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

This crap makes mistakes all the time. Mhmm. 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 junior analyst, who I once was, went and gathered all that information. And after doing this enough times, there's a meta level thinking that's happening where it's like, okay.

Here is what I really understand and how this type of analysis I'm an expert in, actually. I'm very good at. I consistently have a hit rate.

Now I'm the expert. Right? I don't think that meta level learning is there yet.

We'll see if l ones do it right. Everyone who's spending 1 quadrillion dollars in the world thinks it will. It better it better happen, but if you're spending, you know, a trillion dollars and there's not meta level learning.

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

all this time, I just is it okay if I just call you Swiss? Feel like it's the that's that's where my brain is. So I've known you for so long.

You can call me Mule if you aren't. I'm not. 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.

Right? Yeah. No.

I think it was some murder party. Yeah. Yeah.

Yeah. And you were like, hey. Like, who's this tall dude?

I'm like, woah. Okay. Yeah.

Yeah. Well, I mean, it's just like I I knew about you and we've we've like been Internet, you know, pen pals for a long time. So, like, cool meeting in person.

I mean yeah. Yeah. I think that was the first time I ever met you in person.

So yeah. Amazing. I I didn't go to the New Orleans one.

I really wish I did. I love New Orleans, honestly. Yeah.

So We have two New Orleanses in a row, and, yeah, I'll say we should go back there. Yeah. Are you guys going to Melbourne or the Australia one this year?

I have I don't even think that far out. Yeah. But on it but that sounds pretty interesting to me.

I think I can't remember which one. There's a there's something in in in Korea this year. Right?

Yeah. I think ICML. ICML?

Yeah. I think I'm gonna try to go to ICML in Korea. And I know iClear is I don't know, man.

There's so many conferences. I honestly hate to say it. I'm not much of a travel guy.

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

Yeah. Thank you. I really bring yeah.

Actually, yeah, that's fun. Yeah. Yeah.

I did not know that I'll be caught in a snowstorm. Yeah. It's it's funny.

I feel like people recently have been coming, they keep getting stuck in these snowstorms. So, yeah, first blizzard in four years or something like that. Thank you for coming.

Yeah. Yeah. It's a pleasure.

And so you wanna go back. You you used to be anonymous. You used to be Value Mule.

Well, yeah, how I know you. You know what's funny is that Value Mule is, like, the very first one. That's the yeah.

The do y'all know how I don't know how I noticed you? I I was just like, oh, this guy's not seeing smart. Yeah.

I 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, primordial days of Twitter.

Yeah. Honestly, I miss those the most. I it was like 2017, '18, something like that.

Yeah. But yeah. Yeah.

I remember from Value Mule. So if you that's, the deepest cut. If you are even aware of what that is, that is, the deepest cut that you'll possibly have.

And then, yeah, I have another account, then 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 another one. Okay.

I don't wanna dox your other account. Oh, okay. It's semi semi dox.

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. Okay.

I I, you know, I wanna have some privacy, I feel like. Yeah. Yeah.

So so so now you've gone all in on the brand and everything. Yeah. I got a brand and everything.

Yeah. Cool. Cool.

Same profile pic, you know. So Yeah. So let's let's do a little bit of the Doug story because a lot of people hear about Dylan, I wanted to just make this the dog story, make me the fat knowledge story.

You used to be a value investor. That's kind of how you you were value mule.

So I I wouldn't say value because well well, for for everyone who's listening to this podcast, might as well be value. Right? Maybe quality focus back in the day, but we had this whole thing where we wanted to buy quality compounder companies.

And the one I found that nerdstyed me, although, like, single shot me as I found ASML. Yeah. And then I, like, fell in love with it.

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

That'd be amazing. John's a monster, honestly. This one.

Right? Yeah. Yeah.

Yeah. I mean, the thing that's crazy is he has I don't know. He has a whole playlist about it.

Every single aspect of what goes into it. And what what's truly great about it, it's all science fiction. Like, that's my favorite thing is, like, science fiction exists other than, you know, the talking perfectly intelligent robot, whatever information LLM.

Yeah. ASML is all science fiction. So the semiconductor stuff's always been science fiction.

Always loved it. Always thought it was cool. Thought it was the most important thing that we ever made and, yeah, kinda followed from that.

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

I didn't know.

sector to cover. It is absolutely huge. Yes.

Very large. Like, was numbering Sprint. Yeah.

Yeah. And he got like, you know, Viacom. Yep.

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

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

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

Yeah. Yeah.

Yeah. You you you assume that all the Taiwanese people who are buying and selling the rumors of capacity are pretty well informed. You assume all the people who are TMT investors in The United States are pretty well informed.

I think the thing that was, like, the foundational difference for me is, like, you know, real thesis around one, think being young and brash and believing in yourself to be like, no. This is something that really matters and everyone else doesn't see it really helps. So for me, the thing that was, like, I guess, radicalizing was I really believe Moore's Law Off was dead.

And I was like, oh my god. Not only is it this cool new technology is super hard to make and very interesting and technologically very fun to understand and and, like, I get it intuitively. But also, everything all the old playbook is about to be thrown out because it's been like this is a super mature industry.

You really need these primers about it. Like, that's how you learned about things back before ChatGPT knew everything or Mhmm. You had to go and read these primers of all this information.

They're like, oh, it's a very mature industry. A mature they used to be really immature in the eighties and nineties and February, but now you're consolidated, growth doesn't go up a lot. And everyone kinda had this old playbook from the early two thousands.

A lot of people hated hardware. There was just this perception that semiconductors weren't valuable, weren't as valuable. Actually, software was the most valuable thing.

Now software is getting shit on, but that's like outside of the scope of this. But people just thought it was this old, mature business that had nothing new under the sun. Meanwhile, every single day, just making a new chip was like science fiction.

People took that for granted. And when the science fiction ends because you can't make the chips as small as you you could, all of a sudden, all those free gains you got go away and you have to think about it. And what happened for semiconductor specifically is it created a lot of pricing power or value for everyone who knew how to make a good chip.

So NVIDIA is probably the best case. You could talk about parallel computing and all that stuff, but it's not just like they know every aspect of it from the chip to the networking to the design to the scale up, the whole thing. It it is like, you know, versus in the past, it was just CPU gets better, no burr.

Right? Mhmm. And so I think that I had a really deep belief that In this case.

That Moore's Law was Moore's Law was ending and everything would change. And so coming in with that, like, thesis at the top level just, like, made me wanna attack every little assumption and something that really changed as well. I and dude, this is honestly my my favorite post I've ever written.

It's like 20 it's like a check GPT three and the writing on the wall. In like two 2020, you know, my early I get an early pitch to for fabricated knowledge and I'm like, hey, you know, I'm gonna make a release, you know, Moore's Law's over. Scaling Law seemed like a big deal.

If you simplify it all the way through is like, okay, supply, you know, supply divide, you know, demand. Right? Yeah.

Demand is growing a lot because of scaling laws. Supply is actually slowing down because Moore's law is completely screwed. That's probably really good for semiconductors, and parallel compute is gonna be a big deal, blah blah blah blah blah.

My conclusion then was you should just, NVIDIA is pretty much the only one who's gonna benefit. And and, you know, so that's my my my my my, like, good long range prediction, I feel like. Just like Yeah.

I just don't think I don't think I would have expected the magnitude. Think that that's been the kind of the craziest part about this whole story is, like, I had all these beliefs and thesis and, like, I really, really, really believed. The reason why I met Dylan is he's the only person who was as semiconductor pilled in the entire world as me is how I felt.

So I remember, like, yelling at him, arguing about all these kinds of things in, like, our our DMs and stuff like that. Was it just online or It was online. We met in person.

Taiwan and No. No. No.

I I've I've actually only been to Taiwan with him one time, I think. Yeah. So so, I mean, like, look, we we just met in person.

We yapped. We went to conferences. But I think that that's like kinda we were both really early to the thesis, kinda have a different background and perspective.

Dylan is technology first, and, you know, obviously technology matters. I have a little bit more of a financial background, but all was around him. And it was just like, you know, he's the only one guy who, like, cared to the same level.

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

NVIDIA is pretty good. And then it's like, no, NVIDIA is now the most valuable company in the world. And I think if you had me read that and like truly, hey, I wrote that.

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

Exactly. Yeah. Yeah.

Like, there's so many things. Like, what else are you writing at the time, right, that don't work out? You know?

Yeah. Yeah. Yeah.

We can look we can look back. I I I I'm pretty happy with my with my long term track record. I really am.

But, yeah, I'm just really surprised the magnitude of how everything happened. Like, it's crazy to me that, like, co ops is, like, a a not a household term, but, like, relatively well known. It was like an exotic technology.

So all this stuff has been this, like, learning journey, really believing where technology is going, why chips are so important, and then obviously understanding the big scheme of all the things putting it together. And so that's the yeah. That was like the early days, and it's I think it's all been downstream of that, like, you know, one goaded insight pretty much.

Yeah. I mean and 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. Yeah. I I think one of my skills have always been, like, trend following and trend watching.

I think when, you know, for talking, like, on my account, like, Value Mule, you know, full time. Like, I was always pretty good at trends, like, being relatively early. I remember loving and being obsessed with TikTok in, like, 2039, and everyone's like, why are you so obsessed with the dancing music show?

Like, stuff like that. I feel like I've always been decent with the trends, but I think the thing was when you see a really big wave that you have a lot of conviction in, it's worth going all in. And that's that's kinda what it came down to.

It's like, wow. I see this really big wave. It's worth going all in.

And so I reoriented my life around it. Yeah. Yeah.

Cool.

I am Renaud? Where's the chart with the 44% of code? It's it's it's actually go to the top left.

Yeah. Yeah. Send it over.

Oh, you know what's really crazy is we've updated that chart. I think it's like five now.

semiconductor firm when everyone else is super boring and old. Yep. But, like, what are you doing, you know, getting so into cloud code?

Like, you know, I I I shouldn't you be reading reports and stuff? You know, like, tell the story of your code psychosis. So, yeah, I think here's the thing is if you wanna be good at any game, we're we're tool users at the end of the day.

Right? If you are good if you wanna be, like and and obviously this is like outside of my job as analysis. Like, I have all these other things I need to do to to grow and make semi analysis the best research firm ever.

But like, let's say you're a fund manager or an analyst. Right? Your job is to find information edges and like new ways to put information together that no one else has done.

And so like, I've always thought it's really important to know the most important weapons grade tool that you can do all the time, which is essentially chatty bitty, anthropic, all this kind of stuff. And I've been pretty like, I'm a I'm a early adopter in tools as much as I can be. And, like, for example, I've been running the our our case study that we have into Cloud Code since it first came out.

Like, you know, think over a year, like, you know, I wanna say March, April, I started to So which case study? So the case study for people when we were hiring, like, a financial analyst, our core research seed or something. Okay.

Hey. You know, can you can you take this company and do some analysis, blah blah blah, give us this format back? And I've been running it through like the agentic things and like, hey.

