Claude Code for Finance + The Global Memory Shortage: Doug O'Laughlin, SemiAnalysis

This is a free preview of a paid episode. To hear more, visit www.latent.space https://www.latent.space?utm_medium=podcast&utm_campaign=CTA_7 First speakers for AIE Europe https://www.ai.engineer/europe and AIEi Miami https://www.ai.engineer/miami have been announced. If you’re in Asia/Aus, come by Singapore https://www.ai.engineer/singapore and Melbourne https://webdirections.org/ai-engineer/. AI Engineering is going global! One year ago today, Anthropic launched Claude Code https://www.anthropic.com/news/claude-3-7-sonnet, to not much fanfare https://news.smol.ai/issues/25-02-24-ainews-claude-37-sonnet: The word of mouth was incredibly strong however, and so we were glad to be one of the first podcasts to invite Boris and Cat on in early May: As we discussed on the pod, all CC usage was API-based and therefore it was ridiculously expensive to do anything. This was then fixed by the team including Claude Code in the Claude Pro plan https://www.reddit.com/r/ClaudeAI/comments/1l3bwmm/claude_code_is_available_on_pro_plan/ in early June, and then the virality caused us to make a rare trend call in late June https://news.smol.ai/issues/25-06-20-claude-code: Now, 6 months on, Doug has just calculated that around 4% of GitHub is written by Claude Code https://newsletter.semianalysis.com/p/claude-code-is-the-inflection-point: We talk about how Doug uses Claude Code to do SemiAnalysis work. Memory Mania In the second part of this episode, we also check in on Memory Mania https://newsletter.semianalysis.com/p/memory-mania-how-a-once-in-four-decades?utm_source=publication-search, which is going to affect you (yes, you) at home if it hasn’t already: Full Episode on YouTube Timestamps 00:00 AI as Junior Analyst00:59 Meet Swyx and Doug03:30 From Value Mule to Semis06:28 Moore’s Law Ends Thesis12:02 Claude Code Awakening32:02 Agent Swarms Reality Check32:53 Kimi Swarm Benchmarks37:31 Bots vs Zapier Automation39:44 Claude Code Workflow Setup57:54 AGI Metrics and GDP01:04:48 Railroad CapEx Analogy01:06:00 Funding Bubbles and Demand01:08:11 Agents Replace Work Tools01:13:56 Codex vs Claude Race01:21:15 Microsoft and TPU Strategy01:34:13 TPU Window vs Nvidia01:36:30 HBM Supply Chain Squeeze01:39:41 Memory Shock and CXL01:45:20 Context Rationing Future01:54:37 Writing and Trail Lessons Transcript [00:00:00] AI as Junior Analyst [00:00:00] Doug: This crap makes mistakes all the time. All the time. It is still just like a, like I think of it once again as like a junior analyst, right? The analyst goes and does all this like really pain in the ass information and you bring it all together to make a good decision at the top. Historically what happens is that junior analyst, who I once was, went and gathered all that information, and after doing this enough times, there’s a meta level thinking that’s happening where it’s like, okay, here’s what I really understand and how this type of analysis, I’m an expert in, actually I’m very good at, I consistently have a hit rate. [00:00:28] Now I’m the expert, right? I don’t think that meta level learning is there yet. We’ll see if l ones do it, right? Everyone who’s spending one quadrillion dollars in the world thinks it will, it better, it better happen by if you’re spending, you know, a trillion dollars and there’s not meta level learning. [00:00:44] But for me, in our firm, that massively amplifies everyone who is an expert. ‘cause like you have to still do something that you can just like lop it up. It’s very obvious to me. What It’s slop. [00:00:59] Meet Swyx and Doug

• 0:00Speaker 00

  This crap makes mistakes all the time, all the time. It is still just like, like, I think of it once again as like a junior analyst, right? The analyst goes and does all this like really pain in the ass information. You bring it all together to make a good decision at the top. Historically, what happens is that junior analyst, who I once was, went and gathered all that information. And after doing this enough times, there's a meta level thinking that's happening where it's like, okay, here is what I really understand and how this type of analysis I'm an expert in, actually. I'm very good at, I consistently have a hit rate. Now I'm the expert, right? I don't think that meta level learning is there yet. We'll see if L1s do it, right? Everyone who's spending one quadrillion dollars in the world thinks it will. It better, it better happen, right? If you're spending, you know, a trillion dollars and there's not meta level learning.

• 0:44Speaker 00

  But for me in our firm, that massively amplifies everyone who is an expert. Cause like you have to still do something that you can't just like slop it up. It's very obvious to me what it's slop.

• 0:59Speaker 01

  Doug Alford, welcome to Lean Space.

• 1:01Speaker 00

  Yeah, thank you for having me, man. I, after all this time, I just, is it okay if I just call you Swizz? I feel, yeah, it's the, that's, that's where my brain is. I've known you for so long, you can call me Mule if you want, I'm a character, you know?

• 1:12Speaker 01

  Yeah.

• 1:13Speaker 00

  Yeah, I mean, it's been, it's been a long time.

• 1:15Speaker 01

  It's been, it's been a long time coming. I think I first met you at like New Orleans or like one of the, one of the Neuripses.

• 1:22Speaker 00

  Yeah, yeah, I met you at one of the Neuripses in Perth. I think it was Vancouver.

• 1:25Speaker 01

  Right?

• 1:26Speaker 00

  Yeah, no, no. It was like some after party. Yeah, yeah, yeah.

• 1:29Speaker 01

  And you were like, hey, like who's this tall dude? I'm like, whoa, okay.

• 1:32Speaker 00

  Yeah. Yeah, well, I mean, it's just like I, I knew about you and we, we've like been in that, you know, pen pals for a long time. So like cool meeting in person. Yeah, yeah, I think that was the first time I ever met you in person. So, yeah.

• 1:43Speaker 01

  Amazing.

• 1:43Speaker 00

  I, I didn't go to the New Orleans one. I really wish I did. I love New Orleans, honestly.

• 1:47Speaker 01

  Yeah.

• 1:48Speaker 00

  So.

• 1:48Speaker 01

  There are two New Orleanses in a row and yeah, honestly, we should go back there.

• 1:51Speaker 00

  Yeah.

• 1:52Speaker 01

  Are you guys going to Melbourne or the Australia one this year?

• 1:56Speaker 00

  I have, I don't even think that far out, but on it, uh, but that sounds pretty interesting to me. I think, I can't remember which one, there's a, there's something in, in Korea this year, right?

• 2:04Speaker 01

  Yeah, I think ICML.

• 2:05Speaker 00

  ICML?

• 2:06Speaker 01

  Yeah.

• 2:06Speaker 00

  I think I'm going to try to go to ICML in Korea. And I know I clear is, I don't know, man, there's so many conferences. I honestly hate to say it, I'm not much of a travel guy.

• 2:15Speaker 01

  Well, yeah, I mean, I'm glad to catch you. I mean, I am traveling to you.

• 2:19Speaker 00

  Yeah, thank you. I really appreciate it.

• 2:20Speaker 01

  Yeah, it's really awesome. Yeah, yeah. I did not know that I would be caught in a snowstorm.

• 2:24Speaker 00

  Yeah, it's funny. I feel like people recently have been coming and they keep getting stuck in these snowstorms. So yeah, first Blizzard in four years or something like that. Thank you for coming.

• 2:32Speaker 01

  Yeah, yeah, it's a pleasure. And so you want to go back? You used to be Anonymous. You used to be Value Mule, how I know you.

• 2:38Speaker 00

  You know what's funny is that Value Mule is like the very first one.

• 2:41Speaker 01

  That's the, yeah, the, I don't know how I noticed you. I was just like, oh, this guy seems smart.

• 2:45Speaker 00

  Yeah, I don't know, dude. I mean, I remember noticing you too. So it's like, you know, this was in the early, like, the primordial days of Twitter. Honestly, I miss those the most. It was like 2017, 18, something like that.

• 2:55Speaker 01

  Yeah.

• 2:56Speaker 00

  But yeah, yeah, I remember. From Value Mule. So that's like the deepest cut. If you are even aware of what that is, that is like the deepest cut that you possibly have. And then, yeah, I have another account, and I actually have a third account, which is my main account these days.

• 3:08Speaker 01

  Yeah, oh, which one is that?

• 3:09Speaker 00

  I don't have an obolus.

• 3:10Speaker 01

  Okay, I don't want to dox your other account.

• 3:12Speaker 00

  Oh, okay.

• 3:13Speaker 01

  Just semi, semi.

• 3:14Speaker 00

  Yeah, yeah, yeah. So, so, so it is there. That's, it's not, that's okay. That's like my oldest finance account. I think of it as my legacy account.

• 3:21Speaker 01

  Okay.

• 3:21Speaker 00

  I, you know, I want to have some privacy, I feel like.

• 3:24Speaker 01

  Yeah, yeah. So now you've gone all in on the brand and everything.

• 3:26Speaker 00

  Yeah, yeah, I got a brand and everything, yeah. Same profile pic, you know, so.

• 3:30Speaker 01

  Yeah, so let's do a little bit of the Doug story, because a lot of people hear about Dylan. And I wanted to just make this the Doug story, make it the fat knowledge story. You used to be a value investor. That's kind of how you were Value Mule. And you had a mentor or something that nerds sniped you into semis. Is that the story?

• 3:46Speaker 00

  No, actually, I solo nerds sniped myself. So I wouldn't say value, because well, for everyone who's listening to this podcast, might as well be value, right? Maybe quality focus back in the day. But we had this whole thing where we wanted to buy quality compounder companies. And the one I found that nerds sniped me, all the, like, single-shot me, is I found ASML.

• 4:06Speaker 01

  Yeah.

• 4:06Speaker 00

  And I, like, fell in love with it. And then I, like, after ASML, I just, like, read about all this stuff. How complicated it's to make these, who are the people who are able to make them. And then I, you know, semiconductors, the whole downstream is all from there. But it started with ASML in 2018. I really fell in love with it. And then I read, like, textbooks, and I just, like, kept going deeper. And my favorite part about doing that.

• 4:25Speaker 01

  I'm gonna pull out the Asianometry.

• 4:27Speaker 00

  Yeah, yeah. That's perfect. That's a perfect one to do here. John.

• 4:30Speaker 01

  Amazing.

• 4:30Speaker 00

  John's a monster, honestly.

• 4:32Speaker 01

  This one, right?

• 4:32Speaker 00

  Yeah, yeah. I mean, the thing that's crazy is he has, I don't know, he has a whole playlist about it. Every single aspect of what goes into it. And what's truly great about it. It's all science fiction. Like, that's my favorite thing is like, science fiction exists other than, you know, the talking perfectly intelligent robot, whatever information L1. Yeah, ASML is all science fiction. So the semiconductor stuff's always been science fiction. Always loved it, always thought it was cool, thought it was the most important thing that we ever made. And yeah, kind of followed from that.

• 4:59Speaker 01

  Yeah, I don't know if you know, but obviously, you know, I used to be an analyst myself.

• 5:04Speaker 00

  Yeah, I didn't.

• 5:04Speaker 01

  I covered TNT, which is a freaking huge sector to cover. It's absurdly huge.

• 5:10Speaker 00

  Yes, very large.

• 5:11Speaker 01

  Like, I was I'm reading Sprint.

• 5:13Speaker 00

  Yeah, yeah.

• 5:13Speaker 01

  And I think like, you know, Viacom.

• 5:16Speaker 00

  Yep.

• 5:16Speaker 01

  And then there's ASML.

• 5:17Speaker 00

  Yeah, yeah. Yeah, the N, I feel like, the T and the M and the T are actually three completely separate industries. But once upon a time, I think in 2000, they were kind of really close together.

• 5:27Speaker 01

  Right.

• 5:28Speaker 00

  Yeah, but but ever since then, it's really split off. Yeah.

• 5:30Speaker 01

  Yeah, well, I mean, this is my reflection is like, I used to be in, I used to be, I guess, our tech sector guy and like, I did the flights to Taiwan and I took those meetings with like Credit Suisse and all those, all those guys that would, you know, tour you around and all those. I never really felt like I got it because I was always being filtered through like investor relations and all that. And I think you have to do what you did where you sort of go muck mode into like textbooks and stuff and like actually learn about the tech. But then you, you hard, it's like really hard as an investor to like make the connection to, okay, wow, that was that me for this, this quarter? Like, or at least this, this year.

• 6:08Speaker 00

  Yeah.

• 6:09Speaker 01

  Right, because like, one, there's like just so much foundational knowledge and then, then you're like, well, okay, everything here is taken for granted, it's already priced in. So like.

• 6:18Speaker 00

  Yeah, you, you assume that all the Taiwanese people who are buying and selling the rumors of capacity are pretty well informed. You assume all the people who are TMT investors in the United States are pretty well informed. I think the thing that was like, the foundational difference for me is like, you know, real thesis around, one, I think being. Young and brash and believing in yourself to be like, no, this is something that's really matters and everyone else doesn't see it. Really helps. But for me, the thing that was like, I guess radicalizing was I really believe Moore's law was dead. And I was like, oh, my God, not only is it this cool new technology is super hard to make and very interesting and technologically very fun to understand and like I get it intuitively. But also, everything, all the old playbook is about to be thrown out.

• 7:01Speaker 00

  Because it's been like this is a super mature industry. You really need these primers about it. Like, that's how you learned about things. Back before chat GPT knew everything or you had to go and read these primers of all this information. They're like, oh, it's a very mature industry and mature. They used to be really immature in the eighties and nineties and two thousands, but now you're consolidated, growth doesn't go up a lot. And everyone kind of had this old playbook from the early two thousands. A lot of people hated hardware. There's just this perception that semiconductors weren't valuable, weren't as valuable. Actually, software was the most valuable thing. Now, software is getting shit on, but that's like outside of the scope of this. But people just thought it was this old, mature business that had nothing new under the sun. Meanwhile, every single day, just making a new chip was like science fiction. People took that for granted.

• 7:45Speaker 00

  And when the science fiction ends, because you can't make the chips as small as you you could. All of a sudden, all those free gains you got go away and you have to think about it. And what happened for semiconductor specifically is it created a lot of pricing power or value for everyone who knew how to make a good chip. So Nvidia is probably the best case. You could talk about parallel computing and all that stuff, but it's not just like they know every aspect of it. From the chip to the networking, to the design, to the scale up, the whole thing, it is like, you know, versus in the past, it was just CPU gets better, go brr, right? And so I think that I had a really deep belief that In this case. That Moore's law was, Moore's law was ending and everything would change. And so coming in with that like thesis at the top level, just like made me want to attack every little assumption.

• 8:28Speaker 00

  And something that really changed as well. And dude, this is honestly my my favorite post I've ever written. It's like twenty, it's like a check GPT three and the writing on the wall. In like two twenty twenty, you know, my early, I get an early pitch to for fabricated knowledge and I'm like, hey, you know, I'm gonna make a release, you know, more Moore's law is over, scaling law seemed like a big deal. If you simplify it all the way through is like, okay, supply, you know, supply divided, you know, demand, right? Yeah. Demand is growing a lot because of scaling laws. Supply is actually slowing down because Moore's law is completely screwed. That's probably really good for 70 hectares and parallel compute is going to be a big deal, blah, blah, blah, blah, blah. My conclusion then was you should just like Nvidia is pretty much the only one who's going to benefit. And, and you know, so that's my, my, my, like, good long range prediction, I feel like I. Just like.

• 9:12Speaker 00

  Yeah, I just don't think, I don't think I would have expected the magnitude. I think that that's been the kind of the craziest part about this whole story is like, I had all these beliefs and thesis and like, I really, really, really believed. The reason why I met Dylan is he's the only person who is as semi-chauffeur-pilled in the entire world as me is how it felt. So I remember like yelling at him, arguing about all these kinds of things and like our, our DMs and stuff like that. Was it just online or? It was online. We met in person. Taiwan and. No, no, no, I, I've, I've actually only been to Taiwan with him one time, I think. Yeah. So, so I, I mean, like, look, we, we just met in person, we yapped, we went to conferences. But I think that that's like, kind of, we are both really early to the thesis, kind of have a different background and perspective. Dylan is technology first and you know, obviously the technology matters.

• 9:56Speaker 00

  I have a little bit more of a financial background, but always around him and it was just like, you know, he's the only one, a guy who like cared to the same level. So yeah, this, the, the thing that's crazy is like, we called it, we were right, blah, blah, blah, blah, blah. But like, the thing that I think that still shocks me all the time is the magnitude of how right we are, you know? Like, be like, oh, Nvidia was good, right? Nvidia is pretty good. And then it's like, no, Nvidia is now the most valuable company in the world. And I think if you had me read that and like truly, hey, I wrote that, I believed it. Yeah. I still wouldn't have put that together or like, I wouldn't have believed it if you. This is one of many pieces at the time. Exactly. Yeah, yeah. Like, there's so many things. Like, what else are you writing at the time, right? That didn't work out, you know? Yeah, yeah. We can look, we can look back, but. I, I, I, I'm pretty happy with my, with my long-term track record. I really am.

• 10:40Speaker 00

  But yeah, I'm just really surprised the magnitude of how everything happened. Like, it's crazy to me that like, COAS is like a, a, not a household term, but like relatively well-known. It was like an exotic technology. So all this stuff has been this like learning journey, really believing. Where technology is going, why chips are so important. And then obviously, understanding the big scheme of all the things, putting it together. And so that's the, yeah, that was like the early days. And it's, I think it's all been downstream of that, like, you know, one goaded insight, pretty much.

• 11:09Speaker 01

  Yeah, I mean, and probably like a career maker right there, you know, and I just like, I love those kinds of like sort of quarterbacking those career decisions for other people who are also weighing a bunch of things, right? Like I have ADD and like I just chase like whatever is interesting, but at some point you just have to like choose.

• 11:26Speaker 00

  Yeah, I, I think one of my skills have always been like trend following and trend watching. I think when, you know, for talking like on my account, like value meal or, you know, full of time. Like I was always pretty good at trends, like being relatively early. I remember loving and being obsessed with TikTok in like 2019. And everyone's like, why are you so obsessed with the dancing music show, like stuff like that? I feel like I've always been decent with the trends, but I think the thing was when you see a really big wave that you have a lot of conviction in, it's worth going all in. And that's, that's kind of what it came down to. It's like, wow, I see this really big wave, it's worth going all in. And so I reoriented my life around that.

• 12:00Speaker 01

  Yeah.

• 12:01Speaker 00

  Yeah.

• 12:01Speaker 01

  Cool. We're going to talk about other trends that you've spotted, primarily like the sort of memory cycle, which, but also optics, which amazing story. But we wanted to sort of focus this for the Cloud Code launch, Cloud Code anniversary. And you've been a big Cloud Code show.

• 12:17Speaker 00

  Yeah, I, I am.

• 12:19Speaker 01

  Where's the chart with the four, four percent of code?

• 12:22Speaker 00

  It's, it's actually go to the top left. Yeah, yeah. Oh, you know what's really crazy is we've updated that chart. I think it's like five now. I mean, and like, as you know, it's really easy to generate code now. So like that, that number will continue to climb. But it's like just staggering the rate at which this is happening.

