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

27:05He doesn't have the ability to just like create legislation out of thin air. Right. Like he, he is, he is very much. I was trying to look up the history of czars. Right. Uh, 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 President Woodrow Wilson to head the War Industries 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.

27:41Synthetic rubber czar? That's one of the most iconic. People are stoked for that. People don't talk about the need for our ongoing need for a 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, 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.

28:26So anyways, again, I think unless you're just blanket against these 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. So he says, free from those, this is from the New York Times, from the actual article for the screenshot, free of those restrictions, Mr. Sachs 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 feared that China, an ally of the Emirates, would gain access to the technology, these people said.

29:11But the deal was a win for NVIDIA. Analysts estimated that it could make as much as$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 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. Yeah. I mean, it was also just like painting the friendship between Sachs and Jensen as like something that felt wrong was a little bit rough considering it's the most valuable company in the world.

29:53Yeah. One of the most important AI companies, potentially the most important AI company if you just go by weight in various indexes. 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, if you're the average New York times subscriber, this is probably that you were, they were probably like very excited by this story. Right. Yeah. I mean, a lot of, I think a lot of people, um, are, are definitely like, uh, yeah, just riled up by the all in podcast.

30:37Charlie in the chat says all in pot about to be an all timer after this article. Do you think it's possible that David 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 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, we would be bigger if we didn't talk about politics. And that seems crazy to me. I feel like politics is like the ultimate TAM expander in the history of podcasting and media broadly.

31:14Yeah, the audience for political content is like 10 times larger than business. I would think so. I do believe that Jason loves talking about tech. And I think he's kind of - He's an OG. He's an OG. He's said that multiple times. But I would be shocked if politics was not a TAM expander for podcasts broadly. And then the other thing is that he said that they lost money on the all-in events. I don't know how that's possible. Those events, obviously, they're big budgets. But I would imagine that the sponsors and the ticket sales, they're not cheap tickets, right? I would imagine that they'd be making money off that.

31:50I 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 it. They're just underwater. They're like, yeah, we decided to bring it in. They're going to make it back. Podcast production on-prem. And we ordered 100 ,000 black whales. A lot of cat-backs. Blue Owl really has us by the balls. It's rough. Martin Scrawley here says, the Saks 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 real job, Lena Kahn, K Street.

32:22Yeah, and 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 access to the president for$1 million via their podcast business. And Martin Scully says, I doubt it was Sachs who wanted to sell$1 million passes. And Whiskey Titan says, I agree with you. I'm sure it wasn't.

33:00But letting Jason run rampant until Susie Wiles steps in isn't a great look. I happen to think Sachs is doing fine in 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. The timeline truly is in 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 video and image models. They're trusted by millions to power generative media at scale.

33:39So Dan Primack said, lots of people are sending me the New York Times story on David Sachs. Outside of the all-in sponsorship proposal, which feels oblivious at best, corrupt at worst, I'm not seeing much in there that's new, at least to those who've been following. Dan Primack 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 NYT knew this and deliberately published false information.

34:21And Dan Primack says, they included statement that you lost money on it. What did they print that was false. They were somehow make that, that we're somehow making money in this or some gain. And Dan Primack says, just reread, just reread, 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 ads 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?

34:59And 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 House AI Summit pieces, Dan Primmick. Talking in general, don't know how you would not quantify. Sachs' role in White House def 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 on the map. I feel like a lot of people were like, they found out about the White House and about the US government.

35:37Which house? Exactly. Exactly. Because of the all in podcast, they were listening on podcast and they were like, wait, wait, wait, you're telling me - There's people in Washington DC. They run this whole country. They create laws. charge the rules yeah they create sort of laws and framework over how our country should operate which which industries we want to yeah you know support and grow you're telling me you're telling me that there's a group of people and one of my besties is up there running it this is amazing i gotta learn more about this i gotta figure out what a bill is i gotta feel how a bill turns into a law chat what is a bill jason says if anything going deep into politics 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.

36:27That's an interesting take. I still think that politics is made all in so important. It made it so big. Well, yeah, and it made the content polarizing. But I think that polarizing in media is good. you actually get more attention uh not necessarily good from all points of view yeah good from a pure just like reach i mean yeah i was looking at the i think the i think the uh the the the ratings are like the amount of viewers for uh for like cnbc is bigger than bloomberg by like a pretty significant margin because bloomberg's like extra wonky and cnbc is a little bit i mean it's literally called consumer business news.

37:10Like that's what the C stands for, I believe. And then you have Fox, which is even more. Like Fox News is political and it's much bigger ratings than CNBC or Bloomberg. And then ESPN is like by far the biggest because it's like sports. Everyone loves sports. And so like maybe that's the final form. They should go full poker and then full sports. It should just become sports center competitor. I could see it. That might be the way. AB says, I only learned about Trump because Chamath endorsed him. Yes, exactly. I had never heard of this guy. Who? The vodka and social media entrepreneur? He's running for president?

37:50Okay, so Dan Primack is weighing in again, concluding it. 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. I get the complaint, but that's not, but it's really a matter of interpretation, not true, false slash hoax.

38:28Imagine 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 might not be friends with them. You might not be friends with anyone. Sachs and 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, like we now know the correct way to win the AI war. Like 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.

39:00and Sachs has one view that I think has actually played out pretty well, considering that he's been anti-Doomer, anti-fast takeoff, more industrial capacity, more opportunity to grow GDP. 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 Primack's saying here. He says, only time will tell if Saks 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.

39:41Thus, the headline is defensible, albeit pushing an agenda. And that's the timeline in turmoil, folks. Let me tell you about graphite.dev, code review for the age of AI. Graphite helps teams on GitHub ship higher quality software, faster. I can't read this. AI Amblicus, this name. This Iliad 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 holidays, but Fleeting in Bits says, can you say more?

40:19Just 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 in a sea of possibility and not the kind of person. See, I originally read this as the misallocation of capital that I've seen is like the 10th, 11th, 12th foundation model lab that has like$100 to$1 billion that is just like kind of iterating what Ilya already worked on and already developed. right and doesn't necessarily like if they just do if they just create a a model that's like not state-of-the-art like i don't know that there's going to be incredible value in that meanwhile i'm like okay you take the guy that that that whose work led to chat gpt and you give him a few billion dollars and let him you know continue to iterate and and uh he's not just like you know firing a single can you know multi-billion dollar cannon and hoping he hits a target it's like this incremental research that I think is still one of the best shots at like developing the next paradigm, whatever comes after LLM.

41:27So I read this, I think that 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 bearish on the incremental large language model lab. Yeah, I don't know. I mean, 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. 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 companies and different business processes, they do need specific training runs.

42:10They 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, 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.

42:49You're more productizing. Company. Yeah, it's a business. We're 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, it's huge, right? So the expected value is still high. What's crazy is that we're doing a venture-style bet at growth scale, and it's just a massive amount of capital for something that I think the consensus here is that 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 in the sea of research and ideas, and you never really produce anything.

43:33So I love the high-risk bets. I just understand why people are saying, well, at 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. Anyway, what else is in the timeline today? Fin.ai, 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 of good memes.

44:10Cody says, when my wife asks what we should eat for dinner, but says no to my first two suggestions. We are back to the age of research. I like it. And then when she asks what I want for dinner from Bazelord, 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, 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.

44:49We know this is a fact. But of course, Ilya is now joining the ranks of Jan LeCun and Rich Sutton and Andre Kirkpathey 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, Sholto is saying scaling may be not over. So we'll see. This is, this post is, yeah, this post is great. Scaling is over and LLMs are a dead end. Aw, you're sweet. Scaling is over and LLMs are a dead end. Hello, human resources. I love his meme template because it's like, yeah, Jan LeCun has been saying the same thing. He says, Jan says, for the record, my current BMI is 24.

45:36This guy rocks. He's very funny. I thought he would have dropped the meta tag on X by now, but I guess he's still. Oh, he's still wrapping them? Didn't he leave? Reporting to leave. 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 ChatGPT reach millions of consumers who use AI to discover new products and brands 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 was in turmoil lots of people very upset with Semi-Analysis' latest post.

46:27How dare you question NVIDIA? They took a swing at the king, which was the name of their article. They said TPU v7, Google takes a swing at the king. The king is, of course, NVIDIA. And they are asking, is this potentially the end of the coup de moat? Anthropics, they're talking about Anthropics, one gigawatt TPU purchase, the more TPU, meta, SSI, XAI, OpenAI, Anthropic buy, the more GPU CapEx you save, next generation TPU v8, and they're going into what the battle between TPU and the next generation GPU out of NVIDIA will look like. And this upsets some people. There's a lot of folks who are long NVIDIA.

47:13Either they have invested in NVIDIA, they made a lot of money in NVIDIA, or their whole business is tied to NVIDIA or AMD even. Or they bought the local top a month or so. Potentially. There's a whole bunch of reasons. You could also just disagree with this and you could just think that, you know, 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 so thorough. They're extremely thorough. And I think that they do leave you with a lot of new information that you can, you know, do with what you want. 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.

47:57And 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 Anons.

48:14I don't think we should get in the two can play that game camp. It's a little bit rough. 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 3, have the majority of their training and inference infrastructure on Google TPUs and Amazon's Tranium. 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.

48:55Chip 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.

49:33And as I say, 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. 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.