What what when agents really come around, they should be able to one shot multi step hard things to do, things that would take a human twenty four hours to do. Right? And I always wondered because I you know, there's some good submissions and there's some bad submissions.

We pride ourselves in the case study and being good. And and honestly, I always joked like, well, you know, they're gonna start to beat the worst submissions. And so, like, that was our that was always my base level.

I have a base level of is it better than a ChatGPT agent mode or Anthropix Cloud Code Yeah. Or Gemini CLI, whatever. And so I started running these benchmarks a little bit.

And so I was very familiar with how good it could be, but then I was like, oh, it isn't quite there. I vibe coded some stuff on Opus four for sure. But like, you know, it was like kind of interesting projects on the side.

It was really hard. It took a lot of feedback. They would they would mess up.

It just didn't. And then, you know, everyone was freaking out about 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 one shotting everything. Right? Like all these MVPs that, you know, you have to be like, well, the UIs, whatever.

It's like, no. It just one shots it. And then you ask it to do something better and explains what you're doing.

You're like, that's actually really good. And so I was like, wow, generalized, easily one shot MVP of these like projects and able to like really build things on top of it because you can trust what it's doing to a certain extent. And it felt like some level of capability was beaten.

It was very different than what I've done in the past. Oh, I also tried Codex two before this, like like one of those 5.2.

Never really got it to work in the way seamlessly, agentically, like Oh, of course. So this is aft this this is recent. Oh, no.

No. So so so this my most recent when I was like, oh, man, the the awakening, probably December 27. I would say December 20 because you know it to the day.

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

Yeah. Yeah. And and then also there's two x usage limits.

Oh my god. I miss those days. But I mean, now I'm addicted to fast.

But but look, I I was playing around with these coding agents just like everyone else should or or should in the space and like clog code versus it codex. I was like doing, you know, simple testing to see if they can make a thing. And it never really like one shotted like a total idiots thing.

And then four point five just started one shotting stuff. And that to me was like a huge difference. And so I was like, wow.

It could just like one shot stuff. I have all these interesting ideas I pursue. Is it Excel sheets primarily?

No. No. No.

Not Excel sheets primarily. I would say it's usually a mix of like a dashboard or Excel or something like that. But a good example where I like, I I think Excel, it's moderately okay at, like, let's say, one shotting a basic financial model or, like, just taking and and putting information from one place to another.

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

Like, no one's saying, oh, yeah. My estimate is always 1¢ more tighter than everyone else. That's why I'm good at stocks.

No. It it doesn't matter. It's like sell side is ridiculous because, like, everyone's like, I'm bullish because of my EPS estimate is like, 10% higher than than the street.

Yeah. And I'm like, oh, cares? Well, I mean, as you know, sell side, if we're gonna do this as like shots against, across the bow on on sell side.

I mean, look. One of the reasons why Semi Analysis has such a, like, successful business is because I think sell side as a concept is very broken. If you're talking about waves and things that are changing, side in a lot of ways is this hereditary child of like, let's say thirty or forty years of banking where you had, you know, a company go public, so you needed someone to talk about it, to issue securities and You were to have Selling the stock.

You're literally selling the stock. You have to but you have to be independent ish, so your ratings, buy sell hold. One of the biggest sales you could do is like when your when your company IPOs, we'll talk about you so people know who you are.

Yeah. That's the the core original part of the sell side. Right?

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

And so like the primary information process, it's like a forty year old business model on its last legs. And so, I mean, that's one of the reasons why semi analysis is so good. It's because we are not focused on being a 1¢ EPS thing, which I would argue isn't exactly skill.

It's just mechanical maintenance. Mhmm. We are really good at understanding when technology changes and how that impacts everything.

Right? Because it doesn't really matter if one EPS is slightly higher or lower. It does matter if, like, I'm I'm just giving an example of AMD's Helios rack is super on time and is, like, out of the gate ready to make tokens on this day.

Because that's gonna be billions of dollars of difference in revenue for A and D. Right? Yeah.

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

We're a research firm.

literate. But also two three, it's like this, like, x factor that is like, well, focus on things that matter is fuck everything else. And and I don't know what that is, but that that obviously is the alpha.

Yeah. A 100%. A 100%.

That's yeah. That that that's always been the the analyst PM conversation. It's like, hey.

You know, there really is only one or, like, three things that actually matter. Right? Find me those three things.

Find find me those three things. Right? And then there's all this information.

What's actually what you know, that's the hard part. But, yeah, we I think the thing is, like, we're really focused on finding the things that actually matter. Right?

Like, the things that, like, hey. This Certes is better than this Certes. This case doesn't matter.

This one actually matters because now you have a giant opportunity. And so that's that's what the game is all about, I think, in terms of the research Yeah. And, a a, you know, finance perspective.

But on top of that too, it's just like when you do so much research, all these different little industry parts are so hard to understand, man. Mhmm. 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 what you call it, laser.

You know, I I can't even remember what it what email is replacing, blah blah blah. And you're, like, talking about all this stuff, they have PhDs and you don't. Okay?

and you have to kind of throw human attention at all of it to find what matters. And you're you're saying you noticed some kind of breakthrough in December where it was suddenly clicking for you. I I just really wanted to figure out, like, the the task.

The task that I was nailing and the task that is still Okay. Is not great at. Yeah.

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

It's it's like an addiction. Okay? So I I'm like, hey.

You know, here's like all my positions and like, here's some like thoughts on it. Can you just like kinda like start copy pasting some notes over and putting all together? It's like, yeah.

It does that. That's why do you give cloud code? Yeah.

Okay. So I started doing this and then I'm like, well, like add it, make the portfolio, run some basic risk stuff. And it's like, yeah, sure.

Fine. Whatever. And then also like everything you do is perfect.

I'm like, wait, well, like actually, can we, like, make an investment framework for my investment style and start to grade all this stuff and then, attack it and do stuff like that? You could just do, iterative work. And then I was like, woah, woah, woah.

This is, a crazy useful tool that systemized how I think really quickly, like, okay, what else can I do with it? And the answer is like fucking anything. Right?

And my joke on the the podcast is it's all a skill issue now. And so I I've been I've been doing this systematically for every aspect that I can think of. Like, hey, now it's so much easy easier.

Like I was actually, perfect example is this is this chart. Right? Hey, Cloud Code is a really big deal.

Everything's one shotting. I'm reading everyone going into psychosis like me at the same time on the Internet. How do I actually know what's real and what's Well, I wonder.

Right? Yeah. Yeah.

I wonder. Right? So I'm like, okay.

I heard about the fact that the quad code has the commits, right, onto the public onto your your commit. It says, hey. Signed off with I'm like, well, why quad code scrape me all the commits.

Right? And you know what? Lo and behold, it pretty much did.

Like, it was 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 like every single day, the API is relatively open.

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 as a percentage of GitHub? You're like, chart go up.

It's a huge deal. And I'm just like watching your you know, I have like a cron job updating it every single day, blah blah blah. And I'm like, this is a huge deal.

they don't they don't plot it as nicely as you do. Yeah. Well, the and also you wanna okay.

Because you you you asked you had to you you had to you asked the question of what is this as a percentage of GitHub and this this guy didn't. Yeah. That's it.

Yeah. And and also, I mean, the other thing too is, yeah, I have a lot of those as well. Yeah.

But but I thought the quad code because I'm just trying to really, really, really focus on that. So typically yeah. Well and also, you wanna give an example.

Bro, I didn't make that chart. Opus 4.5 did.

Yeah. Or or I think 4.6.

I'm like, hey. I want you to do it in this style. This is the semi analysis color scheme.

Yeah. This I had, like, summarized books about visualization and, like, put in here are little style tips. Yeah.

Here's some Deleted. A tough hiddy. I I don't I don't even know, man.

It has, like, it has, like I had it go read, like, 70 books or something. I'm like, give me, like, you know, the It's probably a waste. Like, you know what?

You're I know. I know. Finding a part.

It it is a waste. Look. Tokens are free.

The the cost of doing this is nothing. That's the part that's so amazing. Yeah.

Yeah. The cost of doing this is nothing. The information gathering and synthesis is like, hey.

If it costs effectively the same doing 70 as 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 in the seminalysis format using any kind of data. And then you can systematically just push this out again. I'm hey, data analyst, please consider all the relationships you can and generate information.

Like, I think it's that one was not chattypie that was not generated. That was not generated, which I hate, honestly. I don't like that much that one as much as the Yeah.

Doesn't have the guidelines that are open. Yeah. And and so you can just that was that was generated.

And so you can just what you can do is you just ask it to do is like, hey. Here's all the dates that we have. Can you, like, visually brainstorm with me a way to better represent this information?

It's like, yeah. Actually, I'm gonna generate you a timeline. Okay.

You can just do things. And I, I mean, it's That that is your catchphrase. Right?

Yeah. That is my catchphrase right now. You can just do things.

And so people were looking at this from the perspective of people who are coding and they're like, hey, just programming is automated. Right? Mhmm.

But like all information work is, you know, I would argue coding is a big subset of all information work. I think there's a a Brian Hobart tweet or something forever ago. He's like, you know, coding and financial, you know, finance people actually are very like different types of abstraction, but, you know, you are doing abstraction.

Excel is a ginormous abstraction. You're building these relationships and you're describing what you think a financial thing is worth. Right?

I think coding is a little harder for me honest with you. And you're telling me the hard one got automated. Why can't the easy one get automated?

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

Right? Hey. I care about x, y, z.

Out of 10 score this. And then you can really do multiple things. It helps with the stochasticness.

after all the tests are done? I I actually have two versions of this. Like, hey, you can pull all this stuff together, just run the Yeah.

For the rubric or whatever. Yeah. Or you can do the task and the rubric.

It just depends on how you wanna do it. Yeah. Because obviously if you put a task and a rubric, then it can iterate itself.

Mhmm. But if you put it after, then it's probably more likely you pay attention to the the rubric. Yeah.

Exactly. And well, the other part part of it too, yeah, it it will iterate, but like the context rot doesn't I kinda like it to be separate because the thing is it's like, oh, it needs to be this like fresh look at it. You have to think of it kind of like it would perceive anything anywhere.

Right? It just each context window is just opening it up. And I think sometimes if you have done if you do it together, it commingles the information to the point where it becomes biased or susceptible.

Opus 4.6, as you know, is, like, super sycophantic. Like, it loves to, like, say, yes.

Okay. Yeah. I'll do this for you.

Yeah. I think having it separate keeps it, like, keeps some of that drifts kind of away, and that's, one of the things that I've really personally, I like the results better, but it's it's just complicated. Like, part of this is really weird because I am I'm weirdly now opinionated on taste in terms of how you should design things because you can, like for example, the context route thing until someone explained it.

I was like, oh my god. I just thank god someone said it. This is a huge deal.

There's this, like, meme where it's like Oh, yeah. These guys. Well, do you see the meme?

It's like, of mice of men. And at the end of you know, at the end of the book, I can't remember which character she's the other guy. I never read it.