• 12:36Speaker 01

  So, so that's recap for people who, let's say, I, I think this is one of the most important pieces I, I've read in a long time. And you know, you, you led it and it's, and it's, it's weird because I think of you as like an analyst, right? Like you, like one of semi analysts is alpha is, is that you're kind of like the fun millennial semiconductor firm when everyone else is super boring and old.

• 12:57Speaker 00

  Yep.

• 12:57Speaker 01

  But like, what are you doing, you know, getting so into cloud code? Like, you know, I, I shouldn't you be reading reports and stuff, you know, like. Tell the story of your go secosis.

• 13:07Speaker 00

  So, yeah, I think here's the thing is if you want to be good at any game, we're tool users at the end of the day, right? If you are good, if you want to be like, and obviously this is like outside of my job of semi-analysis, like I have all these other things I need to do to grow and make semi-analysis the best research firm ever. But like, let's say you're a fund manager or an analyst, right? Your job is to find information edges and like new ways to put information together that no one else has done. And so like, I've always thought it's really important to know the most important weapons grade tool that you can do all the time, which is essentially ChatGPT, Anthropic, all this kind of stuff. And I've been pretty like, I'm a, I'm an early adopter in tools as much as I can be. And like, for example, I've been running the, our, our case study that we have into Claude Code since it first came out.

• 13:51Speaker 00

  Like, you know, I think over a year, like, you know, I want to say March, April, I started

• 13:55Speaker 01

  So which case study?

• 13:56Speaker 00

  So the case study for people when we were hiring like a financial analyst, like our core research seat or something.

• 14:01Speaker 01

  Okay.

• 14:01Speaker 00

  Hey, you know, can you, can you take this company and do some analysis, blah, blah, blah, give us this format back? And I've been running it through like the agentic things, only like, hey, what, what, when agents really come around, they should be able to one-shot, multi-step, hard things to do, things that would take a human 24 hours to do, right? And I always wondered, because I, you know, there's some good submissions and there's some bad submissions. We pride ourselves in the case study and being good. And, and honestly, I always joked like, well, you know, they're going to start to beat the worst submissions. And so like, that was our, that was always my base level. I have a base level of, is it better than a ChatGPT agent mode or Anthropic's Claude Code?

• 14:35Speaker 01

  Yeah.

• 14:35Speaker 00

  Or Gemini CLI, whatever. And so I started running these benchmarks a little bit. And so I was very familiar with how good it could be, but then I was like, oh, it isn't quite there. I've coded some stuff on Opus Four, for sure. But like, you know, it was like kind of interesting projects on the side. It was really hard. It took a lot of feedback. They would, they would mess up. It just didn't. And then, you know, everyone was freaking out about Claude Code 4.5 and I like took it for a spin, especially around the holidays, I had some free time. And then I was like, okay, well, like, how good is this? And it just like one, it started like one-shotting everything, right? Like all these MVPs that like, you know, you have to be like, well, the UI is whatever. It's like, no, just one-shot it. And then you ask it to do something better and it explains what you're doing, and like, that's actually really good. And so I was like, wow.

• 15:15Speaker 00

  Wow, generalized, easily one-shot MVP of these like projects and able to like really build things on top of it. Cause you can trust what it's doing to a certain extent. And it felt like some level of capability was beaten. It was very different than what I'd done in the past. Oh, I also tried Codex 2 before this, like, like 1.5.2, never really got it to work in the way seamlessly, agentically.

• 15:36Speaker 01

  Oh, of course. So this is, this, this is recent.

• 15:39Speaker 00

  Oh, no, no, so, so, so this, my most recent, when I was like, oh, man, the awakening, probably December 27th.

• 15:46Speaker 01

  Okay, you know it's in a day.

• 15:48Speaker 00

  Something like that, something like that. I'm thinking, cause it's between the days and I, I got home from Christmas and I was like, my fiance wasn't feeling so well. So I had some time to mess around just by myself.

• 15:57Speaker 01

  Yeah.

• 15:57Speaker 00

  And, and then also there's 2X usage limits. Oh my God, I miss those days. But I mean, now I'm addicted to Fasta. But, but look, I, I was playing around with these coding agents, just like everyone else should or, or should in the space. And like Claude code versus Codex, I was like doing, you know, simple testing to see if they can make a thing. And I never really like one-shot it like a total idiot's thing. And then 4.5 just started one-shotting stuff. And that to me was like a huge difference. And so I was like, wow, it could just like one-shot stuff. I have all these interesting ideas.

• 16:26Speaker 01

  And can I, is it Excel Sheets primarily?

• 16:29Speaker 00

  No, no, not Excel Sheets primarily. I would say it's usually a mix of like a dashboard or Excel or something like that. But a good example where I, like, I, I think Excel, it's moderately okay at like, let's say, one-shotting a basic financial model. Or like just taking and, and putting information from one place to another. It's not a human level, but honestly, if you know much about investing in the, being in the business, it's like, is your model, you know, being 5% more accurate, really gonna ever make a good investment decision or not? No, never, not once. Like, no one's saying, oh yeah, my estimate is always one cent more tighter than everyone else. And that's why I'm good at stocks. No, it, it doesn't matter.

• 17:05Speaker 01

  It's like sell-side is ridiculous, Chris. Like, everyone's like, I'm bullish, cause my EPS estimate is like 10% higher than, than the street.

• 17:12Speaker 00

  Yeah.

• 17:12Speaker 01

  And I'm like, oh, who cares?

• 17:13Speaker 00

  Well, I mean, as you know, sell-side, if we're gonna do this, like, shots in across the bow on, on sell-side, I mean, look. One of the reasons why Symbianos has such a successful business is because I think sell-side as a concept is very broken. If you're talking about waves and things that are changing, sell-side in a lot of ways is this hereditary child of like, let's say 30 or 40 years of banking, where you had a company, go public, so you needed someone to talk about it, to issue securities.

• 17:38Speaker 01

  You're selling the stock.

• 17:40Speaker 00

  You're literally selling the stock, but you have to be independent-ish, so your ratings, buy, sell, hold. One of the biggest sales you could do is like when your company IPOs, we'll talk about you so people know who you are. That's the core, original part of the sell-side, right? And the problem is like, all the research kind of has this like, really kind of fallen apart, it's just not different, a lot of banking regulations has changed. And so like, the primary information process, it's like a 40-year-old business model on its last legs. And so I mean, that's one of the reasons why Symbianos is so good, it's because we are not focused on being a one-cent EPS thing, which I would argue isn't exactly skill, it's just mechanical maintenance. We are really good at understanding when technology changes and how that impacts everything, right?

• 18:25Speaker 00

  Because it doesn't really matter if one EPS is slightly higher or lower, it does matter if like, I'm just giving an example, if AMD's Helios rack is super on time and is like, out at the gate, ready to make tokens on this day, because that's going to be billions of dollars of difference in revenue for AMD, right? Or some networking technology, or something like that, some bottleneck. Being really right on the timing and the magnitude of those inflection points will make a huge difference in the stocks. And so that's our business, we're a research firm, we're independent, and we've had a really good hit rate and we care deeply about this technology.

• 18:58Speaker 01

  Exactly, yeah. I didn't mean to characterize you as like, you are young and fun, but also you're extremely damn good. It's almost like a triple threat, and I always wonder if it's like, okay, it's like, one, you have like, deep understanding of the tech, two, maybe you're like, sort of financially sort of literate, but also, three, it's like, this like, X-factor that is like, well, focus on things that matters, fuck everything else. And I don't know what that is, but that obviously is the alpha.

• 19:24Speaker 00

  Yeah, 100%, 100%. That's, yeah, that's always been the analyst PM conversation. It's like, hey, you know, there really is only one or like three things that actually matter, right?

• 19:33Speaker 01

  Find me those.

• 19:33Speaker 00

  Find, find me those three things, right? And then there's all this information, what's actually, what, you know, that's the hard part. But yeah, we, I think the thing is like, we're really focused on finding the things that actually matter, right? Like the things that like, hey, this certies is best, this certies, this case doesn't matter. This one actually matters because now you have a giant opportunity. And so that's, that's what the game is all about, I think, in terms of the research.

• 19:54Speaker 01

  Yeah.

• 19:54Speaker 00

  Like a, you know, finance perspective. But on top of that too, is just like, when you do so much research, all these different little industry parts are so hard to understand, man. Like you go to some networking conference and you're talking to a guy who works at a company with, or talking about their new email versus what you would call it laser. You know, I can't even remember what, what email is replacing blah, blah, blah. And you're like talking about all this stuff and they're PhDs and you don't, okay? Everyone has a PhD at the deepest level and they're all doing, so you have to understand all these deep understandings of these parts of these supply chains. But you also have to have a big understanding too, because you know, this little part at the bottom of this supply chain is actually going to impact this giant, you know, business at the top. Because it's all interconnected, but it's so complicated.

• 20:37Speaker 00

  Just paying the tuition to show up is very expensive.

• 20:40Speaker 01

  So I, I think one way I'll bridge this for listeners is that this is the complexity of the problem domain. That's, there's extreme depth, there's extreme width, and you have to kind of throw human attention at all of it to find what matters. And you're, you're saying you noticed some kind of breakthrough in December where it was suddenly clicking for you. I, I just really wanted to figure out like the tasks, the task that I was nailing and the task that is still, is not great.

• 21:04Speaker 00

  Yeah, so let me specifically talk about my use case, because hey, I am still a stock guy. I can't trade or do anything in semi-hector or, or AI world. But you know, I do still really enjoy stocks is one of the reasons, like I'm passionate about it. And it's probably my, my defining skill, what makes me good or bad at stocks, quote unquote. You, you know, the people who are really like stocks, they're like lifers, they just love this shit. It's, it's like an addiction, okay? So I, I'm like, hey, you know, here's like all my positions and like, here's some like thoughts on it. Can you just like, kind of like start copy pasting some notes over and putting it all together? It's like, yeah, it does.

• 21:35Speaker 01

  That's why you give Claude code?

• 21:36Speaker 00

  Yeah.

• 21:37Speaker 01

  Okay.

• 21:37Speaker 00

  So I started doing this and then I'm like, okay, like add it, make the portfolio, run some basic risk stuff. It's like, yeah, sure, fine, whatever. And then also like, everything you do is perfect. I'm like, okay, well, like, actually, can we like make an investment framework for my investment style and start to grade all this stuff? And then like attack it and do stuff like that. You could just do like iterative work. And then I was like, whoa, whoa, whoa, this is like a crazy useful tool that systemize how I think really quickly, like, okay, what else can I do with it? And the answer is like fucking anything, right? And my joke on the, the podcast is it's all a skill issue now. And so I, I've been, I've been doing this systematically for every aspect that I can think of, like, hey, now it's so much easy, easier. Like I was, actually, a perfect example is this, is this chart, right?

• 22:18Speaker 00

  Hey, Claude code is a really big deal. Everything's one-shotting. I'm reading everyone going into psychosis like me at the same time on the internet. How do I actually know what's real and what's not?

• 22:26Speaker 01

  I wonder, right?

• 22:27Speaker 00

  Yeah, I wonder, right? So I'm like, okay, I heard about the fact that the Claude code has the commits, right, on, onto the public, onto your, your commit. It says, hey, signed off with, I'm like, well, why Claude code scraped me all the commits, right? And you know what? Lo and behold, it pretty much did. Like, and it's like, okay, well, like, I'm looking for this signature right here, copy paste. So like, how would you systematically go about doing it? Did like a big query pull for all the stuff, pulls all the, like, every single day, the API is relatively open. And then I'm like, oh my God, let's see how much this is growing. And it's like, okay, chart go up. And you're like, how big is it as a percentage of GitHub? You're like, chart go up. It's a huge deal. And I'm just like watching or, you know, I have like a cron job updating it every single day, blah, blah, blah. And I'm like,

• 23:09Speaker 00

  this is a huge deal. Like this is the, the biggest deal. I, I love watching trends. I love watching exponential trends. And I've never seen one even remotely at this rate. You'd are, you know, 4% in like two weeks.

• 23:21Speaker 01

  Do you know about PR Arena? It's, it's a previous attempt prior to you, but somehow they didn't, they didn't, they didn't talk about, they just talk about merge rates, but they didn't, they just.

• 23:32Speaker 00

  They don't they don't plot it as nicely as you do. Yeah, well, and also you wanna, okay. Because you you asked you had the you you had the you asked the question of what is this as a percentage of, you know, and this this guy didn't. Yeah. That's it. Yeah, and and also, I mean, the other thing too is, yeah, I have a lot of those as well. Yeah. But but I thought the quad code, because I'm just trying to really, really, really focus on that. So typically, yeah, well, and also you want to give an example. Bro, I didn't make that chart. Opus four point five did. Yeah. Or or I think four point six, I'm like, hey, I want you to do it in this style. This is the semi-analysis color scheme. Yeah. This I like summarize books about visualization and like put in here's a little style tips. Yeah, here's some. Tell it a tough idea. I I don't I don't even know, man. It has like it has like I had to go read like seventy books or something. I'm like, give me like, you know, the. It's probably a waste, like. I know, I know.

• 24:17Speaker 00

  Signing it hard. It it is a waste. Look, tokens are free. The the cost of doing this is nothing. That's the part that's so amazing. Yeah, yeah. The cost of doing this is nothing. The information gathering and synthesis is like, hey, if it costs effectively the same doing seventy as three, who cares? Yeah. Right? And so I like, whatever. And the answer, I'm like, oh, this is too many tokens. You better like really summarize this into like ninety tokens or something like that. A really basic whatever, and then you have all the skill. But like, okay, now you can put all that into a skill of how to make charts in the semi-analysis format using any kind of data. And then you can systematically just push this out again. I'm like, hey, data analyst, please consider all the relationships you can. You know, generate information like I think it's that one was not chat G P T G. That was not generated. That was not generated, which I hate, honestly. I don't like that much that one as much as.

• 25:02Speaker 00

  Yeah, it doesn't have the guidelines. Yeah. Yeah. And and so you can just that was that was generated. And so you can just what you can do is you just ask it to do is like, hey, here's all the dates that we have. Can you like visually brainstorm with me a way to better represent this information? It's like, yeah, actually, I'm going to generate you a timeline. You can just do things. And I I mean, it's. That that is your catchphrase, right? Yeah, that is my catchphrase right now. You can just do things. And so people were looking at this from the perspective of people who are coding and they're like, hey, just programming is automated, right? But like, all information work is, you know, I would argue coding is a big subset of all information work. I think there's a a Brian Hobart tweet or something forever ago. He's like, you know. Coding and financial, you know, finance people actually are very like different types of abstraction.

• 25:46Speaker 00

  But you know, you are doing abstraction. Excel is a ginormous abstraction. You're building these relationships and you're describing what you think a financial thing is worth, right? I think coding is a little harder if I'm being honest with you. And you're telling me the hard one got automated. Why can't the easy one get automated? So I started to ask myself, how much can we do? And the answer is, it feels like a skill issue. It makes issue, it makes errors on the, on the margin. But you can kind of force it into like, for me, I love using Rubrics, right? Hey, I care about X, Y, Z. Out of 10, score this and then you can really do multiple things. It helps with the stochasticness.

• 26:20Speaker 01

  Do you put it all in one prompt? Like the, the, the task and the Rubik for the task? Or do you put the Rubik after all the tests then?

• 26:27Speaker 00

  I, I actually have two versions of this. I'm like, hey, you can pull all this stuff together, just run the, for the Rubik or whatever. Or you can do the task and the Rubik. It just depends on how you want to do it.

• 26:36Speaker 01

  Yeah, because obviously if you put it task and a Rubik, then it can iterate itself. But if you put it after, then it's probably more likely to pay attention to the, the Rubik.

• 26:43Speaker 00

  Yeah, exactly. And well, and the other part, part of it too. Yeah, it, it will iterate, but like the context rot doesn't matter. I kind of like it to be separate. Because the thing is, it's like, okay, it needs to be this like fresh look at it. You have to think of it kind of like it would perceive anything, anywhere, right? It just, each context window is just opening it up. And I think sometimes if you have done, if you do it together, it commingles the information to the point where it becomes biased or susceptible. Opus 4.6, as you know, is like super sycophantic. Like it loves to like, say, yes, okay, yeah, I'll do this for you.

• 27:15Speaker 01

  Yeah.

• 27:15Speaker 00

  I think having it separate keeps it like, keeps some of that drifts kind of away. And that's like one of the things that I've really, personally, I like the results better. But it's, it's just complicated. Like part of this is really weird because I am, I'm weirdly now opinionated on taste in terms of how you should design things. Because you can like, for example, the context rot thing, until someone explained it. I was like, oh my God, thank God someone said it. This is a huge deal. There's this like meme where it's like, these guys. Well, do you see the meme? It's like of mice of men. And at the end of, you know, at the end of the book, I can't remember which character she's like.

• 27:50Speaker 01

  I never read it.

• 27:50Speaker 00

  Yeah, so, so. One character shoots the other guy and it's like some guy made a meme about it being like, oh, this is after your after your cod code is garbled, you know, five million tokens. You're like, okay, it's time to put you down because the context raw is huge. So yeah, this, yeah, this is example where.

• 28:05Speaker 01

  So what are your compacts practices? Do you sort of aggressively compact manually or?

• 28:10Speaker 00

  So I personally, with the new one mil, I feel like I try to do it at all in one context window. I'm not doing ginormous.

• 28:17Speaker 01

  The one mil is very new, right?

• 28:18Speaker 00

  Yeah, one mil are very new, okay, very, but it's a big deal too. Because because your skills and whatever your cod MD is a percentage of one mil is so much smaller. So you just get so much more oomph, right? Because the 200 Ks are just wiping over and over and over. That's a big deal. I think it's a huge deal. And and also with how the agents are working, the sub agents will have their own context window. And then the pasting kind of like really saves that that big, you know, the one million, you just want a really high quality. Uh, project within that. That's the best, in my opinion. Compact just kind of start the compression of the noise.

• 28:54Speaker 01

  So yeah.

• 28:54Speaker 00

  Yeah.

• 28:54Speaker 01

  Mentioning sub agents and multi. So first of all, I wanted to give a shout out to this thing from Anthropic Research where they were like, here's our production traffic. And they they did a did a report that was kind of like their equivalent of the meter chart. And there's a lot of people saying that, oh, you should, you know, software engineering has PMF, but here's here's the the next list of everything else. But what if they're all also also just software engineering, right? Like, yeah, like software engineering is like 50% right now, but like there's nothing something from continuing to go to 80.

• 29:23Speaker 00

  I think maybe what's gonna happen, this is like a maybe a giant intake.

• 29:27Speaker 01

  It has like data analysis in here, which that's what you were doing.

• 29:30Speaker 00

  Yeah, that's in my opinion, that is downstream of like that is. So so I think how we should think about it is software engineering might all be downstream of chips, which is downstream. Like like chips is upstream and then it's AI and then it's software engineering. It is all the extension of that same compute hierarchy. And I think the like, you know, teaching where machine and code kind of inter or and the world intermingle right now is code. And so that's just gonna be the bleeding language that's used to to figure out everything else. That's that's my belief, like.

