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

This crap makes mistakes all the time.

All the time. It is still just like a like I think of it once again as like a junior analyst, right? The analyst goes and does all this like really pain in the ass information. You bring it all together to make a good decision at the

top. Historically, what happens is that

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.

good at. I consistently have a hit rate.

Now I'm the expert, right? I don't think that metal level learning is there yet.

Um, we'll see if L1's do it, right?

Everyone who's spending one quadrillion dollars in the world thinks it will. It

better it better happen, right? If

you're spending, you know, a trillion dollars and there's not metal level learning. But for me in our firm, that

learning. But for me in our firm, that massively amplifies everyone who is an expert because like you have to still do something. You can't just like slop it

something. You can't just like slop it up. It's very obvious to me what it's

up. It's very obvious to me what it's sloping.

Doug Loft, welcome to the inspace.

>> Yeah, thank you for having me. Yeah. Um,

I after all this time, I just Is it okay if I just call you Swiss? I feel the that's that's where my brain is. So,

I've known you for so long. You can call me Mule if you want. I'm um you know.

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

>> It's been it's been a long time coming.

I think I first met you at like New Orleans or like one of the one of the Newses. Yeah. Yeah. Yeah. I mentioned

Newses. Yeah. Yeah. Yeah. I mentioned

one of the lyrics is in per I think it was Vancouver, >> right? Yeah. I think it was like some

>> right? Yeah. I think it was like some party.

>> Yeah. Yeah. Yeah.

>> And you were like, "Hey, like who's this called, dude?" I'm like, "Oh, okay."

called, dude?" I'm like, "Oh, okay."

>> Yay. 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. Yeah. Yeah. I think that was

in person. 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, obviously.

>> So, we have two New Orleanses in a row.

And um yeah, honestly, we should go back there.

>> Yeah.

>> Um are you guys going to Melbourne or the Australia one this year?

>> I um have I don't even think that far out, but on but that sounds pretty interesting to me. I think um >> I can't remember which one. There's a

there's something in in in Korea this year right?

>> Yeah, I think um ICML.

>> ICML. I think I'm going to try to go to ICML in Korea. And I know I clear is >> I don't know, man. There's so many conferences. I honestly I hate to say

conferences. I honestly I hate to say it, I'm not much of a travel guy. Well,

yeah. I mean, I'm glad to catch you. Um,

I mean, I I I am traveling to you.

>> Yeah. Thank you. I really Yeah, it was fun.

>> Yeah. Yeah. Uh, I did not know that I'll be caught in a snowstorm.

>> Yeah.

>> Uh, it's it's funny. I feel like people recently have been coming and they keep getting stuck in these snowstorms. So, yeah. Uh, first blizzard in 4 years or

yeah. Uh, first blizzard in 4 years or something like that. Thank you for coming.

>> Yeah. Yeah. It's a pleasure. Um, and so you go back. You you used to be anonymous. You used to be Value Mule,

anonymous. You used to be Value Mule, which how I know you.

>> You know what's funny is that Value Mu was like the very first one. That's the

Yeah, the Do you know how I don't how I noticed you? I I was just like, "Oh,

noticed you? I I was just like, "Oh, this guy seems smart."

>> Yeah. I don't know, dude. I mean, I I remember noticing you, too. So, it's

like, you know, this was in the early like the primordial days of Twitter. Um,

honestly, I miss those the most. It was

like 2017, 18, something like that.

Yeah.

>> But yeah. Yeah, I remember from value mule. So, if that's like the deepest cut

mule. So, if that's like the deepest cut if you are even aware of what that is, that is like the deepest cut that you'll possibly have. Um, and then yeah, I have

possibly have. Um, and then yeah, I have another account and I actually have a third account which is my my main account these days.

>> Yeah. So, wait. Oh, which one is that? I

don't have one.

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

>> Oh, okay. Just semi semi dox.

>> Yeah. Yeah. So, so so so it is there.

That's it's not that's okay. That's like

my oldest finance account. I think of it as my legacy account. Um I I you know I want to have some privacy. I feel like uh >> Yeah. Yeah. So So now you've gone all in

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

>> Yeah. Yeah. I got brand and everything.

Yeah. Cool. Cool. Cool.

>> Um same profile pick, you know. So

>> yeah. So um let's let's do a little bit of the Doug story because a lot of people hear about Dylan. Uh, and I wanted to just make this the Doug story.

Made the fat knowledge story. You used

to be a value investor. That's kind of how you you were value mule and you had a mentor or something that nerd sniped you into semiis. Is that the the story?

>> Um, no. Actually, I solo nerd snipe myself. So, I I wouldn't say value

myself. So, I I wouldn't say value because uh 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 um the one I found that nerd site me all the like single shot me is I found ASML and

I like fell in love with it. And then I like after ASML I just like read about all this stuff how complicated it's to make these who are the people who are able to make them and then I you know semicopters the whole downstreams all

from there but it started with ASML in 2018. I really fell fell in love with it

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 zoom >> I was going to pull out the Asianometry.

>> Yeah. That's perfect. That's a perfect one. be amazing. John's a monster,

one. be amazing. John's a monster, honestly. This one, right? Uh

honestly. This one, right? Uh

>> yeah. Yeah. Um I mean, the thing that's crazy is he has um 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

information LLM. Yeah, ASML is all science fiction. So the semicuncture

science fiction. So the semicuncture stuff's always been science fiction.

Always loved it. Always thought it was cool. Thought it was the most important

cool. Thought it was the most important thing that we ever made. and uh yeah kind of followed from that.

>> Yeah. I don't know if you know but obviously you know I used to be a in analyst myself I covered TMT >> which is a freaking huge sector to cover. It's absurdly huge.

cover. It's absurdly huge.

>> Yes. Very large.

>> Like I was covering Sprint.

>> Yeah. Yeah.

>> And got like uh you know Viacom. Yep.

>> And then there's ASML and Yeah. Yeah.

>> Yeah. The the N I feel like uh the T and the M and the T are actually three completely separate industries. Um but

once upon a time I think in 2000 they were kind of really close together, >> right? Uh but but ever since then it's

>> right? Uh but but ever since then it's really split off. Yeah.

>> Yeah. Well, I mean I just 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 >> and I took those meetings with like

credit Swiss and all those guys that would you know tour you around and all those. I never really felt like I got it

those. I never really felt like I got it uh because I was always being filtered through like investor relations and all that. And um I think you have to do what

that. And um 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, wow, what does that mean for this this quarter like or at least this this year, right? Because

like >> one there's like just so much foundational knowledge and then and then then you're like well okay everything here is taken for granted. It's already

priced in. So like yeah you you assume that all the Taiwanese people who are buying and selling the rumors of capacity are pretty well informed. You

assume all the people who are TMT investors in the United States are pretty well informed. I think the thing that was like the foundational difference for me is like you know real thesis around um one I think being young

and brash and believing in yourself to be like no this is something that's really matters and everyone else doesn't see it really helps. But for me, the thing that was like I guess radicalizing was um I really believe Morris law was

dead. Um and I was like, "Oh my god, not

dead. Um 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 um also everything all the old playbook is about to be thrown out because it's been like this is a super mature industry. you read the these primers about it like that's how you learned about things back before

chat GPT knew everything or um you had to go and read these primers of all this information they're like oh it's a very mature industry and mature they used to be really immature in the 80s and 90s

and 2000s but now you're consolidated growth doesn't go up a lot and everyone kind of had this old playbook from the early 2000s a lot of people hated hardware um there's just this perception

that semiconductors weren't valuable weren't as valuable actually software was the most valuable thing now software is getting [ __ ] on, but that's like outside of the scope of this. But um

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. Um people took that for

science fiction. Um people took that for granted. And when the science fiction

granted. And when the science fiction ends because you can't make the chips as small as you you could, all of a sudden all those free gains you got go away and you have to think about it. And what

happened for semiconductor specifically is it created a lot of pricing power or value for everyone who knew how to make a good chip. So Nvidia is probably the best case. You could talk about parallel

best case. You could talk about parallel comput 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 in the past it was just CPU gets better to bur right and so I think that I had a really deep belief that >> that uh Moors law was uh Mors law was

ending and everything would change. And

so coming in with that like thesis at the top level just like made me want to attack every little assumption 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 uh it's like a check GPT3 and the writing on the wall in like two 2020 you know my early I get an early pitch to um for

fabricated knowledge and I'm like hey you know I'm going to make a really you know mors law's over scaling malls seem like a big deal. If you simplify it all the way through is like okay supply uh

you know supply divide uh you know demand right. Yeah demand is growing a

demand right. Yeah demand is growing a lot because of scaling laws. Uh supply

is actually slowing down because Moor's law is completely screwed. Um that's

probably really good for semiconductors and parallel compute is going to be a big deal blah blah blah blah blah. Um my

conclusion then was um you should just like Nvidia is pretty much the only one who's going to benefit. Um and and you know so that's my my my my like good long range prediction. I feel like I just Yeah. I just don't think I don't

just Yeah. I just don't think I don't think I would have expected the magnitude. I think that that's been the

magnitude. I think that that's been the kind of the craziest part about this whole story is like I had all these beliefs and thesis and like I really really really believed. The reason why I met Dylan is he was the only person who was as semicropilled in the entire world

as me is how I felt. Um and so I remember like yelling at him, arguing about all these kinds of things and like our our DMs and stuff like that.

>> Was it just online or >> online? We met in person and

>> online? We met in person and >> No, no, no. I've I've actually only been to Taiwan with him one time, I think.

Um, so so I mean like look, we we just met in person. We yapped. We went to conferences. Um, but I think that that's

conferences. Um, but I think that that's like kind of we were both really early to the thesis. Kind of have a different background and perspective. Dylan is

technology first and you know, obviously the technology matters. 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 to the same level. Um, so yeah,

this the the thing that's crazy is like we called it, we were right, blah blah blah blah blah. But like the thing that I think that still shocks me all the time is the magnitude of how right we are, you know, like be like, oh, Nvidia

was good, right? Nvidia is pretty good.

Um, and then it's like, no, Nvidia is now the most valuable company in the world. And I think if you had me read

world. And I think if you had me read that and like truly, hey, I wrote that.

I believed it. Um, I still wouldn't >> have put that together or like I wouldn't have believed it if you >> This is one of many thesis at the time.

>> Exactly. Yeah. Yeah. like there's so many things >> like what else are you writing at the time right that didn't work out you know >> um yeah >> we can look we can look back but >> I I I I'm pretty happy with my my long-term track record I really am um

but yeah I'm just really surprised the magnitude of how everything happened like it's crazy to me that like cos is like a a not a household term but like relatively wellknown it was like an

exotic technology so all this stuff has been this like learning journey really believing where technology is going uh 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 uh probably like a career maker right there, you know, and and I just like I love those kinds of like sort of quarterbacking those career decisions for other people who are also weighing a bunch of things, right? Like

I have ADD and like I I just chase like whatever is interesting, but at some point you just have to like really choose and >> Yeah. I I think one of my skills have

>> Yeah. I I think one of my skills have always been like trend following and trend watching. I think when you know if

trend watching. I think when you know if we're talking like on my account like value mule or you know full all the time like I was always pretty good at trends like being relatively early. I remember

loving and being obsessed with Tik Tok in like 20 uh 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 um when you see a really big wave that you have a lot of conviction in it's worth going all in. And that's that's kind of what

all in. And that's that's kind of what it came down to. It's like, wow, I see this really big wave. It's worth going all in. And so, um, I reoriented my life

all in. And so, um, I reoriented my life around it. Um, yeah.

around it. Um, yeah.

>> Cool. Um, we're going to talk about other trends that you've spotted primarily like the sort of memory cycle, which uh, but also uh, optics, which amazing story. Uh, but we wanted to sort

amazing story. Uh, but we wanted to sort of focus this for the cloud code launch, cloud code anniversary, and you've been a big cloud code show.

>> Yeah, I I am. Where's the chart with the four uh 4% of code?

>> Uh it's it's actually go to the top left this one. Yeah. Yeah. Android. Oh, you

this one. Yeah. Yeah. Android. 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

five now. I mean, and and like as you know, it's really easy to generate code now. So like that that number will

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

happening. So So let's recap for people who let's say I think this is one of the most important pieces I I've read in a long time. Um, and uh, you know, you you

long time. Um, and uh, you know, you you let it and it's and it's it's weird because I think of you as like an analyst, right? Like um, you like one of

analyst, right? Like um, 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.

and old. Yep.

>> Um, but like what are you doing, you know, getting so into cloud code? Like,

you know, I shouldn't you be reading reports and stuff, you know, like tell the story of your uh, cloud code psychosis? So yeah, I think here's the

psychosis? So yeah, I think here's the thing is um if you want to be good at any game, we're we're tool users at the end of the day, right? If you are good, if you want to be like and and obviously this is like outside of my job as semi

analysis, like I have all these other things I need to do to to grow and make s analysis the best research firm ever, but like let's say you're a fund manager or an analyst, right? Your job is to

find information edges and like new ways to put information together that no one else has done. Um, and so like I've always thought it's really important to know the most important weaponsgrade uh

tool that you can do all the time which is essentially chache 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 um like for example I've been running the our our case study

that we have into cloud code since it first came out like you know I think over a year like you know I want to say March April I started to >> say which case study >> uh so the case study for people when we we're hiring like a financial analyst like a our core research seat or

something Hey, um, you know, can you can you take this company and do some analysis, blah, blah, blah, give us this format back. Um, and I've been running

format back. Um, and I've been running it through like the agentic things like, hey, what what when agents really come around, they should be able to oneshot multi-step hard things to do, things that would take a human 24 hours to do,

right? And I always wondered cuz I, you

right? And I always wondered cuz I, you know, there's some good submissions and there's some bad submissions. We pride

ourselves in the case study and being good and, and honestly, I always joked like, well, you know, they're going to start to beat the worst submissions. And

so, like, that was, that was always my base level. I have a base level of is it

base level. I have a base level of is it better than a chat GPT agent mode or anthropics cloud code or Gemini CLA 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 ah it isn't quite there >> um I vibe coded some stuff on Opus 4 for sure but like you know it was like kind of interesting projects on the side it

was really hard it took a lot of feedback they would it would mess up it just didn't and then um you know everyone was freaking out about cloud 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 oneshotting everything, right? Like all

these MVPs that like, you know, you have to be like, "Well, the UI is whatever."

