Doug Laughlin (1:43)

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

John (1:47)

Yeah. So there are two New Orleans in a row and. Yeah, obviously we should go back there.

Doug Laughlin (1:51)

Yeah.

John (1:52)

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

Doug Laughlin (1:56)

I have. I don't even think that far out, 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 Korea this year, right?

John (2:04)

Yeah, I think icml.

Doug Laughlin (2:05)

Icml. Yeah. I think I'm going to 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.

John (2:15)

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

Doug Laughlin (2:19)

Yeah.

John (2:19)

Thank you.

Doug Laughlin (2:20)

I really appreciate it.

John (2:20)

Yeah.

Doug Laughlin (2:21)

That was fun.

John (2:21)

Yeah.

Doug Laughlin (2:22)

Yeah.

John (2:22)

I did not know that I'll be caught in a snowstorm.

Doug Laughlin (2:24)

Yeah. It's funny, I feel like people recently have been coming and they keep getting stuck in these snowstorms, so. Yeah. First blizzard in four years or something like that. Thank you for coming.

John (2:32)

Yeah, yeah. It's a pleasure. And so you want to go back? You used to be anonymous. You used to be Value Meal, which is how I know you.

Doug Laughlin (2:38)

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

John (2:41)

That's the.

Doug Laughlin (2:41)

Yeah. I don't know how I noticed you.

John (2:44)

I was just like, oh, this guy's gonna seem smart.

Doug Laughlin (2:45)

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

John (2:55)

Yeah.

Doug Laughlin (2:56)

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

John (3:08)

So, wait. Oh, which one is that?

Doug Laughlin (3:10)

I don't have an option.

John (3:10)

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

Doug Laughlin (3:12)

Oh, okay.

John (3:13)

Just semi. Semi docs. Yeah.

Doug Laughlin (3:14)

Yeah.

John (3:14)

So. So.

Doug Laughlin (3:15)

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.

John (3:21)

Okay.

Doug Laughlin (3:21)

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

John (3:23)

Yeah, yeah.

Doug Laughlin (3:24)

So.

John (3:24)

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

Doug Laughlin (3:26)

Yeah, yeah. And everything. Yeah. Same profile pick, you know, so.

John (3:29)

Yeah. So let's. Let's do a little bit of the Doug story, because a lot of people hear about Dylan, and I wanted to just make this the Doug story. Make me the fat knowledge story. You used to be a value investor. That's kind of how you were value mule. And you had a mentor or something that nerd sniped you into semis. Is that the. The story?

Doug Laughlin (3:46)

No, actually I solo nerd myself, so I, I wouldn't say value because we'll. We'll for. For everyone who's listening to this podcast might as well be value, right? Maybe quality focus back in the day. But we had this whole thing where we wanted to buy quality compounder companies. And the one I found that nerd signed me all the like single shot me as. I found asml. Yeah. And I like fell in love with it. And then I like after asml, I just like read about all this stuff, how complicated it's to make these. Who are the people who are able to make them. And then I, you know, semicopters, the whole downstreams all from there. But it started with ASML in 2018. I really fell. Fell in love with it. And then I read like textbooks and I just like kept going deeper. And my favorite part about doing that,

John (4:25)

I'm gonna pull out the Asianometry.

Doug Laughlin (4:27)

Yeah, that's perfect. That's a perfect one.

John (4:29)

John. Amazing.

Doug Laughlin (4:30)

John's a monster, honestly.

John (4:32)

This one, right?

Doug Laughlin (4:32)

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'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. Yeah. ASML is all science fiction. So the semiconductor stuff's always been science fiction. Always loved it. Always thought it was cool. Thought it was the most important thing that we ever made and. Yeah. Kind of followed from that. Yeah.

John (4:59)

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

Doug Laughlin (5:03)

Yeah.

John (5:04)

I didn't. I covered tmt, which is a freaking huge sector to cover. It's absurdly huge. Yes.

Doug Laughlin (5:10)

Very large.

John (5:11)

Like I was numbering Sprint. Yeah. Yeah. And I think like, you know Viacom. Yep. And then there's ASML and.

Doug Laughlin (5:18)

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.

John (5:30)

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

Doug Laughlin (6:17)

So like, yeah, you assume that all the Taiwanese people who are buying and selling the rumors of capacity are pretty well informed. You assume all the people who are TMT investors in the United States are pretty well informed. I think the thing that was like the foundational difference for me is like, you know, real thesis around one. I think being young and brash and believing in yourself to be like, no, this is something that's really matters and everyone else doesn't see it really helps. But for me, the thing that was like, I guess radicalizing was I really believe Moore's Law was dead. And I was like, oh my God, not only is it this cool, new technology is super hard to make and very interesting and technologically very fun to understand and like, I get it intuitively. But also everything, all the old playbook is about to be thrown out 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 you had to go and read these primers of all this information. They're like, oh, it's a very mature industry. Immature. They used to be really immature in the 80s and 90s and 2000s, but now you're consolidated, growth doesn't go up a lot. And everyone kind of had this old playbook from the early 2000s. A lot of people hated hardware. 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 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 compute and all that stuff, but it's not just like they know every aspect of it from the chip to the networking to the design to the scale up the whole thing. It is like, you know, versus in the past it was just CPU gets better. Go Burr. Right. And so I think that I had a really deep belief that, that Moore's Law was. Moore's Law was ending and everything would change. And so coming in with that like thesis at the top level just like made me want to attack every little assumption and something that really changed as well. And dude, this is honestly my, my favorite post I've ever written. It's like 20, it's like a check GPT3 and the writing on the wall in like 2020 you early. I get an early pitch too for fabricated knowledge and I'm like, hey, you know, I'm going to make a release. You know, Moore's law is over. Scaling laws seem like a big deal if you simplify it all the way through is like okay, supply, you know, supply divide, you know, demand. Right? Yeah, demand is growing a lot because of scaling laws. Supply is actually slowing down because Moore's law is completely screwed. That's probably really good for semiconductors and parallel compute is going to be a big deal. Blah blah, blah blah blah. My conclusion then was you should just like Nvidia is pretty much the only one who's going to benefit. And you know, so that's my, my, my, my like good long range prediction. I feel like I just, yeah, I just don't think, I don't think I would have expected the magnitude. I think that that's been the kind of the craziest part about this whole story is like I had all these beliefs and thesis and like I really, really, really believe the reason why I met Dylan is he's the only person who is 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 and like our, our DMs and stuff like that.

John (9:35)

Was it just online or.

Doug Laughlin (9:36)

It was online. We met in person? No, no, no. 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 kind of, we were both really early to the thesis. Kind of have a different background and perspective. Dylan is technology first and you know, obviously technology matters. I have a little bit more of a financial background, but always around him and it was just like, you know, he's the only one guy who like cared to the same level. So yeah, this, the, the thing that's crazy is like we called it, we were right, blah, blah, blah, blah. But like the thing that I think that still shocks me all the time is the magnitude of how right we are. You know, like be like, oh, Nvidia was good, right? Nvidia is pretty good. And then it's like, you know, 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.

John (10:28)

This is one of many theses at the time.

Doug Laughlin (10:30)

Exactly. Yeah, yeah. Like so many things like what else

John (10:32)

are you writing at the time that didn't work out, you know. Yeah, yeah, we can look, we can look back.

Doug Laughlin (10:36)

But I, 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 coass 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 goated insight pretty much.

John (11:09)

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

Doug Laughlin (11:26)

Yeah. I think one of my skills have always been like trend following and trend watching. I think when, you know, if we're talking like on my account, like value mule or 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 20, 2019. And everyone's like, why are you so obsessed with the dancing music show? Like, stuff like that? I feel like I've always been decent with the trends, but I think the thing was, when you see a really big wave that you have a lot of conviction in, it's worth going all in. And that's. That's kind of what it came down to. It's like, wow, I see this really big wave. It's worth going all in. And so I reoriented my life around it.

John (12:00)

Yeah.

Doug Laughlin (12:01)

Yeah. Cool.

John (12:02)

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

Doug Laughlin (12:17)

Yeah, I. I am.

John (12:19)

Where's the chart with the 4.4percent of code?

Doug Laughlin (12:22)

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

John (12:36)

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

Doug Laughlin (12:57)

Yep.

John (12:57)

But, like, what are you doing, you know, getting so into cloud code? Like, you know, certainly you'll be reading reports and stuff, you know, like, tell the story of your code psychosis.

Doug Laughlin (13:07)

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

John (13:55)

So which case study?

Doug Laughlin (13:56)

So the case study for people when we're hiring like a financial analyst, like our core research seed or something. Ok, hey, you know, can you, can you take this company, do some analysis, blah blah blah, give us this format back. And I've been running it through like the agentic things like hey what, what? When agents really come around they should be able to one shot, multi step hard things to do, things that would take a human 24 hours to do. Right? And I always wondered because I, you know, there's some good submissions and there's some bad submissions. We pride ourselves in the case study and being good and honestly I always joked like well you know, they're going to start to beat the worst submissions. And so like that was our, that was always my base level. I have a base level of is it better than a ChatGPT agent mod or Anthropic's cloud code 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 4 for sure but like, you know, it was like kind of interesting projects on the side. It was really hard, it took a lot of feedback. It 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 like, you know, you have to be like well the UIs, whatever. It's like no, just one shot it and then you ask it to do something better and explains what you're doing. Like that's actually really good. And so I was like, wow, generalized, easily 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 codecs too before this like, like Windows 5.2. Never really got it to work in the way seamlessly, agentically.

John (15:36)

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

Doug Laughlin (15:39)

Oh no, no, no. So, so, so this my re. Most recent when I was like oh man, The Awakening. Probably December 27th, December 20th.

John (15:47)

You know it to the day.

Doug Laughlin (15:48)

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 so I had some time to mess around just by myself. Yeah. And. And then also there's 2x usage limits. Oh my God, I missed 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 do or should in the space and like clog code versus Codex. I was like doing you know, simple testing to see if they can make a thing and it never really like one shotted like a total idiots thing and then 4.5 just started one shotting stuff and that to me was like a huge difference and so I was like wow, it could just like one shot stuff. I have all these interesting ideas.

John (16:26)

Is it Excel sheets primarily?

Doug Laughlin (16:29)

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 going to ever make a good investment decision or not? No, never. Not once. Like no one's saying oh yeah, my estimate is always $0.01 more tighter than everyone else and that's why I'm good at stocks. No, it doesn't matter.

John (17:05)

It's like fell side is ridiculous curse. Like everyone's like I'm bullish because my EPs estimate like 10% higher than than the street.

Doug Laughlin (17:12)

Yeah.

John (17:12)

And I'm like oh who cares?

Doug Laughlin (17:13)

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

John (17:39)

you're selling the stock.

Doug Laughlin (17:40)

You're literally selling the stock. You have the. But you have independent ish so your ratings buy, sell, hold. One of the biggest sales you could do is like when you're, when your company iPodOS, we'll talk about you so people know who you are. 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 falling apart. It's just not different. A lot of banking regulations has changed and so like the primary information process, it's like a 40 year old business model on its last legs. And so I mean that's one of the reasons why Semi Alice is so good is because we are not focused on being a $0.01 EPS thing which I would argue isn't exactly skill, it's just mechanical maintenance. We are really good at understanding when technology changes and how that impacts everything. Right. Because it doesn't really matter if one EPS is slightly higher or lower. It does matter if like I'm just giving an example of AMD's Helios rack is super on time and is like out at the gate ready to make tokens on this day. Because that's going to be billions of dollars of difference in revenue for amd, right? Or some networking technology or, or something like that. Some bottleneck being really right on the timing and the magnitude of those inflection points will make a huge difference in the stocks. And so that's our business. We're a research firm, we're independent and we've had a really good hit rate and we, you know, we care deeply about the technology.

John (18:58)

Exactly. Yeah. You know I didn't mean to characterize you as like. No, no. You are young and fun but also you're extremely damn good. It's like it's almost like a triple threat. And I always wonder if it's like, okay, it's like, like one you have deep understanding of the tech. Two, maybe you're sort of financially sort of literate. But also three, there's like this X factor that is like well focus on things that matter is fuck everything else. And I don't know what that Is. But that obviously is the alpha.