• 30:01Speaker 01

  Yeah.

• 30:02Speaker 00

  It it it doesn't make sense to build like, for example, this is a perfect example. This is like Excel. Claude for Excel is much worse than Claude code using Python to use the Excel skills to then deposit into. It's all.

• 30:14Speaker 01

  Much worse.

• 30:15Speaker 00

  It's much worse.

• 30:16Speaker 01

  Even even all the work they're doing.

• 30:18Speaker 00

  Yes, a hundred percent, because if you think about it, it's it's a legacy. Why make a car engine fit into a horse carriage? It should just be in a car like it's like. It's like a backwards compatibility thing where it does work because. L Lums are like relatively generalizable like this, but why bother because that same abstraction of information on Excel? It's just in that because it's human formatted for us to understand, and I think that that's the important distinction. All of this information stuff, all this software stuff is just to be consumed by humans. Doesn't matter.

• 30:49Speaker 01

  Yeah.

• 30:49Speaker 00

  If they're just as good at putting the data together, we should be much more concerned about. Machine focused of like software consumption and so they can like, you know. The the L Lums and the agents can put and synthesize all the information and deposit, God knows however you want it to be. I don't need to make a chart in in PowerPoint. Or Excel, it will just deposit the MATLAB plot, the MATLAB plot, yeah.

• 31:11Speaker 01

  Matplotlib.

• 31:12Speaker 00

  Matplotlib in a chart to me in an image. Fine.

• 31:15Speaker 01

  Are are you trying to use Matplotlib?

• 31:17Speaker 00

  Yeah.

• 31:18Speaker 01

  Wow.

• 31:18Speaker 00

  Why? Why? You know, it's better. It's better.

• 31:21Speaker 01

  I know.

• 31:21Speaker 00

  Understanding that code.

• 31:22Speaker 01

  Yeah, yeah, yeah.

• 31:22Speaker 00

  So why ever make a chart again?

• 31:25Speaker 01

  Yeah.

• 31:25Speaker 00

  If it if it's better.

• 31:26Speaker 01

  It's just like it could be inconsistent with like the other charts that you do.

• 31:29Speaker 00

  Yeah.

• 31:30Speaker 01

  But I don't do that much about.

• 31:32Speaker 00

  I I don't think we would care that much, but I think one, our new charts are better than our old charts.

• 31:36Speaker 01

  Yeah, yeah.

• 31:36Speaker 00

  And number two, I think if it increases the speed of information, that matters a lot.

• 31:40Speaker 01

  Yeah.

• 31:40Speaker 00

  And so I think we're much more. So pretty much the new charts will outweigh the old charts because they'll just grow. So, yeah, I think it it is a little inconsistent. We have the same watermarking. Honestly, I think it's better than our old formatting anyways.

• 31:53Speaker 01

  Well, the first thing this looks reminds me of is Bloomberg. I was like, you guys are just like, you know, becoming Bloomberg. Which is a nice.

• 32:00Speaker 00

  That's all right.

• 32:00Speaker 01

  There is the beginning of you come.

• 32:01Speaker 00

  Yeah.

• 32:02Speaker 01

  Couple of things I wanted to sort of double click on because this is just a cloud cloud code like brain dump.

• 32:06Speaker 00

  Yeah.

• 32:07Speaker 01

  And one of the the biggest sort of cloud code shills in the world, which is. Agent swarms, I don't know if you've tried.

• 32:14Speaker 00

  I have tried them.

• 32:15Speaker 01

  Pick either one, whatever you want.

• 32:17Speaker 00

  I have a controversial opinion that Claude does not do RL on the agent swarms or agent team.

• 32:22Speaker 01

  Yeah, it's just an experiment.

• 32:23Speaker 00

  It's just an experiment. Thank you, thank you, because no one.

• 32:26Speaker 01

  Not really.

• 32:27Speaker 00

  We exactly, because it's just via prompt and it's actually very bad. I think subagents are okay because they usually have a Claude MD to go do whatever. But the agent team is actually really bad.

• 32:37Speaker 01

  Okay, well, you know, we can't knock it, because it's experimental, so.

• 32:40Speaker 00

  Yeah, yeah, no, no, it is.

• 32:41Speaker 01

  But what do you try it on?

• 32:42Speaker 00

  Well, it was like some big data analysis of like many, many different companies with different KPIs into a dashboard all at once. And I was like, hey, can you just make this all whatever, split up the teams? You know, speaking of that, though, you say that, but can we, can we, to one, an agent swarm is actually good.

• 32:58Speaker 01

  I have also tried that.

• 32:58Speaker 00

  It is, that is actually really good. So I did some like, oh, example of things I was never available to me. Like internal benchmarking of these models and be like, hey, here's a set of problems I'd like you to do 20 times. Can you do them? And then I can measure the performance between them and then like do qualitative, but like, what's the difference between X and Y?

• 33:15Speaker 01

  Yeah.

• 33:16Speaker 00

  That, that was completely out of the hands of me, a normal guy, like three months ago, okay? Now it is completely available to me. That's awesome. Like, I am very, I care about this stuff. And now I have the tools that's able to automate and do a lot of this stuff because, hey, all the software engineering is like partially automated. And so, I mean, my experience is the 2.5 swarm actually improves the model's performance meaningfully. The agent team makes it meaningfully worse because there's clearly not RL done. So it isn't conscious aware of what's the best thing to be done. And yeah, so I think, I think it's interesting. I like subagents because it's usually a little bit cleaner on a task to go do it and then come back. But the agent team is just very.

• 33:56Speaker 01

  They had some post about how they did stuff.

• 33:59Speaker 00

  Yeah.

• 33:59Speaker 01

  Where it was, yeah, there's a bunch of RL for this. And I tried it myself. I thought it was, I thought it was like pretty, it's cute how they do all these like little games and stuff.

• 34:07Speaker 00

  Yeah, yeah. Also, it's crazy how like the setup, you have to, it's a lot of compute. Did you just run the swarm? I think it's like a 16 node of H100s.

• 34:14Speaker 01

  Okay.

• 34:15Speaker 00

  And you're just like, dang, so you and I are not going to be running, and this is just to run, and I'm sure there's concurrency available. But yeah, I think it's really cool. And that's like, I think that that's the sign of what's next because. You know, these agents are going to get better to a certain extent. They're, they're, you know, it's another benchmark and like another benchmark to hill climb. Right. But then it's going to be how many of these together in a bigger chain can you get to work? That you could argue it's kind of like a scale out of the reasoning problem too. Hey, how do you get these like this one agent to essentially get a verified whatever? Put it into a bigger process and do more information work? That, that's the next thing and it's important to have context windows that, that don't garble up into random stuff. And is able to do just like good enough with token efficiency, I think is a huge part of that.

• 34:59Speaker 01

  Yeah.

• 34:59Speaker 00

  So yeah, that's, that's kind of what our experiments have shown, at least in terms of like the agent swarm versus like not. I think it's very clear the agent team out of Claude is an experiment.

• 35:09Speaker 01

  But Kimi.

• 35:09Speaker 00

  Show better. No, they definitely better. But the Kimi 2.5 tells you that this is already boom. Perfectly great new place to do more work on, completely available to us right now. I think it's huge because if these agents get any better, like, I don't know, I'm never going to sleep again. So.

• 35:25Speaker 01

  Honestly, like it's very interesting, this sort of moonshot AI. And this is a tangent, we're not, we're not really going to focus on this very much. But you know how like the, the sort of AI tigers out of China were, were DeepSeek and Quen? And then you were like, well, who are these like Kimi guys? And, and these, these sort of newer names, like I guess Minimax as well.

• 35:44Speaker 00

  Yep.

• 35:45Speaker 01

  Yeah. And ZAI has been, been around longer, but only recently much more active.

• 35:50Speaker 00

  Yes.

• 35:50Speaker 01

  So like I, I noticed that Kimi is much more in the productization phase, like as seen. As opposed to like the Quens of the world, the DeepSeek's of the world, who don't really care that much.

• 35:59Speaker 00

  I mean, Quen because of how it's attached to Alibaba, right?

• 36:02Speaker 01

  Like. Yeah, yeah.

• 36:03Speaker 00

  They, they have a way to productize it, but it's like, it's like kind of like the Gemini version. They have so much stuff to do elsewhere, right?

• 36:09Speaker 01

  Yeah.

• 36:09Speaker 00

  But yeah, Kimi, Kimi's pretty interesting.

• 36:12Speaker 01

  They're pushing so hard. They got everything.

• 36:13Speaker 00

  I know.

• 36:14Speaker 01

  They got Kimi Manus, Kimi Claw, Kimi Claw.

• 36:17Speaker 00

  Yeah, I know. Kimi Claw. I haven't, yeah. Dude, I was gonna say, have you messed around with OpenClaw? Cause I did, I, oh, yes. I remember, what was it first called?

• 36:24Speaker 01

  Claudebot.

• 36:25Speaker 00

  Claudebot? Yeah. Dude, I was gonna say it was really, really euphoric. I was like having it read all my emails and my calendar and do all this stuff. And I was like, whoa, whoa, whoa. This is really, really, really prompt injectable. And I was like, this is pretty secure and important stuff. So I like, I was like, you know, Claude Code psychosis is good enough for me at this point.

• 36:41Speaker 01

  I mean, so what I do is I just have multiple emails, right? And there's, there's a safer email to give to bots. And I can let it use that. And if it impresses me, then I can upgrade it. But Claude Bot didn't impress me at the.

• 36:51Speaker 00

  I'm being honest with you, I wasn't impressed either. That was the reason why people were freaking out about this molbook. I was like, bro, have you actually used this shit? Cause it's not, even right now on Claude Code in a relatively focused terminal, it will be like, oh, blah, blah, blah. I'm like, dude, in the dot env, there is an, like, in the dot env, there is an API I told you to use for this sub case of problems and it's in your Claude MD. Like, please focus up. Like, it still is like making mistakes.

• 37:16Speaker 01

  Yeah.

• 37:16Speaker 00

  This is not like truly AGI and there is harness. You still have to wrangle this thing, but it's not like a perfect skill follower. And the, the context in each attention window is gonna like change. And sometimes it'll be lazy, sometimes it won't be. But it's definitely good enough to do a lot of information with.

• 37:31Speaker 01

  Yeah, I was gonna, so I, I use, I use our, I use our Discord as basically like a, a way to just bring information in and out. I, I just saw, I saw this too where basically like a lot of people are just setting up things that they could have done in Zapier with Claude Bot. Because they're like, well, you know, now I'm like AI pilg. But actually they just done it more securely with Zapier. Like, I think it's kind of interesting.

• 37:51Speaker 00

  I guess, I, I do think it's kind of interesting, but I think there is, but the, the difference though is Zapier. I mean, I remember, I've tried to use Zapier before.

• 38:00Speaker 01

  Yeah, it's also not very.

• 38:01Speaker 00

  It's not also not very good. The difference though is like, and that's okay. Like, it's okay to be early to something and just wrong because you weren't the one that made it happen, right? Claude Bot, the Claude Code, Claude Bot, whatever, all this stuff. The reason why it's so powerful is it gets to completion, right? And, and like, okay, Zapier, maybe you can get to completion all the time, but like, man, it probably took you like eight hours of clicking through things and like copy pasting crap to make sure it all works and it's all secure. And it's like, well, Claude Bot did it, or Claude Code did it in like, you know, four and a half minutes. And that's good enough for me, you know? That, that's a faster achievement. And so like, it's totally okay that they were, they're right, but they were just not the right mechanism, right? You, you see. Happen in information like in the history of like computer.

• 38:46Speaker 01

  I think there's also like an innovator's dilemma thing where Zapier as a pre-existing business had this view of the world of automations as like very strict sort of on rails workflow type of things that their giant user base already uses. They couldn't like really pivot that much.

• 39:01Speaker 00

  Yeah.

• 39:02Speaker 01

  So that's why I like, I think like, you know, one of the co-founders left too because they were like, well, I can't exist within this like.

• 39:08Speaker 00

  Yeah, like, like, yeah, you, you end up becoming with, you know, the, the, the box will control you.

• 39:13Speaker 01

  Yeah, yeah.

• 39:13Speaker 00

  You, you are.

• 39:14Speaker 01

  It's your, it's your golden handcuffs.

• 39:15Speaker 00

  Yeah, it's just like your cage, you know, you're gonna act like how you are in the cage. And so, yeah, that, that sucks for, honestly, that's, I feel like that sucks for.

• 39:21Speaker 01

  The framing I have is like your priors become your prison.

• 39:24Speaker 00

  Ooh, that's pretty good. That's pretty good. That's pretty good. Your priors become, yeah.

• 39:27Speaker 01

  I haven't bogged that yet, but I should.

• 39:29Speaker 00

  You should, you should. Your priors become your prison. I like that a lot.

• 39:32Speaker 01

  Coming back to Cloud Code, I also wanna make this like the sort of Cloud Code. No, no, no, that, like, I wanna indulge because like that's how natural conversation goes and I think people like enjoy that, right? And probably that's the only time we'll talk, we'll talk about Kibbe.

• 39:44Speaker 00

  Yeah.

• 39:44Speaker 01

  So like, do you use hooks? Do you like, give me like the, the Doug O'Laughlin Cloud Code setup.

• 39:50Speaker 00

  I had just like essentially a few base skills and then I have a lot of APIs. And then we've also made sure to work, and this is like all work in progress as well, to have APIs for some of the semi-analysis information out as well.

• 40:02Speaker 01

  Yeah.

• 40:02Speaker 00

  And so that way we have.

• 40:03Speaker 01

  Like an internal server.

• 40:04Speaker 00

  An internal server that is, that, that is accessed by people with an API. So that like all the semi-analysis researchers are able to hit like some basic level of context, because I think the context is really what matters.

• 40:16Speaker 01

  Yeah.

• 40:16Speaker 00

  I'm too like, too dumb to be really smart in, in order to have, well, I guess, I guess I do have some hooks, if that makes sense, in terms of like.

• 40:23Speaker 01

  I think hooks are very underrated, right?

• 40:25Speaker 00

  Yeah, I, I do think.

• 40:26Speaker 01

  Because you can do like a Ralph loop, yes, with a hook.

• 40:28Speaker 00

  Yeah, yeah. I feel like I underutilize hooks. I, I think that is true, but I do, I do run some version of them on like skill calls effectively. Like, hey, on this, then you have to start pulling all this stuff. But I think in the beginning, I tried to do all this like hook stuff and like compounded stuff like that. And I found that like, you know, the gas town Ralph loop era, it's like, it, it is a sign of what will come, but I just don't think there's enough fidelity to like. Make crazy multi-turn, something happens. So like, okay, actually less is more. Try to have like a strong set of smaller skills with a good amount of context information to be pulled in. And then at the beginning of every session, ask and focus on what you want to do. So that like it prompts the like, not like, you know, a cloud within a cloud, whatever.

• 41:12Speaker 00

  So here's the goal to finish within this single context window and then get it done. And this is like my generalized research thing. Hey, I want to look at the price of NAND since nineteen eighty-four, something like that. This is what I want to do. I want to, so like the, actually no, let me just give you the best example. That is probably not going to work. I would like to fine tune a time series foundation model to predict NAND and DRAM prices. Okay? I'm going to first start by gathering as much information as possible from all this stuff, blah, blah, blah. And then we're going to fine tune it, evaluate which ones we're going to do. I chose Cronus two because of covariates, blah, blah, blah, blah. Try to set this all project up. We'll make it a Versal dashboard internally for, for semi-analysis. Maybe we'll external if we want, if it's a good enough product. Okay?

• 41:55Speaker 00

  So then it like does all this stuff and then I just like start planning away. Hey, can you go research series of search API, CERPER or Exa or whatever you want to use? To go look for all these different information sources and then bring it together. Right? So this agent goes and gathers all this information. This agent goes and like works on like considering the fact that the price isn't perfect to do all this fine tuning on. And then we like throw it in. I also had it of like, oh, what do I use? Showed me which GPU, whatever we're renting on an hourly basis. And so, yeah, we just pull all this stuff together and we fine tune it. And I'm like, okay, cool. How did this work? And then we just have this constant iterative loop until I try to finish something. I got to the point where I was like, okay, this, this time series alone is probably not going to work. Unfortunately, unfortunately, the

• 42:40Speaker 01

  You said it was because of regimes or something else?

• 42:42Speaker 00

  I think so. It's regimes. Yeah, there's no way. This regime was so messed up.

• 42:45Speaker 01

  For, for a lot of people who are like new to finance, this is why I have, I have an issue with all these kids doing like stock trading games with L L Ms. They have no idea. They, they've never studied finance. And like, like, you know, some, like a past does predict the future a lot until something fundamental change. And like the macro shifts and like risk on versus risk off. They've never heard, they've never heard those terms.

• 43:07Speaker 00

  Yeah.

• 43:07Speaker 01

  Had to explain it to people at Cognition. And like, yeah, the, the rules invert, like completely invert. Like what used to work is exactly the opposite of what you need to do in, when you have a regime change.

• 43:18Speaker 00

  Exactly. And it's very, very, very hard because, and the other thing too is you, you really like, okay, each of these, each of these are almost like a one-off onto their, onto their own.

• 43:28Speaker 01

  Right. Which reduces your sample size.

• 43:29Speaker 00

  Yeah. Which reduces your sample size. And so then at the end of the, at the end of the day, you end up being like, well,

• 43:35Speaker 00

  it kind of just like, I guess it's here's some heuristics. Good luck. Have fun. Right? Here's your checklist to see it might be over, but you really don't know anything until then. So, but, but like, okay, an example of where this project was helpful and it's like, okay, I'm not going to have the magic L L and tell me what the price of memory is going to be. Hey, it was a good weekend project and I did burn quite a few tokens. But I do happen to have, after all this like information synthesis and analysis, all of the memory prices of everything I could possibly find, plus the things up behind API that we paid for, plus, you know, enhanced data sources. And I have all the covariates. So like, hey, W F E, what was the consumer sentiment, every macro thing of all time? And you know, it's really interesting is I am going to just be like, okay, well now can you go

• 44:18Speaker 00

  make a summary of each and every memory regime and what it looked like and what, what, what created the beginning, middle, end? And put that in a dashboard. So it's relatable and like easy shareable consumable within my firm and company. Yes, I'll probably be done with that today. And that, okay, so that you're like, well, that's just gathering, doing information of like, you don't understand. No one's ever done that in the history of time. I know for a fact, as the guy who like, is like the cycle semiconductor guy, I've written and done more work on the cycles that I think anyone else has at this point, especially for like the older ones, like the eighties and nineties and two thousands and twenty teens. And like, when I did it first time, the human gropped way, brain is I went and I read these old annual reports and I put it together and I tried to string an air.

• 45:00Speaker 01

  Really through it.

• 45:01Speaker 00

  And I, and I brought through all, I'm like, okay, what was GDP this grow, what this year, what was all this stuff? And you have to like make all this giant sheet to, to whatever, and then make the narratives. No, no, no shit, dude. I mean, this is like too much information to gather. It's like a lifetime of work. It's like a PhD project. I did it in a day, two days.