Yeah. So so one character treats the other guy, and it's like, some guy made a meme about it being like, this is after your after your cloud code is garbled, you know, 5,000,000 tokens, you're like, okay. It's time to put you down.

Because the context raw is huge. So, yeah, this, yeah, this is the example where. So what what are your compact practices?

Do you sort of aggressively compact manually? Or So I personally with the one new one mil, it I feel like I try to do it at all in one context window. I'm not doing ginormous The the one mil is very new.

Right? Like Yeah. One mil are very new.

Okay. Very but it's a big deal too. Because Yeah.

Because your skills and whatever your caught MD is a percentage of one mil is so much smaller. So you just get so much more oomph. Right?

Because the the 200 k's are just wiping over and over and over. That's a big deal. I think it's a huge deal.

And and also with how the agents are working, the sub agents will have their own contacts window, and then the pasting kind of, like, really saves that that big, you know, the 1,000,000.

I think maybe what's gonna happen this is like a maybe a giant intake. It has, like, data analysis in here, which that's what you were doing. I I yeah.

That's in my opinion, that is downstream of like, that is so so I think how we should think about it is software engineering might all be downstream of chips, which is downstream. Like like, chips is upstream, and then it's AI, and then it's software engineering. It is all the extension of that same compute hierarchy.

And I think the, like, you know, teaching where machine and code kind of inter or in the world intermingle right now is code. And so that's just gonna be the bleeding language that's used to to figure out everything else. That's that's my belief.

Like Yeah. It it doesn't make sense to build like, for example, this is a perfect example. 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 now.

Yes. A 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 Mhmm.

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 at putting the data together, we should be much more concerned about machine focused of, software consumption. And so they can, like, you know, the the LLMs and the agents can put and synthesize all the information and deposit, and god knows however you want it to be. I don't need to make a chart in in PowerPoint or Excel.

It will just deposit it, the Matplot the Matplot Yeah. Matplotlib. Matplotlib in a chart to me in an image.

Fine. That Oh, are you trying to use Matplotlib? Yeah.

Wow. Why? Why?

You know, it's better it's better I know. Understanding that code. Yeah.

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. But I don't think you would 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. Yeah.

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

So, yeah, I think it it it is a little inconsistent, we have the same watermarking. Honestly, I think it's better than our old formatting anyways. Well, the first thing this looks reminds me of is Bloomberg.

or agent team or something. It's just an experiment. It's just an experiment.

Yeah. Thank you. Thank you.

Because no one Not very good. Beat them. We exactly.

Because the pro it's just via prompt, and it's actually very bad. I think sub agents are okay because they usually have a QuadMD to go do whatever, but the agent team is is actually really Well, you know, we we can't we can't knock it because it's experimental. So Yeah.

Yeah. No. No.

It is what do you what do you try it on? Well, it was like some big data analysis of, like, many, many different companies with different KPIs into a dashboard all in one. Oh, I was like, hey.

Can you just make this all whatever, split up the teams? You know, speaking of that though, you say that, but can we can we do one month? 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 would be like, hey.

Here's a set of problems I would like you to do 20 times. Can you do them? And then I can measure the performance between them and then, like, do qualitative, 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, three months ago. Okay?

Now it is completely available to me. That's awesome. Like, I am very I care about this stuff and now I have the tools that's able to automate and do a lot of this stuff because, hey, all of software engineering is like partially automated.

And so, I mean, experience is the 2.5 swarm actually improves the model's performance meaningfully. The agent team makes it meaningfully worse because there's clearly not RL done.

So it isn't aware of what's the best thing to be done. And yeah. So I think I think it's interesting.

I like sub agents because it's usually a little bit cleaner on a task to go do it and then come back. But the agent team is just very They had some post about how they did stuff Yeah. Where it was yeah.

There's there's a bunch of RL for for this. And I tried it myself. I thought it 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 h one hundreds. Okay. And you're just like, dang.

So you and I are not gonna be running. And this is just to run, and I'm sure there's concurrency available. But, yeah, I think it's really cool.

And that's like I think that that's the sign of what's next because, you know, these agents are gonna get better to a certain extent. They're they're, know, it's another benchmark and bench like another benchmark to hill climb. Right?

But then it's gonna be how many of these together in a bigger chain can you get to work? That you could argue it's kinda like a scale out of the reasoning problem too. Hey, how do you get these like this one agent to essentially get a verified whatever, put it into a bigger process and do more information work?

That that's the next thing. And it's important to have context windows that that don't garble up into random stuff and is able to do just like good enough with token efficiency, I think is a huge part of that. Yeah.

So, yeah, that's that's kind of what our experiments have shown, at least in terms of like the agent sworn versus like not, I think it's very clear the agent team out of Claude is an experiment. But Kimi better. They'll they'll definitely do better.

But the Kimi 2.5 tells you that this is already boom, perfectly great new places to do more work on, completely available to us right now. I think that's huge because if these Asians get any better, like, don't know.

I'm never gonna sleep again as a podcast.

phase, like, as as seen, as opposed to, like, the Quens of the world, the Deepseaks of the world who don't really care that much. I mean, Quentin, because of how it's it's attached to Alibaba. Right?

Like Yeah. Yeah. They they have a way to productize it, but it's like it's like kinda like the Gemini version.

They have so much stuff to do elsewhere. Right? Yeah.

Yeah. But, yeah, Kimi Kimi's pretty interesting. So hard.

They got everything. I know. They got Kimmy Manus.

Kimmy Claw. Kimmy Claw. Yeah.

I know. Kimmy Claw. I haven't yeah.

Dude, I was gonna say, have you messed around with OpenClaw? Because I did I oh, boss. I remember what was it first called?

Claw. Clawbot. Clawbot?

Yeah. I was gonna say it was really, really euphoric. I was, like, having it read all my emails and my calendar and do all this stuff.

And I was like, wait. Wait. Wait.

This is really, really, really prompt injectable. And I was like, this is pretty secure and important stuff. 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 Box Yeah.

And I can let it use that. And if it impresses me, then I can upgrade it. But Cloudbot didn't impress me at the with I'm gonna honest with you.

I'm gonna honest with you. I wasn't impressed either. That was the reason why people were freaking out about this molt book.

Was like, bro, have you actually used this shit? Because it's not even right now on Cloud Code in a relatively focused terminal, it will be like, oh, blah blah blah. I'm like, dude, in the dot a n v, there is an like, in the dot a EMV, there is an API I told you to use for this sub case of problems and it's in your Cloud MD.

Like, please focus up. Like, still is like making mistakes. Yeah.

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

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

I I I do think it's kinda interesting, but I think there is but the the difference, though, is Zapier I mean, I remember I've tried to use Zapier before. Yeah. It's And it's also not very good.

It's not also not very good. The difference, though, is, like, and that's okay. Like, it's okay to be early to something and just wrong because you weren't the one that made it happen.

Right? Claude bot the Claude code, Claude bot, whatever, all this stuff, the reason why it's so powerful is it gets to completion. Right?

And and like, okay, Zapier, maybe 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, four and a half minutes. Mhmm.

And that's good enough for me. You know? That that's a faster achievement.

And so, like, it's totally okay that they were they were right, but they were just not the right mechanism. Right? You you see this happen in information like, in the history of, like, compute.

exist within this, like Yeah. Like like yeah. You you end up becoming with you know, the the the box will control you.

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 seminalysis information out as well. Yeah.

And so that way we have Like an internal server. An internal server that is that that is accessed by people with an API so that, like, all the seminalysis researchers are able to hit, like, some basic level of context. Because I think the context is really what matters.

Yeah. I'm too, like, too dumb to be really smart in in order to have well, I guess I guess I do have some hooks, if that makes sense, in terms of, like think I hooks are very under underrated. Right?

Yeah. I I do think because you can do, like, a Ralph loop, I guess, with a hook. Yeah.

Like Yeah. I feel like I underutilized hooks. I I think that is true, but I do I do run some version of them on, like, skill calls.

So, like, it was 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, cook stuff and, like, compounded stuff like that.

And I found that, like, you know, the Gastown, Ralph Loop era, it's like it it is a sign of what will come, but I just don't think there's enough fidelity to, like, make crazy multi turn when something happens. So was like, okay. Actually, less is more.

Try to have, like, a strong set of smaller skills with a good amount of context information to be pulled in. And then at the beginning of every session, ask and focus on what you wanna do so that like it prompts the like not like, you know, a 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 NAND since 1984 or something like that. This is what I wanna do.

I wanna so like the actually, no. Let me just give you the best example that is probably not gonna work. I would like to fine tune a time series foundation model to predict NAND and and DRAM prices.

Okay? I'm gonna first start by gathering as much information as possible for all this stuff, blah blah blah, and then we're gonna fine tune it, evaluate which ones we're gonna do. I chose Cronos two because of covariates, blah blah blah.

Try to set this all project up. We'll make it a Vercel dashboard internally for for semi analysis. Maybe we'll external if we want if it's a good enough product.

Okay? So then it like does all this stuff and then I just like start plowing away. Hey, can you go research So here's the search API, Serper or Exa or whatever you wanna use to go look for all these different information sources and then bring it together.

Right? So this agent goes and gathers all this information. This agent goes and like works on, like, considering the fact that the price isn't perfect to do all this fine tuning on.

And then we like throw it in. I also had it of like, oh, what do I use? It showed me which GPU, whatever we're renting on an hourly basis.

And so, yeah, we just pull all this stuff together and we fine tune And I'm like, alright. Cool. How did this work?

And then we just have this constant iterative loop until I try to finish something. I got to the point where I was like, okay. This this time series, Elon, is probably not gonna work.

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 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 the sample size. Yeah. Which reduces your sample size.

And so then at the end of the time, at the end of the day, end up being like, well, it kind of just like, I guess it's, here's some heuristics. Good luck, have fun. Right?

Here's your checklist to see it might be over, but you really don't know anything until then. So but but, like, okay. An example of of where this project was helpful, was like, oh, hey.

I'm not gonna have the magic LLM tell me what the price of memory is gonna be. Hey. It was a good weekend project.

I did burn quite a few tokens. But I do happen to have, after all this like information synthesis and analysis, all of the memory prices of everything I could possibly find, plus the things that behind API that we've paid for, plus, you know, enhanced data sources. And I have all the covariates.

So like, hey, WFE, what was the consumer sentiment? Every macro thing of all time. And, you know, what's really interesting is I am gonna just be like, okay, well now can you go make a summary of each and every memory regime and what it looked like and what what what created the beginning, middle, end, and put that in a dashboard so it's relatable and, like, easy, shareable, consumable within my firm and company, yes.

I'll probably be done with that today. And that okay. So that you're like, well, that's just gathering, doing information stuff like, you don't understand.

No one's ever done that in the history of time. Mhmm. 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 eighties and nineties and February.

And, like, when I did it first time, the human grokked way brain is I went and I read these old annual reports and I put it together and I try to string an narrative through it. And I and I brought through all I'm like, okay. What was GDP this grow?

What this year? What was all this stuff? And you have to, like, make all this giant sheet to to whatever and then make the narratives.

