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

This crap makes mistakes all the time. [laughs] 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 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, it better, it better happen, right? [laughs] 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 '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. [upbeat music] Doug Lofquist, welcome to LeanSpace. Yeah, thank you for having me, man. [laughs] I, after all this time, I just... Is it okay if I just call you Swix? I feel- Yeah, of course...

it's the, that's, that's where my brain is 'cause I've known you for so long. You can call me Mule if you want. I'm not care. [laughs] You know? Yeah. Yeah, I mean, it's been, it's been a long time. It's been a, 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. Yeah, yeah. I met you at one of the NeurIPSes in per- I think it was Vancouver, right?

Was that? Yeah, no. I think it was- It was like some after party... yeah, yeah, yeah. And you were like, "Hey," like, "Who's this tall dude?" Yeah. I'm like, "Whoa, okay." [laughs] Yeah. [laughs] Yeah, well, I mean, it's just like I, I knew about you, and we, we've, like, been internet, you know, pen pals for a long time, so it was, like, cool meeting in person. I mean- Yeah.

Yeah, I think that was the first time I ever met you in person, so yeah. Amazing. I, I didn't go to the New Orleans one. I really wish I did. I love New Orleans, honestly. Yeah. So. There are two New Orleanses in a row, and yeah, honestly, we should go back there. Yeah. Are you guys going to Melbourne or- I-... like, the Australia one this year? I have, I don't even think that far out. Okay.

But on a, uh, but that sounds pretty interesting to me. I think, I can't remember which one. There's a, there's something in, in, in Korea this year, right? Yeah, I think ICML. ICML? Yeah. I think I'm gonna try to go to ICML in Korea. Yeah. And then I know iClear is... I don't know, man. There's so many conferences. I honestly, I hate to say it, I'm not much of a travel guy. Well, yeah.

I mean, I'm, I'm glad to catch you. I mean, I, I, I am traveling to you. Yeah. Thank you. I really appreciate it. Yeah, absolutely. Yeah, it was fun. Yeah, yeah. I did not know that I would be caught in a snowstorm. Yeah. It's, 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. Yeah, yeah.

It's a pleasure. And so you and I go back. You, you used to be anonymous. You used to be Value Mule- Yeah... which is how I know you. You know what's funny is that Value Mule is, like, the very first one. That's the be- I- Yeah. The... I don't know how, I don't know how I noticed you. I, I was just like, "Oh, this guy s- seems smart." Yeah. I, I don't know, dude. I mean, I- Yeah...

I remember noticing you too, so it's like, you know- Yeah... this was in the early, like, the primordial days of Twitter. Yeah. Honestly, I miss those the most. Yeah. I, it was like 2017, '18, something like that. Yeah. But yeah, yeah, I remember. From Value Mule, so if you, 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 then I actually have a third account, which is my, my main account these days. Yeah. So yeah. Wait. Oh, which one is that? I don't, I don't want to- Which... Okay. I don't wanna dox your other account. Oh, it's okay. You can semi, semi-dox it. Yeah, yeah, yeah. Sure. So, so, so, so it is there. That's, it's, it's not... That's okay. That's like my oldest finance account.

[laughs] I think of it as my legacy account. Okay. I, I, you know, I wanna have some privacy, I feel like. Yeah, yeah. So, so, so now you've gone all in on the brand and everything. Yeah, yeah. Okay, yeah. Got the brand and everything. Yeah. Cool, cool, cool. Same profile pic, you know, so. Yeah. So let's, let's do a little bit of the Doug story 'cause a lot of people hear about Dylan, and I wanted to just make this the Doug story, make, make the Fat Knowledge story. You used to be a value investor.

That's kind of how you, you were Value Mule. Mm-hmm. And you had a mentor or something that nerd sniped you into semis. Is that the, the story? No, actually, I solo nerd sniped myself. You soloed, soloed. Um, so I, I wouldn't say value because well, well, for, 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 nerd sniped me, all the, like, single-shot me, is I found ASML. Yeah. And then I, like, fell in love with it, and then I, like, after ASML, I just, like, read about all this stuff, how complicated it is to make these, who are the people who are able to make them.

And then I, you know, semicopters, the whole downstream's all from there. But it started with ASML in 2018. I really fell, 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- I was gonna pull out the Asianometry video. Yeah, yeah, yeah. That's perfect. That's a perfect one for you. [laughs] John. [laughs] Amazing. John's a monster, honestly.

This one, right? Yeah, 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, 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, LLM.

Yeah, ASML is all science fiction. So the semicopter 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. Yeah. I don't know if you know, but obviously, you know I used to be a inve- Yep... uh, an analyst myself. Yeah, I didn't know. I covered TMT- Mm-hmm... which is a freaking huge sector to cover. It is absurdly huge.

Yes, very large. It, like, I was covering Sprint- Yeah, yeah... and [laughs] got, like, you know, Viacom. Yep. And then there's ASML. And- [laughs] Yeah, yeah. Yeah, the, 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. Right. Yeah, yeah.

But, but ever since then, it's really split off, yeah. Yeah. Well, I mean, I guess that's my reflection is, like, I used to be in, I, I used to be, I guess, our tech sector guy, and, like, I did the flights to Taiwan. Mm. And I took those meetings with, like, Credit Suisse and all those, those guys that would- Mm... you know, tour you around and all those.

I never really felt I got it because I was always being fi- filtered through, like, investor relations and all that. Mm-hmm. 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, the tech. But then you, you hard- It's, like, really hard as an investor to, like, make the connection to, okay, well, how does that mean for this, this quarter? Like, or at least this, this year. Yeah. Right? Because, like- One, there's, like, just so much foundational knowledge- Yes... and then, and then, then you're like, "Well, okay, everything here is taken for granted. It's already priced in." So like [laughs] Yeah.

You, 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 y- 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 believed 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, and like I get it intuitively," but also everything, all the old playbook is about to be thrown out.

Because it's been like this is super mature industry. You read these primers about it. Like, that's how you learned about things back before ChatGPT knew everything or- [laughs]... you had to go and read these primers of all this information. They're like, "Oh, it's a very mature industry. It matured. It used to be really immature in the '80s and '90s and 2000s, but now you're consolidated.

Growth doesn't go up a lot." And everyone kinda had this old playbook from the early 2000s. A lot of people hated hardware. There was 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. And when the science fiction ends because you can't make the chips as small as you c- you could, all of the sudden, all those free gains you got go away, and you have to think about it. And what happened for semiconductors specifically is it created a lot of pricing power or value for everyone who knew how to make a good chip. So NVIDIA's 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, it is like... You know, versus it w- in the past, it was just CPU gets better and over, right? Mm-hmm. And so I think that I had a really deep belief that- In this case...

that M- 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 wanna attack every little assumption. And something that really changed as well... I, and dude, this is honestly my, my favorite post I've ever written. It's like 20- it's like a ChatGPT-3 and the writing on the wall. In like 2- 2020, you know, my early...

I get a early pitch to... for fabricated knowledge, and I'm like, "Hey, you know, I'm gonna make a release. You know, Moore's law's over. Scaling laws seem like a big deal." If you simplify it all the way through, is like, okay, supply... You know, supply divide, 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 semiconductors, and parallel compute is gonna be a big deal, blah, blah, blah, blah, blah. My conclusion then was you should just, like, NVIDIA's pretty much the only one who's gonna benefit. And, and, you know, so that's why my, my, my, my like good long range prediction, I feel like.

I- Just like- [laughs] Yeah, I just don't think, I don't think I would've expected the magnitude. I think that that's been the kinda 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 was the only person who was as semiconductor pilled in the entire world as me, is how I felt.

So I remember, like, yelling at him, arguing about all these kinds of things in, like, our, our DMs and stuff like that. Was it just online or- It was on-... did you also- On- online. We met in person... in 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, kinda we were both really early to the thesis, kinda have a different background and perspective. Dylan is technology first, and, you know, obviously the technology matters. 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- guy who, like, cared the, 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, you'd be like, "Oh, NVIDIA was good," right? "NVIDIA's pretty good."

And then it's like, no, NVIDIA's now the most valuable company in the world. [laughs] 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, you know- This is one of many thesis at the time. Exactly. Yeah, yeah. Right?

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, I'm pretty happy with my m- with my long-term track record. [laughs] I really am. But yeah, I'm just really surprised the magnitude of how everything happened.

Like, it's crazy to me that, like, CoWoS is, like, a, a, not a household term, but, like, we- relatively well-known. It was, like, an exotic technology. Mm-hmm. 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 goated insight pretty much. Yeah. I mean... And probably, like, a career maker right there, you know? And, and I just li- I love those kinds of, like, sort of quarterbacking those q- career decisions for other people who are also weighing a bunch of things, right?

Like, I have ADD, and, like, I, I just chase, like, whatever is interesting, but at some point you just have to, like- Choose... choose and- Yeah. I, I think one of my skills have always been, like, trend following and trend watching. I think when, you know, if we're talking, like, on my account, like Value Mill or, you know, full time, like, I was always pretty good at trends, like being relatively early. Mm.

I remember loving and being obsessed with TikTok in, like, 20- 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- Yeah...

that's kinda what it came down to is, like, wow, I see this really big wave. It's worth going all in. Yeah. And so I reoriented my life around it. Yeah. Yeah. Cool. We're gonna talk about other trends that you've spotted, primarily, like, the sort of memory cycle [laughs] which... But also optics, which amazing story.

But we wanted to sort of focus this, uh, for the CloudCode launch, CloudCode anniversary, and you've been a big CloudCode show. [laughs] Yeah. I- I am no- Where's, where's the chart with the four- 4% of code? It's, it's, it's... Actually, go to the top left. This one. Yeah, yeah. That's it right there. Oh, you know what's really crazy is we've updated that chart. I think it's like five now.

I mean, and, 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. So, so let's recap for people who... Let's say, like, I think this is one of the most important pieces I- I've read in a long time, and y- you know, you, you led it, and it's...