Doug Laughlin (19:24)

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

John (19:33)

Find me those three things.

Doug Laughlin (19:34)

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 30s is better than this 30s. 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.

John (19:54)

Yeah.

Doug Laughlin (19:54)

And like a, you know, finance perspective, but on top of that too is just like when you do so much research, all these different little industry parts are so hard to understand, man. Like, you go to some 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 can't remember what it. What emails replacing blah, blah, blah. And you're like talking about all this stuff and they have PhDs and you don't. Okay, everyone has a PhD at the deepest level and they're all doing so you have to understand all these deep understandings of these parts, of these supply chains, but you also have to have a big understanding too, because, you know, this little part at the bottom of this supply chain is actually an impact this giant, you know, business at the top because it's all interconnected, but it's so complicated. Just paying the tuition to show up is very expensive.

John (20:40)

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

Doug Laughlin (21:04)

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 AI world. But, you know, I do still really enjoy stocks. It's one of the reasons, like, I'm passionate about it. And it's probably my, my defining skill, what makes me good or bad at stocks, quote, unquote. You, you know, the people who are really like stocks that are like lifers, they just love this shit. It's. It's like an addiction. Okay, So I. I'm like, hey, you know, here's like all my positions and like, here's some, like, thoughts on it. Can you just like, kind of like start copy pasting some notes over and putting all together? It's like, yeah, it does.

John (21:35)

That's. Why do you give quote code?

Doug Laughlin (21:36)

Yeah, okay. So I started doing this and then I'm like, okay, like, add it. Make the portfolio run some basic risk stuff. And it's like, yeah, whatever. And then all of a sudden, like, everything you do is perfect. I'm like, okay, well, like, actually, can we like, make an investment framework for my investment style and start to grade all this stuff and then like, attack it and do stuff like that? You could just do like iterative work. And then I was like, whoa, whoa, whoa, whoa. This is like a crazy useful tool that systemize how I think really quickly. Like, okay, what else can I do with it? And the answer is like, fucking anything, right? And my joke on the 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 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.

John (22:26)

I wonder, right?

Doug Laughlin (22:27)

Yeah, I wonder, right? So I'm like, okay, I heard about the fact that the cloud code has the commits right onto the public, onto your. Your commit. It says, hey, signed off with like, why cloud code scrape me all the commits, right? And you know what? Lo and behold, it pretty much did like. And it's like, okay, well, like, I'm looking for this signature right here. Copy paste it. Like, how would you systematically go about doing it? Did like a big query poll for all the stuff pulls all the, like, every single day, the API is relatively open. And then I'm like, oh, my God, let's see how much this is growing. And it's like, okay, chart go up. And you're like, how big is as a percentage of GitHub? You're like, chart, go up. It's a huge deal. And I'm just like watching here, you know, I have like a cron job updating it every Single day, blah, blah, blah. And I'm like, this is a huge deal. Like, this is the, the biggest deal. I, I love watching trends. I love watching exponential trend. Never seen one even remotely at this rate, you would are, you know, 4% in like two weeks or so.

John (23:21)

Do you know a PR arena? It's the, it's the previous attempt prior to you, but somehow they didn't, they didn't, they didn't talk about, they just talk about merge rates, but they don't plot it as nicely as you do.

Doug Laughlin (23:34)

Yeah, well, and also you want to.

John (23:36)

Okay, because you, you asked, you had to, you had to, you asked the question of what is this as a percentage of GitHub. And this, this guy didn't.

Doug Laughlin (23:42)

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 really, really focus on that specifically. Yeah, well, and also you want to 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 in this style. This is the semi analysis color scheme. This I like summarize books about visualization and like put in here style tips. Yeah, here's some style. It's how fitty. I don't, I don't even know, man. It has like, has like I had it go read like 70 books or something. I'm like, give me like, you know

John (24:14)

the, it's probably a waste.

Doug Laughlin (24:16)

Like I know finding it hard. It is a waste. Look, tokens are free. Cost of doing this is nothing. That's the part that's so amazing. Yeah, yeah. The cost of doing this is nothing. The information gathering and synthesis. Like, hey, if it costs effectively the same, doing 70 is 3. 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 semi analysis format using any kind of data. Data. And then you can systematically just push this out again. I'm like, hey, data analyst, please consider all the relationships. You can generate information. Like I think it's that one was not ChatGPT. That was not generated. That was not generated, which I hate. Honestly, I don't like that much. That one as much as.

John (25:02)

Yeah, it doesn't have the guidelines.

Doug Laughlin (25:03)

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, you can. Yeah, actually, I'm going to generate you a timeline. You can just do things. And I. I mean, it's that.

John (25:20)

That is your catchphrase, right?

Doug Laughlin (25:21)

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, programming is automated. Right. But, like, all information work is. You know, I would argue coding is a big subset of all information work. I think there's a. A Brian Hobart tweet or something forever ago. He's like, you know, coding and financial. You know, finance people actually are very, like, different types of abstraction. But you are doing abstraction. Excel is a ginormous abstraction. You're building these relationships, and you're describing what you think a financial thing is worth. Right. I think coding is a little harder. If I'm being honest with you, and you're telling me the hard one got automated, why can't the easy one get automated? So I started to ask myself, how much can we do? And the answer is, it feels like a skill issue. It makes issue. It makes errors on the. On the margin, but you can kind of force it into, like, for me, I love using rubrics, right. Hey, I care about X, Y, out of 10. Score this. And then you can really do multiple things. It helps with the stochastic.

John (26:20)

Do you put it all in one prompt, like the task and the rubric for the task, or do you put the rubric after all the tests?

Doug Laughlin (26:27)

Then I actually have two versions of this. You can pull all this stuff together, just run the. For the rubric or whatever. Or you can do the task and the rubric. It just depends on how you want to do it.

John (26:36)

Yeah. Because obviously, if you put it task and the rubric, then it can iterate itself, but if you put it after, then it's probably more likely to pay attention to the rubric.

Doug Laughlin (26:43)

Yeah. And well, in the other part of it, too. Yeah, it will iterate, but, like, the context rot doesn't matter. I kind of like it to be separate because the thing is, it's like, okay, it needs to be this, like, fresh look at it. You have to think of it kind of like it would perceive anything anywhere. Right. It just. Each context window is just opening it up. And I think sometimes if you have done, if you do it together, it commingles the information to the point where it becomes biased or susceptible. Opus 4:6, as you know, is like super sycophantic. Like, it loves to, like, say, yes, okay, yeah, I'll do this for you. I think having it separate keeps it like, keeps some of that drifts kind of away. And that's like one of the things that I've really, personally, I like the results better, but it's. It's just complicated. Like, part of this is really weird because I am. I'm weirdly now opinionated on taste in terms of how you should design things. Because you can. Like, for example, the context rot thing. Until someone explained it, I was like, oh, my God, thank God someone said it. This is a huge deal. There's this, like, meme where it's like these guys. Well, do you see the meme? It's like Of Mice of Men. And at the end of, you know, at the end of the book, I can't remember what you care. Character shoots the other.

John (27:50)

I never read it.

Doug Laughlin (27:50)

Yeah, so. So one character shoots the other guy. And it's like some guy made a meme about it being like, oh, this is after your. After your quad code is garbled, you know, 5 million tokens, you're like, okay, it's time to put you down. Because the context raw is huge. So, yeah, this. Yeah, this is the example where.

John (28:05)

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

Doug Laughlin (28:10)

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

John (28:17)

The one mil is very new, right?

Doug Laughlin (28:18)

Yeah, one mil, very new.

John (28:19)

Okay, very.

Doug Laughlin (28:20)

But it's a big. Because your skills and whatever your cloud MD as a percentage of 1 mil is so much smaller. So you just get so much more oomph. Right. Because the 200ks are just wiping over and over and over. That's a big deal. I think it's a huge deal. And also with how the agents are working, the sub agents will have their own contacts window. And then the pasting kind of like really saves that. That big. You know, the 1 million, you just want a really high quality, quality project within that. That's the best, in my opinion. Compacts just kind of start the compression of the noise. Oh, yeah, yeah.

John (28:55)

Mentioning subagents and multi. So first of all, I wanted to Give a shout out to this thing from anthropic research where they were like here's our production traffic. And they did a report that was kind of like their equivalent of the meter chart. And there's a lot of people saying that oh you should, you know, software engineering has pmf, but here's the next list of everything else. But what if they're all also just software engineering? Right. Software engineering is like 50% right now but there's nothing stopping it from continuing to go to 80.

Doug Laughlin (29:23)

I think maybe what's going to happen this maybe a giant intake.

John (29:27)

It has data analysis in here which. That's what you were doing.

Doug Laughlin (29:31)

Yeah, that's in my opinion that is downstream. So I think how we should think about it is software engineering might all be downstream of chips, which is downstream. Chips is upstream and then it's AI and then it's software engineering. It is all the extension of that same compute hierarchy. And I think the like, you know, teaching where machine and code kind of inter or and the world intermingle right now is code. And so that's just going to be the bleeding language that's used to, to figure out everything else. That's, that's my belief. Like it doesn't make sense to build like for example, this is a perfect example. This is like Excel. Claude for Excel is much worse than Claude code. Using Python to use the Excel skills to then deposit into. It's all much worse.

John (30:15)

Worse.

Doug Laughlin (30:15)

It's much worse.

John (30:16)

Even, even all the work they're doing.

Doug Laughlin (30:18)

Yes, 100% because if you think about it, it's a legacy. Why make a car engine fit into a horse carriage? It should just be in a car. Like it's like, it's like a backwards compatibility thing where it does work because alums 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.

John (30:48)

Yeah.

Doug Laughlin (30:49)

If they're just as good at putting the data together. We should be much more concerned about machine focused of like software consumption. And so they can like you know, the, the LLMs and the agents can put and synthesize all the information and deposit God knows, however you want it to be. I don't need to make a chart in, in PowerPoint or Excel. It will just deposit the Matlab. The Matlab. Yeah. Matplotlib Matplotlib in a chart to me in an image. Fine.

John (31:16)

Oh, you try to use matplotlib.

Doug Laughlin (31:17)

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

John (31:21)

I know.

Doug Laughlin (31:21)

Understanding that code.

John (31:22)

Yeah.

Doug Laughlin (31:22)

So why ever make a chart again?

John (31:25)

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

Doug Laughlin (31:29)

Yeah.

John (31:30)

I don't care that much about.

Doug Laughlin (31:32)

I don't think we would care that much. But I think one, our new charts are better than our old charts. Yeah. And number two, I think if it increase the speed of information, that matters a lot. And so I think we're much more. So pretty much the new charts will outweigh the old charts because it'll just grow. So. Yeah, I think it, it is a little inconsistent. We have the same watermarking. Honestly, I think it's better than our old formatting anyways.

John (31:53)

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

Doug Laughlin (32:01)

Yeah.

John (32:02)

Couple of things I wanted to sort of double click on because this is just a cloud code like brain dump in one of the biggest set of cloud code shows in the world, which is sub agents and agent swarms. I don't know if you've tried.

Doug Laughlin (32:14)

I have tried them.

John (32:15)

Pick either one. Whatever you want.

Doug Laughlin (32:17)

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

John (32:22)

Yeah.

Doug Laughlin (32:23)

It's just an experiment. It's just an experiment. Thank you. Thank you. Because we. Exactly. Because it's just via prompt and it's actually very bad. I think sub agents are okay because they usually have a cloud MD to go do whatever, whatever. But the agent team is, is actually really okay.

John (32:37)

Well, you know, we can't, we can't knock it because it's experimental.

Doug Laughlin (32:40)

So. Yeah.

John (32:40)

Yeah.

Doug Laughlin (32:40)

No, no.

John (32:41)

What do you, what do you try it on?

Doug Laughlin (32:42)

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

John (32:58)

I have also tried that.

Doug Laughlin (32:58)

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

John (33:20)

Okay.

Doug Laughlin (33:21)

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

John (33:56)

They had some post about how they did stuff.

Doug Laughlin (33:59)

Yeah.