• 45:18Speaker 01

  Yeah, I mean, I think the kind of pushback would be that then you don't have enough expert information to criticize the reasoning that went into the report that you're slopping out.

• 45:30Speaker 00

  Yeah, there is some slop. I definitely agree with the slop.

• 45:33Speaker 01

  You know.

• 45:34Speaker 00

  So, so I think of it once, so right now.

• 45:37Speaker 01

  By the way, that's also essential for you guys if you get caught doing like putting out some slop to your clients, right? Like you have to.

• 45:44Speaker 00

  Yep.

• 45:45Speaker 01

  At one point be like extremely AI-pilled and like, you know.

• 45:48Speaker 00

  Yep.

• 45:49Speaker 01

  Number one in the world that applying AI to your productivity, great, but also like you gotta.

• 45:53Speaker 00

  Yeah, you have to. So, so I think, I think the thing that's really interesting is this whole thing is a game of hygiene now.

• 45:58Speaker 01

  Yeah.

• 45:59Speaker 00

  Because I, I think it's like, this is really hard. I think about it all the time. I feel very comfortable with doing all this work because the thing is my, at the end of the day.

• 46:07Speaker 01

  It's done the work.

• 46:08Speaker 00

  And I've done the work. I have like a lot of like embeddings in my brain, a lot of information. The vibes that have gotten me in here is actually like tons and tons and tons of information, set up scenarios and like pattern recognition, right? But yeah, you're right. This, this crap makes mistakes all the time, all the time. It is still just like, like I think of it once again as like a junior analyst, right? The analyst goes and does all this like really pain in the ass information. You bring it all together to make a good decision at the top. But the problem is historically what happens is that junior analyst, who I once was, went and gathered all that information and after doing this enough times, there's a meta-level thinking that's happening where it's like, okay, here is what I really understand and how this type of analysis I'm an expert in, actually, I'm very good at, I consistently have a hit rate. Now I'm the expert, right?

• 46:52Speaker 00

  I don't think that meta-level learning is there yet. We'll see if L1s do it, right? Everyone who's spending one quadrillion dollars in the world thinks it will. It better, it better happen, right? If you're spending, you know, a trillion dollars and there's not meta-level learning. But for me in our firm. That massively amplifies everyone who is an expert, right? And we are a firm full of experts. And so it's this hard part where I wonder if new people, we will be less lenient in terms of like how much AI tools.

• 47:21Speaker 01

  Are you like junior or junior to the firm?

• 47:25Speaker 00

  Yeah. Or junior and like, like, cause like you have to still do something that you can't just like slop it up. It's very obvious to me when it's slopped, right? When it's slopped and there's no cognition, then it's like, like whatever the artisanal last 5% is like that really matters.

• 47:38Speaker 01

  Yeah.

• 47:38Speaker 00

  But for me, I know inherently what the 5% is. I can like write it away with some really easy heuristics in time and like be like, okay, well, this is the last 5% you fix. This is what I believe. Just fucking make up these assumptions instead, press enter. Okay, cool. We're good to go, you know?

• 47:53Speaker 01

  Yeah.

• 47:53Speaker 00

  And so that's kind of the hard part. That's a real hard part. There is still a human in the loop right now. One day, someday it will be superhuman, but I definitely believe the, where we're at today, where we're, it's not there. Like you just compound all this noise and it becomes just like garbled, just like all context is rotten. But in terms of like the capability that is over hit, like, you know, the human CPU in this, this agentic swarm is very, very powerful now.

• 48:18Speaker 01

  Yeah.

• 48:19Speaker 00

  You know, a huge, huge, huge multiplier of what you're able to do. And for me, that was enough to be like, I feel AGI pilled, honestly. Because if I define AGI as many common jobs, not like, I'm not, I'm not doing ASI that's like religion. Can it automate or change or, or take or, you know, completely shift a lot of the information work? Yes, a hundred percent.

• 48:39Speaker 01

  Yeah, yeah.

• 48:39Speaker 00

  Like data analysis is a perfect example. Hey, every quarter I want you to just find me some examples of some information that might be interesting. I just can't imagine if I was an entry level worker doing data analysis that a 22 year old, an average 22 year old would, would murder the hell out of a relatively well thought out agentic system. And so you're like, yeah, that job actually does seem at risk. And so that, yeah, that, the 4.5 capability enough like that, that we hit some level with agentically where it seems to work and do bigger information work. That's when I'm like, okay, yeah, this, this does change everything. And so, yeah, there's, there's all kinds of mistakes. I, it's a new level of hygiene that we have to do. You're gonna have to understand what. The absolutely of Gentik work is back to you, right?

• 49:22Speaker 00

  I catch it making errors all the time. It doesn't always pull skills. Like you can definitely tell, like context windows, like it gets dumber over time. It's not AGI today, but it can do these crazy long tasks. And as long as you finish it at the end and deposit as information work, that's very valuable.

• 49:38Speaker 01

  Yeah, amazing. So you do a lot of like client visits, obviously. By the way, transistor radio, amazing for like understanding like what your world is like. Yeah. Are you also Claude code pilling your analysts and your, you know, on the other side?

• 49:52Speaker 00

  I've definitely Claude code pilled the analysts. Everyone in the New York office, I'm like, must try it. I like really tried.

• 49:58Speaker 01

  Not, I mean, not your, like, not the semi analysis or your customers and all that. And like, my perception is they don't adopt any of this stuff.

• 50:06Speaker 00

  Okay, so yes and no. Some people are interested, but you have to remember it's relatively more conservative. But I think, but if you ask any analyst if they're using AI, every single one of them will tell you, yes. I use it every single day. Of course, how could I not? This is like a vital skill. And so the, the, the basic, the basic inference that I'm doing is I am a bleeding edge adopter. I'm a relatively smart dude who knows what he's doing and if a tool is used for or not. And I've evaluated the tool and I'm like, wow, this is an amazing tool that I literally like pry it out of my dead fucking cold hands. Okay? I'm like this, even if it's like makes mistakes, I will be using this for all kinds of work forever.

• 50:44Speaker 00

  Then I look around to everyone else and be like, most of these guys are enough like me that if they have an opportunity and an edge, they will obviously apply it. And they look at this tool and they start to use it. If, if they start to use it and they're thinking like me, they're gonna obviously adopt it. I'm like, well, I don't understand why everyone doesn't adopt it. I would argue, well, we'll see in the 24 month view, it will be a base level, I think. I think Claude code cowork, whatever is going to be a base level of all information work very soon. Yeah. And you know, you see one, my, my friend was telling you how his portfolio manager well have found cowork and he's like getting it to read his emails. And he's like, oh my God, I love this, right? Everyone's moment is going to be a little different, but I think my moment, it feels like GPT 3.5 or four for me.

• 51:29Speaker 00

  Where there's that first time where you're like, okay, I know I made some shit up, but like, this is better than like if I went for hours searching, putting information together, it can, and then also like the analogy power, you know, where you can say, hey, this is the setup. Can you describe it in this? These like really strong pattern matching skills that are really powerful. I just think it hits some level of capability. I can't tell you what it is. It is like my taste, my personal taste where I'm like, oh, wow. This is completely over the chasm of what needs to happen for it to be a very, very powerful tool. And so, yeah, that's my Claude code moment, I think.

• 52:01Speaker 01

  There's some kind of automation chart that, you know, XCCD has this.

• 52:05Speaker 00

  Yeah, yeah, yeah.

• 52:06Speaker 01

  And I think we need a version of this that is the Claude code, like.

• 52:09Speaker 00

  It needs to be much, but what's crazy is this, the Claude code thing like murders the access.

• 52:14Speaker 01

  Exactly. It just shifts everything like right or something.

• 52:17Speaker 00

  Yeah.

• 52:17Speaker 01

  But also like what I was trying to figure out is, well, okay, it is maybe dumber, less, less human attention. But because you can spin it up so quickly and it can spin parallel so quickly. And it gets that, you get more turns at the wheel.

• 52:30Speaker 00

  Yes.

• 52:30Speaker 01

  Whereas in, as a human, you'd get one turn.

• 52:33Speaker 00

  You get one turn.

• 52:34Speaker 01

  But with Claude code, maybe you get three turns. And the sort of review process is the thinking.

• 52:39Speaker 00

  Yeah.

• 52:40Speaker 01

  And you just need to get very good at review or hygiene.

• 52:43Speaker 00

  Yeah, I think of it as hygiene. The thing that's like really going to be painful though is like, a lot of my expert opinion has been built by like, you know, it's like pre phones and not, right? Like your attention span, like, you know, the children are cooked, okay? Like, you know, the attention spans are really bad, all this stuff. Like, I read this like really sad thing, be like, oh, we're getting dumber or something, first generation. I don't know. I'm not gonna, maybe it's like.

• 53:05Speaker 01

  You see that the Coinbase earnings.

• 53:07Speaker 00

  Yeah, I saw the Coinbase. So it's like, you have this thing where it's like, okay, and it's cute and all, but like, it's such an addictive technology that like, I feel very grateful that I'm like, well, I understand what I'm doing, have this history of doing stuff and able to apply a tool. But like people who are riding this curb, it's gonna be very dangerous. It's like giving everyone.

• 53:23Speaker 01

  Right now, I'm struggling.

• 53:24Speaker 00

  Yeah, that's so funny.

• 53:28Speaker 01

  I think you should just do that.

• 53:29Speaker 00

  Yeah, we do. We do with some of the semi analysis memes, you know? And the thing is, you say some of this brain rot is like so bad, which is is terrible. But some of it is also like, you know, it is hitting some attention mechanism. In my, in my, my deep primordial monkey brain.

• 53:45Speaker 01

  Stimming you.

• 53:45Speaker 00

  Yeah, it's stimming me and you're like, you know, I can't look away from the subway surfers, so.

• 53:51Speaker 01

  You couldn't look away. I had to pause it.

• 53:53Speaker 00

  Yeah, I was like, I literally, I will. Well, hey, there's like, have you ever been at like a bar where they play like these like weird? They'll be like, like TikTok videos for lack of whatever and you just watch.

• 54:03Speaker 01

  There's TikTok bars in New York?

• 54:05Speaker 00

  Not TikTok bars, not TikTok.

• 54:06Speaker 01

  Okay.

• 54:06Speaker 00

  It's like, there, there's like essentially a B-roll channel that they'll like sometimes play in public spaces. And you will just find yourself like being engaged with it. Like there are certain things that just, it works. So. Yeah, sorry, that's completely off. But, but I wonder. This quote code pill is very powerful for me. I believe it will change how it all works. It'll shift all of that over massively. The, the chart. But it's just really weird because if you didn't pay any like human cognition to get there. I don't think you're going to be a great reviewer. One of the reasons why you know what, what makes that, that human feat, that loop? Well, is because once upon a time you did that and you could make the three like. Yeah, idiot, you're not thinking about this problem in this way. You're missing this, this.

• 54:51Speaker 00

  Like, you know, whatever. You're not considering this 90%. You know, like the 10% tail, something like that.

• 54:57Speaker 01

  Yeah.

• 54:57Speaker 00

  And so it's like, yeah, I know you said this, but like, you know, I, I know you, I know that I told you the valuation is the only thing that matters. But like, it's also a fraud. You can't do both, right? Like if you think about like the analysis stuff. You have to know when your own personal embedded model is like, yeah, actually this one overrides this one. And that, that's through learned experience. And I wonder if we're just reviewing, we won't be building and embedding those assumptions to understand judgment.

• 55:23Speaker 01

  Right, right. Because you're just checking for mistakes rather than trying to do original thought. By just doing the work.

• 55:31Speaker 00

  Yeah.

• 55:31Speaker 01

  Yeah, I think that's, that is, that is a danger.

• 55:33Speaker 00

  Yeah, and that's what hygiene sounds like to me. Like, hey, it's really addicting to be like, you know, whatever, press the button over and over and over. But sometimes you do actually have to like think, you know, so. So I think that that's it's gonna be really interesting.

• 55:46Speaker 01

  I mean, have you tried like so? So I mean, the way to model the sort of meta learning as element is like once a night, you do a batch job of like, look over everything I've done, like extract some learnings. You know, and open claw, I think one of the interesting things I really liked about it was this heartbeat.

• 56:03Speaker 00

  Yeah, heartbeat.

• 56:04Speaker 01

  And I'm like, people aren't like excited enough about this because like, well, this is the first instance where like the agents are just always on, like always living, always reflecting.

• 56:12Speaker 00

  Yes.

• 56:12Speaker 01

  And.

• 56:13Speaker 00

  Like, what is it sold on MD too?

• 56:15Speaker 01

  Sort of thing is much more for character and like whatever, but like, yeah.

• 56:18Speaker 00

  Yeah, it's heartbeat.

• 56:19Speaker 01

  Heartbeat is the crown.

• 56:20Speaker 00

  Yeah, I mean, I think, yeah, that's a good way to put it. Yeah. And so like, that's the powerful thing about all this stuff is that like, okay, yes, we know that the contact like it gets garbled. We know that open, open claw doesn't always do everything you ask to it, ask it to do initially. But you can see the design patterns, like the heartbeat MD is a perfect example, can see the, the design patterns where it's like, well, you know, is all of our tasks every single day actually us having this like genius thing? Or do we like sit down in a single session, finish a single project and get up and get some coffee, then come back? If it's that and you could just fuck, you can make the heartbeat dot M D, consider the like the session to session and like, hey, meta learnings, all this stuff.

• 57:00Speaker 00

  And it's only specialized and focused on one form of doing something. So it actually does have a context of all the like, let me, I'm thinking like a customer service agent or something like that. It does have a context. In fact, it can look at every single time it's ever happened. That's actually information and context. No human could ever hold. You're like, wait, that, that feels like a G like, like that's effectively good enough to do a huge information.

• 57:23Speaker 01

  Yeah.

• 57:24Speaker 00

  And have enough context and be able to fetch it. And maybe like, there'd be some verification to make sure it doesn't just totally mess it up. But that to me feels like a design pattern that you can build something on. And so that's the, that's the vibe is that we've hit some capability that you can, you can do, you can build these much bigger blocks now. And those bigger blocks are not just like the single line of code. It might actually be a business. It's kind of crazy. Like I, I wouldn't have put myself as AGI pill. I think 4.5 is like actually.

• 57:52Speaker 01

  I think my own timelines have moved up a lot.

• 57:53Speaker 00

  Yeah.

• 57:54Speaker 01

  Are you guys watching GDP Val?

• 57:55Speaker 00

  I to the best I can, but I feel like I'm mostly just trying to.

• 57:59Speaker 01

  No, no, no. To me, when GDP Val came out, so I all just.

• 58:03Speaker 00

  Yeah.

• 58:04Speaker 01

  GDP Val is like a basically a like a broader street bench, let's call it, where it's applied on every discipline, every profession that is white collar that you can model. And it is above like something like two to five percent of GDP, something like that. That's why it's called GDP Val. And they they had human experts do the task and as well as UPTs. And here's the results, right? Like, and where fifty percent is parity with industry expert.

• 58:28Speaker 00

  Yeah, yeah.

• 58:29Speaker 01

  Coin flip, exactly. Where so like you can see the the nice increase from four oh to Opus four one. And since then, obviously, five two and Opus four five have already exceeded. We're at seventy something now.

• 58:40Speaker 00

  Yeah.

• 58:40Speaker 01

  Which means models are consistently better than industry experts.

• 58:43Speaker 00

  Yes.

• 58:44Speaker 01

  At these things.

• 58:44Speaker 00

  Yeah.

• 58:45Speaker 01

  So to me, like, this is the AGI definition, isn't it?

• 58:48Speaker 00

  Yeah, yeah, this is this is. And so like, I think I think the problem now, yeah, I would say that that is the definition. So the thing that's crazy is because there's like this ASI element that people are like really, really focused on.

• 58:58Speaker 01

  We're moving to goalpost.

• 58:59Speaker 00

  Yeah, we're moving the goalpost, but I'm like, bro, I the the goalpost like I I mean, we'll see if this is actually the machine god. And Shogeth will come and talk to us and vibrate on our same.

• 59:08Speaker 01

  But what if I do think so?

• 59:09Speaker 00

  I was okay. I I don't, I I mean, honest with you, I'm very open. I will change my mind often. I'm not, this is not something I feel intuitively I got today. Maybe it's the next next next thing. But when it comes to like the the GDP Val version of this, yes.

• 59:23Speaker 01

  Yeah.

• 59:23Speaker 00

  This is this is.

• 59:24Speaker 01

  Do white collar work.

• 59:25Speaker 00

  The white collar work, which is most of the most of the time.

• 59:28Speaker 01

  Very boring, yeah.

• 59:29Speaker 00

  Knowledge more like like actually, it's almost all, not almost all, but it's a huge portion of all of work in the world.

• 59:35Speaker 01

  Yeah.

• 59:35Speaker 00

  It's like now we just made like my favorite stat is like, once upon a time, ninety percent of people were farming, right? Now, today, less than one percent of people are farmers. It's kind of like this crazy shift where technology is going to massively change the relationship with all of that. And it's going to be like this ninety nine one thing. I don't know if it'll be quite that drastic or whatever. Maybe, you know, everyone's just doing leisure. So far, my experience is everyone just works harder and it's been my experience. But it just it just feels. Like a massive moments happen like the the steam engines invented and you know the the trains are here and and everything is gonna change in knowledge work. And it's kind of crazy.

• 1:00:13Speaker 01

  This is this is an economic cycle from my macro days that I'm I can't remember the name. I can't look it up. But it's basically like there's this stages of economic development where like your your economy starts out majority agriculture. Then it discovers like manufacturing, then it discovers white collar work, then it discovers they they build like a very mature financial sector. And like though these are like like a layer cake that all declining over time.

• 1:00:38Speaker 00

  Yeah.

• 1:00:38Speaker 01

  Then the new thing is increasing. So my my theory is like there's this like fifth layer that's like has to open up. That starts to happen because I do fundamentally believe we'll just invent new work.

• 1:00:47Speaker 00

  I do believe that, a hundred percent. Like humans are very adaptable. That's like my favorite thing I've learned. You're able to adapt to God like coldest, coldest place in the entire world, the warmest place, humans are in every latitude. That's in a physical sense, but I think we're gonna find a way to make utilization go up, but we'll we'll we'll invent more work for sure. But I think the thing that's crazy is just like things change so quickly. And that five to 10 year period, like 10 year gap can be drastic and crazy. And that's just societally wild.

• 1:01:19Speaker 01

  But yeah, it's happening in our lifetimes.

• 1:01:21Speaker 00

  It's happening in our life very like it's like happening like right now. Like it's it's it's really crazy. It's like very. And like, no, so this is like a complete side task on.

• 1:01:29Speaker 01

  Yeah, yeah.

• 1:01:30Speaker 00

  I'm like really curious of when we start to see it in a much bigger way in the real economy. That's like my my my pet.

• 1:01:35Speaker 01

  Yeah, why is it not showing on GDP yet, right?