And it's, it's weird because I, I think of you as, like, a, an analyst, right? Like, you... Like, one of Semi Analysis alphas is that you're kind of like the fun millennial semiconductor firm when everyone else is super boring and old. Yep. Uh, but, like, what are you doing, you know, getting so into Claude Code, right? [laughs] You know, I- I... Shouldn't you be reading reports and stuff? You know, like, tell the story of your psychosis. So yeah.

I think here's the thing, is if you wanna be good at any game, we're, we're tool users at the end of the day, right? Mm-hmm. If you are good... If you wanna be, like... And, and obviously this is, like, outside of my job at Semi Analysis, like, I have all these other things I need to do to, 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, n- 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. Like, you know, I think over a year, like, you know, I wanna say March, April, I started to- Sorry, which case study?

So the case study for people when we're, we're hiring, like, a financial analyst, like a- our core research seat or something. Okay. "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, and I'm 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, 'cause I... You know, there's some good submissions and there's some bad submissions. We pride ourselves in the case study in being good.

And, and honestly I always joke, like, well, you know, they're gonna start to beat the worst submissions. And so, like, that was our... That was always my base level. I have a base level of ca- is it better than a ChatGPT agent mode or Anthropic's Claude Code- Yeah... 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." Um, I've web coded some stuff on Opus 4, for sure, but, like, you know, it was, like, kinda 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 UIs, whatever.

It's like, no, it just one-shots it. And then you ask it to do something better, and it explains what you're doing, and you're like, "That's actually really good." And so I was like, 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. Mm.

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, when it was 5.2. Never really got it to work in the way seamlessly, agentically, like- Oh, so this is af- this, this is recent... Oh, no, no, no. So, so, so this...

My re- most recent when I was like, "Oh, man, the, the awakening", probably December 27th. Okay. December 26- You know it to the day. [laughs] Something like that. Something like that. I'm thinking 'cause it's between the days, and- Oh... I, I got home from Chri- Christmas, and I was like... My fiance wasn't feeling so well, so I had some time to mess around just by myself. Yeah, yeah.

And, and then also there's 2X usage limits. Oh my God, I miss those days. But I mean, now I'm addicted to Fast. But, but look, I, I was playing around with these coding agents just like everyone else should or d- 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 it never really, like, one-shotted 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 I can pursue. And, and can I... Is it Excel sheets in- primarily? Uh, no, no, no. Not Excel sh- 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 at human level, but honestly, i- 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- [laughs]... and that's why I'm good at stocks." No. It d- it doesn't matter. That's why sell-side is ridiculous 'cause, like, everyone's like, "I'm bullish 'cause, like, my EPS estimate is, is like 10% higher than, than the street." Yeah. And I'm like, "Oh, who cares?"

[laughs] Well, I mean, as you know, sell side, if we're gonna do this as, like, shots in s- uh, across the bow on si- on sell side, I mean, look, one of the reasons why Semi Analysis has such a, like, 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, you know, a company go public, so you needed someone to talk about it to issue securities, and so you had- So you're selling the stock...

So you're literally selling the stock. You have the... But you have to be independent-ish- Yeah... so your ratings, buy, sell, hold. [laughs] One of the biggest sales you could do is, like, when your, when your company IPOs, we'll talk about you so people know who you are. Yeah. That's the, 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 Semi Analysis is so good is 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. Mm-hmm. Um, we are really good at understanding when technology changes and how that impacts everything, right?

Because it doesn't really matter if one EPS is slightly higher or lower. It does matter if, like, I'm, I'm just giving an example, if AMD's Helios rack is super on time and is, like, out of the gate ready to make tokens on this day, because that's gonna be billions of dollars of difference in revenue for AMD, right?

Yeah. 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, you know, we care deeply about the tech- technology. Exactly, yeah. Yeah.

You know, I, I didn't mean to characterize you as like- No, no... you are young and fun, but al- also you're extremely damn good. Yeah. It's like a, it's almost like a triple threat, and I always, almost 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, uh, literate. But then also t- three, there's like this, like, X factor that is like, well, focus on things that matters, fuck everything else.

And, and I don't know what that is, but that, that obviously is the alpha. Yeah, 100%. [laughs] 100%. That's, yeah, that, that, that's always been the, the analyst PM conversation. It's like, "Hey, you know, there really is only one or, like, three things that actually matter," right? Yeah. Find me those three things. 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 SerDes is better than this SerDes. 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- Yeah... and like a, 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. Mm-hmm. Like, you go to some networking conference and you're talking to a guy who works at a company with, they're talking about their new email versus whatchamacallit laser, you know, I can't even remember what it, what email it's replacing, blah, blah, blah.

And you're, like, talking about all this stuff, and they have PhDs and you don't, okay? [laughs] 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 gonna impact this giant, you know, business at the top because it's all interconnected, but it's so complicated.

Just paying the tuition to show up is very expensive. So I, I think one way I'll bridge this for listeners is that this is the complexity of the problem domain. That's ex- there's extreme depth, there's extreme width, and you have to kind of throw human attention at all of it- Yeah... 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, the tasks, the task that it was nailing and the task that it's still- Okay... is not great at. Yeah, so let me specifically talk about my use case 'cause, hey, I am still a stock guy. I can't trade or do anything in semiconductor or, or AI world, but you know, I do still really enjoy stocks.

It's 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, kinda, like, start copy-pasting some notes over and putting it all together?"

And it's like, yeah, it does- That's why do you give Cloud Code? Yeah. Okay. [laughs] So I started doing this, and then I'm like, "Okay, but, like, add it, make the portfolio, run some basic risk stuff." And 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 can just do, like, iterative work. And then I was like, whoa, whoa, whoa, this is, like, a crazy useful tool that systemized 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? Hey, Cloud 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- Well, I wonder, right? Yeah. Yeah. I wonder, right?

So I'm like, okay, I heard about the fact that the Cloud Code has the commits, right, o- onto the public, onto your, your commit. It says, "Hey, signed off with..." I'm like, well, what, Cloud Code, scrape me all the commits, right? And you know what? Lo and behold, it pretty much did. Like, and it's like- [laughs]...

okay, well, like, I'm looking for this signature right here, copy-paste. It was 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's 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 perc- percentage of GitHub? You're like, chart go up.

[laughs] It's a huge deal, and I'm just, like, watching your... You know, I have, like, a cron job updating it every single day, blah, blah, blah. And I'm like, this is a huge deal. Like, this is the b- 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 would ar- you know, 4% in like two weeks or so. Do you know of PR Arena? Yeah.

It, it's the, it's the previous attempt prior to you. But somehow they didn't, they didn't, they didn't talk about... They, they just talk about merge rates. They didn't, they, they just [laughs] they don't, they don't plot it as nicely as you do. Yeah. Well, the, and also you wanna... Okay- 'Cause you, you, you asked, you had to te- You, you had to, you asked the question of what is this as a percentage of GitHub, 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 Cloud Code 'cause I'm just trying to really, really, really focus on that specifically. Yeah. Well, and also you wanna get, give an example. Bro, I didn't make that chart. Opus 4.5 did. Yeah. Or, or I think 4.6. I'm like, "Hey, I want you to do it in this style. This is the semi-analysis color scheme." Yeah.

This re- I had, like, summarized books about visualization- Yeah... and, like, put in, "Here's some style tips. Yeah, here's some-" Still a toughie. I, I don't, I don't even know, man. It has like, it has like... I had it go read, like, 70 books or something. I'm like, "Give me, like, you know, the, like- It's probably a waste. Like, you know, you're- I know, I know... defining a chart. It, it is a waste.

[laughs] 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, like, hey, if it costs effectively the same doing 70 as 3, 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, 90 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- Yeah... and generate information." Like, I think it's... That one was not ChatGPT. That was not generated. That was not generated, which I hate, honestly. I don't like that much, that one as much as, like- Yeah, it doesn't have the guidelines as the other thing, 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 gonna generate you a timeline." Okay. You can just do things.

[laughs] And I, I mean, it's, it- That, that is your catchphrase, right? Yeah, it- that is my catchphrase right now. You can just do things. Yes. And so people were looking at this from the perspective of people who are coding, and they're like, "Hey, just c- programming is a- automated, right?" Mm-hmm. 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, 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's 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? Yeah. "Hey, I care about X, Y, Z. Out of 10, score this," and then you can really do multiple things. It helps with the stochastic then. Do you, do you put it all in one prompt, like the, the, the, the task and the rubric for the task- Yeah...

or do you put the r- rubric after all the tasks are done? I, I actually have two versions of this. Okay. I'm like, "Hey, you can pull all this stuff together and just ru- run the pr- Yeah, yeah... for the rubric or whatever." Yeah. Or you can do the task and the rubric. It just depends on how you wanna do it. Yeah, because obviously if you put it task and the rubric, then it can iterate itself. Mm-hmm.

But if you put it after, then it's probably more likely to pay attention to the, the rubric. Yeah, exactly. And well, and the other part, part of it, too, yeah, it, it will iterate, but, like, the context rot doesn't ma- 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 sus- 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." Yeah.

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." [laughs] "Just thank God someone said it. This is a huge deal." There's this, like, meme where it's like- Oh, yeah. These guys. Yeah. Um, 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 shoots who. I've never read it.

Yeah, like, so, so one ch- 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 cloud code is garbled, you know, 5 million tokens. You're like, "Okay, it's time to put you down," because the context rot is huge. So yeah, this, yeah, this is an example where- So what, what are your compact practices?

Do you s- sort of aggressively compact manually or...? So I personally, with the one- new 1 mil, it, I feel like I try to do it all in one context window. I'm not doing ginormous projects. And the, the 1 mil is very new, right? Like- Yeah, 1 mil, very new. Okay. Very... But it's a big deal, too. Because- Yeah...