50:23And so basically what he's explaining is that because of the competitive dynamic between NVIDIA and Google with TPU now, you can use TPU as a stalking horse and say, hey, if you don't cut your prices, NVIDIA, 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. NVIDIA would rather invest back into your business instead of cutting prices. Yes. And so it says, we think the more realistic explanation is that NVIDIA aims to protect its dominant positions at the foundation labs by offering equity investment rather than cutting prices, which would lower gross margins and cause widespread investor panic.

51:07Below, we outline 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 OpenAI NVIDIA, 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 in investment. And so how that works out is a 29 % partner discount. And 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 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 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?

52:19I mean, certainly it didn't today. Isn't NVIDIA up today, right? NVIDIA is up 1%, adding a casual, you know, what,$10 trillion or$100 billion or something? $100 trillion. A 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, 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, So can some analysis be trusted? Because they're writing about NVIDIA and Dillin.

53:08I think some people didn't understand that he was joking. Zephyr here has a post. Dillin is being tongue-in-cheek, but he's not wrong. NVIDIA was extremely dominant for the last three years, as we saw in the stock. It's up 10x over the last three years. New competitors will cause a reduction in market share and margin compression. but TAM is big, so revenue profits won't go down. 75 % of GM is just unsustainable. Hyperscalers will also use the cheap TPUs threat to extract better deals from Jensen, priority access for Rubin, Feynman, or discounts on GPUs. Jensen called Altman and initiated the$10 billion deal after he saw the information article about OpenAI testing TPUs.

53:49And so this is in reaction to 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, 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 old semi-analysis research, some of which they did before the paywall, some of which they did under the paywall.

54:45But 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 the surprise here is just how much faster this conversation has really come to a head than people may have expected. I think at least 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. Yeah. Yeah. The other buried lead in the article was, of course, about pre-training. So there's a snippet in here. OpenAI's leading researchers have not completed a successful full-scale pre-training run that was broadly for a new frontier model since GPT-40 in May of 2024.

55:39And, 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 people or, like, proxies or surrogates, right? People quote reading and being like, that's just not true. Wow, something 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, 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. That's not the goal.

56:15That'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 tiers and rank a bunch 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.

56:54But I'm sure we'll get into the discussion of ClusterMax and 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 stock should you buy, right? But like, that's not necessarily the read. The other read is like, which product is the best to work with as a customer? But it's like, what customer are you? 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 NeoClouds that was particularly upset with Dylan is in a very niche market.

57:34But if you're in that niche market, it's probably a great product. It's probably great for you if you satisfy 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 given that he lives with Sholto from Anthropic. We got to ask him why he has roommates. It's not even, I'm not even concerned about a conflict. It's roommate gate. Yeah, it's roommate gate. But yeah, that's a big question. Is this a tinfoil hat post from JuCon? My theory is that Meta deliberately leaked the story to the information about Google's, about acquiring Google's TPUs.

58:19For Meta, it's a classic risk-free power play. The moment Jensen Wong reaches wind, catches wind of Meta using Google Silicon, NVIDIA is likely to rush 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 when other hyperscalers will catch on to this magic wand. All you have to do is hint at using TPUs. But the issue is how many red flags would be waving if Jensen was like, yeah, we're investing$20 billion in Meta.

59:01We're 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 a discount. And it's not like an open AI where he can just announce an investment or an Anthropic, et cetera. So how does any type of rebate actually happen is the question. Yeah. Well, before we bring in our next guest, let me tell you about Turbo Puffer, serverless vector and full-text search. built from first principles and object storage, fast, 10x cheaper, and extremely scalable.

59:44Let's read through some more TPU stuff to set the table. So, Clive 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, going back to pre-GPT models like BERT. TPUs predate NVIDIA's own Tensor Cores. Anthropic and Character and SSI and MidJourney 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 C-U-D-N-N on benchmarks. There's nothing magical or structural about any of this, just good engineers doing good work.

1:00:25TPUs 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 NVIDIA's and Google's moats are small, and we are still at the point 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 OpenAI, is a little bit of like first time in the public markets. It's 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.

1:01:11Like it's a completely separate ecosystem. And that's why organizations like Semi Analysis exist. And I believe we have Dylan Patel from Semi Analysis in the restroom waiting room. Let's bring him in. Dylan, how are you doing? What's happening? I'm doing 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 to show you. Let's go. 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.

1:01:50It's nice to have the family pamper me a little bit because I broke my foot a couple weeks ago. I'm sorry to hear that. How'd you break your foot? Tripped over a TPU. Family reunion playing football in Texas. We're American as we can get there you go. There you go. 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 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.

1:02:26We've been saying this for one year. and even referencing Google's own content about the TPU dating back even further. Yeah, 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 been tied together. It hasn't had a narrative around it, right? Because when we think about what we put out 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?

1:03:02They're either in the industry or they're 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 TPU v7 versus Blackwell. We've even put out updates on here's what we think TPV8 is versus what we think Rubin is. And so generally it was making it into a narrative and explaining the technology and the corporate, I would say, politics or dynamicism around it, right?

1:03:36So 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, So 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? Whereas people externally are, you know, last week when Gemini 3 was launched, or two weeks ago, people were just learning that TPUs are training Google's models, right?

1:04:14So it's where are you in that information spectrum, right? Yeah, totally. So on that information spectrum, the finance bros, they can probably just like, if they read into this, oh, bullish Google or bearish NVIDIA or whatever, like they can kind of trade in and out as they please. But on the more technical side, like are people using semi-analysis research to understand like, okay, I'm a neocloud. What do I want to rack for next year? 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?

1:04:55Which is calculating the TCO of all these different hardware's 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. So there we put out research on TPUs because as soon as Neocloud 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 a should cost, right? You go and calculate what it costs for the company versus what it costs for me to deploy.

1:05:25And then you think about, oh, what's the margins they have? What is ridiculous to offer them what is not, right? Because everyone always wants to know, hey, what margin are they making off of me? Can I push that down a little bit? what is ridiculous to demand in a negotiation versus what's not. So we've already been working 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, 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?

1:06:01And so when you say, hey, on the cost side of things, On the technical side of things, there's a lot of network engineers now out there who have never deployed Google hardware that are now like, okay, I need to figure out how to do this. Techs, right? So there's people who have DMed me that are like, oh, 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? 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 CUDA.

1:06:34How much of that are they going to actually give to their customers who are buying TPU? Because that feels like you, it feels like potentially you could over-rotate 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 prowess, multi-data center training, all this fancy stuff that 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 soft software will remain closed source, but you can still use it. And then some of the software, they are trying to open source aggressively.

1:07:07And then some of the software, they're just never going to get out there anywhere. So it sits in three kind of buckets. The interesting, I guess, newer thing that we did in the piece was we looked across all these different open source software, whether it's PyTorch, whether it's VLLM, all these different open source libraries. And we calculated and counted up how many Google commits there were. And you can see there's a chart in the article where the number of commits that Google's doing on TPOs has exploded over the last handful of months. As they've decided to shift their strategy, sell TPs externally, they also recognize software has to be open for this.

1:07:43Only the giga brains like Anthropic can figure out how to do everything themselves. It's those people outside of Anthropic types that need a bunch of open source software that builds on top of it. And what's interesting is when you look at like, hey, NVIDIA, the biggest argument that NVIDIA doesn't really make for GPUs, but they should, is that about 40 % of the software that's open sourced is actually just from China on CUDA. And that's the CUDA mode. 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?

1:08:15ByteDance, open sourcing stuff, DeepSeek, open sourcing stuff. And Google, they don't have people open source. you know, Anthropik's not going to open source software. 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, you know, Google Cloud is selling the TPU.

1:08:46Gemini is the one actually using it and developing a lot 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 like 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, structurally, 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.

1:09:18So 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, 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 TPUs, right? But when they did all of this stuff, they didn't give a crap about, hey, this has to go in 50 ,000, 100 ,000 different people's data centers, right? This has to go in my data centers that I designed myself.

1:09:57So everything is super vertical. The entire liquid cooling supply chain is super vertical. The racks aren't even the standard width, right? So when I look at a data center, it's like the door, the loading bays, because they're 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 physically through the doors. Okay. So there's like all sorts of random, like, I wouldn't say random, it's Google from first principles design stuff. Totally. Yeah, yeah, yeah. But if you're NeoCloud and you're like, the hot thing's going to be TPU next year, 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.

1:10:32You have to maybe build a whole new building. Like, it might be that significant. Right, or knock down some walls. And then I need to go get liquid cooling, not from Dell and Supermicro and HPE, who serviced me already. 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 some gigabrain engineer who has a team of 20 people working on liquid cooling instead of my one guy who does liquid cooling, procurement, and negotiations, and also does procurement of 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.

1:11:19I 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 in Meta. It'd be very suspicious. It'd be very concerned. I totally get the OpenAI investment. That seems like it makes much, much more sense than saying, hey, we're going long Meta as a$4 trillion company. 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.

1:11:53And so what really is challenging here is, you know, Meta looks at the numbers. You know, it's like, okay, OpenAI, I'm getting 30 % off because they're investing in me as a role. Obviously, they get equity, but they're investing in me, and I get 30 % off on these GPUs 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 NVIDIA when they buy AMD? No, there are many engineers, right? They're developing all the software. They're actually deploying. Llama405B was exclusively on AMD for a number of months, right, for inference, right?

1:12:29So when you look across, hey, is Meta just 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 TPU v7 that it is. So they'd be dumb not to look at it, right? And they have the people, they have the team. Now, NVIDIA at the same time has to play the game of chicken, right? Yeah, sure, they could discount the pricing somewhat.