It's like, "No, it just oneshots it."

And then you ask it to do something better and explains what you're doing.

Like that's actually really good. And so

I was like, "Wow, generalized easily oneshot MVP of these like projects and able to like really build things on top of it because you can trust what it's doing to a certain

extent." and it felt like some level of

extent." and it felt like some level of capability was beaten. It was very different than what I've done in the past. Oh, I also tried Codeex 2 before

past. Oh, I also tried Codeex 2 before this like um like Windows 5.2. Never

really got it to work in the way seamlessly agentically like >> course. So this was this was recent.

>> course. So this was this was recent.

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

>> Oh December 20th.

>> You know it's a day >> something like that. Something like

that. I'm thinking cuz it's between the days and I I got home from Christmas and I was like uh um my fiance wasn't feeling so well so I had some time to mess around just by myself 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. Um but but look, I I was playing around with these coding agents just like everyone else should or

or should in the space and like cloud code versus codeex. I was like doing, you know, simple testing to see if they can make it thing and it never really like oneshotted like a total idiots thing and then 4.5 just started

oneshotting stuff and that to me was like a huge difference and so um I was like wow it could just like oneshot stuff. I have all these interesting

stuff. I have all these interesting ideas.

>> Uh is it Excel uh sheets primarily?

>> No, no, no, not Excel uh sheets primarily. I would say it's usually a

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

oneshotting a basic financial model or like just taking and and putting information from one place to another.

It's not a human level but honestly if you know much about investing in the being in the business it's like is your model you know being 5% more accurate really going to ever make a good investment decision or not? No. Never.

Not once. Like no one's saying oh yeah my estimate is always one cent more tighter than everyone else and that's why I'm good at stocks. No, it it doesn't matter.

>> It's like Southside is ridiculous cuz like everyone's like I'm bullish because my DPS estimate is like 10% higher than than the street. Yeah.

>> And I'm like, "Oh, who cares?"

>> Well, I mean, as you know, sellside, if we're going to do this, like Shots across the bow on on cellside, I mean, look, one of the reasons why semi analysis has such like a successful business is because I think sellside as

a concept is very broken. If you're

talking about waves and things that are changing, uh, sellside in a lot of ways is this hereditary child of like let's say 30 or 40 years of banking where you had um, you know, a company go public.

So, you needed someone to talk about it to issue securities and selling the stock.

>> You're literally selling the stock. You

have, but you have to be independentish.

So, your ratings, buy, sell, hold. Uh,

one of the biggest sales you could do is like when your when your company IPOs, um, we'll talk about you so people know who you are. That's the the core original part of the cell side, right?

And the problem is like all the research kind of has just like really kind of fallen apart. It's just not different. A

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 is so good is because we are not focused on being a 1-centent EPS thing, which I would argue isn't exactly skill.

It's just mechanical maintenance. um we

are really good at understanding when technology changes and how that impacts everything, right? Um because it doesn't

everything, right? Um 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 of AMD's Helios rack is super on time and is like

out at the gate ready to make tokens on this day because that's going to be billions of dollars of difference in revenue for AMD, right? or some

networking technology or something like that, some bottleneck, being really right on the timing and the magnitude of those inflection points will make a huge difference in the stocks. And so that's

our business. We're a research firm.

our business. We're a research firm.

We're independent and um we've had a really good hit rate and we, you know, we care deeply about the technology.

>> Exactly. Yeah. You know, I I didn't mean to characterize you as like No, you are young and fun, but also you're extremely damn good. Yeah, it's like it's almost

damn good. Yeah, it's like it's almost like a triple threat. And I always almost always wonder if it's like okay, it's like one, you have like deep understanding of the tech. Two, maybe

you're like sort of financially sort of uh literate, but also two, three, there's like this like X factor that is like, well, focus on things that matters. [ __ ] everything else. And and I

matters. [ __ ] everything else. And and I don't know what that is, but that that obviously is the alpha.

>> Yeah. 100% 100%. That's Yeah, that that that's always been the analyst PM conversation. It's like, hey, you know,

conversation. It's like, hey, you know, there really is only one or like three things that actually matter, >> right? Find me those three things.

>> right? 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,

right? Like the things that like, hey, this 30s is better than this 30. This

case doesn't matter. This one actually matters because now you have a giant opportunity. Um, and so that's that's

opportunity. Um, and so that's that's what the game is all about, I think, in terms of the research. Yeah. And like a, you know, finance perspective. But on

top of that too is just like when you do so much research, all these different little industry parts are so hard to understand, man. Like you go to some

understand, man. 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 um you know I can't even remember what what email is

replacing blah blah blah and you're like talking about all this stuff and they have PhDs and you don't. Okay, everyone

has a PhD at the deepest level and they're all doing so you have to understand all these deep understandings of these parts of these um supply chains, but you also have to have a big understanding too because you know this

little part at the bottom of this supply chain is actually going to impact this giant you know business at the top because it's all interconnected but it's so complicated just paying the tuition

to show up is very expensive. So I I think one of way I'll bridge this for listeners is that um this is the complexity of the problem domain that's there's extreme depth there's extreme

width and uh you have to kind of throw human attention at all of it to find what matters and you're you're saying you noticed some kind of breakthrough in December where it was suddenly clicking for you. I I just really wanted to

for you. I I just really wanted to figure out like the tasks the task that I was nailing and the task that is still is not great at.

>> Yeah. So, let me specifically talk about my use case cuz hey, I am still a stock guy. I can't trade or do anything in

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 know, the people who are really like stocks, they're like lifers. They just love this [ __ ] Um it's it's like an addiction.

Okay. Um so, I I'm like, "Hey, you know, um here's like all my positions and like here's some like thoughts on it. can you

just like kind of like start copy pasting some notes over and putting all together? It's like yeah it does. That's

together? It's like yeah it does. That's

why you give cloud code. Yeah. Okay. So

I started doing this and then I'm like okay like add it make the portfolio run some basic risk stuff. It's like yeah sure fine whatever. And then also like everything you do is perfect. I'm like

okay well like actually can we like make an investment framework for my investment style and start to grade all this stuff and then like attack it and do stuff like that. You could just do like iterative work and then I was like whoa whoa whoa. this is like a crazy

useful tool that's systemized how I think really quickly like okay what else can I do with it and the answer is like [ __ ] anything right and my joke on the the podcast is it's all a skill

issue now um 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 um

actually perfect example is this is this chart right hey cloud code is a really big deal everything's oneshotting I'm reading everyone going into psychosis like me at the same time on the internet. How do I actually know what's

internet. How do I actually know what's real and what's >> I wonder, right? I wonder, right? So,

I'm like, "Okay, uh I heard about the fact that the cloud code has the commits, right, onto the public uh onto your your commit. It says, "Hey, signed off with much of like, well, why cloud code, scrape me all the commits, right?"

And uh you know what? Lo and behold, it pretty much did like and it's like, "Okay, well, like I'm looking for this signature right here." Copy paste was like, "How would you systematically go about doing it?" Did like a big query

pull for all the stuff. Pulls all the uh like every single day. the API is relatively open and then I'm like oh my god let's see how much this is growing and it's like okay chart go up and you're like how big is it as a percent

percentage of GitHub you're like chart go up it's a huge deal and I'm just like watching your uh you know I have like a crown job updating it every single day blah blah blah and I'm like this is a

huge deal like this is the the biggest deal I I love watching trends I love watching exponential trends and I've never seen one even remotely at this rate you would you know 4% in like two weeks or something.

>> Do you have a PR Arena?

>> Uh, >> it's a it's a previous attempt prior to you, but somehow they didn't they didn't uh they didn't talk about they just talk about merge rates, but they didn't they don't they don't plot it as nicely as

you do.

>> Yeah. Well, and also you want to Okay.

>> You you asked you have to you have 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 um I mean the other thing too is Yeah, I have a lot of those as well. Um but but I thought the quad

as well. Um but but I thought the quad code cuz I'm just trying to really really really really focus on that specifically. Yeah. Well, and also you

specifically. Yeah. Well, and also you want to give an example. Um bro, I didn't make that chart.

>> Uh Opus 4.5 did.

>> Yeah. Or or I think 4.6. I'm like, "Hey, um I want you to do in this style. This

is the semi analysis color scheme. This

uh I like summarize books about visualization and like put in here little style tips. Yeah. Here's some

Yeah. Um I don't I don't even know, man.

It has like 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 part it is a waste look tokens are free the the cost of doing this is nothing that's the part that's so amazing

>> the cost of doing this is nothing the information gathering and synthesis is like hey if it cost effectively the same doing 70 is three who cares 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 um 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 uh please consider all the relationships you can and you generate information like I think it's um that one was not chat that was not generated that was not generated

which I hate honestly I don't like that much that one as much as it doesn't have the guidelines. Yeah.

the guidelines. Yeah.

>> And um and so you can just that was that was generated. And so you can just what

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

have. Can you like visually brainstorm with me a way to better represent this information? It's like yeah actually I'm

information? It's like yeah actually I'm going to generate you a timeline. Um you

can just do things. Um and I I mean it's that that is your catchphrase right?

>> Yeah that is my catchphrase right now.

You can just do things. And um so people were looking at this from the perspective of people who are coding and they're like hey just programming is automated right? But like all

automated right? But like all information work is, you know, I would argue coding is a big subset of all information work. I think there's a a

information work. I think there's a a Brian Hobart tweet or something forever ago. He's like, you know, coding and

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

doing abstraction. Excel is a ginormous abstraction. You're building these

abstraction. You're building these relationships and you're describing what you think a financial thing is worth, right? Um, I think coding is a little

right? Um, I think coding is a little harder if I'm being honest with you. And

you're telling me the hard one got automated. Why can't the easy one get

automated. Why can't the easy one get automated? So I started to ask myself

automated? So I started to ask myself how much can we do? And the answer is it feels like a skill issue. It makes issue it makes errors on the on the margin but you can kind of force it into like for

me I love using rubrics right. Hey um I care about XYZ uh out of 10 uh score this and then you can really do multiple things. It helps with the stochcastic

things. It helps with the stochcastic dance.

>> Do you do you put it all in one prompt like the the the the task and the rubric for the task or do you put the rubric after all the test is done? I I actually have two versions of this. I'm like,

hey, you can pull all this stuff together, just run the uh the rubric or whatever. Or you can uh do the task and

whatever. Or you can uh do the task and the rubric. It just depends on how you

the rubric. It just depends on how you want to do it.

>> Yeah. Because obviously if you put it task and a rubric, then it can iterate itself. But if you put it after, then

itself. But if you put it after, then it's probably more like you pay attention to the the rubric.

>> Yeah, exactly. And well, in the other part part of it too, um yeah, it will iterate, but like the context rot doesn't matter. I kind of like it to be

doesn't matter. I kind of like it to be separate because the thing is it's like okay, it needs to be this like fresh look at it. You have to think of it kind of like it would perceive anything anywhere, right? It just each context

anywhere, right? It just each context window is just opening it up. And I

think sometimes um if you have done if you do it together, it co-mingles the information to the point where it becomes biased or sus susceptible. Opus

4.6 as you know is like super cyclopantic like it loves to like say yes okay yeah I'll do this for you. Um,

I think having it separate keeps it like keeps some of that drift 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

results better. But it's it's just complicated. Like part of this is really

complicated. Like part of this is really weird because I am I'm weirdly now opinionated on taste in terms of how you should design things because you can like for example the context rot thing until someone explained it. I was like,

"Oh my god, thank god someone said it.

This is a huge deal." Um there's this like meme where it's like uh these guys um well do you see the meme? It's like

um of mice of men and at the end of um you know at the end of the book I can't remember which character shoots the other I never read it.

>> Yeah. So so one character shoots the other guy and it's like some guy made a meme about it being like oh this is after your um after your cloud code is garbled you know 5 million tokens you're

like okay it's time to put you down. Um

because the context raw is huge. Um so

yeah this yeah this is example where >> so what what are your compact practices?

Do you sort of aggressively compact manually or >> um so I personally with the one new 1 mil it I feel like I try to do it at all in one compact window. Um I'm not doing ginormous projects.

>> The one is very new right yeah 1 mil very new. Okay. Very uh but it's a big

very new. Okay. Very uh but it's a big deal too because cuz your skills and whatever your cod MD as a percentage of 1 mil is so much smaller. So you just get so much more oomph, right? Because

um the the 200ks are just wiping over and over and over. Um that's a big deal.