'cause your skills and whatever, your Claude MD is a percentage of 1 mil is so much smaller, so you just get so much more oomph, right? Because the s- the 200Ks are just wiping over and over and over. That's a big deal. I think that's a huge deal. And, and also with how the agents are working, the sub-agents will have their own co- context window, and then the pasting kind of, like, really saves that, that big, you know, the 1 million.

You just want a really high-quality, uh, project within that. That's the best in my opinion. Compacts just kind of start the compression of the noise. So- Yeah. Yeah. Mentioning sub-agents and multi-agent. Uh, 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, they did a report that was kind of like their, 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? [laughs] Like- Yeah... like, software engineering is, like, 50% right now, but what, like, there's nothing stopping it from continuing to go to 80.

[laughs] I think maybe what's gonna happen, this is, like, maybe a giant brain take. It has, like, data analysis in here, which that's what you were doing. [laughs] I, I... Yeah. That's, in my opinion, that is downstream of soft- Like that is- Yeah. 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 m- that's my belief. Like- Yeah... it, it, it doesn't make sense to build...

Like for example, this is a perfect example of 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 lo- Much worse. It's much worse. Even, even all the work they're doing on it? Yes, 100%. Okay. 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- [laughs]...

it's like a backwards compatibility thing where it does work because LLMs 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. Mm.

All of this information stuff, all this software stuff is just to be consumed by humans. Doesn't matter. Yeah. If they're just as good at, 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 LLMs 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 it, the Matlab pl- the Matlab pl- Matplotlib. Yeah. Matplotlib, yes. Matplotlib in a chart to me in a image. Fine. That- Oh, oh, you're trying to use Matplotlib? Yeah. Wow. Why? Why? You know, it's better, it's better- I know... understanding that code. Yeah, yeah, yeah. So why ever make a chart again? Yeah.

That's- If it, if it's better- It, it's just like it could be inconsistent with like the other charts that you do- Yeah... which I don't think you would care that much about. But, but I, I don't think we would care that much, but I think, one, our new charts are better than our old charts. Yeah, yeah. And number two, I think if it increases the speed of information, that matters a lot. Yeah.

And so I think we're much more ex- So pretty much the new charts will outweigh the old charts because it'll just grow. [laughs] So yeah, I think it, it, it is a little inconsistent. We have the same watermarking. Honestly, I think it's better than our old for- formatting anyways. Well, the first thing this looks, reminds me of is Bloomberg. I was like, "You guys are just like, you know, becoming Bloomberg." Ah.

Which is a nice- That's something, yeah... honor that it is to be in view company. Yeah. Couple things I wanted to sort of double-click on because th- th- this is just a Cla- Claude Code like brain dump- Yeah... right? One of the, the biggest sort of Claude Code shows in the world, which is subagents and agent swarms. Maybe if you, I don't know if you've tried. I have tried them. Any- Pick, pick either one. Uh, whatever you want.

I have a controversial opinion that Claude does not do RL on agents swarms or agent teams. Yeah, it's just an experiment. It's, it's just an experiment. Um- Yeah. Thank you. Thank you because- No, not [laughs] he could- We, we- exactly, 'cause the pro- 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 h- is actually really bad. Okay. Well, well, you know- It's, it's- We, we can't, we can't knock it because it's experimental, so. Yeah. Yeah. No, no, it's- But what do you, what do you try it on?

Well, c- it was like some big data analysis of like many, many different companies with different KPIs into a dashboard all in one. Mm-hmm. Um, 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 Kimi, Kimi 2.0 agent swarm is actually good. I have also tried that.

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

That, that was completely out of the hands of me, a normal guy, like three months ago. [laughs] 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 of 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 is in context- Yeah... aware of what's the best thing- Yeah... to be done. And yeah, so I think, I think it's interesting.

I like subagents 'cause 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- They had some post about how they did stuff- Yeah... where it was... Yeah, there's, there's a bunch of RL for, 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 names and stuff. Yeah, yeah.

Also, it's crazy how like the setup, you have to... It's a lot of compute. Yeah. To just run the swarm, I think it's like a 16 node of H100s. Okay. And you're just like, "Dang," so you and I are not gonna 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 gonna get better to a certain extent. They're, they're, you know... It's another benchmark and bench- like another benchmark to hill climb, right?

But then it's gonna 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. Yeah. 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, but Kimi show- It'll do better. It, they'll definitely do better. But the Kimi 2.5 tells you that this is already, boom, perfectly great new places to do more work on completely available to us right now. I think that's huge because if these agents get any better, like, I don't know, I'm never gonna sleep again. That's what happens. [laughs] So.

Honestly, like I, it's very interesting this sort of moonshot AI and, and this is a tangent. We're not, we're not really gonna focus on this very much, but you know how like the, the sort of AI tigers out of China were, were DeepSeek and Qwen and- Mm-hmm... then you were like, "Well, who, who are these like Kimi guys and, and these, these sort of newer names like I guess, I guess Minimax as well- Yep... with, with- Minimax, yeah...

and ZAI's been, been around longer but only recently much more active. Yes. So like I, I noticed that Kimi is much more in the productization phase like as, as seen, as opposed to like the Qwens of the world, the DeepSeeks of the world who don't really care that much. I mean, Qwen, because of how it's, it's attached to Alibaba, right? Like- Yeah, yeah... they, they have a way to productize it, but it's like, it's like kinda like the Gemini version.

They have so much stuff to do elsewhere, right? Yeah, yeah. But yeah, Kimi, Kimi's pretty interesting. They're pushing so hard. They're pushing- They've got everything. I know. They got Kimi Manus, Kimi Claw. Kimi Claw. [laughs] 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? Claw. Claude Bot. Claude Bot, 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, "Wait, wait, wait. 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 in time." Yeah.

I mean, so what I do is I just have multiple emails, right? Yeah. And there's, there's a safer email to give to bots- Yeah... 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, at- I, I'm gonna be honest with you, I'm gonna be honest with you, I wasn't impressed either. Yeah. That was the reason why people were freaking out about this whole book.

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.env there is an... Like, in the.em- env there is an API I told you to use for this subcase of problems, and it's in your Claude MD. Like, please focus up." Like, it still is, like, making mistakes. Yeah.

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.

Yeah. I was gonna... So I, I use, I use our, I use our Discord as, uh, basically, like, a, a, a way to just bring information in and out. I, I, I just saw, I saw this too where, where basically, like, a lot of people are just setting up things that they could've done in Zapier- Mm-hmm... with Claude Bot because they're like, "Well, you know, now I'm, like, AI pilled." But actually they just done it more securely with Zapier. [laughs] I- Which I think is kind of interesting. I guess.

I, I do think it's kinda interesting, but I think there's... But the, the difference though is Zapier... I mean, I remember I've tried to use Zapier before. Yeah. It's, it's also not very good. 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 even gets 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. Mm. 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. Yeah. Right? You, you see this happen in information, like, in the history of, like, compute.

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

So that's why, like, I think, like, you know, one of the co-founders left because they were like, "Well, I can't exist within this, like- Yeah... like, constraints." Like, like, yeah, you, you end up becoming wi- you know, the, the, the box will control you. Yeah, yeah, yeah. You, you, you are- It's your, it's your golden handcuffs. Yeah. It's just like your cage, you know?

You're gonna act like how you are in a cage. And so yeah, that, that sucks for... Honestly, that, I feel like that sucks for- The framing I have is, like, your priors become your prison and- Ooh. [laughs] That's pretty good. That's pretty good. That's pretty good. Your priors become... Yeah. I haven't vlogged that yet, but I should. You should. You should. Your priors become your prison. I like that a lot.

Coming back to Claude Code, just, I, I also wanna make this, like, the sort of Claude Code- Yeah, yeah. No, I'm sure, sure. No, no, no. That, like... I wanna indulge because, like, that's how a natural conversation goes- Mm-hmm... and I think people, like, enjoy that, right? Mm-hmm. And probably that's the only time we'll talk, we'll talk about Kimi. Yeah.

So, like, do you use hooks? Do you... Like, give me, like, the, the Dougal Offen Claude Code setup. I have 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. Yeah.

And so that way we have- Like an internal server that just has- An internal server that is, that, that is accessed by people with an API- Ooh... so that, like, a- all the semi-analysis researchers are able to hit, like, some basic level of context, 'cause I think the context is really what matters. Yeah.

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- I think hooks are very under- underrated, right? Yeah. I, I do think- 'Cause you can do, like, a Ralph Loop- Yes... with a hook. Yeah. Like- Yeah. I feel like I underutilize hooks. Okay.

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 Gastown 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 I was 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 wanna do so that, like, it prompts the, like... Not like, you know, a Claude within a Claude whatever. 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 1984," something like that. This is what I wanna do. I wanna... So, like, the... Actually, no. Let me just give you the best example. That is probably not gonna work. I would like to fine-tune a time series foundation model to predict NAND and, and DRAM prices, okay?

I'm gonna first start by gathering as much information as possible from all this stuff, blah, blah, blah, and then we're gonna fine-tune it, evaluate which ones we're gonna do. I chose Chronus 2 because of covariates, blah, blah, blah, blah. Try to set this all project up. We'll make it a Vercel dashboard internally for, for semi-analysis.

Maybe we'll external if we want if it's a good enough product, okay? 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, Serper or Exa or whatever you wanna 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, "Well, what do I use?" And- Showed me which GPU, whatever we're renting on an hourly basis. And so yeah, we just pull all this stuff together, then 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 LLM is probably not gonna work." Unfortunately [laughs]. Unfortunately, the- You said it was because of regimes or something else? I think so, it's regimes, yeah. Yeah, yeah. There's no way. This regime- For, for-... is so messed up...

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 LLMs. Mm-hmm. They have no idea. They, they've never studied finance. And like [laughs]- Yeah... and like, the, 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- Yeah... they've never heard those terms. Yeah. I had to explain it to the people at Cognition. And, like, yeah, the, 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. Exactly. And it's very, very, very hard because...