No. None of that shit, dude. I mean, this is, too much information to gather.

It's like a lifetime of work. It's like a PhD project.

this is really hard and I think about it all the time. I feel very comfortable with doing all this work because the thing is my at the end of the day It's done to work. 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 set up scenarios and like pattern recognition. Right?

But yeah, you're right. This this crap makes mistakes all the time. Mhmm.

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. But the problem is, historically, what happens is that junior analyst, who I once was, went and gathered all that information.

And after doing this enough times, there's a meta level thinking that's happening where it's like, okay. Here's what I really understand and how this type of analysis I'm an expert in, actually. I'm very good at.

I consistently have a hit rate. Now I'm the expert. Right?

I don't think that meta level learning is there yet. We'll see if l ones do it right. Everyone who's spending 1 quadrillion dollars in the world thinks it will.

It better it better happen, but if you're spending, you know, a trillion dollars and there's not meta level learning. But for me in our firm, that massively amplifies everyone who is an expert. Right?

And we are a firm filled with experts. And so it's this hard part where I wonder if new people, we will be less lenient in terms of like how much AI tools you're Are you, like, junior or are to the firm? Junior.

Oh. Junior. To the firm.

Yeah. Junior. And like like because, like, you have to still do something that you can't just, like, slop it up.

It's very obvious to me what it's slopped. Yeah. Right?

When it's slopped and there's no cognition, then it's like like whatever the artisanal last 5% is, like, that really matters. Yeah. But for me, I know inherently what the 5% is.

I can like write it away with some really easy heuristics and time and like be like, okay. Well, this is the last 5% you fixed. This is what I believe.

Just fucking make up these assumptions instead. Press enter. Okay.

Cool. We're good to go. You know?

Yeah. And so that's kind of the hard part. That's a real hard part.

There is still a human in the loop right now. One day, someday, it'll be superhuman, but I definitely believe the where we're at today, where we're there it's not there. Like, you just compound all this noise and it becomes just like garbled, just like all context around.

But in terms of like the capability that is over hit, like, you know, the human CPU in this this agentic swarm is very, very powerful now. Yeah. 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 you if I define AGI as many common jobs, not like I'm not I'm not doing ASI, that's like religion. Can it automate or change or or take or, you know, completely shift a lot of the information work?

Yes. A 100%. Yeah.

Yeah. Like, data analysis is a perfect example. Hey.

Every quarter, I want you to just find me some examples of some information that might be interesting. I just can't imagine if I was an entry level worker doing data analysis that a 22 year old, an average 22 year old would would murder the hell out of a relatively well thought out agentic system. And so you're like, yeah.

That job actually does seem at risk. And so that yeah. That the 4.

5 capability enough, like, that we hit some level where it seems to work and do bigger information work, that's when I'm like, okay. Yeah. This this does change everything.

And so, yeah, there's there's all kinds of mistakes. I it's a new level of hygiene that we have to do. You're gonna have to understand what the absolutely of work is back to you.

Right? I catch it making errors all the time. It doesn't always pull skills.

Like, you can definitely tell like, context windows def like, gets dumber over time. It's not AGI today, but it can do these crazy long tasks. And as long as you finish it at the end and deposit it as information work, that's very valuable.

Yeah. Amazing. So you do a lot of, like, client visits, obviously.

Some people are interested, but you have to remember it's relatively more conservative. But I think but if you ask any analyst if they're using AI, every single one of them will tell you, yes. I use it every single day.

Of course, how could I not? This is like an an a vital skill. And so the the the basic the basic inference that I'm doing is I am a bleeding edge adopter.

I'm a relatively smart dude who knows what he's doing and if a tool is useful or not. And I've evaluated the tool and I'm like, wow, this is an amazing tool that I literally like pry it out of my dead fucking cold hands. Okay?

I'm like this even if it's like makes mistakes, I will be using this for all kinds of work forever. Then I look around to everyone else and being like, most of these guys are enough like me that if they have an opportunity and an edge, they will obviously apply it. And they look at this tool and they start to use it.

If if they start to use it and they're thinking like me, they're gonna obviously adopt it. I'm like, well, I don't understand why everyone doesn't adopt it. I would argue well, we'll see in the twenty four month view, it will be a base level, I think.

I think Yeah. Clog code, co work, whatever is gonna be a base level of all information work very soon. Yeah.

And, you know, you see one my my friend was telling me how his portfolio manager 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 gonna be a little different, but I think my moment it feels like GPT 3.5 or four for me.

Or there's that first time where you're like, okay, I know it made some shit up, but like, this is better than like if I went for hours searching, putting information together. It can and then also 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 level of capability.

I can't tell you what it is. It is, my my personal taste where I'm like, oh, wow. This is completely over the the the the chasm of what needs to happen for it to be a very, very powerful tool.

And so, yeah, that's my Cloud Code moment, I think. There there's some kind of automation chart that you know, XUCD has this automation chart.

Yeah. I think of it as hygiene. But the thing that's, like, really gonna be painful though is, like, a lot of my expert opinion has been built by, like you know, it's like pre phones and not.

Right? Like, your attention span like, you know, the children are cooked. Okay?

Like, you know, the attention spans are really bad, all this stuff. Like, I don't I read this, like, really sad thing. We're like, oh, we're getting dumber or something, first generation.

I don't know. I'm not gonna maybe that's like You see the the Coinbase earnings? Yeah.

I saw the Coinbase. Yeah. So so, like, you have this thing where it's like, okay.

And it's cute and all, but, like, it's such an addictive technology that, like, I feel very grateful that I'm like, well, I understand what I'm doing, have this history of doing stuff and able to apply a tool, but like people who are riding this curve, it's gonna be very dangerous. It's like giving everyone Right now I'm struggling. Yeah.

That's so funny. I I think you should just do that. Yeah.

He well, we we we do we do with some of the seminalysis memes, 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 what?

Well, hey. There's like have you ever been at, like, a bar when they play, like, these, like, weird like, there'll be, like, like, TikTok videos from whack or whatever, and you just watch them. There's TikTok bars in New York?

No. Not TikTok bars. Not TikTok bars.

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

So yeah. I'm sorry. That's completely off.

But but I wonder this quadcopill is very powerful for me. I believe it will g m 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 think you're gonna be a great reviewer. One of the reasons why you know what what makes that that human feat that loop well is because once upon a time you did that and you could make the three like, yeah, you idiot. You're thinking about this problem in this way.

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

And so it's like, yeah, I know you said this, but like, you know, the I I know g I know that I told you the valuation is the only thing that matters, but like it's also a fraud. You can't do both. Right?

Like if you think about like the analysis stuff, you have to know when your own and personal embedded model is like, yeah, actually, this one overwrites this one. That that's through learned experience. And I wonder if we're just reviewing, we won't be building and embedding those assumptions to understand judgment.

Right. Right.

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, know, whatever, press the button over and over and over, but sometimes you do actually have to, like, think. You know?

but you can see the design patterns. Like, the HeartbeatMD is a perfect example. You 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 fuck you can make the heartbeat.md consider the, like, the session to session and, like, hey, meta learnings, all this stuff, and it's only specialized and focused on one form of doing something.

So it actually does have a context of all the like, let me I'm thinking, like, a customer service agent or something like that. It does have the context. In fact, it can look at every single time it's ever happened.

That's actually information and context no human could ever hold. You're like, wait, that that feels like 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, the single line of code. It might actually be a business.

It's kinda crazy. Like, I I wouldn't have put myself as AGI Pilled. I think 4.

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 of AGI. So the thing that's crazy is because there's, like, this ASI element that people are, like, really, really focused on. Yeah.

Yeah. Moving the goalpost. Yeah.

We're moving the goalpost, but I'm like, bro, I the the goalpost, like, I I I mean, we'll see if this is actually the machine god, and Shogeth will come and talk to us and vibrate on our same But why don't you if I do think so? Okay. I I I don't I'm being honest with you.

I'm very open. I will change my mind often. I'm not this is not something I feel intuitive in my gut today.

Maybe it's the next next next thing. But when it comes to, like, the the the GDP valve version of this, yes. Yeah.

This is this is do white collar work. The white collar work, which is in most of the most of the tangible Very boring. Yes.

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

It's like now we just made like, my favorite stat is, like, once upon a time, 90% of people were farming. Right? Now today, less than 1% of people are farmers.

It's kinda like this crazy shift where technology is gonna massively change the relationship with all of that, and it's gonna be like this ninety nine one thing. I don't know if it'll be quite that drastic or whatever. Maybe, you know, everyone's just doing leisure.

So far, my experience is everyone just works harder, and it's been my experience. But it just it just feels like a massive moments happen. Like, the the steam engine's invented and, you know, the the trains are here and and everything is gonna change in knowledge work.

things change so quickly, and that five to ten year period, like, ten year gap can be drastic and crazy, and that's just societally wild. But yeah. And it's happening in our lifetimes.

It's happening in our library. Like, it's, like, happening, like, right now. Like, it's it's just it's really crazy.

It's, like, very and, like no. So this is, like, a complete side task on Yeah. Yeah.

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

Where why is it not showing our GDP yet? Right? So there's gonna be, you know, some people are gonna be like, oh, you know, the fax.

Fax to the internet, same thing, information transfer, whatever. I think I'm actually scared for a third worst thing, which is like, now now this is a complete crackpot theory. Please don't hold me to this Internet.

But what if AI is massively deflationary? Yep. And and and also, I think one of the more interesting conversations I've had in a bit is like, what would GDP was invented once upon a time as a way to figure out how much we could divert, you know, normal economy away just to war during World War, like, one or two or something like that.

Okay. My spiciest take is I feel like GDP itself is gonna be very, very challenged by AI because information work yeah. So how we, how we capture it effectively is all of an economic good and then the service hours divided by hours.

Okay? So there isn't like a widget to widget difference, but in theory, we could break all of information work down into units, we're gonna have a lot more information work for sure. Like, more work will be done.

I don't know what the value of that's gonna be. Is it gonna be so much increase in supply? It's deflationary.

great depression of AI Yeah. Where, like, we figure it out. Yeah.

Well, I I wrote this whole thing about railroad stuff because it's my favorite Okay. My my favorite capital cycle. Is it on Fab or It's on Fab.

Yeah. Okay. I can't remember.

It's like railroad, Fab. It's about all the railroad stuff over time. Okay?

Pretty much because we're like everyone was first looking for the Internet. We've well massively passed the Internet in terms of the absolute size of the build out. It's not even close.

Like, we What what numbers are are you thinking of? Like, what what I a trillion was a trillion all in was essentially the real dollars for Okay. Version, and I think we are well past like, we like, whatever this year, and it's cumulative.

Right? We will well past that. I think Railroads, the reason why it's so interesting is because honestly, it's way crazier.

But but prob part of the problem and craziness of it too is, like, Railroad was literally, like, one of the first added layers of the layer cake, if you think about it. Before it was agriculture, and railroad was like, okay. Well, how do we move this agriculture around faster?