John (33:59)

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

Doug Laughlin (34:07)

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 H1 hundreds. Okay. And you're just like, dang. So you and I are not going to be running and this is just to run and I'm sure there's concurrency available. But yeah, I think it's really cool. And that's like, I think that that's the sign of what's next because you know, these agents are going to get better to a certain extent. They're, they're, you know, it's another benchmark and bench, like another benchmark to hill climb. Right. But then it's going to be how many of these together in a bigger chain can get to work that you could argue it's kind of like a scale out of the reasoning problem too. Hey, how do you get these like this one agent to essentially get a verified whatever put it into a bigger process and do more information work that that's the next thing. And it's important to have context windows that, that don't garble up into random stuff and is able to do just like good enough. Enough with token efficiency I think is a huge part of that. Yeah. So yeah, that's, that's kind of what our experiments have shown, at least in terms of, like, the agent swarm versus, like, not. I think it's very clear the agent team out of Claude is an experiment. But Kimmy should Better. No, they'll definitely do better. But the Kimi 2.5 tells you that this is already, boom, perfectly great new place to do more work on. Completely available to us right now. I think that's huge because if these agents get any better, like, I don't know, I'm never going to sleep again.

John (35:25)

So, honestly, like, it's very interesting, this sort of moonshot AI and this is a tangent, we're not really going to focus on this very much. But you know how the sort of AI tigers out of China were Deepseek and Quen? And then you were like, well, who are these Kimmy guys? And these sort of newer names, like, I guess Minimax as well would matter. Zai has been around longer, but only recently much more active. So I noticed that Kimmy is much more in the productization phase, as seen as opposed to the Quens of the world, the Deep Seiks of the world, who don't really care that much.

Doug Laughlin (35:59)

I mean, Quentin, because of how it's attached to Alibaba, right? Like, yeah, yeah, they. They have a way to productize it, but it's like. It's like kind of like the Gemini version. They have so much stuff to do elsewhere. Right?

John (36:09)

Yeah.

Doug Laughlin (36:09)

But, yeah, Kimmy. Kimmy's pretty interesting. They're pushing so hard.

John (36:12)

They got everything.

Doug Laughlin (36:13)

I know.

John (36:14)

They got Kimmy mammoths.

Doug Laughlin (36:15)

Kimmy Claw. Kimmy claw. Yeah, I know. Kimmy Claw. I haven't. Yeah, dude, I was gonna say, have you messed around with open claw? Because I did. I. Oh, yes, I remember. What was it first called? Claw? Cloudbot. Cloudbot. Yeah. Dude, I was gonna say it was really, really euphoric. I was like, having it read all my emails and my calendar and do all this stuff. And I was like, wait, wait, wait. This is really, really, really prompt, injectable. And I was like, this is pretty secure and important stuff. So I like. I was like, you know, Claude, code psychosis is good enough for me at this point in time.

John (36:41)

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

Doug Laughlin (36:52)

I'm honest with you. I wasn't impressed either. That was the reason why people Were freaking out about this mo. Like, bro, have you actually used this shit? Because it's not even right now on cog code in a relatively focused terminal, it will be like, oh, blah, blah, blah. I'm like, dude, in the env, there is an, like in the env 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 it still is like making mistakes.

John (37:16)

Yeah.

Doug Laughlin (37:16)

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 context in each attention window is going to like change and sometimes it'll be lazy, sometimes it won't be, but it's definitely good enough to do a lot of information with.

John (37:31)

Yeah, that's gonna. So I use, I use our, I use our discord as basically like a, a way to just bring information in and out. I, I just saw, I saw this to you where basically like a lot of people are just setting up things that they could have done in Zapier with cloudbot because they're like, well, you know, now I'm like AI pilled, but actually they just done it more securely with Zapier.

Doug Laughlin (37:50)

Okay.

John (37:50)

I think it's kind of interesting.

Doug Laughlin (37:51)

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

John (38:00)

Yeah.

Doug Laughlin (38:00)

And it's also not very, it's not. Also not very good. The difference though is like, and that's okay. Like it's okay to be early to something and just wrong because you weren't the one that made it happen. Right. Claudebot, the cloud code, cloudbot, whatever, all this stuff. The reason why it's so powerful is it gets to completion. Right. And like, okay, Zapier, maybe you can get to completion all the time, but, 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, codbot did it or COD Code did it in like you know, four and a half minutes. And that's good enough for me, you know, that that's a faster achievement. And so like it's totally okay that they were, they were right, but they were just not the right mechanism. Right. You see this happen in information, like in the history of like compute.

John (38:46)

I think there's also like an innovator's dilemma thing with where Zapier as a pre existing business had this view of the world of automations as like very strict sort of on rails workflow type things that their giant user base already uses. They couldn't really pivot that much. So that's why I think one of the co founders left because they were like, well, I can't exist within this constraint.

Doug Laughlin (39:09)

You end up becoming. The box will control you.

John (39:15)

It's your golden handcuffs.

Doug Laughlin (39:16)

Yeah, it's just like your cage. You're going to act like how you are in the cage and so yeah, that, that sucks for. Honestly that's. I feel like that sucks for sure.

John (39:21)

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

Doug Laughlin (39:24)

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

John (39:27)

Yeah, I haven't vlogged that yet, but I should.

Doug Laughlin (39:29)

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

John (39:31)

Coming back to cloud coaches, I also want to make this like the sort of cloud coach. No, no, no. Like I want to indulge because like that's how natural conversation goes and I think people like enjoy that. Right. And probably that's the only time we'll talk, we'll talk about Kimmy.

Doug Laughlin (39:44)

Yeah.

John (39:45)

So like do you use hooks? Do you, you like give me like the, the Doug o' Laughlin cloud code setup.

Doug Laughlin (39:50)

I had just like essentially a few base skills and then I have a lot of APIs and then we've also made sure to work and this is like all work in progress as well to have APIs for some of the semi analysis information out as well. Yeah. And so that way we have an internal server. An internal server that is that, that is accessed by people with an API so that like all the semi analysis researchers are able to hit like some basic level of contracts because I think the contract context is really what matters. I'm too like too dumb to be really smart in, in order to have. Well, I guess, I guess I do have some hooks if it makes sense

John (40:22)

in terms of like I think hooks are very underrated.

Doug Laughlin (40:25)

Right. Yeah, I, I do think.

John (40:25)

Because you can do like a RALPH loop just with a hook.

Doug Laughlin (40:28)

Yeah, yeah. I feel like I underutilize hooks. I, I think that is true. But I do, I do run some version of them on like skill calls effectively. Like hey, on this then you have to start pulling all this stuff. But I think in the beginning I tried to do all this like hook stuff and like compounded stuff like that and I Found that like you know, the Gastown Ralph Loop era, it's like it. It is a sign of what will come. But I just don't think there's enough fidelity to like make crazy multi turn something happens. So like okay, actually less is more. Try to have like a strong set of smaller skills with a good amount of context information to be pulled in and then at the beginning of every session ask and focus on what you want to do so that like it prompts the like not like you know, a clock Claude within a Claude whatever. So here's the goal to finish within this single context window and then get it done. And this is like my generalized research thing. Hey, I want to look at the price of NAND since 1984 or something like that. This is what I want to do. I want to. So like the. Actually no, let me just give you the best example. That is probably not going to work. I would like to fine tune a time series foundation model to predict NAND and DRAM prices. Okay, I'm going to first start by gathering as much information as possible from all this stuff, blah blah blah. And then we're going to fine tune it, evaluate which ones going to do. I chose Chronos 2 because of covariates blah blah blah blah. Try to set this all project up. We'll make it a vercel dashboard internally for. For semi analysis. Maybe we'll external if we want if it's a good enough product. Okay, so then it like does all this stuff and then I just like start plowing away. Hey, can you go research? Here's a search API serper or exa or whatever you want to use to go look for all these different information sources and then bring it together. Right. So this agent goes and gathers all this information. This agent goes and like works on like considering the fact that the price isn't perfect to do all this fine tuning on. And then we like throw it in. I also had it of like well what do I use? It showed me which gpu, whatever we're renting on an hourly basis. And so yeah, we just pull all this stuff together. Then we fine tune it and I'm like okay, cool, how did this work? And then we just have this constant iterative loop until I try to finish something. I got to the point where I was like okay, this, this time series Ellen is probably not going to work. Unfortunately. Unfortunately the.

John (42:40)

You said it was because of regimes or something else?

Doug Laughlin (42:42)

I think so. Regimes, yeah. There's no way this regime is so

John (42:45)

messed up for A lot of people who are like new to finance. This is why I have an issue with all these kids doing stock trading games with LLMs. They have no idea, they've never studied finance.

Doug Laughlin (42:56)

And

John (42:59)

past does predict the future a lot until something fundamental change and the macro shifts and risk on versus risk off. They've never heard those terms. Had to explain it to the people at Cognition and, and like, yeah, 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.

Doug Laughlin (43:18)

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

John (43:28)

Right. Which reduces your sample size.

Doug Laughlin (43:29)

Yeah, which reduces your sample size. And so then at the end of the, at the end of the day you end up being like, well, it kind of just like, I guess it's, here's some heuristics. Good luck, have fun.

John (43:38)

Right?

Doug Laughlin (43:39)

Here's your checklist to see. It might be over, but you really don't know anything until then. So, but, but like, okay, an example of where this project was helpful and it's like, okay, I'm not going to have the magic LLM tell me what the price of memory is going to be. Hey, it was a good weekend project and I did burn quite a few tokens. But I do happen to have after all this like information synthesis and analysis, all of the memory prices of everything I could possibly find, plus the things that behind API that we paid for, plus the, 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, it's really interesting is I am going to just be like, okay, well now can you go make a summary of each and every memory regime and what it looked like and what, what, what created the beginning, middle, end and put that in a dashboard so it's relatable and like easy, 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. I know for a fact as the guy who like is like the cycle semiconductor guy, I've written and done more work on the cycles than I think anyone else has at this point. Especially for like the older ones, like the 80s and 90s and 2000s and 2010s and like when I did it first time, the human gro way brain is, I went and I read these old annual reports and I put it together. I try to string a narrative through it and I, I brought through all. I'm like, okay, what was GDP this grow, what this year? What was all this stuff? And you have to like make all this giant sheet to com to whatever and then make the narratives. No, none of that, dude. I mean this is like too much information to gather. It's like a lifetime of work. It's like a PhD project. I did it in a day, two days.

John (45:18)

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

Doug Laughlin (45:31)

There is some slop. I, I definitely agree with the slop, you know, so, so I think a bit once again.

John (45:36)

So right now, by the way, that's also is essential for you guys if you get caught doing like putting out some slop to your clients. Right. Like you have to.

Doug Laughlin (45:44)

Yep.

John (45:45)

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

Doug Laughlin (45:48)

Yeah.

John (45:49)

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

Doug Laughlin (45:54)

So. So I think, I think the thing that's really interesting is that this whole thing is a game of hygiene now. Yeah. Because I, I think it's like this is really hard and I think about it all the time. I feel very comfortable with doing all this work because the thing is my, at the end of the day it's done 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 all the time. All the time. It is still just like a, like I think of it once again as like a junior analyst. Right. The analyst goes and does all this like really pain in the ass information. You bring it all together to make a good decision at the top. But the problem is historically what happens is that junior analyst who I once was went and gathered all that information and after doing this enough times, there's a meta level thinking that's happening where it's like, okay, here is what I really understand and how this type of analysis I'm an expert in Actually, I'm very good at. I consistently have a hit rate. Now I'm the expert, right? I don't think that meta level learning is there yet. We'll see if L ones do it right. Everyone who's spending $1,000,000,000 in the world thinks it will. 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 want wonder if new people, we will be less lenient in terms of like, how much AI tools are you,

John (47:22)

like, junior or you to the firm?

Doug Laughlin (47:23)

Junior.

John (47:24)

Junior to the firm. Yeah, a junior.

Doug Laughlin (47:26)

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 when it's slopped, right? When it's slopped and there's no cognition, then it's like, like whatever the artisanal last 5% is, like, that really matters. But for me, I know inherently what the 5% is. I can like write it away with some really easy heuristics in time and like be like, okay, well this is the last 5%. You fix. This is what I believe. Just make up these assumptions instead. Press Enter. Okay, cool, we're good to go. You know, 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 rot. But in terms of like the capability that is overhit. Like, you know, the human CPU in this agentic swarm is very, very power. Powerful now.