And the other thing too is you, you'll be like, "Okay, each of these, each of these are almost like a one-off onto their s- onto their own." Right, which reduces your sample size. Yeah, which reduces your sample size. And so then at the end of the ta- at the end of the day, you end up being like, well- [laughs]... it kind of just, like... I guess it's, "Here's some heuristics. Good luck. Have fun," right?

[laughs] "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 gonna have the magic LLM tell me what the price of memory is gonna be. Hey, it was a good weekend project. 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've paid for, plus, you know, en- enhanced data sources, and I have all the covariates.

So like, hey, WFE, what was the consumer sentiment? Every macro thing of all time. And you know what's really interesting is I am gonna just be like, "Okay, well, now can you go 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, e- 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 stuff." Like, you don't understand. No one's ever done that in the history of time. Mm-hmm. 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 than I think anyone else has at this point, especially for, like, the older ones, like the '80s and '90s and 2000s and 2010s.

And, like, when I did it first time the human grokked way brain is I went and I read these old annual reports, and I put it together, and I try to string a narrative through it. Oh, God. 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 com- to whatever and then make the narratives.

No, none of that 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. Yeah, I, I mean, I think the, the kind of pushback would be that then you don't have enough expert information to criticize the reasoning that- Yeah... went into the report that you're s- slopping out- Yeah...

and then on this- There, there is some slop. I, I definitely- But-... agree with the slop... you know- So, so I think of it once aga- so right now- By the way, that's also is essential for you guys if you get caught do- like, putting out some slop to your clients, right? Yeah. Like, you, you have to- Yep...

at, at one point be, like, extremely AI pilled and, like, you know- Yeah... you number one in the world at applying AI to your productivity, great, but also, like, you gotta- Yeah. You have to... So, so I think, I think the thing that's really interesting is this whole thing is a, a game of hygiene now. Yeah. Because I, I think it's like this is really hard, and 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- It's done the work... and I've, I've done the work. I have, like, a lot of, like, embeddings in my brain- Yeah... a lot of information. The vibes that have got 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. [laughs] 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.

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? I don't think that meta-level learning is there yet. We'll see if LLMs do it, right? Everyone who's spending $1 quadrillion in the world thinks it will. It, it better, it better happen, right? [laughs] 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 filled with 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 you- Are you, like, junior or new to the firm? Yeah, junior. Junior- Oh... to the firm. Yeah. Or junior, and, like, like, 'cause, like, you have to still do something. You can't just, like, slop it up. It's very obvious to me when it's slopped. Yeah. 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. Yeah. But for me, I know inherently what the 5% is. I can, like, write it away with some really easy heuristics and time and, like, be like, "Okay, well, this is the last 5% you fixed. This is what I believe. Just fucking make up these assumptions instead. Press Enter.

Okay, cool. We're good to go." You know? Yeah. 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'll be superhuman, but I definitely believe the... where we're at today, where we're... There... It's not there, right? You'll just compound all this noise, and it becomes just, like, garbled, just, like, all context rot.

But in terms of, like, the capability that is over hit, like, you know, the human CPU in these, this agentic swarm is very, very powerful now. Yeah. You know, a huge, huge, huge multiplier of what you're able to do. And for me that was enough to be like, "The... I feel AGI pilled," honestly. Yeah. Because if, 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, 100%. Yeah, yeah. 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- [laughs]... 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 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 agentic work is back to you, right?

I catch it making errors all the time. It doesn't always pull skills. Like, you can definitely tell, like context windows def- 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 it as information work, that's very valuable. Yeah. Amazing. So you do a lot of like client visits obviously.

I- 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- I-... you know, on the other side? I've definitely Claude Code pilled the analysts. Everyone in the New York office, I'm like, "You must try it." I like really tried to like- No, not, I mean, not your, like not the semi-analysis- Oh...

but your customers and, and all that. I don't think- Like I- so my perception is they don't adopt any of this stuff. 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." Yeah. "Of course. How could I not?" Yeah. This is like a, an, 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 useful or not, and I've evaluated the tool and I'm like, "Wow, this is an amazing tool that I literally like pried 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. Then I look around to everyone else and being 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- Yeah... Claude Code, Cowork, whatever is gonna be a base level of all information work very soon. Yeah.

And you know, you see one- my, my friend was telling me how his portfolio manager w- 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 gonna be a little different, but I think my moment, it feels like GPT 3.5 or 4 for me, where there's that first time where you're like, "Okay, I know it 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 it's like the analogy power, you know, where you can say, "Hey, this is the setup. Can you com- 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 ta- my personal taste where I'm like, "Oh wow, this is completely over the, the, the, 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. [laughs] There, there's some kind of automation chart that, you know, XCD has this automation- Yeah, yeah, yeah... chart, and I think I, we need a version of this that is the Claude Code, like- It needs to be much... But it- Yeah... but what's crazy is this, the Claude Code thing like murders the axis. Exactly.

It just strips everything like le- right or something. Yeah. But, but also like it's... 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, and it gets done, you get more turns at the wheel.

Yes. Whereas in, as a human, you, you get one turn. You get one turn. But with, with, with Claude Code, maybe you get three turns, and th- they the sort of review process is the thinking. Yeah. And you just need to get very good at review or- Yeah... or hygiene. Yeah, I think of it as hygiene. The thing that's like really gonna be painful though is like a lot of my expert opinion has been built by like, you know, it, it's like pre-phones and not, right? Like your attention span, like, you know, the children are cooked, okay?

[laughs] Like, you know, the attention spans are really bad, all this stuff. Like I don't- I read this like really sad thing where like, oh, and we're getting dumber or something first generation. I don't know. I'm not gonna... Maybe that's like- Well, you see the, the Coinbase earnings call? Yeah, I saw the current pricing.

[laughs] So, so 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 curve, it's gonna be very dangerous. It's like giving everyone- I know, it's true. [laughs] Yeah. That's so funny. [laughs] I, I think you should just do that. Yeah.

He- well, we als- we, we, we do, we do with some of the semi-analysis memes, you know? [laughs] And, and the thing is, you say some of this brain rot is like so bad, which is, it 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.

Stimming you. Yeah, it's stimming me, and you're like, "You know what? I can't look away from the, the Subway Surfers," so- Yeah. Yeah, you, you couldn't look away. I was-- I had to pause it. Yeah. Yeah, I was-- I literally, I wa- [laughs] Like, well, hey, there's like... Have you ever been to like a bar when they play like these like weird...

Like, there'll be like, like TikTok videos for lack of whatever, and you just watch it. And- There's TikTok bars in New York? Well, not TikTok bars, not TikTok bars. Okay. It's like there, there's like essentially a B-roll channel that they'll, they'll like sometimes play in public spaces. Sure. 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. [laughs] But, but I wonder, this Claude 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 gonna be a great reviewer.

One of the reasons why you know what, what makes that, that human fe- that loop be- Well, is because once upon a time you did that, and you could make the, to me like, "Yeah, yeah, idiot, you're n- you're not thinking about this problem in this way." Yeah. "You're missing this, this..." Like, you know, whatever. "You're not considering this 90%... You know, like the 10% tail," something like that. Yeah, yeah.

And so it's like, yeah, I know you said this, but, like, you know, the... I, I know g- I know that I told you the valuation is the only thing that matters, but, like, it's also 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 overwrites 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. Right, right. Because you're just checking for mistakes rather than trying to do original thought by just doing the work. Yeah. Yeah, I think that's, that is, that is a danger. Yeah. Yeah.

And that's, and that's what hygiene sounds like to me. You know, 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? [laughs] So, so I think that that's... It's gonna be really interesting- I, I mean, have you tried, like...

So, so I mean, the, the s- the way to model the sort of meta learning as, as element is, like, once a night you do a batch job of, like, look over everything I've done, like, like, extract some learnings, you know? And OpenClau, I, I think one of the interesting things I really liked about it was this Heartbeat.md- The Heartbeat, yeah, Heartbeat.md...

and I'm like, people aren't, like, excited enough about this, 'cause, like, well, this is the first instance where, like, the agents are just always on. Like always living, always reflecting- Yes... and- Like what is it, Soul.md too? Or- Soul I think is much more for character and like- Yeah... whatever, but like, yeah, Heartbeat is- Yeah, Heart- Heartbeat's the crown. 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 conte- like, it gets garbled. We know that Open, OpenClau 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's 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, get up and get some coffee, then come back? Mm. If it's that, and you could just fuck- you can make the Heartbeat.md consider the, like, the session to session, and like, "Hey, meta learnings," all this stuff, 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 the 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 AG." Like, like that's effectively good enough to do a huge information task- Yeah, yeah...

and have enough context, and be able to fetch it, and maybe, like, there would 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 kinda crazy. Like I, I wouldn't have put myself as AGI pilled. I think 4.5 was like actually- I think my own timelines have moved up a lot. Yeah. Are you guys watching GDPVal? I... To the best that I can, but I'm feel like I'm mostly just trying to- No, no, no.

So d- to me, when GDPVal came out, so I, I, I'll just- Yeah... GDPVal is, like, an, basically a, like a, a broader suite bench, let's call it, where it's like applied on every disci- every profession that is white collar that you can model, and, and it's above, like, something like 2% to 5% of GDP, something like that.

That's why it's called GDPVal. And they, they had human experts do the tasks and, a- as well as GPTs, and here's the results, right? Like, and where 50% is parity with industry expert. Yeah, yeah. Co- coin flip, exactly. Where, so like you can see the, the nice increase from 4.0 to Opus 4.1, and since then obviously 5.2 and Opus 4.5 have already exceeded, that we're at 70-something now. Yeah.

Which means models are consistently better than industry experts- Yes... at c- at these things. Yeah. So to me, like, this is the AGI definition, isn't it? Yeah, yeah. This is, this is- [laughs] And so, like, I think, I think the problem though, yeah, I would say that, that is the definition of AGI. So the thing that's crazy is 'cause there's like this ASI element that people are like really, really focused on. Yeah, we're moving the goalpost. Yeah, we're moving the goalpost, but I'm like, bro, I...