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

Oh. Because there's no need to finance it. Finance it.

Yeah.

Andrew Carnegie was the federal reserve. Yes. Yeah.

There were individuals. Yes. Yeah.

And so all this stuff so it's like the whole thing. I kind of did some work on the Gilded Age, all this stuff. But, like, my takeaway is, like, that was a really interesting cycle because it was so big and took so long to deploy.

It actually forty five years of like there's three cycles, actually. There's three boom busts. Same.

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

Or that Because, you know, information Information. Moves around faster. Exactly.

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

Yeah. Where it's like it's all in one instantaneous up down, I think it's gonna look like some multiple cycles. So yeah.

Kinda just worry about railroads. The there was like a baby railroad cycle, then there was huge railroad cycle. The modern world was invented out of it.

That's, like, my favorite analogy for this because, like, I think it was, like, GDP percentage of CapEx each year were, like, high single digits for sustained for, like, ten years. Yeah. 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? Because I think that's I think so. The the way you make it convertible.

Stargate itself, 2% of US GDP.

Yeah. Yeah. That's a yeah.

And it's not all gonna be in one year. Right? But it's okay.

So yeah. So so total CapEx for it was 4.8% of GNP and 25% of total gross fixed capital investment.

Okay. So 25% of investment every year and four 5% of GNP. Yeah.

I think we're there. You know? StarGate plus Anthropic plus whatever.

Yeah. We're we're right there. X AI.

Yeah. So we're at a railroad build up. Yeah.

Yeah. So it's like, at one point, like, but the thing is crazy. We should exceed it.

Like Probably. Yeah. Yeah.

No. Not not probably. Like, we should.

Yeah. 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 worried. I'm like, dude, where are we gonna get all the money? That's, such a, like, the pedestrian concern.

Yeah. It's not a pedestrian I mean, that's what happens every capital cycle. I worry, like, goon must hands in The Middle East.

He'll fit the thing. We must we must the what what this happens every single time. This is reason why the, like, bubbles happen.

Right? It's, like, we essentially get so big where it's like, this must be built. It doesn't matter the price.

And then all of a sudden we look at it and we're like, oh, that was a steep ass price. But I think, I mean, the thing I think about this is like how I think about the big picture is there is a demand curve and a supply curve. 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 know when that number is.

I'm not I don't wanna say a number go up forever because I feel like that's like intellectually dishonest. But clock code for me is the first time where I'm like and we're bringing it all back together, where you're like, demand go up so much. I am now guzzling as an individual.

Like, for example, I'm we're we're off I'm off max. It's not enough. It's not even anywhere near enough.

Like, I mean, some people buy, like, five maxes and then they will Well, yeah. So so I I so I'm on fast I'm on fast with 1,000,000 on API, which is that is like an addiction level, if any sense. But, yeah, I I I really think 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. Yeah.

Like, if not more. Like, I don't understand, like, what's the like, I can't price it. I have no idea of the elasticity.

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

Right? And so what's able to that cost? Like, yeah, 90 k.

That's able to work in parallel. Yeah. Like, you can have a 100 of them.

It's kinda crazy. Yeah.

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 1.1 in time, but, in the very first month of us having it, no one's gonna be like, yeah, you idiot, you rube.

You don't know how to use your completely new technology that got birthed last month. Yeah. I think it's just about a it's a bit of time, and it's, like, kind of interesting because you're, like, watching I mean, it's cool is that if you're, like, on this acid bleeding edge, you get to see the design patterns, like, blossom in real time.

And, like, we have this, like, really old older guy who's, like, been through the history of technology since, like, forever back then. He's, like, one of the most interesting intelligent people of semi analysis. And he talks about how wait.

Who is it? Tange? Oh, wait.

So, like, he said this, like, we we we had the conversation one time. He was talking about, like, early Internet, how, like, it wasn't actually sure if the browser was gonna win. It was like a remote web file service.

Some people thought, like, well, I just I'm just gonna reach and play with someone else's web files remotely. Right? Who knows?

Right? And that kind of, you know, it kind of is a remote web file. Who who the hell knows?

There were design pattern searching back then, and I think we're at that again, where all the design patterns are open and it's like really interesting because there's many different ways this could go. And we're gonna have to kind of collectively agree what's the best set of hygiene, set of design patterns, what's the level of abstraction, and then like all the rest of how much SaaS it will disrupt all everything else, who the hell knows. But you get to watch it, like, front row seat right now.

Yeah. Yeah. I mean, my biggest one, and I I I do wanna bring it to Sami's in in a little bit, but is the IDE.

really wondering too because, like, IDE so so I think that same my like, my the reason why I'm so excited about this is I get to like look. I never my daily driver was never an IDE. Right?

My daily driver was like Bloomberg or Excel or something like that. But I have a personal belief. It's not happening yet because we're not quite there in the maturity curve, like software is just gonna be first.

But the year, like, of, you know, Excel is dead for finance. Yeah. Like, it's it's it's Excel is the IDE for analysts.

Excel is the IDE for analysts. Bloomberg is the IDE for analysts. I believe every one of these IDEs are done.

It's dead over and gone and dead. I just think it's why? Why?

It doesn't like just imagine the concept of you. I remember when I learned Bloomberg. I had to watch videos to learn about all the random The sub and keys, How to use this, how to use this, know, the tactic knowledge of using this function versus that function.

That's like crazy to think about. That is like that is like horse and buggy. Okay?

The agent with the information that can perfectly retrieve and analyze stuff is gonna have the ability to to to pull that all together in a better UI than it was with no legacy, whatever, I think all of that is dead. And like, this is why I'm like my my spiciest take of all is like Microsoft is a lot to lose. I think they have the most to lose of everyone.

Yeah. Because Excel is a human IDE for information work that's generalizable. So is PowerPoint, so is email.

Those are the base core level of abstraction that decided to be broadly general. But I I just don't think that matters anymore. I think Claude Cote or coworker or whatever is gonna be the year that, like, it will destroy all of that.

All that information work that that where you sat every single year, it's it's over. I think that that's the the one that's, like, more shocking and scary that, like, people don't believe. Like, I believe in my stomach with conviction because I have already had that moment for me.

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

Yeah. It's hard to let go because I I have so much, like, ingrained knowledge of of, like, manipulating things directly in in Excel.

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 and some stuff. I think that if you are marginal and on the curious and not hyper interconnected, which I would argue that I am at some analysis.

Like, for example, I'm trying my absolute best to just rip Bloomberg out. We're going to fax it API. Like, all all in API with a cloud code is my belief of the future.

Hey. Verifiable data source that you trust. Yeah.

Yeah. Hey. Scale up.

For you guys, you can do it. Yeah. For traders, we're so For for no way.

Yeah. I understand. Like, there's an information network that's, like, outside of this event.

But then you do deals in IB. Yeah. I know which are tracked by the regulators.

A 100%. Yeah. A 100%.

It's totally I completely But as an analyst, yes. For as an analyst. Yeah.

And so, like but I just think that, like, k, 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 gonna be using Bloomberg.

Right? This first time, I was like, actually, no. I don't care anymore.

Yeah. The IBs my util utils of, like, marginal value from from IB is, like, now outweighed by how, like, clunky this is, and I wanna just make some charts. Right?

And so that's Immediately, you save 10 to 20 k Yeah. For switching down. Yeah.

There you go.

Yeah. I want you know, I sandbag the ever living shit. Okay.

I I just believe '25 is very okay. Like, because 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.

Mhmm. I think 25 is within the 95 confidence interval. Sure.

So it's so it's between twenty five and fifty. Something like that. Yeah.

Yeah. It's just it's just absurd. But, you know And it could also be Codex.

Codex are you also watching Codex? Yeah. Yeah.

Yeah. So just to be clear, I am I actually even willing to comment on that? Because, like, I know we've been on a lot of shit of being Codex haters.

Yeah. I I think I I'm by the way, when I put Codex code, Codex agent, whatever, all in percentage that we can publicly see, I would argue that for the ratio outside of that's probably gonna be higher too, but whatever. Yeah.

I think together. Yeah. We're watching Codex.

I actually think Codex is Codex is pretty good. 5.3, I think.

So we had the whole thing. I because, like, I wrote most of the articles. I got token efficiency to context throttle.

Is the same one or Yeah. Yeah. It's in the bottom.

It's in the it's in the the paid section. Okay. Okay.

But, like, TLDR, I was like, well, you know, the reason why Cloud Code is so good, Anthropic is so good is because all of this token efficiency, the token efficiency is better than ChatGPT, all this stuff, blah blah blah blah. And then, like, 5.3 Codex came out and it's like, yeah, that that completely doesn't matter anymore.

They're like they're they're so back. I really think 5.3 Codex is awesome in in coding though, but you can watch it.

Like the reason why I like Opus 4.6 so much is because when I'm using it, I'm using it for like coding in is the way I interact with it, but I'm using it for broad generalized information work. Right?

But I think the difference is Codex wants to code because it's RL to be so good at coding to win on Suitebench that like you're trying to use it for general information. Like, hey. I'm trying to can you go research and search all these websites?

And, like, I don't even think they have web search in it or whatever. Maybe maybe you can give it to API or whatever. But it's like, great.

I'm 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, like, just ingest tokens of what's on the website.

It's like, okay. Great. I'm still like, it's so coding pilled on the URL that I think it isn't generalizable in the way that that 4.

6 is where it's like, oh, I could have it I could have it make some rubric or do some research or do something like that versus codex. It's very it's very it coding Codex is coding pill. And so that's what but I I am very optimistic actually on Codex, we do track quite a bit.

I I you can they have a meaningful amount of thing. Share, you could see the Bloomberg. They have the the the chart, bloomberry.

com. So the Cloud Code definitely is in the lead, but I think the part of it too is like the the like to like comparison. There's a ratio of 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 OpenAI people like Roon will tell like, blah blah blah, we're not accounting for it.

Yes. We didn't account for it. But like Okay.

I I I think Codex is better. I think there is some real problems and issues, but I bet you the second that they have a new pre trained with the RL, because the RL stack on Codex 5.3 is amazing, like, it's very coding code, that's when that's when the it 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?

Yes. So that on this the daily install counts, right Mhmm. Is an order Which is, by the way, just the Versus Code extension.

Right? So there's Yeah. Know.

I know. That's not even a default way to use Yeah. Core code.

Yeah. You're right. You're right.

Yeah. Well, CLI, NPM, Domo is another way to track it. Mhmm.

But I think they have like their own cost their own installer now.

ChatGPT portal that could be, like, Tri Atlas. Like, the modal that pops up can really move big users. Like, they're not quite a google.

com in terms of having so much ability to, like, siphon off users off. But I I wonder, like, the like to like, but but that's, like, my skepticism.

Embryos was just on the Lenny pod saying that they actually haven't invested enough in the web experience. So, like, I I I think I think the attribution for that is zero. Okay.

Yeah. I guess I just saw a modal. Be like, oh, try co try co I mean, but the but a modal isn't and and to be clear, Codecs in the in Mac is great.