John (48:18)

Yeah.

Doug Laughlin (48:19)

You know, a huge, huge, huge multiplier of what you're able to do. And for me, that was enough to be like, I feel AGI pilled, honestly. Because if I define AGI as many common jobs, not like, I'm not, I'm not doing asi. That's like religion. Can it automate or change or, or take or, you know, completely shift a lot of the information work. Yes. 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 work doing data analysis that a 22 year old, an average 22 year old would, would murder the hell out of a relatively well thought out agentic system. And so you're like yeah, that job actually does seem at risk. And so that, yeah that the 4.5 capability enough like that that we hit some level W gentically where it seems to work and do bigger information work. That's when I'm like okay, yeah this, this does change everything. And so yeah there's, there's all kinds of mistakes. I. It's a new level of hygiene that we have to do. You're going to have to understand what the absolutely. Of gentic work is back to. Right. I catch it making errors all the time. It doesn't always pull skills. Like you can definitely tell like context windows definitely like it gets dumber over time. It's not AGI today but it can do these crazy long tasks and as long as you finish it at the end and deposit it as information work, that's very valuable. Yeah. Amazing.

John (49:39)

So you do a lot of like client visits obviously by the way, transistor radio. Amazing for like understanding like what your world is like.

Doug Laughlin (49:47)

Yeah.

John (49:47)

Are you also cloud code pilling your analysts and your I on the other side?

Doug Laughlin (49:52)

I've definitely clock code pilled the analysts. Everyone in the New York office. I'm like must try it. I like really tried to like not,

John (49:59)

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

Doug Laughlin (50:06)

Okay. So yes and no. Some people are interested but you have to remember it's relatively more conservative. But I think. But if you ask any analysts if they're using AI, every single one of them will tell you yes, I use it every single day. Of course. Course. How could I not? This is like a vital skill. And so the, the, the basic, the basic inference that I'm doing is I am a bleeding edge adopter. I'm a relatively smart dude who knows what he's doing and if a tool is 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 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 going to obviously adopt it. I'm like, well, I don't understand why everyone doesn't adopt it. I would argue, well, we'll see in the 24 month view. It will be a base level, I think, I think cloud code, cowork, whatever is going to be a base level of all information work very, very soon.

John (51:12)

Yeah.

Doug Laughlin (51:12)

And you know, you see one, my friend was telling me how his portfolio manager found cowork and he's like getting it to read his emails and he's like, oh my God, I love this. Right. Everyone's moment is going to be a little different. But I think my moment, it feels like GPT 3.5 or 4 for me where there's that first time where you're like, okay, I know it made some up but like this is better than like if I went for hours searching, putting information together. It can. And then also like the analogy power, you know, where you can say, hey, this is the setup. Can you describe it in this, these like really strong pattern matching skills that are really powerful. I just think it hits some level of capability. I can't tell you what it is. It is like my taste, my personal taste where I'm like, oh wow, this is completely over the chasm of what needs to happen for it to be a very, very powerful tool. And so yeah, that's my Claude code moment.

John (52:01)

I think there's some kind of automation chart. You know XACD has this automation chart.

Doug Laughlin (52:05)

Yeah.

John (52:05)

And I think we need a version of this that is the cloud code. Like it's being much.

Doug Laughlin (52:09)

But what's crazy is this the Claude code thing like murders the axis.

John (52:14)

Exactly. It just shifts everything like. Right. Or something.

Doug Laughlin (52:17)

Yeah.

John (52:17)

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

Doug Laughlin (52:30)

Yes.

John (52:30)

Whereas in as human you can get

Doug Laughlin (52:32)

one turn, you get one turn.

John (52:34)

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

Doug Laughlin (52:39)

Yeah.

John (52:40)

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

Doug Laughlin (52:43)

Yeah, I think of it as hygiene. But the thing that's like really going to be painful though is like a lot of my expert opinion has been built by like, you know, it's like pre phones and not. Right. Like your attention span, like, you know, the children Are cooked. Okay. Like, you know, the attention spans are really bad. All this stuff. Stuff. Like, I read this, like, really sad thing. Be like, oh, we're getting dumber or something. First generation. I don't know. I'm not gonna. Maybe that's like.

John (53:05)

You see the. The coinbase earnings.

Doug Laughlin (53:07)

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. And I'm strong. Yeah. That's so funny.

John (53:28)

I think you should just do that.

Doug Laughlin (53:29)

Yeah, well, we do it with some of the semianalysis memes, you know, and the thing is, you say some of this brain rot is, like, so bad, which is terrible, but some of it is also, like, you know, it is hitting some attention mechanism in my. In my deep primordial monkey brain.

John (53:45)

Stimming you.

Doug Laughlin (53:45)

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

John (53:50)

So you couldn't look away. I was. I had to pause it.

Doug Laughlin (53:53)

Yeah. Yeah, I was. I literally. I walked.

John (53:56)

Hey, there's like.

Doug Laughlin (53:57)

Have you ever been at, like, a bar when they play, like, these, like, weird. Like, there'll be like. Like TikTok videos for lack of whatever, and you just watch and TikTok bars in New York. Not Tick Tock bars. Not TikTok.

John (54:06)

Okay.

Doug Laughlin (54:06)

It's like there. There's like, essentially a B roll channel that they'll, like, sometimes play in public spaces, and you will just find yourself, like, being engaged with it. Like, there are certain things that just. It works. So, yeah, sorry, that's completely off. But. But I wonder. This club code pill is very powerful for me. I believe it will cheat how it all works. They'll shift all of that over massively the. The chart. But it's just really weird because if you didn't pay any, like, human cognition to get there. I don't think you're going to be a great reviewer. One of the reasons why, you know, what. What makes that. That human feet that loop well, is because once upon a time, you did that and you could make the three, like, yeah, you idiot, you. You're not 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, I, I know you, I know that I told you the valuation is the only thing that matters. But like, it's also fraud. You can't do both. Right. Like, if you think about like the analysis stuff, you have to know when your own personal embedded model is like, yeah, actually this one overwrites this one. 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.

John (55:23)

Right, right. Because you're just checking for mistakes rather than trying to do original thought by just doing the work. Yeah, yeah, I think that's, that is, that is a danger.

Doug Laughlin (55:33)

Yeah. And that's what hygiene sounds like to me. Like, hey, it's really addicting to be like, you know, whatever, press the button

John (55:39)

over and over and over.

Doug Laughlin (55:40)

But sometimes you do actually have to like think, you know, so, so I think that that's, it's going to be really interesting.

John (55:46)

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

Doug Laughlin (56:03)

Yeah, heartbeat.

John (56:04)

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

Doug Laughlin (56:12)

And is it sold on MD too?

John (56:15)

So I think it's much more for character and whatever. But heartbeat, Heartbeat is the Quran.

Doug Laughlin (56:20)

Yeah, I mean, I think, yeah, that's a good way to put it. Yeah. And so like, that's the powerful thing about all this stuff is that like, okay, yes, we know that the content, like it gets garbled. We know that open. OpenClaw doesn't always do everything you ask to ask it to do initially, but you can see the design patterns. Like the heartbeat MD is a perfect example. Can see the, the design patterns where it's like, well, you know, is all of our tasks every single day actually us having this like genius thing or do we like sit down in a single session. Session, finish a single project, get up and get some coffee, then come back if it's that. And you could just, you can make the heartbeat md consider the, like the session to session and like, hey, meta learnings, all this stuff and it's only specialized and focused on one form of doing something. So it actually does have a context of all the, like, let me. I'm thinking like a customer service agent or something like that. It does have the context. In fact, it can look at every single time it's ever happened. That's actually information and context no human could ever hold. You're like, wait, that, that feels like ag. Like that's effectively good enough to do a huge information test and have enough context and be able to fetch it and maybe like there would be some verification to make sure it doesn't just totally mess it up. But that to me feels like a design pattern that you can build something on. And so that's the, that's the vibe is that we've hit some capability that you can, you can do. You can build these much bigger blocks now. And those bigger blocks are not just like this single line of code. It might actually be a business. It's kind of crazy. Like I, I wouldn't have put myself as AGI pilled. I think 4.5 is like.

John (57:51)

Actually, I think my own timelines have moved up a lot.

Doug Laughlin (57:53)

Yeah.

John (57:54)

Are you guys watching GDP val?

Doug Laughlin (57:55)

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

John (57:59)

No, no, no. To me when GDP VAL came out. So I'll just. GDP VAL is like basically a, like a broader suite bench, let's call it, where it's applied on every profession that is white collar that you can model and it's above something like 2 to 5% of GDP, something like that. That's why it's called GDPVAL. And they had human experts do the tasks as well as GPTs. And here's the results, right, where 50% is parity with industry expert coin flip. Exactly. So you can see the nice increase from 4.0 to Opus 4.1. And since then, obviously 5.2 and Opus 1 for 5 have already exceeded. We're at 70 something now, which means models are consistently better than industry experts at these things. So to me, this is the AGI definition, isn't it?

Doug Laughlin (58:48)

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

John (58:58)

We're moving the goalpost.

Doug Laughlin (58:59)

Yeah, we're moving the goalpost. But I'm like, bro, the goalpost. Like, I mean, we'll see if this is actually the machine. God and Shogath will Come and talk to us and vibrate on our same

John (59:08)

if I do think so.

Doug Laughlin (59:09)

Yeah. Okay. I, I, I don't, I'm be 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 thing but when it comes to like the, the, the GDP valve version of this. Yes.

John (59:23)

Yeah.

Doug Laughlin (59:23)

This is, this is do white collar work. Literally the white collar work which is most of the, most of the tam. Very boring knowledge. Like, like actually it's almost all, not almost all, but it's a huge portion of all of work in the world. It's like now we just made like my favorite stat is like once upon a time 90% of people were farming right now today less than 1% of people are farmers. It's kind of like this crazy shift where technology is going to massively change the relationship with all of that and it's going to be like this 991 thing. I don't know if it'll be quite that drastic or whatever. Maybe you know, everyone's just doing leisure. So far my experience is everyone just works harder. That's been my experience. But it just, it just feels like amazing massive moments happen like the, the steam engines invented and you know the, the trains are here and, and everything's going to change in knowledge work and it's kind of crazy.

John (60:13)

This, this is sort of economic cycle from my macro days that I'm, I, I can't remember the name, I can't look it up. But it's basically like there's this stages of economic development where like your, your economy starts out majority agriculture. Then it discovers like manufacturing, then it discovers why call it work. Then it discovers it builds like a very mature financial sector. And like these are like, like a layer cake all declining over time, then the new things increasing. So my, my theory is like there's this like fifth layer that's like has to open up that starts to happen because I do fundamentally believe we'll just invent new work.

Doug Laughlin (60:47)

I do believe that 100% like humans are very adaptable. That's like my favorite thing I've learned. You're able to adapt to God like coldest, coldest place in the entire world. The warmth, warmest place. Humans are in every latitude. That's in a physical sense. But I think we're gonna find a way to make utilization go up. But we'll, we'll invent more work for sure. But I think the thing that's crazy. Is just like things change so quickly and that 5 to 10 year period, like 10 year gap can be drastic and crazy and that's just societally wild.

John (61:19)

But yeah, it's happening in our lifetimes.

Doug Laughlin (61:21)

It's happening in our library. Like it's like happening like right now. It's just, it's really crazy. It's like very. And like. So this is like a complete side task on. I'm like really curious of when we start to see it in a much bigger way in the real economy. That's like my, my, my pet. Yeah.

John (61:35)

Where. Why is it not showing out of GDP yet? Right.

Doug Laughlin (61:37)

So there's going to be, you know, some people are going to be like, oh, you know, the facts. Facts. The Internet, same thing. Information transfer, whatever. I think I'm actually scared for a third worst thing which is like now, now this is a complete crackpot theory. Please don't hold me to this Internet. But what if AI is. Is massive, massively deflationary. 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 going to be very, very challenged by AI because information work. Yeah. So how we, how we capture it effectively. Effectively is all of an economic good. And then the service hours divided by hours. Okay. So there isn't like a widget to widget difference. But in theory, if we could break all of information work down into units, we're gonna have a lot more information work for sure. Like more work will be done. I don't know what the value of that's going to be. Is it going to be so much increase in supply? It's deflationary. That seems to be like, like a real concern.

