhouse era. The clubhouse back end, I believe, was at one point, you know, was working with a Chinese company, or maybe it was that there was a company that did, like, peer-to-peer audio streaming that was based in China. And so if you were building a competitor, you might use that company.

Starting point is 03:25:04 Yeah. And so. I became familiar with. Air Wallach's through the 20VC episode that Harry did with Jack, the founder. Is it ripping? I mean, it seems like the business is doing really well. Yeah. Yeah, yeah, yeah.

Starting point is 03:25:18 Oh, well. Anyways. Well, what else? We'll hear more about it to say. We got to get on with Menlo Park. Okay. Well, thank you so much for listening. Hang out with us today.

Starting point is 03:25:30 We'll see you tomorrow. Please leave us five stars on Apple Podcast and Spotify. The break, the Thanksgiving break was absolutely brutal for us, I will say, every single day. But hopefully you had a great Thanksgiving. Wake up and just twiddle my thumbs, wishing we were podcasting. It's great to be back. Hope you had an amazing break or a little holiday, and we'll see you tomorrow.

Starting point is 03:25:50 See you tomorrow. Cheers. Goodbye.

TBPN - David Sacked by NYT, Sir Dylan Patel Joins, Kushner & Sama are Thriving | Ro Khanna, Jonathan Swerdlin, Cristóbal Valenzuela, Vincent Weisser, Ben Hylak, Alby Churven

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