I think it's a huge deal. And and also with how the agents are working, the sub agents will have their own contacts 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. Um compacts just kind of start the compression of the noise. So

>> yeah. Yeah.

>> Uh mentioning sub aents and multi. So

first of all I wanted to give a shout out to this thing from enthropic research where they were like here's our production traffic. Um and they uh they

production traffic. Um and they uh they did a they did a report that was kind of like their their equivalent the meter chart. Um and there's a lot of people

chart. Um 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 alo also

else. But what if they're all alo also just software engineering, right? Like

like software engineering is like 50% right now, but what like there's nothing stopping it from continuing to go to 80.

>> I think maybe what's going to happen um this like a maybe a giant take.

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

>> yeah that's in my opinion that is downstream of like that is um 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 uh code kind of inter or and the world intermingle right now is code and so that's just going to be the bleeding language that's used to to figure out everything else. Um, that's that's my

everything else. Um, that's that's my belief like it doesn't make sense to build like for example this is a perfect example of this is like Excel cloud for Excel is much worse than cloud code

using Python to use the Excel skills to then deposit into it's all much worse.

>> It's much worse >> even even all the work they're doing.

>> Yes, 100%. Because if you think about it, it's it's a legacy. Why make a car engine fit into a horse carriage? It

should just be in a car. like it's like it's like a backwards compatibility thing where it does work because LMS are like relatively generalizable like this but why bother because that same

abstraction of information on Excel it's just in that because it's human formatted for us to understand and I think that that's the important distinction all of this information stuff all this software stuff is just to

be consumed by humans doesn't matter yeah if they're just as good at putting the data together we should be much more concerned about machine focused of like software consumption and so they can

like you know um the the LLM and the agents can put and synthesize all the information and deposit god knows however you want it to be. I don't need to make a chart in in PowerPoint or

Excel. It will just deposit the mat lab

Excel. It will just deposit the mat lab uh the mattplot. Yeah. Mattplot lib.

>> Mattplot lib um in a chart to me in an image. Fine. That

image. Fine. That

>> Oh, you're trying to use mlot lib.

>> Yeah.

>> Wow.

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

better understanding that code.

>> Yeah.

>> So, why ever make a chart again?

>> Yeah.

>> If it if it's better, >> it's just like it could be inconsistent with like the other charts that you do.

>> Yeah. I don't care that much about.

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

>> And number two, I think uh if it increases the speed of information, that matters a lot. Um and so I think we're much more so pretty much the new charts will outweigh the old charts because it'll just grow.

>> Um so yeah, I think it it it is a little inconsistent. We have the same

inconsistent. We have the same watermarking. Honestly, I think it's

watermarking. Honestly, I think it's better than our old for formatting anyways. Um

anyways. Um >> well, the first thing this looks reminds me of is Bloomberg. I was like, you guys are just like, you know, becoming Bloomberg, >> which is a nice view. Uh couple of things I wanted to

view. Uh couple of things I wanted to sort of double click on because this is just a cloud cloud code like brain dump for one of the the biggest sort of cloud code uh shows in the world. Uh which is

sub agents and agent swarms. I don't know if you've tried >> I have tried them.

>> Pick pick either one you want.

>> I have a controversial opinion that cla does not do RL on agent swarms or agent team or something.

>> Yeah, it's just an experiment.

>> It's just an experiment. Um thank you.

Thank you. Because no we Exactly. cuz

the pro it's just via prompt and it's actually very bad. Um I think sub aents are okay because they usually have a quad MD to go do whatever. Um but the agent team is is actually really

>> okay well we can't we can't knock it experimental. So

experimental. So >> yeah. Yeah. No no is what do you try it

>> yeah. Yeah. No no is what do you try it on?

>> Um uh well it was like some big data analysis of like many many different companies with different KPIs into a dashboard all in one. 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 Kimmy Kimmy 21 agent swarm is actually good. I

have also tried that. It is that is actually really good. So I did some like oh example of things that I was never available to me like internal benchmarking of these models and be like hey here's a set of problems I'd like

you to do 20 times. Um can you do them and then I can measure the performance between them and then like do qualitative but like what's the difference between X and Y? Yeah,

>> that that was completely out of the hands of me a normal guy like 3 months ago. Okay. Now it is completely

ago. Okay. Now it is completely available to me. That's awesome. Like I

am very I care about this stuff and now I have the tools that's able to automate and do a lot of this stuff because hey all of software engineering is like partially automated. And so I mean my

partially automated. And so I mean my experience is the 2.5 swarm actually improves the model's performance meaningfully. Um the agent team makes it

meaningfully. Um the agent team makes it meaningfully worse because there's clearly not RL done. So it isn't context aware of what's the best thing to be done. And yeah so I think I think it's

done. And yeah so I think I think it's interesting. I like sub agents because

interesting. I like sub agents because it's usually a little bit cleaner on a task uh to go do it and then come back, but the agent team is just very >> They had some post about how they did

stuff uh where it was Yeah. There's a

bunch of RL for for this and I tried it myself. I thought it was I thought it

myself. I thought it was I thought it was like pretty it's cute how they do all these like little games and stuff.

>> Yeah. Yeah. Also, it's crazy how like the setup you have to it's a lot of compute to just run the swarm. I think

it's like a 16 node of H100's. Um Okay.

>> And you're just like dang. So you and I are not going to be running and this is just to run and I'm sure there's concurrency available. But um yeah, I

concurrency available. But um yeah, I think it's really cool and that's like I think that that's the sign of what's next because you know these agents are going to get better to a certain extent.

They're they're you know it's another benchmark and bench like another benchmark to hill climb, right? But then

it's going to be how many of these together in a bigger chain can you get to work that you could argue it's kind of like a scale out of the reasoning problem too. Hey, how do you get these

problem too. Hey, how do you get these like this one agent to essentially get a verified whatever? Put it into a bigger

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 um 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.

>> Um so yeah, that's that's kind of what our experiments have shown at least in terms of like the agent sworn versus like not I think it's very clear the agent team out of Claude is an

experiment. Um, but Kimmy better they'll

experiment. Um, but Kimmy better they'll definitely do better. But the Kimmy 2.5 tells you that this is already boom perfectly great new place to do more work on completely available to us right

now. I think it's huge. Um, because if

now. I think it's huge. Um, because if these agents get any better, like I don't know, I'm never going to sleep again. So,

again. So, >> honestly, like uh it's very interesting this sort of moonshot AI and and this is a tangent. we're not we're not really

a tangent. we're not we're not really going to focus on this very much, but um you know how like the the sort of AI tigers out of China were were Deep Seek and Quen and >> then you were like well who are these

like Kimmy guys and um and these these sort of newer names like um I guess Miniax as well would matter >> and uh Zi has been been around longer but only recently much more active. So

like I I noticed that Kimmy is much more in the productization phase like as as seen as opposed to like the Quens of the world, the Deep Seeks of the world who don't really care that much.

>> I mean Quinn because of how it's uh it's attached to Alibaba, right? Like

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

yeah, Kimmy Kimmy's pretty interesting.

>> They're pushing so hard. They got

everything.

>> I know.

>> They got Kimmy Manis. Kimmy Claw. Kimmy

Claw.

>> Yeah, I know. Kimmy Claw. I haven't.

Yeah, dude. I was gonna say um Have you messed around with OpenClaw? Because I

did I Oh, I remember um what was it first called?

>> Clawbot.

>> Clawbot. Yeah, dude. I was going to 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 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. I mean, so what I do is I just have multiple emails, right? And

there's there's a safer email to give to bots and I can let it use that and if it impresses me, then I can upgrade it. Uh,

but CloudBot didn't impress me at the I'm honest with I'm honest with you, I wasn't impressed either. That was the reason why people were freaking out about this mold book. I was like, bro, have you actually used this [ __ ] Cuz it's not even right now on cloud code in

a relatively focused terminal. It will

be like, oh, blah blah blah. Like dude,

in the DMV there is an like in the env there is an API I told you to use for this subcase of problems and it's in your cloudmd like please focus up like it still is like making mistakes. This

is not like truly AGI and there is harness you still have to wrangle this thing but um it's not like a perfect skill follower and the the context in each attention window is going to 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 going to

information with. Yeah, I was going to uh So, I I use I use our um I use our Discord as a basically like a a way to just bring information in and out. I I

just saw I saw this too where basically like a lot of people are just setting up things that they could have done in Zapier with Cloudbot because they're like, well, you know, now I'm like AIDS, but actually they just done it more securely with Zapier.

>> Okay. I think it's kind of interesting.

I guess I I I do think it's kind of interesting. But I think there's But the

interesting. But I think there's But the difference though is Zapier. I mean, I remember I've tried to use Zapier before.

>> Yeah. And it's also not very >> It's not also not very good. The

difference though is like and that's okay. Like it's okay to be early to

okay. Like it's okay to be early to something and just wrong because you weren't the one that made it happen.

Right. Cloudbot, the Cloud Code, Cloudbot, whatever, all this stuff. The

reason why it's so powerful is it gets to completion, right? and and like, okay, Zapier, maybe you can get to completion all the time, but like, man, it probably took you like eight hours of clicking through things and like copy

pasting crap to make sure it all works and it's all secure. And it's like, well, Cloudbot did it or Cloud Code did it in like, you know, 4 and a half minutes and that's good enough for me, you know, that that's a faster

achievement. And so, like, it's totally

achievement. And so, like, it's totally okay that they were they were right, but they were just not the right mechanism, right? You see this happen in

right? You see this happen in information like in the history of like computer. I think there's also like an

computer. I think there's also like an innovator's dilemma thing where Zapier as a pre-existing business had this view of the world of automations as like very strict sort of on rails workflow type

things that their giant user base already uses. They couldn't like really

already uses. They couldn't like really pivot that much. Yeah.

>> So that's why I think like you know one of the co-founders left because they were like well I can't exist within this like >> you you end up becoming with you know the the the box will control you.

you you are >> it's your it's your golden handcuff.

>> Yeah, it's just like your cage. You

know, you're going to act like how you are in the cage. And so yeah, that that sucks for honestly. I feel like that sucks for >> the framing I have is like your priors become your prison. Oo,

>> that's pretty good. That's pretty good.

That's pretty good. Your prior Yeah, >> I haven't bogged that yet, but I should.

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

your prison. I like that a lot.

>> Coming back to clog coaches. I also want to make this like the sort of clog cob.

>> Uh no, no, no. That like I want to indulge because like that's how natural conversation goes and I think people like enjoy that, right? And probably

that's the only time we'll talk we'll talk about Kibby.

>> Yeah.

>> So like do you use hooks? Do you like give me like the the Dougloff cloud code setup?

>> I had just like essentially a few base skills. Uh and then I have a lot of APIs

skills. Uh 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. And so

that way we have an internal server >> an internal server that is that that is accessed by people with an API so that like all the semi analysis researchers are able to hit like some basic level of

context cuz I think the context is really what matters. Um 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 it makes sense. Um in terms of like >> I think hooks are very underrated, right?

>> Yeah. I I do think >> cuz you can do like a Ralph loop just with a hook.

>> Yeah. Yeah. I feel like I underutilize hooks. I I think that is true. But I do

hooks. I I think that is true. But I do >> I do run some version of them on like skill calls effectively like hey on this then you have to start pulling all this stuff but I think in the beginning I

tried to do all this like hook stuff and like compounded stuff like that and I found that like you know the gas town Ralph loop era it's like it it is a sign of what will come but I just don't think

there's enough fidelity to like make crazy multi-turn something happens. So

like okay actually less is more. Try to

have like a strong set of smaller skills with a good amount of context um information to be pulled in and then at the beginning of every session ask and focus on what you want to do so that

like it prompts the like not like you know a cloud within a cloud whatever 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 nan since 1984 or something like that this is what I want to Do I want to uh so like the uh actually no let me just give you the best example

that is probably not going to work. I

would like to fine-tune a time series foundation model to predict NAND and and DRAM prices. Okay, I'm going to first

DRAM prices. Okay, I'm going to first start by gathering as much information as possible from all this stuff blah blah blah and then we're going to fine-tune it evaluate which ones we're going to do. I chose Kronos 2 because of coariantss blah blah blah blah try to

set this all project up. We'll make it a versell dashboard internally for for semi analysis. Maybe we'll external if

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 plowing away. Hey, can you go research here's a

away. Hey, can you go research here's a search API serper or exa or whatever you want to use to go look for all these different um information sources and then bring it together. Right? So this

agent goes and gathers all this information. This agent goes and like

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 like well what do I

in. I also had it like well what do I use? it showed me which GPU whatever

use? it showed me which GPU whatever we're renting on a hourly basis and so yeah we just pull all this stuff together and we fine-tune it and I'm like okay cool um 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 uh Ellen is probably not going to work um unfortunately um unfortunately the >> you said it was because of regimes or

something else >> I think so regimes yeah there's no way is so messed up >> for for a lot of people who are like new to finance this is why I have I an issue with all these kids doing like stock

trading games with LLMs. They have no idea. They they've never studied finance

idea. They they've never studied finance and like like know some like a past does predict the future a lot until something fundamental change and like the macro shifts and like risk on versus risk off.

They've never heard they've never heard those terms. I had to explain it to people at cognition and like yeah like 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 uh you you be like okay each of these each of these are almost like a one-off onto their onto their own >> right which reduces your sample size.

>> Yeah. Which reduces your sample size.