John (62:48)

It's possible.

Doug Laughlin (62:49)

Yeah.

John (62:49)

And then we'll figure out how to use it. But like there may be a Great depression of AI. Yeah. Where like we figure it out.

Doug Laughlin (62:56)

Yeah. Well I, I wrote this whole thing about railroad stuff because it's my favorite. Okay. My, my favorite capital site.

John (63:02)

Fab.

Doug Laughlin (63:02)

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.

John (63:08)

Pretty much.

Doug Laughlin (63:09)

Because we're like everyone that's first looking for the Internet. We've well massively passed the Internet in Terms of the absolute size of the, the build out. It's not even close. Like we, what numbers are you thinking of? Like I think a trillion was a trillion all in was essentially the real dollars ver version. And I think we are well past like we like whatever this year and it's cumulative. Right. We were well past that. I think railroads. The reason why it's so interesting is because honestly it's way crazier. But, but pro. Part of the problem and craziness of it too is like railroad was literally like one of the first added layers of the layer cake. If you think about it before it was agriculture and railroad was like okay well how do we move this agriculture around faster? And then banking got, I, I, I kid you not like one of my big takeaways is banking effectively got invented by railroads.

John (63:53)

Oh.

Doug Laughlin (63:53)

Because there's no need to finance it. Finance it. Yeah. So much money was needed that like effectively 85% of all paper or whatever was essentially just railroad debt.

John (64:01)

Yeah. One of my favorite anecdotes was before there was a Federal Bank, a Federal Reserve. Andrew Carnegie was the Federal Reserve. Yes. Yeah.

Doug Laughlin (64:09)

There were individuals. Yes. Yeah. And so all this stuff, so it's like the whole thing. I kind of did some work on the Gilded Age, all this stuff. But like my takeaway is like that was a really interesting cycle because it was so big and took so long to deploy. It actually was 45 years of like there's three cycles actually there's three boom busts same. I don't know if it'll be quite that long. All the cycles kind of collapse. Yeah. Are that because you know, information, information moves around fast. Exactly. Yeah. And so you, you have all this stuff where I think it's going to happen faster but like I would be really, 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 going to look like some multiple cycles. So yeah. Kind of just wrote about railroads. There was like a baby railroad cycle. Then there was a huge railroad cycle. The modern R 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 10 years. Yeah. But what's crazy is like that amount of to spend is like we're, we're like well on track for that.

John (65:09)

Did you do percent or gdp? Because I think that's, I think the, the way you make it convertible. Stargate itself, 2% of US GDP and I mean it's going to go up like.

Doug Laughlin (65:21)

Yeah, yeah, that's a. Yeah. And it's not all going to be in one year. Right. But it's okay. So. Yeah, so, so total capex 4, it was 4.8% of G. GNP and 25% of total gross fixed capital investment. Okay, so 25% of investment every year and 4.5% of GNP.

John (65:41)

I think we're there. You know, Stargate plus anthropic plus whatever.

Doug Laughlin (65:44)

Yeah, we're, we're right there, Xai. Yeah. So we're at a railroad build up. It was like at one point like, but the thing is crazy.

John (65:49)

We should exceed it like probably. Yeah, yeah, no, not probably. Like we should, but okay, like this is bigger.

Doug Laughlin (65:54)

Yeah, okay. I, I'm, I'm like, I would like to say, yeah, sure, yeah, we will do it. I'm worrying, I'm like dude, where we going to get all the money? That's like such a, like the pedestrian concern. Yeah, it's not a pedestrian. I mean this is what happens every capital like we must hand to the Middle East. We must, we must, this happens every single time. This reason why the like bubbles happen, right, is like we essentially get so big was like this must be built. It doesn't matter the price. And then all of a sudden we look at it was like, ooh, that was a steep ass price. But I think, I mean the thing I think about this is like how I think about the big picture is there is a demand curve and a supply curve and we have no idea when they cross. They will cross one day and every single year. The demand, the, we're finding that demand curve and then the supply curve, we're just like, we're doing our best to deploy it. And I think for me, like I don't know when that number is. I'm not, I don't want to say 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 all back together where you're like demand go up so much. I am now going to 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.

John (67:01)

Like, I mean some people buy like five maxes and then.

Doug Laughlin (67:04)

Yeah, yeah, so, so I, I, so I'm on fast. I'm on fast with 1 million on API, which is, that is like an addiction level, if any sense. 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. Like, if not more. Like, I don't understand, like, what's the, like, I can't price it. I have no idea. The elasticity.

John (67:26)

Yeah. You pay for a perfectly compliant junior analyst.

Doug Laughlin (67:29)

Yeah.

John (67:30)

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

Doug Laughlin (67:33)

That's able to work in parallel.

John (67:35)

Yeah.

Doug Laughlin (67:35)

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

John (67:40)

if you cannot manage a junior analyst that is 20k a year.

Doug Laughlin (67:43)

Yeah, 100%.

John (67:44)

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

Doug Laughlin (67:49)

Yeah.

John (67:49)

It's like it's at, it's, it's three months old.

Doug Laughlin (67:51)

Exactly. It is two months. That, that's the correct way to put it. It's like, it's, it was definitely a skill issue that you didn't know how to get like, your settings on your iPhone to work. We know at one point, one point in time, but, like, in the very first month of us having it, no one's going to be like, yeah, you idiot. You rube. You don't know how to use your completely new technology that got birthed last month. 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 massive bleeding edge, you get to see the design patterns, like, blossom in real time. And like, we have this like, really old, older guy who's like, been through the history of technology since, like, forever back then. He's like one of the most interesting, intelligent people at semianalysis. And he talks about how. Who is it? Tan. Okay, so, like, you 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 going to win. It was like a remote web file service. Some people thought like, well, it just, I'm just going to reach and play with someone else's web files remotely. Right. Who knows? Right. And that kind of, you know, it kind of is a remote web file. Who, who the hell knows? So they were Design pattern searching back then. And I think we're at that again where all the design patterns are open. And it's like really interesting because there's many different ways this could go and we're going to 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.

John (69:20)

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

Doug Laughlin (69:39)

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

John (70:08)

Excel is the IDE for analysts.

Doug Laughlin (70:10)

Excel is the IDE for analysts. Bloomberg is the ID for analysts. Like, I believe every one of these IDE keys are done. It's dead, overhang and dead. I just think it's why, why it doesn't like, just imagine the concept of you. Like, I remember when I learned Bloomberg, I had to like watch videos to learn about all the random folders, keys, how to use this, how to use this. You know, the tactic knowledge of using this function versus that function, that's like crazy to think about. That is like, that is like horse and buggy, okay? The agent with the information that can perfectly retrieve an animal stuff is going to have the ability to pull that all together in a better UI than it was with no legacy whatever. I think all of that is dead. And this is why my spiciest take of all is Microsoft is a lot to lose. I think they have the most to lose of everyone. Because Excel is a human IDE for information work that's generalizable. So is PowerPoint, so is email. Those are the base core level of abstraction. I decided to be broadly Generable. But I just don't think that matters anymore. I think Claude Code or coworker or whatever is going to be the year that like it will destroy all of that. All that information work that. That where you sat every single year. It's over. I think that's the. The one that's like more shocking and scary that like people don't believe. Like I believe in my stomach with conviction because I have already had that moment for me. Yeah. I will never make a chart in Excel again. I actually. Wow.

John (71:33)

Yeah. It's hard to let go because I have so much like ingrained knowledge of like many things directly in Excel. Bloomberg I have. So there's no way that you know this but like my very first startup was an attempted Bloomberg killer Sentio decade office. I remember.

Doug Laughlin (71:47)

I remember. Yeah. Yeah. No, no, I. Oh yeah, yeah.

John (71:50)

You are one of the few.

Doug Laughlin (71:51)

You had OD Sentio. I was a Sentio customer. You're a Sentio customer. How does a Sentio customer that got rolled in.

John (71:55)

Dude.

Doug Laughlin (71:55)

I remember. How dare they acquire Sentio.

John (71:58)

I had a. I had a patent. We filed for a patent for similar tables. Anyway, one of my conclusions was like Bloomberg is just like three things. It's.

Doug Laughlin (72:04)

It's.

John (72:04)

It's slack and it's the journalism which is amazing. And then it's the, the data feeds. It's actually not really the ui.

Doug Laughlin (72:10)

Yeah. Yeah. But I, I think for the first time in my life where I just think that like. I just wonder if that like. Okay. If you can get. Obviously. So you're telling me that the future, the undisputable future is just like it's IB and nothing else and then like a terminal that types in some stuff. I think that if you are marginal and on the curious and not hyper inter connected which I would argue that I am at semi analysis. Like for example, I'm trying my absolute best to just rip Bloomberg out. We're going to fax it API like all. All in API with a cloud code is my belief of the future. Hey, verifiable data source that you trust. Yeah. Hey, scale for you guys.

John (72:46)

You can do it. Yeah. For traders. We're so.

Doug Laughlin (72:48)

We're. No way. I understand. Like there's an information network that's like outside of this and you do deals in IB.

John (72:54)

Yeah, right. Which are tracked by the regulators.

Doug Laughlin (72:55)

100%. 100%. It's totally. I completely agree.

John (72:58)

But as an analyst. Yes. For.

Doug Laughlin (72:59)

As an analyst. Yeah. And so like. But I just think that like. Okay, that doesn't really. So you're right. The core cash flow CAL thing will continue onward but like each iteration of this AI thing I was like yeah, I'm still going to be using Bloomberg right this first time. I was like actually no, I don't care anymore. The IB is my utils of like marginal value from from IB is like now outweighed by how like clunky this is. And I want to just make some charts. Right.

John (73:21)

And so there immediately you save 10 to 20k.

Doug Laughlin (73:24)

Yeah.

John (73:25)

For switching down.

Doug Laughlin (73:25)

Yeah. There you go.

John (73:28)

It's amazing.

Doug Laughlin (73:28)

Yeah.

John (73:29)

By the way, what was your cloud code end of year prediction? 25.

Doug Laughlin (73:32)

Yeah. I want you to know I sandbagged the ever living share. Oh okay. I just believe 25 is very. 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. I think 25 is within the 95 confidence interval.

John (73:51)

Sure. So it's between 24. 5 by 50, something like that.

Doug Laughlin (73:54)

Yeah, yeah.

John (73:55)

It's just absurd. But you know Codex watching code.

Doug Laughlin (73:59)

So to be clear I, I'm actually even willing to comment on that because like I know we've done a lot of being Codex haters. Yeah. I, I think I, I by the way when I put COD code Codex agent whatever all in percentage that we can publicly see. I would argue the. For the ratio outside of that's probably going to be higher too but whatever. Yeah I think together yeah we're watching Codex. I actually think Codex Codex is. Codex is pretty good. 53 I think so we had the whole thing. I cause like I wrote most of the articles like oh token efficiency to context Rod all this the same one or. Yeah yeah. It's in the bottom. It's in the, it's in the. The paid section. Okay so 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 five three Codex is awesome in. In co 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 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 swedbench that like you're trying to use it for general information. Like hey, I'm trying to can you go research and search all these websites and like I don't even think they have web search in it or whatever. Maybe, maybe you can give it an API API or whatever. But it's like great. I mean 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 in the RL that I think it isn't generalizable in the way that that 4.6 is where it's like oh I could have it. I could have it make some rubric or do some research or do something like that versus Codex. It's very is there a building Codex is co coding pill and so that's what. But I am very optimistic actually on Codex and we do track quite a bit. I, I, you can, they have a meaningful amount of thing share. You could see the bloomberry they have the, the the chart bloomberg.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 more closer. So all the OpenAI people like run will tell like blah blah. We're not accounting for it. Yes, we didn't account for it but like okay, I, I think Codex is better. I think there is some real problems and issues but I bet you the second that they have a new pre train with the RL because the RL stack on Codex 5.3 is amazing. Like it's very coding code. That's when, that's when the it flips over and yeah, look at the other players here. It's just like. I mean my favorite thing is 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?

John (76:42)

Yeah, look. Okay, that's a bubble talking, right?