• 1:01:37Speaker 00

  So there's gonna be, you know, some people are gonna be like, oh, you know, the fax, fax, the internet, same thing, information transfer, whatever. I think I'm actually scared for a third worst thing, which is like now now this is a complete crackpot theory. Please don't hold me to this internet. But what if AI is massively deflationary? And and also, I think one of the more interesting conversations I've had in a bit is like what was GDP was invented once upon a time. As a way to figure out how much we could divert, you know, normal economy away just to war during World War like one or two or something like that. My spiciest take is I feel like GDP itself is gonna be very, very challenged by AI because information work.

• 1:02:19Speaker 00

  Yeah, so how we how we capture it effectively is all of an economic good. And then the service hours divided by hours. Okay? So there isn't like a widget to widget difference. But in theory, if we could break all of information work down into units, we're gonna have a lot more information work for sure. Like more work will be done. I don't know what the value that's gonna be. Is it gonna be so much increase in supply? It's deflationary? That seems to be like a real concern.

• 1:02:48Speaker 01

  It's possible.

• 1:02:49Speaker 00

  Yeah.

• 1:02:49Speaker 01

  And then we'll figure out how to use it, but like there may be a great depression of AI.

• 1:02:55Speaker 00

  Yeah.

• 1:02:55Speaker 01

  Where like we figure it out.

• 1:02:56Speaker 00

  Yeah, well, I I wrote this whole thing about railroad stuff because it's my favorite.

• 1:03:00Speaker 01

  Okay.

• 1:03:00Speaker 00

  My my favorite capital cycle.

• 1:03:02Speaker 01

  It's on Fab or?

• 1:03:02Speaker 00

  It's on Fab, yeah.

• 1:03:03Speaker 01

  Okay.

• 1:03:03Speaker 00

  I can't remember. It's like railroad, Fab. It's about all the railroad stuff over time. Okay? Pretty much because we're like everyone was first looking for the internet. We've well massively passed the internet in terms of the absolute size of the build out. It's not even close like.

• 1:03:18Speaker 01

  What what numbers are you thinking of like?

• 1:03:19Speaker 00

  I think I think a trillion was a trillion all in was essentially the real dollars for.

• 1:03:24Speaker 01

  Okay.

• 1:03:25Speaker 00

  Version. And I think we are well past like we like whatever this year and it's cumulative, right? We will well past that. I think railroads, the reason why it's so interesting is because honestly, it's way crazier. But but part of the problem and craziness of it too is like railroad was literally like one of the first added layers of the layer cake. If you think about it before it was agriculture and railroad was like, okay, well, how do we move this agriculture around faster?

• 1:03:47Speaker 01

  Yeah.

• 1:03:47Speaker 00

  And then banking got, I I kid you not, like one of my big takeaways is banking effectively got invented by railroads.

• 1:03:53Speaker 01

  Oh.

• 1:03:53Speaker 00

  Because there's no need to finance it. Yeah, so much money was needed that like effectively eighty five percent of all paper or whatever was essentially just railroad debt.

• 1:04:02Speaker 01

  Yeah, one of my favorite anecdotes was before there was a federal bank, a Federal Reserve. Andrew Carnegie was the Federal Reserve.

• 1:04:08Speaker 00

  Yes, yeah, there were individuals. Yes. Yeah. And so all this stuff. So it's like the whole thing, I kind of. Did some work on the Gilded Age, all this stuff, but like my takeaway is like that was a really interesting cycle because it was so big and took so long to deploy. It actually was forty five years of like, there's three cycles, actually. There's three boom busts.

• 1:04:26Speaker 01

  Same.

• 1:04:27Speaker 00

  I don't know if it'll be quite that long. All the cycles kind of collapsed.

• 1:04:30Speaker 01

  Yeah.

• 1:04:31Speaker 00

  Or that.

• 1:04:31Speaker 01

  Because, you know, information.

• 1:04:33Speaker 00

  Information.

• 1:04:33Speaker 01

  Moves around faster.

• 1:04:34Speaker 00

  Exactly, yeah. And so you, you have all this stuff where I think it's gonna happen faster, but like I would be really shocked if it was all in one go. That's my vibe.

• 1:04:41Speaker 01

  Yeah.

• 1:04:42Speaker 00

  Where it's like, it's all in one instantaneous up down. I think it's gonna look like some multiple cycles. So yeah, kind of just wrote about railroads. There was like a baby railroad cycle. Then there was a huge railroad cycle, the modern railroads invented out of it. That's like my favorite analogy for this because like, I think it was like GDP percentage of capex each year, where like high single digits sustained for like ten years.

• 1:05:04Speaker 01

  Yeah.

• 1:05:05Speaker 00

  But what's crazy is like that amount of spend is like, we're like well on track for that.

• 1:05:09Speaker 01

  Did you do a percent of GDP? Cause I think that's.

• 1:05:11Speaker 00

  I think.

• 1:05:12Speaker 01

  The, the way you make it convertible. Stargate itself, two percent of US GDP. And I mean, it's gonna go up, like.

• 1:05:21Speaker 00

  Yeah. Yeah, that's a, yeah. And it's not all gonna be in one year, right? But it's, okay, so yeah, so, so total capex, four, it was four point eight percent of GNP and twenty five percent of total gross fixed capital investment.

• 1:05:36Speaker 01

  Okay.

• 1:05:36Speaker 00

  So twenty five percent of investment every year and four, five percent of GNP.

• 1:05:41Speaker 01

  I think we're there, you know.

• 1:05:42Speaker 00

  Yeah.

• 1:05:42Speaker 01

  Stargate plus entropic plus whatever.

• 1:05:43Speaker 00

  Yeah, we're, we're right there.

• 1:05:45Speaker 01

  XAI.

• 1:05:45Speaker 00

  Yeah, so we're at the railroad buildup. Cause like at one point like, but the thing is crazy.

• 1:05:49Speaker 01

  We, we should exceed it.

• 1:05:50Speaker 00

  Probably, yeah.

• 1:05:51Speaker 01

  Yeah. No, no, not probably, like we should.

• 1:05:53Speaker 00

  Yeah. Okay.

• 1:05:53Speaker 01

  Like this is bigger.

• 1:05:54Speaker 00

  Yeah. Okay, I, I'm, I'm like, I, I would like to say, yeah, sure. I, yeah, we, we will do it. I, I'm worrying, I'm like, dude, we're gonna get all the money. That's like such a like, the pedestrian concern. Yeah, it, it's not a pedestrian, I mean, that's what happens every capital.

• 1:06:09Speaker 01

  I'm worried like, we must.

• 1:06:10Speaker 00

  Hands in the Middle East, you'll fit the thing. We must, we must, the, what, what, this happens every single time. This is the reason why like bubbles happen, right? It's like, we essentially get so big, we're like, this must be built. It doesn't matter the price. And then all of a sudden we look at it, it was like, ooh, that was a steep ass price. But I think. I mean, the thing I think about this is like how I think of the big picture is there is a demand curve and a supply curve. And we have no idea when they cross. They will cross one day. And every single year, the demand, we're finding that demand curve and then the supply curve, we're just like, we're doing our best to deploy it. And I think for me, like, I don't know when that number is. I'm not, I don't want to say a number go up forever because I feel like that's like intellectually dishonest. But Quadcode for me is the first time where I'm like, and we're bringing it all back together, where you're like, demand go up so much.

• 1:06:54Speaker 00

  I am now guzzling as an individual, like, for example, I'm, we're, we're off, I'm off max. It's not enough. It's not even anywhere near enough, like.

• 1:07:02Speaker 01

  I mean, some people buy like five maxes.

• 1:07:04Speaker 00

  Yeah. Well, yeah. So, so I, so I'm on fast, I'm on fast with one million on API. Which is, that is like an addiction level of any sense. But yeah, I, I, I really think it's the first time where like, okay, well, actually, how much is this worth to me on a yearly basis? I think it's like 20 to 30,000 dollars easily. Yeah. Like if not more, like, I don't understand, like, what's the, like, I can't price it. I have no idea of the elasticity.

• 1:07:26Speaker 01

  Yeah. You pay for a perfectly compliant junior analyst.

• 1:07:29Speaker 00

  Yeah.

• 1:07:30Speaker 01

  Right? And so what's able to that cost like 90K?

• 1:07:33Speaker 00

  That's able to work in parallel.

• 1:07:35Speaker 01

  Yeah.

• 1:07:35Speaker 00

  Like you can have a hundred of them. It's kind of crazy. Yeah. So it's.

• 1:07:39Speaker 01

  It's a skill issue if you cannot manage a junior analyst that is 20K a year.

• 1:07:43Speaker 00

  Yeah, 100%.

• 1:07:44Speaker 01

  Which like, I mean, okay, like, you know, skill issues, like it's your fault, but no, like, we have to learn how to do this. It's, it's.

• 1:07:49Speaker 00

  Yeah, it's like.

• 1:07:49Speaker 01

  It's two, it's three months old.

• 1:07:51Speaker 00

  Exactly. It is, that's the correct way of putting it. It's like, it was definitely a skill issue. That you didn't know how to get like your settings on your iPhone to work, you know, at one point, one point in time, but like in the very first month of us having it. No one's going to be like, yeah, you idiot, you rube. You don't know how to use your completely new technology that got birthed last month.

• 1:08:11Speaker 01

  Yeah.

• 1:08:11Speaker 00

  I think it's just about a, it's a bit of time and it's like kind of interesting because you're like watching. I mean, it's cool is that if you're like on this acid bleeding edge, you get to see the design patterns like blossom in real time. And like we have this like really older guy who's like been through the history of technology since like forever back then. He's like one of the most interesting, intelligent people of semi analysis. And he talks about how.

• 1:08:33Speaker 01

  Who is it?

• 1:08:33Speaker 00

  Tanj. Okay. So like he said this like. We had the conversation one time, he was talking about like early internet, how like it wasn't actually sure if the browser was gonna win. It was like a remote web file service. Some people thought like, well, I'm just gonna reach and play with someone else's web files remotely, right? Who knows, right? And that kind of, you know, it kind of is a remote web file. Who the hell knows? There were design pattern searching back then. And I think we're at that again, where all the design patterns are open. And it's like really interesting because there's many different ways this could go. And we're gonna have to kind of collectively agree what's the best set of hygiene, set of design patterns? What's the level of abstraction? And then like all the rest of how much SaaS it will disrupt, all, everything else, who the hell knows?

• 1:09:18Speaker 00

  But you get to watch it like front row seat right now.

• 1:09:20Speaker 01

  Yeah, I mean, my biggest one, and I do wanna bring it to Sami's in a little bit, but is the IDE. Two months ago, they, we had Steve Yegi from Gastown talk about how 2026 would be the year IDE died. And I, like, two weeks ago, three weeks ago, I recently was like, shit, he's absolutely fucking right.

• 1:09:39Speaker 00

  It's over.

• 1:09:41Speaker 00

  I'm really wondering too, because like IDE, so I think that same, my, like, my, the reason why I'm so excited about this is I get to like, look, I never, my daily driver was never an IDE, right? My daily driver was like Bloomberg or Excel or something like that. But I have a personal belief, it's not happening yet. Because we're not quite there in the maturity curve, like software is just gonna be first. But the year like of, you know, Excel is dead for finance, like it's, it's.

• 1:10:08Speaker 01

  Excel is the IDE for analysts.

• 1:10:10Speaker 00

  Excel is the IDE for analysts, Bloomberg is the IDE for analysts. Like, I believe every one of these IDEs are done. It's dead over and dead. I just think it's, why? Why? It doesn't like, just imagine the concept of you, like I remember when I learned Bloomberg, I had to like watch videos to learn about all the random subfolders, keys, how to use this, how to use this, you know, the tactic knowledge of using this function versus that function. That's like crazy to think about. That is like, that is like horse and buggy, okay? The agent with the information that can perfectly retrieve and analyze stuff is gonna have the ability to to to pull that all together in a better UI than it was with no legacy, whatever. I think all of that is dead.

• 1:10:52Speaker 00

  And like, this is why I'm like, my my spiciest take of all is like, Microsoft is a lot to lose. I think they have the most to lose of everyone.

• 1:10:59Speaker 01

  Yeah.

• 1:10:59Speaker 00

  Because Excel is a human IDE for information work that's generalizable. So is PowerPoint, so is email. Those are the base core level of abstraction that decided to be broadly generable. But I I just don't think that matters anymore. I think Claude Code or CoWork or whatever is gonna be the year that like it will destroy all of that. All that information work that that where you sat every single year, it's it's over. I think that's the the one that's like more shocking and scary that like people don't believe like. I believe in my stomach with conviction because I have already had that moment for me.

• 1:11:30Speaker 01

  Yeah.

• 1:11:30Speaker 00

  I will never make a chart in Excel again. I actually.

• 1:11:33Speaker 01

  Wow, yeah. It's hard to let go because I I have so much like ingrained knowledge of of like manipulating things directly in in Excel. Bloomberg, I have, so there's no way that you know this, but like my very first startup was that attempted Bloomberg killer. Sent to a decay office, I remember, I remember.

• 1:11:48Speaker 00

  Yeah, yeah, no, no, I.

• 1:11:49Speaker 01

  Oh, yeah, yeah, you're one of the few.

• 1:11:51Speaker 00

  I was a sent you, I was a sent you customer.

• 1:11:53Speaker 01

  You're a sent you customer.

• 1:11:53Speaker 00

  I was a sent you customer, like a roll in, dude. I remember, like, how dare they acquire sent you.

• 1:11:58Speaker 01

  I had a I had a patent. I we filed for a patent for similar tables. Anyway, one of my conclusions was like Bloomberg is just like three things. It's it's it's Slack and it's the journalism, which is amazing. And then it's the the data feeds. It's actually not really the UI.

• 1:12:10Speaker 00

  Yeah.

• 1:12:10Speaker 01

  It's not really the UI.

• 1:12:11Speaker 00

  Yeah, but I I think for the first time in my life where I just think that like, I just wonder if that like, okay, if you can get, obviously, so you're telling me that the future, the undisputable future is just like, it's IB and nothing else, and then like a terminal that like types in some stuff. I think that if you are marginal and only curious and not hyper interconnected, which I would argue that I am at some analysis, like, for example, I'm trying my absolute best to just rip Bloomberg out. We're going to fax it API like all, all in API with a Claude code is my belief of the future. Hey, verifiable data source that you trust.

• 1:12:44Speaker 01

  Yeah.

• 1:12:45Speaker 00

  Hey, scale.

• 1:12:45Speaker 01

  For you guys, you can do it. Yeah, for traders, we're so.

• 1:12:47Speaker 00

  For for trade, no way. I understand, like, there's. There's an information network that's like outside of this. Then you do deals in IB, right? Which are tracked by the regulators. A hundred percent, yeah, a hundred percent. It's totally, I completely. But as an analyst, yes. As an analyst, yeah. And so like, but I just think that like, okay, that doesn't really, so you're right. The core cash flow, cow thing will continue onward. But like each iteration of this AI thing, I was like, yeah, I'm still gonna be using Bloomberg, right? This first time was like, actually, no, I don't care anymore. The IB is my util, uddles of like marginal value from, from IB is like now outweighed by how like clunky this is. And I want to just make some charts, right? And so that's like, immediately you save ten to twenty K. Yeah. For switching down. Yeah, there you go.

• 1:13:28Speaker 00

  It's amazing. Yeah. But by the way, what was your Claude code end of year prediction, twenty five? Yeah, I want you know, I sandbagged the ever living share of it. Oh, okay. I, I just believe twenty five is very, like I, like it's like a, the rate it's on is like, whatever, fifty or something like that. But I think, I feel, I wanted to give a ninety five confidence interval. I think twenty five is within the ninety five confidence interval. Sure. So it's, so it's between twenty five and fifty. Something like that, yeah. Yeah, this is absurd. But you know. And it could also be Codex. Are you watching? So to be clear, I, I'm actually even willing to comment on that. Cause like, I know we've gotten a lot of shit of being Codex haters. Yeah, I, I think, I, I'm, by the way, when I put Claude code, Codex agent, whatever, all in percentage that we can publicly see.

• 1:14:11Speaker 00

  I would argue the, for the ratio outside of that's probably going to be higher too, but whatever. Yeah, I think together, yeah, we're watching Codex. I actually think Codex is, Codex is pretty good. Five point three, I think. So we had the whole thing. I, cause like I wrote most of the articles, like, oh, token efficiency, the context draw, all this. The same one or? Yeah, it's in the bottom. It's in the, it's in the paid section. Okay. So but like TLDR, I was like, well, you know, the reason why Claude code is so good. Anthropic is so good is because all of this token efficiency, the token efficiency is better than ChatGPT, all this stuff, blah, blah, blah, blah. And then like five point three Codex came out and it's like, yeah, that, that completely doesn't matter anymore. They're like, they're, they're so back. I really think five point three Codex is awesome in, in coding though. But you can watch it, like the reason why I like Opus four point six so much is because when I'm using it, I'm using it for like coding.

• 1:14:56Speaker 00

  In, is the way I interact with it, but I'm using it for broad, generalized. Information work, right? But I think the difference is Codex wants to code because it's RL to be so good at coding to win on Sweetbench. That like you're trying to use it for general information. Like, hey, I'm trying to, can you go research and search all these websites? And like, I don't even think they have web search in it or whatever. Maybe, maybe you can give it an API or whatever. But it's like, great, I'm, I'm scraping, I'm creating a piece of scraping software to go look at these websites. I was like, no, no, no, no, just like, just ingest tokens of what's on the website. It's like, okay, great, I'm still, like, it's so coding pilled on the RL. That I think it isn't generalizable in the way that that 4.6 is. Where it's like, oh, I could have it, I could have it make some rubric or do some research or do something like that versus Codex.

• 1:15:39Speaker 00

  It's very, it's very, coding Codex is coding pilled. And so that's what, but I am very optimistic actually on Codex and we do track quite a bit. I, you can, they have a meaningful amount of thing. Share, you could see the Bloomberg, they have the, the chart, bloomberry.com. So the Claude code definitely is in the lead, but I think the part of it too is like the, the like to like comparison. There's a ratio of Codex that's not available because it doesn't sign off every commit. It does sign off on pull requests. That, that ratio is much closer. So all the OpenAI people like Rune will tell like, blah, blah, we're not accounting for it. Yes, we didn't account for it, but like, I, I, I think Codex is better.

• 1:16:19Speaker 00

  I think there is some real problems and issues, but I bet you the second that they have a new pre-train with the RL. Because the RL stack on Codex 5.3 is amazing, like it's very coding code. That's when, that's when the, it flips over. And yeah, look at the other players here. It's just like, I mean, my favorite thing is that how GitHub Copilot is like number one. And like, I've never heard of like, do you know anyone who uses GitHub Copilot?

• 1:16:42Speaker 01

  Yeah, look, okay, that's, that's a bubble talking, right?

• 1:16:45Speaker 00

  Okay, that's a bubble?

• 1:16:46Speaker 01

  Yeah.

• 1:16:46Speaker 00

  Yeah, that's, that's the, that's the SF bubble?

• 1:16:48Speaker 01

  Yeah, yeah, like, you know, there's, there's like all these Windows users and you don't talk to them, right? Like you do, but we don't in San Francisco and like, that's just, that's fine. That's definitely bubble talking, but yes, Copilot has a billion in ARR, I think at least.