And so then at the end of the at the end of the day, you end up being like, well, it kind of just like I guess it's um here's some heristics. Good luck, have fun, right? Here's your checklist to see

fun, right? Here's your checklist to see it might be over, but you really don't know anything until then. Um so but but like okay, an example of where this project was helpful is like okay, I'm not going to have the magic LLM tell me

what the price of memory is going to be.

Hey, it was a good weekend project and I did burn quite a few tokens, but I do happen to have after all this like information synthesis and analysis all of the memory prices of everything I

could possibly find plus the things behind API that we paid for plus um you know enhanced data sources and I have all the coariants so like hey WF what was the consumer sentiment every macro

thing of all time and you know what's really interesting is I am going to 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 easy, sharable, consumable within my firm and company.

Yes. Um I'll probably be done with that today. And that okay, so that you're

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

understand. No one's ever done that in the history of time. I know for a fact as the guy who like is like the cycle semiconductor guy, I've written and done more work on the cycles than I think anyone else has at this point,

especially for like the older ones like the 80s and '9s and 2000s and 2010s. And

like when I did it first time, the human groin is I went and I read these old annual reports and I put it together and I tried to string an air through it and I I brought through all I'm like, "Okay, what was GDP this girl? What this year?

What was all this stuff?" You have to like make all this giant sheet to to whatever and then make the narratives.

No, none of that [ __ ] dude. I mean,

this is like too much information to gather. It's like a lifetime of work.

gather. It's like a lifetime of work.

It's like a PhD project. I did it in a day. Two days.

day. Two days.

>> Yeah. I I mean, I think the the push back would be that then you don't have enough expert information to criticize the reasoning that went into the report

that you're slopping out. You know,

>> there there is some slop. I I definitely agree with the sloppiness. So, so I think of it once. So, right now, >> by the way, that's also is essential for you guys. If you get caught doing like

you guys. If you get caught doing like putting on some slop to your clients, right? Like you have to y

right? Like you have to y >> at at one point be like extremely AI pill and like you know number one in the world and applying AI to your productivity. Great. But also like you

productivity. Great. But also like you got to you have to so so I think I think the thing that's really interesting is this whole thing is like a game of hygiene now 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 since done and I've done the work. I have like a lot of like embeddings in my brain, a

lot of information. The vibes that have got me in here is actually like tons and tons and tons of information setup scenarios and like pattern recognition, right? But um yeah, you're right. This

right? But um yeah, you're right. This

this crap makes mistakes all the time.

All the time. It is still just like a like I think of it once again as like a junior analyst, right? The analyst goes and does all this like really pain in the ass information. you bring it all together to make a good decision at the

top. Um but the problem is um

top. Um but the problem is um 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

metal level learning is there yet um we'll see if L1's do it right everyone who's spending one quadrillion dollars in the world thinks it will it better it better happen, right? If you're

spending, you know, a trillion dollars and there's not metal 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 >> are you like junior or new to junior junior to the firm? Yeah.

>> Junior and like like because like you have to still do some of that. You can't

just like slop it up. It's very obvious to me what it's slopped, right? When

it's slopped and there's no cognition, then it's like like whatever the artisal last 5% is like that really matters. But

for me, I know inherently what the 5% is. I can like write it away with some

is. I can like write it away with some really easy heruristics and time and like be like, "Okay, well this is the last 5% you fix. This is what I believe.

Just [ __ ] make up these assumptions instead. Press enter. Okay, cool. We're

instead. Press enter. Okay, cool. We're

good to go." You know? Um and so that's kind of the hard part. That's a real hard part. There is still a human in the

hard part. There is still a human in the loop right now. one day someday it'll be super human but I definitely believe the where we're at today um where we're there it's not there like you'll just

compound all this noise and it becomes just like garbled just like all context uh 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 you know a huge huge huge multiplier of what you're able to do and for me that was enough to be like I feel agi pilled honestly because if if I define AGI is

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%. Like data analysis is a perfect example. Hey every quarter I

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

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 wellthoughtout

agentic system. And so you're like,

agentic system. And so you're like, yeah, that job actually does seem at risk. And so that yeah, that the 4.5

risk. And so that yeah, that the 4.5 capability enough like that that we hit some level with 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 going to have to

to do. You're going to have to understand what the absolutely of uh 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 defin 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 uh client visits obviously. Um I by the way transistor radio amazing for like understanding like what your world is like.

>> Yeah. Are you also quadcoping your analysts and your you know on the other side?

>> I've definitely quadcopled the analysts.

Um everyone in the New York office like must try it. I like really tried >> pilling. Not I mean not your like not

>> pilling. Not I mean not your like not the semi analysis your customers and and all that like 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. Of course, how could I not?

single day. Of course, how could I not?

This is like 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

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 it out of my dead [ __ ] 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

use it. If if they start to use it and they're thinking like me, they're going to obviously adopt it. I'm like, well, I don't understand why everyone doesn't adopt it. I would argue well we'll see

adopt it. I would argue well we'll see in the 24 month view it will be a base level I think I think claude code co-work whatever is going to be a base level of all information work very soon

and you know you see one uh my friend was telling me how his portfolio manager found co-work and he's like getting it to read his emails and he's like oh my god I love this right everyone's moment

is going to be a little different but I think my moment it feels like GPT 3.5 or 4 for me where there's that first time where you're like okay I I know it made some [ __ ] up, but like this is better

than like if I went for hours searching, putting information together. It can and then also has like the analogy power, you know, where you can say, "Hey, this is the setup. Can you describe it in this these like really strong pattern

matching skills that are really powerful?" I just think it hits some

powerful?" I just think it hits some level of capability. I can't tell you what it is. It is like my t my personal taste where I'm like, "Oh, wow. This is

completely over the the the chasm of what needs to happen for it to be a very very powerful tool." And so yeah, that's my cloud code moment. I think um >> there there's some kind of automation chart um that you know XCD has this

automation >> and I think I we need a version of this that is the cloud code like >> it needs to be much but but what's crazy is this the cloud code thing like murders the access.

>> Exactly. It just shifts everything like right or something. Yeah. But but also like it um 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 uh 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

turn. You get one turn >> but with with with clock maybe you get three turns and they the sort of review process is the thinking.

>> Yeah.

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

>> Yeah. I think of it as hygiene. The

thing that's like really going to be painful though is like a lot of my expert opinion has been built by like you know it's like prephones and not right like your attention span like you

know the children are cooked okay like you know the attention spans are really bad all this stuff like I don't I read this like really sad thing be like oh we're getting dumber or something first generation I don't know I'm not going to maybe that's like

>> you see the the Coinbase uh earnings all the coins 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 going to be very dangerous it's like giving everyone right now I'm strong

>> yeah it's so funny >> I I think you should just do that well we we we do we do deal with some of the seal analysis memes you know 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, I can't look away from the the subway surfers. So,

subway surfers. So, >> you couldn't look away. I was I had to pause it.

>> Yeah. Yeah. I was I literally I Well, well, hey, there's like Have you ever been at like a bar where they play like these like weird like there'll be like like Tik Tok videos for lack of whatever and you just watch and Tik Tok bars in New York?

>> No, not Tik Tok bars. Not Tik Tok. Okay.

It's like there there's like essentially a B-roll channel that that they'll like sometimes play in public spaces and you will just find yourself like being engaged with it. Like there are certain

things that just it works. So yeah,

sorry that's a completely off uh but but I wonder this cloud code pill is very powerful for me. I believe it will how it all works. It'll shift all of that

over massively the the chart. But it's

just really weird because if you didn't pay any like human cognition to get there, I don't think you're going to be a great reviewer. One of the reasons why you know what what makes that that human

feed that loop well is because once upon a time you did that and you could make that to me like yeah idiot. You you're

not thinking about this problem in this way. You're missing this this like you

way. You're missing this this like you know whatever. You're not considering

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, I I know you I know that I told you the valuation is the only thing that matters, but like it's also a fraud. You can't do both, right?

Like if you think about like the analysis stuff, you have to know when your own personal embedded model is like, yeah, actually this one overwrites this one. That that's through learned

this one. That that's through learned experience. And I wonder if we're just

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. And that's what hygiene sounds like to me. Like, hey, it's really addicting to be like, you know, whatever, press the button over and over and over, but sometimes you do actually

have to like think um you know, uh so so I think that that's uh it's going to be really interesting. I mean have you

really interesting. I mean have you tried like uh so so I mean the this the way to model the sort of metalarning SS element is like once a night you do a batch job of like look over everything

I've done like like extract some learnings um you know and open claw I I think one of the interesting things I really liked about it was this heartbeat heartbeat and I'm like people aren't like excited enough about this because

like well this is the first instance where like the agents are just always on like always living always reflecting >> yes and like what is it sold on MD2 story I think is much more for character and like uh whatever but like yeah

heartbeat is heartbeat >> harp is 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 cont like it gets garbled. We know that open open claw doesn't always do

everything you asked to it asked it to do uh initially but you can see the design patterns like the heartbeat MD is a perfect example can see the the design patterns where it's like well you know

is all of our tasks every single day actually us having this like genius thing or do we like sit down in a single session finish a single project get up and get some coffee then come back

>> if it's that and you could just [ __ ] you can make the heartbeat MD consider the like the session to session and like hey metal alertarnertings 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 a like like that's effectively good enough to do a huge information test 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 this single line of code. It might

actually be a business. It's kind of crazy like I I wouldn't have put myself as AGI pled. I think 4.5 is like actually >> I think my own timelines have moved up a lot. Yeah.

lot. Yeah.

>> Um are you guys watching GDP val?

>> I to the best I can but I'm feeling like I'm mostly just trying to >> No, no, no. So to me when GDP val came out so uh I I mean I I'll just GDP vow

is like basically a like a broader sweet bench let's call it where it's like applied on every discip every profession that is white collar that you can model and it's

above like something like 2 to 5% of GDP something like that that's why it's called GDP val and they they had human experts do the task and as well as GPTs and here's the results right like um and where 50% is parody within industry

expert.

>> Yeah.

>> Yeah.

>> Coin flip. Exactly. Where so like you can see that the nice uh increase from 40 to 41 and since then obviously 52 and uh Opus 45 have already exceeded that we're at 70ome now. Yeah.

>> Which means models are consistently better than industry experts as at these things.

>> Yeah.

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

>> Yeah. Yeah. This is this is and so like I think I think the problem though Yeah.

I would say that that is the definition.

Um, so the thing that's crazy is cuz 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 gold post. 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 shogith will come and talk to us and vibrate on our same >> if I do think so. Yeah.

>> Okay. Uh I I I don't I'm be honest with you. I'm very open. I will change my

you. I'm very open. I will change my mind often. I'm not This is not

mind often. I'm not This is not something I feel intuitive in my gut today. Maybe it's the next next X next X

today. Maybe it's the next next X next X next X next X next X next X next X next X next X next X next X thing but when it comes to like the the the GDP valve version of this yes >> yeah this is do white collar work the white collar work which is most of the

most of the t >> knowledge like like actually it's almost all not almost all but it's a huge portion of all of work in the world 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 kind of like this crazy shift where technology is going to massively change

the relationship with all of that and it's going to be like this 991 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. Been my experience, but it just

harder. Been my experience, but it just it just feels like a massive moment's happened like the the steam engines invented and you know the the trains are here and and everything's going to

change in knowledge work >> and it's kind of crazy. There's a

there's a sort of economic cycle uh from my macro days that I'm I I can't remember the name I can't look it up but it's basically like there's this stages of economic development where like your

your economy starts out majority argiculture then it discovers like manufacturing then it discovers white collar work then it discovers it builds like a very mature financial sector and

like these are like like a layer cake all declining over time then the new things increasing. So my my theory is

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

>> I do believe that. Yeah. 100%. Like um

humans are very adaptable. That's like

my favorite thing I've learned. Um

>> you're able to adapt to God like coldest coldest place in the entire world, the warmest place. Humans are in every

warmest place. Humans are in every latitude. That's in a physical sense,

latitude. That's in a physical sense, but I think we're going to find a way to make ilization go up. Um but we'll we'll we'll invent more work for sure. But um

I think the thing that's crazy is just like things change so quickly and that five to 10 year period like 10 year gap can be drastic and crazy and that's just

society wild but yeah it's happening in our lifetimes.

>> It's happening in our lifet like it's like happening like right now like it's it's just it's really crazy. It's like

very and like so this is like a complete side task on >> 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.

real economy. That's like my my my pet.

>> Yeah. where why is it not showing out in GDP yet? Right?

GDP yet? Right?

>> So, there's going to be, you know, some people are going to be like, oh, you know, the facts facts of the internet, same thing, information transfer, whatever. I think I'm actually scared

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 um

theory. Please don't hold me to this um internet. Um, but what if AI is mass

internet. Um, but what if AI is mass massively deflationary and and and also I think uh one of the more interesting conversations I've had in a bit is like

what was GDP was invented once upon a time as a way to figure out how much we could divert you know normal economy away just to war during World War something like that. Okay.

>> My spiciest take is I feel like GDP itself is going to be very very challenged by AI because information work Yeah. So how we how we capture it

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

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 going to have a lot more information work for sure. Like more work will be done. I don't know what the value of

done. I don't know what the value of that's going to be. Is it going to be so much increase in supply it's deflationary? That seems to be like a

deflationary? That seems to be like a real concern. It's possible. Yeah. And

real concern. It's possible. Yeah. And

then we'll figure out how to use it. But

like there may be a great depression of AI where like we figure it out.