Doug Laughlin (76:45)

Okay, that's a bubble. Yeah, yeah, that's, that's the CSF bubble.

John (76:49)

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

Doug Laughlin (77:01)

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

John (77:07)

Yes.

Doug Laughlin (77:08)

So that on this the daily install counts. Right.

John (77:12)

Is an order which is by the way just the VS code extension.

Doug Laughlin (77:14)

Right.

John (77:15)

So yeah I know, I know that's not even a default way to use car code.

Doug Laughlin (77:18)

Yeah, you're right, you're right. CLI NPM dominoes is another way to track it but I think they have like their own cost their own installer now anyways all in all definitely heard understand it's very hard for us to like actually track it but like I'm

John (77:33)

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

Doug Laughlin (77:41)

Yeah. Okay so, so I have some not skepticism just because they have such a big ChatGPT portal that could be like try 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 users off. But I wonder like the like to like but, but that's like my skepticism.

John (78:04)

I have an answer for that. Alexander Americos was just on the lennypod 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.

Doug Laughlin (78:15)

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

John (78:26)

it's they actually read the app. The app launch.

Doug Laughlin (78:28)

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

John (78:39)

my, you know you use Conductor.

Doug Laughlin (78:41)

No, I've not used Conductor.

John (78:42)

Oh okay. I thought I heard you say on

Doug Laughlin (78:44)

a podcast no, I've not used Conductor.

John (78:45)

So so basically like the, the, the argument for any lab, any first party app is that they're only going to prefer their own first party. Yes, 100% which like they're already okay

Doug Laughlin (78:55)

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

John (79:00)

Well then, then you have a conductor where you can use codecs and cloud code for different tasks as you see fit. And so this is the, the clean superset.

Doug Laughlin (79:08)

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 money and whatever. Like, I just think my favorite one is like platform as a service, if you remember that one is like infrastructure as service, platform as a service, SaaS software as a service. And like, oh, this platform is 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 dies. But that being said, in this moment, I agree. I actually, you. I like to have them like review each other. Like having them yell at each other is really great. I might actually try this soon. I haven't used, I haven't used con doctor Personally, I've mostly just been going deeper into the psychosis.

John (79:58)

Yeah. And this is, as a former cloud analyst, very typical of like, do you want a multi cloud or do you want to go all in one cloud? And the classic argument for multi cloud is, well then you can use the best of your. Exactly. But if you go all in one cloud, you can exploit the sort of minor features of everything and you know, it makes a market and there's no right answer for everyone.

Doug Laughlin (80:19)

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

John (80:23)

of those things where like even the really small percentages in AI still really matter because they're, they're huge. And like, yes, people are very happy, very productive.

Doug Laughlin (80:31)

Okay. It's good to be an analyst in the space because it's fun to keep up with it.

John (80:35)

Right?

Doug Laughlin (80:35)

Like, I agree. Like, I, I think everything.

John (80:37)

Like we like the horse race.

Doug Laughlin (80:38)

Yeah, I like the horse number one. Number two. Ooh, yeah, yeah. But, yeah, no, no, I know. But then you have to. Your brain also have to be like, number two is really big too. And then I just think like for me, someone who likes the history of all this, like, like likes history of innovation and competition and disruption and stuff, likes new technology, it's like a very fun time to be following this stuff

John (80:57)

altogether tech during the, like, 2017 and 2020 years.

Doug Laughlin (81:02)

So boring. Yeah.

John (81:04)

At least for me, anyway.

Doug Laughlin (81:05)

I thought it was pretty boring too. Yeah.

John (81:07)

Sorry I interrupted you in mid.

Doug Laughlin (81:09)

No, no, I remember. I was talking about boring.

John (81:11)

That's okay.

Doug Laughlin (81:11)

It's just fun time. It's a fun time to be. Things are happening.

John (81:14)

Okay. I wanted to transition to a little bit of a small, spicy thing where you were on tvpn and their title that they chose for you was Douglas Laughlin thinks Microsoft is out of AI And. Ooh, did you not see this?

Doug Laughlin (81:28)

Okay, so I wouldn't say out of AI. No, I did. Okay, so I didn't watch it. I never rewatched these things. Okay. So how I think about it is.

John (81:36)

But like you said, things like Microsoft is scaling back investment, so.

Doug Laughlin (81:40)

It was the previous conversation. I was talking about how Microsoft has the most to lose. They had the most to lose of everyone in the entire world.

John (81:48)

They're.

Doug Laughlin (81:49)

They are.

John (81:49)

They're the software.

Doug Laughlin (81:50)

The horizontal software company. Yeah, exactly. They're the horizontal software company that humans use their software to do information work.

John (81:58)

Okay.

Doug Laughlin (81:59)

No, like, I cannot paint a bigger target. Okay. I cannot paint a bigger target. And Salesforce. Yeah, well, okay, that's another two numbers.

John (82:07)

Microsoft is ordering two bigger.

Doug Laughlin (82:08)

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, 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 buy the Golden Army. Exactly.

John (82:28)

From Game of Thrones.

Doug Laughlin (82:29)

Yeah, yeah, the Golden Army. And the problem is, like, each year they become more powerful and then. And then at some point they're just like, you know, we could just like, scale these. These shitty walls. So, like the. So that's the problem is the moat. The wall is. 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, like. Yeah, it. It is exactly like that. And so it's just like this weird process where that's a terrible setup too, because what happens in the history of that is you have to choose one or another. Okay. If you do either poorly, you're. You're like you're like somehow in a third worst place. You either all in become Azure, like maybe in the telecom era, right? Because your team guy, you become dumb pipes. Okay, that is the, the Azure becomes what is it? Charter, right? Ooh yeah. But then, oh, the other version of this is you say no, no, screw these guys. I have to like reinvest back in and like essentially steal copy their their features and build up my moat. That means I need to stop investing in Azure for the stock. That really sucks because the stock is very much weighed on out your Azure revenue. And meanwhile, if we actually had to value Microsoft Excel 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 8 times earnings, 10 times earnings. Like it was trading like that before actually. Oh geez. And like, like remember the 2010s era when it went all the way down to like 10 times earnings in the Steve Ballmer era and then it inflected outward as it did oak with Azure. Yeah, yeah. Azure and O365. Yeah, there we go. That's right. Yeah, yeah, yeah.

John (84:02)

Okay, I don't think you have the answer, but I just like this is far the most bizarre I want to call it, but I don't know if it's a or not even because it's a clear decision where they were the lead investors in OpenAI. They had the deal and they consciously obviously stepped back. They're still good partners but like what happened?

Doug Laughlin (84:21)

So I think the biggest blunder of all time that the part that is kind of crazy to me about that one, one is like, yeah, I, I, I definitely think there was a financial decision because when you look at it, it looks like a conversation of shareholders roic and how much are you willing to burn cash because like, you know, effectively you look at all the other peers and Google I would argue is going to free cash for zero. I think Meta will go to free cash for 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. Responsible. Yeah, yeah, he's being responsible. Responsible, right. But the problem is that responsible this like an innovator's dilemma, right? Like do I maintain maximize shareholder value and cash flow today or do I have a deep belief that AI will kill the hell out of my core business and I need to all in invest to, you know, am I ready to bet the entire company on on a trend? And it seems like Satya is not a believer. You know, we've been talking about AGI. He is not Asi pilled, okay? He doesn't have any fear of the Shoga. He thinks it's just like a new tool. It's a new Lotus. It's a, you know, Lotus. And Excel came around, right? Like, it's just a new tool. But I think at the same time, this, this conflict between renting GPUs to the barbarians who will disrupt your business or, you know, your actual core business. It's clear how they're feeling in the call of earnings. They talked about they could grow a lot faster if they wanted to, but they're trying to reinvest back into the internal capabilities. That to me sounds like we are not going to hire as many barbarians we're going to pull. You know, we're reinvesting these walls, pull in together and try to defend the core moat. Right? Because the, the dream of this, in theory, you're like, oh, remember in 23 when they did the first big deal? You're like, wow, Microsoft's going to win it all because they already have all the distribution and they're going to have the perfect product and boom, they're going to have this giant business that makes them, you know, whatever, 100 billion, $100 trillion. Okay. Whatever number you want to say. But reality is, Claude for Excel, Claude for, for PowerPoint is literally exactly what it's supposed to be. Microsoft should have built it. Microsoft should have built it. Yeah. And so now you see the barbarians, and this isn't even your primary barbarian issue. The guy who, you know, this is like, this is the, this is like the tribe over the hill barbarian. Yeah, this is the tribe over the hill, you know, and the tribe over the hill is like, like, you know, on an, on a nightly raid, easily sacked the hell out of your castle. And you're like, dang, this is an issue. So, so Microsoft now is super stuck in the middle. And so how they're gonna have to do this is totally different. I think they're going to keep, I think they're going to keep pulling back in. We're starting to see that, like, they're going to do internal training, they're going to try to do more foundational models, they're going to try to use the weights that they have access to this Mei. Yeah, okay. Yeah. But I'm very skeptical because their execution has been kind of dismal.

John (86:58)

Well, you know, maybe seen they, they, they, they do have. You know, they are one of the big, biggest Companies, companies in the world with all these resources. Yeah, I, I, I always want to push back on the, the sort of like you know, so Oracle picked up the slack.

Doug Laughlin (87:11)

Yeah.

John (87:11)

Oh, is Oracle being irresponsible?

Doug Laughlin (87:13)

You know so I, I'm actually if we're going to talk about Oracle I think so let's talk specifically about Oracle because this is where we're going to go. I think Oracle was irresponsible because the magnitude of what they did.

John (87:22)

Okay.

Doug Laughlin (87:23)

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 happened is they went so aggressive out the gate did the quarter where they said like $400 billion right. They, they said RM is the wolves. They promised the world. Then they proceeded to raise as much money as possible and like this is the first time they've ever done these giant build outs and so now there's delays. Everyone's like whoa, whoa, you did this much right. Capitalism is kind of like hey, hey pump the brakes. And, and seriously I think that if they just teared it out better meaning that they didn't do it all in one period played a little bit of expectations man. 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 kind of funny because in high yield TMT it's such a big part of the entire index. Like the issuance is so big. It's like debt index. Yes.

John (88:34)

Of I, I have zero familiarity.

Doug Laughlin (88:36)

Of okay I, I'm pulling some numbers up. I did the numbers forever ago. I'm like I, I hallucinate and whatever. Forget all the precision. Let's just say all of the investment grade TMT is like 500 billion. Okay. I think Oracle is like 135 of it. So that's like, that's so big. And so each time you have to, you put up a huge new issuance, you have to give someone an incentive to go buy your debt instead of someone else's and so you just of kind, kind of like they're screwing up the liquidity because these issuance are so big diluting the whole Pie it, it makes all the terms a little better or more favorable for investors. So literally the entire index is selling off because it's like yeah, 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 were like a weird bottleneck. I never ever, ever, ever, ever thought, thought would ever, ever hit. And I think you could appreciate this. Uniquely weekly is like one of the bottlenecks is like supply of debt into the market. Like, like capitalism cannot like absorb that much capital demand because the order of magnitude, it's totally different. These hyperscaler businesses have been completely self funded since the history of time had never gone out and issued anything. First time they wanted, they turn around and they're like, hey, instead of like can you give me a $10 billion loan limit? Like we've never done that them before, right. So the absolute size is kind of screwing it up. And I think that Oracle specifically was way, way, way too aggressive into a relatively illiquid market. And so like you have to do this, like you have to kind of let yourself into it if it's going to be like that. But they, they like Super Jolty did these big huge incremental ads and kind of flip the whole thing. Oracle cds people all freaking out. I think a lot of it's mechanical specifically on how badly it was done from a supply demand perspective. And I think they can pay for it. You want to what I'm hearing it. All right.