1:13:00And because what's funny is NVIDIA is more vertically integrated than Google is when selling hardware. Google has to pay Broadcom who pays TSMC, whereas NVIDIA gets to pay TSMC directly. 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. The whole point is you charge the highest price possible. And then the last thing is they've got this view about antitrust. You don't want to cut deals for specific customers because that looks bad. Instead, you want... Right now, Dell pays the same price for GPUs Gigabyte as Meta.

1:13:35Now, 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. Fair in the sense that they're making a shitload of money off of everyone. You know? Yeah. Yeah. Talk about kind of Jensen's leverage that he has around Rubin allocations as some of these customers start to at least consider TPUs. 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. Anthropic's got a bunch and then there's some sprinkled elsewhere.

1:14:14But 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 OpenAI started their chip team, they poached like 15 Google people overnight, right? In one week, like, someone I knew I heard was like, oh, yeah, I'm joining OpenAI. And then I text like another three people I know, and they're like, oh, yeah, I'm also joining OpenAI. 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 TPV7.

1:14:47That's affecting TPV8. At the same time, Google is trying to diversify their supply chain, get from not just Broadcom, but also MediaTek. And so Google's got a real challenge on TPV8 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 NVIDIA is just pedal to the floor, paranoid as fuck. 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 nvidia nvidia 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 um there's a lot of unknowns to go through 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 um there's 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 NVIDIA.

1:15:47Maybe they don't want to, right? Or, hey, they could execute everything. NVIDIA has a three, six-month delay. Now, all of a sudden, they're a lot more competitive, right? And so, all these things are still open questions, but 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 ByteDance, 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 neoclouds, right?

1:16:26When there's, you know, Nebius and Iris Energy and all these other, you know, CoreWeave and all these folks are deploying for OpenAI anyways, right? You know, this sort of ends up being like, well, sure, I could stiff 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? Is that the primary goal of ClusterMax? Because I feel like some people look at it and they're like, this is a buy rating.

1:17:06This is a sell rating on the stock. So the funniest thing is like ClusterMax V1, the title of it was ClusterMax, how to rent a GPU, right? Because we discussed all of that. And then, and then in ClusterMax V1, I believe we put Iris Energy and underperform, right? At the same time, the research side of the business, we explicitly were like, dude, they've got these data centers. It doesn't matter if they suck at 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. So like, at the same time as like, um, Jordan, right.

1:17:39Jordan, he's running cluster max is like, Iris kind of sucks. Uh, 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, what, what, 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 right jeremy did ask the team like hey what do you think of iris energy i think it's a log and the team working on cluster max is like i don't know like you know it's 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 um but at the end of the day right like cluster max is the the goal purpose sole purpose it'll 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?

1:18:33The 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 saying we're giving a bunch of feedback for people to make the cloud ecosystem better. The unsung hero between ClusterMax v1 and v2 is that we moved 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 gotta move keep moving the goalposts right people keep improving the ecosystem and actually you know this is this is the funny thing it's like cluster max is evil it's like when i when we look at the quotes and we've got hundreds of quotes on cluster max.ai 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 right or like hey it helped me understand the reliability help me understand this or that.

1:19:23People are like, love ClusterMax. 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. It is people who hire us to do 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.

1:19:55And then lastly, it's people who want to invest in an EO 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. We're not, 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, or I'll send an email to the CEO like, dude, just so you know, the people working on this are not the people who are doing cluster max rating, right? You know, the people who are buying, 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.

1:20:28That'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, Flukdac and like, you know, those kind of companies, right? And yet 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 midsize companies in the world. Okay. Question from the chat.

1:20:57And the price is discriminated based on that. Would you change the rating of a NeoCloud if Sholto promised to do the dishes for two weeks straight? You know, there was an argument. I saw someone was 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 dishes. But frankly, we're working so much. And I think Dorkesh has ordered pizza from the same spot three nights in a row before. Wait, so question. Is being an adult man with roommates underrated? So I haven't lived with people in years.

1:21:39And then when I moved to SF this year. This is crazy. 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 um and the first house kind of fell apart so i moved into this house with these guys and we've been talking about it for months um i love it right it's like look we we 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 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 what is the negative here is like, well, we're living with our friends, but we have enough space to wear like...

1:22:15And the beauty is if you do bunk beds, you have more room for activities. Exactly. Anyway, no, no, sorry. Sorry. 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. Funnily enough, actually, we triggered a security warning for this Google engineer. Kimbo went to a Jax conference, right? Jax is the opposite. It's like PyTorch, but for TPUs. It's the most simple. It's Google's own internal thing, right? That people do use externally. He went to this PyTorch or this Jax conference. A Google engineer presented something.

1:22:55He's like, can I get the slides? They send it to him. And then Google's security alert locks him out of his computer because he sent us some technical information. For three days, the guy can't work and he's freaking the fuck out. I emailed 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, we're trying to implement it. We have access to some TPUs. The software stack is different, right? So you basically have to rewrite or reimplement Inference Max, like the code that actually runs.

1:23:27I won't say it's that much work, as much as completely redoing Inference Max, but there's a ton of work, right? So we're moving as fast as we can. Internal target is this year. Okay. Well, then the obvious question is, I feel like Inference Max is my north star for TCO relative in AMD versus NVIDIA land. There was a bar chart of TCO for GPU versus NVIDIA. It looked like TPU was doing really well on that chart. The bars were very low. Where did those numbers come from? Do you have confidence in those numbers, or do you think the numbers will change once you actually get TPU on Inference Max? Yeah, so inference max shows performance TCO, right?

1:24:09You know, it's great, great. Like, you know, like, guess what? Like, you know, TCO of like a Raspberry Pi is incredible. It's like five bucks, right? Sure. You know, versus a GPU is$50 ,000. Okay. Performance divided by TCO is what matters. So that bar chart is saying, look, TPUs 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. Here's a range. Worst case, it's like a little bit worse than GPUs. Best case, it's way better than GPUs, right?

1:24:40And 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 us to just take the open source software and say that's the performance, right? Because that's obviously not real, right? Anyone who 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 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 TPV7 can do today.

1:25:12It'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 VLLM, are 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 cost? How much do the racks cost? How much does the liquid cooling cost?

1:25:42How 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 explain more about Google and Broadcom's relationship? Max Hodak from Neuralink 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 TSMC themselves?

1:26:18But what's your read on that relationship and how durable it is? 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 in the on the chip and then there's like the whole supply chain side of things right negotiating contracts getting allocations etc that takes like 18 months right isn't that like an 18 month process basically 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 has been doing it for longer.

1:26:58They have a bigger team, right? And they, you know, 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. 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, reorg it, 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 the gates, everything, right?

1:27:28As 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? 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 super big advantage. NVIDIA, they acquired Mellanox, call it five, six, seven years ago, huge acquisition. Who's the biggest networking company in the world? Broadcom. Broadcom is the biggest networking company in the world.

1:28:00And when you talk about AI, it's the architecture of the actual processing elements. It's memory, which you're buying from the memory companies, 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 NVIDIA in networking is so low, or rather, it's just not as good as them. They're actually good, but it's like Broadcom and NVIDIA 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?

1:28:37Yes, they're defining how the network topology is. But when you're talking about the physical network certies, 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 or four years ago, 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 MediaTek, right?

1:29:09MediaTek at times has helped Cisco with their network chips. MediaTek has a lot of work on some of this networking stuff. They're nowhere close to Broadcom, right, on revenue, right? That's one metric. On technical competence, that's another metric. I think MediaTek is good, right? But they're just nowhere close to Broadcom. So now Google is having to work with, I don't want to say subpar vendors, but inferior vendors to Broadcom. And that's just to increase their margin on TPU-8. I would even say their angle when they started this project was never, we're going to sell TPUs externally. It was, dude, we're paying, you know, a 3X markup to Broadcom.

1:29:44Um, and half the cost of this chip is memory. Like, what the fuck are we doing? Right. Um, 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 is fair or what we think we can charge. and Google's like, oh no, we're stuck to you, right? So MediaTek is taking way, way, way less margin. They're not passing the memory through them, right?

1:30:12And so this ends up being like, hey, that's a huge advantage for them. Flip side 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 MediaTek, 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. And Google, because it's risky, is going down both paths, right? They're continuing to work with Broadcom on TPV8. And then separate TPV8 project, they're working with MediaTek, right?

1:30:47Because 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. Yeah. Can you can you give any takes on the NVIDIA's two billion dollar investment in Synopsys that got announced this morning? I don't know if you saw it. I'm assuming you did. 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.

1:31:16Everyone's 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. We've got a really long article coming on that soon that will sort of explain the landscape.

1:31:48But AI chip design is going to shake up everything. And to be clear, this is AI chip design. Correct. AI helping chip design, whether it's for AI chips or for like power chips. Okay. Got it. And so the question is like, you know, NVIDIA has a lot of tools internally, right? 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 design a lot of their tooling internally, although they are massive customers of all three vendors, right? So it's kind of like an oligopoly where the customers also contribute a lot.

1:32:23And 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 FPGAs. A lot of it's running on CPUs. And 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, right? And I don't have enough engineers to work on all the software. They've open sourced a lot of software, right? Like Koolitho, 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 those other things.