>> Yeah. Well, I I wrote this whole thing about railroad stuff cuz it's my favorite my my favorite capital. It's on

fab. Yeah. Um I can't uh it's like railroad fab. Uh it's about all the

railroad fab. Uh it's about all the railroad stuff over time. Okay. Pretty

much because um 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 buildout. It's not even close. Like we

buildout. It's not even close. Like we

>> What numbers are you thinking about?

>> Um I think a trillion was a trillion allin was essentially the real dollars 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 railroads the reason why it's so interesting is because um honestly it's way crazier but but pro 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 um 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 to finance it finance it yeah so much money was needed that like effectively 85% of all paper whatever was essentially just railroad debt.

>> Yeah. Um, one of my favorite anecdotes was before there was a Federal Bank, uh, a Federal Reserve, Andrew Carnegie was the Federal Reserve.

>> Yes. Yeah. There were individuals. Yes.

Um, yeah. And so all this stuff, uh, so it's like the whole thing, I kind of did some work on the guilded 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

deploy. It actually was 45 years of like there's three cycles actually. There's

three boom busts. Damn. Um I don't know if it'll be quite that long. All the

cycles kind of collapse. Um that you know information moves around faster.

>> Exactly. Yeah. And so you you have all this stuff where I think it's going to happen faster but like I would be really shocked if it was all in one go.

>> That's my vibe. Um where it's like it's all in one instantaneous up down. I

think it's going to look like mult some multiple cycles. So yeah kind of just

multiple cycles. So yeah kind of just wrote about railroads. The there was like a baby railroad cycle then there was a huge railroad cycle. the modern

rurals 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 sustained for like 10 years.

But what's crazy is like that amount of spend is like we're we're like well on track for that. Did you do percent of GDP? Cuz I think that's I think the the

GDP? Cuz I think that's I think the the way you make it convertible.

>> Uh Stargate itself 2% of US GDP. Um and

uh I mean it's going to go up like >> Yeah. Yeah. That's a Yeah. And it's not

>> Yeah. Yeah. That's a Yeah. And it's not all going to be in one year, right? But

um it's okay. So yeah, so total capex four it was 4.8% of G&P and 25% of total gross fixed capital investment.

>> Okay.

>> So 25% of investment every year and four 5% of G&P.

>> I think we're there. You know, Stargate plus entropic plus whatever. We're We're

right there. Xai.

>> Yeah. So, we're at the railroad buildout, which is like at one point like But the thing is crazy.

>> We should exceed it like >> probably. Yeah.

>> probably. Yeah.

>> Yeah. No, no, not probably. Like we

should, but Okay. Like this is bigger.

>> Yeah.

>> Okay. I I'm I'm like I I would like to say Yeah, sure. I Yeah, we we will do it. I I'm worrying. I mean, like, dude,

it. I I'm worrying. I mean, like, dude, where are we going to get all the money?

That's like such a like the pedestrian concern. Yeah, it's not a pedestrian. I

concern. Yeah, it's not a pedestrian. I

mean, that's what happens every capital.

I'm worried like we must hands in the Middle East. You'll flip the thing. we

Middle East. You'll flip the thing. we

must we must what this happens every single time. That's the reason why the

single time. That's the reason why the like bubbles happen, right? Is like we essentially get so big where it's like this must be built. It doesn't matter the price and then all of a sudden we look at it, we're like, "Oo, that was a steep ass price." Um, but I think I mean

the thing I think about this is like how I think about the big picture is there's 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 then 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

it. And I think for me, like I don't know when that number is. I'm not I don't want to say a number go up forever because I feel like that's like intellectually dishonest, but quad code for me is the first time where I'm like

and we're bringing you all back together where you're like demand go up so much.

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

max. It's not enough. It's not even anywhere near enough. Like I mean some people buy like five maxes and then they >> Yeah. So, so I I so I'm on fast I'm on

>> Yeah. So, so I I so I'm on fast I'm on fast with 1 million on API which is that is like an addiction level if any sense.

Um but yeah I I I really think um it's the first time we're 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 if not more. Like I don't

understand like what's the like I can't price it. I have no idea the elasticity.

price it. I have no idea the elasticity.

>> Yeah. You pay for a perfectly compliant junior analyst. Yeah.

junior analyst. Yeah.

>> Right. And so what's able to that cost like get 90k >> that's able to work in parallel? Yeah.

Like you can have a 100 of them. It's

kind of crazy. Yeah. So

>> it's it's a skill issue if you cannot manage a junior analyst that is 20k a year.

>> Yeah. 100%.

>> Which like I mean okay like you know skill issue is like it's your fault but no like we have to learn how to do this.

It's it's >> Yeah. It's like it's it's two it's three

>> Yeah. It's like it's it's two it's three months old.

>> Exactly. It is two month that that's the correct way to put it. It's like 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 going to be like, "Yeah, you idiot, you rube." You don't know how to use your completely new technology that got birthed last month. Um I think it's

just about a it's a bit of time. And

it's like kind of interesting cuz you're like watching I mean it'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 um we

have this like really old uh older guy who's like been through the history of technology since like forever back then.

He's like one of the most interesting uh intelligent people semi analysis and he talks about how who is he? Uh, Tanch.

Okay. So, like you said this like we we we had this conversation one time. He's

talking about like early internet how like it wasn't actually sure if the browser was going to win. It was like a remote web file service. Some people

thought like, well, it just I'm just going to reach and play with someone else's web files remotely, right? Who

knows, right? And that kind of, you know, it kind of is a remote web file.

Who 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 because there's many

different ways this could go. And we're

gonna have to kind of collectively agree what's the best set of hygiene, set of design patterns, what's the level of abstraction, and then like all the rest of how much SAS will disrupt all

everything else. Who the hell knows? But

everything else. Who the hell knows? But

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

>> Yeah. Yeah. I mean, my biggest one uh and I I I do want to bring it to semi in a little bit, but uh is the IDE. two

months ago they uh we had Steve from Gastown talk about how66 would be your idea died and I like two weeks ago three weeks ago I recently was like [ __ ] he's absolutely [ __ ] right

>> it's over I'm really wondering too because like ID 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 is just going to be first. But the year like of you know Excel is dead for finance and like it's it's it's >> Excel is the ID for analysts.

>> Excel is the IDE for analyst. Bloomberg

is the ID for analysts. Like I believe every one of these IDEs are done. It's

dead over and dead. I just think it's why why it doesn't like just imagine the concept of you like I remember when I learned Bloomberg I had to like watch videos to learn about all the random

folders keys how to use this how to use this you know the tactic knowledge of using this function versus that function that's like crazy to think about that is like that is like horse and buggy okay

the agent with the information that can perfectly retrieve and analyze stuff is going to have the ability to to to pull that all together in a better UI than it was with no legacy whatever. I think all

of that is dead. And like um this is why like my my spiciest take of all is like Microsoft is a lot to lose.

>> I think they have the most to lose of everyone >> because Excel is a human IDE for information work that's generalizable.

So is PowerPoint, so is email. Those are

the base core level of abstraction that decided to be broadly generable. But I I just don't think that matters anymore.

or I think Claude Code or co-worker whatever is going to 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. Um I think that's the the one

over. Um I think that's the the one that's like more shocking and scary that like people don't believe like I believe in my stomach with conviction because I have already had that moment for me.

Yeah.

>> I will never make a chart in Excel again. I actually believe um

again. I actually believe um >> it's hard to let go because I I have so much like ingrained knowledge of of like manipulating things directly in Excel.

Uh Bloomberg I have so um there's no way that you know this but like my very first startup was an attempted Bloomberg killer >> uh sent office. I remember I remember you.

>> Yeah. Yeah. No. No. I

>> Oh yeah. Yeah. You one of the few you had >> Centio. I was a Centio customer. Centio.

>> Centio. I was a Centio customer. Centio.

How does a Centio customer like rolled in dude?

>> I remember how dare they acquire Centio.

>> I had a I had a patent. I we filed for a patent for similar tables. Anyway, one

of my conclusions was like Bloomberg is just like three things. It's it's it's Slack and it's the journalism which is amazing and then it's the the data feeds. It's actually not really the UI

feeds. It's actually not really the UI UI. Yeah. But I I think for the first

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 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 uh marginal and only curious and not hyper interconnected, which I would argue that I am at Sony analysis. Uh like for example, I'm trying my absolute best to just rip Bloomberg out. We're going to

fax it API like all allin API with a cloud code is my belief of the future.

Hey, verifiable data source that you trust. Hey, scale

trust. Hey, scale >> for you guys. You can do it for traders.

We're so >> for no way. I understand like there's an information network that's like outside of this >> and you do deals in IB right are tracked by the regulators 100% 100% it's totally I completely

>> but as an analyst yes >> but as an analyst yeah and so like but I just think that like okay that doesn't really so you're right the core cash flow cal thing will continue onward but like each iteration of this AI thing I was like yeah I'm still going to be

using Bloomberg right this first time it's like actually no I don't care anymore um the IBS my util utils of like marginal value from from IB is like now outweighed by how like clunky this is

and I want to just make some charts, right? And so

right? And so >> immediately you save 10 to 20k.

>> Yeah.

>> For switching down.

>> Yeah. There you go.

>> It's amazing.

>> Yeah.

>> Uh by the way, what was your cloud code end of year prediction? 25.

>> Yeah. I want you know I sandbag the ever living [ __ ] out of that.

>> Oh, okay. I I just believe 25 is very like I like it's 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.

>> Mhm.

>> I think 25 is within the 95 confidence interval.

>> Sure. So it's so it's between 25 and 50 >> something like that. Yeah.

>> Yeah. It's just absurd. But you know >> also be Codex. Are you also watching?

Yeah. So to be clear I I'm actually even willing to comment on that because like I know we've done a lot of [ __ ] of being Codex haters. Yeah. I I think I I'm by

Codex haters. Yeah. I I think I I'm by the way when I put cloud codeex agent whatever allin percentage that we can publicly see I would argue the for the ratio outside of that's probably higher

too but whatever yeah I think together um yeah we're watching Codex um I actually think Codex is Codex is pretty good 5.3 I think so we had the whole thing I cuz like I wrote most of the articles I was like oh token efficiency

the context rottle this the same one or >> um yeah yeah it's in the bottom it's in the it's in the the paid section okay so but like TLDDR I was like well you know the reason why quad code is so good, enthropic is so good is because all of

this token efficiency, the token efficiency is better than chat, all this stuff blah blah blah blah. And then like 5.3 CEX came out and it's like yeah that that completely doesn't matter anymore.

They're like they're they're so back. Um

I really think 5.3 CEX is awesome um in coding though. But you can watch it like

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

right? But I think the difference is codeex wants to code because it's RL to be so good at coding to win on Sweetbench that like you're trying to use it for general information like hey

I'm trying to can you go research and search all these websites and like I don't even think they have web search in it or whatever maybe maybe you can give an API or whatever but it's like great I'm scrape I'm creating a piece of scraping software to go look at these

websites. I was like no no no no just

websites. I was like no no no no just like just ingest tokens of what's on the website. It's like, okay, great. I'm

website. It's like, okay, great. I'm

still like, it's so coding pill on the RL that I think um it isn't generalizable in the way that that 4.6 is where it's like, oh, I could have it I could have it make some rubric or do some research or do something like that

versus Codex. It's very

versus Codex. It's very >> it's very it coding codeex is coding pill and so that's what uh but I am very optimistic actually on CEX and we do track them um quite a bit. I I you can

they have a meaningful amount of thing share you could see the Bloomberry they have the the the chart bloomberry.com so the cloud code definitely is in the lead um but I think the part of it too is

like the the like to like comparison there's a ratio of codecs that's not available because it doesn't sign off every commit it does sign off on pull requests that that ratio is much closer

so all the openi people like rune will tell like blah blah we're not accounting for it yes we didn't account for it but like >> I I I 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 because the RL stack on Codex 5.3 is amazing. Like it's

very coding code. Um that's when that's when the it flips over. And yeah, look at the other players here. It's just

like I mean my favorite thing is that how GitHub Copilot is like number one.

And like I've never heard of like do you know anyone who uses GitHub Copilot?

>> Yeah. Look look. Okay. That that's

that's a bubble talking, right? That's a

bubble. Yeah. Yeah. That's that C that CSF bubble like >> yeah like you know there's there's like all these Windows users and you don't talk to them right like you do but we don't in San Francisco and like that's

just that's fine but that's definitely bubble talking but yes co-pilot has a billion in ARR I think at least >> uh what's crazy is cloud code is has a ratio their attribution of cloud code uh

and AR is 2.5 >> yes >> so that on this the daily install counts right is an order >> which is by the way just the VS code extension right Yeah, I know. I know.

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

>> Yeah, you're right. You're right. CLI,

mpm, domino is another way to track it, but I think they have like their own c their own installer now. Anyways,

>> all in all, definitely heard understand it's very hard for us to like actually track it. But like

track it. But like >> I I'm I'm not criticizing. I'm just like I I think Codex uh a big thing I'm watching is well is Codex back? Uh

because they they reported like Jan to Feb, they doubled users.

>> Yeah.

>> Okay. So, so I have some not skepticism just because they have such a big chatbt portal that could be like trilas 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 wonder like the like to like but but that's like my skepticism but

>> I have an answer for that. Uh Alexander

and Beros 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 try co I mean but the but a modal isn't and and to be clear codeex in the in Mac is great. I'm actually I mean like >> Yeah. Yeah. It's they actually the app

>> Yeah. Yeah. It's they actually the app the app launch >> the the app launch is actually pretty good. So So yeah and and I think I'm

good. So So yeah and and I think I'm pretty bullish that honestly especially for coding because it's like very coding code. I just can't get it to work as

code. I just can't get it to work as well for non-coding stuff. Then my you know you use conductor.