I'm actually I mean, like Yeah. Yeah. It's they they actually have the app.

They The the app launch. The the app launch is actually pretty good. So so yeah.

And and I think I'm pretty bullish on that, honestly, especially for coding because it's, very coding filled. I just can't get it to work as well for non coding stuff. Then my know, I you use Conductor?

No. I've not used Conductor. Oh, okay.

I I thought I heard you say on a podcast No. I've not used Conductor.

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 I guess my design pattern on that is really skeptical of building on top of something that is growing very quickly and has more money in whatever. Like, I just think my favorite one is, like, platform as a service.

If you remember that one, it was, like, infrastructure as a service, platform as a service, SaaS software as a service. And I'm like, oh, this platform as a service. And it's like it always just ends up being in the middle, so it just gets eaten by one or the other.

I I think of that, like, middleware layer, unless if it's a really, really, really compelling case, often die. 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 haven't used I haven't used Conductor personally.

Yep. I've mostly just been, you know, going deeper into the psychosis. Yeah.

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

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

It's good to be an analyst in the space because it's fun to keep up with it. Right? Like, I agree.

Like, I I think everything everything We like the horse race. Yeah. I like the horse race.

Number one, number two. Oh. Yeah.

Yeah. But You know? Yeah.

No. No. I know.

But then you have to your brain also have to be like, number two is really big too. And then I I just think, like, for me, someone who likes the history of all this, like, 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 altogether.

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

No. I did okay. So I didn't watch it.

I never rewatched these things. K. So how I think about it is But, like, you you said things like Microsoft is scaling back investment and Yep.

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. You because they're the they're they're the software company. The horizontal software company.

Their their butt. Yeah. Exactly.

They're the horizontal software company that humans use their software to do information work. Okay? No like, I cannot paint a bigger target.

Okay? I cannot paint a bigger target. And my Salesforce.

Yeah. Well, so okay. That's another two number.

Microsoft is a automatic too big at Salesforce. Yeah. But the other thing too is they have this Azure business.

I don't think I'm completely out of the race. I'm like, you know, it's a really great clickbait title, but the the problem is the Azure business with OpenAI. Right?

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

The Golden Army. Exactly. The Golden 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 the that's the problem is the moat the the wall and the moats every year are getting more dilapidated as they continue to rent GPUs to to the barbarians.

So it's just like Google, Yahoo again? Yeah. It 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, like somehow in a third worst place.

You either all in become Azure, maybe in the telecom era, right, because you're team t guy, you become dumb pipes. K? That is the the Azure becomes call what is it?

Charter. Right? Mhmm.

Yeah. But then the other version of this is you say, no. No.

Screw these guys. I have to, like, reinvest back in and, essentially steal, copy their their features and build up my moat. That means I need to stop investing in Azure for the stock.

That really sucks because the stock is very much weighed on out your Azure revenue. And meanwhile, if we actually had to value Microsoft x Azure, the multiple would be really low right now. I think about that all the time.

What would this trade x x Azure? This is just Microsoft. Yeah.

Eight times earnings, 10 times earnings. Like, it was trading like that before, actually. Oh, jeez.

And, like like, remember the twenty tens era when it went all the way down to, like, 10 times earnings in the Steve Ballmer era, and then inflected outward as it did o's Oak with Azure. Yeah. Yeah.

Azure and o March.

I I think the biggest blunder of all time that the part that, like, is kinda 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, know, effectively, you look at all the other peers and Google, I would argue, is going to free cash flow zero.

I think Meta will go to free cash flow zero. Microsoft is still like, you know, Satya did not make the company. He is a professional manager and there is a board and there's a conversation.

He's being responsible. Yeah. Yeah.

He's being responsible. Right? But the problem is that responsible of this like an innovator's dilemma.

Right? Like, do I maintain maximize shareholder value and cash flow today? Or do I have a a deep belief that AI will kill the hell out of my core business?

And I need to all in invest in you know, am am I ready to bet the entire company on on a trend? And it seems like Satya is not a believer. You know, we've been talking about AGI.

He is not ASI pill. Okay? He doesn't have any fear of the Shogeth.

Mhmm. He thinks it's just like a new It's new loading it's a, you know, Lotus and Excel came around. Right?

Like, it's just a new tool. But I think at the same time, this this conflict between renting GPUs to the barbarians who will disrupt your business or, you know, your actual core business, it's clear how they're feeling. In the call of earnings, they talk about they could grow a lot faster if they wanted to, but they're trying to reinvest back into the internal capabilities.

That to me sounds like we are not gonna hire as many barbarians. We're gonna pull you know, we're reinvesting in these walls, pull in together, and try to defend the core moat. Right?

Because the the dream of this and in theory, you're like, oh, remember in '23 when they did the first big deal, you're like, wow, Microsoft's gonna win it all. Yeah. Because they already have all the distribution and they're gonna have the perfect product and boom, they're gonna have this giant business that makes them, you know, whatever, a 100,000,000,000, $100,000,000,000,000.

Okay. Whatever number you wanna But reality is, Claude for Excel, Claude for PowerPoint is literally exactly what it's supposed to be. Microsoft should have built it.

My 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, know, you this is like This this is the this is like the tribe over the hill. Clone. Yeah.

Yeah. Barbarian. Yeah.

This is the tribe over the hill, you know, and and though the tribe over the hill is like like, you know, on an on a nightly raid, easily sack the hell out of your castle. And you're like, dang, this is an issue. So so Microsoft now is super stuck in the middle.

And so how they're gonna have to do this is totally different. I think they're gonna keep I think they're gonna keep pulling back in. We're starting to see that.

Like, they're gonna do internal training. They're gonna try to do more foundational models. They're gonna try to use the weights that they have access to.

This MEI? Yeah. Okay.

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

part. Like, you know, so Oracle picked up the Slack. Yeah.

Or is Oracle being irresponsible? You know? So so I I'm actually if we're gonna talk about Oracle, I think so.

Let's talk specifically about Oracle because this is where we're gonna go. I think Oracle was irresponsible because the magnitude of what they did. Okay.

So it like, the thing is, like, I think the Slack, they should have done it, but, like, the whole setup, in my opinion, on Oracle is own goal. They messed up the messaging. They messed up the fundraising.

And in my opinion, if they were not like like, one of the things that happened is they went so aggressive out the gate, did the quarter where they said, like, $400,000,000,000. Right? They they said our Prize the wolf.

They promised the world. Then they proceeded to raise as much money as possible. And, like, this is the first time they've ever done these giant build outs.

And so now there's delays. Everyone's like, woah. Woah.

You did this much. Right? Capitalism is kinda 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, a little bit of expectations management.

This year's revenue from the deployed GPUs should partially help start to keep self funding, and that's how you make this work in a glide path without going up, down, up, down, 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 kinda 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 Not debt index.

Yes. Of the I I have zero familiarity of stuff. Hey.

I'm I'm pulling some numbers up. I did the numbers forever ago. I'm like, I I co say to him, whatever, forget all the precision.

Let's just say all of investment grade TMT is, 500,000,000,000. K? I think Oracle was like a 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. The entire index is selling off because it's like Yeah. It's supply.

Yeah. It's a supply thing. Right?

And that's the thing that's, like, crazy to me is, like so they they massively overshot. And I think that we're, like, a weird bottleneck I never ever ever ever ever thought us thought 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.

Mhmm. Like like, capitalism cannot, like, absorb that much capital demand because the order of magnitude, it's totally different. These hyperscaler businesses have been completely self funded since the history of time, had never gone out and issued anything.

First time they wanted, they turn around. They're like, hey. Instead of like, can you give me a a $10,000,000,000 loan limit?

Like, we've never done that before. Right? So the absolute size is kind of screwing it up, and I think that Oracle specifically was way, way, way too aggressive into a relatively illiquid market.

And so, like, you have to do this, like, you have to kind of lay yourself into it if it's gonna be like that. But they they would, like, super jolty, did these big, huge incremental ads and kinda flipped the whole thing. Oracle, CDS, people all freaking out.

I think a lot of it's mechanical, specifically on how badly it was done from a supply demand perspective.

could have internally funded this. They would have been totally fine. A 100% of your being.

And, like, this example where it's like, yeah. I think that that's a blunder. It'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. This is Correct. And, like, just from a, like, a, like, a p time like the math perspective, no one else they they're better than Oracle.

They they just by their credit rating, they have a 2% more profitability at a a capital basis. That's that's like, you can't beat that. I don't know why they decided not to, but now they're in this weird thing where they're like they're they're they're kind of wavering.

Like like, to win, you have to be, like, really bold. Right? And they're kind of like doing this one thing over here, being really defensive with Copilot.

Satya is now, you know, the the product manager of Copilot. And then they're also pulling back from Azure. Meanwhile, the competitors are pull are pushing in for the supply.

It's a really weird game. I think Microsoft has to choose choose a direction. We'll see.

We'll see. We'll see. And it's Okay.

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

And I'm sure it will will have more information that emerges.

I think Sergei

pre Gemini three, it was like, dude, we have all these TPUs. We're gonna 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 we're getting our asses kicked in consumer? I think it's an interesting thing too because the other thing I think about is there's a lot of different ways to to like break this down.

One, we wrote about it in t p v eight, like whatever we think Rubin will be much more competitive. I think Ironwood v seven is the peak gap between on between TCO, between NVIDIA and TPU. Right?

So if you are at your absolute strongest point, what do you do? There's two ways you could do it. You could try to maximize and, like, squeeze the juice and, like, make margins, or you can you can gain market share.

I think the perspective of doing this externally with with Anthropic is to gain market share because the biggest gap you have and and one of the reasons why there hasn't been a second merchant ship, 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? It's like a trillion.

Yeah. It's like a trillion or something like that. Assuming it gets, like, 30% market share or something like that.

Yeah. Everyone has been trying to crack the merchant silicon mode. Right?

And now they have the biggest absolute outperformance. All a lot of the people who did the original TPU program are, like, now at OpenAI. Some of them are medics.

Some some of them are yeah. You're exactly some of them are med they're all over. Right?

Like, the the core team that did most engineering have, like, since really dispersed. And so I think the gap might might close over time. And so at this absolute period of time, they're gonna they're gonna win market share.

And then then what happens is if you have an install base, you have an incentive to upgrade your install base. That's like the hugest problem with AMD, for example. No one wants to buy new AMD chips because it's not like they have old AMD chips.

No. They're not upgrading from anything. And so when you have that number two place, you have to, like you have to win definitively, and then also you have an opportunity to win win again next year.

I think the install base issue has been a kind of huge one. And so TPU is at the point where the software ecosystem is mature enough. The hardware is definitely mature.

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

at least in, like, the academic arena, which is a leading indicator of what it's gonna be used in India. I do not have as I I don't give a special purview on that. The thing I'm most excited about and, like, very much TBD will see is Infrance X will have TPUs eventually.

Mhmm. That's something we wanna do longer term. I think that that will really show in numbers.

What's As a benchmark? Yeah. As a benchmark.