John (90:22)

Microsoft could have just internally funded this

Doug Laughlin (90:24)

and like, yeah, Microsoft could have internally funded this. It would have been totally fine. 100% agree. And like this example where it's like, yeah, I think that that's a blunder. That's a perfect example of a blunder because Microsoft's cost of debt is the same as the United States government. It's like the cheapest you'll get anywhere else is correct. And like just from like a, like a P type, like the math perspective, no one else, they're better than Oracle. They, they just by their credit rating they have a 2% more profitability at a capital basis. That's, that's like you can't beat that. I don't know why they decided not to. But now they're in this weird thing where they're like, they're, they're, they're kind of wavering like, like to win you have to be like really bold, right? And they're kind of like doing this one thing over Here being really defensive with Copilot. Satya is now, you know, the product manager of Copilot and then they're also pulling back from Azure. Meanwhile, the competitors are pull are pushing in for the supplier applied. It's a really weird game. I think Microsoft has to choose a direction.

John (91:18)

We'll see, we'll see, we'll see.

Doug Laughlin (91:20)

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

John (91:25)

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

Doug Laughlin (91:45)

I think Sergey doesn't want to lose and I think the thing that happened was up until like you know, he wasn't. No one was there A part of

John (91:53)

the whole DeepMind story was we will hoard all the TPUs because we were first, you know and so like why, so you know, why give anything to enthalpic?

Doug Laughlin (92:00)

I think it's because at least last year it became pre Gemini 3 it was like dude, we have all these TPUs, we're going to hold support 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 TPV8, like whatever. We think Ruben will be much more competitive. I think Ironwood V7 is the peak gap between, on between TCO, between Nvidia and TPU. Right. So if you are at your absolute strongest point, what do you do? There's two ways you could do it. You could try to maximize and like squeeze the juice and like make margins or you can, you can gain market share. I think the perspective of doing this externally with, with anthropic is to gain market share because the biggest gap you have and, and one of the reasons why there hasn't been a second merchant chip and also you can argue Nvidia's most valuable company in the world. What's the value of TPU in Google? It's, it's huge.

John (93:03)

You might have done the math.

Doug Laughlin (93:04)

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. 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.

John (93:22)

Some of them are medics, some, some of them are.

Doug Laughlin (93:24)

Yeah, you're exact. Some of them are. 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 going to, they're going to win the market share. And then, then what happens is if you have an install base, you have an incentive to upgrade your install base. That's like the hugest problem with amd, for example, no one wants to buy new AMD 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. If you want market share, now's the time.

John (94:13)

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

Doug Laughlin (94:36)

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

John (94:50)

As a benchmark.

Doug Laughlin (94:51)

Yeah, as a benchmark, how do you

John (94:52)

expect going to come in pretty good

Doug Laughlin (94:54)

on a price basis? I mean our expectation is like they're they're the best TCO by a meaningful amount right now. Anthropic is very clear how they feel. Like, like everyone is very clear. I think even open AI would take. I think everyone would eat as much TPU V7 as possible. If you had it in a perfectly unconstrained world, it would probably be at this exact moment like you know, the, the hottest kid on the block until Reuben comes out. But the reality is supply chain really matters and you're. That's just, that's not available. And so that tco, that TCO advantage is at this absolute biggest aperture. Then like Jensen essentially gets 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. What, what can you do?

John (95:38)

It's this cascade. Right. Which I think you've talked about.

Doug Laughlin (95:40)

Yeah, all the way.

John (95:41)

Goes all the way back to the fabs.

Doug Laughlin (95:43)

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

John (95:47)

Is there a link I should be pulling up?

Doug Laughlin (95:48)

Yeah, that's it. That's it. Anything? Yeah. So yeah, it all goes back to the fabs. It all goes to who's making the chips. And 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 you can pre train big models on it, which is like a huge, huge swipe at OpenAI right now. That being said, like, I think OpenAI will get their act together very quickly. And so yeah, that's kind of like the narrative narrative. I think it's going to be a good story for probably like a year or two. But then the real question is V8 we just don't think will be as competitive to Ruben. And that's when your special window starts to close.

John (96:30)

What's the technical reason why HBM HBM 4 versus 3 and that's as secure as the strategic decision by Nvidia.

Doug Laughlin (96:37)

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 trip chip. It is a, it is an F1. Like it is as maxed out as possible. TPU is kind of like this like replicatable pod in a very large with like very high stability. Right.

John (96:54)

Which if you know the history of Google, that's what that's what they do.

Doug Laughlin (96:57)

That's with Infra. Yeah, that's what they do with Infra. Yeah. But I think GB200 would have completely mogged, you know, V7 if it came out on time and stable. It came out a little delayed and it wasn't stable. And so I think there's a lot of different ways to kind of, 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 non, Nvidia is the best. They own the entire supply chain. They really do. Like you think all those HBM price increases, they're going to 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, he's doing love shots with everyone. Okay. It's because he, he needs to get the chips. Okay.

John (97:43)

So, yeah, this is Samsung's chairman.

Doug Laughlin (97:45)

Yeah, this is Samsung's chairman.

John (97:46)

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

Doug Laughlin (97:49)

But let's put it this way. That's, that's a huge deal. That's a huge, huge, huge deal. Do you think, do you think Google was out Hyundai. Hyundai, yeah, yeah. Do you think Google was what, what? You know, do you think Sergey was out in Taiwan drinking to, to get supplied? No. A hundred percent. There's an opportunity here, but there's Only so many TPUs that can be made because the, because of all the bottlenecks. Right. And so Nvidia has all the supply chain locked up and so they're going to have like so much of that kind of constrained there. And so it's going to be really interesting. They're going to, they're going to get the best, most performant hbm. They're going to be first on the road maps for even more rack density. They're going to have like the best connectors, the best, you know, the whole system will be once again turbo jammed again for as hard as it can be. And the people who made V7 like they made the chip was done like three or four years ago. Like the 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 think our current read is that like the HBM specifically and the memory scale up is going to really go in Ruben's favor. And so that's the big difference. And I think as you know that's what makes the context windows, that's what I'm able to do. Bigger, bigger.

John (99:01)

Everything.

Doug Laughlin (99:01)

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

John (99:07)

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

Doug Laughlin (99:13)

Yeah.

John (99:14)

Every other part of the memory. It's like such an interesting cascade waterfall of like just like a short squeeze and everything. It's not a short squeeze, it's like a surprise squeeze. Yeah, I mean it was like I just want to, I mean if I one ratio of like.

Doug Laughlin (99:28)

Yeah, yeah. Okay so it's a three to one, if I three to one to four to one ratio. I think, I think next generator. So, so it's in the memory mania post that we just put out of like the 4 to 1 or the trade off ratio. Yeah. Scroll down somewhere and you'll see.

John (99:40)

Yeah. So so basically for, for listeners it's the idea that like when you convert to HBM because there's a huge amount of hbm, it takes three times one HBM sort of unit is like three times of the other sort of DDR whatever. Right.

Doug Laughlin (99:53)

Yeah. So some amount will always be lost in production because yield isn't perfect. And so effectively you're trading some like you're trading. I, I, I actually wrote a really funny piece like I, I called it like super oil but let's, this is a better one but pretty much like you in order for this higher grade of jet fuel has been invented and the only way to make it is to like actually get rid of all, all your other fuel and you have to like massively condense it and refine it. Okay so now what happens is if there's any demand here, it's an instant shortage. And so we, we hilariously enough came out of like the biggest shortage ever in Nan and Dram. Like terrible, like catastrophic. The worst one ever. Like the, the, the last analysis I could put to is like 96 or something like that. Seriously, it's like a 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 4x multiplier onto DRAM. And then now so we, we've completely constrained took all the DRAM capacity, we just came out of this shortage. So no one invested in any clean rooms or capital equipment or anything like that. People got like massively free cash flow negative. No one's spending a cent. Okay. People could go bankrupt. You know, so you, they haven't invested in these three year long lead time items. And then now there's the like more demand than God. And it also evaporates the middle layer because the KB cash offload and then boom. You're just looking at the supply demand. You're like, yeah, this is not going to catch up for like two years. I think the thing that's like so interesting is the supply chain squeeze because these clean rooms take two years to make, man. And effectively everyone paused and how bad the last cycle was really forced everyone to completely pause altogether in terms of adding any new capacity.

John (101:33)

Yeah.

Doug Laughlin (101:33)

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

John (101:55)

What does that look like?

Doug Laughlin (101:55)

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 have, this thing is supposed to come on in 12 months. It's coming on 2018. 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 HBM guys are like, oh my God, how. Look at all this demand. And then at some point in time what happens is you say well we pulled this all forward. You know, we have the power is going to constrain us anyways. We're going to like kind of chill out the orders. And historically that's when the memory market, that, that's what causes the crisis, the, the prices to drop. Realistically, just looking at, at the aggregate demand of how much we've purchased in 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, it's you could say SK Hynix and, and Sam Micron and all the, all the semi like semicap has

John (103:11)

been ripping which like by the way when I was in Baliasney we a majority of a lot of money we made which is being on my coin.

Doug Laughlin (103:17)

Yeah.

John (103:17)

In, in the last like.

Doug Laughlin (103:18)

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

John (103:28)

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

Doug Laughlin (103:31)

Yeah. Like yeah, I. Apple. I think Apple's moving.

John (103:35)

I had to buy a SD card for this thing.

Doug Laughlin (103:36)

Yeah, it was a bucks. Yeah, that's nothing too because that's just the NAN side. Dude, have you looked up, I want to say like 64 gigabytes of dram.

John (103:50)

Like I'm moving up. I need to refresh my iPhone. I'm moving it up because I'm doing this research. Oh yeah, you need to do as soon as buy your iPhone now.

Doug Laughlin (103:58)

Yeah, you buy your iPhone now because what's going to happen is when iPhones go into the spot market, prices are going to go up 100% on them. That's insane. And so they have to pass.

John (104:06)

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

Doug Laughlin (104:10)

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

John (104:48)

Oh, exactly that.

Doug Laughlin (104:49)

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 bowl for once upon a time, then became very clear as going to die and I was like, it's back. But only because the entire express intent is to have these DD like old chips, pool the old chips, attach it to something new. That's what it's going to be like. The, the memory shortage is just like, it's crazy. So yeah, it's incredible. Yeah.

John (105:20)

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

Doug Laughlin (105:36)

I've actually been thinking about that a lot and like this is not going

John (105:39)

to go to 100 million context windows. It's not going to go to a trillion. Like this is it. Yeah, this is it for like five years, ten years pretty much.

Doug Laughlin (105:48)

Okay, so the question is, will. Yeah, I mean, yeah, probably actually. Will capitalism work? Will we, will there be a way for supply to show up? Probably. But on top of that, I wonder if there's gonna be like history of compute. What happens is you have to like, you have to like make a curve of the, of the context windows. Like does free context windows go to like 1000? Hey, you can use ChatGPT free now, but you, your context window is like a thousand tokens or something like that. And then you can just like somehow do a tiny, like a tiny parcel for that just so that you can then charge like you know, a hundred x more for 1 million. The 1 million context window is like a mansion, you know, that's the real.

John (106:25)

You live in a mansion right now.

Doug Laughlin (106:26)

I live in a mansion right now. Yeah.

John (106:27)

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

Doug Laughlin (106:37)

Yeah, you have to learn how to use it well because of the dram. Yeah. So I actually have a question. I know because long context to me makes a lot of sense. Right? Hey, that's like the memory scale up version if you're thinking about chips. But in the AI world I just am always been curious because it does feel like, at least in my stated experience, really long context. Like you see in the papers, they kind of like drop off they like actually don't use all the context. So that's like kind of the thing I've been most interested in is like, does the 100 million context actually matter if it's not possible to use it?

John (107:09)

All versions of the 100 million do exist today. They just suck in various ways. They're not actually applying full attention. Right. You can use state space models or even like a lstm to process 100 million tokens, but you're not paying full attention to those 100 million tokens. And so I think the way that we have context about today and those curves, they will improve over time and they have been improving a lot, but we're never going to use all of them. But we'll improve on the algorithm side. I think for me, what matters is you represent the physical constraints that us, the software side can never surmount because it's a physical constraint. And, well, I mean, it just physically, we can't even double notice it's 10x.

Doug Laughlin (107:51)

Yeah.

John (107:52)

What's the point of talking about anything? Yeah, what's the point?

Doug Laughlin (107:54)

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

John (108:11)

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

Doug Laughlin (108:17)

Over and on and on. I've been pretty interested in that. But, like, to be clear, I'm the total idiot. I have no idea of. Claude tells me what's.