• 1:17:01Speaker 00

  Yeah. What's crazy is Claude code is, has a ratio, their attribution of Claude code and ARR is 2.5.

• 1:17:07Speaker 01

  Yes.

• 1:17:08Speaker 00

  So that on this, the daily install counts, right? Is an order-

• 1:17:12Speaker 01

  Which is, by the way, just the VS Code extension.

• 1:17:15Speaker 00

  Yeah, I know, I know.

• 1:17:16Speaker 01

  That's not even a default way to use Claude code.

• 1:17:18Speaker 00

  Yeah, you're right, you're right. Well, CLI, MPM, Domo is another way to track it. But I think they have like their own, their own installer now. Anyways, all in all. Definitely heard, understand it's very hard for us to like actually track it, but like.

• 1:17:32Speaker 01

  I'm not, I'm not criticizing. I'm just like, I, I think Codex, a big thing I'm watching as well is Codex back. Because they, they reported like Jan to Feb, they doubled users.

• 1:17:41Speaker 00

  Yeah.

• 1:17:42Speaker 01

  Okay.

• 1:17:42Speaker 00

  So, so I have some not skepticism, just because they have such a big chat GPT portal. That could be like Tri Atlas, like the modal that pops up can really move big users. Like they're not quite a Google.com in terms of having so much ability to like siphon off users off. But I, I wonder like the like to like, but, but that's like my skepticism.

• 1:18:04Speaker 01

  Oh, and I have an answer for that. Alexander and Bericles was just on the Lenny pod. Saying that they actually haven't invested enough in the web experience. So like, I, I, I think, I think the attribution for that is zero.

• 1:18:15Speaker 00

  Okay, yeah. I guess I just saw a modal be like, oh, try code, try code. I mean, but the, but a modal isn't. And, and to be clear, Codex in the, in Mac is great. I'm actually, I mean.

• 1:18:26Speaker 01

  Yeah, yeah. It's, they actually were the app, the, the app launch.

• 1:18:28Speaker 00

  The, the app launch is actually pretty good. So, so yeah, and, and I think. I'm pretty bullish that honestly, especially for coding, because it's like very coding code. I just can't get it to work as well for non-coding stuff.

• 1:18:39Speaker 01

  Then my, you know, you use Conductor?

• 1:18:41Speaker 00

  No, I've not used Conductor.

• 1:18:42Speaker 01

  Oh, okay. I, I thought I heard you say on a podcast.

• 1:18:44Speaker 00

  No, I've not used Conductor.

• 1:18:45Speaker 01

  So, so basically like the, the, the argument for any, like any first party app. Is that they're only going to prefer their own first party.

• 1:18:53Speaker 00

  Yes, a hundred percent.

• 1:18:54Speaker 01

  Which like.

• 1:18:54Speaker 00

  They're already. They're already doing it. Like, like, I feel like this is how they're going to differentiate, right? Like they're going to.

• 1:19:00Speaker 01

  Well, then, then you have a conductor where you can use Codex and Claude code for different tasks as you see fit. And so this is the, the clean superset. No?

• 1:19:09Speaker 00

  In theory, yeah. But, but, I mean, this is like, okay, so, so, so then you can argue this is the clean superset. It feels kind of like, I guess my design pattern on that is really skeptical. Of building on top of something that is growing very quickly and has more money and whatever. Like I just think my favorite one is like platform as a service. If you remember that one is like infrastructure as a service. Platform as a service, SaaS, software as a service. And I'm like, oh, this platform is a service. And it's like, it always just ends up being in the middle. So it just gets even by one or the other. I, I think of that like middleware layer, unless if it's a really, really, really compelling case. Often dies, but that being said, in this moment, I agree. I actually, I like to have them like review each other, like having them yell at each other is really great.

• 1:19:51Speaker 00

  I might actually try this soon. I haven't used, I haven't used Conductor personally.

• 1:19:55Speaker 01

  Yeah.

• 1:19:55Speaker 00

  I'm mostly just been, you know, going deeper into the psychosis.

• 1:19:58Speaker 01

  Yeah, and this is, as a former cloud analyst, very typical of like, do you want a multi-cloud? Or do you want to go all in one cloud? And the, the, the classic argument for multi-cloud is, well, then you can use the best of everything.

• 1:20:09Speaker 00

  Exactly.

• 1:20:10Speaker 01

  But if you go all in one cloud, you can exploit.

• 1:20:12Speaker 00

  Yeah.

• 1:20:12Speaker 01

  The sort of minor features of everything and, you know, the, it makes a market and you, there's no right answer for everyone.

• 1:20:19Speaker 00

  Exactly. Yeah. Yeah, I mean, it's, yeah.

• 1:20:22Speaker 01

  That's one, that's one of those things where I like, even the really small percentages in AI still really matter because they're, they're huge and like, yes, or very happy, very productive, very money.

• 1:20:31Speaker 00

  Okay, it's good to be an analyst in this space because it's fun to keep up with it, right? Like, I agree, like, I, I think everything, everything.

• 1:20:38Speaker 01

  We like the horse race.

• 1:20:38Speaker 00

  Yeah, I like the horse.

• 1:20:39Speaker 01

  Number one, number two, ooh.

• 1:20:40Speaker 00

  Yeah, yeah, but, yeah, no, no, I know, but then you have to, your brain also has to be like, number two is really big too. And then I, I just think, like, for me, someone who likes the history of all this, like, likes history of innovation and competition and disruption and stuff. Likes new technology. It's like a very fun time to be following this stuff altogether.

• 1:20:58Speaker 01

  Tech during the like 2017 and 2020 years, so boring.

• 1:21:03Speaker 00

  Yeah.

• 1:21:04Speaker 01

  At least for me, anyway.

• 1:21:05Speaker 00

  I thought it was pretty boring too.

• 1:21:06Speaker 01

  Yeah.

• 1:21:07Speaker 00

  Sorry.

• 1:21:07Speaker 01

  I was, I interrupted you in mid.

• 1:21:09Speaker 00

  No, no, I, I remember what I was talking about, bro.

• 1:21:11Speaker 01

  That's it.

• 1:21:11Speaker 00

  It's just fun time. It's fun time to be, you know, but things are happening.

• 1:21:15Speaker 01

  Okay, I wanted to transition to a little bit of a spicy thing where you were on TVPN and their title that they chose for you was Doug Loffin Thinks Microsoft Is Out Of AI. And, ooh, did you, did you not see this?

• 1:21:27Speaker 00

  Okay, so, so, so I wouldn't say out of AI, no, I did, okay, so I didn't watch it. I never rewatch these things. Okay, so how I think about it. Is. But like you you said things like Microsoft is scaling back investment. Yeah, so so so it was the previous conversation I was talking about. Yes. How Microsoft has the most to lose. They had the most to lose of everyone in the entire world. Because they're the they're they're the software company. The horizontal software company. That is dead, they're fucked. Yeah, exactly. They're the horizontal software company that humans use their software to do information work. Okay, no, like I cannot paint a bigger target, okay? I cannot paint a bigger target. And my source. Yeah, well, okay, that's another two. Microsoft is automatically too big, it goes worse. Yeah, but the other thing too is they have this Azure business.

• 1:22:11Speaker 00

  I don't think I'm completely out of the race. I'm like, you know, it's a really great clickbait title. But the the problem is the Azure business with OpenAI, right? You're essentially renting barbarians at the gate. You're you're like, you know, this is ancient Rome and you're like, hey, we need some extra guys. So we're gonna we're gonna pay money for these barbarians to. The golden army. Exactly, from Game of Thrones. Yeah, yeah, the golden army. And the problem is like each year they become more powerful. And then and then at some point, they're just like, you know, we could just like scale these these shitty walls. So like the so that's the problem is the moat, the the wall and the moats every year are getting more dilapidated as they continue to rent GPUs to to the barbarians. So it's just like Google Yahoo again? Yeah, it is exactly like that. And so it's just like this weird process where that's a terrible setup too.

• 1:22:55Speaker 00

  Because what happens in the history of that is you have to choose one or another. Okay, if you do either poorly, you're you're like you're like somehow in a third worst place. You either all in become Azure, like maybe in the telecom era, right? Because you're Team T guy, you become dumb pipes, okay? That is the the Azure becomes what is it Charter, right? Uh huh. Yeah, but then oh, the other version of this is you say, no, no, screw these guys. I have to like reinvest back in and like essentially steal, copy their their features and build up my moat. That means I need to stop investing in Azure for the stock. That really sucks because the stock is very much weighed on out your Azure revenue. And meanwhile, if we actually had to value Microsoft X Azure,

• 1:23:37Speaker 00

  the multiple would be really low right now. I think about that all the time. What would this trade XX Azure? This is just Microsoft.

• 1:23:44Speaker 01

  Yeah.

• 1:23:45Speaker 00

  Eight times earnings, ten times earnings. Like, it was trading like that before, actually.

• 1:23:48Speaker 01

  Oh, jeez.

• 1:23:49Speaker 00

  And like, like, remember the 2010s era when it went all the way down to like 10 times earnings in the Steve Ballmer era? And then it inflected outward as it did.

• 1:23:57Speaker 01

  Oh, with Azure.

• 1:23:58Speaker 00

  Yeah, yeah. Azure and oh, 365. Yeah, there we go. That's right.

• 1:24:01Speaker 01

  Yeah.

• 1:24:02Speaker 00

  Yeah.

• 1:24:02Speaker 01

  Okay, I don't think you have the answer, but I just like, this is far the most bizarre. I want to call it, but I don't know if it's a or not even.

• 1:24:10Speaker 00

  Yeah.

• 1:24:10Speaker 01

  Because it's a clear decision where they were the lead investors in OpenAI. They had the deal and they consciously, obviously stepped back. They're still good partners. But like, what happened?

• 1:24:21Speaker 00

  Like, so. I, I think the biggest blunder of all time, that, the part that like is kind of crazy to me about that one is like, yeah, I, I, I definitely think there was a financial decision. Because when you look at it, it looks like a conversation of shareholders, ROIC, and how much are you willing to burn cash? Because like, you know, effectively you look at all the other peers and Google, I would argue, is going to free cash flow zero. I think Meta will go to free cash flow zero. Microsoft is still like, you know, Satya did not make the company. He is a professional manager and there is a board and there's a conversation.

• 1:24:55Speaker 01

  Be responsible.

• 1:24:56Speaker 00

  Yeah, he, he's being responsible, right? But the problem is that responsible of this like an innovator's dilemma, right? Like, do I maintain, maximize shareholder value and cash flow today? Or do I have a, a deep belief that AI will kill the hell out of my core business? And I need to all in invest, you know, am, am I ready to bet the entire company on, on a trend? And it seems like Satya is not a believer. You know, we've been talking about AGI. He is not ASI pill, okay? He doesn't have any fear of the Shogeth. He thinks it's just like a new, it's a new Lotus. It's a, you know, Lotus and Excel came around, right? Like, it's just a new tool. But I think at the same time, this, this conflict between renting GPUs to the barbarians who will disrupt your business, or, you know, your actual core business, it's clear how they're feeling.

• 1:25:40Speaker 00

  In the call of earnings, they talked about they could grow a lot faster if they wanted to, but they're trying to reinvest back into the internal capabilities. That to me sounds like we are not going to hire as many barbarians. We're going to pull, you know, we're reinvesting these walls, pull in together and try to defend the core mode. Right? Because the, the dream of this, and in theory, you're like, oh, remember in 23 when they did the first big deal? You're like, wow, Microsoft's going to win it all. Because they already have all the distribution and they're going to have the perfect product. And boom, they're going to have this giant business that makes them, you know, whatever, 100 billion, 100 trillion dollars. Okay, whatever number you want to say. But reality is Claude for Excel, Claude for PowerPoint is literally exactly what it's supposed to be.

• 1:26:19Speaker 01

  Microsoft should have built it.

• 1:26:20Speaker 00

  Microsoft should have built it.

• 1:26:22Speaker 01

  Yeah.

• 1:26:22Speaker 00

  And so now you see the barbarians, and this isn't even your primary barbarian issue. The guy who, you know, this is like, this is the, this is like the tribe over the hill.

• 1:26:29Speaker 01

  Alone.

• 1:26:29Speaker 00

  Yeah, yeah.

• 1:26:29Speaker 01

  Barbarian.

• 1:26:30Speaker 00

  This is the tribe over the hill, you know? And the, the tribe over the hill is like, like, you know, on a, on a nightly raid, easily sacked the hell out of your castle. And you're like, dang, this is an issue. So, so Microsoft now is super stuck in the middle. And so how they're going to have to do this is totally different. I think they're going to keep, I think they're going to keep pulling back in. We're starting to see that, like, they're going to do internal training. They're going to try to do more foundational models. They're going to try to use the weights they have access to.

• 1:26:52Speaker 01

  This MEI?

• 1:26:53Speaker 00

  Yeah. Yeah. But I'm very skeptical because their execution has been kind of dismal.

• 1:26:58Speaker 01

  Well, you know what makes me see, they, they, they do have, you know, they are one of the big.

• 1:27:02Speaker 00

  Biggest companies.

• 1:27:03Speaker 01

  Companies in the world with all these resources.

• 1:27:05Speaker 00

  Yeah.

• 1:27:05Speaker 01

  I, I always want to push back on the, the sort of responsibility part, like, you know? So Oracle picked up the slack.

• 1:27:11Speaker 00

  Yeah.

• 1:27:11Speaker 01

  Are is Oracle being irresponsible?

• 1:27:13Speaker 00

  You know, so I, I'm actually, if we're going to talk about Oracle, I think so. Let's talk specifically about Oracle, because this is where we're going to go. I think Oracle was irresponsible because of the magnitude of what they did. Okay, it's like, the thing is like, I think the slack they should have done in, but like, the whole setup, in my opinion, on Oracle is on goal. They messed up the messaging, they messed up the fundraising. And in my opinion, if they were not like, like, one of the things that happened is they went so aggressive out the gate. Did the quarter where they said like 400 billion dollars, right? They, they said, "R and P is the wolf." They promised the world. Then they proceeded to raise as much money as possible. And like, this is the first time they've ever done these giant build outs. And so now there's delays. Everyone's like, "Whoa, you did this much?" Right? Capitalism is kind of like, "Hey, hey, pump the brakes."

• 1:27:57Speaker 00

  And, and seriously, I think that if they just teared it out better, meaning that they didn't do it all in one period. Played a little bit of expectations management. This year's revenue from the deployed GPUs should partially help start to keep self-funding. And that's how you make this work in a glide path without going up, down, up, down, a big bang. And so they, I think what really happened is the big bang that really screwed them up was the debt side. They, they just offered so much debt. It's kind of funny because in high-yield TMT, it's such a big part of the entire index. Like, the issuance is so big, it's like- Debt index, yes. I have zero familiarity with this stuff. Okay, I'm, I'm pulling some numbers up. I did the numbers forever ago.

• 1:28:40Speaker 00

  I'm like, I, I co-stated them, whatever, forget all the precision. Let's just say all of the investment-grade TMT is like 500 billion, okay? I think Oracle is like 135 of it. So that's like, that's so big. And so each time you have to, you put up a huge new issuance, you have to give someone an incentive to go buy your debt instead of someone else's. And so you just kind of like, they're screwing up the liquidity. Because these issuance are so big, diluting the whole pie. It, it makes all the terms a little better or more favorable for investors. So literally, the entire index is selling off. Because it's like- Yeah, it's supply. Yeah, it's a supply thing, right? And that's the thing that's like crazy to me is like, so they, they massively overshot. And I think that we're like a weird bottleneck I never, ever, ever, ever, ever thought, thought would ever, ever hit.

• 1:29:24Speaker 00

  And I think you could appreciate this uniquely is like, one of the bottlenecks is like- Supply of debt into the market. Like, like capitalism cannot like absorb that much capital demand. Because the order of magnitude, it's totally different. These hyperscaler businesses have been completely self-funded since the history of time, had never gone out and issued anything. First time they wanted, they turn around and they're like, hey, instead of like, can you give me a $10 billion loan limit? Like, we've never done that before, right? So the absolute size is kind of screwing it up. And I think that Oracle specifically was way, way, way too aggressive into a relatively illiquid market. And so like, you have to do this, like, you have to kind of let yourself into it if it's gonna be like that.

• 1:30:05Speaker 00

  But they, they like super jolty, did these big, huge incremental adds and kind of flipped the whole thing. Oracle, CDS, people all freaking out. I think a lot of it's mechanical, specifically on how badly it was done from a supply-demand productive. And I think they can pay for it.

• 1:30:21Speaker 01

  You wanna, wanna hear it all right? Microsoft could have just internally funded this and like.

• 1:30:26Speaker 00

  Microsoft could have internally funded this, it would have been totally fine, 100% agree. And like this example where it's like, yeah, I think that that's a blunder, that's a perfect example of blunder. Cause Microsoft's cost of debt is the same as the United States government. It's like the cheapest you'll get anywhere else.

• 1:30:37Speaker 01

  Correct.

• 1:30:38Speaker 00

  And like, just from like a, like a P time, like the math perspective, no one else, they're better than Oracle. They, they just by their credit rating, they have a 2% more profitability at a capital basis. That's, that's like, you can't beat that. I don't know why they decided not to, but now they're in this weird thing where they're like, they're, they're, they're kind of wavering. Like, like to win, you have to be like really bold, right? And they're kind of like doing this one thing over here, being really defensive with Copilot. Satya is now, you know, the, the product manager of Copilot. And then they're also pulling back from Azure. Meanwhile, the competitors are pulling, are pushing in for the supply. It's a really weird game. I think Microsoft has to choose, choose a direction.

• 1:31:19Speaker 01

  We'll see, we'll see.

• 1:31:20Speaker 00

  We'll see.

• 1:31:20Speaker 01

  I mean, it's okay.

• 1:31:20Speaker 00

  That's what's gonna make it fun. I'm more than happy to change all of my opinions when new information comes around.

• 1:31:25Speaker 01

  Yeah, and I'm sure we'll, we'll have more information emerges. I wanted to touch on TPUs and then go into memory. TPUs will hopefully, I don't know, maybe, maybe a short one, but like, you know, for a long time, you could not buy TPUs, at least like current gen TPUs externally. And now you can. And Google's open as a, as a, as a supplier, I guess.

• 1:31:45Speaker 00

  I think Sergey doesn't want to lose. And I think the thing that happened was up and like, you know, he wasn't, no one was there. A part of the whole DeepMind story was we will hoard all the TPUs because we were first, you know? And so like, why so? You know, why give anything to Anthropic? I think it's because at least last year, it became pre Gemini 3. It was like, dude, we have all these TPUs, we're gonna hoard them all. But like, people haven't seen our products anyways. And like, hey, what's all, what, what good is all these TPUs if we're getting our asses kicked in consumer? I think it's an interesting thing too, because the other thing I think about is there's a lot of different ways to like, break this down. One, we wrote about it in TPV8, like whatever, we think Ruben will be much more competitive.