The, the goalpost, like I, I, I mean, we'll see if this is actually the machine god and Shogoth will come and talk to us and vibrate on our same- But what if I do think so? Okay. I, I, I don't... I, I'm being honest with you, I'm very open. I will change my mind often. I'm not... This is not something I feel intuitive in my gut today.

Maybe it's the next, next X thing, but when it comes to like the, the, the GDPVal version of this, yes. Yeah. This is, this is- Do white collar work... literally the white collar work, which is most of the, most of the Very boring, yes... knowledge mo- like, like actually it's almost all, not almost all, but it's a huge portion of all of work in the world. Yeah.

It's like now we just made... Like my favorite stat is like once upon a time 90% of people were farming, right? Now today less than 1% of people are farmers. It's kinda like this crazy shift where technology's gonna massively change the relationship with all of that, and it's gonna be like this 99.1 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- [laughs]... been my experience. But it just, it just feels like a massive moment's happened, like the, the steam engine's invented and, you know, the, the trains are here and, and everything- Yeah... is gonna change in knowledge work, and it's kinda crazy. The, there's a, there's a sort of economic cycle from my macro days that I'm, I c- I can't remember the name. I can 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. Mm-hmm. Then it discovers white collar work. Then it discovers... Then it builds like a very mature financial sector. Mm-hmm. And like though these are like, like a layer cake that all declining over time- Yeah, yeah... the, then, then the new thing's increasing.

So at, 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 I do believe that, yeah, 100%. Like, humans are very adaptable. [laughs] That's like my favorite thing that 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 5 to 10-year period, like 10-year gap can be drastic and crazy, and that's just societally wild.

But yeah, it's, it's happening in our lifetimes. It's happening in our lifetimes. Like, it's, like, happening, like, right now. [laughs] Like, it's just... It's really crazy. It's, like, very... And, like, ah, no, 'cause so this is, like, a complete side task on- Yeah, yeah... 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- Yeah.

Where... Why is it not showing up in our GDP yet, right? So there's gonna be... You know, some people are gonna be like, "Oh, you know, the facts." The facts, 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 mash- massively deflationary? Yeah.

And, and, and also I think one of the more interesting conversations I've had in a bit is, like, what w- 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, I or II or something like that. Okay. My spiciest take is I feel like GDP itself is gonna be very, very challenged by AI because information work...

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 of that's gonna be. Is it gonna be so much increase in supply it's deflationary? That seems to be, like, a real concern. [laughs] It, it's possible. Yeah. And, and then we'll figure out how to use it, but, like, there may be a Great Depression of AI- Yeah... where, like, we figure it out. [laughs] Yeah.

Well, I, I wrote this whole thing about railroad stuff 'cause it's my favorite- Okay... my, my favorite capital cycle. Is this on Fab or? It's on Fab, yeah. Okay. I can't rem- it's, like, railroad Fab. It's about the, 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. Mm-hmm. It's not even close. Like, we- What, what numbers are, are you thinking of? Like, what, what could- I think, I think a trillion was... A trillion all in was essentially the real dollars for- Okay... version, and I think we are well past.

Like, we, like, whatever, this year, and it's cumulative, right? We were well past that. I think railroad, the reason why it's so interesting is 'cause honestly it's way crazier. But, but prob- 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?" Yeah. And then banking got... I, I, I kid you not, like, one of my big takeaways is banking effectively got invented by railroads. Oh. Because there's no need- You have to finance it... to finance it. Yeah.

So much money was needed that, like, effectively 85% of all paper or whatever was essentially just railroad debt. Yeah. One of my favorite anecdotes was before there was a federal bank, a federal reserve, Andrew Carnegie was the federal reserve. [laughs] Yes. Yeah, there were individuals. Yes. [laughs] 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 45 years of, like... There's three cycles actually. There's three boom busts. Same. I don't know if it'll be quite that long. All the cycles kind of collapse. Yeah.

Or that the- Because, you know, information- Information... moves around faster now. 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. Yeah. Where it's like it's all in one instantaneous up down, I think it's gonna look like mult- some multiple cycles. So yeah, kind of just worried about railroads. The...

There was, like, a baby railroad cycle, then there was a huge railroad cycle. The modern railroad was 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 were, like, high single digits for... su- sustained for, like, 10 years. Yeah.

But what's crazy is, like, that amount of spend is, like, we're, we're, like, well on track for that. It- Did you do percent of GDP 'cause I think that's- I think-... the, the way you make it convertible. Stargate itself, 2% of US GDP, and I mean, it's gonna go up, like [laughs] 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, 4...

It was 4.8% of GNP and 25% of total gross fixed capital investment. Okay. So 25% of investment every year and 4, 5% of GNP- Yeah, yeah. I think we're there. Yeah. You know, Star- Stargate plus Anthropic plus whatever. Yeah, yeah. We're, we're right there. xAI. Yeah, so we're at- Meta... the railroad build-out- Yeah, yeah... which is, like, at one point, like...

But the thing that's crazy- And we, we, we should exceed it, like- Probably, yeah... yeah. No, not, not probably, like, we should. Yeah. Okay. Like, this is bigger. Yeah. [laughs] 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 worried. I'm like, "Dude, where are we gonna get all the money?" That's, like, such a, like- The pedestrian concern. [laughs] Yeah.

It, it, it's not a pedestrian... I mean, this is what happens every capital cycle. I worry, like, we must answer the Middle East. We'll flip the thing. We must, we must. The... Well, well, this happens every single time, and this is the reason why the, like, bubbles happen, right? It's like we essentially get so big where it's like this must be built. It doesn't matter the price. Yeah.

And then all of a sudden we look at it, and it's like, ooh, that was a steep ass price. [laughs] But I think... I mean, the thing I think about this is, like, how... I think one of the big pictures, 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, the...

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 wanna say a number go up forever 'cause I feel like that's, like, g- intellectually dishonest. Yeah. But Claude Code, 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- [laughs]... 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- I mean, some people buy, like, five Maxes, and then- Yeah... they will- 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, if any sense. [laughs] 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 easily. Yeah. Like, if not more.

Like, I don't understand, like, what's the ca- Like, I can't price it. I have no idea the elasticity. Yeah. You pay for a perfectly compliant junior analyst. Yeah. Right? And so- What's able to- What's that cost? Like- Yeah... 90K. That's able to work in parallel. Yeah. Like, you can have 100 of them. It's kinda crazy. Yeah. Yeah. Oh, yeah. So it's- It's a skill issue if, if you cannot manage a junior analyst that is- 100%...

20K a year. Yeah, 100%. Which, like, I mean, okay, like, you know, skill issue is, like, is your fault. But no, like, we have to learn how to do this. It's, it's- Yeah, it's like- It's at, it's two, it's three months old. Exactly. It is two month- [laughs] That- that's the correct way to put it. It's like- [laughs] It's, it was definitely a skill issue that you didn't know how to get, like, your settings on your iPhone to work. We know at one point, one point in time.

But, like, in the very first month of us having it, no one's gonna be like, "Yeah, you idiot, you rube. You don't know how to use your n- completely new technology that got birthed last month." Yeah. I think it's just about a ti- It's a bit of time, and it's, like, kind of interesting 'cause you're, like, watching...

I mean, what's cool is that if you're, like, on this absolute bleeding edge, you get to see the design patterns, like, blossom in real time, and, like, we have this, like, really old- 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 at Semi Analysis, and he talks about how- Wait, who is it? Tanch. Okay, so, like, he said this, like... We, we, we had the conversation one time.

He was talking about, like, early internet, how, like, it wasn't actually sure if, uh, the browser was gonna win. It was, like, a remote web file service. Some people thought, like, "Well, it just... I'm just gonna reach in 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, who the hell knows?

They 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 'cause there's many different ways this could go. Yeah. And we're gonna have to kinda 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? But you get to watch it, like, front row seat right now. Yeah, yeah. I, I mean, m- my biggest one, and I, I'll, I, I do wanna bring it to Semis in, in a little bit, but is the IDE. Two months ago, they... We had Stevie Aggie from Gastown talk about how 2026 would be the year IDE died, and I al- Like, two weeks ago, three weeks ago, I recently was like, "Shit, he's absolutely fucking right." It's over. Yeah.

[laughs] I, I'm really wondering too because, like, IDE... So, 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's just gonna be first, but the year, like, of, you know, Excel is dead for finance- Yeah... and, like, it's, it's, it's- Excel is the IDE for analysts. E- 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 done- And gone...

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- The series... subfolders- Yeah... keys, how to use this, how to use this one, 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? Yeah. The agent with the information that can perfectly retrieve and analyze stuff is gonna have the ability to, to s- to pull that all together in a better UI than it was with no legacy whatever. I think all of that is dead, and, like, this is why I'm, like, my, my spiciest take of all is, like, Microsoft has a lot to lose. Mm. I think they have the most to lose of everyone. Yeah.

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 generalable. 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 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. Yeah. I will never make a chart in Excel again. I actually believe that. 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 would know this, but, like, my very first startup was a, an attempted Bloomberg killer. Mm-hmm. Uh, Sentieo- Oh, okay... DK Office. I remember. I remember. Oh, you remember DK Office? Yeah.

Yeah. No, no, I- Oh, yeah, yeah. You are one of the few. You had OD- Sentieo. I was a Sentieo customer. You're a Sentieo customer. [laughs] I was a Sentieo customer, like, I rolled in, dude. I remember being like- Uh... "How dare they acquire Sentieo?" I had a, I had a patent. I, we filed for a patent- Wow... 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 w- amazing, and then it's the, the data feeds. It's actually not really the UI. Yes. Yeah. It's usually the UI. 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, uh...

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 on the curious and not hyper interconnected, which I would argue that I am at semi analysis. Like, for example, I'm trying my absolute best to just rip Bloomberg out.