1:32:59They just want to make it all run on GPUs. And so that's what their goal here is, right? And now they've given Synopsys a huge, huge, they're buying Synopsys at the lowest valuations that Synopsys has ever had with all this cash that they were going to give away in dividends or buybacks anyways. And they're getting Synopsys to now make GPUs first class, right? And so I think this is a win-win for or Synopsys and NVIDIA? Well, we can go way longer, but I know what's on your calendar. You 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.

1:33:35Enjoy the holidays with your family. Great catching up. We'll talk to you soon. Cheers. Goodbye. See you guys. Let me tell you about public.com investing for those who take it seriously. They got multi-asset investing and they're trusted by millions. We have Ro Khanna in the Restream waiting room. Let's bring him in to the TVP and Ultradome. Ro, 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. 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.

1:34:09Thank you so much for doing the time. What was that? Are the all-in guys jealous or do they respect you? I think that they have left. They're in the stratosphere. They have left the, you know, the low. We're picking up the scrap. Yeah, we're picking up the scraps compared to them. I think like any great Silicon Valley startup, you're looking at their heels. Well, it's funny. We had a New York Times piece about us. It was very nice, you know, just like, here's what TBPN is doing. Just kind of an explainer piece. David Sachs, of course, got a little bit more of the investigative journalism treatment, got five reporters.

1:34:43We 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 when our guests 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. 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 is there, but in DC, it's going to be uniform. Yeah. It's the uniform in DC. But I was hoping you could, you could sort of, uh, take us through a little bit of the prehistory since it's the first time on the show, just explain, uh, how you wound up in this position, a little bit of your backstory.

1:35:26And then, um, obviously there's so many hot topics that I want to talk about in, uh, 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. My parents came from India in the late 1960s. 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.

1:36:07That'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. I was 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. 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're one of the first American beginning of the American dynamism movement, would you say?

1:36:51Yeah, I was going to say to President Trump, he stole all my ideas in terms of manufacturing. But, you know, that's good. That's good. That's good. That's good. then, right? 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. And 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 metals and magnets.

1:37:33I mean, we should have a Manhattan project to do that in the United States or New Zealand, Australia, Chile. But, you know, so I, after my time in 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. I ran against an incumbent again, lost again. California is a machine dominated state. It's very hard to break in. And I persisted and won on my third try. There you go. Third time's the charm.

1:38:07And 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. 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.

1:38:48But 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. Obviously, in the last few months, what's been a highest priority is getting these Epstein files released. 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 or a thousand 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.

1:39:28And 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. So are you optimistic that the American people will ever get 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.

1:40:11I think 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? No, there's always going to be some sense that we didn't get a 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.

1:40:49And to your point about Apple, it's not AI literally as just AI, as Grok or ChatGPT 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 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.

1:41:27They 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 is the challenge, in my view, for time. And, you know, I've gotten attacked from some people in Silicon Valley saying, oh, he's kind of a Luddite.

1:42:07And 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. 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 deeper in the bowels of some enterprise software product that no human was ever interacting with to begin with.

1:43:04and 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 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. I would say that the key distinction is, is AI going to enhance human capability or eliminate human beings? That is the distinction.

1:43:51and that we need to figure out as a society how we get more AI that is enhancing human beings as opposed to just eliminating them. Let me share two thoughts on this, both of people who influenced 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. Smoglu won the Nobel Prize at MIT, has this idea of total factor productivity. 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 our society, right?

1:44:40I mean, who wants to just get a check without contributing? People have pride. But you also wouldn't actually maximize total production because you have all these people who could be doing things who are not productive and who are 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.

1:45:20And so you have the benefit of the synthesis of the technology and the worker. And that is actually what transforms 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? OK, but so let's make it more specific because I agree at a high level with a lot of that. But let's talk about like a specific role or job like truck driving.

1:46:02you've generally come out against or have concerns around AI based job displacement with and with long haul trucking and truck drivers on the other side of that if I'm a if I'm running a trucking company and I wanted to live deliver the best possible service for my customers it's possible that AI would be able to support that. What kind of policy do you think is right in order to create? You want some guardrails around the industry, how AI should be used in trucking? 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'm on a plane, A lot of it is automated, but we still have a pilot there, and I'm glad we have a pilot.

1:46:58I 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 an assist from a technology that maybe allows them to rest more, that's less taxing, 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.

1:47:41That 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 we 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 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. But the ratio of teleoperators to cars on the road is potentially higher than one-to-one right now, according to some reports.

1:48:26But 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? And you're sort of proposing maybe try and make that as gradual 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 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.

1:49:06But 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 who once 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, 40s, 50s, 60s 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.

1:49:44We'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. 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, displaced by the concentration of wealth in some areas.

1:50:31And 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? 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 in the transition being managed in a way that considers their interests as well? And that's, you know, and I get that this is a good nature debate and people say, OK, kind of you're adding some cost to the issue. And if all you cared about was shareholder profits and minimizing consumer costs is 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.

1:51:23But 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 in communities not as dispositive, but as a factor, just like we consider consumer costs and shareholder profits. Yeah. What's your look 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.

1:52:07But 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 that it was actually okay and delivered enough value to the consumer? Because Uber is one of those weird examples where the amount of, you know, taxicab-like activity, ride-sharing activity, it 10xed. And more people take 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.

1:52:51I take Ubers all the time. Yeah, right? I'm the same way. I'd be like an expose, you know, next time I do an Uber. But I'll say this, we should have done more for the medallion owners, right? I tried actually in New York. This is before Zoran Mamdani became Zoran Mamdani when he was an assembly member. He was really focused on a lot of these taxi drivers who had lost their medallion value. and we're 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 through JP Morgan, and it ended up not working out. But we should have, as a government, done more to help those folks who had medallions, who lost all their value.

1:53:40And 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 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 healthcare on an exchange that has soaring premiums. So there are better things we need to be doing to help that Uber driver. But do I, am I glad that there is a technology like Uber?

1:54:16Yes, I am. I think 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. And it was around basically around S &B deregulation, making it easier to get a small business off the ground. Is that 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 like a finished restaurant that's just has its door closed because they're waiting on some some permit or something like that.

1:55:05And 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? Yes, it does. Yes. And look, Zoran became famous in part with his 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.

1:55:44That'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. 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 a wealth. Completely agree. I completely agree. We love small businesses here too. But here's the but. Okay. Most Americans, most Americans are not going to go just start a small business. Like this idea that every person in Bucks County, Pennsylvania, where I grew up, or Western Pennsylvania, should start a startup or build a business.

1:56:26Like 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 I'm for a better life. But like a lot of people just don't want to do that. And they still want to have the American dream.

1:56:57And 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. 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? 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? Yes. Three takeaways. One, one third of the AI talent is in China.

1:57:37What does that mean? That means it would be totally counterproductive 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%.

1:58:18It's really high, right? 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 and control economy. I don't know. It's always been crazy. They have enough empty sky rises, I think. Maybe. I don't know. Yeah. What was your takeaway from that? 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. Is it banned? Yeah. I mean, it's such a directed economy. They want everyone to like make stuff, manufacture stuff, not do things that they would consider frivolous.

1:58:55Sure, sure. Like a sports app, a music app, all the cultural stuff that we do that improves consumer life or thinks about consumer needs. And, you know, so you're someone who gets this fancy education in college and then they're like, OK, 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 the next factory of robotics.

1:59:40And it was hilarious because I would talk to the premier, Li Chang or others, and they'd say, well, it's a voluntary unemployment problem. These are just folks, they should be doing these jobs. But what if in America we said, OK, you know, as 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 has overvalued manufacturing, doesn't have that diversity. We do. Our problem has been the opposite, that we undervalued making things.

2:00:19We 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 who is 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, OK, Xi Jinping gets rich and a bunch of people are running these companies get rich and the rest. And then you have 20 percent unemployment and you have consumer welfare declining.

2:00:55And 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. China is not the model. America needs to be more like America of how we built America in the 1940s, 50s. I completely agree. Quick couple, I 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 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?

2:01:42I'm a Yimby. 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$5 trillion companies in my district and expect to live like where the Valley of the Heart's delight. Like if you've 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 wealthy people can live and that the working and middle class is getting shafted.

2:02:22We 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 home. 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.

2:03:00and whoever is elected the next governor, I can't imagine it won't be on an abundance agenda when it comes to housing. And it's not going to be, okay, let me do it at the last year or 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 regulation? We didn't get to touch on that earlier. and you had some comments recently on SB 1047, the bill in California, but what's your updated view on where regulation should be happening?

2:03:46Well, 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, OK, you're going to stop all the growth. I mean, my district is$18 trillion of value.

2:04:31We've got five companies over a trillion dollars. East of the Mississippi, there's not a single trillion dollar. California is 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. They're producing more wealth than ever before. Like what we want to know is how is how are our kids going to fit into this? Yeah. And I just think that that I wish more tech leaders, you know, sometimes gets it as a Jensen Wong is talking about this. Like, yeah, I mean, how do we create economic development opportunities in places that have been left out?

2:05:10how do we make sure that everyone comes along on the AI revolution? I just think it's in tech companies' interest to embrace this in a similar way as the economic royalists embrace the New Deal eventually. I mean, you can't have just a capitalism that is 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, 10 years to develop. I mean, two years ago, we were reflecting on this. People were worried about AI killing everyone and creating the Terminator.

2:05:51And 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. It's 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, you know, maybe there should be regulations, but how, when will we be confident that we know how to regulate it? 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.

2:06:29We 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 a democratic society. I think that's fair. The places I focus on are jobs and American citizenship. And I agree with you on the jobs part, but it just feels like the jobs, we haven't seen a collapse. 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.