>> Uh no I've not used conductor.

>> Oh okay. Uh I thought I heard you say on a podcast this.

>> No no I've not used conductor.

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

>> Yes 100%.

>> Which like they're already playing.

>> They're already doing it like like I feel like this is how they're going to differentiate right like they're going to >> well then then you have a conductor where you can use codeex and cloud code for different tasks as you see fit. And

so this is the the clean superers set.

No, >> in theory. Yeah. 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 um I guess my design pattern on that is really skeptical of building on top of something that is growing very quickly and has all the money and whatever. Like I just think my favorite one is like platform as a

service. If you remember that when it

service. If you remember that when it was like infrastructure as a service, platform as a service, SAS software as a service 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 even by one or the other. I I think of that like middleware layer unless if it's a really really really compelling case >> often dies. But that being said in this moment I agree I actually you 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

great. I might actually try this soon. I

haven't used I haven't used conductor personally. I mostly just been, you

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

>> Yeah. And this is as a former cloud analyst very typical of like do you want to multiloud or do you want to go all in one cloud? And the the the classic

one cloud? And the the the classic argument for multiloud is well then you can use the best of everything. Exactly.

>> But if you go all in one cloud, you can exploit >> uh the the sort of minor features of everything and um you know the >> it makes a market and you there's no right answer for everyone.

>> Exactly. Yeah. Yeah. I mean it's Yeah.

That's one 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 people are very happy, very productive, make money.

>> Okay. It's good to be an analyst in the space because um it's fun to keep up with it, right? Like I agree like I I think everything >> we like the horse race. I like the number one number two. Oo.

>> Yeah. Yeah. But yeah. No, no, I know.

But then you have to your brain also have to be like number two is really big too. Uh and then I I just think like for

too. Uh 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 the stuff all together >> tech during the like 2017 and 2020 years so boring.

>> Yeah. At least for me anyway.

>> I thought it was pretty boring too.

>> Yeah. Yeah.

>> Sorry. I was I interrupted you in mid >> No. I I was talking about it's just fun

>> No. I I was talking about it's just fun time. It's a fun time to be here. It's

time. It's a fun time to be here. It's

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 uh Dougloff thinks Microsoft is

out of AI and Oo, did you did you not see this? Okay. So, so I wouldn't say

see this? Okay. So, so I wouldn't say out of AI. No, I did. Okay. So, I didn't watch it. I never rewatched these

watch it. I never rewatched these things. Okay. So how I think about it is

things. Okay. So how I think about it is >> but like you you said things like Microsoft is scaling back investment and >> so so so it was the previous conversation I was talking about yes >> how Microsoft has the most to lose

>> they had the most to lose of everyone in the entire world if >> they're the they're the software the horizontal software company but >> yeah exactly they're the horizontal

software company that humans use their software to do information work okay no like I cannot paint a bigger target okay I cannot paint a bigger Um and Salesforce. Yeah. Well, okay. That's

Salesforce. Yeah. Well, okay. That's

another two number.

>> Microsoft is an automatic two bigger Salesforce.

>> Yeah.

>> But the other thing, too, is they have this Azure business. I don't think you're completely out of the race. I'm

like, you know, it's a really great clickbait title. But the the problem is

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,

the gate. You're you're like, you know, this is ancient Rome and you're like, hey, we need some extra guys, so we're going to we're going to pay money for these barbarians to burn.

>> The golden army. Exactly. From Game of Thrones.

>> Yeah. Yeah. The golden army. And the

problem is like each year they become more powerful and then and then at some point they're just like you know we could just like scale these these shitty walls. So like the so that's the problem

walls. So like the so that's the problem is the mo the the wall and the moes every year are getting more dilapitated as they continue to rent GPUs to to the barbarians.

>> So it's just like Google Yahoo again.

Yeah it is exactly like that. And so

it's just like this weird process where that's a terrible setup too 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

place. You either all in become Azure like maybe in the telecom era, right?

Cuz you're team T guy. You become dumb pipes. Okay, that is the the Azure

pipes. Okay, that is the the Azure becomes co uh what is it? Charter,

right? Um yeah. Uh but then the other version of this is you say no no screw these guys. I have to like reinvest back

these guys. I have to like reinvest back in and like essentially steal copy their their features and build up my moat.

That means I need to stop investing in Azure for the stock.

That really sucks because the stock is very much weighed on out your Azure revenue. And meanwhile, if we actually

revenue. And meanwhile, if we actually had to value Microsoft X Azure, the multiple would be really low right now.

I think about that all the time. What

would this trade XX Azure? This is just Microsoft. Eight times earnings, 10

Microsoft. Eight times earnings, 10 times earnings. Like it was trading like

times earnings. Like it was trading like that before actually. Oh jeez. And like

like remember the 2010s era when it went all the way but down to like 10 times earnings in the Steve Balmer era and then it inflected outward as it did o with Azure.

>> Yeah. Yeah. Azure and 0 365. Yeah. There

we go. That's

>> Yeah.

>> Okay. I don't think you have the answer but I just like this is the most bizarre I want to call it, but I don't know if it's a or not even because it's a clear decision where they were the lead

investors in OpenAI. They had the deal and they consciously obviously stepped back. They're still good partners, but

back. They're still good partners, but like what happened? Like, so

I I think the biggest blunder of all time that the part that like is kind of crazy to me about that one is like, yeah, I I I definitely think there was a financial decision because when you look

at it, it looks like a conversation of shareholders, ROIC, and how much are you willing to burn cash? Because like, you know, effectively, you look at all the other peers and Google, I would argue,

is going to free cash with zero. I think

Meta will go to free cash flow zero.

Microsoft is still like you know Satia did not make the company. He is a professional manager and there is a board and there's a conversation >> being responsible. Yeah. Yeah. He's

being responsible, right? But the

problem is that responsible this like an innovator's dilemma, right? Like do I maintain maximize shareholder value and cash flow today or do I have a a deep belief that AI will kill the hell out of

my core business and I need to allin invest to you know am I ready to bet the entire company on on a trend and it seems like Satia is not a believer. You

know, we've been talking about AGI. He

is not ASI pill, okay? He doesn't have any fear of the shogith.

>> He thinks it's just like a new. It's a

new Lotus. It's a, you know, Lotus and Excel came around, right? Like it's just a new tool. But I think at the same time, this this um 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

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 internal capabilities. That to me sounds

internal capabilities. That to me sounds like we are not going to hire as many barbarians. We're going to pull, you

barbarians. We're going to pull, you know, we're reinvest in these walls, pull in together, and try to defend the core mode, right? Because the the dream of this and in theory you're like, "Oh, remember in 23 when they did the first

big deal, you're like, "Wow, Microsoft's going to win it all because they already have all the distribution and they're going to have the perfect product and boom, they're going to have this giant business that makes them, you know, whatever 100 billion, hundred trillion

dollar." Okay, whatever number you want

dollar." Okay, whatever number you want to say, but reality is Claude for Excel, Claude for PowerPoint is literally exactly what it's supposed to be.

>> Microsoft should have built it.

>> Microsoft should have built it. Yeah.

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

issue. The guy who you know this is like this this is the this is like the tribe over the hill barbarian.

>> Yeah. This is the tribe over the hill, you know, and and 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 going to have to do this is totally different. I think they're going

totally different. I think they're going to keep I think they're going to keep pulling back in. Um we're starting to see that like they're going to do internal training. They're going to try

internal training. They're going to try to do more foundational models. is

they're going to try to use the weights that they have access to >> this MEI. 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 are they do have in uh you know, they are one of the big uh biggest companies companies in the world with all these resources.

>> Yeah.

>> Uh I I always want to push back on the the sort of responsibility part like you know so Oracle picked up the slack.

>> Yeah.

>> Is Oracle being irresponsible? you know,

so I I'm actually if we're going to talk about Oracle, I think so let's talk specifically about Oracle cuz this is where we're going to go. I think Oracle was irresponsible because the magnitude of what they did 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, right? They they said R& fries

billion, right? They they said R& fries the wolf. They promised the world. Then

the wolf. They promised the world. Then

they pro proceeded to raise as much money as possible and like this is the first time they've ever done these giant buildouts. And so now there's delays. Uh

buildouts. And so now there's delays. Uh

everyone's like, "Whoa, whoa, you did this much, right?" Capitalism is kind of like, "Hey, hey, pump the brakes." And

and seriously, I think that if they just tiered it out better, meaning that they didn't do it all in one period, played a little bit of expectations management, this year's revenue from the deployed

GPUs should partially help start to keep self-unding it. And that's how you make

self-unding it. And that's how you make this work in a glide path without going up, down, up, down, a big bang. And so

they I think what really happened is the big bang that really screwed them up was the debt side. They they just offered so much debt. It's kind of funny because in

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 >> uh debt indexes of I have zero

familiarity of I'm pulling some numbers up I did the numbers forever ago I'm like I I hallucinated whatever forget all the precision let's just say all of investment grade uh TMT is like 500

billion okay um I think Oracle is like 135 of it so that's like that's so big and so each time you have to you put up a huge new issuance you have to give someone an incentive to go buy your debt

instead of someone else's. And so you just kind of like they're screwing up the liquidity because these issuance are so big diluting the whole pie. It it

makes all the terms a little better or more favorable for investors. So it

literally the entire index is selling off because it's like supply.

>> Yeah, it's a supply thing, right? And

that's the thing that's like crazy to me is like so they they massively overshot and I think that we're like a weird bottleneck I never ever ever ever ever ever thought us 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 >> like like capitalism cannot like absorb

that much capital demand um because the order of magnitude it's totally different. These hyperscaler businesses

different. These hyperscaler businesses have been completely self-funded since the history of time. Had never gone out and issued anything. First time they want to they turn around and they're like, hey, instead of like, can you give

me a a $10 billion loan limit? Like

we've never done that before, right? So

the absolute size is kind of screwing it up. And I think that Oracle specifically

up. And I think that Oracle specifically was way way way too aggressive into a relatively illquid market. And so like you have to do this like you have to kind of leg yourself into it if it's going to 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

whole thing. Oracle CDS people all freaking out. I think a lot of it's

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. What what are you all right?

it. What what are you all right?

Microsoft could have just internally funded this and like fine >> Microsoft could have internally funded this. It would have been totally fine

this. It would have been totally fine 100%. And like this example where it's

100%. And like this example where it's like yeah I think that that's a blunder.

That's a perfect example of blunder because Microsoft's cost of debt is the same as the United States government.

It's like the cheapest you'll get anywhere else.

>> Correct.

>> And like just from a like a like a P time like the math perspective, no one else they're better than Oracle. They

they just by their credit rating they have a 2% more profitability at a a capital basis. That's that's like you

capital basis. That's that's like you can't beat that. Um, I don't know why they decided not to, but now they're in this weird thing where they're like, uh, 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.

Satia 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

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. Um, and that's what's going to make it fun. I mean, I'm more than happy to change all of my opinions when new information comes around.

>> Yeah. And I'm sure we we'll have more information that emerges. I wanted to touch on TPUs and then go into memory.

>> TPUs will hopefully I don't know maybe maybe a short one, but like you know uh for a long time you could not buy TPUs uh at least like current gen TPUs externally and now you can. And Google's

open as a as a as a supplier. I guess

>> I think Sergey doesn't want to lose and I think the thing that happened was um up 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

because we were first you know and and so like why so you know why why give anything to the topic >> I think it's because at least last year it beca um pre-Gemini 3 it was like dude

we have all these TPUs we're going to hoard them all but like people aren't using our products anyways and and like like Hey, what's all what what good is all these TPUs if um we're getting our

asses kicked in consumer? I think it's an interesting thing, too, cuz 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

down. One, we wrote about it in TPV8 like whatever we think Reuben will be much more competitive. I think Ironwood V7 is the peak gap between on between

TCO between Nvidia and um 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 Enthropic is to gain market share because the biggest gap you have um and and one of the reasons why there hasn't been a second merchant chip and also you can argue Nvidia's most valuable company in the world. What's

the value of TPU in Google? It's it's

huge.

>> You must have done the math.

>> I've done the math. It could be like it's like >> a trillion trillion. Yeah, it's like a trillion or something like that, assuming it gets like 30% market share or something like that. 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 exactly Some of them are they're all over, right? Like the the core team that

over, right? Like the the core team that did most engineering have like since really dispersed. And so I think the gap

really dispersed. And so I think the gap might might close over time. And so at this absolute period of time, they're they're going to win the market share.

And then then what happens is if you have an install base, you have an incentive to upgrade your install base.

That's like the hugest problem with AMD, for example. No one wants to buy new AMD

for example. No one wants to buy new AMD chips cuz it's not like they have old AMD chips. No, they're not upgrading

AMD chips. No, they're not upgrading from anything. And so when you have that

from anything. 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

next year. I think the install base issue has been a kind of a huge one. And

so TPU is at the point where the software ecosystem is mature enough. The

hardware is definitely mature. The

networking is really mature. You have a really good external customer who actually knows how to use your product.

If you want market share, now's the time.

>> Yeah, that would be insane if they if they actually sort of pump the pump the gas on that stuff. Um, 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 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

going to be used in.