We're going to Fax.it API. Like, all- Yeah... all-in API with a cloud code is my belief of the future. Hey, verifiable data source that you trust. Yeah, yeah. Hey, scale- But you guys could do it. Yeah. For traders, we're so- For, for tr- no way. Yeah. I understand. Like, there's an information network- Yeah... that's, like, outside of this- Then you do deals in IB. Yeah. Right, which are tracked by the regulators.

Yep, 100%. Yeah. Yeah, 100%. It's totally... I completely- But as an analyst, yes. For as a analyst, yeah. And so, like... But I just think that, like, okay, that doesn't really... So you're right, the core cashflow cal 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 I was like, "Actually, no, I don't care anymore." Yeah. [laughs] The IB's... My util- utils of, like, marginal value from, from IB is, like, now outweighed by how, like, clunky this is, and I wanna just make some charts. Mm. Right? And so that's- Like, like immediately you save 10 to 20K- Yeah...

for switching down. Yeah. [laughs] There you go. [laughs] It's amazing. Yeah. By, by the way, what was your cloud code end of year prediction? 25? Yeah. I want you to know, I sandbagged the ever-living shit out of that. Oh, okay. I, I just believe 25 is very... Like, I... Like, this is, like, a... The rate it's on is, like, whatever, 50 or something like that, but I think... I feel... I wanted to give a 95 confidence interval. Mm-hmm.

I think 25 is within the 95 confidence in- interval. Sure. So it's, so it's between 25 and 50. Something like that, yeah. Yeah. It's, it's just absurd. But, you know- And it could also be Codex. Co- Are you also watching Codex? Yeah, yeah, yeah. So, 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 cloud code, Codex, agent, whatever all in percentage that we can publicly see, I would argue the f- the ratio outside of that's probably gonna be higher too, but whatever. Yeah, I think together. Yeah, we're watching Codex. I actually think Codex is... Codex is pretty good. 5.3 I think. So we had the whole thing. I...

'Cause, like, I wrote most of the articles, like, oh, token efficiency, the context rot, all this stuff. Is it the same one or? Yeah, yeah. It's in the bottom. It's in the, it's in the, the paid section. Okay. Like I said, but, like, TLDR, I was like, "Well, you know, the reason why cloud code is so good, Anthropic is so good is 'cause all of this token efficiency.

The token efficiency is better than ChatGPT," all this stuff, blah, blah, blah, blah, blah. And then, like, 5.3 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 5.3 Codex is awesome in, in coding though. But you can watch it.

Like, the reason why I like Opus 4.6 so much is because when I'm using it, I'm using it for, like, coding 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 'cause it's RL'ed to be so good at coding to win on SWE-bench that, like, you're trying to use it for general information, be 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, uh, maybe you can give it to API or whatever. But it's like, great, I'm ma- I'm scra- I'm creating a piece of scraping software to go look at these websites. So 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 in the RL that I think it isn't generalizable- Mm. Mm... 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 it's very, it's very co- it...

Coding Codex is coding pilled, and so that's what... But I, I am very optimistic actually on Codex, and we do track quite a bit. I, I... You can... They have a meaningful amount of thing share. You could see the Bloomberry. They have the, the sh- the, the chart bloomberry.com. So the cloud 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 'cause 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 Roon will tell like, blah, blah, blah, we're not accounting for it. Yes, we didn't account for it, but, like- Okay... I, I, I think Codex is better.

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, 'cause the RL stack on Codex 5.3 is amazing, like, it's very coding code. That's when, that's when the... it, 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 Co- Copilot? Yeah, ta- look, look, okay, that, that's, that's a bubble talking, right? Okay. That's a bubble? Yeah. Yeah, that's, that's the, that's the SF bubble, like the- Yeah, yeah. Yeah.

Like, you know, there's, there's, like, all these Windows users and you don't talk to them, right? Like, uh, you do, but we don't in, in San Francisco, and, like, that's just, that's fine. That, that's definitely bubble talking. But yes, Copilot has a billion in ARR I think at least. Yeah. What's crazy is cloud code is, has a ratio. The- their attribution of cloud code and ARR is 2.5. Yes.

So that on this, the daily install counts, right- Mm-hmm... is an order- Which, which is by the way, just the VS Code extension, right? Yeah, I know, I know. That's not even the default way to use cloud code. Yeah, you're right, you're right, you're right. Well, CLI, NPM, DOML is another way to track it. Mm-hmm. But I think they have, like, their own cust- their own installer now.

Anyways, all in all, definitely heard, understand it's very hard for us to, like, actually track it, but, like- I, I'm not, I'm not gonna say anything. I'm just, like, I, I think Codex, a big thing I'm watching as well is Codex back because they re- they reported, like, Jan to Feb they doubled users. Yeah. Okay.

So, so I have some s- not skepticism, just 'cause they have such a big ChatGPT 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. But- I have an answer for that.

Uh, Alexander Ambrigos 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. Okay. Yeah. I guess I just saw a modal be like, oh, try Co- try Co-... 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, like- Yeah, yeah, it's...

They, they actually read the app, the, the app launch. The a- the app launch is actually pretty good. So, so yeah. And, and I think- I'm pretty bullish on that, honestly, especially for coding, 'cause it's, like, very coding code. I just can't get it to work as well for non-coding stuff. Then my, you know... Uh, you use Conductor?

No, I've not used Conductor. Oh, okay. I, I thought I heard you say on a podcast that you use- No, I've not used Conductor. But so, so basically, like, the, the, their argument for any la- any first-party app is that they're only gonna prefer their own first-party PN. Yes, 100%. Which, like- W- they're already- Fair play...

they're already doing it. Like, like, I feel like this is how they're gonna differentiate, right? Like, they're going to- Well, then, then you have a Conductor where you can use Codex and Cloud Code for different tasks, uh, as you see fit, and so this is the, the clean superset. No? In theory, yeah. [laughs] But, 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- Mm...

of building on top of something that is growing very quickly and has- Oh, okay... all the money and whatever. Yeah. Like, I just think my favorite one is, like, platform as a service, if you remember that one. It was, like, infrastructure as a service, platform as a service, SaaS, s- software as a service. Mm-hmm.

And like, "Oh, this platform as a service," and it's like it always just ends up being in the middle, so it just gets eaten by one or the other. Mm. 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 u- I like to have them, like, review each other, like, having them yell at each other is really great. I might actually try this soon. I haven't used, I haven't used Conductor personally. Yeah. Uh, I've mostly just been, you know, going deeper into the psychosis. Yeah, and this is, as a c- 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. Exactly, exactly. But if you go all in one cloud, you can exploit- Yeah... uh, the sort of minor features of everything, and, you know, the, it makes a market, and you, there's no right answer for everyone. Exactly. [laughs] Yeah. Yeah, I mean- It's- Yeah...

that, that's why, that's one of those things where, like, even the really small percentages in AI still really matter because they're, they're huge, and, like- Yes... people are very happy, very productive, make money. 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- Like, we like the horse race, right? Yeah, I like the horse race.

Number one, number two. Ooh. Yeah, yeah. You know? [laughs] Yeah, no, no, I know, but then you have to, your brain also have to be like, "Number two is really big, too." [laughs] And then I, I just think, like, for me, someone who likes the history of all this, like, 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 all together. Tech during the, like, 2017 and 2020 years, so boring. Yeah. [laughs] Yeah, at least for me anyway.

I thought it was pretty boring too. Yeah. Sorry. Oh, I, I interrupted you in mid- No, no, no, I, I, I remember what I was talking about, bro. That's it. It's just fun time, it's a fun time to be- It, it's a- Yeah, things are happening. 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 "Dougal Loughlin thinks Microsoft is out of AI," and... Ooh. Did you, did you not see this?

Okay, so, so, so I wouldn't say out of AI. No, I did s- Okay, so I didn't watch it. I never re-watch these things. It's- Okay, so how I think about it is- But, like, uh, uh, you, you said things like- I-... Microsoft is scaling back investment and- Yep... yeah. So, so, so it was the previous conversation I was talking about- Yes... how Microsoft has the most to lose. Mm.

They have the most to lose of everyone in the entire world if you- 'Cause they're the, they're, they're- They are-... they're the software company... they're the horizontal software company. If ChatGPT is dead, they're [censored]. Yeah, exactly. They're the horizontal software company that humans use their software to do information work. [laughs] Okay? No... Like, I cannot paint a bigger target, okay?

[laughs] I cannot paint a bigger target. And m- m- So, so it's worse. Yeah. Well, so, okay, that's another two numbers. But Microsoft is automatically bigger than Salesforce. Yeah. Yeah. But the other thing too is they have this Azure business. I don't think they're completely out of the race. I'm like, you know, that'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 r- 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 protect us." 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 m- the, so that's the problem is the moa- 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. Like- Yeah. Yeah. It, it is exactly like that, and so it's just, like, this weird process where that's a terrible setup too 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, 'cause you're a Team T guy. You become dumb pipes, okay? That is the, the Azure becomes ca- what is it? Charter, right? Ooh. Yeah. But then, or 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-year Azure revenue. And meanwhile, if we actually had to value Microsoft ex-Azure, the multiple would be really low right now.

I think about that all the time. What would this trade ex, ex-Azure? So it's just Microsoft. Yeah. 8 times earnings, 10 times earnings. Mm. Like, it was trading like that before actually. Oh, geez. And, like, like, remember the 2010s era when it went all the way b- down to, like, 10 times earnings in the Steve Ballmer era, and then it inflected outward as it did O- Oh, with Azure? Yeah, yeah, Azure and O365.

Yeah, there we go. That's right. Yeah. Yeah. Okay, I don't think you have the answer, but I just, like, this is one of the most bizarre, I wanna call it a [censored], but I don't know if it's a [censored] or not even. Yeah. 'Cause 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? Like [laughs] 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- He's being responsible. Yeah, he- he's being responsible, right?

But the problem is that responsible, this is 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 to... 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 pilled, okay? He doesn't have any fear of the Shogoth.

Mm. He thinks it's just like a new- It's a tool... it's a new Lotus- Yeah... or 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.