Or 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.

Anthropic's very clear how they feel. Like like, everyone is very clear. I think even OpenAI would take I think everyone would eat as much TPU v seven as possible if you had it in a perfectly unconstrained world.

It would probably be at this exact moment, like, you know, the the hottest kid on the block until Reuben comes out. But the reality is supply chain really matters, and they're that just that's not available. And so that TCO that TCO advantage is at this absolute biggest aperture, then, like, Jensen essentially gets its it gets their stuff together.

It's competitive, and boom, it closes. So this door only open right now. Probably, TSMC is the biggest blocker, so there's no Yeah.

Yeah. What what can you do? It's this cascade, right, that which I think you've talked about Yeah.

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

Well, it's interesting because it's, like, even more than the fabs, like like, on the optical I believe there's link I should be pulling up. Yeah. That that that's it.

That's it. Just an EV seven or something. Yeah.

So so, yeah, it all goes back to the fabs. It all goes to who's making the chips. And like, I think one of the big differences too is just like the per it's just like a really good cleverly designed system architecture, and it's relatively stable.

And it's clear that they you can pre train big models on it, which is like a huge huge swipe at OpenAI right now. That being said, like, think OpenAI will get their their act together very quickly. And so, yeah, that's kinda like the narrative.

I think it's gonna be a good story for probably, like, a year or two, but then the real question is v v eight, we just don't think we'll be as competitive to Ruben, and that's when your special window starts to close. What's the technical reason why? HPM.

Okay. Four versus three. And then And that's a secure is the strategic decision by NVIDIA?

Yeah. I think one so NVIDIA is always if you think about NVIDIA, they're always trying to gas it as hard as they can. Like, they they like, it is a high performance chip.

It is a it is an f one. Like, it is as maxed out as possible. TPU is kinda like this, like, replicatable pod in a very large with, like, very high stability.

Right? Which if you know the history of Google, that's what that's what they do. That's with infra.

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

Yeah. But I think g v 200 would have completely mocked, you know, v seven if it came out on time and stable. It came out a little delayed and it wasn't stable.

And so I think there's a lot of different ways to kind of course correct that. And the one thing that's important is like, I think on the infrastructure side or sorry, on the supply chain side, bar none, NVIDIA is the best. They own the entire supply chain.

They really do. Like, you think all those HPM price increases, gonna come for TPU just like Nvidia, but Nvidia was literally in Asia. You saw him drinking with everyone, with the SK, with the everyone, with all the Korean guys, all the TSMC.

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

It's because he he needs to get the chips. Okay? So yeah.

This is Samsung's chairman? Yeah. This is Samsung's chairman.

Yeah. I know. Who's the who's the other guy for everything?

Let's put it this way. That's that's a huge deal. That's a huge huge huge deal.

Do you think do you think Google was out Hyundai. Hyundai. Yeah.

Yeah. Do you think Google was what what you know, do you think Sergei was out in Taiwan drinking to to get it supplied? No.

A 100%. There's an opportunity here, but there's only so many TPUs that can be made because the because of all the bottlenecks. Right?

And so NVIDIA has all the supply chain locked up, and so they're gonna have, like, so much of that kinda constrained there. And so it's it's gonna be really interesting. They're gonna they're gonna get the best, most performing HPM.

They're gonna be first on the road maps for even more rack density. They're gonna have, like, the best connectors, the best you know, the whole system will be once again turbojammed again for as hard as it can be. Mhmm.

And the people who made v seven, like, they made the the Chippewas done, like, three or four years ago. Like, the the talent dispersion aspect where people who worked really hard on this team to make this great chip has really kind of gone all over, that starts to get worse. And so if that gets better, which takes some time, I I think our current read is that, like, the HBM specifically and the memory scale up is gonna really go in Rubin's favor.

And so that's the big difference. And I think, as you know, that's what makes the context windows. That's what I'm able to do bigger bigger everything.

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

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

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

It's like a supply squeeze. Yeah. It's a I mean I was like, I just wanna I mean, if I can one ratio of, like Yeah.

Yeah. Okay. So it's a To three to one Quantify to So I have three to one to four to one ratio.

I I may I actually wrote a really funny piece. Like, I I called it, like, super oil, but let's see. This is a better one.

But pretty much, like, you in order for this higher grade of jet fuel has been invented, and the only way to make it is to, like, actually get rid of all all your other fuel and you have to like massively condense it and refine it. Okay? So now what happens is if there's any demand here, it's an instant shortage.

And so we we hilariously enough came out of like the biggest shortage ever in NAND and DRAM, like terrible, like catastrophic. The worst one ever. Like, the the the last analysis I could put to is, 96 or something like that.

Seriously, it's like an 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 of HBM is essentially a four x multiplier onto DRAM.

And then now, so we we completely constrained, took all the DRAM capacity. We just came out of this shortage, so no one invested in any clean rooms or capital equipment or anything like that. People got like massively free cash flow negative.

No one's spending a cent. Okay? People could go bankrupt.

You know? Yeah. So you they haven't invested in these three year long lead time items, and then now there's the, like, more demand than God, and it also evaporates the middle layer because the KB cash offload.

And then boom. You're just looking at the supply demand. You're like, yeah.

This is not gonna catch up for, like, two years. I think the thing that's, like, so interesting is the supply chain squeeze because these clean rooms take two years to make, man. And effectively, everyone paused, and how bad the last cycle was really forced everyone to completely pause altogether in terms of adding any new capacity.

Yeah. And so now we're a few years later and all the supply is gone. So, I mean, people are mean, it's just it's crazy.

We I I our post our conclusion is, like, we we could see DRAM prices, like, go up a 100% again. Like, it's it's gonna be the point where and this is, like, also example, like, really interesting in the whole thing. Another 100, I think, is demand I think you will start to have demand destruction from What does that look like?

Where hyperscalers maybe purchase less or something like that on the margin on the margin. Right? Because they're like, okay.

Well, what if I just really focus on this energy aspect instead? And also, ironically, all the energy so like every not every data center in America, but like many, many, many, most of the data centers in America are delayed. So you had this thing that's supposed to come on in twelve months.

It's coming on in eighteen. Maybe what you can do is you can play chicken with memory prices and you can kind of push out. Of course, everything you have in the pipeline, you you pull forward as hard as you can.

Okay? You pull forward, you double triple order, and then the DRAM and the 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 gonna constrain us anyways. We're gonna, like, kinda chill out the orders. And historically, that's when the memory market that that's what causes the crisis the the prices to drop.

Realistically, just looking at at the aggregate demand of how much we've purchased in term terms of power, it just seems like the gap is just huge. It's completely off to the point where the most obvious logical leg of the AI the AI trade is effectively investing in memory capacity. Yeah.

Well, not taste oh, yeah. Mean, it's you could say SK hynix and and Sam Micron. Micron and all the all the semi like, semi cap has been ripping off.

Which is like, by way, when I was in Baliasseny, we majority of a lot of money we made was just being on my card, yeah, in in the last, like Yeah. It's a good example. Yeah.

So, like, you have all the semi cap stuff. Right? Like, all everything that is even remotely related to investing in capacity for memory, that is, like, the ultimate bottleneck right now.

And also for listeners, it's gonna affect, like, your phones. Yeah. Like Yeah.

I Apple I think Apple's I bought a I bought a SD card for this thing. Yeah. I'm like, oh, it's a bucks.

Yeah. That's that's nothing too. That's and and that because because, like, that that's just the NAND side.

Dude, have you looked up, like like, I wanna say, like, 64 gigabytes of of DRAM? Like like, I'm moving up like, I I need to refresh my iPhone. Mhmm.

I'm moving it up because I'm doing this research work. Like, oh, yeah. You need to do it as soon as buy your iPhone now.

Yeah. Yeah. You buy your iPhone now because what's gonna happen is when iPhones go into the spot market Mhmm.

It prices are gonna go up a 100% alone. That's insane. And so they have to pass.

We're gonna be buying, like, old iPhones and then taking them out for them. Yes. There's no.

No. That's actually there's a there's a whole super duper deep in the weeds. There's this whole, like, technology that was very focused on cloud era called CXL, which is memory expanders for CPUs in order to have, like, whatever, just, like, elastic pools of compute of CPU and DRAM and whatever memory attach and whatever.

It never really took off because essentially HBM was like the way that really crushed it all, high performance, best of breed wins. But this CXL technology that kind of never really took off is gonna take off just because what they're gonna do is they're gonna take DDR four. They're gonna take the oldest, every bit of spare memory they can find, and they're gonna put them into racks, and then they're gonna attach them via CXL.

So like this Oh, yeah. So exactly that. Yeah.

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

Yeah. Like, yeah, that that, you know, I I was like a CXL bull for once upon a time, then it became very clear it was gonna die. And I was like, it's back.

But only because the entire express intent is to have these d like old chips, pull the old chips, attach it to something new. That's what it's gonna be like.

Hey. You can use ChatGPT 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, a 100 x more for 1,000,000.

The 1,000,000 context window is like a mansion, you know? That's the real You live in a mansion. I live in a mansion right now.

Yeah. Oh my god. The word just context rationing just came to me.

I'm like, fuck. Like, we're gonna have, like, vouchers for, like, okay, you can have this amount of context today.

You yeah. Have to, like, you have to learn how to use it well because of the DRAM. Yeah.

Context frugality or, like, budget or something. I feel like everyone's gonna be like, woah. Woah.

Woah. Woah. Running out of context windows today.

You know? Like, maybe that's what happens next year where we're we're She is on context window. And then the the one of the more recent obsessions is recursive language models, which again is just reusing the same context window on Yeah.

Over and under and Yeah. I've been pretty interested in that, but like to be clear, I'm a total idiot. I have no idea if Claude tells me what's You're the you're the semis guy, man.

Like, you're you're really good on your stuff.

the weights into the chip. Yep. So you don't need memory.

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

This comes 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 sorry. What do you mean?

A lot of the prod the the production. Yeah. They they get distilled to shit.

Yeah. They get distilled to shit. So it's like the push and pull there is gonna be like, okay.

Can you just burn in a us like, enough efficient Pareto frontier in terms of performance to be burned in straight onto the silicon that doesn't need memory and then boom, you can scale this forever versus, like, you know, the performance edge of the long thing. It's pretty clear to me that, like, Thales has a place because you're kinda seeing this market bifurcate a little bit. Mhmm.

You could argue the prefill decode disaggregation, stuff like that. It's like the focus on performance inference serving is gonna be a subset of the market and then the training and the whatever and the big production, like the you're we need to kind of break it in into smaller parts in order Yeah. At least for the using the same chips.

Yeah. That makes no sense. Just in order for the compute to be even remotely okay.

I don't know. Okay. I'm not I mean, speculate.

Yeah. I mean, I'm not kinda like super speculate. I mean, the thing the thing is like, their thing is like, how do we have a a big systolic array?

Right? But like, 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 bet on on on this really large systolic array is effectively everything is compute bound.