• 1:32:28Speaker 00

  I think Ironwood V7 is the peak gap between on, between TCO, between Nvidia and TPU, right? So if you are at your absolute strongest point, what do you do? There's two ways you could do it. You could try to maximize and like squeeze the juice and like make margins. Or you can, you can gain market share. I think the perspective of doing this externally with Anthropic is to gain market share. Because the biggest gap you have, and, and one of the reasons why there hasn't been a second merchant chip. And also you can argue Nvidia's most valuable company in the world. What's the value of TPU in Google? It's huge. You must have done the math. I've done the math. It could be like, it's like A trillion? It's like a trillion, yeah. It's like a trillion or something like that. Assuming it gets like 30% market share or something like that.

• 1:33:11Speaker 00

  Everyone has been trying to crack the merchant silicon mode, right? And now they have the biggest absolute outperformance. All, a lot of the people who did the original TPU program are like now at OpenAI. Some of them are medics. Some, some of them are, yeah, you're exact. Some of them are, they're all over, right? Like the, the core team that did most of the engineering have like sense really dispersed. And so I think the gap might, might close over time. And so at this absolute period of time, they're gonna, they're gonna win the market share. And then, then what happens is if you have an install base, you have an incentive to upgrade your install base. That's like the hugest problem with AMD, for example. No one wants to buy new AMD chips because it's not like they have old AMD chips. No, they're not upgrading from anything. And so when you have that number two place, you have to like, you have to win definitively.

• 1:33:54Speaker 00

  And then also you have an opportunity to win, win again next year. I think the install base issue has been a kind of huge one. And so TPU is at the point where the software ecosystem is mature enough. The hardware is definitely mature. The networking is really mature. You have a really good external customer who actually knows how to use your product. If you want market share, now's the time.

• 1:34:13Speaker 01

  Yeah, that would be insane if they if they actually sort of pump the pump the gas on that stuff. Are you also hearing? I don't know if this is something that affects your analysis at all, because I don't have any appreciation for the sizes that we're talking about here. The Jax is helping TPUs win. Or Jax is winning relatively to PyTorch, at least in like the academic arena, which is a leading indicator of what it's going to be using in.

• 1:34:36Speaker 00

  I do not have as I don't have a special purview on that. The thing I'm most excited about and like very much TBD will see is inference X will have TPUs eventually. That's something we want to do longer term. I think that that will really show in numbers.

• 1:34:50Speaker 01

  As a benchmark?

• 1:34:51Speaker 00

  Yeah.

• 1:34:51Speaker 01

  As a benchmark. How do you expect them to come in?

• 1:34:53Speaker 00

  Pretty good on a price basis. I mean, our expectation is like they're they're the best TCO by a meaningful amount right now. Anthropic's very clear how they feel like like everyone is very clear. I think even OpenAI would take, I think everyone would eat as much TPUV7 as possible. If you had it in a perfectly unconstrained world, it would probably be at this exact moment. Like, you know, the hottest kid on the block until Ruben comes out. But the reality is supply chain really matters and that's just that's not available. And so that TCO that TCO advantage is at this absolute biggest aperture. Then like Jensen essentially gets it gets their stuff together, it's competitive and boom, it closes. So this door only open right now. Probably TSMC is the biggest blocker, so there's no.

• 1:35:37Speaker 01

  Yeah.

• 1:35:37Speaker 00

  Yeah, what can you do?

• 1:35:38Speaker 01

  It's this cascade, right? They wish I think you've talked about.

• 1:35:40Speaker 00

  Yeah.

• 1:35:41Speaker 01

  Like all the way goes all the way back to the fabs.

• 1:35:43Speaker 00

  Yeah. Yeah, well, it's interesting because it's like even more than the fabs, like on the optical.

• 1:35:47Speaker 01

  I did their link I should be pulling up.

• 1:35:48Speaker 00

  Yeah, that that's it. That's it.

• 1:35:52Speaker 00

  Yeah, so. So, yeah, it all goes back to the fabs. It all goes to who's making the chips and like, I think one of the big differences too. Is just like the per. It's just like a really good cleverly designed system architecture. And it's relatively stable and it's clear that they you can pre-train big models on it, which is like a huge. Huge swipe at OpenAI right now. That being said, like I think OpenAI will get their their act together very quickly. And so, yeah, that's kind of like the narrative. I think it's gonna be a good story for probably like a year or two. But then the real question is the V8. We just don't think will be as competitive to Ruben. And that's when your special window starts to close. What's the technical reason why? HPM, HPM four versus three. And that's as secure as the strategic decision by Nvidia?

• 1:36:37Speaker 00

  Yeah, I think one, so Nvidia is always, if you think about Nvidia, they're always trying to gas it as hard as they can. Like they, they, like, it is a high-performance chip. It is a, it is an F1, like it is as maxed out as possible. TPU is kind of like this, like replicatable pod in a very large with like very high stability. Right? Which if you know the history of Google, that's what, that's what they do. That's what they do. Yeah, that's what they do with infra, yeah. But I think GB200 would have completely mugged, you know, V7 if it came out on time and stable. It came out a little delayed and it wasn't stable. And so I think there's a lot of different ways to kind of course correct that. And the one thing that's important is like, I think on the infrastructure side, or sorry, on the supply chain side, bar none Nvidia is the best. They own the entire supply chain.

• 1:37:22Speaker 00

  They really do. Like you think all those HPM price increases, they're gonna come for TPU just like Nvidia. But Nvidia was literally in Asia. You saw him drinking with everyone, with the SK, with the everyone, with all the Korean guys, all the TSMC, he's doing the shots with everyone. Why do you think he's doing love shots with everyone? Okay? It's because he, he needs to get the chips, okay? So, yeah, this is Samsung's chairman? Yeah, this is Samsung's chairman. Yeah, I know, who's the, who's the other guy? But let's put it this way, that's, that's a huge deal. That's a huge, huge, huge deal. Do you think, do you think Google was out? Hyundai, Hyundai. Yeah, yeah. Do you think Google was what, what, you know? Do you think Sergey was out in Taiwan drinking to, to get supplied? No, 100%.

• 1:38:03Speaker 00

  There's an opportunity here, but there's only so many TPUs that can be made because the, because of all the bottlenecks, right? And so Nvidia has all the supply chain locked up. And so they're going to have like so much of that kind of constraint there. And so it's going to be really interesting. They're going to, they're going to get the best, most performant HPM. They're going to be first on the roadmaps for even more rack density. They're going to have like the best connectors, the best, you know, the whole system will be once again turbojammed again for as hard as it can be. And the people who made V7, like they made, the chip was done like three, four years ago. Like the, the talent dispersion aspect where people who worked really hard on this team to make this great chip has really kind of gone all over. That starts to get worse.

• 1:38:46Speaker 00

  And so if that gets better, which takes some time, I, I think our current read is that like the HPM specifically and the memory scale up is going to really go in Ruben's favor. And so that's the big difference. And I think, as you know, that's what makes the context windows. It's when able to do bigger, bigger.

• 1:39:01Speaker 01

  Everything.

• 1:39:01Speaker 00

  Everything. Yeah. And so they're going to really jam it. And that's, that's going to be a huge advantage in performance.

• 1:39:07Speaker 01

  One thing I love about your analysis is not actually just the context windows. It's not just the KV cache. We also have to offload it to non-HBM.

• 1:39:13Speaker 00

  Yeah.

• 1:39:14Speaker 01

  Every other part of the memory is, it's like such an interesting cascade waterfall of like just like a short squeeze and everything. It's not a short squeeze. It's like a surprise squeeze.

• 1:39:24Speaker 00

  Yeah.

• 1:39:24Speaker 01

  I mean, I just want to.

• 1:39:26Speaker 00

  I mean, if I.

• 1:39:26Speaker 01

  Three to one ratio of like.

• 1:39:28Speaker 00

  Yeah. Okay. So it's a three to one.

• 1:39:29Speaker 01

  If I.

• 1:39:30Speaker 00

  Three to one to four to one ratio. I think, I think next year, so, so it's in the memory mania post that we just put out of like the four to one or the trade off ratio.

• 1:39:38Speaker 01

  Yeah.

• 1:39:38Speaker 00

  Scroll down somewhere and you'll see.

• 1:39:40Speaker 01

  Yeah. So, so basically for, for listeners, it's the idea that like when you convert to HPM, because there's a huge amount of HPM, it takes three times. One HPM sort of units is like three times of the other sort of DDR or whatever, right?

• 1:39:53Speaker 00

  Yeah. So some amount will always be lost in production because yield isn't perfect. And so effectively you're trading some, like you're trading. I, I may, I actually wrote a really funny piece, like I called it like super oil, but let's say this is a better one. But pretty much like you, in order for this higher grade of jet fuel has been invented. And the only way to make it is to like actually get rid of all, all your other fuel. And you have to like massively condense it and refine it. Okay? So now. What happens is if there's any demand here, it's an instant shortage. And so we hilariously enough came out of like the biggest shortage ever in NAND and DRAM. Like terrible, like catastrophic, the worst one ever. Like the last analysis I could put to is like 96 or something like that. Seriously, it's like a history one.

• 1:40:37Speaker 00

  And then meanwhile, we have all this new demand, HBM specifically, the highest end, you need the most memory, the trade ratio is crazy. So each, you know, each bit of HBM is essentially a 4X multiplier onto DRAM. And then now, so we completely constrained, took all the DRAM capacity. We just came out of this shortage, so no one invested in any clean rooms or capital equipment or anything like that. People got like massively free cash from negative, no one's spending a cent. Okay, people could go bankrupt, you know?

• 1:41:03Speaker 01

  Yeah.

• 1:41:03Speaker 00

  So you, they haven't invested in these three-year-long lead time items. And then now there's the like more demand than God and it also evaporates the middle layer because the KB cash offload. And then boom, you're just looking at the supply demand, you're like, yeah, this is not going to catch up for like two years. I think the thing that's like so interesting is the supply chain squeeze because these clean rooms take two years to make, man. And effectively everyone paused and how bad the last cycle was really forced everyone to completely pause altogether in terms of adding any new capacity.

• 1:41:33Speaker 01

  Yeah.

• 1:41:33Speaker 00

  And so now we're a few years later and all the supply is gone. So, I mean, people are, I mean, it's just, it's crazy. We, our post, our conclusion is like we could see DRAM prices like go up 100% again. Like it's, it's going to be the point where, and this is like also example, like really interesting in the whole thing. Another 100% I think is demand destruction. I think you will start to have demand destruction from.

• 1:41:55Speaker 01

  What does that look like?

• 1:41:55Speaker 00

  Where hyperscalers maybe purchase less or something like that on the margin, on the margin, right? Because they're like, okay, well, what if I just really focus on this energy aspect instead? And also ironically, all the energy, so like every, not every data center in America, but like many, many, many, most of the data centers in America are delayed. So you had this thing that's supposed to come on in 12 months, it's coming on in 18. Maybe what you can do is you can play chicken with memory prices and you can kind of push out. Of course, everything you have in the pipeline, you pull forward as hard as you can, okay? You pull forward, you double, triple order and then the DRAM. And HBM guys are like, oh my god, how, look at all this demand. And then at some point in time, what happens is you say, well, we pulled this all forward. You know, we have, the power is going to constrain us anyways.

• 1:42:39Speaker 00

  We're going to like, kind of chill out the orders. And historically, that's when the memory market, that's what causes the crisis, the prices to drop. Realistically, just looking at the aggregate demand of how much we've purchased in terms of power. It just seems like the gap is just huge, is completely off. To the point where the most obvious logical leg of the AI, the AI trade is effectively investing in memory capacity.

• 1:43:03Speaker 01

  Yeah.

• 1:43:04Speaker 00

  Well, not to, you could say SK Hynix and Samsung.

• 1:43:08Speaker 01

  Micron.

• 1:43:08Speaker 00

  Micron and all the, all the semi, like semi cap has been ripping.

• 1:43:12Speaker 01

  Which like, by the way, when I was in Ballyasney, we, a majority of, a lot of money we made was just being on Micron.

• 1:43:17Speaker 00

  Yeah.

• 1:43:17Speaker 01

  In, in the last sec.

• 1:43:18Speaker 00

  Yeah, it's a good example. Yeah. So like, you have all the semi cap stuff, right? Like all, everything that is even remotely related to investing in capacity for memory, that is like the ultimate bottleneck right now.

• 1:43:28Speaker 01

  And also for listeners, it's going to affect like your phones.

• 1:43:31Speaker 00

  Yeah.

• 1:43:31Speaker 01

  Like.

• 1:43:32Speaker 00

  Yeah, I, Apple, I think Apple's.

• 1:43:34Speaker 01

  I had to buy a, I had to buy an SD card for this thing.

• 1:43:36Speaker 00

  Yeah.

• 1:43:36Speaker 01

  It was a box.

• 1:43:37Speaker 00

  Yeah, that's, that's nothing too. That's, and that, because like, that's just the nan side. Dude, have you looked up like, like, I want to say like 64 gigabytes of, of DRAM?

• 1:43:50Speaker 01

  Like, I'm moving up, like, I, I need to refresh my iPhone. I'm moving it up because I'm doing this research for.

• 1:43:56Speaker 00

  Oh yeah, you need to do as soon as.

• 1:43:57Speaker 01

  Buy your iPhone now.

• 1:43:58Speaker 00

  Yeah. Yeah, you buy your iPhone now because what's going to happen is when iPhones go into the spot market, it, prices are going to go up a hundred percent on them.

• 1:44:05Speaker 01

  That's insane.

• 1:44:05Speaker 00

  And so they have to pass.

• 1:44:06Speaker 01

  We're going to be buying like old iPhones and then taking them out for the.

• 1:44:10Speaker 00

  There's, no, that's actually, there's a, there's a whole, super duper deep in the weeds. There's this whole like technology that was very focused on cloud era called CXL, which is memory expanders for CPUs in order to have like whatever, just like elastic pools of compute of CPU and DRAM and whatever, memory attach and whatever. It never really took off because essentially HBM was like the way that really crushed it all. High performance, best of three wins. But this CXL technology that kind of never really took off is going to take off just because what they're going to do is. They're gonna take DDR4, they're gonna take the oldest, every bit of spare memory they can find, and they're gonna put them into racks and then they're gonna attach them via CXL. So like this.

• 1:44:48Speaker 01

  Oh, exactly that.

• 1:44:49Speaker 00

  Yeah, it's exactly that. But the thing that's so crazy is like this dead technology is like having a shot on goal. Because of how bad the storage, or how bad the memory constraint is.

• 1:44:59Speaker 01

  Yeah.

• 1:44:59Speaker 00

  Like, yeah, that, that, you know, I was like a CXL bull for once upon a time, then it became very clear it was gonna die. And I was like, it's back. But only because the entire express intent is to have these D, like old chips, pool the old chips, attach it to something new. That's what it's gonna be like. The, the memory shortage is just like, it's crazy. So.

• 1:45:19Speaker 01

  Yeah, it's incredible.

• 1:45:20Speaker 00

  Yeah.

• 1:45:20Speaker 01

  So obviously, this is lower level than I usually go to, which is, which is why I'm having so much fun. One thing I, I do tell people about is like, well, you know, everyone, including Sam, by the way, is like predicting longer context windows. We've been kind of effectively stuck at a million for two years now.

• 1:45:36Speaker 00

  I've actually been thinking about that a lot.

• 1:45:38Speaker 01

  And like, this is not gonna go to a hundred million context windows. It's not gonna go to a trillion. Like, we're, this is it.

• 1:45:45Speaker 00

  Yeah.

• 1:45:45Speaker 01

  This is it for like five years, 10 years.

• 1:45:47Speaker 00

  Pretty much. Okay, so the question is, will, yeah, I mean, yeah, probably, actually. Will capitalism work? Will we, will there be a way for supply to show up? Probably, but on top of that, I wonder if there's gonna be like, like, hey, his, his history of compute, what happens is you have to like, you have to like make a curve of the, of the context windows. Like, does free context windows go to like 1,000? Hey, you can use ChatGPT free now, but you, your context window is like 1,000 tokens or something like that. And then you can just like somehow do a tiny, like a tiny parcel for that, just so that you can then charge like, you know, 100X more for 1 million. The 1 million context window is like a mansion, you know? That's the real.

• 1:46:25Speaker 01

  You live in a mansion.

• 1:46:26Speaker 00

  I live in a mansion right now, yeah.

• 1:46:27Speaker 01

  Oh my god. And the word just context rationing just came to me. I'm like, fuck, like, we're gonna have like vouchers for like, okay, you can have this amount of context today.

• 1:46:36Speaker 00

  It's like, yeah, you have to, you have to learn how to use it well because of the DRAM, yeah. Okay, so I actually have a question. I know, because like, okay, long context to me makes a lot of sense, right? Hey, that's like, that's like the memory scale up version, like if you're thinking of chips, but in the AI world. I just am like, always been curious, because it does feel like, at least in my stated experience, really long contexts, like, you see in the papers, they kind of like drop off, they like actually don't use all the context. So that's like, kind of the thing I've been most interested in is like, does the hundred million context actually matter if, if it's not possible to use at all?

• 1:47:09Speaker 01

  They, you know, versions of a hundred million do exist today, they just suck in various ways. They're not actually applying full attention, right?

• 1:47:15Speaker 00

  Yeah.

• 1:47:16Speaker 01

  You can, you can use state space models, or even like a LSTM to like, you know, to process a hundred million tokens, but you're not paying full attention to those hundred million tokens. And so I think like, the way that we have context about today and those curves, they will improve over time, and they have, they have been

• 1:47:30Speaker 00

  Yeah.

• 1:47:30Speaker 01

  improving a lot, but we're just not, we're never going to use all of them, but we'll, we'll improve like on the algorithm side.

• 1:47:36Speaker 00

  Yeah.

• 1:47:36Speaker 01

  I think for me, what matters is you represent the physical constraints that us, the software side can never surmount, because there's a physical constraint.

• 1:47:44Speaker 00

  Yeah.

• 1:47:45Speaker 01

  And, well, I mean, it just, it just, like, physically, we cannot double, we can't even, we can't even double in the much as 10x.

• 1:47:51Speaker 00

  Yeah.

• 1:47:51Speaker 01

  Yeah, what's the point of talking about any of this?

• 1:47:53Speaker 00

  Yeah, what's, what's the point? Yeah. I was gonna say, like, we could, we could, we can invent a lot of things. Context rationing is pretty good. I really like that one. Context frugality, or like budget or something. I feel like everyone's gonna be like, whoa, you're running out of context window today, you know? Like, maybe that's what happens next year, where we're, we're charged on context window.

• 1:48:11Speaker 01

  And then the, one of the more recent observations is recursive language models, which again, is just reusing the same context window on.

• 1:48:17Speaker 00

  Yeah, over and over again, yeah. I've been pretty interested in that, but like, to be clear, I'm a total idiot. I have no idea. Claude told me what's.

• 1:48:23Speaker 01

  You're the, you're the semis guy, man. Like, you're really good on your stuff. One thing I wanted to spot check on was Talos.

• 1:48:29Speaker 00

  Uh, which I have not. I have not messed around with it.

• 1:48:31Speaker 01

  Okay, you don't have to mess around with it. It's just the, this general theory of custom basics, burning the weights into the chip.

• 1:48:37Speaker 00

  Yep.

• 1:48:37Speaker 01

  So you don't need memory.