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 gonna hire as many barbarians. We're gonna pull... You know, we're reinvesting these walls, pull in together, and try to defend the core moat," 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 gonna win it all." Yeah. Because they already have all the distribution, and they're gonna have the perfect product, and boom, they're gonna have this giant business that makes them, you know, whatever, 100 billion, $100 trillion, okay?

Whatever number you wanna say. But reality is Claude for Excel, Claude for PowerPoint is literally exactly what it's supposed to be. Microsoft should have built it. Mi- Microsoft should have built it. Yeah. And so now you see the barbarians, and this isn't even your primary barbarian issue. [laughs] The guy who... You know, this is like the- This is the, the- This is like the tribe over the hill... clone- Yeah, yeah...

of barbarian. Yeah, this is the tribe over the hill. You know, and tho- the tribe over the hill is like, like, you know, on a, on a nightly raid, easily sack 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 gonna have to do this is totally different.

I think they're gonna keep, I think they're gonna keep pulling back in. We're starting to see that. Like, they're gonna do internal training. They're gonna try to do more foundational models. They're gonna try to use the weights they have access to. This MEI? Yeah. Okay. Yeah. But I'm very skeptical because their execution has been kind of dismal. Well, you know, it remains to be seen. They, they, they're...

They do have in- you know, they are one of the big- Biggest companies... biggest companies in the world with all these resources. Yeah. I, I, I just wanna push back on the, the sort of i- responsibility part. Like, you know, so Oracle picked up the slack. Yeah. Is Oracle being irresponsible? You know? [laughs] So, so I, I'm actually... If we're gonna talk about Oracle, I think...

So let's talk specifically about Oracle- [laughs]... 'cause this is where we're gonna go. I think Oracle was irresponsible because the magnitude- Oh... of what they did. Okay. It, it... Like, the thing is, like, I think the slack, they should have done it, but, like, the whole setup in my opinion on Oracle is own 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. [laughs] Right? They, they said- Promised the world... they promised the world. Then they pr- 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, whoa. You did this much?" Right?

Capitalism is kind of like, "Hey, hey, pump the brakes." Hmm. And, and seriously, I think that if they just tiered it out better, meaning that they didn't do it all in one periods, 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 g- glide path without going up, down- Back...

up, down- Yeah... 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- The debt index. Yes, of the- I, I have zero familiarity with this stuff. Yeah, of the... Hey, I'm, I'm pulling some numbers up.

I did the numbers forever ago. I'm like, I, I hallucinated them. Whatever, forget all the precision. Let's just say all of the investment grade TMT is, like, 500 billion, okay? I think Oracle was, like, 135 of it. Right. 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 it lit- 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've massively overshot, and I think that we're, like, a weird bottleneck I never ever, ever, ever, ever thought of s- thought we would ever, ever hit, and I think you could appreciate this uniquely, is like one of the bottlenecks is, like, supply of debt into the market. Mm-hmm. Like, like, capitalism cannot, like, absorb that much capital demand- Mm... because the order of magnitude, it's totally different.

These hyper-scalar 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, a $10 billion l- 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 leg yourself into it if it's gonna be like that. But they, they like super jolty did these big huge incremental ads 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 perspective, and I think they can pay for it. Hey, you wanna, wanna hear me go the right way? Microsoft could have just internally funded this and, like- Yeah. [laughs] Fine. Microsoft could have internally funded this. It would've 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 a 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. United States, correct. Yeah. And, like, just from a, like a, like a p time, like the math perspective, no one else... They, they're better than Oracle.

They, they just by their credit rating, they have a 2% more profitability at a, at a capital basis. That's, that's like you can't beat that. Mm. 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 pull- are pushing in for the supply. It's a really weird game. I think Microsoft has to choose, choose a direction. We'll see, we'll see. We'll see.

I mean, it's- That's what's gonna make it fun. That's yeah, man. I'm more than happy to change all of my opinions when new information comes around. Yeah. And I'm sure it will- It will... we'll have more information as it emerges. I wanted to touch on TPUs and then go into memory. Mm-hmm. TPUs will hopefully, I, I 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. [laughs] I think Sergey doesn't want to lose. Yeah. And I think the thing that happened was up until like, you know, he wasn't- no one was there. A part of the whole Deep Mind story was we will hoard all the TPUs- Yes... because we were first, you know? Yeah. And, and so like why so? You know, why, 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 are [laughs] using our products anyways." And, and like, 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, 'cause the other thing I think about is there's a lot of different ways to, to like break this down. One, we wrote about it in TPV8, like whatever we think Rubin will be much more competitive. 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, 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 the most valuable company in the world, what's the value of TPU in Google?

It's, 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. Yeah. 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 right, some of them are Me- they're all over, right? Yeah. Like the, the core team that did most of the engineering have like since 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 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 'cause it's not like they have old AMD chips. Mm. No, they're not upgrading from anything. Mm. And so when you have that number two place, you have to like, you have to win definitively and then also you have an opportunity to win, win again next year. I think the install base issue's been a kinda huge one.

And so TPU is at the point where the software ecosystem is mature enough, the hardware is definitely mature, the networking's 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. 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, I don't know if this is something that affects your analysis at all because I don't have any appreciation for the, or the, the sizes that we're talking about here, that JAX is helping TPUs win or, or JAX is winning relatively to PyTorch, at least in like the academic arena, which is a leading indicator of what it's gonna be used in, in- I do not have as-- I, I don't have a special purview on that.

The thing I'm most excited about and like very much TBD we'll see is Inference X will have TPUs eventually. Mm-hmm. That's something we wanna do longer term. I think that that will really show in numbers what's- As a benchmark? Yeah, as a benchmark. Well, how do you expect them to come in? Pretty good on a price basis.

[laughs] 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 TPU v7 as possible if you had it in a perfectly unconstrained world.

It would probably be at this exact moment, like, you know, the, the hottest kid on the block until Rubin comes out. But the reality is supply chain really matters, and they're, that just, that's not available. And so that TCO, that TCO advantage is at this absolute biggest aperture than like Jensen essentially gets his-- 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- Yeah. Yeah. What, what can you do? It, it's this cascade, right? They-- which I think you've talked about. Yeah. Like all the way, it goes all the way back to the fabs. Yeah. [laughs] Yeah. Well, it's interesting 'cause it's like even more than the fabs, like, like on the optical that- Is there a link I should be pulling up? Yeah.

Yeah, that, that, that's it. That's it. Just V7 or- Cool. Yeah. 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 kinda 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 V8 we just don't think will be as competitive to Rubin, and that's when your special window starts to close. What's the technical reason why? HBM. Okay. HBM4 versus 3. And then- And that's a secure, is- the strategic decision by Nvidia? 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 ship. It is a, it is an F1, like it is as maxed out as possible. TPU is kinda 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... with infra. Yeah, that's what they do with infra. Yeah. But I think GB200 would have completely mogged, 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's the best. They own the entire supply chain. Mm-hmm. They really do. Like, you think all those HBM 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 'cause he- he needs to get the chips, okay? So yeah. This is Samsung's chairman? Yeah, this is Samsung's chairman. Yeah. And then who's the, who's the other guy up here? 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, Hyun- yeah. Do you think Google was what, what, you know-- Do you think Sergey was out in Taiwan drinking to, to get supply? No, 100%. 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 gonna have, like, so much of that kind of constraint there. And so it's, it's gonna be really interesting. They're gonna, they're gonna get the best, most performing HBM. They're gonna be first on the roadmaps for even more rack density. They're gonna have, like, the best connectors, the best...

You know, the whole system will be once again turbo jammed again for as hard as it can be. Mm-hmm. And the people who made V7, like, they made-- the, the chip was done like three or 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.

And so if that gets better, which takes some time, I, I think our current read is that, like, the HBM specifically and the memory scale-up is gonna really go in Rubin's favor, and so that's the big difference. And I think, as you know, that's what makes the context windows, that's when we're able to do bigger, bigger- Everything. Everything, yeah.

And so they're gonna really jam it, and that's, that's gonna be a huge advantage in performance. One thing I love about your analysis is it's not actually just the context windows. It's not just the KV cache. We also have to offload it to non-HBM. Yeah. Every other part of the memory, it, the, the-- it's, it's like su- such an interesting cascade, uh, waterfall of, like, just, like a dim- a short squeeze and, and everything.

It's not a short squeeze. It's like a supply squeeze. Yeah. The sup- I mean- So it's like, I just wanna like- I mean, if I- Three to one ratio of like- Yeah, yeah. Okay, so to- So three to one- To quantify to- Sorry. No... three to one to four to one ratio. I think, I think next generation-- 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. Yeah.

Scroll down somewhere and you'll see. Yeah. So, so basically for, for listeners, it's the idea that, like, when you convert to HBM, because there's a huge demand for HBM, it takes three times. One HBM sort of unit is like three times of the other sort of DDR or whatever, right? 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 ma- I actually wrote a really funny piece, like I, I called it like super oil, but let's-- 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, 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, the, the last analysis I could put to is like '96 or something like that. Seriously, it's like a, a, a historied one.

And then meanwhile, we have all this new demand, HBM specifically. The highest end, you need the most memory. The, the trade ratio is crazy. So each, you know, each bit of, of HBM is essentially a four X multiplier onto DRAM. And then now-- So we, we've 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 flow negative. No one's spending a cent. Okay? People could go bankrupt, you know? Yeah. 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 KV cache offload. And then boom, you're just looking at the supply-demand, and you're like, "Yeah, this is not gonna 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. Yeah. 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-- I, our post, our conclusion is like we, we could see DRAM prices like go up 100% again.

Like it's, it's gonna 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- What does that look like? Where hyperscalers maybe purchase less or something like that on the margin. On the margin. Yeah. 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 li- 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, you pull forward as hard as you can. Okay? You pull forward, you double, triple order, and then the DRAM and the 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 gonna constrain us anyways. We're gonna like kinda chill out the orders."