>> I do not have as I I don't have a special perview on that. The thing I'm most excited about and like very much TBD will see is uh inference X will have TPUs eventually. That's something we

TPUs eventually. That's something we want to do longer term. I think that that will really show in numbers what's >> as a benchmark.

>> Yeah, as a benchmark.

>> How do you expect them to come in?

>> Pretty good on a price basis. I mean,

our expectation is like they're they're the best TCO by a meaningful amount right now. Enthropic is very clear how

right now. Enthropic is very clear how they feel like like everyone is very clear. I think even OpenAI would take I

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

unconstrained world. it would probably be at this exact moment like you know the the hottest kid on the block until Ruben comes out. But the reality is supply chain really matters and that

just that's not available. And so that TCO um that TCO advantage is at this absolute biggest aperture then like Jensen essentially gets it gets their stuff together is competitive and boom

it closes. So this door only open right

it closes. So this door only open right now probably TSMC is the biggest blocker. So there's no

blocker. So there's no >> Yeah. Yeah. What what can you do? It's

>> Yeah. Yeah. What what can you do? It's

this cascade, right, that which I think you've talked about where all the way it goes all the way back to the fabs.

>> Yeah. Yeah. Well, it's interesting cuz it's like even more than the fabs like like on the optical.

>> Is there a link I should be pulling up?

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

EV7.

>> Yeah. So, so yeah, it all goes back to the fabs. It all goes to who's making

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 open air right now. That being said like I think open air will get their their act together very quickly and so yeah that's

kind of like the narrative. I think it's going to be a good story for probably like a year or two, but then the real question is V u V8 we just don't think will be as competitive to Reuben and that's when your special window starts

to close.

>> What's the technical reason why?

>> HPM HPM 4 versus three and >> and that's a secure is the strategic decision by Nvidia.

>> Uh yeah, I think one uh so Nvidia is always if you think about Nvidia they're always trying to gas it as hard as they can like they like it is a high performance chip. It is a it is an F1

performance chip. It is a it is an F1 like it is as maxed out as possible. Um

TPU is kind of like this like replicatable pod in a very large with like very high stability right which if you know the history of Google that's what that's what they do that's what they do. Yeah that's what they do with

they do. Yeah that's what they do with infra. Yeah

infra. Yeah >> but um 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 um 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 um supply chain side bar none Nvidia is the best. They own the entire supply chain. They really do like

um you think all those HBM price increases they're going to come for TPU just like uh 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

the TSMC. He's doing the shots with everyone. Why do you think he's doing

everyone. Why do you think he's doing love shots with everyone? Okay. It's

because he he needs to get the chips.

Okay.

>> Uh so yeah, this is Samsung's chairman.

>> Yeah, this is Samsung's chairman.

>> Yeah. And who's the other guy? I

>> But let's put it this way. Um that's

that's a huge deal. That's a huge huge huge deal. Do you think do you think uh

huge deal. Do you think do you think uh Google was out?

>> Hundai. Hundai.

>> Yeah. Yeah. Do you think Google was uh what what you know, do you think Sergey was out in Taiwan drinking to to get supply? No. 100%. there's an opportunity

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 going to have like so much of that um kind of constraint there. And so

it's going to be really interesting.

They're going to they're going to get the best most performant HPM. They're

going to be first on the road maps for even more rack density. They're going to have like the best connectors, the best, you know, the whole system will be once again turbo jammed again for as hard as

it can be. And the people who made V7 like they made the chip was done like three or four years ago. Like the the talent um 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 going to really go in Grubin's favor. And so that's the big

Grubin's favor. And so that's the big difference. Um and I think as you know

difference. Um and I think as you know that's what makes the context windows that's able to do bigger bigger >> everything >> everything. Yeah. And so they're going

>> everything. Yeah. And so they're going to really jam it and that's that's going to 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

windows. It's not just the KV cache. We

also have to offload it to nonHBM.

>> Yeah. Every other part of the memory it um it's it's like s such an interesting cascade waterfall of like just like a short squeeze and and everything. It's

not a short squeeze. It's like a surprise squeeze. Yeah. I mean, so it's

surprise squeeze. Yeah. I mean, so it's like uh I just want to one ratio of like >> Yeah.

>> Yeah. Okay. So, it's a 3:15 if >> 3 to one to 4:1 ratio. I think I think next So, so it's in the memory mania um post that we just put out of like the 4:1 or the trade-off ratio. Yeah. Um

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 amount of HBM, it takes three times uh one HBM sort of unit is like three

times uh of the other sort of DDR or whatever, right? Yeah. So, some amount

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 uh I I actually wrote a really funny piece like I I called it like super oil, but let's this is a better one. Um but pretty much like you

better one. Um 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 uh get rid of all all your other fuel and you

have to like massively condense it and refine it. Okay. So um now what happens

refine it. Okay. So um now what happens is if there's any demand here it's an instant shortage. And so we we

instant shortage. And so we we hilariously enough came out of like the biggest shortage ever in Nan and DM.

Like terrible like catastrophic the worst one ever. Like the the last analysis I could put to is like 96 or something like that. Seriously, it's

like a a history 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 uh of HBM is essentially a 4x

multiplier onto DRAM. And then now, so we we completely constrained, took all the DM 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 scent. Okay? People

could go bankrupt, you know? So you they haven't invested in these three-earong lead time items and then now there's like more demand than God and it also evaporates the middle layer because the

KB cash offload and then boom you're just looking at the supply demand you're like yeah this is not going to catch up for like 2 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 DM prices like go up 100% again. like it's it's going to be the

again. like it's it's going to be the point where and this is like also example like really interesting in the whole thing another 100% I think is demand destruction I think you will start to have demand destruction from >> what does that look like

>> um where hyperscalers maybe purchase less or something like that on the margin on the margin right because they're like okay well what if I just really focus on this energy aspect instead and also ironically all the

energy um so like every not every data center in America but like many many many most of the data centers in America are delayed so you had this thing that's supposed to come on in 12 months. It's

coming on in 18. Maybe what you can do is you can play chicken with memory prices and you can kind of push out. Of

course, everything you have in the pipeline, you you pull forward as hard as you can. Okay, you pull forward. You

double triple order. And then the DRM and HPM guys are like, "Oh my god, how look at all this demand." And then at some point in time, what happens is you say, "Well, we pulled this all forward.

You know, we have the power is going to constrain us anyways. We're going to like kind of chill out the orders." And

historically, that's when the memory market that that's what causes the crisis uh the the prices to drop.

Realistically, just looking at at the aggregate demand of how much we've purchased in ter 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 the AI trade is effectively investing in memory capacity. Yeah. Well, not not

memory capacity. Yeah. Well, not not it's uh you could say skinix and and Samsung Micron and all the all the semi like Sunny Cap has been ripping >> which like by the way when I was in

Basne we a majority of a lot of money we made would just be long micron yeah 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 going to affect like your phones >> yeah like Yeah, I Apple I think Apple's moving >> to buy I had to buy an SD card for this

thing. Yeah, like it was bucks.

thing. Yeah, like it was bucks.

>> Yeah, that's that's nothing too. That's

and that cuz like that that's just the nan side. Dude, have you looked up like

nan side. Dude, have you looked up like like I want to say like 64 GB of of DRAM >> like like I'm moving up like I I need to refresh my iPhone.

>> I'm moving it up because I'm doing this research for >> Oh yeah, you need to do as soon as buy your iPhone now.

>> Yeah. Yeah, you buy your iPhone now because what's going to happen is when um iPhones go into the spot market, >> it prices are going to go up 100% of them.

>> That's insane.

>> And so they have to pass.

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

>> There's no that's actually there's a there's a whole super duper deep in the weeds. There's this whole like

weeds. There's this whole like technology that was very focused on cloud era um 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 attached and whatever. It never

really took off because essentially HBM was like the way that really crushed it all. High performance best of wins. But

all. High performance best of wins. But

this CXL technology that kind of never really took off is going to take off just because what they're going to do is they're going to take DDR4. They're

going to take the oldest every bit of spare memory they can find and they're going to put them into racks and then they're going to attach them via CXL. So

like this >> Oh, exactly that.

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

that that um you know I I was like a CXL bull for once upon a time then became very clear it was going to die and I was like it's back but only because the entire express intent is to have

these DD like old chips pull the old chips attach it to something new that's what it's going to be like the the memory shortage is just like it's crazy so >> yeah it's incredible >> so obviously this is lower level than I

usually go to which is which is why I'm having so much fun thing I I do tell people about is like well you know everyone including Sam by the way is like predicting longer context windows.

We've been kind of effectively stuck at a million for 2 years now.

>> I've actually been thinking about that a lot and like this is not going to go to 100 million context windows. It's not

going to go to trillion like we're this is it. Yeah, this is it for like 5

is it. Yeah, this is it for like 5 years, 10 years pretty much. Okay, so

the question is will Yeah, I mean yeah, probably actually. Will capitalism work?

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 going to be like like I okay his his history of compute what happens is you have to like

you have to like make a curve of the of the context windows like does free context windows go to like 1,000. Hey

you can use strategy BT free now but you your context window is like a thousand tokens or something like that and then you just like somehow do a tiny like a tiny parcel for that just so that you can then charge like you know 100x more

for 1 million. The one million context window is like a mansion, you know?

That's the real >> You live in a mansion, right?

>> I live in a mansion right now. Yeah.

>> Uh oh my god. The word just context rationing just came to me. I'm like,

[ __ ] Like we're going to have like vouchers for like, okay, you can have this amount of context today. Like it's

like, yeah, you have to you have to learn how to use it well because of the DM. Yeah. So, I actually have a

DM. Yeah. So, I actually have a question. I know cuz like Okay, long

question. I know cuz like Okay, long context to me makes a lot of sense, right? Hey, that's like like that's like

right? Hey, that's like like that's like the memory scale up version like if you're think chips but in the AI world.

I just am like always been curious because it does feel like at least in my stated experience really long context like you see in the papers they kind of like drop off. They like actually don't use all the context. So that's like kind

of the thing I've been most interested in is like does the 100 million context actually matter if if it's not possible to use it all. They you know versions of 100 million do exist today. They just

suck in various ways. They're not

actually applying full attention. Right?

you can you can use a space models or even like a LSTM to like uh you know uh to to process 100 million tokens but you're you're not paying full attention to those 100 million tokens and so I think like the way that we have context

today and those curves they will improve over time and they have they have been improving a lot >> but we're we're just not we're never going to use all of them but we'll we'll improve like on the algorithm side I

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

And well, I mean it just it just like physically we cannot d we can't even we can't even double say 10x. Yeah.

>> Yeah. What what's the point of talking about that?

>> Yeah. What's what's the point? Yeah. I

was going to say uh like we could we could we can invent a lot of things.

Context rationing is pretty good. I

really like that one. Context fality or like budget or something. I feel like everyone's going to be like whoa you're running out of context uh window today.

You know like maybe that's what happens next year where we're we're charged on context window. And then the the one of

context window. And then the the one of the more recetive sessions is recursive language models, which again is just reusing the same context window on >> Yeah. over 100 artist. Yeah. I've been

>> Yeah. over 100 artist. Yeah. I've been

pretty interested in that. But like to be clear, I'm the total idiot. I have no idea. Claude tells me what's

idea. Claude tells me what's >> You're this you're the semiis guy, man.

Like you're really good on your stuff.

One thing I wanted to spot check on was talis. Uh which

talis. Uh which >> I have not messed around with it.

>> Okay. You don't have to mess around with it. Just this general theory of custom

it. Just this general theory of custom basics burning the weights into the chip. Y so you don't need memory. Yeah,

chip. Y so you don't need memory. Yeah,

>> that's pretty good actually. I think I think I think that that that makes sense to me. Um

to me. Um >> 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?

>> A lot of the produ the the production >> Yeah. They they get distilled to [ __ ]

>> Yeah. They they get distilled to [ __ ] >> Yeah. They get distilled to [ __ ] So

>> Yeah. They get distilled to [ __ ] So it's like the push and pull there is going to be like okay can you just burn in a a like enough efficient paro frontier in terms of performance to be burned in straight onto the silicon that

doesn't need memory and then boom you can scale this forever uh versus like you know the performance edge of the long thing. It's pretty clear to me that

long thing. It's pretty clear to me that like talis has a place because you're kind of seeing this market bifurcate a little bit. You could argue the prefill

little bit. You could argue the prefill uh decode disagregation stuff like that is like the focus on performance inference serving is going to be a subset of the market and then the training and the whatever and the big

production like you're we need to kind of break it in into smaller parts in order for using the same that makes no sense >> just in order for the compute to be even remotely okay.

>> It makes sense to you and uh I mean TBD on the sort of practical implementation but otherwise burning their ways into the chip. Why didn't Etched or some of

the chip. Why didn't Etched or some of the other guys get there first? Um, Etch

is pretty interesting. I don't know.

>> Yeah, I'm going to speculate.

>> I mean, I'm not going to like super speculate. I mean, the the thing is like

speculate. I mean, the the thing is like their thing is like, okay, how do we have a a big systolic array? Um, 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 Yeah. I

just think the way to speed things up is to never transfer anything. Yeah.