2:07:02And 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. And I can't just say, hey, you know, trucker, like, I want the AI to handle this one. 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 when things will happen is, you know, fortunes are won and lost on that alone.

2:07:37Totally agree with you. John Maynard Cain said we would all be working 15-hour work weeks. And he was on 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 Darren A. Smoglu, who 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 and kids.

2:08:11Two 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 sparring with folks. And I appreciate sort of the criticism I've gotten from 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.

2:08:55Because if 70 percent of the American people believe the American dream is dead and have a concern about AI, like the answer to that for anyone who's been like in a relationship is not to dismiss it and say they're dumb. problem it's to say okay how do i address that anxiety so that we can move forward and i guess i i guess my hope would be that uh there'll be more tech leaders uh 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 i i think it's in silicon valley's interest to have that kind of view no i think you really you really freaked people out with uh there should be a tax on mass job displacement well there is a tax on the profits, right?

2:09:33Like we, we tax profits. So, I mean, it, it, there, 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. I don't know. 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, I'm like a politician like this. I just talk like I talked 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 two years later on Face the Nation.

2:10:11I just don't think that's what our politics are. It's like put your ideas out there. Like what human being doesn't have some ideas that are dumb? Like maybe, maybe Einstein didn't or something. Most of us, we put up good ideas, we put up bad ideas. I love it. We think, experiment. 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 it's fascinating i'm learning a ton and uh we really appreciate you taking the time to come talk to us thank you so much for coming on you guys are doing great seriously you're elevating the conversation and yeah silicon valley and it's an honor to be on and uh i look forward to being back yeah we'd love to have you back on the show and and go way deeper on all of these and i'm sure uh by the time the next time you're on uh all 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.

2:11:03And so thank you so much for taking the time to come talk to us. I appreciate your approach. Thank you. Have a great day. Thank you. 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, so you can focus on growth. Our next guest is Jonathan Swardlin. Hey, sorry for keeping you waiting. we were in a political quagmire. We were in the swamp. We were in the swamp. 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.

2:11:40He did get us going though. He was telling us how much value has been created in his district. It's in the trillions. 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. 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.

2:12:12It 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 and everybody can agree that preventable death shouldn't happen yeah so it it comes at 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 well that or or the uh well this diet this diet is right wing that diet oh working out that's a right wing thing or like no this is left wing like you know different ingredients became politically charged over the last few years my powder is better than your powder.

2:12:57For 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. 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 love it. I like it. 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 what's the value prop to consumers uh what's the progress been how big is the company kind of set the table for us 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 a new platform it's one dollar per day to join and the platform includes twice a year comprehensive lab testing at over 2200 locations any quest diagnostics around the country you go and you test everything heart, hormones, liver, kidney, thyroid, cancer signals, you name it.

2:13:54Up and down, all of that data goes into a platform, into an app that explains 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. And what a function has created is this entirely new standard for your health that every year for 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, that's the, the value proposition.

2:14:25And that's what function delivers right now. We started with lab testing because that's, that's like, that's the most impactful data. 70 % of medical decisions are based on lab testing. And recently we acquired a company. You might've heard about this called Ezra and Ezra 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 sighted in an MRI machine typically. Two, it also massively reduces the cost and it picks up the efficiency.

2:15:08And so what we've actually done is we've introduced lab testing, one of the largest, most powerful lab testers in the country. and then we went into imaging on the imaging side and bringing down the cost and what you're seeing actually emerges this new standard for health we took the most impactful parts of the health system for capturing your data and we actually packaged it up into something that's really simple to understand and really affordable for for many many people talk about how talk about how mris were used historically are these things that that get done when uh like your act you have like acute pain or you have an issue and then you're doing it and this feels like your ACL.

2:15:49Yeah. Or this, this feels like kind of flipping it and saying, using it as like preventative, uh, preventative cares. Is that the right read? That that's where I read it. Not just preventive. I would just, 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 early are a little tricky for me because the word early, it's like, why is it early detection? We just call it detection. Can we just get rid of the word early? What MRI does is traditionally it allows somebody to look inside the body.

2:16:23But to do that, it's been really, really expensive. You get an 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 every single organ and look for tumors that are 0.2 centimeters, two millimeters. It can look for stroke risk, aneurysm risk, endometriosis, hernias, 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.

2:17:00It'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 in a hospital, you broke something, you have an issue, you go look at this one particular area. In Function's case, you can actually look at most of the body through an MRI and you can detect cancers early, you can detect the aneurysm, stroke risk, et cetera. 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 is the first time in history it's been possible geographically from a cost perspective, technologically, and culturally it's changing.

2:17:40People 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 10, 15 minutes each time. You go in, you get your whole body testing, you find out what's actually happening inside. 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.

2:18:16And 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. You're good to go. John, Jordy, 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 narrow 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.

2:18:50I'll give you a crazy stat. A new study just came out. 45 % of people that were hospitalized for their first 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. 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 man for a minute. They might say it's Lindy. So look, there is no world where any top cardiologists say, I'm just going to rely on LDL cholesterol based.

2:19:41What every top cardiologist tell you, let's look at ApoB. Let's look at LB little a. Let's look at lipid particle size. For most people, those words are foreign to them. But they should be. I mean, it's just 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 as status quo. And what functions that is for hundreds of thousands 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.

2:20:14Women don't have to go to their doctor and ask for a horn panel and get chased around. 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, you're four times more likely to survive cancer if you catch it early. But right now, status quo is you have to wait to have symptoms to catch cancer. Yeah, it's crazy. There's no way 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 out of these things so i can talk about yeah yeah so i'm i'm sold on the product uh i think it's i think it's hard i think it's hard hard not to be it's it's the best kind of like value offering i think in health like period uh and i was sold obviously uh when you were raising uh pre-seed uh back in the day, however, two and a half years ago or something like that.

2:21:11Feels like forever ago. Can you give us, I'd love to get your view on an update of the market structure. A lot of companies have seen. You guys weren't, Function wasn't the first lab 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 that we're seeing out of last year. Give us an update on the shape of the market, how you see the market evolving, because like I was saying, a lot of people are trying to ride your coattails, but I'm curious for an update there. The market is realizing that the word consumer health has been this like dirty word for 20 years or something.

2:22:08And it's not, it's what it is, is it's premise on the most primary thing that we experience as human beings is our biology. 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. Give it up for$7 trillion TAMs. John, hit the size gong for a$7 trillion TAM. Had to, had to. Anyways, continue. I love it. I love it.

2:22:57No. So, so, so look, this is, this is, people have been spending absurd amounts of money on their health 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 bring scientific and medical rigor directly into a platform that people, they themselves can sign up for, they themselves can manage. And so they can make decisions for themselves. And that gets them way ahead of disease, as you know, as I've been saying before, like this is not a trivial space.

2:23:34I think it is the best, is the most anticipated 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 we're surprised that that the category and all the competitors aren't, it's not that it's not bigger, that more people are jumping into this. Like we know that people are gonna try to ride these coattails, but our head is down and our focus is on 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 actually delivering something to somebody that has real, real, real substantial value.

2:24:19And at a time, a lot of technology can do a lot. It's like, what are we really paying for? And it's like, where can people get started? You mentioned it's a dollar a day. 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 going. Okay. So it used to be$999 when we started. It was manual. It was per day. $1 ,000 per day. $1 ,000 per day. Sign me up. $1 ,000. he said per year don't don't worry john's just messing with you so it started at a thousand bucks per year then we worked really hard to bring up the efficiencies in tech got it down to 499 and and a couple weeks ago we announced it's now 365 it's like when it's actually 365 dollar per day because health it helps an everyday thing and it's an understandable price and when it's health care actually been deflationary yeah and then so so uh go to a quest labs probably twice a year.

2:25:17You go to functionhealth.com. Functionhealth.com. You just sign right up. Right there on the scheduler, you sign up for your lab appointment. 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. Now your app is live. All the data is coming in. It's making sense of it. You test every six months. I think people, one of the reasons people underestimated this kind of category as it was emerging is so many people got burned on like DNA testing. What would DNA, DNA like the 23andMe is you test it once and then there's like zero incentive to retest, right?

2:25:57You did 23andMe, 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 rewritten my entire DNA. All of it. From start to finish, every base pair is different now. Sign me up again. I'm ready to go. We have to study you if that's the case. We're going to have to bring you in. John needs to be studied, honestly. What does 25 ,000 Diet Cokes do to the human body? We're going to find out. What does 500 Diet Cokes a year do?

2:26:37A 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. 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... Mate yina, yerba mate. Podcast in a can from Andrew Huberman, of course. And we're going to find out. 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 I want to get to before our next guest.

2:27:11So 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 raised,$2.5 billion valuation. Congratulations. 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. I love it. I love it. Appreciate you guys. Thank you so much for taking the time to stop by. We will talk to you soon. Great to have you here, Jonathan. Long live TVPN. Good to see you guys. Hey, for the next one, for the C, come in person.

2:27:46We got a seat here for you. I'd be honored to have you in person. Be great. Let's do it, brother. We'll talk to you soon. Great to see you. Goodbye. Let me tell you about Vanta. Automate compliance and security. Vanta is the 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. SOC 2 compliance is a big factor in actually making it up the tier rankings for ClusterMax because, of course, if you're training on customer data, you need SOC 2 compliance. You need the whole process.