And historically, that's when the memory market-- That, that's what causes the crisis, the, the prices to drop. Realistically, just looking at, at the aggregate demand of how much we've purchased in term- terms of power, it just seems like the gap is just huge. It's completely off to the point where the most obvious logical leg of the AI p- The AI trade is effectively investing in memory- Capacity... capacity. Yeah. Well, not, not to Oh, yeah...

it's, you could say SK hynix and, and Samsung- Uh, Micron... Micron, and all the, all the semi- like, Semicap has been ripping off. Which, like, by the way, when I was in Valley as me, we, a majority of m- a lot of money we made was just being on Micron. Yeah. In, in the last, like- Yeah, it's a good example.

Yeah, so, like, you have all the Semicap stuff, right? Like, all the- everything that is even remotely related to investing in capacity for memory, that is, like, the ultimate bottleneck right now. And also for listeners, it's gonna affect, like, your phones. Yeah. Like [laughs] Yeah. I- Apple, I think Apple's moving the point- I had to buy a, I had to buy a SD card for this thing. Yeah. How much?

I think it was $100. Yeah. That's, that's nothing too. [laughs] That's- and, and that- 'cause it was like that- that's just the NAND side. Dude, have you looked up, like, like, I wanna say, like, 64 gigabytes of, of DRAM? Like, like I'm moving up. Like, I, I need to refresh my iPhone. Mm-hmm. I'm moving it up because I'm doing this research for [laughs] Big- oh, yeah, you need to do as soon as- Just buy your iPhone now. Yeah, yeah- Do it... you buy your iPhone now because what's gonna happen- [laughs]...

is when iPhones go into the spot market- Mm-hmm... it- prices are gonna go up 100% on them. That's insane. And so they have to pass that. We're gonna be buying, like, old iPhones and then taking them out for the memory [laughs] There's- no, no, that's actually ha- 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 breed wins. But this CXL technology that kinda never really took off is gonna take off just because what they're gonna 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- Oh, okay. Yeah, yeah. Exactly that. Yeah. 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 con- or how bad the memory constraint is. Yeah. Like, yeah, that, that... You know, I, I was, like, a CXL bull for once upon a time, and then became very clear it was gonna die, and now 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.

Yeah, it's incredible. Yeah. 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 in- 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.

I've actually been thinking about that a lot. And, like, this is not gonna go to 100 million context windows. It's not gonna go to trillion. Like, we're... This is it. Yeah. This is it for, like, five years, 10 years. Pretty much. Okay, so the question is, will c- 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 I- hey, his- his- history of compute, what happens is you have to, like, you have to, like, make a curve of the sup- of the context windows. Like, does free context windows go to, like, 1,000? "Hey, you can use ChatGPT free now, but you c- 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 one million. The one million context window is like a mansion, you know? That's the real- You live in a mansion right now. I live in a mansion right now, yeah. Oh my God. And, 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." [laughs] Like [laughs] That's like, that's like... Yeah, you're gonna have to, you have to learn how to use it well because of the DRAM, yeah. [laughs] No, so I actually have a question. I know 'cause, like... Okay, long context to me makes a lot of sense, right?

Hey, that's like, like, that's like the memory scale-up version, like, if you're thinking about chips but in the AI world. I just am, like, always been curious 'cause it does feel like, at least in my stated experience, really long context, like, you see in the papers, they kinda, 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 100 million context actually matter if, if we- if it's not possible to use it all?

They... You know, versions of 100 million do exist today. They're, they just suck in various ways. They, they're not actually applying full attention, right? Yeah. You can, you can use a state-space models or even, like, a LSTM to, like, you know, to, to process 100 million tokens, but you're, you're not paying full attention to all those 100 million tokens. Yes.

And so I think, like, the way that we have context models today and those curves, they will improve over time- Yeah... and they have, they have been- Yeah... improving a lot, but we're, we're just not- we're never gonna use all of them, but we'll, we'll improve, like, on the algorithm side. Yeah. I think for me, what matters is you're, you represent the physical constraints that us, the software side, can never surmount- Yeah... because it's a physical constraint. Yeah, but- And, well, I mean, it just, it just...

Like, physically we cannot dou- we can't even- Yeah... we can't even double the number unless it's 10X. Yeah. Yeah, like, what, what, what's the point of talking about any of this? Yeah, what's, what is- what's the point? Yeah. I was gonna say, like, we could, we could in- we can invent a lot of things. Context rationing's pretty good. [laughs] I really like that one.

Context frugality or, like, budget or something. I feel like everyone's gonna be like, "Whoa, whoa, whoa. You're running out of context wi- window today." You know? [laughs] Like, maybe that's what happens next year. We're, we're, we're- Jesus Christ... we're charged on context window. And then the, the one of the more recent obsessions is recursive language models, which again is just reusing the same context window on- Yeah...

over and over and over. Yeah. I've been pretty interested in that, but, like, to be clear, I'm a total idiot. I have no idea. Claude tells me what's- You're the, you're the semis guy, man. Yeah. Like, you're, you're really good in your stuff. One thing I wanted to, to spot check on was Talus, uh, which can help you- I have not, I have not messed around with it. Okay. And you don't have to mess around with it.

It, it's just the, this general theory of custom ASICs burning the weights into the chip- Yep... so you don't need memory. Yep. That's pretty good, actually. I think- Right? I think, I think that that, that makes sense to me. This came, this comes at the perfect time. 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? I don't know. A lot, a lot of the produc- the, the production- Yeah, they, they get distilled to shit. Yeah. 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 mean 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. Mm-hmm. You could argue the prefill decode, disaggregation, stuff like that is, 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, the... You're- we need to kind of break it in- into smaller parts in order for- Yeah, we've been using the same terms. Yeah, yeah. And it makes no sense. Just in order for the compute to be even remotely okay. 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?

Um, that's just pretty interesting. I don't know. Okay, I'm- I mean-... just gonna speculate, yeah... I mean, I'm not gonna s- 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 a little different, right?

I mean, like, look, like, I mean- I- Yeah... I just think- The way to speed things up is to never transfer anything. [laughs] 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?

But, 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 d- 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 accelerator chip is, like, very...

Like, I just don't believe in them is 'cause, like, where are they? Until Cerebras and Groq, honestly, they were all considered failures, and even then we're like, "What are they gonna do with Groq? What are they gonna do with Cerebras?" So. Is, is AmaNova up? No, I think AmaNova is, like, a much more interesting one- Yeah... but I think there's, like, there's, like, all kinds of deal issues with that.

I haven't been keeping up with that one as much. Yeah. I, I always, I always try to mention them as, as part of that cohort. Yeah. Yeah, 'cause I kinda forget about them, too, but yeah, 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, yeah. Yeah.

You mentioned actually some CPU shortage stuff or CPU- Yeah... like, sort of opinion. What's, what's going on there? I'm, I'm less- I think it's- Less informed... I think it's okay. So okay, I have one. We'll start with a 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. It has been down a lot.

I, like, and, like, me- okay, this is pure, like, you know, a schizophrenic hat brain 'cause I have a schizophrenic trend hat brain. I'm wondering if it's two things: shipping vibe code slop to prod, 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 a AWS thing. It's not just a G- 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 $100 billion 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 it... I think Azure's just, or, like, AWS is pissed tonight."

You know? Mm-hmm. Something like that. But I think, so we have this five-year thing. Everyone scrounged every single dollar they could to essentially invest in as much as AI as possible and just do maintenance CapEx on CPU. Ironically, at the same time for all this cloud 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, uh, CPUs. So the or- not quite, like, the orders of magnitude of the GPU stuff, but it's just such a big trend even when it steps slightly in a, in a place. Massive amounts demand. Yeah.

So, like, we might actually be seeing a CPU shortage partially 'cause of this refresh cycle- Yeah... but partially also because, like, I legitimately believe the cloud code, cloud code is increasing c- software creation, and then on top of that there is real demand from RL. Yeah, yeah, and just general d- uh, production agents as well. You know, we just got... Yeah, every, like, RLMs take compute and, you know, cl- Open Cloud takes more compute. Yeah.

And, uh, you know, it's just, it's just different slope but at the same sort of direction. But still an up slope- Yeah... and, and a slope that, to be clear, has had massive underinvestment for the last two years 'cause everybody like, "How do..." It, it, the same problem that happened. Massive underinvestment 'cause they're like, "Screw it. We're doing maintenance only. We're j- all we're gonna do is maintan- 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. Yeah, yeah. Amazing. Yeah. So, Semi's guys say Semi's numbers go up. Yeah. Is, is that [laughs] That's, that's one way to put it, yeah. [laughs] I, I... The thing that's crazy is we talked about the demand just- But it's like, it's like you're right... w- I mean, it's for sure.

Like, you, like, show me where I'm wrong. 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 prices go up. Exactly. Yeah. That's the crazy part to me. Yeah. 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're n- you can't do these things because your price is half the market. That's what's crazy. I was gonna say- 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. So- No, that is amazing. Tha- thank you for that breakdown.

I feel like I, I really understood it, like, talking to you. Yeah. To be able to transition to a couple personal things and- Yeah, sure... as, as the end. How do you write? 'Cause you, you write- A fuck ton. 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. And, like, you kept going with Fab, because I- For a while.

Okay, dude, to be clear, that was really... So, so, so look, I am still trying to do Fab because I, I, I do feel deeply connected to writing. Let's just specifically talk o- on this a little. Yeah. Just, just, just, like, explain yourself, you know? Okay. So the thing... Before L- 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 i- idea of what you're doing." I was like, like, hey, when I was interested in semiconductor book, I literally raw-dogged some textbooks. Whatever, the 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 skill set differentiator, 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 21, like, consecutive streak for a long time. The streak has been a little broken as of late. Semi-analysis plus Fabricate Knowledge is pretty hard to do. Yeah.

But, like, all of '24, 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- Okay... one a week. Yeah. At least an attempt to two. Yeah. And so I think one of the best ways all the people who write other... who write about writing all say the same thing. You need to just be writing more. Yeah.

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

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