• 1:48:38Speaker 00

  Yep. That's pretty good, actually. I think, I think, I think that that, that makes sense to me.

• 1:48:43Speaker 01

  This comes, this comes at the perfect time.

• 1:48:45Speaker 00

  It does, but I guess, okay, so historically, the question is how big does it scale, right? But like, I mean, you know, a lot of the models are kind of actually smaller than you think, right? So like, that's like, sorry, what do you mean? A lot of the produ- the, the produ-

• 1:48:58Speaker 01

  Yeah, they, they get distilled to shit.

• 1:48:59Speaker 00

  Yeah, they get distilled to shit. So it's like, the push and pull there is gonna be like, okay, can you just burn in a, a s- like enough efficient Pareto frontier in terms of performance to be burned in straight onto the silicon? That doesn't need memory and then boom, you can scale this forever? Versus like, you know, the performance edge of the long thing. It's pretty clear to me that like, Talos has a place because you're kind of seeing this market bifurcate a little bit. You could argue the pre-fill, decode, disaggregation, stuff like that. It's like, the focus on performance inference serving is gonna be a subset of the market. And then the training and the whatever and the big production, like, you're, we need to kind of break it in into smaller parts.

• 1:49:37Speaker 01

  Yeah, we've been using the same pips.

• 1:49:39Speaker 00

  Yeah.

• 1:49:39Speaker 01

  And it makes no sense.

• 1:49:40Speaker 00

  Just in order for the compute to be even remotely okay.

• 1:49:43Speaker 01

  It makes sense to you and, I mean, TBD on the sort of practical implementation, but otherwise, burning the weights into the chip. Why didn't Etched or some of the other guys get there first?

• 1:49:53Speaker 00

  Um, actually, it's pretty interesting. I don't know.

• 1:49:57Speaker 01

  Okay, I'm asking to speculate, yeah.

• 1:49:58Speaker 00

  I mean, I'm not kind of like, super speculate. I mean, the thing, the thing is like, their thing is like, okay, how do we have a, a big systolic array? Right? But like, they, they didn't burn weights into the chip. That's all different, right?

• 1:50:07Speaker 01

  I mean, like, look, like, I mean, yeah, I just think the way to speed things up is to never transfer anything.

• 1:50:12Speaker 00

  Yeah, yeah, that's, that's the fastest way possible. And so, so, but the thing is, the bet on, on, on this really large systolic array is effectively everything is compute bound. Right? Like, I don't think that that's really the case in, in terms of like, where we're actually seeing issues in production markets today. It's like, you're actually seeing all the issues in the memory. Right? And so like, I, I just don't know if that's like, gonna be the perfect solution. There is definitely a world and space and like a design space where they're gonna be very valuable and cool. But like, also, the reason why my hit rate for every AI salarator chip is like very, like, I just don't believe in them. It's cause like, where are they? Until Strubris and Grok, honestly, they were all considered failures. And even then, we're like, what are they gonna do with Grok? What are they gonna do to Strubris? So.

• 1:50:55Speaker 01

  Is, is someone over?

• 1:50:56Speaker 00

  No, I think someone over is like a much more interesting one, but I think there's like, there's like all kinds- To deal issues with that. I haven't been keeping up with that one as much. Yeah, I always I always try to mention them as as part of that cohort. Yeah, yeah, cuz I kind of forget about them too. But yeah, I honestly, I was gonna say they were. You know, once once a year, they show up. Yeah, they they do, and they're not. They're not so bad. Yeah. Yeah. You mentioned actually some CPU shortage stuff for CPU. Yeah. Sort of opinion. What's what's going on there? I think it's I think it's okay. So, okay, I have one. We'll start with the conspiracy theory that I think is really funny. Love this. Have you been noticing just like I feel like web services have become really unstable? Give us with Donald. I like and like, okay, this is pure, like, you know, schizophrenic hat brain cuz I have a schizophrenic trend hat brain. I'm wondering if it's two things, shipping vibe code slot to prod.

• 1:51:40Speaker 00

  That's number one. That's definitely something that's possible, but it's happening to all the clouds at once. I feel like it's not just the AWS thing. It's not just like a GitHub Azure thing. We are kind of right at the exact five to six year period of the refresh cycle of COVID. So COVID, we had this big 2020, 2021. You bought like hundred billion dollars of CPUs and stuff like that. And so we're right at the natural end of life for these chips. And so usually what you do is you have this big refresh of all these chips. But what what's been happening instead is everyone has essentially scrounged all of their budget as hard as they can. But then, like, I feel like I've seen it in like Azure, like, hey, last night, my Amazon Prime thing doesn't work. And I was like, it'll probably work in the in the morning, babe. Don't worry about it. I think I think Azure is just are like AWS pissed tonight, you know, something like that.

• 1:52:25Speaker 00

  But I think so. We have this five year thing. Everyone scrounged every single dollar they could to essentially invest in as much as AIs as possible and just do maintenance CapEx on CPU. Ironically, at the same time for all this Claude Code stuff is actually if you have this coding agent just generate you god knows how much compute, how much like software. Where is the software gonna run on CPUs? So I think we're gonna see some increasing utilization as well as the fact that RL is like actually heavily used for like RL gyms. You have to you have to simulate software and it uses a lot of CPUs. So the not quite like the orders of magnitude of GPU stuff, but it's just such a big trend, even when a step slightly in a in a place. Mass mouse demand. I feel like we might actually be seeing a CPU shortage, partially cuz this refresh cycle, but partially also because.

• 1:53:10Speaker 00

  Like I legitimately believe the Claude code. Claude code is increasing software creation. And then on top of that, there is real.

• 1:53:17Speaker 01

  Yeah.

• 1:53:17Speaker 00

  And from RL.

• 1:53:17Speaker 01

  Yeah, and just general production agents as well. You know, we just, yeah, every like RLMs take compute and, you know, open cloud takes more compute. And it's just, it's just a different slope, but at the same sort of direction.

• 1:53:30Speaker 00

  Still an up slope and in a slope that, to be clear, has had massive underinvestment for the last two years. Cause everybody like, how do you, the same problem that happened, massive underinvestment. Cause they're like, screw it, we're doing maintenance only. We're all we're gonna do is maintain, maintain the past. We're not gonna add anything else. And then all of a sudden, just a little tiny slope on top of it. You're like, boom, shortage.

• 1:53:49Speaker 01

  Yeah, amazing. Yeah. Semi's guys say semi's numbers go up.

• 1:53:55Speaker 00

  That's, that's one way to put it, yeah. I, I, the thing that's crazy is we talked about the demand.

• 1:53:59Speaker 01

  But it's like, it's like, you're right.

• 1:54:00Speaker 00

  Well, like, I mean, it's for sure.

• 1:54:02Speaker 01

  Like, you're like, show me where I'm wrong.

• 1:54:03Speaker 00

  Yeah, like, show me where I'm, I, I mean, definitely not. But the thing that's crazy is like, memory prices are gonna go up so much that we're gonna have to choose which, which.

• 1:54:10Speaker 01

  Exactly.

• 1:54:10Speaker 00

  So why? That's the crazy part to me. Historically, memory has never been a constraint like this where it said, actually, you're not gonna get your low end, you're not gonna get your low end phone, you're not gonna get a GPU this year for gaming. None of that stuff. You, you can't do these things because your price out of market. That's what's crazy.

• 1:54:26Speaker 01

  I didn't say.

• 1:54:27Speaker 00

  That is the first thing that's happened in a long time. That's gonna be really interesting to see where that shortage and how it, how it's like digested and felt.

• 1:54:33Speaker 01

  That is amazing. Thank you for that breakdown. I feel like I really understood it, like talking to you.

• 1:54:37Speaker 00

  Yeah.

• 1:54:37Speaker 01

  Let's go to transition to a couple of personal things and.

• 1:54:39Speaker 00

  Yeah, sure.

• 1:54:40Speaker 01

  As the end, how do you write? Cause you, you write a fuck ton.

• 1:54:43Speaker 00

  Yeah, I do. I have been writing a little bit less these days now that I'm like in the semi analysis mega mind. I definitely write a lot.

• 1:54:50Speaker 01

  And like you kept going with Fab.

• 1:54:52Speaker 00

  Cause I, wow, okay, to, to clear, that was really, so, so, so look, I'm still trying to do Fab. Cause I, I, I do feel deeply connected to writing. Let's just specifically talk on this a little.

• 1:55:01Speaker 01

  Yeah, just, just like explain yourself, you know?

• 1:55:03Speaker 00

  Okay, so the thing before LLMs came around, the thing I felt the strongest about my, my number one information skill is I was able to read and synthesize and process at like really high speed, really high throughput, decently high comprehension. The adjustment is speed in terms of. Comprehension, almost anything like when my like when my friend gets a PhD, I go read their paper. I was like, oh, I have a pretty good idea of what you're doing. I was like, like, hey, when I was interested in semi-cofter book, I literally raw dogged some textbooks. Whatever their comprehension was not very high, but like, hey, whose comprehension is, you know? But I was able just to like push through these books and learn. So I've always loved reading. That's like my, my number one original competitive skillset differentiator.

• 1:55:44Speaker 00

  And also something I like loved as a kid, crazy reader when I was a kid, always have been. And then starting the Substack, which has been really fun, actually, because I just really wanted to get my story out, like the things I cared about. Closed the loop for writing for me because I love, I love reading so much. It makes a lot of sense that I love writing. I think what really helped is I wrote every single week for like since October twenty one, like consecutive streak for a long time. The streak has been a little broken as of late. Semi-analysis plus fabricated knowledge is pretty hard to do. Yeah. Like all of twenty four, I think like we're just talking just like every single day, every single week I will put something out, right? Is it like a hard rule, like one a week? It was a hard rule, one a week, at least an attempt to two.

• 1:56:25Speaker 00

  And so I think one of the best ways, all the people who write, who write about writing all say the same thing. You need to just be writing. Yeah. And so that's how I, that's how I start. I'm writing every weekday now. Yeah, it really helps. Well, I was gonna say, what's crazy is like, it's, it's kind of hard these days and I, and L ones kind of have really, I don't know, I don't like L M writing. I do like it for ideation, like making outline. Yeah, yeah, research. Here's my un, unorganized thoughts, make it into an outline and then like, you know, I'll even be like, put bullet points in the outline and I'll literally read the outline. And then like ideate and write in parallel. But yeah, that's, that's how I feel about writing, I guess. Write more. I have a strong for nonfiction writing. I really like this book called On Writing Well. That's just a really good classic book.

• 1:57:08Speaker 00

  It's actually summarized and synthesized into a, into a skill for me. Oh, yeah? Yeah, yeah, yeah. So, hey, please edit this, use these, use this style guide, use the like learning from this book. So, yeah, just stuff like that. Yeah. And then do you like have a, a topic idea list that you groom? Like I, I've put mine in Apple Notes now, but. Bro, it's. No, never, never. I'm one. I'm just a one shot.

• 1:57:31Speaker 01

  Whatever's on your head.

• 1:57:32Speaker 00

  Yeah, usually I one shot the the idea all the way.

• 1:57:34Speaker 01

  Yeah, yeah.

• 1:57:34Speaker 00

  Usually I think about it for quite a bit, so it's been bouncing around in my brain. And then at some point in time, I've like condensed enough information to make a really crappy outline. And that's usually when I just one shot go.

• 1:57:46Speaker 01

  For me, like, it's hard to one shot and bounce because you will forget, right? And sometimes you you have like really good stuff that you forget. And sometimes it's actually, so I call this mise en place writing, where you basically just have a store where you're just kind of writing, working your ideas in parallel. And then every now and then you cook.

• 1:58:03Speaker 00

  Yeah.

• 1:58:04Speaker 01

  And so this is async and this is sync, right? This is like passive, like, oh, here's a data point, here's a quote, here's a thing. I'll just slot it in the right thing and then and then I bake it.

• 1:58:13Speaker 00

  Historically, okay, so how that prewriting actually works today is probably in the semi analysis slack. It's just like all the.

• 1:58:18Speaker 01

  Then you just search it up when you need.

• 1:58:20Speaker 00

  Yeah, I'd search it up when I need it or something like that. But like, I do most of the prewriting, I think, in my brain and I have places that I put it out.

• 1:58:26Speaker 01

  Yeah, yeah.

• 1:58:27Speaker 00

  That I reference it later. But my favorite thing too is like when it comes to the, because like, okay, well, once upon a time, much more on the beat. Oh, hey, here's earnings, read every single one and put it all together. But like, my favorite skill or tip or whatever is like, hey, do the prewriting, think about it, all that stuff. And go to sleep and wake up the next, the fresh context window in the morning is my number one advice on writing.

• 1:58:48Speaker 01

  Help you decode?

• 1:58:49Speaker 00

  Better? It helps so much better. Like literally, if I'm like, hey, I need to write something right now, I will do, I'll write it all down, I'll make outlines, I'll do all kinds of crap except for writing it. And then I'll be like, and I'll go to sleep and then wake up and the first thing I do, I'll open up a new tab and I will write it.

• 1:59:03Speaker 01

  Go.

• 1:59:04Speaker 00

  And then so usually that will get me into sixty, seventy five percent of something. Even if it's like an outline where I like have gotten all the ideas enough to know how to fill it out the rest of the way. And then that's that's how I take it from there.

• 1:59:14Speaker 01

  Cool, amazing. Last thing, hike.

• 1:59:16Speaker 00

  Yeah.

• 1:59:17Speaker 01

  So one bit of context for me is I I just, I just, I've never taken a break, never. And I feel like, you know, if you take a break in this time, you're like, just gonna be so behind, you're just gonna so miss out. I just found out my friend from, I took a break a year off to bike through Japan. And I'm like, how how could you? I'm like, you're gonna miss it, you're gonna miss everything. But he's like, I'm good, you know, like, I'm, you know, having kids, whatever. You did a sabbatical as well, and like, it was pre-AI. But it was interesting. I, I, you did, you did the Appalachian Trail.

• 1:59:51Speaker 00

  Which one? So there's three big ones in the United States. It's the Appalachian, the Pacific Crest Trail, and then there's the Continental Divide Trail. So I did the Continental Divide Trail, which is the longest and most remote of the three.

• 2:00:02Speaker 01

  Okay.

• 2:00:02Speaker 00

  Sometimes considered like the, the, the older, bad, whatever. But like, honestly, the PC, they're all different trails. Like, I'm pretty steeped in hiking culture. I think mile for mile, AT is actually the hardest. But I did the CDT as my first trail, as my first through hike. You know, you learn a little bit about the three when I was choosing which one I wanted to do. And the CDT was the one that scared me the most. I was like, hey, this would be the hardest, biggest accomplishment I could possibly imagine. And I thought, if I never have an opportunity to ever do this ever again, which so far seems to be pretty correct. Which one I'm gonna do to feel the most like, hey, I did the thing that I really wanted to do, because I've always wanted to do a long distance hike. And so I, I chose the Continental Divide Trail. I did that in 2021, pre-AI, and-

• 2:00:42Speaker 01

  But after the GPC three essay?

• 2:00:44Speaker 00

  After the GPC three essay, yeah. I felt like I was missing out a lot, and there's like a huge, it was a huge year for Substack. I feel like I missed out like a very big year of like the big growth.

• 2:00:53Speaker 01

  You're doing okay.

• 2:00:53Speaker 00

  I'm, I'm doing, I'm doing fine. But I, I just think that for me is something I always deeply wanted to do from an intrinsic perspective. I think something is like, like-

• 2:01:01Speaker 01

  Life fulfillment.

• 2:01:02Speaker 00

  Yeah, life fulfillment. And, and I would definitely do it again, but I probably-

• 2:01:05Speaker 01

  And to be, for people, it's like four months, five months, six months, six months.

• 2:01:09Speaker 00

  Six months, six months, twenty eight hundred miles, we'll, we'll call it on the route, twenty eight fifty or whatever the miles I went.

• 2:01:14Speaker 01

  And like, you meet people on the way, but you're mostly alone.

• 2:01:17Speaker 00

  Mostly alone, did it alone, you get the trail name, it's a whole audiobooks. I listened to audiobooks until I hated them, listened to music until I hated it, got bored as hell, like you just, you just, you go, you, you go through all of it, actually. Yeah, it was awesome, six months. I think the thing I think about is so far in most, in, in, in my life, up until that point, you get, kind of get kicked from situation to situation, right? You create a, a view, a form of yourself, you think you know yourself, you have ideas of what motivates you, how do you react in situations, blah, blah, blah, blah. I think the one, the CDT amount, like, it's just like, I like, I like the outdoors, I like hiking, I'm like, good at it, whatever. Is this something I really, appealed to me from an adventure perspective? Like, when in modern life you get to say, hey, I'm going on an adventure?

• 2:02:01Speaker 00

  Never, like, and that's what it was. It was it was an adventure for me. And one that I got to like really, you you you know, it's like, oh, the journey is a destination, or whatever. You learn a lot about yourself. In fact, I learned, it didn't grow me up per se, but I feel like I am more well-defined of my view of myself. I understand how I react. I actually know where my exact line, or it's like, you know, you're like, oh, I'll go do this. It's like, actually, no, I know my exact line where I'm like, I would not do that. I know exactly where I'm not, that's too scary, too hard to whatever.

• 2:02:28Speaker 01

  Yeah.

• 2:02:28Speaker 00

  I know my limits a little better. I feel like I know just more about myself. It is a very condensed version of a very intense life. And yeah, I wouldn't give up that experience for anything in the entire world. It was extremely personally meaningful to me. I think it's very fun to go back to the lower part of the Maslow's hierarchy of needs. Like, all this crap what we're talking about today is so abstract. It's like totally fake. And we were not born and built for it. We were born to, like, you know, our human evolution got us to like scrape a living in the mud, okay?

• 2:02:58Speaker 01

  Hunt and gather.

• 2:02:59Speaker 00

  Hunt and gather and just not die. It's kind of interesting to go backwards and to see what feels like, dude, I was so hungry, so scared, so alone, so like, but also like, super low. The the the phrase is like lowest lows and highest highs. These crazy lows where you're like, what am I doing? What does all mean? Highest highs and me like, holy crap, it's so good just to be alive. All these things where it's like, it's just so like, the raw experience of life is so meaningful. And you don't get to experience it without doing it that way. And so, yeah, I wouldn't. I highly recommend it. It's very, I would do it when you're younger. I wish I did it after college.

• 2:03:33Speaker 01

  Yeah.

• 2:03:33Speaker 00

  Like, right after college and said, hey, like, whatever kicks out of the year. I think it's good to learn about yourself.

• 2:03:38Speaker 01

  Yeah.

• 2:03:38Speaker 00

  It's really important. Your your self mastery is your most important tool use of all, so.

• 2:03:43Speaker 01

  Yeah, that. Yeah, self mastery is one of my tools. Yeah, amazing. Well, thank you for jumping on and like covering everything.

• 2:03:50Speaker 00

  Yeah.

• 2:03:50Speaker 01

  I feel like I got like, got to go through the sort of quad code psychosis all the way to semi is all, all the way to the hiking.

• 2:03:56Speaker 00

  Yeah, thank you. Thank you for having me on. Yeah, so this, yeah, great to catch up.