2:28:22Anyway, the breaking news that I wanted to get to really quickly is Josh Kushner is partnering with OpenAI. OpenAI, he says, we are excited to announce a strategic partnership between OpenAI and Thrive Holdings Through our partnership, OpenAI 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. We view the businesses that we own and operate as the right reward system to build, test, and improve industry-specific products and models.

2:29:06So the race is on. Is it inside out or outside in transformation? What's going to happen? These are the new fast takeoff, 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. 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.

2:29:44Taylor says, deal guy Yuga. This is the deal guy Yuga. It's happening. It's happening. Well, before we bring in our next guest, let me tell you 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 with a reintroduction on where the company is today. And then the news. I'd love to know about the news. Yeah. Thank you for having me again.

2:30:19It's been a while. Yeah. So big news. because we just released our latest Frontier model, runway Gen 4.5. It's a model we've been working on for quite some time. It's the best video model right now in the world, which is a pretty remarkable fit. So I think it's pretty good. It's pretty fun to play with. To be clear, that's my audio. I'm at it. It's not your video. I didn't drop that, but perfect timing. 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.

2:31:03They have a very serious asset in YouTube, and I'm fascinated by – so first, give me the news. Video Arena leaderboard, that's the ranking that you're using. How is that scored? How does that actually work? So it's kind of like a way of crowdsourcing performance. You basically ask people on 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 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.

2:31:49And we got results a couple days ago. And yes, we managed to basically outcompete all other video models, including both Google and OpenAI, which is a very remarkable feat if you think about the scale of resources. Like, I think it's the era of, Ilya was saying this is the era of research again, and I agree, but it's also the era 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 what you optimized for here because Sora seems it's an incredible model. And it was for like a minute, like, whoa, really mind blowing.

2:32:28Then 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 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. They've also degraded the model dramatically. They have degraded the model a lot. Whereas VO3, 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, 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.

2:33:21It 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 clear or 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 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 slob and like quick, like shareable stuff, you're going to train the models just from the ground up very differently. That for the stuff that we're trying to do, which is a much more professional, like high quality, very controllable sort of like tools.

2:33:56And so a lot of what you're like basically outlining is, 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 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, like what do you want to work on? Like having vision, like having, okay, I want to pick these specific problems I want to work on and this is how we're going to solve them.

2:34:30And 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 like and that's like beavers on a construction site this is actually very good that's your taste yeah look at this hilarious right look at the motion of the donkey moving like the camera the angles like the amount of data creation our team of artists and like filmmakers and like people have spent it's not 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. I mean, I guess that's the point.

2:35:05It's really had to summon the demon on this one. Have you been inspired by Anthropic 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 like a mercenary versus like visionary type of like, I would say, bet. It's like you want to have people who feel like very committed to the vision long term. And the way you do that is like you're very focused on like the culture and like that culture eventually shines in the product.

2:35:46I think Entropic has also that. You can tell like who works there and like how they think. And it's also very cohesive in a way. I think we spent somehow a similar amount of time doing that in a way. And I hope you can tell via the models themselves that that personality comes across nicely as well. Yeah, and I agree. That at the end will be perhaps the most defining part of the companies that stay in the long run. I think if you just throw money at the problem, you're not going to get too far, to be honest. Yeah. What went into the actual training run? Are you at a scale now where it's a meaningful capital investment to build a model like this?

2:36:30We saw the scaling paradigm change from like, you know, maybe it's$100 million to do a big frontier language model run. Then we were talking about 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. So you definitely have to spend more money, more resources. But I think we're proven that we are not spending tens of billions of dollars to get there and to overcome the challenges.

2:37:13And look, to be honest, the model is not perfect. There's a lot of things that are 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 most expensive thing is like the natural intuition the team builds around what kind of works and what doesn't work. It's going to go back to the idea of research stage. Like you can't throw money at it. You just have to spend enough time. We've been working on running for almost a decade. And so there's a lot that you've learned over time about what works and what doesn't that informs a lot of the efficiencies on training.

2:37:44And yes, expensive models, you'll need more money to train larger and bigger models. If this is the worst the models will be, imagine them in two years. 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 only on training, but on inference. This is a price point that's very comparable to our previous models. So it's actually very usable. And hopefully you'll be using it in real time very soon.

2:38:18And 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 pretty remarkable. 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 a credit system. No, so we have a queue. We have compute, and there's a queue, and you get into the queue, and you generate as the queue becomes available. If you just want to generate fast, you pay for credits.

2:39:01But depending on how anxious you are with your generations, it's a measurement of how fast you want it. But eventually, you can just literally generate unlimited. By the way, I think no one else has a plan like that. but it's a pretty good deal. What are the length of generations 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? And are you trying to get to two minutes or two hours? Like how do you think about durations? 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.

2:39:47And that's actually been trending down. It's like the scene, right? The scene itself, the cut, it's like two to three seconds long on average. And so when you actually, when people mean like, I want to join the 45 minute like long thing, you don't want 45 minutes of like one camera, like fixed. You want like scene 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 reality where like you can generate consistent narrative work like really 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?

2:40:41And so that's a different problem, I would say, altogether. There was some debate on the why doesn't the cursor for video exist yet. Do you have any thoughts there? What's the cursor for video? Basically, a nonlinear editor, like a Premiere 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 day was for Greenscreen, for Chromacab, basically. And it was fantastic for that. And it feels like that, building a canvas, building an NLE, that feels like one potential pathway to victory.

2:41:30it's way it's also very difficult because you can't just fork vs code there are no leading open source nles on the flip side uh when you 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 and um you know there's a variety of ways to to win i'm interested in hearing your approach yeah that's definitely definitely interesting question and uh by the way shout out for you for being an OG on runway since 2019. Yeah, something like that. Crazy. I love that. So my two thoughts are, first, the art of NLE and editing and film, it's an art.

2:42:13And there's a lot of pacing and details that are very nuanced and specific. It's about granular details. And it's hard for, I would say, model or assistant to automate that level of decisions. That's on a purely NLE side, right? 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 these 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 razors and now we have digital razors, you're cutting things together.

2:42:50My bet is that you probably won't need like NLEs. Like that whole paradigm will feel like a fax machine like in a few more years. And so I feel that's somewhat what's happening with like the, the, the, the devins and the clogged 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 fond of like, you know, pushing like better versions of NLEs out there. I think there's something around how you make videos and how you interact with this AI system that just naturally allows itself for different primitives.

2:43:32And if you think also about the fact that very soon you'll start to see this happen in real time. Like when you make real time like narrative work or videos or experiences, how are you going to call them? 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'm saying. It changes the nature of like those things in the first place. and there's a transitional period where like you'll you were seeing like NLEs being augmented with AI but I think it's that's transitory I don't think it's gonna play out like in the long run yeah yeah no I think uh has has Hollywood capitulated yet what's going on there we we had uh it's funny I've been hearing I've been hearing more and more about Suno from from not just uh guests and friends of the show but just like random people out in the world It sounds like every single musical artist now is using it in some degree, even if they're not willing to talk about it.

2:44:27What 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 negative energy that gets focused on it, specifically from people that are within the industry. you know you know i think the the negative energy is like the water problem with ai you know like it's kind of this this unrealistic like and very noisy not representative sample of what's actually happening within the industry if you go to la you speak with the agencies with the talent 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 more advanced than others.

2:45:17But I would say that the narrative publicly hasn't yet catch up with that, mostly because some people might not want to speak about it. It's much more interesting to say all the negative things than 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 to what AI can help them and how they can use it. So yeah, I would say that some of those narratives are a bit fake, to be honest.

2:45:52Yeah. Well, thank you so much for taking the time to come on the show on a busy day. We appreciate it. And I can't wait to play around with the new model. We have a benchmark here, BezelBench, where we try and recreate a very complicated shot that we shot practically with a bunch of different watches with our intern or gap semester, Tyler Cosgrove. And the shot's very long. It pulls out. It twists around. It's a pretty complex shot. 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 taking the time to come chat with us. We'll talk to you soon.

2:46:29Thank you for the chat update. Goodbye. Let me tell you about Julius.ai, 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? Great to see you. It's been too long since last weekend. Thanks for having me. Congratulations. Master of 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. Yes, I'm so excited to give you a rundown.

2:47:11So basically, for the broader context, our broader goal is really creating open frontier models and infrastructure for everyone to create them. And last week, we released Intellect 3, which is basically really like a scale-up towards scaling RL and post-training and creating a SOTA model, especially for more agentic tasks. 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 parameter MOE model and really kind of like that whole infrastructure is kind of quite a challenge like from like the RL environments to the broader like code send boxes and the whole stack to do post training that's basically what we built over the last half year I think Will Brown came on the show to unpack some of it on the verifiers and environment side um so basically that's kind of like what we released last week and really proved that kind of like we got performance um at 100 billion scale that um does find open source only 300 to 600 billion parameter models like deep seek are on for example achieved before so basically getting to better performance actually at a much smaller scale um and i think in general it showcases that like um open models are starting to catch up obviously i think quite interesting um is in general seeing the trend that um not just with our model but also more broadly with other releases like deep seek today um and over the weekend that actually um they're 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 sorta is um we basically released like our early checkpoint and we're actually scaling it much further um also on more like agentic capabilities, but basically really like making it sort of across like a range of tasks.

2:49:04And really, I think the foundation of this, which is quite interesting, is that we created this environment software. Anyone in the world can create one of these RL 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. 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.