John (108:23)

You're this. You're the semis guy, man. Like, you're really good on your stuff. One thing I wanted to spot check on was Thales, which I have not

Doug Laughlin (108:31)

messed around with it.

John (108:31)

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

Doug Laughlin (108:37)

Yep.

John (108:37)

So you don't need memory.

Doug Laughlin (108:38)

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

John (108:43)

This Kim, comes at the perfect time.

Doug Laughlin (108:45)

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. Sorry, what do you mean a lot of the product? The. The product?

John (108:58)

Yeah, they get distilled to shit.

Doug Laughlin (108:59)

Yeah, they get distilled to Shit. So it's like the push and pull there is going to be like, okay, can you just burn in a 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 kind of seeing this market bifurcate a little bit. You could argue the pre fill, decode, disaggregation stuff like that is like the focus on performance inference serving is going to be a subset of the market and then the training and the whatever and the big production like you're. We need to kind of break it in into smaller parts in order.

John (109:38)

Yeah, we should be using the same cubes. Yeah, that makes no sense.

Doug Laughlin (109:40)

Just in order for the compute to be even remotely okay, it makes sense to you.

John (109:44)

And I mean TBD on a sort of practical implementation, but otherwise burning the weights into the chip, why didn't etched

Doug Laughlin (109:50)

or some of the other guys get there first? Etched is pretty interesting. I don't know.

John (109:57)

I'm not going to speculate.

Doug Laughlin (109:58)

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

John (110:07)

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

Doug Laughlin (110:12)

Yeah, yeah, that's the fastest way possible. And so that's. But the thing is the bet on this really large systolic array is effectively everything is compute bound. Right. I don't think that that's really the case in terms of 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 I just don't know if that's going to be the perfect solution. There is definitely a world in space and a design space where they're going to be very, 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 Cerebras and Grock, honestly they were all considered failures. And even then we're like what are they going to do with Grock? What are they going to do with Cerebrus?

John (110:54)

So is, is Salmonova.

Doug Laughlin (110:56)

No, I think Salonova is like a much more interesting one, but I think there's like, there's like all kinds of deal issues with that. I haven't been keeping up with that one this much.

John (111:02)

Yeah, I, I always, I always try to mention them as, as part of that cohort.

Doug Laughlin (111:06)

Yeah, yeah. Because I kind of forget about them too. But yeah, I honestly, I was going to say they were, you know, once,

John (111:09)

once a year they show up.

Doug Laughlin (111:10)

Yeah, they, they do and they're not, they're not so bad. Yeah. Yeah.

John (111:13)

You mentioned actually some CPU shortage stuff or cpu. What's going on there?

Doug Laughlin (111:18)

I think it's okay. So. Okay, I have one. We'll start with the conspiracy theory that I think is really funny. Have you been noticing? Just like I feel like web services have become really unstable.

John (111:28)

It has been down a lot.

Doug Laughlin (111:30)

This is pure like, you know, schizophrenic hat brain. Because I have a schizophrenic trend hat brain. I'm wondering if it's two things. Shipping vibe code slot 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 the 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 20, 20, 21. You bought like $100 billion of CPUs and stuff like that. And so we're right at the natural end of life for these two chips. And so usually what you do is you have this big refresh of all these chips. But what, what's been happening instead is everyone has essentially scrounged all of their budget as hard as they can. But then like I feel like I've seen it in like Azure, like, hey, last night my Amazon prime thing doesn't work. And I was like, it'll probably work. And then in the morning, babe, don't worry about it. I think, I think Azure just are like AWS is pissed tonight. You know, something like that. But I think so we have this five year thing. Everyone scrounged every single dollar they could to essentially invest in as much as AI as impossible and just do maintenance capital clicks on cpu. Ironically at the same time for all this cloud code stuff is actually if you have this coding agent just generate you God knows how much compute how much like software, where is the software going to run on CPUs? So I think we're going to see some increasing utilization as well as the fact that RL is like actually heavily used for like RL gyms. You have to, you have to simulate software and it uses a lot of CPUs so the, or not quite like the orders of magnitude of GPU stuff, but it's just such a big trend even when it steps slightly in a place. Massive amounts of demand. But like we might actually be seeing a CPU shortage partially because of this refresh cycle and 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.

John (113:18)

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 you know it's just, it's just different slope but at the same sort of direction, still

Doug Laughlin (113:30)

an upslope slope and in a slope that to be clear, has had massive underinvestment for the last two years because everyone's like how the same problem that happened massive under investment because they're like screw it, we're doing maintenance only. All we're going to do is maintain the past. We're not going to add anything else. And then all of a sudden just a little tiny slope on top of it, you're like, boom shortage. Yeah, amazing.

John (113:50)

Semi sky, semi numbers go up.

Doug Laughlin (113:55)

That's one way to put it. Yeah. The thing is crazy is we talked about the demand.

John (113:59)

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

Doug Laughlin (114:00)

Like, I mean it's for sure like,

John (114:02)

like show me where I'm wrong with.

Doug Laughlin (114:03)

Show me where I'm. I mean I. Definitely not. But the thing that's crazy is like memory prices are going to go up so much that we're going to have to choose which. Which. That's the crazy part to me. Historically memory has never been a constraint like this where I said actually you're not going to get your low end, you're not going to get your low end phone, you're not going to get a GPU this year for gaming. None of that stuff. You, you can't do these things because you're probably out of the market. That's what's crazy. Insane. That is the first thing that's happened a long time that's going to be really interesting to see where that shortage and how that, how it's like digested and felt is amazing.

John (114:34)

Thank you for that breakdown. I feel like I really understood it like talking to you. It's going to transition to a couple of personal things. And at the end, how do you write? Because you write a fuck ton.

Doug Laughlin (114:44)

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.

John (114:50)

And you kept going with fab for a while.

Doug Laughlin (114:53)

Okay, dude, to clear. That was really so, so, so look, I am 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.

John (115:01)

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

Doug Laughlin (115:03)

Okay. So the thing before LLMs came around, the thing I felt the strongest about my, my number one information skill is I was able to read and synthesize and process at like really high speed, really high throughput, decently high comprehension. The adjustment is speed in terms of comprehension. Almost anything. Like when my, like when my friend gets a PhD, I go read their paper, I was like, oh, I have a pretty good idea of what you're doing. I was like, like, hey, when I was interested in semicuffer 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 loving as a kid. Crazy reader when I was a kid. Always have been. And then starting the substack, which has been really fun actually, because I just really wanted to get my story out. Like the things I cared about closed the loop for writing for me because I love, I love reading so much. It makes a lot of sense that I love writing. I think what really helped is I wrote every single week for like since October 21st, like consecutive streak for a long time. The streak has been a little broken as of late. I mean, Alice's plus fabricated knowledge is pretty hard to do. But like all of 24, I think, like we're just talking just like every single day, every single week, I will put something out. Right.

John (116:20)

Is it like a hard rule? Like one a week?

Doug Laughlin (116:22)

It was a hard rule. One a week. least an attempt to. 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 more.

John (116:32)

Yeah.

Doug Laughlin (116:33)

And so that's how I, that's how I'm writing every weekday though. Yeah, it really helps. Well, I was going to say what's crazy? Crazy is like it's it's kind of hard these days and I and LLMs kind of have really. I don't know. I don't like LLM writing. I do like it for ideation. Like making outline.

John (116:46)

Yeah, yeah.

Doug Laughlin (116:47)

Research. Here's my un. Un organized thoughts. Make it into an outline. And then like, you know, I'll even be like put bullet points in the outline and I'll literally read the outline and then like ideate and write in parallel. But yeah, that's. That's how I feel about writing, I guess. Write more. I have a strong for non fiction writing. I really like this book called On Writing. Well that's just a really good class 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, stuff like that. Yeah. Okay.

John (117:22)

And then do you like have a topic idea list that you groom? Like I. I've put mine in Apple notes now.

Doug Laughlin (117:27)

But bro, it's. No, never, never. I'm one. 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.

John (117:34)

Yeah, yeah.

Doug Laughlin (117:34)

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

John (117:46)

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

Doug Laughlin (118:03)

Yeah.

John (118:04)

And so this is async and this is sync. Right. This is like passive. Like oh, here's a data point, here's a quote. Here's the thing. I'm just gonna slot it in the right thing and then.

Doug Laughlin (118:12)

And then I bake it historically. Okay, so how that prewriting actually works today is probably in the semianalysis slack. Just like all the little. Then you just search it up when you need it. Yeah, I'd search it up when I need it or something like that. But like I do most of the pre writing I think in my brain and I have places that I put it out 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 pre writing, 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.

John (118:48)

Tells you decode better.

Doug Laughlin (118:49)

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, 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. And then. So usually that will get me to 60, 75% of something, even if it's like an outline, where I like have gotten all the ideas enough to know how to fill it out the rest of the way. And then that's, that's how I take it from there.

John (119:14)

Cool. Amazing. Last thing. Hike.

Doug Laughlin (119:16)

Yeah.

John (119:17)

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

Doug Laughlin (119:51)

So there's three big ones in the United States. It's the Appalachian, the Pacific Crest Trail, and then there's a Continental Divide Trail. So I did the Continental Divide Trail, which is the longest and most remote of the three. Sometimes considered like the, the, the older, bad, whatever, but like, honestly, the PC, they're all different trails. Like, I'm, I'm pretty steeped in hiking culture. I think mile for mile at is actually the hardest. But I did the CDT as my first trail, as my first through hike. You know, you learn a little bit about the three. When I was choosing which one I wanted to do, and the CDT was the one that scared me the most. I was like, hey, this would be the hardest, biggest accomplishment I could possibly imagine. And I thought if I never have an opportunity to ever do this ever again, which so far seems to be pretty correct. Which one am I going to do to feel the most like, hey, I did the thing that I really wanted to do because I've always wanted to do a long distance hike. And so I, I chose the content of Divide Trail. I did that in 2021. Pretty AI and. But after the GPC3 essay. After the GPC3 essay. Yeah. I felt like I was missing out a lot and there's like a huge. It was a huge year for Substack. I feel like I missed out like a very big year of like the big growth.

John (120:53)

You're doing okay?

Doug Laughlin (120:54)

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. Life fulfillment. And I would definitely do it again. But I probably to be for people

John (121:07)

it's like four months, five months, six months.

Doug Laughlin (121:09)

Six months. Six months, six months. 2800 miles. We'll. We'll call it on the route 2850 or the miles I went and like

John (121:15)

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

Doug Laughlin (121:17)

Mostly alone. Did it alone. You get the trail name. It's a whole audiobooks. I listened to audiobooks until I hated them. Listen to music till I hated it. Got bored as hell. Like you just, you just, you go, you, you go through all of it actually. Yeah, it was awesome. Six months. I think the thing I think about is so far in most in my life up until that point, you get kind of get kicked from situation to situation, right. You create a view, a form of yourself. You think you know yourself. You have ideas of what motivates you. How do you react in situations, blah, blah, blah, blah. I think the one. The CDT amount like it's just like I like, I like the outdoors. I like hiking. I'm like good at it. Whatever. Is just something I really appealed to me from an adventure perspective. Like when in modern life do you get to say, hey, I'm going on an adventure. Never like. And that's what it was. It was, it was an adventure for me and one that I got to like. Really you, you. You know, it's like, oh, the journey is the destination or whatever. You learn a lot about yourself. And 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, too 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, 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. Why? 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. Their 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. And me like, holy crap, it's so good just to be alive. All these things where it's like. It's just so. Like, the raw experience of life is so meaningful and you don't get to experience it while doing it that way. And so. 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.

John (123:32)

Yeah.

Doug Laughlin (123:33)

Like, right after college. And said, hey, like, whatever kicked us out of the year. I think it's good to learn about yourself. It's really important. You're. Your Self Mastery is your most important tool. Use them all. So. Yeah. Yeah.

John (123:44)

Self Masters was more to this. Amazing. Well, thank you for jumping on and, like, covering everything.

Doug Laughlin (123:50)

Yeah.

John (123:50)

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

Doug Laughlin (123:56)

Yeah. Thank you.

John (123:57)

Thank you for having John.

Doug Laughlin (123:59)

Yeah. Great to catch up.