Right? Like, I don't think that that's really the case in in terms of, like, where we're actually seeing issues in production markets today. It's like you're actually seeing all the issues in the memory.

Right? And so, like, I I just don't know if that's, like, gonna be the perfect solution. There is definitely a world and space and, like, a design space where they're gonna be very valuable and cool, but, like, also, the reason why my hit rate for every AI accelerator trip is, like, very like, I just don't believe in them is because, like, where are they?

Until Cerberus and Grok, honestly, they were all considered failures. And even then, we're like, what are they gonna do with Grok? What are they gonna do with Cerberus?

So Is is Simonova up? No. I think someone that was, 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 one as well. Yeah. I I always I always try to mention them as as part of that cohort.

Yeah. Yeah. Because I kinda forget about them too.

But, yeah, I honestly was gonna say they were You know, once once a year, they show up. Yeah. They they do, and they're not they're not so bad.

Yeah. Yeah. Yeah.

Yeah. You mentioned actually some CPU shortage stuff or CPU Yeah. Sort of opinion.

What's what's going on there? Think it's I think it's okay. So, okay, I have one we'll start with conspiracy theory that I think is really funny.

Love this. Have you been noticing just like I feel like web services have become really unstable. It has been down a lot.

I like and like me okay. This is pure, like, know, schizophrenic hat brain because I have a schizophrenic trend hat brain. I'm wondering if it's two things, shipping vibe code swap to prod.

That's number one. That's definitely something that's possible. But it's happening to all the clouds at once, I feel like.

It's not just a AWS thing. It's not just a like a GitHub Azure thing. We are kind of right at the exact five to six year period of the refresh cycle of COVID.

So COVID, we had this big 2020, 2021. You bought, like, $100,000,000,000 of CPUs and stuff like that. And so we're right at the natural end of life for these chips.

And so usually what you do is you have this big refresh of all these chips. But what what's been happening instead is everyone has essentially scrounged all of their budget as hard as they can. But then, I feel like I've seen it in, like, Azure, like, hey.

Last night, my Amazon Prime thing doesn't work. And I was like, it'll probably work in the in the morning, babe. Don't worry about it.

I think it's I think Azure's just are like AWS pissed tonight, you know, something like that. But I think so we have this five year thing. Everyone's scrounged every single dollar they could to essentially invest in as much as AI as possible and just do maintenance CapEx on CPO.

Ironically, at the same time for all this Cloud Code stuff, is actually if you have this coding agent just generate you god knows how much compute, how much software, where is the software gonna run on CPUs? So I think we're gonna see some increasing utilization as well as the fact that RL is, like, actually heavily used for, like, RL gyms. You have to you have to simulate software, and it uses a lot of CPUs.

So the not quite, like, the orders of magnitude of GPU stuff, but it's just such a big trend even when it steps slightly in a in a place, mass amounts demand. Yeah. But, like, we might actually be seeing a CPU shortage, partially because of this refresh cycle, partially also because, like, I legitimately believe the Cloud Code Cloud Code is increasing software creation.

And then on top of that, there is real demand from RL. Yeah. And just general production agents as well.

You know, we just yeah. Every like, RLMs take compute and, you know, Open Cloud takes more compute. And and and, you know, it's just it's just a different slope, but at the same sort of direction.

An up slope and in a slope that, to be clear, has had massive underinvestment for the last two years because everyone's like, how do it it the same problem that happened. Massive underinvestment because they're like, screw it. We're doing maintenance only.

We're all we're gonna do is maintain maintain the past. We're not gonna add anything else. And then all of a sudden, just a little tiny slope on top of it, you're like, boom.

Shortage. Yeah. Yeah.

Amazing. So semi skies say semi's numbers go up. Yeah.

Is this the That's that's one way to put it. Yeah. I I the thing that's crazy is we talked about the demand issue.

But it's like, yeah. It's like, you're right. I mean, it's it's for sure.

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

Go up. That's the crazy part to me. Historically, memory has never been a constraint like this where I said, actually, you're not gonna get your low end you're not gonna get your low end phone.

You're not gonna get a GPU this year for gaming. None of that stuff. You're you can't do these things because you're priced out of the market.

That's what's crazy. It's insane. That is the first thing that's happened in long time.

That's something really interesting to see where that shortage and how how it's like digested and felt. That's amazing. Thank you for that breakdown.

I feel like I I really understood it when I was talking to you. Yeah. It's bit of transition to a couple of personal things and Yeah.

Sure. As the the end. How do you write?

Because you you write a fuck ton. Yeah. I do.

I have been writing a little bit less these days now that I'm, like, in the semi analysis mega mind. I definitely write a lot. And, like, you kept going with fab.

Because I For a while. Okay. To to clear, that was really so so so look, I'm still trying to do FAB because I I I I do feel deeply connected to writing.

Let's just specifically talk on this a little bit. Yeah. Just just just, like, explain yourself.

You know? Okay. So the thing LLMs came around, the thing I felt the strongest about my my number one information skill is I was able to read and synthesize and process at like really high speed, really high throughput, decently high comprehension.

The adjustment is speed in terms of comprehension, Almost anything. Like, when my like, when my friend gets a PhD, I go read their paper. I was like, oh, I have a pretty good idea of what you're doing.

I was like like, hey, when I was interested in a semiconductor book, I literally raw dog some textbooks. Whatever the comprehension was not very high, but like, hey, whose comprehension is? You know?

But I was able just to like push through these books and learn. So I've always loved reading. That's like my my number one original competitive skill set differentiator, and also something I like loved as a kid.

Crazy reader when I was a kid, always have been. And then starting the Sub Sack, which has been really fun actually because I just really wanted to get my story out, like the things I cared about, closed the loop for writing for me because I love I love reading so much. It makes a lot of sense that I love writing.

I think what really helped is I wrote every single week for, like, since October 21, like, consecutive streak for a long time. The streak has been a little broken as of late. Semi analysis plus fabricated knowledge is pretty hard to do.

Yeah. But, like, all of '24, I think, like, we're just talking just, like, every single day, every single week, I will put something out. Right?

Is it, a hard rule, like, one a week? It was a hard rule Okay. One a week.

At least in a tenth to two. Yeah. And so I think one of the best ways, all the people who write who write about writing all say the same thing.

You need to just be Writing. Yeah. And so that's how I that's how I start.

I'm writing every weekday now. Yeah. It really helps.

Well, I was gonna say what's crazy is like, it's it's kind of hard these days and I and LMs kind of have really I don't know. I don't like LLM writing. I do like it for ideation, like making outline.

Yeah. Yeah. Research.

Here's my un unorganized thoughts, make it into an outline, and then, like, you know, I'll even be, put bullet points in the outline, and I'll literally read the outline and then, like, ideate and write in parallel. But, yeah, that's that's how I feel about writing, I guess. Write more.

I have a strong for non fiction writing, I really like this book called On Writing Well. That's just a really good classic book. It's actually summarized and synthesized into a into a skill for me.

Oh, yeah? Yeah. Yeah.

Yeah. So, hey, please edit this. Use these use this style guide.

Use the, like, learnings from this book. So, yeah. Do stuff like that.

Yeah. Okay. And then do you, like, have a a topic idea list that you groom?

Like, I I put mine in Apple Notes now, but it's Bro, it's No. Never. Never.

I'm one and I'm just I'm just a one shot. Whatever is on your head. Yeah.

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

Usually, I think about it for quite a bit, so it's been bouncing around in my brain.

Yeah. 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'll 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 seminalysis Slack.

It's just like all the little things search it up when you need it. Yeah. It 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 Yeah. Yeah. That I reference it later.

But my favorite thing too is like when it comes to the because like okay. Well, once upon a time, much more on the beat. Oh, hey.

Here's earnings. Read every single one and put it all together. But like, my favorite skill or tip or whatever is like, hey, do the prewriting, think about it, all that stuff, and go to sleep and wake up.

The next the fresh context window in the morning is my number one advice on writing. Kelsey Decode? Yeah.

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. Go. And then so usually, that will get me into 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.

Last thing, hike. Yeah.

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. K.

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.

I think mile from mile 80 is actually the hardest, but I did the CDT as my first trail, as my first through hike. You know, you learn a little bit about the three when I was choosing which one I wanted to do, and the CDT was the one that scared me the most. I was like, hey.

This would be the hardest, biggest accomplishment I could possibly imagine. And I thought, if I never have an opportunity to ever do this ever again, which so far seems to be pretty correct, which one I'm gonna do to feel the most like, hey. I did the thing that I really wanted to do because I've always wanted to a long distance hike.

And so I I chose the Continental Divide trial. I did that in 2021, pre AI. And But after the GPC three essay?

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

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

Yeah. 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, six months.

Six months. Six months. Six months, 2,800 miles.

We'll we'll call it on the route. Twenty eight fifty or whatever the miles I went. And like you meet people on the way, but Yeah.

You're mostly alone. Mostly alone. Did it alone.

You get the trail name. Like, it's a whole audiobooks. I listened to audiobooks until I hated them, listened to music till I hated it, got bored as hell.

Like you just you just you go you've you go through all of it actually. Yeah. Yeah.

It was awesome. Six months. I think the thing I think about is so far in most in in in my life up until that point, you get kinda 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 CTE about, like, it's just like, I like I like the outdoors.

I like hiking. I'm, like, good at it, whatever. Just something I really appealed to me from an adventure perspective.

Like, when in modern life you get to say, hey. I'm going on an adventure. Never.

Like and that's what it was. It was a was an adventure for me. And one that I got to, like, really you you you know, it's like, oh, the journey is a destination or whatever.

You learn a lot about yourself. In fact, I learned it didn't grow me up per se, but I feel like I am more well defined of my view of myself. I understand how I react.

I actually know where my exact line or it's like, you know, you're like, oh, I'll go do this. It's like, actually, no. I know my exact line where I'm like, I would not do that.

I know exactly where I'm not that's too scary, too hard to whatever. Yeah. I know my limits a little better.

I feel like I know just more about myself. It is a very condensed version of a very intense life. And, yeah, I wouldn't give up that experience for anything in the entire world.

It was extremely personally meaningful to me. I think it's very fun to go back to the lower part of the Maslov'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 gather. Hunt and gather and just not die. It's kind of interesting to go backwards and to see what feels like, dude, I was so hungry, so scared, so alone, so like, but also, like, super low.

The the the phrase is, like, lowest lows and highest highs. These crazy lows where you're like, what am I doing? What does it all mean?

Highest highs would mean, holy crap. It's so good just to be alive. All these things where it's, like, it's just so like, the raw experience of life is so meaningful, and you don't get to experience it while doing it that way.

And so, yeah, I wouldn't I highly recommend it. It's very I would do it when you're younger. I wish I did it after college.

Yeah. Like right after college and said, hey, like whatever kick us out of here. I think it's good to learn about yourself.

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

Love that. Yeah. Self mastery always more joyous.

Yeah. Amazing. Well, thank you for jumping on and, like, covering everything.

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

Thank you. Thank you for having John. Yeah.

So this yeah. Great to catch up.