2:49:42But 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 opportunities, 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 uh creating our environments for the labs like the doordish clones etc so basically to push really capability so i think we're in this paradigm right now obviously we're ultimately like scaling rl is the main way on on how these models improve right like we've seen it with opus or um with gpd5 and gemini like that was mainly like i think a scale up in rl um but basically what we are seeing are two things.

2:50:30It's like on the one side, it's like there's a lot of demand for these RL environments, but then the other side, RL is very sample efficient, so you can take an open model and then really create an RL environment for the specific use case you care about and scale capabilities for that. So I think a good example of this was, for example, a cursor with Composer. That was what's widely believed or known to be a scale-up of an open source model, and the RL environment was Cursor. They basically just gave it the tools and the things within the harness and application of Cursor itself. But it trained basically that model on getting really good at using Cursor.

2:51:09And I think we'll see the same play out across all the applications where basically the broader theory is every application, every company will be an AI company or AI native and will have an opportunity to really post-train and use RL to make the models work specifically on the application. So even if you take examples of like, say, a Figma, right? Like if they want to make their platform agentic, really they need to create an RL environment around Figma and post-train on that environment to be able to serve that within Figma, like kind of like out of the box, like the closed models won't be perfect at like really navigating and making those applications agentic.

2:51:51So I think that's the broader theory. I think really it's also like the capital requirements are much, much lower than I think the big labs want to believe you. In a sense where it's like you can, for like hundreds of thousands of dollars, like post-train a model, right? It's like to be much better on your application. And then also you are able to like serve the model cheaper. Big labs hate this one weird trick. Post-train a model for$100K and create a better. So, I mean, that's basically what you're saying 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.

2:52:28I can go in RL on just my particular application and have a smaller model that's fine-tuned on open source, 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, post-training their own model with like first post-training their own model, Composer. And Composer is also like, it's much cheaper to serve.

2:53:03It's much faster, like same for the model 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 at RL. I think we're basically starting to see a huge pull in terms of enterprises realizing that if they want to get a specific capability, RL is the way to get it and ultimately enables them quite capital efficiently to train those models and serve those models. And then really get to a point where even in deployment, all the interactions from the user help improve the model. So I think with the cursor example, like every, for example, cursor tap interaction, every yes and no that a user gives to the model, is updating the model every two hours so it's what like drakesh talks a lot about two hours like online rl yeah like like they basically reach like continuously training the model in two hour interval and pushing updates every two hours to cursor tap so basically every user using cursor for the last two hours is um is being post trained on so to speak like with kind of like an online rl loop i think that's something which we'll see more and more that basically applications will do their own rl their own post training yep um actually then and i think it's like really how we on unhubble basically towards AGI.

2:54:15It's like the question is like, why haven't we say automated like specific valuable knowledge work yet? And I think the answer that also like Sholter was speaking about, for example, on the example of like automating taxes and accounting firm, right? It's like no one has really created our own environments, post-trained on them, and then served the model in the application where the end user is. And then ultimately 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 the applications win to some extent through distribution.

2:54:55Like ultimately they own the end customer interaction, right? Where it's like even the cursors and cognitions have like an advantage there over folks who are basically just model providers and who don't interact with like millions of developers. and I think we'll see the same play out like across all the different applications and it's something like from the start that I talked about also in the context of like Copilot and Microsoft like they own distribution they can like create the cursor for Excel or like PowerPoint or other things right and then whole strain on all those interactions 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 you know just like every company needs to become AI native right like and own also to keep owning the distribution They don't want to give all of it up to the big HR labs.

2:55:41Yep, that makes sense. We got a question from our intern, Tyler, if 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 MOE base model, which achieves like pretty sort of results. So we released that I think like an hour ago with them.

2:56:22And we're actually now like ramping up with them towards like a much bigger base model. So fully like pre-trained from scratch. So we actually just had like 2000 B300s going live, I think, yesterday to ramp up towards a much bigger retrain. And I think the broader pattern is since 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 model stack. 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.

2:57:05So I think that's our broader goal is really serving the world more globally, but also the West and the U.S. with an end-to-end pipeline, right? That's from data to pre-training to mid-training to post-training, like the full stack and making that accessible to enterprises and people who are trained on models. So I think there's a huge, I think, pull where a lot of enterprises or even like sovereign nation states, et cetera, like can't train on Chinese open 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 of really like serving kind of like that whole segment.

2:57:42Do 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. And I believe every tech company, maybe every founder-led tech company under 10 years might be 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.

2:58:29I'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 like a truly legacy, you know, non-tech company adopting a fine-tuned LLM or an RL model? model totally no i i think there's like early adopters and later like later adopters i think go-kola might be more like a later doctor might not need to adopt it early on but i think they are even more adopting it just like in less obvious places right it's like ultimately i think they're initially just like using the ai tools that use us for example yeah in a sense where it's like say customer service right it's like it's 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-chain using Coca-Cola data to serve them a better model.

2:59:23So I think what we'll see play out, I think, is really just making a lot of that so accessible to your point that it feels more like using SaaS, where I think one element of it is we are launching also our whole RFT platform, basically, and offering to make it extremely 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 the 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 of it for you.

2:59:58Like that you basically just like plug it into your system and like the AI even like creates AI for you. Like, 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 Vibe coding is today, right? In a sense, it'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. And that's one of the big things that we're playing into. We're kind of like pushing towards like autonomous AI research, where AI can do most of it for you.

3:00:32Well, thank you so much for taking the time to come and talk to us on the show. Congratulations on all the progress. Thanks for having me. And we will talk to you soon. Great to see you, Vincent. Goodbye. See you guys. Have a good one. Let me tell you about Privy. Privy makes it easy to build on crypto. 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 Hilack.

3:01:02Did he do the Jaguar rebrand? That's him. Ben, welcome to the show. And we'll follow him forever. How are you? Grab a seat. Hang out. Good to see you. Oh, you brought hats. It's fantastic. Thank you. Please, grab a seat. Introduce yourself. Introduce the company. What's the news? Yes. So my name's Ben Heilach. Yes. Let's take a second for the flow. Fantastic. Thank you. Thank you. This is kind of like a vintage Silicon Valley flow. Somewhat of a lost art. I appreciate it. You guys have great hair as well. I know. I feel a lot of pressure. You'll notice I'm not wearing a hat today, and it's because I did notice, actually.

3:01:38Actually, I discovered a blow dryer, I think, around nine months ago, ten months ago. So that was a big deal. Your life's never been the same. It's never been the same since. But, yeah, my name is Ben Hilek, as you guys know. I'm the CTO of a company called Raindrop. So really simply put, we monitor agents in production. So we were 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, Like if you're using Sentry, if you're using traditional analytics, you know, they're covering like the things the users are clicking.

3:02:14And almost everything that's happening in your product, if you're making an agent, is just not covered. So you just have no idea what's going on. 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 that was not necessarily intuitive to us in the beginning. You know, you think like, oh, well, agents are going to get better. Maybe this problem becomes less important.

3:02:40But it's like actually as they 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 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, something goes wrong, suddenly it's like, it's a real issue.

3:03:13So that was not intuitive before. So agent's a pretty overloaded term at this point. I think of, you know, when I fire off a deep research report in ChatGPT, 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 agent's overloaded.

3:03:50We're very hesitant to use it for a really long time. And then we realize it actually matters. Of course. So we focus on products that have some sort of user input and 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 going to focus on like specific like ML pipelines or things like maybe like translating text or like summarizing text even. It's like we want to see like the user, 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 assistant actually responds.

3:04:30And then what's the go-to-market for you? I mean, it's been a little crazy, actually. We've had a lot of inbounds. So some of our biggest customers have been inbound. A lot of it has been like when we first launched, I think, 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 that were like, okay, like I have evals. I think we'll need this. 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.

3:05:00Okay, now I get it. We need you. So it's actually been a ton of inbound. We don't really pay for advertising, anything like that. 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. How are you thinking about the 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 of the startup that just kind of gets it and they can hop on really quickly?

3:05:31Like 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, PostHog has this metric where they look at, like, what percentage of YC companies in 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 faster. So, for example, like, when a new model comes out – just to give actually a very specific example.

3:06:03So GPT-5 introduced intermediate reasoning, right? It was kind of one of the first models to do this, 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. And it really helped. So where startups will just, like, throw everything out the next day. And they'll ship a whole new thing in a week.

3:06:36You don't see, like, if you look at the biggest enterprises, they're not going to do that. So you can learn really fast with startups. That being said, on the flip side, I think that the problem we're solving is actually most painful for enterprises. It's like the most critical, high-stakes environments are where failures cost the most in every single sense. Yeah. 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.

3:07:15Yeah, 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. Yeah, yeah, yeah. because they don't want their competitors to know. And then obviously, like, CX feels sufficiently hyped. But what else are you seeing? Man, there's so many different things. Like, I think, you know, speak, for example, language learning. I think the better, like, as models get better, that experience just actually starts to become really, really, really viable.

3:07:53So, 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 voice models get better, as, like, the models themselves get better, it's actually not just like if you try to use chat CPT for example to learn a language you sort of can but if you ask it to 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 you're absolutely right don't de esta la biblioteca is the most complicated Spanish sentence 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 start critiquing everything, which is also not what you want as you're learning a language.

3:08:35So it turns out, and I think we see this with a lot of products, that 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, whether it's marketing, whether it's even just the idea of having a personal assistant. Notably, we don't have that yet, which is crazy, right? 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. And so I think we're starting to see products actually like nail that, like smaller mostly.

3:09:05How are you thinking about just, 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 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 evals is that they don't catch those sort of issues. Like, you know, you're kind of testing just like the model.