>> Yeah. That's that's the fastest way possible. And so so but the thing is the

possible. And so so but the thing is the bet on on on this really large systolic array is effectively everything is computebound, right? Like I don't think

computebound, right? Like I don't think that that's really the case in in terms of like where we're actually seeing issues in production markets today. It's

like you're actually seeing all the issues in the memory. Uh right. And so

like I I just don't know if that's like going to be the perfect solution. There

is definitely a world and space and like a design space where they're going to be very valuable and cool. But like also the reason why my hit rate for every AI accelerator trip is like very like I just don't believe in them is cuz like

where are they until Cerebrus and Grock?

Honestly, they were all considered failures and even then we're like what are they going to do with Grock? What

are they going to do with Cerebrus? So

>> is is Salmanova?

>> Uh no I think Smanov is like a much more interesting one but I think there's like there's like all kinds of deal issues with that. I haven't been keeping up

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. Because I kind of forget about them, too, but yeah. Honestly, I was going to say they were, >> you know, once once a year they show up.

>> Yeah, they they do. And they're not they're not so bad. Yeah. Yeah. You

mentioned actually some CPU shortage stuff for CPU uh that have been what's going on there.

>> I think it's I think Okay, so okay, I have uh one we'll start with the conspiracy theory that I think is really funny. Have you been

funny. Have you been >> noticing just like I feel like web services have become really unstable.

>> It has been down about I like and like me. Okay, this is pure like, you know, a

me. Okay, this is pure like, you know, a schizophrenic hatbrain because I have a schizophrenic trend hatbrain. I'm

wondering if it's um two things.

Shipping vibe code slop to prod. That's

number one. That's definitely something that's possible. But it's happening to

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 like a GitHub Azure thing. We are kind of right at the exact five to six year

period of the refresh cycle of co. So

COVID we had this big 2020 2021 you bought like hundred billion dollars of CPUs and stuff like that and so we're right at the natural end of life for these chips and so usually what you do is you have this big refresh of all

these chips but what what's been happening instead is everyone has essentially scred all of their budget as hard as they can but then like I feel like I've seen it in like Azure like hey last night my Amazon Prime thing doesn't

work and I was like it'll probably work in the in the morning babe don't worry about it I think I think Azure is just or like AWS is pissed tonight you know something like that um but I think uh so we have this 5year thing everyone scrged

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 computer uh how much like software where is the software going to run on CPUs so I think we're going to see some increasing utilization

as well as the fact that RL is like actually heavily used for like RL gyms you have you have to simulate software and it uses a lot of uh CPUs. So the or not quite like the orders of magnitude of

GPU stuff but it's just such a big trend even when it steps slightly in in a place mass amounts demand. So like we might actually be seeing a CPU shortage partially because of this refresh cycle but partially also because like I

legitimately believe the cloud code cloud code is increasing software creation um and then on top of that there is real demand from >> RL. Yeah. Yeah. And just general

>> RL. Yeah. Yeah. And just general production agents as well. uh you know we just um yeah every like RLMs take compute and um you know open cloud takes

more compute and know it's just it's just um different slope but at the same sort of direction >> still an ups slope and in a slope that to be clear has had massive underinvestment for the last two years cuz everyone like

>> how do the same problem that happened massive underinvestment cuz they're like screw it we're doing maintenance only we're all we're going to do is m maintain the past we're not going to 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. Semi guys say semi numbers go up the >> That's That's one way to put it. Yeah. I

I The thing that's crazy is we talked about the demand, >> but it's like it's like you're right.

Like I mean for sure like show me where I'm wrong with like show me where I'm I mean definitely not. But the thing that's crazy is like memory prices are going to go up so much that we're going to have to choose which which I want.

That's the crazy part to me.

Historically, memory has never been a constraint like this where I said actually you're not going to get your low-end uh you're not going to get your low-end phone. You're not going to get a

low-end phone. You're not going to get a GPU this year for gaming. None of that stuff. You're you can't do these things

stuff. You're you can't do these things because you're priced out market. That's

what's crazy. That is the first thing that's happened in a long time. That's

going to be really interesting to see where that shortage and how how it's like digested and felt.

>> So, it's amazing that thank you for that breakdown. I feel like I really

breakdown. I feel like I really understood it talking to you. Uh let's

transition to a couple personal things and yeah sure as the end. How do you write cuz you you write a [ __ ] ton.

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

definitely write a lot. Um

>> and like you kept going with fab.

>> Oh wow. Okay dude to clear that was really so so look I'm still trying to do fab cuz I I I do feel deeply connected to writing. Um let's just specifically

to writing. Um let's just specifically talk on this a little bit.

>> Yeah. Just just just like explain yourself, you know.

>> Okay. So um the thing uh before LLM's came around the thing I felt the strongest about my my number one information skill is I was able to read and synthesize and process at like

really high speed really high throughput decently high comprehension the adjustment is speed in terms of comprehension almost anything like when my like when my friend gets a PhD I go read their paper I was like oh I have a

pretty good idea of what you're doing I was like like hey when I was interested in semicer book I literally raw dog some textbooks whatever the comprehension was not very high but like hey whose comprehension is you know um 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 um starting the subsack which has been really fun actually cuz I just really wanted to get my story out like the things I cared about closed the loop for writing for me cuz 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 fabricated

of late. Semi analysis plus fabricated knowledge is pretty hard to do 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 one a week. Uh at

least an attempt to two. And so I think one of the best ways all the people who write who write about writing all say the same thing. You need to just be writing. Um and so that's how I that's

writing. Um and so that's how I that's how I writing every week. They don't.

Yeah. It really helps. Um well I was going to say what's crazy is like it's it's kind of hard these days and I and LMs kind of have really I don't know. I

don't like LM writing. I do like it for ideiation like making outline.

>> Yeah. Yeah. We here's my un unorganized thoughts. make it into an outline and

thoughts. make it into an outline and then like you know I'll even be like put bullet points in the outline and I'll literally read the outline and then like ideulate and write in parallel but yeah that's that's how I feel about writing I

guess write more I have a strong uh for non-fiction writing I really like this book called on writing well um that's just a really good classic book it's actually um

>> summarized and synthesized into a into a skill for me oh yeah yeah yeah so hey uh please edit this use these uh use this style guide use the like learnings from

this book. So yeah, stuff like that.

this book. So yeah, stuff like that.

Yeah.

>> Okay. And then uh do you like have a a topic idea list that you groom like I I've put mine in Apple notes now but it's >> bro it's no never I'm one I'm just I'm just a oneshot on your head. Yeah.

Usually I oneshot the the idea all the way. Usually I think about it for quite

way. Usually I think about it for quite a bit so it's been bouncing around in my brain. Um, and then at some point in

brain. Um, and then at some point in time I've like condensed enough information to make a really crappy outline and that's usually when I just oneshot go for me like it's hard to

oneshot and bounce because you will forget right and sometimes you you have like really good stuff that you forget and sometimes it's actually uh so I call this mis plus writing where you basically just have a store where you're

just kind of writing working your ideas in parallel and then every now and then you cook. Yeah. Um and so this is async

you cook. Yeah. Um and so this is async and this is sync right? This is like passive like, "Oh, here's a data point.

Here's here's a quote. Here's a thing. I

just slot it in the right thing and then and then I bake it." Historically, okay.

So, how that prewriting actually works today is probably in the semi analysis slack.

>> Um, just like all the little then you just search it up when you need it.

>> Yeah. Search it up when I need it or something like that. But like I do most of the prewriting I think in my brain and I have places that I put it out that I reference it later. But um my favorite thing too is like when it comes to the

because like okay uh well once upon a time much more on the beat. Oh, hey,

here's earnings. Read every single one and put it all together. But like, um, my favorite skill or tip or whatever is like, hey, do the prewriting, think about it, all that stuff, and go to sleep and wake up the next the fresh

context window in the morning is my number one advice on writing. Kelsey,

decode. It helps so much better. Like,

literally, if I'm like, hey, I need to write something right now, I will do I'll write it all down. I'll make

outlines. I'll do all kinds of crap except for writing it. And then I'll be like and I'll go to sleep and then wake up and the first thing I do I'll open up a new tab and I will write it. Got it.

>> Um and then so usually that will get me to 60 75% of something even if it's like an outline where I like have gotten all the ideas enough to know how to fill it out the rest of the way and then that's

that's how I take it from there. Cool.

Amazing. Uh last thing hike. Yeah. Uh so

one bit of context for me is uh I I just I just I've never taken a break. Never.

Um, and I feel like, you know, if you take a break in this time, you're like just going to be so behind. You're just

going to so miss out.

>> Uh, I just found out my friend from OMI took a break a year off to bike through Japan.

>> And how how could you? I like you're going to miss it. You're going to miss everything. But he's like, I'm good, you

everything. But he's like, I'm good, you know, like I'm I'm, you know, having kids, whatever. You did a sabbatical as

kids, whatever. You did a sabbatical as well, and like it was preai, but uh, it was interesting. I You did you did the

was interesting. I You did you did the Appalachian Trail. Which one? Uh, so

Appalachian Trail. Which one? Uh, so

there's three big ones in the United States. There's the Appalachian, the

States. There's the Appalachian, the Pacific Crest Trail, and then there's a Continental Divide Trail. So, I did the Continental Divide Trail, which is the longest and most remote of the three.

Sometimes considered like the the the older bad whatever. But like honestly, the PC, they're all different trails.

Like I'm I'm pretty steeped in hiking culture. Um, I think Mile form AT is

culture. Um, I think Mile form AT is actually the hardest, but I did the CDT as my first trail, as my first through hike. Um, you know, you learn a little

hike. Um, you know, you learn a little bit about the three when I was choosing which one I wanted to do. And the CDT was the one that scared me the most. Uh,

I was like, hey, this would be the hardest, biggest accomplishment I could possibly imagine. And I thought if I

possibly imagine. And I thought if I never have an opportunity to ever do this ever again, which so far seems to be pretty correct, um, which one am I going to do to feel the most like, hey, I did the thing that I really wanted to

do because I've always wanted to do a longdistance hike. And so I I chose the

longdistance hike. And so I I chose the Continental Divide Trail. I did that in 2021 preI and >> but after the GPC3 essay >> after the GPC3 essay. Yeah, I felt like I was missing out a lot and there's like

a huge it was a huge year for Substack.

I feel like I missed out like a very big year of like the big growth. You're

doing okay.

>> Um I'm doing I'm doing fine. Um but I I just think that for me is something I always deeply wanted to do from an intrinsic perspective. I think something

intrinsic perspective. I think something is like like fulfillment life fulfillment and and I would definitely do it again but I probably >> and to be for people it's like four months five months >> uh 6 months >> 6 months

>> 6 months uh 6 months 2800 miles we'll we'll call it on the route 2850 or whatever the miles I went and like you meet people on the way but you're mostly alone >> mostly alone did it alone you get the trail name it's a whole

>> audio books I listened to audiobooks until I hated them listen to music till I hated it bored as hell like you just you just you go you you go through all of it actually um >> yeah it was awesome some 6 months. I

think the thing I think about is so far in most in in my life up to that point, you get kind of get kicked from situation to situation, right? You

create a a view, a form of yourself, you think you know yourself, you have ideas of what motivates you, how do you react in situations, blah blah blah blah. I

think the one the CDT about like it's just like I like I like the outdoors, I like hiking, I'm like good at it, whatever. It's just something I really

whatever. It's just something I really appealed to me from an adventure perspective. like when in modern life do

perspective. like when in modern life do you get to say hey I'm going on an adventure >> never like and that's what it was it was it was an adventure for me um and one that I got to like really you you know it's like oh the journey is a

destination or whatever you learn a lot about yourself in fact I learned it didn't grow me up per se but I feel like I am more well- definfined of my view of myself I understand how I react I actually know where my exact line or

it's like you know you're like oh I'll go do this it's like actually no I know my exact line where I'm like I would not do that I know exactly where I'm not that's too scary, too hard, too whatever. I know my limits a little

whatever. I know my limits a little better. I feel like I know

better. I feel like I know >> just more about myself. It is a very condensed version of a very intense life. And yeah, I wouldn't give up that

life. And yeah, I wouldn't give up that experience for anything in the entire world. It was extremely personally

world. It was extremely personally meaningful to me. I think it's very fun to go back to the lower part of the Mazov's hierarchy of needs. Like all

this crap what we're talking about today is so abstract. It's like totally fake.

And we were not born and built for it.

We were born to like, you know, our human evolution got us to like scrape a living in the mud. Okay.

>> Hunt and hunt and gather and just not die. It's kind of interesting to go

die. It's kind of interesting to go backwards and to see what feels like, dude, I was so hungry, so scared, so alone, so like but also like super low that the the phrase is like lowest lows

and highest highs. These crazy lows where you're like, what am I doing? What

does all mean? Highest highs and mean like holy crap, it's so good just to be alive. all these things where it's like

alive. all these things where it's like it's just so like the raw experience of life is so meaningful and you don't get to experience it without doing it that way. And so yeah, I wouldn't I highly

way. And so yeah, I wouldn't I highly recommend it. It's very I would do it

recommend it. It's very I would do it when you're younger. I wish I did it after college.

>> Um like right after college instead of hey like whatever kicked us out a year.

I think it's good to learn about yourself.

>> It's really important. You're the you're selfmastery is your most important tool use of all. So

>> yeah that yeah self mastery is your most important tool use. Amazing. Well, thank

you for uh jumping on and like covering everything. Yeah, I feel like I got like

everything. Yeah, I feel like I got like got to go through the sort of quad code psychosis all the way to the semis all the way to the hiking.

>> Yeah, thank you. Thanks for having us.

Yeah, so this Yeah, great to catch up.