3:09:40What 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 upload is taking too long. It's like, okay, well, it's not 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.

3:10:11And so users all started complaining like, hey, that's not what I, like what are you talking about? That's not what I said. So it was like an increase in that, in like confusion. 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. But there was a situation where like people could share. 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.

3:10:34So you can actually catch those sort of problems. It's actually 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 a 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 if you can see when it actually starts to affect users, like that's really powerful. 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.

3:11:04I'm not benchmarking everything every night. My slop agent was degraded. But it does feel like that sometimes, right? My slop agent. It feels like sometimes I'm like, wait a minute. It 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. This is real, right? I think it's real. 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. So that's it. You know what I mean? I can't say who. It was a big company. And because I noticed this and I thought I was going to – I can't say whose hands were caught red-handed.

3:11:39I can't say which one of the – But I caught some red hands. 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 Chachup Tea and it was doing the same – oh, I just said. but anyway yeah like i i think that the reality is that like every single one of these uh providers are like having these sort of problems and they're trying to optimize costs they're trying to like make changes like 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 i kind of sure yeah i don't really mind that you put me on the lower tier 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.

3:12:20A hundred percent. 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, yeah. Hit the gong. Tell us how much you raised. How much did you raise? How much did you raise? 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. Bucky, we love Bucky. This one's for you, Bucky. This one's for Bucky. Clean hit. Yeah, we're big fans of Bucky up here, so I just wanted to get him a shout-out. Us too, us too, us too. I think the moment we met him, we were like, okay, he matched our energy, great vibe.

3:12:59Yeah, yeah, he's doing great stuff. How's building the team going? It's 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. It's honestly really, really small still. Well, if you want to get out of San Francisco, you could book a wanderer with inspiring views. Hotel, great amenities, dreamy beds, top tier cleaning, 20 % cost of your service. It's a vacation home, but better. You could do an offsite there. We could do our offsite. That's beautiful. You could do your offsite. I once used a team offsite as a recruiting tactic.

3:13:31I said, we are going on an offsite in two weeks. Okay. And I posted a picture. Oh, yeah. You want to come? We got an amazing hire. Totally, totally. We'll do it. I'll do it. It creates emergencies. We're doing it. So if you're watching right now, I'll post a picture soon of the house. Okay, fantastic. But we have an amazing team. I figure if you're picky and you're in San Francisco, it's like the most ruthless talent war constant. The other thing is 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.

3:14:04If you start, whether you're work telling, whatever, it's like if they're just, you know, if it doesn't fit, like everybody knows and feels that. Have you had to bring anyone 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 on the podcast. So he had Mark Chen, OpenAI's research chief on the show as part of a post-Gemini 3 sit-down to get the update from OpenAI. And he said, I knew the AI talent wars were rough, but not this rough. Zuck is out there apparently delivering handmade soup.

3:14:37Whoa. Wow. And OpenAI has soup counters. And so I guess... Wait, they count how many soups? I don't even know what this means. Oh, I see. No, I think it's like a counter. It's just like a cafeteria. A place where you have soup. Aggressive. What exactly is this tit for tat? We can play this on the show later. But yes. No, no. My partner has cooked meals for someone. You have to come up with creative ways, for sure. That sort of thing works. Home cooking. We do typewritten. I'll write a note on a typewriter when we do our offer letter. Typewriter, I like that. So that adds something a little pizzazz.

3:15:16That's good. Are you messing with us? No, I'm serious. I love typewriters. No, I like that. It's just a way to prove I can actually value this message. All the text is AI generated, I'm sure. Of course, yeah. I'm just copying from Chatsy. You're absolutely right. No, I think it's a little bit of a paperwork thing. You're not just the newest hire. You're a revelation. This is a statement. Yeah, yeah. We're having fun. Well, that's great. Congratulations on all the progress. Very excited. I'm sure you'll be back on the show soon. I will. for giving us plenty more updates. And it's been fun, because I believe that we started tracking your journey via your viral joke post about doing the Jaguar rebrand or something.

3:15:54But we've always had fun featuring your post. It's great to have you here. Live in person. 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, it was the first time I ever heard of you guys. You were reading one of my tweets, and it was just so surreal that people from the internet are reading my tweets. One of our customers sent it to us, actually. We had to print it out. You had to print it out, yeah. So I called my mom today. I was like telling her, I was like, hey, I'm going to be on the tweet. I was like, you're not going to know what it is.

3:16:20But remember those guys that were talking about that tweet? This was the whole shtick. It was like little love letters to Silicon Valley folks. Just like little messages of just, hey, we found something that you did fun. Because anyone can like, anyone can repost. It's easy to send a small thing. It's very hard to actually print it out, sit down, talk about it. It means something. But we appreciate your post. And we appreciate you coming on the show. I appreciate you guys. and hanging out today. So thanks so much. Thank you. We're going to close out the show and we're going to talk to you in just a second.

3:16:49While he's walking off, let me tell you about getbezel.com. Shop over 26 ,500 luxury watches. Super intelligent for watches. Fully authenticated in-house by Bezel's team of experts. I also need to tell you about 8sleep.com. 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 on a 98 98 98 98 that is remarkable um well is there there are a bunch of yeah we'll see if you want to go through some breaking news um buco capital bloke is on the timeline you can feel the panic behind the urgency and intensity with which people are defending nvidia it feels visceral and quite intense you can tell how much there's riding on this it makes a lot of sense.

3:17:39What else did you want to cover? I thought it was notable. PagerDuty has fallen to a$1.1 billion market cap. And they're at 500 million of ARRs. They're not growing anymore. They're trading at 2.1x ARR. It's profitable, according to Jason Lemkin 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 I was like there's no way 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.

3:18:32Benson was like, I'll take that spot. November 29th. Obviously, that's not how it works. It is much more mathematical than that, I believe. Standard & Poor's picks 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 Enrod comparisons are just so silly to me obviously it's like you know the discussion is like is like 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 it does not seem like it's some ridiculous enron situation that's like so so insane uh people are just having fun with that headline but what is incredible is this this branded shirt he's wearing look at this thing fantastic so awesome i love not enough people trying to go snipe vintage nvidia 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-silicon valley 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 following?

3:19:59Tyler, you got to follow the account. No, this is not my account. I think this is more of like a burner account situation. Oh, it's a scraper that we use for the show. It is, it is. You got to correct that, Tyler. Come on. That's not it. Gorkum over at Fall 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 Gorkum says, Series A pitch meeting. 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?

3:20:39Why 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 had delays and weren't ready to take delivery yet, and orders got pushed back at the same time. China raced to get as many machines as possible before export controls tightened. Okay, that makes sense. Let's hope. Sash Zatz says, Oxford Dictionary didn't get the memo. Apparently, Ragebait named word of the year. What?

3:21:18I think it... No, no, no. I think they're actually right. It's appropriate that it would be the word of the year, But it is so funny that you posted this and then Oxford Dictionary. Yeah, so this is true. According to the BBC, Ragebait named Oxford Word of the Year 2025. It certainly feels that way on the timeline. Your post, 1 million views on this, 3.6 thousand likes. People really, this really set the agenda for a little bit. Wow. Congratulations. What a banger essay. Should TVPN do a Word of the Year? I like that. or motion. Motion. Motion might be our word of the year. Word of the year. Motion's a big word of the year.

3:22:01Motion, named word of the year 2025 by TVPF. If you have it, you'll know. You'll know. We'll call you. Tyler has motion. In other breaking news, Keith Reboys is taking shots at AirwallX. AirwallX 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. It's how fast the business is accelerating. It takes more than six years to 100 million AR. What does Airwallex do exactly? I think they provide payment rails for a bunch of American fintechs to handle international. Okay. Okay. And so Keith Raboy, been on the show multiple times, says, Cool growth chart.

3:22:45Have you disclosed to U.S. customers like Rippling, Bill.com, Brax, Navon, that you're quietly sending their customers' data to China? Airwallex has become a Chinese backdoor into sensitive American data 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 espionage upon request. Through Airwallex, 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.

3:23:25Obviously, many companies do business in China, and that's not 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 uh what's happening uh you route global payments for u.s companies and critical sectors without disclosing that you're under a chinese jurisdiction you moved your hq to singapore well that seems like a step in the right direction maybe uh but your largest operational footprint is in china okay no so maybe one step back um and hundreds of your engineers in mainland china touch production payment systems.

3:24:02You are subject to Chinese law that requires Air Wallach's employees to support CCP intelligence requests 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 access to sensitive financial information. uh this is a big story this is a this is a crazy scoop from keith reboy um and uh i i will be interested to see where this goes how how how quickly they can uh they can um uh you know remedy this this this popped up uh a couple years ago with during the clubhouse era the clubhouse back end i believe was was at one point you know i was working with a chinese company or maybe maybe was that there was a company that did peer-to-peer audio streaming that was based in China.

3:25:02And so if you were building a competitor, you might use that company. I became familiar with Airwallex through the 20 BC 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. Oh, well. Anyways, I'm sure we'll hear more about it. to say. We got to get on with Menlo Park. Okay. Well, thank you so much for listening. Hanging out with us today. We will see you tomorrow. Please leave us five stars on Apple Podcasts and Spotify. I can't wait. The break, the Thanksgiving break was absolutely brutal for us, I will say, every single day.

3:25:40But 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 will see you tomorrow. See you tomorrow. Cheers. Goodbye. Thank you.